diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..b023908 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,36 @@ +# Build artifacts & deps (rebuilt inside the image) +**/node_modules +**/dist +**/.vite +frontend/dist +backend/static + +# Python envs / caches +.venv +.conda-py312 +**/__pycache__ +**/*.pyc +.pytest_cache +htmlcov +.coverage + +# Tiled server config, data & catalog (external in production — never bundled) +.tiled +tiled/ + +# Local data, backups, runtime files +data +.drafts +.reset-backups +*.pid +.run + +# VCS / editor / OS +.git +.gitignore +.idea +.vscode +.DS_Store + +# Plans / docs not needed in the image +*.md diff --git a/.gitignore b/.gitignore index 54e9b46..8ad0477 100644 --- a/.gitignore +++ b/.gitignore @@ -14,11 +14,15 @@ build/ .env # Tiled catalog database (binary, regenerated on first run via start_all.sh) -# The zarr data under .tiled/data/ IS committed so clones get demo data. .tiled/catalog.db .tiled/catalog.db-shm .tiled/catalog.db-wal +# Tiled data written at runtime (drag-and-drop ingest, etc.). +# Demo datasets already committed under .tiled/data/ stay tracked; new arrays +# ingested locally are ignored. To commit a new demo dataset, use `git add -f`. +.tiled/data/ + # Runtime Tiled thumbnail containers created on save **/*__v_thumbs/ @@ -45,3 +49,14 @@ Thumbs.db htmlcov/ .coverage coverage.xml + +# Vendored SlimSAM weights (~75 MB) — fetch with: +# node frontend/scripts/fetch-sam-model.mjs +frontend/public/models/ + +# Mark25 / TomoJEPA weights (~365 MB) — keep local; path via TOMOJEPA_WEIGHTS +ipred/models/*.pth +ipred/models/*.onnx + +# Local runtime caches (feature banks, ipred catalog, etc.) +.local_data/ diff --git a/.reset-backups/20260630_132922/drafts/0ba9bd25134dd13b.json b/.reset-backups/20260630_132922/drafts/0ba9bd25134dd13b.json new file mode 100644 index 0000000..92e86de --- /dev/null +++ b/.reset-backups/20260630_132922/drafts/0ba9bd25134dd13b.json @@ -0,0 +1 @@ +{"source_key": "tiled:http://127.0.0.1:8010:20260221_135217_petiole22_00006", "saved_at": "2026-06-25T18:59:49.077371+00:00", "payload": {"classes": [{"classId": 1, "label": "sample", "color": "#1f77b4", 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b/.reset-backups/20260630_132922/drafts/0ffceac8ef656d85.json new file mode 100644 index 0000000..cbd53c9 --- /dev/null +++ b/.reset-backups/20260630_132922/drafts/0ffceac8ef656d85.json @@ -0,0 +1 @@ +{"source_key": "tiled:http://127.0.0.1:8010:20260221_135217_petiole22_00342", "saved_at": "2026-06-30T18:52:54.249209+00:00", "payload": {"classes": [{"classId": 1, "label": "air", "color": "#1f77b4", "isVisible": true}, {"classId": 2, "label": "xylem", "color": "#ff7f0e", "isVisible": true}], "slices": {}, "split_by_slice": {}, "negative_slices": []}} \ No newline at end of file diff --git a/.reset-backups/20260630_132922/drafts/1729d6dc86766d96.json b/.reset-backups/20260630_132922/drafts/1729d6dc86766d96.json new file mode 100644 index 0000000..69d9a63 --- /dev/null +++ b/.reset-backups/20260630_132922/drafts/1729d6dc86766d96.json @@ -0,0 +1 @@ +{"source_key": "tiled:http://127.0.0.1:8010:20260221_135217_petiole22_00022", "saved_at": "2026-06-24T22:44:46.981750+00:00", "payload": 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a/.reset-backups/20260630_132922/tiled/data/browse/dataset/20260221_135217_petiole22_00008/c/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/dataset/20260221_135217_petiole22_00008/c/0/0 new file mode 100644 index 0000000..1d22e6d Binary files /dev/null and b/.reset-backups/20260630_132922/tiled/data/browse/dataset/20260221_135217_petiole22_00008/c/0/0 differ diff --git a/.reset-backups/20260630_132922/tiled/data/browse/dataset/20260221_135217_petiole22_00008/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/dataset/20260221_135217_petiole22_00008/zarr.json new file mode 100644 index 0000000..6ed0ad4 --- /dev/null +++ b/.reset-backups/20260630_132922/tiled/data/browse/dataset/20260221_135217_petiole22_00008/zarr.json @@ -0,0 +1,42 @@ +{ + "shape": [ + 2560, + 2560 + ], + "data_type": "float32", + "chunk_grid": { + "name": "regular", + "configuration": { + "chunk_shape": [ + 2560, + 2560 + ] + } + }, + "chunk_key_encoding": { + "name": "default", + "configuration": { + "separator": "/" + } + }, + "fill_value": 0.0, + "codecs": [ + { + "name": "bytes", + "configuration": { + "endian": "little" + } + }, + { + "name": "zstd", + "configuration": { + "level": 0, + "checksum": false + } + } + ], + "attributes": {}, + "zarr_format": 3, + "node_type": "array", + "storage_transformers": [] +} \ No newline at end of file diff --git a/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_2/c/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_2/c/0/0 new file mode 100644 index 0000000..754e02c Binary files /dev/null and b/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_2/c/0/0 differ diff --git a/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_2/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_2/zarr.json new file mode 100644 index 0000000..faacc2d --- /dev/null +++ b/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_2/zarr.json @@ -0,0 +1,39 @@ +{ + "shape": [ + 414, + 2418 + ], + "data_type": "uint8", + "chunk_grid": { + "name": "regular", + "configuration": { + "chunk_shape": [ + 414, + 2418 + ] + } + }, + "chunk_key_encoding": { + "name": "default", + "configuration": { + "separator": "/" + } + }, + "fill_value": 0, + "codecs": [ + { + "name": "bytes" + }, + { + "name": "zstd", + "configuration": { + "level": 0, + "checksum": false + } + } + ], + "attributes": {}, + "zarr_format": 3, + "node_type": "array", + "storage_transformers": [] +} \ No newline at end of file diff --git a/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_3/c/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_3/c/0/0 new file mode 100644 index 0000000..d785b7a Binary files /dev/null and b/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_3/c/0/0 differ diff --git a/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_3/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_3/zarr.json new file mode 100644 index 0000000..faacc2d --- /dev/null +++ b/.reset-backups/20260630_132922/tiled/data/browse/dataset/260_R1_Oct_slice_3/zarr.json @@ -0,0 +1,39 @@ +{ + "shape": [ + 414, + 2418 + ], + "data_type": "uint8", + "chunk_grid": { + "name": "regular", + "configuration": { + "chunk_shape": [ + 414, + 2418 + ] + } + }, + "chunk_key_encoding": { + "name": "default", + "configuration": { + "separator": "/" + } + }, + "fill_value": 0, + "codecs": [ + { + "name": "bytes" + }, + { + "name": "zstd", + "configuration": { + "level": 0, + "checksum": false + } + } + ], + "attributes": {}, + "zarr_format": 3, + "node_type": "array", + "storage_transformers": [] +} \ No newline at end of file diff --git a/.reset-backups/20260630_132922/tiled/data/browse/dataset/dino_synaps/c/0/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/dataset/dino_synaps/c/0/0/0 new file mode 100644 index 0000000..99f1303 Binary files /dev/null and b/.reset-backups/20260630_132922/tiled/data/browse/dataset/dino_synaps/c/0/0/0 differ diff --git a/.reset-backups/20260630_132922/tiled/data/browse/dataset/dino_synaps/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/dataset/dino_synaps/zarr.json new file mode 100644 index 0000000..2a5684b --- /dev/null +++ b/.reset-backups/20260630_132922/tiled/data/browse/dataset/dino_synaps/zarr.json @@ -0,0 +1,41 @@ +{ + "shape": [ + 1254, + 1254, + 3 + ], + "data_type": "uint8", + "chunk_grid": { + "name": "regular", + "configuration": { + "chunk_shape": [ + 1254, + 1254, + 3 + ] + } + }, + "chunk_key_encoding": { + "name": "default", + "configuration": { + "separator": "/" + } + }, + "fill_value": 0, + "codecs": [ + { + "name": "bytes" + }, + { + "name": "zstd", + "configuration": { + "level": 0, + "checksum": false + } + } + ], + "attributes": {}, + "zarr_format": 3, + "node_type": "array", + "storage_transformers": [] +} \ No newline at end of file diff --git a/.reset-backups/20260630_132922/tiled/data/browse/dataset/octopus_synaps/c/0/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/dataset/octopus_synaps/c/0/0/0 new file mode 100644 index 0000000..4c0f891 Binary files /dev/null and b/.reset-backups/20260630_132922/tiled/data/browse/dataset/octopus_synaps/c/0/0/0 differ diff --git a/.reset-backups/20260630_132922/tiled/data/browse/dataset/octopus_synaps/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/dataset/octopus_synaps/zarr.json new file mode 100644 index 0000000..2a5684b --- /dev/null +++ b/.reset-backups/20260630_132922/tiled/data/browse/dataset/octopus_synaps/zarr.json @@ -0,0 +1,41 @@ +{ + "shape": [ + 1254, + 1254, + 3 + ], + "data_type": "uint8", + "chunk_grid": { + "name": "regular", + "configuration": { + "chunk_shape": [ + 1254, + 1254, + 3 + ] + } + }, + "chunk_key_encoding": { + "name": "default", + "configuration": { + "separator": "/" + } + }, + "fill_value": 0, + "codecs": [ + { + "name": "bytes" + }, + { + "name": "zstd", + "configuration": { + "level": 0, + "checksum": false + } + } + ], + "attributes": {}, + "zarr_format": 3, + "node_type": "array", + "storage_transformers": [] +} \ No newline at end of file diff --git a/.tiled/data/browse/generated_data/gen_010005/c/0/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010005/c/0/0/0 similarity index 100% rename from .tiled/data/browse/generated_data/gen_010005/c/0/0/0 rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010005/c/0/0/0 diff --git a/.tiled/data/browse/generated_data/gen_010005/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010005/zarr.json similarity index 100% rename from .tiled/data/browse/generated_data/gen_010005/zarr.json rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010005/zarr.json diff --git a/.tiled/data/browse/generated_data/gen_010006/c/0/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010006/c/0/0/0 similarity index 100% rename from .tiled/data/browse/generated_data/gen_010006/c/0/0/0 rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010006/c/0/0/0 diff --git a/.tiled/data/browse/generated_data/gen_010006/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010006/zarr.json similarity index 100% rename from .tiled/data/browse/generated_data/gen_010006/zarr.json rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010006/zarr.json diff --git a/.tiled/data/browse/generated_data/gen_010007/c/0/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010007/c/0/0/0 similarity index 100% rename from .tiled/data/browse/generated_data/gen_010007/c/0/0/0 rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010007/c/0/0/0 diff --git a/.tiled/data/browse/generated_data/gen_010007/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010007/zarr.json similarity index 100% rename from .tiled/data/browse/generated_data/gen_010007/zarr.json rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010007/zarr.json diff --git a/.tiled/data/browse/generated_data/gen_010011/c/0/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010011/c/0/0/0 similarity index 100% rename from .tiled/data/browse/generated_data/gen_010011/c/0/0/0 rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010011/c/0/0/0 diff --git a/.tiled/data/browse/generated_data/gen_010011/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010011/zarr.json similarity index 100% rename from .tiled/data/browse/generated_data/gen_010011/zarr.json rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010011/zarr.json diff --git a/.tiled/data/browse/generated_data/gen_010021/c/0/0/0 b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010021/c/0/0/0 similarity index 100% rename from .tiled/data/browse/generated_data/gen_010021/c/0/0/0 rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010021/c/0/0/0 diff --git a/.tiled/data/browse/generated_data/gen_010021/zarr.json b/.reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010021/zarr.json similarity index 100% rename from .tiled/data/browse/generated_data/gen_010021/zarr.json rename to .reset-backups/20260630_132922/tiled/data/browse/generated_data/gen_010021/zarr.json diff --git a/.tiled/README.md b/.tiled/README.md index 720cdf9..5f55434 100644 --- a/.tiled/README.md +++ b/.tiled/README.md @@ -6,9 +6,10 @@ SQLite catalog and sample array data (~1 MB). - **`catalog.db`** — Tiled catalog metadata (paths, structure, metadata keys). - **`data/`** — Writable storage (Zarr chunks) for `browse/generated_data/*` sample datasets. -After clone, run `./start_all.sh` from the repo root. The script still creates -`backend/.env` from `.env.example` and may generate a `TILED_API_KEY` if missing; -that key is only for authenticating to the local server, not stored inside these files. +After clone, run `./start_all.sh` from the repo root. The script creates +`backend/.env` from `.env.example` if missing. Local writes use +`authentication.single_user_api_key` from `tiled/config.yml` (anon access is +read-only); that key is not stored in this catalog directory. To replace this with an empty catalog, delete `.tiled/` and run `tiled catalog init` (see `start_all.sh`). To add more datasets, use `backend/scripts/seed_generated_data_to_tiled.py`. diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..12ff00c --- /dev/null +++ b/Dockerfile @@ -0,0 +1,36 @@ +# syntax=docker/dockerfile:1 +# Lightweight production image: builds the SPA and serves it from the FastAPI +# backend (single container, one port). Tiled is NOT included — point the app at +# an external Tiled via TILED_URI/TILED_API_KEY. Local dev still uses start_all.sh. + +# --- Stage 1: build the frontend (same-origin: VITE_API_BASE left empty) --- +FROM node:22-alpine AS web +WORKDIR /web +COPY frontend/package.json frontend/package-lock.json ./ +RUN npm ci +COPY frontend/ ./ +RUN npm run build # → /web/dist + +# --- Stage 2: backend + built SPA --- +FROM python:3.12-slim AS app +WORKDIR /app + +# Install Python deps first (cached until pyproject changes). py-modules=[] means +# this installs dependencies only; the app code runs from the copied source below. +# pyproject pins tiled[client] (not [all]/[server]), so the Tiled server is NOT +# installed into this image — the app only connects to an external Tiled. +# If a wheel is unavailable for pycocotools/imagecodecs on this platform, add: +# RUN apt-get update && apt-get install -y --no-install-recommends build-essential +COPY backend/pyproject.toml ./pyproject.toml +RUN pip install --no-cache-dir . + +# App source + the built SPA (served from ./static by annotation_server.py). +COPY backend/ ./ +COPY --from=web /web/dist ./static + +# Drafts/versions/exports persist here — mount a volume in production. +ENV LOCAL_DATA_ROOT=/data +VOLUME ["/data"] + +EXPOSE 8002 +CMD ["uvicorn", "annotation_server:app", "--host", "0.0.0.0", "--port", "8002"] diff --git a/backend/.env.example b/backend/.env.example index cace58b..73f882e 100644 --- a/backend/.env.example +++ b/backend/.env.example @@ -1,5 +1,26 @@ TILED_URI=http://127.0.0.1:8010 +# Leave empty to use authentication.single_user_api_key from tiled/config.yml +# (needed for local ingest/writes; anon access is read-only). Never commit a real +# production key. A blank *exported* value breaks the tiled client (illegal +# "Apikey " header) — start_all.sh / tiled_config clear or replace blanks. TILED_API_KEY= BROWSE_CACHE_TTL_SECONDS=300 LOCAL_DATA_ROOT=~/data EXPORT_ROOT= +# Disjoint ipred (preprocess / train / conformal). start_all.sh sets this. +IPRED_URL=http://127.0.0.1:8003 +# Optional Mark25 TomoJEPA weights (defaults to ipred/models/tomojepa25.pth). +# Requires: pip install -e '.[torch]' inside ipred/. +TOMOJEPA_WEIGHTS= +# Optional Mark11 TomoJEPA weights (defaults to ipred/models/tomojepa11.pth). +TOMOJEPA11_WEIGHTS= +# Optional ONNX exports (after: python -m ipred.scripts.export_tomojepa_onnx). +TOMOJEPA_ONNX= +TOMOJEPA11_ONNX= + +# Production / Docker notes: +# - The container serves the SPA same-origin, so BROWSE_ALLOWED_ORIGINS can stay +# empty. Set it (comma-separated) only for split frontend/backend origins. +# - Point TILED_URI/TILED_API_KEY at your external Tiled; mount LOCAL_DATA_ROOT +# as a volume so annotation drafts/versions/exports survive restarts. +BROWSE_ALLOWED_ORIGINS= diff --git a/backend/annotation_server.py b/backend/annotation_server.py index 8145fa9..a263bd7 100644 --- a/backend/annotation_server.py +++ b/backend/annotation_server.py @@ -20,23 +20,47 @@ import json import logging import os +import shutil +import tempfile +import threading from concurrent.futures import ThreadPoolExecutor, as_completed from pathlib import Path -from typing import Optional +from typing import Any, Optional -from fastapi import FastAPI, HTTPException, Query, Response +import numpy as np +from fastapi import FastAPI, File, Form, HTTPException, Query, Response, UploadFile from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import FileResponse from pydantic import BaseModel import arrays as arrays_mod import drafts as drafts_mod +import export_jobs import images as images_mod +import ingest as ingest_mod import local_fs -from browse_helpers import FieldMapping, build_field_mapping, tiled_distinct_values, tiled_search_items, _STUDIO_RAW_KEYS +import clf_shelf as clf_shelf_mod +import label_sets as label_sets_mod +import feature_manifold as manifold_mod +import multiscale_features as features_mod +import pixel_clf as pixel_clf_mod +from browse_helpers import ( + FieldMapping, + build_field_mapping, + distinct_from_rows, + scoped_metadata_rows, + tiled_distinct_values, + tiled_search_items, + _SINGLE_VALUE_FACET_RAW_KEYS, +) from cache import TTLCache -from schemas import DraftPayload, ExportRequest, ExportSourceItem, ImageMeta, RenderOpts, SaveVersionRequest +from schemas import DraftPayload, ExportRequest, ExportSourceItem, ImageMeta, SaveVersionRequest from thumbnails import render_thumbnail -from tiled_clients import api_key_for_uri, get_browse_container, get_tiled_client +from tiled_clients import ( + api_key_for_uri, + get_browse_container_for, + get_tiled_client, +) from tiled_config import get_tiled_api_key, get_tiled_servers logging.basicConfig(level=logging.INFO) @@ -92,9 +116,11 @@ class ServerConfig(BaseModel): _field_mapping_cache: TTLCache = TTLCache(ttl_seconds=_FIELD_MAPPING_TTL, max_entries=32) -def _resolve_field_mapping(container: object, server_uri: str, technique: str) -> FieldMapping: +def _resolve_field_mapping( + container: object, server_uri: str, technique: str, container_path: str = "" +) -> FieldMapping: """Return a cached :class:`FieldMapping` for the given container.""" - key = (server_uri, technique) + key = (server_uri, technique, container_path or "") cached = _field_mapping_cache.get(key) if cached is not None: return cached @@ -127,6 +153,7 @@ async def browse_facets( server_uri: Optional[str] = None, server_api_key: Optional[str] = None, technique: str = Query("GIWAXS"), + container_path: Optional[str] = Query(None, description="Tiled container to browse"), refresh: bool = Query(False), # noqa: ARG001 — kept for client API compat ) -> dict[str, list[str]]: """Return ordered list of browsable metadata fields, discovered live. @@ -137,17 +164,27 @@ async def browse_facets( """ def _discover() -> dict[str, list[str]]: client = get_tiled_client(server_uri, server_api_key) - container, _ = get_browse_container(client) - mapping = _resolve_field_mapping(container, server_uri or "", technique) + container, _ = get_browse_container_for(client, container_path) + mapping = _resolve_field_mapping(container, server_uri or "", technique, container_path or "") + + # `container.distinct()` is catalog-global; when browsing a specific + # container, read its children once and compute values scoped to it. + # A sample is enough to detect which fields have >=2 distinct values; + # exact value lists are computed per-field (scoped) by /api/browse/column. + scoped = bool(container_path) + scoped_rows = scoped_metadata_rows(container, limit=300) if scoped else [] def _facet_for_key(disp_key: str) -> tuple[list[str], list[str]]: raw_key = mapping.display_to_raw.get(disp_key, disp_key) - min_values = 1 if raw_key in _STUDIO_RAW_KEYS else 2 - try: - result = container.distinct(raw_key, counts=True) - except Exception: - return [], [] - raw_values = result.get("metadata", {}).get(raw_key, []) + min_values = 1 if raw_key in _SINGLE_VALUE_FACET_RAW_KEYS else 2 + if scoped: + raw_values = distinct_from_rows(scoped_rows, raw_key) + else: + try: + result = container.distinct(raw_key, counts=True) + except Exception: + return [], [] + raw_values = result.get("metadata", {}).get(raw_key, []) non_null = [ v for v in raw_values if v.get("value") is not None @@ -193,13 +230,14 @@ async def browse_column( technique: str = Query("GIWAXS"), field: str = Query(..., description="Display-key metadata field to group by"), filters: str = Query("{}", description="JSON dict of upstream display_key=value selections"), + container_path: Optional[str] = Query(None, description="Tiled container to browse"), limit: int = Query(500, ge=1, le=5000), refresh: bool = Query(False), ) -> dict: """Return distinct values (+ counts) for *field* via Tiled ``distinct()``.""" filter_dict = _parse_json_filters(filters) - cache_key = ("column", server_uri or "", technique, field, filters, limit) + cache_key = ("column", server_uri or "", technique, container_path or "", field, filters, limit) if not refresh: cached = _column_cache.get(cache_key) if cached is not None: @@ -207,8 +245,8 @@ async def browse_column( def _build() -> dict: client = get_tiled_client(server_uri, server_api_key) - container, _ = get_browse_container(client) - mapping = _resolve_field_mapping(container, server_uri or "", technique) + container, _ = get_browse_container_for(client, container_path) + mapping = _resolve_field_mapping(container, server_uri or "", technique, container_path or "") raw_key = mapping.display_to_raw.get(field, field) return tiled_distinct_values( container, @@ -216,6 +254,7 @@ def _build() -> dict: filters=filter_dict, field_mapping=mapping, limit=limit, + scoped=bool(container_path), ) try: @@ -233,13 +272,14 @@ async def browse_items( server_api_key: Optional[str] = None, technique: str = Query("GIWAXS"), filters: str = Query("{}", description="JSON dict of display_key=value selections"), + container_path: Optional[str] = Query(None, description="Tiled container to browse"), limit: int = Query(500, ge=1, le=2000), refresh: bool = Query(False), ) -> dict: """Return sample records (path + metadata) matching *filters* via ``search()``.""" filter_dict = _parse_json_filters(filters) - cache_key = ("items", server_uri or "", technique, filters, limit) + cache_key = ("items", server_uri or "", technique, container_path or "", filters, limit) if not refresh: cached = _items_cache.get(cache_key) if cached is not None: @@ -247,8 +287,8 @@ async def browse_items( def _build() -> dict: client = get_tiled_client(server_uri, server_api_key) - container, prefix = get_browse_container(client) - mapping = _resolve_field_mapping(container, server_uri or "", technique) + container, prefix = get_browse_container_for(client, container_path) + mapping = _resolve_field_mapping(container, server_uri or "", technique, container_path or "") return tiled_search_items( container, filters=filter_dict, @@ -266,6 +306,40 @@ def _build() -> dict: return result +@app.get("/api/browse/slices") +async def browse_slices( + path: str = Query(..., description="Tiled container path of a multi-slice dataset"), + server_uri: Optional[str] = None, + server_api_key: Optional[str] = None, + limit: int = Query(2000, ge=1, le=10000), +) -> dict: + """List a dataset container's array children as individually-openable slices. + + Used by Browse drill-in: each returned record is ``{path, sample, metadata}`` + where ``path`` points at a single array node that opens as a 2-D image. + """ + cache_key = ("slices", server_uri or "", path, limit) + cached = _items_cache.get(cache_key) + if cached is not None: + return cached + + def _build() -> dict: + client = get_tiled_client(server_uri, server_api_key) + container, prefix = get_browse_container_for(client, path) + result = tiled_search_items(container, limit=limit, container_path_prefix=prefix) + # Order slices by key (ingest zero-pads, so lexical == slice order). + result["items"].sort(key=lambda it: it["sample"]) + return result + + try: + result = await asyncio.to_thread(_build) + except Exception as exc: + raise HTTPException(status_code=502, detail=f"Failed to list slices: {exc}") from exc + + _items_cache.set(cache_key, result) + return result + + @app.get("/api/browse/thumbnail") async def browse_thumbnail( tiled_path: str = Query(..., description="Slash-separated Tiled path"), @@ -300,25 +374,28 @@ def _build() -> bytes | None: @app.get("/api/local/list") async def local_list( - rel: str = Query("", description="Relative path under LOCAL_DATA_ROOT"), + rel: str = Query("", description="Relative path under the granted root"), + root: Optional[str] = Query(None, description="Granted absolute browse root"), ) -> list[dict]: - """List directory entries under LOCAL_DATA_ROOT.""" - return await asyncio.to_thread(local_fs.list_dir, rel) + """List directory entries under the granted local root.""" + return await asyncio.to_thread(local_fs.list_dir, rel, root) @app.get("/api/local/samples") async def local_samples( - rel: str = Query(..., description="Relative path to a folder under LOCAL_DATA_ROOT"), + rel: str = Query(..., description="Relative path to a folder under the granted root"), + root: Optional[str] = Query(None, description="Granted absolute browse root"), ) -> dict: """Return all image files under a local folder (used by the Browse tab). Args: - rel: Relative folder path under ``LOCAL_DATA_ROOT``. + rel: Relative folder path under the granted root. + root: Granted absolute browse root (defaults to ``LOCAL_DATA_ROOT``). Returns: ``{"items": [{"name", "path"}], "total": int}`` """ - items = await asyncio.to_thread(local_fs.list_image_files, rel) + items = await asyncio.to_thread(local_fs.list_image_files, rel, root) return {"items": items, "total": len(items)} @@ -327,6 +404,8 @@ async def connect_summary( kind: str = Query(..., description="'tiled' or 'local'"), server_uri: Optional[str] = None, rel: str = Query("", description="Local folder path (kind=local only)"), + root: Optional[str] = Query(None, description="Granted absolute browse root (kind=local)"), + container_path: Optional[str] = Query(None, description="Tiled container to browse (kind=tiled)"), ) -> dict: """Return a connection summary: sample count and display label. @@ -337,22 +416,35 @@ async def connect_summary( ``{"kind", "label", "sample_count", "server_uri"}`` """ if kind == "local": - count = await asyncio.to_thread(local_fs.count_image_files, rel) - label = rel or "Local Data Root" - return {"kind": "local", "label": label, "sample_count": count, "server_uri": None} + count = await asyncio.to_thread(local_fs.count_image_files, rel, root) + label = (root or "Local Data Root") + (f"/{rel}" if rel else "") + return { + "kind": "local", + "label": label, + "sample_count": count, + "server_uri": None, + "local_root": root, + } if kind == "tiled": - def _count() -> int: + def _count() -> tuple[int, str]: client = get_tiled_client(server_uri) - container, _ = get_browse_container(client) - result = tiled_search_items(container, filters={}, limit=10_000) - return int(result.get("total", 0)) + container, prefix = get_browse_container_for(client, container_path) + # An unfiltered count is just the container size — a single request. + # Avoid iterating every child and building per-item metadata dicts, + # which is O(N) HTTP round trips and stalls the connect UI. + try: + n = int(len(container)) + except Exception: + result = tiled_search_items(container, filters={}, limit=10_000) + n = int(result.get("total", 0)) + return n, prefix try: - count = await asyncio.to_thread(_count) + count, discovered_prefix = await asyncio.to_thread(_count) except Exception as exc: logger.warning("connect_summary tiled count failed: %s", exc) - count = 0 + count, discovered_prefix = 0, "" servers = get_tiled_servers() label = next( @@ -360,7 +452,16 @@ def _count() -> int: if (cfg.get("uri") or "").rstrip("/") == (server_uri or "").rstrip("/")), server_uri or "Tiled Server", ) - return {"kind": "tiled", "label": label, "sample_count": count, "server_uri": server_uri} + resolved_path = (container_path or discovered_prefix or "").strip("/") or None + if resolved_path: + label = f"{label} · {resolved_path}" + return { + "kind": "tiled", + "label": label, + "sample_count": count, + "server_uri": server_uri, + "container_path": resolved_path, + } raise HTTPException(400, f"Unknown kind: {kind!r}; must be 'tiled' or 'local'") @@ -430,10 +531,11 @@ async def image_meta( source: str = Query(...), kind: str = Query(...), server_uri: Optional[str] = None, + root: Optional[str] = Query(None, description="Granted absolute root (kind=local)"), ) -> ImageMeta: """Return shape / dtype metadata for an image source.""" def _run() -> ImageMeta: - node = arrays_mod.resolve_array(source, kind, server_uri) + node = arrays_mod.resolve_array(source, kind, server_uri, root) meta = arrays_mod.array_shape_meta(node) sl = arrays_mod.read_slice(node, meta, 0) flat = sl.ravel().astype(float) @@ -461,6 +563,7 @@ async def image_slice( kind: str = Query(...), slice_index: int = Query(0), server_uri: Optional[str] = None, + root: Optional[str] = Query(None, description="Granted absolute root (kind=local)"), norm: str = Query("global"), scale: str = Query("linear"), vmin_pct: float = Query(1.0), @@ -477,7 +580,7 @@ async def image_slice( } def _run() -> bytes: - node = arrays_mod.resolve_array(source, kind, server_uri) + node = arrays_mod.resolve_array(source, kind, server_uri, root) meta = arrays_mod.array_shape_meta(node) sl = arrays_mod.read_slice(node, meta, slice_index) global_range = None @@ -501,6 +604,526 @@ def _run() -> bytes: ) +class FeatureComputeRequest(BaseModel): + """Parameters for multiscale feature computation on one slice.""" + + source: str + kind: str + slice_index: int = 0 + server_uri: Optional[str] = None + root: Optional[str] = None + sigma_min: float = 1.0 + sigma_max: float = 8.0 + intensity: bool = True + edges: bool = True + texture: bool = True + clahe: bool = True + include_sam: bool = False + + +@app.get("/api/image/features/sam-status") +async def features_sam_status() -> dict: + """Whether SlimSAM vision encoder ONNX is available for feature concat.""" + import sam_embed as sam_mod + + return {"available": sam_mod.sam_available()} + + +@app.post("/api/image/features") +async def compute_image_features(body: FeatureComputeRequest) -> dict: + """Compute multiscale features for one slice; cache stack; return channel list.""" + + def _run() -> dict: + node = arrays_mod.resolve_array(body.source, body.kind, body.server_uri, body.root) + meta = arrays_mod.array_shape_meta(node) + sl = arrays_mod.read_slice(node, meta, body.slice_index) + job = features_mod.compute_and_store( + sl, + sigma_min=body.sigma_min, + sigma_max=body.sigma_max, + intensity=body.intensity, + edges=body.edges, + texture=body.texture, + clahe=body.clahe, + include_sam=body.include_sam, + ) + h, w, _ = job.stack.shape + return { + "job_id": job.job_id, + "width": w, + "height": h, + "channels": [{"index": i, "label": lab} for i, lab in enumerate(job.labels)], + "has_sam": job.sam_emb is not None, + "sam_shape": list(job.sam_emb.shape) if job.sam_emb is not None else None, + } + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + except ValueError as exc: + raise HTTPException(400, str(exc)) from exc + except Exception as exc: + logger.error("compute_image_features failed: %s", exc) + raise HTTPException(500, f"Failed to compute features: {exc}") from exc + + +@app.get("/api/image/features/{job_id}/{index}") +async def get_feature_channel(job_id: str, index: int) -> Response: + """Return one cached feature channel as a grayscale PNG.""" + + def _run() -> bytes: + job = features_mod.get_job(job_id) + if job is None: + raise HTTPException(404, "Feature job not found or expired") + try: + return features_mod.encode_channel_png(job.stack, index) + except IndexError as exc: + raise HTTPException(404, str(exc)) from exc + + try: + png = await asyncio.to_thread(_run) + except HTTPException: + raise + except Exception as exc: + logger.error("get_feature_channel failed: %s", exc) + raise HTTPException(500, f"Failed to encode feature channel: {exc}") from exc + + return Response( + content=png, + media_type="image/png", + headers={"Cache-Control": "private, max-age=600"}, + ) + + +class ManifoldSampleRequest(BaseModel): + """Greedy feature-variance inducing boxes on a feature job.""" + + job_id: str + k: int = 24 + """Full square box side length in image pixels.""" + box_size: int | None = None + stride: int | None = None + pca_dims: int = 16 + """Optional placement-region shapes (union mask); omit for full image.""" + shapes: list[dict] | None = None + + +@app.post("/api/image/features/manifold/sample") +async def manifold_sample(body: ManifoldSampleRequest) -> dict: + """Sample diverse annotation boxes via variance + exclusion.""" + + def _run() -> dict: + job = features_mod.get_job(body.job_id) + if job is None: + raise HTTPException(404, "Feature job not found or expired") + place_mask = None + if body.shapes: + from coco_export import shape_to_mask + + h, w = job.float_stack.shape[:2] + place_mask = np.zeros((h, w), dtype=bool) + for shape in body.shapes: + try: + place_mask |= shape_to_mask(shape, h, w) + except (KeyError, ValueError, TypeError) as exc: + raise HTTPException(400, f"invalid placement shape: {exc}") from exc + if not bool(place_mask.any()): + raise HTTPException(400, "placement mask is empty") + try: + result = manifold_mod.sample_inducing_points( + job, + k=body.k, + box_size=body.box_size, + stride=body.stride, + pca_dims=body.pca_dims, + mask=place_mask, + ) + except ValueError as exc: + raise HTTPException(400, str(exc)) from exc + cached = manifold_mod.store_sample(result) + return { + "sample_id": cached.sample_id, + "points": cached.points, + "k": result.meta["k"], + "n_picked": result.meta["n_picked"], + "n_subsample": result.meta["n_subsample"], + "pca_dims": result.meta["pca_dims"], + "explained_variance": result.meta["explained_variance"], + "stride": result.meta["stride"], + "radius": result.meta["radius"], + "box_size": result.meta["box_size"], + "mask_pixels": result.meta.get("mask_pixels", 0), + "n_windows_in_mask": result.meta.get("n_windows_in_mask"), + "has_mask": result.meta.get("has_mask", False), + } + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + except Exception as exc: + logger.error("manifold_sample failed: %s", exc) + raise HTTPException(500, f"Failed to sample manifold: {exc}") from exc + + +@app.get("/api/image/features/manifold/{sample_id}/heatmap.png") +async def manifold_heatmap_png(sample_id: str) -> Response: + """Coverage heatmap PNG (grayscale 0–255).""" + + def _run() -> bytes: + cached = manifold_mod.get_sample(sample_id) + if cached is None: + raise HTTPException(404, "Manifold sample not found or expired") + return cached.heatmap_png + + try: + png = await asyncio.to_thread(_run) + except HTTPException: + raise + except Exception as exc: + raise HTTPException(500, str(exc)) from exc + return Response( + content=png, + media_type="image/png", + headers={"Cache-Control": "private, max-age=300"}, + ) + + +class ClfTrainRequest(BaseModel): + """Train CatBoost on sparse annotations + a feature job's float stack.""" + + job_id: str + shapes: list[dict] + iterations: int = 200 + depth: int = 6 + learning_rate: float = 0.1 + max_samples: int = 200_000 + + +class ClfPredictRequest(BaseModel): + """Mondrian conformal predict at misfire level *alpha*.""" + + model_id: str + job_id: str + alpha: float = 0.05 + # When set, annotated pixels are zeroed (keep originals on commit). + shapes: list[dict] | None = None + + +@app.post("/api/image/features/clf/train") +async def clf_train(body: ClfTrainRequest) -> dict: + """Fit CatBoost on labeled pixels from *shapes* and the feature job float stack.""" + + def _run() -> dict: + job = features_mod.get_job(body.job_id) + if job is None: + raise HTTPException(404, "Feature job not found or expired") + try: + model = pixel_clf_mod.train_classifier( + job, + body.shapes, + iterations=body.iterations, + depth=body.depth, + learning_rate=body.learning_rate, + max_samples=body.max_samples, + ) + except ValueError as exc: + raise HTTPException(400, str(exc)) from exc + return pixel_clf_mod.train_result_dict(model) + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + except Exception as exc: + logger.error("clf_train failed: %s", exc) + raise HTTPException(500, f"Failed to train classifier: {exc}") from exc + + +@app.get("/api/image/features/clf/{model_id}/tree/{tree_index}") +async def clf_tree(model_id: str, tree_index: int = 0) -> dict: + """Return one CatBoost oblivious tree (splits + leaf values) for the UI.""" + + def _run() -> dict: + model = pixel_clf_mod.get_model(model_id) + if model is None: + raise HTTPException(404, "Classifier model not found or expired") + try: + return pixel_clf_mod.tree_view(model, tree_index) + except IndexError as exc: + raise HTTPException(404, str(exc)) from exc + except ValueError as exc: + raise HTTPException(400, str(exc)) from exc + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + except Exception as exc: + logger.error("clf_tree failed: %s", exc) + raise HTTPException(500, f"Failed to read tree: {exc}") from exc + + +@app.post("/api/image/features/clf/predict") +async def clf_predict(body: ClfPredictRequest) -> dict: + """Conformal predict; returns meta + pred_id for commit/status PNG GETs.""" + + def _run() -> dict: + model = pixel_clf_mod.get_model(body.model_id) + if model is None: + raise HTTPException(404, "Classifier model not found or expired") + job = features_mod.get_job(body.job_id) + if job is None: + raise HTTPException(404, "Feature job not found or expired") + try: + result = pixel_clf_mod.predict_conformal( + model, + job, + alpha=body.alpha, + preserve_shapes=body.shapes, + ) + except ValueError as exc: + raise HTTPException(400, str(exc)) from exc + cached = pixel_clf_mod.store_prediction(result) + return cached.meta + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + except Exception as exc: + logger.error("clf_predict failed: %s", exc) + raise HTTPException(500, f"Failed to predict: {exc}") from exc + + +@app.get("/api/image/features/clf/predict/{pred_id}/commit.png") +async def clf_predict_commit_png(pred_id: str) -> Response: + """Singleton commit map (classId or 0).""" + + def _run() -> bytes: + cached = pixel_clf_mod.get_prediction(pred_id) + if cached is None: + raise HTTPException(404, "Prediction not found or expired") + return cached.commit_png + + try: + png = await asyncio.to_thread(_run) + except HTTPException: + raise + except Exception as exc: + raise HTTPException(500, str(exc)) from exc + return Response(content=png, media_type="image/png", headers={"Cache-Control": "private, max-age=300"}) + + +@app.get("/api/image/features/clf/predict/{pred_id}/status.png") +async def clf_predict_status_png(pred_id: str) -> Response: + """Status map: 0 abstain, 1 singleton, 2 multi.""" + + def _run() -> bytes: + cached = pixel_clf_mod.get_prediction(pred_id) + if cached is None: + raise HTTPException(404, "Prediction not found or expired") + return cached.status_png + + try: + png = await asyncio.to_thread(_run) + except HTTPException: + raise + except Exception as exc: + raise HTTPException(500, str(exc)) from exc + return Response(content=png, media_type="image/png", headers={"Cache-Control": "private, max-age=300"}) + + +class ClfShelfSaveRequest(BaseModel): + """Persist a trained in-memory model + feature recipe to the shelf.""" + + model_id: str + name: str + feature_recipe: dict + + +class ClfShelfPredictRequest(BaseModel): + """Recompute features with the shelf recipe and run conformal predict.""" + + source: str + kind: str + slice_index: int = 0 + server_uri: Optional[str] = None + root: Optional[str] = None + alpha: float = 0.05 + shapes: list[dict] | None = None + + +# ############################################################################## +# # REMOVE THIS AND USE YOUR OWN STUFF +# Scaffold CatBoost model-shelf + reusable label-set APIs. Replace with your +# own model registry / label taxonomy endpoints, then delete these routes and +# ``clf_shelf.py`` / ``label_sets.py``. +# ############################################################################## + + +@app.post("/api/clf/models/save") +async def clf_shelf_save(body: ClfShelfSaveRequest) -> dict: + """# REMOVE THIS AND USE YOUR OWN STUFF — save shelf model.""" + + def _run() -> dict: + model = pixel_clf_mod.get_model(body.model_id) + if model is None: + raise HTTPException(404, "Classifier model not found or expired — train again") + try: + meta = clf_shelf_mod.save_model( + model, name=body.name.strip() or "unnamed", feature_recipe=body.feature_recipe + ) + except Exception as exc: + raise HTTPException(500, f"Failed to save model: {exc}") from exc + return { + "id": meta.id, + "name": meta.name, + "class_ids": meta.class_ids, + "n_train": meta.n_train, + "n_cal": meta.n_cal, + "train_accuracy": meta.train_accuracy, + "uses_sam": meta.uses_sam, + "feature_recipe": meta.feature_recipe, + "created_at": meta.created_at, + "n_features": meta.n_features, + } + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + + +@app.get("/api/clf/models") +async def clf_shelf_list() -> list[dict]: + """List persisted classifier shelf models.""" + return await asyncio.to_thread(clf_shelf_mod.list_models) + + +class LabelSetSaveRequest(BaseModel): + """Create a reusable annotation label set (classes).""" + + name: str + classes: list[dict] + + +@app.get("/api/label-sets") +async def label_sets_list() -> dict: + """# REMOVE THIS AND USE YOUR OWN STUFF — list scaffold label sets.""" + sets = await asyncio.to_thread(label_sets_mod.list_label_sets) + return {"sets": sets} + + +@app.post("/api/label-sets") +async def label_sets_save(body: LabelSetSaveRequest) -> dict: + """# REMOVE THIS AND USE YOUR OWN STUFF — save scaffold label set.""" + + def _run() -> dict: + try: + return label_sets_mod.save_label_set(name=body.name, classes=body.classes) + except ValueError as exc: + raise HTTPException(400, str(exc)) from exc + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + + +@app.get("/api/label-sets/{set_id}") +async def label_sets_get(set_id: str) -> dict: + """Load one label set.""" + data = await asyncio.to_thread(label_sets_mod.get_label_set, set_id) + if data is None: + raise HTTPException(404, "Label set not found") + return data + + +@app.delete("/api/label-sets/{set_id}") +async def label_sets_delete(set_id: str) -> dict: + """Delete a label set.""" + + def _run() -> dict: + ok = label_sets_mod.delete_label_set(set_id) + if not ok: + raise HTTPException(404, "Label set not found") + return {"ok": True} + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + + +@app.delete("/api/clf/models/{shelf_id}") +async def clf_shelf_delete(shelf_id: str) -> dict: + """Delete a shelf model.""" + + def _run() -> dict: + ok = clf_shelf_mod.delete_model(shelf_id) + if not ok: + raise HTTPException(404, "Shelf model not found") + return {"ok": True} + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + + +@app.post("/api/clf/models/{shelf_id}/predict") +async def clf_shelf_predict(shelf_id: str, body: ClfShelfPredictRequest) -> dict: + """Apply a shelf model to an image: recompute features + conformal predict.""" + + def _run() -> dict: + try: + meta = clf_shelf_mod.get_meta(shelf_id) + model = clf_shelf_mod.load_into_cache(shelf_id) + except FileNotFoundError as exc: + raise HTTPException(404, str(exc)) from exc + recipe = meta.get("feature_recipe") or {} + node = arrays_mod.resolve_array(body.source, body.kind, body.server_uri, body.root) + arr_meta = arrays_mod.array_shape_meta(node) + sl = arrays_mod.read_slice(node, arr_meta, body.slice_index) + job = features_mod.compute_and_store( + sl, + sigma_min=float(recipe.get("sigma_min", 1.0)), + sigma_max=float(recipe.get("sigma_max", 8.0)), + intensity=bool(recipe.get("intensity", True)), + edges=bool(recipe.get("edges", True)), + texture=bool(recipe.get("texture", True)), + clahe=bool(recipe.get("clahe", True)), + include_sam=bool(recipe.get("include_sam", False)), + ) + try: + result = pixel_clf_mod.predict_conformal( + model, + job, + alpha=body.alpha, + preserve_shapes=body.shapes, + ) + except ValueError as exc: + raise HTTPException(400, str(exc)) from exc + cached = pixel_clf_mod.store_prediction(result) + return { + **cached.meta, + "job_id": job.job_id, + "shelf_id": shelf_id, + "width": int(job.float_stack.shape[1]), + "height": int(job.float_stack.shape[0]), + } + + try: + return await asyncio.to_thread(_run) + except HTTPException: + raise + except Exception as exc: + logger.error("clf_shelf_predict failed: %s", exc) + raise HTTPException(500, f"Shelf predict failed: {exc}") from exc + + @app.get("/api/annotations/draft") async def get_draft(source_key: str = Query(...)) -> dict: """Return the saved draft for source_key, or 404.""" @@ -697,20 +1320,66 @@ async def export_coco(payload: ExportRequest) -> dict: negative_slices=payload.negative_slices, )] - def _run() -> dict: - from coco_export import build_export_plan, write_coco_split - import images as images_mod_local - import arrays as arrays_mod_local + # Dry run = counts only. Resolve splits from the payload without touching + # Tiled, reading slices, sampling stats, or rasterizing — so the preview is + # instant. Returned synchronously (no job). + if payload.dry_run: + from coco_export import _resolve_split + summary: dict = {"skipped_zero_area": 0, "splits": {}} + for item in source_items: + neg_keys = {str(k) for k in item.negative_slices} + all_keys = list(set(item.slices.keys()) | neg_keys) + resolved = _resolve_split( + all_keys, + {str(k): v for k, v in item.split_by_slice.items()}, + payload.auto_split, + ) + for k in all_keys: + split = resolved.get(k, "train") + bucket = summary["splits"].setdefault(split, {"n_images": 0, "n_annotations": 0}) + bucket["n_images"] += 1 + bucket["n_annotations"] += len(item.slices.get(k, [])) + return summary + + # Real export runs on a background thread; the UI polls /api/export/status + # for phase/progress/log lines and downloads the .zip when done. + jid = export_jobs.new_job(str(out_root)) + threading.Thread( + target=_run_export_job, + args=(jid, source_items, payload, out_root), + daemon=True, + ).start() + return {"job_id": jid, "dataset_path": str(out_root)} + + +def _run_export_job( + jid: str, + source_items: "list[ExportSourceItem]", + payload: "ExportRequest", + out_root: Path, +) -> None: + """Background worker: render+rasterize all sources, write the dataset tree + (images + masks + COCO), zip it for download, then sync Tiled metadata.""" + from coco_export import build_export_plan, write_coco_split + import images as images_mod_local + import arrays as arrays_mod_local + + try: + export_jobs.update(jid, state="running", phase="reading") + total = sum( + len(set(item.slices.keys()) | {str(k) for k in item.negative_slices}) + for item in source_items + ) + export_jobs.set_total(jid, total) - # Merged splits accumulator across all sources. merged_splits: dict[str, dict] = {} skipped_total = 0 merged_categories: list[dict] = [] merged_info: dict = {} for item in source_items: + export_jobs.log(jid, f"Reading {item.source} …") node = arrays_mod_local.resolve_array(item.source, item.kind, item.server_uri) - # Build a temporary single-source payload object for reuse of build_export_plan. tmp = ExportRequest( kind=item.kind, source=item.source, @@ -722,76 +1391,129 @@ def _run() -> dict: render=payload.render, auto_split=payload.auto_split, ) + + def _cb(message: str, _jid: str = jid) -> None: + export_jobs.bump(_jid, 1) + export_jobs.log(_jid, message) + plan = build_export_plan( node, tmp, render_slice_fn=images_mod_local.render_slice, array_shape_meta_fn=arrays_mod_local.array_shape_meta, read_slice_fn=arrays_mod_local.read_slice, sample_global_stats_fn=images_mod_local._sample_global_stats, + progress_cb=_cb, + include_polygons=payload.include_polygons, ) skipped_total += plan["skipped_zero_area"] if not merged_categories: merged_categories = plan["categories"] merged_info = plan["info"] for split_name, split_data in plan["splits"].items(): - if split_name not in merged_splits: - merged_splits[split_name] = {"images": [], "annotations": []} - merged_splits[split_name]["images"].extend(split_data["images"]) - merged_splits[split_name]["annotations"].extend(split_data["annotations"]) - - summary: dict = {"skipped_zero_area": skipped_total, "splits": {}} - if payload.dry_run: - for split_name, split_data in merged_splits.items(): - summary["splits"][split_name] = { - "n_images": len(split_data["images"]), - "n_annotations": len(split_data["annotations"]), - } - return summary - + bucket = merged_splits.setdefault(split_name, {"images": [], "annotations": []}) + bucket["images"].extend(split_data["images"]) + bucket["annotations"].extend(split_data["annotations"]) + + # Write files AND build the download .zip in one pass. ZIP_STORED: the + # PNGs are already compressed, so re-deflating them is wasted CPU. + import zipfile + export_jobs.update(jid, phase="writing") + zip_path = f"{out_root}.zip" written: dict = {} - for split_name, split_data in merged_splits.items(): - split_dir = out_root / split_name - result = write_coco_split( - split_dir, - images=split_data["images"], - categories=merged_categories, - annotations=split_data["annotations"], - mode=payload.mode, - info=merged_info, - ) - written[split_name] = result - summary["written"] = written - summary["dataset_path"] = str(out_root) + with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_STORED) as zf: + for split_name, split_data in merged_splits.items(): + export_jobs.log(jid, f"Writing split '{split_name}' ({len(split_data['images'])} images + masks)…") + written[split_name] = write_coco_split( + out_root / split_name, + images=split_data["images"], + categories=merged_categories, + annotations=split_data["annotations"], + mode=payload.mode, + info=merged_info, + zf=zf, + arc_prefix=f"{split_name}/", + ) - # Sync annotation flags back onto source Tiled nodes for Browse discovery. + export_jobs.update(jid, phase="syncing") import tiled_annotation_sync - from source_keys import parse_source_key - for item in source_items: if item.kind != "tiled": continue sk = f"tiled:{item.server_uri or ''}:{item.source}" try: tiled_annotation_sync.sync_annotation_metadata( - sk, - { - "classes": payload.classes, - "slices": item.slices, - }, + sk, {"classes": payload.classes, "slices": item.slices}, ) except Exception as sync_exc: logger.warning("Export Tiled sync failed for %s: %s", sk, sync_exc) - return summary - - try: - result = await asyncio.to_thread(_run) - return result + result = { + "skipped_zero_area": skipped_total, + "written": written, + "dataset_path": str(out_root), + "zip_available": True, + "splits": { + k: {"n_images": len(v["images"]), "n_annotations": len(v["annotations"])} + for k, v in merged_splits.items() + }, + } + export_jobs.update(jid, zip_path=zip_path, result=result, phase="done", state="done") + export_jobs.log(jid, "Export complete.") except FileExistsError as exc: - raise HTTPException(409, str(exc)) from exc - except Exception as exc: - logger.error("Export failed: %s", exc) - raise HTTPException(500, f"Export failed: {exc}") from exc + export_jobs.update(jid, state="error", phase="error", error=f"{exc} (use overwrite or merge)") + except Exception as exc: # noqa: BLE001 + logger.error("Export job failed: %s", exc) + export_jobs.update(jid, state="error", phase="error", error=str(exc)) + + +@app.get("/api/export/status/{job_id}") +async def export_status(job_id: str) -> dict: + """Poll an export job's progress (state, phase, done/total, log, result).""" + job = export_jobs.get_job(job_id) + if job is None: + raise HTTPException(404, "Unknown job_id") + return job + + +@app.get("/api/export/download/{job_id}") +async def export_download(job_id: str) -> Response: + """Stream the finished export .zip (browser save dialog picks the location).""" + zp = export_jobs.zip_path(job_id) + if not zp or not Path(zp).exists(): + raise HTTPException(404, "Export zip not ready") + return FileResponse(zp, media_type="application/zip", filename=Path(zp).name) + + +@app.post("/api/masks/to-tiled") +async def masks_to_tiled(payload: ExportRequest) -> dict: + """Write rasterized masks into Tiled as stacked volumes (standalone action). + + Reuses the export payload (classes/slices/negative_slices per source) but, + instead of building a training zip, rasterizes each Tiled source's shapes and + writes a ``__masks`` sibling container. Runs on a background + thread; poll ``/api/export/status/{job_id}`` for progress. + """ + import tiled_mask_sync + + if payload.sources: + source_items: list[ExportSourceItem] = payload.sources + else: + source_items = [ExportSourceItem( + kind=payload.kind, # type: ignore[arg-type] + source=payload.source, + server_uri=payload.server_uri, + slices=payload.slices, + split_by_slice=payload.split_by_slice, + negative_slices=payload.negative_slices, + )] + + jid = export_jobs.new_job("") + threading.Thread( + target=tiled_mask_sync.run_mask_sync_job, + args=(jid, source_items, payload), + daemon=True, + ).start() + return {"job_id": jid} @app.post("/api/import/coco") @@ -810,11 +1532,482 @@ def _run() -> dict: raise HTTPException(500, f"Import failed: {exc}") from exc +@app.post("/api/ingest/upload") +async def ingest_upload( + server_uri: Optional[str] = Query(None, description="Target Tiled server URI"), + container_path: str = Form(..., description="Target container, e.g. 'browse/myset'"), + description: str = Form("", description="Optional keyword(s) stored on every ingested node"), + files: list[UploadFile] = File(..., description="Image files to copy into Tiled"), +) -> dict: + """Stream uploaded files to temp storage and start a background ingest job. + + Each supported image becomes its own browsable node in *container_path* on + the connected Tiled server. Returns a ``job_id`` to poll for progress. + """ + tmp_dir = Path(tempfile.mkdtemp(prefix="ingest_")) + saved: list[tuple[str, Path]] = [] + for index, upload in enumerate(files): + ext = Path(upload.filename or "").suffix.lower() + if ext not in ingest_mod.IMAGE_EXTS: + await upload.close() + continue + dest = tmp_dir / f"{index:06d}{ext}" + # Stream in 1MB chunks — files can be 26MB+, never read() whole into memory. + with dest.open("wb") as out: + while chunk := await upload.read(1024 * 1024): + out.write(chunk) + await upload.close() + saved.append((upload.filename or dest.name, dest)) + + if not saved: + shutil.rmtree(tmp_dir, ignore_errors=True) + raise HTTPException(400, "No supported image files in upload") + + jid = ingest_mod.new_job(len(saved), server_uri, container_path) + threading.Thread( + target=ingest_mod.run_ingest_job, + args=(jid, server_uri, container_path, saved, description), + daemon=True, + ).start() + return {"job_id": jid, "total": len(saved), "container_path": container_path} + + +@app.get("/api/ingest/status/{job_id}") +async def ingest_status(job_id: str) -> dict: + """Return progress for an ingest job started by ``/api/ingest/upload``.""" + job = ingest_mod.get_job(job_id) + if job is None: + raise HTTPException(404, "Unknown job_id") + return job + + @app.get("/health") async def health() -> dict[str, str]: return {"status": "ok"} +# --------------------------------------------------------------------------- +# ipred proxy (GUI never calls port 8003 directly) +# --------------------------------------------------------------------------- + + +class IpredSessionRequest(BaseModel): + """Open an ipred session for one image/stack project.""" + + kind: str + source: str + server_uri: Optional[str] = None + root: Optional[str] = None + + +class IpredPreprocessRequest(BaseModel): + """Proxy featurize request.""" + + session_id: str + feature_setup_id: Optional[str] = None + composition_id: Optional[str] = None + slice_index: int = 0 + array_ref: Optional[str] = None + + +class IpredCompositionUpsertRequest(BaseModel): + """Create/update composition on ipred.""" + + name: str + nodes: list[dict[str, Any]] + outputs: list[str] + composition_id: Optional[str] = None + builtin: bool = False + + +class IpredArrayUploadRequest(BaseModel): + """Upload float array for remote ipred preprocess.""" + + session_id: str + shape: list[int] + dtype: str = "float32" + data_b64: str + array_ref: Optional[str] = None + + +class IpredTrainRequest(BaseModel): + """Proxy train request.""" + + session_id: str + shapes: list[dict[str, Any]] + feature_id: Optional[str] = None + trainer_id: str = "catboost" + config: Optional[dict[str, Any]] = None + + +class IpredInferRequest(BaseModel): + """Proxy infer request.""" + + session_id: str + model_id: Optional[str] = None + feature_id: Optional[str] = None + alpha: float = 0.05 + + +class IpredRethresholdRequest(BaseModel): + """Proxy rethreshold request.""" + + session_id: str + alpha: float + run_id: Optional[str] = None + + +class IpredSetupUpsertRequest(BaseModel): + """Create or update a Feature Setup on ipred.""" + + name: str + kind: str + procedure_id: Optional[str] = None + params: Optional[dict[str, Any]] = None + encoder_setup_id: Optional[str] = None + weights_path: Optional[str] = None + weights_format: Optional[str] = None + inference: Optional[dict[str, Any]] = None + setup_id: Optional[str] = None + + +class IpredManifoldSampleRequest(BaseModel): + """Suggest Labels via ipred feature bank.""" + + feature_id: str + k: int = 24 + box_size: Optional[int] = None + stride: Optional[int] = None + pca_dims: int = 16 + shapes: Optional[list[dict[str, Any]]] = None + + +def _ipred_http_error(exc: Exception) -> HTTPException: + import httpx + + import ipred_client as ipred_client_mod + + if isinstance(exc, httpx.HTTPStatusError): + detail = exc.response.text + try: + detail = exc.response.json() + except Exception: + pass + return HTTPException(exc.response.status_code, detail) + if isinstance(exc, httpx.ConnectError): + return HTTPException( + 503, + f"ipred unreachable at {ipred_client_mod.ipred_url()}", + ) + return HTTPException(500, str(exc)) + + +@app.get("/api/ipred/health") +async def ipred_health() -> dict: + """Liveness of the disjoint ipred.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.health() + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/sessions") +async def ipred_open_session(body: IpredSessionRequest) -> dict: + """Open/create an engine session for a project.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.open_session( + kind=body.kind, + source=body.source, + server_uri=body.server_uri, + root=body.root, + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/setups") +async def ipred_list_setups() -> dict: + """List Feature Setups from the ipred.""" + import ipred_client as ipred_client_mod + + try: + return {"setups": ipred_client_mod.list_setups()} + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/setups/{setup_id}") +async def ipred_get_setup(setup_id: str) -> dict: + """Get one Feature Setup from ipred.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.get_setup(setup_id) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/setups") +async def ipred_upsert_setup(body: IpredSetupUpsertRequest) -> dict: + """Create or update a Feature Setup on ipred.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.upsert_setup(body.model_dump(exclude_none=True)) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/trainers") +async def ipred_list_trainers() -> dict: + """List trainer plugin ids from ipred.""" + import ipred_client as ipred_client_mod + + try: + return {"trainers": ipred_client_mod.list_trainers()} + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/modules") +async def ipred_list_modules() -> dict: + """List composable feature modules.""" + import ipred_client as ipred_client_mod + + try: + return {"modules": ipred_client_mod.list_modules()} + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/compositions") +async def ipred_list_compositions() -> dict: + """List feature compositions.""" + import ipred_client as ipred_client_mod + + try: + return {"compositions": ipred_client_mod.list_compositions()} + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/compositions/{composition_id}") +async def ipred_get_composition(composition_id: str) -> dict: + """Get one composition.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.get_composition(composition_id) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/compositions") +async def ipred_upsert_composition(body: IpredCompositionUpsertRequest) -> dict: + """Create or update a composition.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.upsert_composition(body.model_dump(exclude_none=True)) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/compositions/preview") +async def ipred_preview_composition(body: IpredCompositionUpsertRequest) -> dict: + """Preview concat labels for a composition draft.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.preview_composition(body.model_dump(exclude_none=True)) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/sessions/{session_id}/arrays") +async def ipred_upload_array(session_id: str, body: IpredArrayUploadRequest) -> dict: + """Upload an array blob to ipred for remote preprocess.""" + import ipred_client as ipred_client_mod + + try: + payload = body.model_dump(exclude_none=True) + payload["session_id"] = session_id + return ipred_client_mod.upload_session_array(session_id, payload) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/manifold/sample") +async def ipred_manifold_sample(body: IpredManifoldSampleRequest) -> dict: + """Suggest Labels on an ipred feature bank.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.manifold_sample(body.model_dump(exclude_none=True)) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/manifold/{sample_id}/heatmap.png") +async def ipred_manifold_heatmap(sample_id: str) -> Response: + """Proxy manifold residual heatmap PNG.""" + import ipred_client as ipred_client_mod + + try: + return Response( + content=ipred_client_mod.manifold_heatmap_png(sample_id), + media_type="image/png", + headers={"Cache-Control": "private, max-age=300"}, + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/preprocess") +async def ipred_preprocess(body: IpredPreprocessRequest) -> dict: + """Cache-aware featurize via ipred.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.preprocess( + session_id=body.session_id, + feature_setup_id=body.feature_setup_id, + composition_id=body.composition_id, + slice_index=body.slice_index, + array_ref=body.array_ref, + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/features/{feature_id}/channels/{index}") +async def ipred_feature_channel(feature_id: str, index: int) -> Response: + """Proxy a feature-channel PNG from the ipred.""" + import ipred_client as ipred_client_mod + + try: + data = ipred_client_mod.feature_channel_bytes(feature_id, index) + return Response(content=data, media_type="image/png") + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/train") +async def ipred_train(body: IpredTrainRequest) -> dict: + """Train via ipred trainer plugin.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.train( + session_id=body.session_id, + shapes=body.shapes, + feature_id=body.feature_id, + trainer_id=body.trainer_id, + config=body.config, + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/infer") +async def ipred_infer(body: IpredInferRequest) -> dict: + """Infer + conformal products via ipred.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.infer( + session_id=body.session_id, + model_id=body.model_id, + feature_id=body.feature_id, + alpha=body.alpha, + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.post("/api/ipred/rethreshold") +async def ipred_rethreshold(body: IpredRethresholdRequest) -> dict: + """Rethreshold from cached proba via ipred.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.rethreshold( + session_id=body.session_id, + alpha=body.alpha, + run_id=body.run_id, + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/runs/{run_id}/commit.png") +async def ipred_run_commit(run_id: str) -> Response: + """Proxy commit map PNG.""" + import ipred_client as ipred_client_mod + + try: + return Response( + content=ipred_client_mod.run_commit_png(run_id), + media_type="image/png", + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/runs/{run_id}/status.png") +async def ipred_run_status(run_id: str) -> Response: + """Proxy status map PNG.""" + import ipred_client as ipred_client_mod + + try: + return Response( + content=ipred_client_mod.run_status_png(run_id), + media_type="image/png", + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +@app.get("/api/ipred/runs/{run_id}/proba/{class_index}.png") +async def ipred_run_proba(run_id: str, class_index: int) -> Response: + """Proxy softmax class heatmap PNG from ipred.""" + import ipred_client as ipred_client_mod + + try: + return Response( + content=ipred_client_mod.run_proba_png(run_id, class_index), + media_type="image/png", + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + +class IpredThresholdClassRequest(BaseModel): + class_id: int + threshold: float = 0.5 + + +@app.post("/api/ipred/runs/{run_id}/threshold-class") +async def ipred_threshold_class( + run_id: str, body: IpredThresholdClassRequest +) -> dict: + """Threshold one softmax class into a dense label map via ipred.""" + import ipred_client as ipred_client_mod + + try: + return ipred_client_mod.threshold_class_map( + run_id, + class_id=body.class_id, + threshold=body.threshold, + ) + except Exception as exc: + raise _ipred_http_error(exc) from exc + + # --------------------------------------------------------------------------- # Helpers # --------------------------------------------------------------------------- @@ -827,6 +2020,28 @@ def _parse_json_filters(raw: str) -> dict: return value if isinstance(value, dict) else {} +# --------------------------------------------------------------------------- +# Static SPA (production container only) +# --------------------------------------------------------------------------- +# In the Docker image the built frontend is copied to backend/static/, and +# FastAPI serves it so the app is a single same-origin service. In local dev +# this directory is absent (Vite serves the SPA), so the mount is skipped. +# Registered AFTER all /api routes so the catch-all never shadows them. +_STATIC_DIR = Path(__file__).parent / "static" +if _STATIC_DIR.is_dir(): + from fastapi.staticfiles import StaticFiles + + app.mount("/assets", StaticFiles(directory=str(_STATIC_DIR / "assets")), name="assets") + + @app.get("/{full_path:path}") + async def spa_fallback(full_path: str) -> FileResponse: + """Serve a real static file when it exists, else index.html (SPA routing).""" + candidate = _STATIC_DIR / full_path + if full_path and candidate.is_file() and _STATIC_DIR in candidate.resolve().parents: + return FileResponse(str(candidate)) + return FileResponse(str(_STATIC_DIR / "index.html")) + + if __name__ == "__main__": # pragma: no cover — convenience entry point import uvicorn diff --git a/backend/arrays.py b/backend/arrays.py index db2360d..8a27e38 100644 --- a/backend/arrays.py +++ b/backend/arrays.py @@ -42,15 +42,21 @@ _node_cache: TTLCache = TTLCache(ttl_seconds=300.0, max_entries=32) -def resolve_array(source: str, kind: str, server_uri: str | None = None) -> Any: +def resolve_array( + source: str, + kind: str, + server_uri: str | None = None, + root: str | None = None, +) -> Any: """Return a lazily-sliceable array node for *source*. - Results are cached by ``(kind, source, server_uri)`` for 5 minutes. + Results are cached by ``(kind, source, server_uri, root)`` for 5 minutes. Args: source: Tiled path (slash-separated) or local relative path. kind: ``"tiled"`` or ``"local"``. server_uri: Tiled server URI; only used when ``kind == "tiled"``. + root: Granted absolute local root; only used when ``kind == "local"``. Returns: A Tiled array node or a NumPy-compatible array. @@ -58,7 +64,7 @@ def resolve_array(source: str, kind: str, server_uri: str | None = None) -> Any: Raises: HTTPException: 404 if the path does not exist; 422 for unknown kind. """ - key = (kind, source, server_uri or "") + key = (kind, source, server_uri or "", root or "") cached = _node_cache.get(key) if cached is not None: return cached @@ -72,8 +78,12 @@ def resolve_array(source: str, kind: str, server_uri: str | None = None) -> Any: node = node[part] except KeyError as exc: raise HTTPException(404, f"Tiled path not found: {source!r}") from exc + # Drag-and-drop ingest nests datasets as browse//, so + # a Browse selection resolves to the wrapping container — descend to the + # array or slice-stack it represents. Direct array paths are unchanged. + node = _descend_to_stack(node) elif kind == "local": - node = local_fs.open_array(source) + node = local_fs.open_array(source, root) else: raise HTTPException(422, f"Unknown source kind: {kind!r}") @@ -81,6 +91,41 @@ def resolve_array(source: str, kind: str, server_uri: str | None = None) -> Any: return node +def _is_container_node(node: Any) -> bool: + """True if *node* is a Tiled container (vs. an array/leaf) node.""" + sf = getattr(node, "structure_family", None) + return str(getattr(sf, "value", sf)) == "container" + + +def _descend_to_stack(node: Any, max_depth: int = 8) -> Any: + """Resolve a Browse selection to the array/stack it should open. + + Drag-and-drop ingest nests datasets as ``browse//``. This + descends through *wrapper* containers (a container whose only/first child is + itself a container) but STOPS at a container whose children are arrays — + returning that container so it can be treated as a slice stack (one array + node per slice). Array nodes (and non-Tiled inputs) are returned unchanged. + """ + depth = 0 + while _is_container_node(node) and depth < max_depth: + try: + first = next(iter(node)) + except StopIteration: + return node # empty container — nothing to descend into + child = node[first] + if not _is_container_node(child): + return node # container of arrays → the stack itself + node = child + depth += 1 + return node + + +def _stack_keys(node: Any) -> list[str]: + """Sorted child keys of a container-stack (ingest zero-pads, so lexical + order == slice order).""" + return sorted(node) + + def array_shape_meta(node: Any) -> dict[str, Any]: """Return shape-dispatch metadata for *node*. @@ -94,6 +139,9 @@ def array_shape_meta(node: Any) -> dict[str, Any]: Raises: HTTPException: 422 for unsupported array shapes (e.g. 1-D or 5-D). """ + if _is_container_node(node): + return _stack_shape_meta(node) + raw = ( np.asarray(node) if hasattr(node, "__array__") and not hasattr(node, "shape") @@ -145,6 +193,41 @@ def array_shape_meta(node: Any) -> dict[str, Any]: raise HTTPException(422, f"Unsupported array shape: {shape}") +def _stack_shape_meta(node: Any) -> dict[str, Any]: + """Shape-dispatch metadata for a container-of-arrays treated as a stack. + + Each child is one slice; ``n_slices`` is the child count and the per-slice + H/W/dtype/is_rgb come from the first child. The sorted child keys are stored + under ``"keys"`` so :func:`read_slice` maps a slice index to its node. + + Raises: + HTTPException: 422 for an empty container or unsupported slice shape. + """ + keys = _stack_keys(node) + if not keys: + raise HTTPException(422, "Container has no array slices") + first = node[keys[0]] + fshape = tuple(first.shape) + dtype = str(first.dtype) + + if len(fshape) == 2: + h, w, is_rgb = fshape[0], fshape[1], False + elif len(fshape) == 3 and fshape[2] in (3, 4): + h, w, is_rgb = fshape[0], fshape[1], True + else: + raise HTTPException(422, f"Unsupported slice shape in stack: {fshape}") + + return { + "n_slices": len(keys), + "height": h, + "width": w, + "dtype": dtype, + "is_rgb": is_rgb, + "shape_kind": "STACK", + "keys": keys, + } + + def read_slice(node: Any, meta: dict[str, Any], idx: int) -> np.ndarray: """Read one slice from *node* and return it as a NumPy array. @@ -168,4 +251,10 @@ def read_slice(node: Any, meta: dict[str, Any], idx: int) -> np.ndarray: return np.asarray(node[idx]) if kind == "NHWC": return np.asarray(node[idx]) + if kind == "STACK": + keys = meta.get("keys") or _stack_keys(node) + if not keys: + raise HTTPException(422, "Container has no array slices") + key = keys[idx] if 0 <= idx < len(keys) else keys[0] + return np.asarray(node[key]) raise HTTPException(422, f"Cannot slice shape kind: {kind}") diff --git a/backend/browse_helpers.py b/backend/browse_helpers.py index c897c86..73cb3f1 100644 --- a/backend/browse_helpers.py +++ b/backend/browse_helpers.py @@ -36,6 +36,20 @@ "studio_updated_at", ) +# Ingest-time keys worth showing as Browse facets even when only ONE distinct +# value exists (e.g. a single dropped file, or a whole batch sharing one +# user-supplied description). Without this they'd be hidden by the >=2 rule. +_INGEST_FACET_RAW_KEYS: tuple[str, ...] = ( + "description", + "sample_name", + "original_filename", +) + +# Keys that qualify as a facet with a single distinct value (vs. the default >=2). +_SINGLE_VALUE_FACET_RAW_KEYS: frozenset[str] = frozenset( + (*_STUDIO_RAW_KEYS, *_INGEST_FACET_RAW_KEYS), +) + # Raw keys that are stored at the array-node level rather than on the parent # sample container. When a filter references one of these, we switch to a # per-sample search path. @@ -116,12 +130,17 @@ def build_field_mapping(container_node: Any) -> FieldMapping: ) +# Ingest writes these at the dataset-container level, so they're always worth +# offering as facets even when the scanned sample metadata didn't surface them. +_INJECTED_INGEST_RAW_KEYS: tuple[str, ...] = ("description", "sample_name") + + def _inject_studio_keys( display_to_raw: dict[str, str], raw_to_display: dict[str, str], ) -> None: - """Ensure studio annotation fields are always available in Browse.""" - for raw_key in _STUDIO_RAW_KEYS: + """Ensure studio annotation + ingest description fields are always in Browse.""" + for raw_key in (*_STUDIO_RAW_KEYS, *_INJECTED_INGEST_RAW_KEYS): display_key = _display_name(raw_key) display_to_raw.setdefault(display_key, raw_key) raw_to_display.setdefault(raw_key, display_key) @@ -157,14 +176,52 @@ def _display_name(raw_key: str) -> str: # Distinct values # --------------------------------------------------------------------------- +def scoped_metadata_rows(node: Any, limit: int = 5000) -> list[dict]: + """Read each child's metadata once for a *specific* container. + + Tiled's ``container.distinct()`` aggregates across the whole catalog, not + just the node it's called on, so it can't be used to compute values scoped + to a chosen sub-container. Iterating children gives correctly-scoped data + at the cost of one metadata read per child. + """ + rows: list[dict] = [] + try: + for key in list(node)[:limit]: + try: + meta = node[key].metadata + rows.append(dict(meta) if meta else {}) + except Exception: # noqa: BLE001 — skip children that fail to open + continue + except Exception as exc: # noqa: BLE001 + logger.warning("scoped_metadata_rows iteration failed: %s", exc) + return rows + + +def distinct_from_rows(rows: list[dict], raw_key: str) -> list[dict]: + """Tally distinct values of *raw_key* across pre-read metadata *rows*.""" + counts: dict[Any, int] = {} + for meta in rows: + val = meta.get(raw_key) + if val is None: + continue + counts[val] = counts.get(val, 0) + 1 + return [{"value": v, "count": c} for v, c in counts.items()] + + def tiled_distinct_values( container_node: Any, raw_key: str, filters: Optional[dict] = None, field_mapping: Optional[FieldMapping] = None, limit: int = 500, + scoped: bool = False, ) -> dict: - """Call ``container.distinct(raw_key)`` with optional upstream filters. + """Return distinct values (+ counts) for *raw_key* under optional filters. + + When *scoped* is True, values are computed by iterating the (filtered) + container's children so they reflect only that container — use this when + browsing a specific ``container_path``. Otherwise the faster but + catalog-global ``container.distinct()`` is used. Returns:: @@ -189,12 +246,15 @@ def tiled_distinct_values( except Exception: # noqa: BLE001 — Tiled raises a range of errors here pass - try: - result = node.distinct(raw_key, counts=True) - raw_values = result.get("metadata", {}).get(raw_key, []) - except Exception as exc: - logger.warning("distinct() failed for key %r: %s", raw_key, exc) - raw_values = [] + if scoped: + raw_values = distinct_from_rows(scoped_metadata_rows(node), raw_key) + else: + try: + result = node.distinct(raw_key, counts=True) + raw_values = result.get("metadata", {}).get(raw_key, []) + except Exception as exc: + logger.warning("distinct() failed for key %r: %s", raw_key, exc) + raw_values = [] non_null = [entry for entry in raw_values if _is_valid_value(entry.get("value"))] sorted_vals = sorted(non_null, key=lambda e: (-e.get("count", 0), str(e["value"])))[:limit] @@ -262,8 +322,26 @@ def _search_container_only( for k in list(node)[:limit]: try: entry = node[k] - meta = dict(entry.metadata) if hasattr(entry, "metadata") else {} - items.append({"path": _join(prefix, k), "sample": k, "metadata": meta}) + meta = dict(entry.metadata) if hasattr(entry, "metadata") and entry.metadata else {} + n_slices = 1 + # Drag-and-drop ingest nests as browse//: the + # dataset is a container of array slices, with the descriptive + # metadata on the children. Report the slice count (so the UI can + # offer drill-in) and, if the container itself is metadata-less, + # borrow the first child's metadata (cheap metadata-only read). + if _is_container(entry): + child_keys = list(entry) + if child_keys and not _is_container(entry[child_keys[0]]): + n_slices = len(child_keys) # flat stack of array slices + if not meta: + cmeta = entry[child_keys[0]].metadata + meta = dict(cmeta) if cmeta else {} + items.append({ + "path": _join(prefix, k), + "sample": k, + "metadata": meta, + "n_slices": n_slices, + }) except Exception: # noqa: BLE001 continue except Exception as exc: # noqa: BLE001 @@ -272,6 +350,12 @@ def _search_container_only( return {"items": items, "total": len(items)} +def _is_container(entry: Any) -> bool: + """True if *entry* is a Tiled container (vs. an array/leaf) node.""" + sf = getattr(entry, "structure_family", None) + return str(getattr(sf, "value", sf)) == "container" + + def _search_array_only( container_node: Any, raw_filters: list[tuple[str, str]], @@ -398,7 +482,9 @@ def _typed_query_value(raw: str) -> Any: except (ValueError, TypeError): pass try: - return float(raw) + fv = float(raw) + if str(fv) == raw: + return fv except (ValueError, TypeError): pass return raw diff --git a/backend/clf_shelf.py b/backend/clf_shelf.py new file mode 100644 index 0000000..aff5145 --- /dev/null +++ b/backend/clf_shelf.py @@ -0,0 +1,223 @@ +"""# REMOVE THIS AND USE YOUR OWN STUFF + +Persist trained CatBoost + Mondrian cal scores + feature recipe to disk. + +TEMPORARY scaffold under ``$LOCAL_DATA_ROOT/.clf_models//``. Replace with +your own model registry, then delete this module and the ``/api/clf/models`` +shelf routes + ModelShelfPanel. +""" + +from __future__ import annotations + +import json +import logging +import os +import tempfile +import uuid +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np +from catboost import CatBoostClassifier + +import pixel_clf as pixel_clf_mod +from pixel_clf import ClfModel + +logger = logging.getLogger(__name__) + +# ############################################################################## +# # REMOVE THIS AND USE YOUR OWN STUFF — on-disk CatBoost model shelf +# ############################################################################## + + +def _shelf_root() -> Path: + return Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve() / ".clf_models" + + +@dataclass(frozen=True) +class ShelfMeta: + """Indexed metadata for a persisted classifier.""" + + id: str + name: str + class_ids: list[int] + n_train: int + n_cal: int + train_accuracy: float + uses_sam: bool + feature_recipe: dict[str, Any] + created_at: str + n_features: int + + +def _dir_for(model_id: str) -> Path: + return _shelf_root() / model_id + + +def save_model( + model: ClfModel, + *, + name: str, + feature_recipe: dict[str, Any], +) -> ShelfMeta: + """Write CatBoost, cal scores, PCA, and recipe to disk; return shelf meta.""" + from datetime import datetime, timezone + + shelf_id = uuid.uuid4().hex + dest = _dir_for(shelf_id) + dest.mkdir(parents=True, exist_ok=True) + + cb_path = dest / "model.cbm" + model.model.save_model(str(cb_path)) + + cal: dict[str, list[float]] = { + str(k): [float(x) for x in v.tolist()] for k, v in model.cal_scores_by_class.items() + } + (dest / "cal_scores.json").write_text(json.dumps(cal), encoding="utf-8") + + if model.sam_pca_mean is not None and model.sam_pca_components is not None: + np.savez_compressed( + dest / "sam_pca.npz", + mean=model.sam_pca_mean, + components=model.sam_pca_components, + ) + + n_features = int(getattr(model.model, "feature_count_", 0) or 0) + if not n_features and model.feature_labels: + n_features = len(model.feature_labels) + + meta = { + "id": shelf_id, + "name": name, + "class_ids": list(model.class_ids), + "n_train": model.n_train, + "n_cal": model.n_cal, + "n_samples": model.n_samples, + "train_accuracy": model.train_accuracy, + "uses_sam": model.uses_sam, + "feature_recipe": feature_recipe, + "feature_labels": list(model.feature_labels), + "params": model.params, + "n_trees": model.n_trees, + "n_features": n_features, + "created_at": datetime.now(timezone.utc).isoformat(), + } + (dest / "meta.json").write_text(json.dumps(meta, indent=2), encoding="utf-8") + logger.info("Saved classifier shelf model %s (%s)", shelf_id, name) + return ShelfMeta( + id=shelf_id, + name=name, + class_ids=list(model.class_ids), + n_train=model.n_train, + n_cal=model.n_cal, + train_accuracy=model.train_accuracy, + uses_sam=model.uses_sam, + feature_recipe=feature_recipe, + created_at=meta["created_at"], + n_features=n_features, + ) + + +def list_models() -> list[dict[str, Any]]: + """List saved shelf models (meta only).""" + root = _shelf_root() + if not root.is_dir(): + return [] + out: list[dict[str, Any]] = [] + for child in sorted(root.iterdir(), key=lambda p: p.stat().st_mtime, reverse=True): + meta_path = child / "meta.json" + if not meta_path.is_file(): + continue + try: + meta = json.loads(meta_path.read_text(encoding="utf-8")) + out.append(meta) + except (OSError, json.JSONDecodeError) as exc: + logger.warning("skip corrupt shelf entry %s: %s", child, exc) + return out + + +def delete_model(shelf_id: str) -> bool: + """Remove a shelf model directory. Returns False if missing.""" + dest = _dir_for(shelf_id) + if not dest.is_dir(): + return False + for p in dest.iterdir(): + p.unlink(missing_ok=True) + dest.rmdir() + return True + + +def load_into_cache(shelf_id: str) -> ClfModel: + """Load a shelf model into the in-memory CatBoost cache for predict.""" + dest = _dir_for(shelf_id) + meta_path = dest / "meta.json" + cb_path = dest / "model.cbm" + if not meta_path.is_file() or not cb_path.is_file(): + raise FileNotFoundError(f"shelf model {shelf_id} not found") + + meta = json.loads(meta_path.read_text(encoding="utf-8")) + clf = CatBoostClassifier() + clf.load_model(str(cb_path)) + + cal_raw = json.loads((dest / "cal_scores.json").read_text(encoding="utf-8")) + cal_scores = {int(k): np.asarray(v, dtype=np.float64) for k, v in cal_raw.items()} + + pca_mean = None + pca_comp = None + pca_path = dest / "sam_pca.npz" + if pca_path.is_file(): + z = np.load(pca_path) + pca_mean = z["mean"] + pca_comp = z["components"] + + # Dump model json for tree browsing (best-effort) + model_json: dict[str, Any] = {} + try: + with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as tmp: + tmp_path = Path(tmp.name) + try: + clf.save_model(str(tmp_path), format="json") + model_json = json.loads(tmp_path.read_text(encoding="utf-8")) + finally: + tmp_path.unlink(missing_ok=True) + except Exception: # noqa: BLE001 + model_json = {} + + n_trees = len(model_json.get("oblivious_trees") or []) + feat_labels = tuple(meta.get("feature_labels") or []) + fi_raw = np.asarray(clf.get_feature_importance(), dtype=np.float64) + if len(feat_labels) != fi_raw.size: + feat_labels = tuple(f"f{i}" for i in range(fi_raw.size)) + order = np.argsort(-fi_raw) + feature_importances = tuple((feat_labels[int(i)], float(fi_raw[int(i)])) for i in order) + + entry = ClfModel( + model_id=f"shelf:{shelf_id}", + feature_job_id=f"shelf:{shelf_id}", + class_ids=tuple(int(x) for x in meta["class_ids"]), + n_samples=int(meta.get("n_samples") or 0), + n_train=int(meta.get("n_train") or 0), + n_cal=int(meta.get("n_cal") or 0), + model=clf, + feature_labels=feat_labels, + train_accuracy=float(meta.get("train_accuracy") or 0), + feature_importances=feature_importances, + params=dict(meta.get("params") or {}), + n_trees=n_trees, + cal_scores_by_class=cal_scores, + _model_json=model_json, + sam_pca_mean=pca_mean, + sam_pca_components=pca_comp, + uses_sam=bool(meta.get("uses_sam")), + ) + pixel_clf_mod._model_cache.set(entry.model_id, entry) + return entry + + +def get_meta(shelf_id: str) -> dict[str, Any]: + """Return meta.json for a shelf id.""" + meta_path = _dir_for(shelf_id) / "meta.json" + if not meta_path.is_file(): + raise FileNotFoundError(f"shelf model {shelf_id} not found") + return json.loads(meta_path.read_text(encoding="utf-8")) diff --git a/backend/coco_export.py b/backend/coco_export.py index 7fbd14f..0c528d3 100644 --- a/backend/coco_export.py +++ b/backend/coco_export.py @@ -98,24 +98,61 @@ def _brush_mask(strokes: list[dict[str, Any]], h: int, w: int) -> np.ndarray: return mask +def _apply_erased(mask: np.ndarray, erased: list[dict[str, Any]] | None) -> np.ndarray: + """Subtract erase carve-outs from a vector shape's mask (in-place-safe).""" + if not erased: + return mask + for stroke in erased: + stamp = np.zeros(mask.shape, dtype=bool) + _stamp_stroke(stamp, stroke["points"], stroke["radius"]) + mask &= ~stamp + return mask + + def shape_to_mask(shape: dict[str, Any], h: int, w: int) -> np.ndarray: """Rasterize one Shape (image-pixel coords) to an (h, w) boolean mask.""" kind = shape["kind"] if kind == "polygon": - return _polygon_mask(shape["points"], h, w) - if kind == "rectangle": - return _rect_mask(shape["x"], shape["y"], shape["w"], shape["h"], h, w) - if kind == "ellipse": - return _ellipse_mask(shape["cx"], shape["cy"], shape["rx"], shape["ry"], h, w) - if kind == "brush": + mask = _polygon_mask(shape["points"], h, w) + # Carve inner rings (holes), e.g. from "invert shape", so exports match. + for hole in shape.get("holes") or []: + mask &= ~_polygon_mask(hole, h, w) + elif kind == "rectangle": + mask = _rect_mask(shape["x"], shape["y"], shape["w"], shape["h"], h, w) + elif kind == "ellipse": + mask = _ellipse_mask(shape["cx"], shape["cy"], shape["rx"], shape["ry"], h, w) + elif kind == "brush": + # Brush erase strokes are part of its own stroke list, not `erased`. return _brush_mask(shape["strokes"], h, w) - raise ValueError(f"Unknown shape kind: {kind!r}") + else: + raise ValueError(f"Unknown shape kind: {kind!r}") + # Vector shapes can carry eraser carve-outs applied after rasterization. + return _apply_erased(mask, shape.get("erased")) # --------------------------------------------------------------------------- # Mask -> COCO annotation # --------------------------------------------------------------------------- +def _encode_png(arr: np.ndarray, compress_level: int = 0) -> bytes: + """Encode a single-channel uint8 array as a grayscale PNG. + + Masks are tiny and pre-binarized; ``compress_level=0`` (store) is the fastest + and the size cost is negligible. + """ + import io + from PIL import Image as PILImage + + buf = io.BytesIO() + PILImage.fromarray(arr, mode="L").save(buf, format="PNG", compress_level=compress_level) + return buf.getvalue() + + +def _safe_name(name: str) -> str: + """Filesystem-safe folder name for a class label.""" + return "".join(c if c.isalnum() or c in "-_" else "_" for c in name) or "class" + + def _mask_to_polygons(mask: np.ndarray, min_pts: int = 6) -> list[list[float]]: """Outer+inner contours as COCO-style flat polygons (holes NOT encoded -- see RLE).""" polys: list[list[float]] = [] @@ -131,14 +168,26 @@ def mask_to_coco_ann( ann_id: int, image_id: int, category_id: int, + *, + include_polygons: bool = False, + poly_override: list[list[float]] | None = None, ) -> dict[str, Any]: - """Build a COCO annotation: RLE in segmentation, polygons in segmentation_poly. + """Build a COCO annotation: RLE in segmentation, optional polygon copy. RLE is exact (holes, multiple components) and is what SAM3's pycocotools - segm path consumes. The polygon copy is a convenience for external viewers. + segm path consumes — always present. The polygon copy (``segmentation_poly``) + is a convenience for external viewers; generating it via ``find_contours`` is + costly, so it is opt-in. ``poly_override`` lets a polygon-kind shape reuse its + own points instead of re-tracing the mask. """ rle = mask_utils.encode(np.asfortranarray(mask.astype(np.uint8))) rle["counts"] = rle["counts"].decode("ascii") + if not include_polygons: + seg_poly: list[list[float]] = [] + elif poly_override is not None: + seg_poly = poly_override + else: + seg_poly = _mask_to_polygons(mask) return { "id": ann_id, "image_id": image_id, @@ -147,7 +196,7 @@ def mask_to_coco_ann( "area": float(mask_utils.area(rle)), "bbox": [float(v) for v in mask_utils.toBbox(rle)], "segmentation": rle, - "segmentation_poly": _mask_to_polygons(mask), + "segmentation_poly": seg_poly, } @@ -189,9 +238,15 @@ def write_coco_split( *, mode: str = "fail", info: dict[str, Any] | None = None, + zf: Any = None, + arc_prefix: str = "", ) -> dict[str, Any]: """Write/merge one split directory. + If ``zf`` (an open ``zipfile.ZipFile``) is given, every file is also added to + it under ``arc_prefix`` from the same in-memory bytes — a single pass with no + disk re-read (used to build the download .zip cheaply). + In merge mode existing image entries are matched by file_name: matched images are replaced (their old annotations dropped), new images get ids above the existing max; annotation ids likewise @@ -215,7 +270,11 @@ def write_coco_split( """ split_dir.mkdir(parents=True, exist_ok=True) coco_path = split_dir / "_annotations.coco.json" - sidecar_path = split_dir / "_studio_shapes.json" + + def _emit(rel: str, data: bytes) -> None: + """Mirror a just-written file into the download zip (if building one).""" + if zf is not None: + zf.writestr(arc_prefix + rel, data) existing_coco: dict[str, Any] = {"images": [], "annotations": [], "categories": []} if coco_path.exists(): @@ -269,12 +328,30 @@ def write_coco_split( max_img_id += 1 img_id = max_img_id png_bytes: bytes | None = img.pop("png_bytes", None) + # Pop mask payloads so they don't leak into the COCO json. + label_png: bytes | None = img.pop("label_png_bytes", None) + class_masks: dict[str, bytes] = img.pop("class_masks", {}) or {} out_img = {**img, "id": img_id} out_images.append(out_img) - # Write PNG + fname = img["file_name"] + # Write PNG (+ mirror into zip) if png_bytes is not None: - (split_dir / img["file_name"]).write_bytes(png_bytes) + (split_dir / fname).write_bytes(png_bytes) + _emit(fname, png_bytes) + + # Write masks: masks/semantic/ (label map) + masks//. + if label_png is not None: + sem_dir = split_dir / "masks" / "semantic" + sem_dir.mkdir(parents=True, exist_ok=True) + (sem_dir / fname).write_bytes(label_png) + _emit(f"masks/semantic/{fname}", label_png) + for cname, cbytes in class_masks.items(): + safe = _safe_name(cname) + cls_dir = split_dir / "masks" / safe + cls_dir.mkdir(parents=True, exist_ok=True) + (cls_dir / fname).write_bytes(cbytes) + _emit(f"masks/{safe}/{fname}", cbytes) # Write annotations for this image img_anns = [a for a in annotations if a.get("_image_file_name") == img["file_name"]] @@ -297,7 +374,17 @@ def write_coco_split( "categories": final_cats, "annotations": out_anns, } - coco_path.write_text(json.dumps(coco_doc, indent=2)) + coco_json = json.dumps(coco_doc, indent=2) + coco_path.write_text(coco_json) + _emit("_annotations.coco.json", coco_json.encode("utf-8")) + + # Legend mapping semantic label index -> class name/color (for mask viewers). + masks_dir = split_dir / "masks" + if masks_dir.exists(): + legend = [{"id": c["id"], "name": c["name"], "color": c.get("color")} for c in final_cats] + legend_json = json.dumps(legend, indent=2) + (masks_dir / "legend.json").write_text(legend_json) + _emit("masks/legend.json", legend_json.encode("utf-8")) return { "n_images": len(images), @@ -313,6 +400,8 @@ def build_export_plan( array_shape_meta_fn: Any, read_slice_fn: Any, sample_global_stats_fn: Any, + progress_cb: Any = None, + include_polygons: bool = False, ) -> dict[str, Any]: """Build the full export plan (rasterize all shapes, render PNGs). @@ -328,6 +417,7 @@ def build_export_plan( Dict with splits (each has images, categories, annotations, info). """ import io + from concurrent.futures import ThreadPoolExecutor from PIL import Image as PILImage meta = array_shape_meta_fn(node) @@ -345,63 +435,113 @@ def build_export_plan( if render_opts_mapped["norm"] == "global": global_range = sample_global_stats_fn(node, meta) - classes_by_id = {c.classId: c for c in payload.classes} cat_id_map: dict[int, int] = {} + cat_id_to_name: dict[int, str] = {} categories: list[dict[str, Any]] = [] for i, cls in enumerate(payload.classes, 1): - categories.append({"id": i, "name": cls.label, "supercategory": "object"}) + # `color` is an extra key (COCO ignores it) used for the mask legend. + categories.append({ + "id": i, "name": cls.label, "supercategory": "object", + "color": getattr(cls, "color", None), + }) cat_id_map[cls.classId] = i + cat_id_to_name[i] = cls.label all_slice_keys = list(payload.slices.keys()) neg_keys = set(str(k) for k in payload.negative_slices) - all_keys = list(set(all_slice_keys) | neg_keys) + all_keys = sorted(set(all_slice_keys) | neg_keys, key=int) resolved_splits = _resolve_split(all_keys, {str(k): v for k, v in payload.split_by_slice.items()}, payload.auto_split) - splits_data: dict[str, dict[str, Any]] = {} - skipped_zero_area = 0 - - for slice_key in all_keys: - split = resolved_splits.get(slice_key, "train") - if split not in splits_data: - splits_data[split] = {"images": [], "annotations": []} + source_stem = str(payload.source).replace("/", "_").replace("\\", "_")[-30:] + def _process_slice(slice_key: str) -> dict[str, Any]: + """Read → render → PNG-encode → rasterize one slice. Pure & independent, + so slices run concurrently — the read/encode I/O dominates wall time.""" slice_idx = int(slice_key) arr = read_slice_fn(node, meta, slice_idx) rgb = render_slice_fn(arr, render_opts_mapped, global_range) buf = io.BytesIO() - PILImage.fromarray(rgb).save(buf, format="PNG") + # compress_level=1: PNG encode of a large frame is a big chunk of export + # time; level 1 is ~3x faster than the default for a small size cost. + PILImage.fromarray(rgb).save(buf, format="PNG", compress_level=1) png_bytes = buf.getvalue() - source_stem = str(payload.source).replace("/", "_").replace("\\", "_")[-30:] file_name = f"{source_stem}_{slice_idx:04d}.png" - - shapes = payload.slices.get(slice_key, []) anns: list[dict[str, Any]] = [] - for shape in shapes: + skipped = 0 + # Semantic label map (class index per pixel, 0 = bg) + per-class binary + # masks, built from the same rasterization used for the COCO annotations. + label = np.zeros((h, w), dtype=np.uint8) + class_acc: dict[str, np.ndarray] = {} + for shape in payload.slices.get(slice_key, []): shape_dict = shape if isinstance(shape, dict) else shape.model_dump() mask = shape_to_mask(shape_dict, h, w) - area = float(mask.sum()) - if area < 1: - skipped_zero_area += 1 + if float(mask.sum()) < 1: + skipped += 1 logger.warning("Zero-area shape %r skipped", shape_dict.get("id")) continue - class_id = shape_dict.get("classId", 1) - cat_id = cat_id_map.get(class_id, 1) - ann = mask_to_coco_ann(mask, ann_id=0, image_id=0, category_id=cat_id) + cat_id = cat_id_map.get(shape_dict.get("classId", 1), 1) + # A polygon shape can reuse its own points instead of re-tracing. + poly_override = ( + [shape_dict["points"]] + if include_polygons and shape_dict.get("kind") == "polygon" and shape_dict.get("points") + else None + ) + ann = mask_to_coco_ann( + mask, ann_id=0, image_id=0, category_id=cat_id, + include_polygons=include_polygons, poly_override=poly_override, + ) ann["_image_file_name"] = file_name anns.append(ann) + # Paint label map (last shape wins on overlap) + accumulate per class. + label[mask] = cat_id + cname = cat_id_to_name.get(cat_id, str(cat_id)) + if cname not in class_acc: + class_acc[cname] = np.zeros((h, w), dtype=bool) + class_acc[cname] |= mask + + # Semantic map is always emitted (zeros for negative slices) so every + # exported image has a matching label; per-class only where present. + class_masks = {name: _encode_png((m * 255).astype(np.uint8)) for name, m in class_acc.items()} + + result = { + "split": resolved_splits.get(slice_key, "train"), + "image": { + "file_name": file_name, + "height": h, + "width": w, + "source_key": str(payload.source), + "slice_index": slice_idx, + "png_bytes": png_bytes, + "label_png_bytes": _encode_png(label), + "class_masks": class_masks, + }, + "annotations": anns, + "skipped": skipped, + } + if progress_cb is not None: + progress_cb(f"slice {slice_idx}: {len(anns)} object(s)") + return result - splits_data[split]["images"].append({ - "file_name": file_name, - "height": h, - "width": w, - "source_key": str(payload.source), - "slice_index": slice_idx, - "png_bytes": png_bytes, - }) - splits_data[split]["annotations"].extend(anns) + splits_data: dict[str, dict[str, Any]] = {} + skipped_zero_area = 0 + + # I/O-bound (Tiled reads) + partially GIL-releasing (numpy/PIL/pycocotools) + # → thread pool. EXPORT_WORKERS tunes parallelism. ex.map preserves order. + import os + workers = min(int(os.getenv("EXPORT_WORKERS", "16")), max(1, len(all_keys))) + with ThreadPoolExecutor(max_workers=workers) as ex: + results = list(ex.map(_process_slice, all_keys)) + + for r in results: + split = r["split"] + if split not in splits_data: + splits_data[split] = {"images": [], "annotations": []} + splits_data[split]["images"].append(r["image"]) + splits_data[split]["annotations"].extend(r["annotations"]) + skipped_zero_area += r["skipped"] info = { "description": "SAM3 fine-tune dataset -- SAM3 Annotation Studio", diff --git a/backend/coco_import.py b/backend/coco_import.py index 9fde5b3..bc19404 100644 --- a/backend/coco_import.py +++ b/backend/coco_import.py @@ -80,7 +80,6 @@ def _import_from_coco(coco: dict[str, Any]) -> dict[str, Any]: from skimage import measure images_by_id = {img["id"]: img for img in coco.get("images", [])} - cats_by_id = {c["id"]: c for c in coco.get("categories", [])} classes = [ {"classId": c["id"], "label": c["name"], "color": "#1f77b4", "isVisible": True} diff --git a/backend/export_jobs.py b/backend/export_jobs.py new file mode 100644 index 0000000..315fe13 --- /dev/null +++ b/backend/export_jobs.py @@ -0,0 +1,79 @@ +"""In-memory job registry for COCO/mask exports. + +Mirrors the ingest job pattern (ingest.py): export runs on a background thread +and reports phase/progress/log lines that the UI polls via +``/api/export/status/{job_id}``. Kept deliberately simple — a process-local dict +guarded by a lock; jobs are ephemeral and fine to lose on restart. +""" +from __future__ import annotations + +import threading +import uuid +from typing import Any + +_jobs: dict[str, dict[str, Any]] = {} +_lock = threading.Lock() +_MAX_LOG = 200 + + +def new_job(dataset_path: str) -> str: + """Register a new export job and return its id.""" + jid = uuid.uuid4().hex + with _lock: + _jobs[jid] = { + "state": "pending", # pending | running | done | error + "phase": "queued", + "done": 0, + "total": 0, + "log": [], + "result": None, + "error": None, + "dataset_path": dataset_path, + "zip_path": None, # internal; surfaced as result.zip_available + } + return jid + + +def get_job(jid: str) -> dict | None: + """Return a snapshot copy of the job (without the internal zip_path).""" + with _lock: + job = _jobs.get(jid) + if not job: + return None + snap = dict(job) + snap.pop("zip_path", None) + return snap + + +def zip_path(jid: str) -> str | None: + with _lock: + job = _jobs.get(jid) + return job.get("zip_path") if job else None + + +def update(jid: str, **kw: Any) -> None: + with _lock: + if jid in _jobs: + _jobs[jid].update(kw) + + +def set_total(jid: str, total: int) -> None: + with _lock: + if jid in _jobs: + _jobs[jid]["total"] = total + + +def bump(jid: str, n: int = 1) -> None: + with _lock: + if jid in _jobs: + _jobs[jid]["done"] += n + + +def log(jid: str, message: str) -> None: + with _lock: + job = _jobs.get(jid) + if not job: + return + job["log"].append(message) + if len(job["log"]) > _MAX_LOG: + del job["log"][: len(job["log"]) - _MAX_LOG] diff --git a/backend/feature_manifold.py b/backend/feature_manifold.py new file mode 100644 index 0000000..e201fb6 --- /dev/null +++ b/backend/feature_manifold.py @@ -0,0 +1,358 @@ +"""Greedy feature-variance box sampling for annotation guidance. + +Sliding windows scored by whitened feature variance; picks suppress nearby +(spatial Chebyshev exclusion ≥ box side) and feature-similar boxes. +""" + +from __future__ import annotations + +import logging +import math +import uuid +from dataclasses import dataclass +from io import BytesIO +from typing import Any + +import numpy as np +from PIL import Image as PILImage +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler + +from cache import TTLCache +from multiscale_features import FeatureJob + +logger = logging.getLogger(__name__) + +_sample_cache: TTLCache = TTLCache(ttl_seconds=600.0, max_entries=32) +_COS_HARD = 0.95 +_COS_ALPHA = 2.0 + + +@dataclass(frozen=True) +class ManifoldSample: + """Inducing boxes + residual interestingness heatmap.""" + + job_id: str + points: list[dict[str, Any]] + heatmap: np.ndarray # HxW float32 in [0, 1] + meta: dict[str, Any] + + +@dataclass(frozen=True) +class CachedManifoldSample: + """Server-side manifold sample for PNG GET.""" + + sample_id: str + heatmap_png: bytes + points: list[dict[str, Any]] + meta: dict[str, Any] + + +def derived_exclusion_radius(h: int, w: int, k: int) -> float: + """Disk radius so roughly K disks pack the image plane.""" + k = max(1, int(k)) + area = float(h * w) + r = math.sqrt(area / (math.pi * k)) + r_max = min(h, w) / 4.0 + return float(max(8.0, min(r, r_max))) + + +def sample_inducing_points( + job: FeatureJob, + *, + k: int = 24, + box_size: int | None = None, + stride: int | None = None, + pca_dims: int = 16, + seed: int = 0, + mask: np.ndarray | None = None, +) -> ManifoldSample: + """Greedy variance boxes with spatial + feature-space exclusion. + + Args: + job: Cached feature bank (``float_stack``). + k: Number of inducing boxes (clamped to [2, 128]). + box_size: Full square side length in image pixels. When omitted, derived + from the K-based exclusion radius (``2 * round(r)``). + stride: Optional hop override; default ``max(4, box_half)``. + pca_dims: PCA dimensionality after standardization. + seed: RNG for PCA solver stability. + mask: Optional HxW placement mask. When set, only windows whose full + box lies inside the mask are eligible. + + Returns: + ManifoldSample with box centers, boxes, radius, and residual heatmap. + """ + feats = np.asarray(job.float_stack, dtype=np.float32) + if feats.ndim != 3: + raise ValueError(f"float_stack must be HxWxC, got {feats.shape}") + h, w, c = feats.shape + if c < 1: + raise ValueError("empty feature stack") + + place_mask: np.ndarray | None = None + mask_pixels = 0 + if mask is not None: + m = np.asarray(mask) + if m.shape != (h, w): + raise ValueError(f"mask shape {m.shape} does not match image {(h, w)}") + place_mask = m.astype(bool, copy=False) + mask_pixels = int(place_mask.sum()) + if mask_pixels < 1: + raise ValueError("placement mask is empty") + + k = int(max(2, min(128, k))) + r_pack = derived_exclusion_radius(h, w, k) + if box_size is None: + side = int(max(8, 2 * round(r_pack))) + else: + side = int(box_size) + # Clamp: at least 8px side, at most half the short image edge + side = int(max(8, min(side, min(h, w) // 2 * 2))) # even-ish via floor + if side % 2 == 1: + side += 1 + b = max(4, side // 2) # box half-size + # Spatial exclusion in Chebyshev (L∞) distance so axis-aligned boxes do + # not overlap: centers must be ≥ side+1 apart (1px gap for stroke). + # Also respect K-packing floor. + r = float(max(r_pack, side + 1)) + hop = int(stride) if stride is not None else int(max(4, b)) + + # Coarse feature grid for PCA fit + window stats + grid_stride = max(2, hop // 2) + gy = np.arange(0, h, grid_stride, dtype=np.int32) + gx = np.arange(0, w, grid_stride, dtype=np.int32) + yy, xx = np.meshgrid(gy, gx, indexing="ij") + flat_y = yy.ravel() + flat_x = xx.ravel() + X = feats[flat_y, flat_x, :].astype(np.float64) + X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0) + n = X.shape[0] + if n < 4: + raise ValueError("too few pixels to score boxes") + + scaler = StandardScaler() + Xs = scaler.fit_transform(X) + d = int(max(1, min(pca_dims, c, n - 1, 16))) + pca = PCA(n_components=d, random_state=seed) + Z = pca.fit_transform(Xs) + explained = float(np.sum(pca.explained_variance_ratio_)) + + # Map (y,x) -> Z row via nearest grid sample (for window aggregation) + # Store Z as grid gh×gw×d + gh, gw = len(gy), len(gx) + Z_grid = Z.reshape(gh, gw, d) + + # Sliding window centers on hop grid + cy = np.arange(b, h - b + 1, hop, dtype=np.int32) + cx = np.arange(b, w - b + 1, hop, dtype=np.int32) + if cy.size == 0: + cy = np.array([h // 2], dtype=np.int32) + if cx.size == 0: + cx = np.array([w // 2], dtype=np.int32) + win_yy, win_xx = np.meshgrid(cy, cx, indexing="ij") + centers_y = win_yy.ravel() + centers_x = win_xx.ravel() + n_win = int(centers_y.size) + + scores = np.zeros(n_win, dtype=np.float64) + means = np.zeros((n_win, d), dtype=np.float64) + + for i in range(n_win): + y0 = int(centers_y[i] - b) + y1 = int(centers_y[i] + b) + x0 = int(centers_x[i] - b) + x1 = int(centers_x[i] + b) + # Grid indices covering the box + gi0 = max(0, (y0 + grid_stride - 1) // grid_stride) + gi1 = min(gh, (y1 // grid_stride) + 1) + gj0 = max(0, (x0 + grid_stride - 1) // grid_stride) + gj1 = min(gw, (x1 // grid_stride) + 1) + patch = Z_grid[gi0:gi1, gj0:gj1, :].reshape(-1, d) + if patch.shape[0] < 2: + continue + means[i] = patch.mean(axis=0) + # tr(Cov) = sum of feature variances + scores[i] = float(np.var(patch, axis=0).sum()) + + residual = scores.copy() + n_windows_in_mask = n_win + if place_mask is not None: + for i in range(n_win): + cx_i = int(centers_x[i]) + cy_i = int(centers_y[i]) + x0 = max(0, cx_i - b) + y0 = max(0, cy_i - b) + x1 = min(w, cx_i + b) + y1 = min(h, cy_i + b) + if x1 <= x0 or y1 <= y0 or not bool(place_mask[y0:y1, x0:x1].all()): + residual[i] = 0.0 + scores[i] = 0.0 + n_windows_in_mask = int(np.count_nonzero(residual > 0)) + if n_windows_in_mask < 1: + raise ValueError( + "no candidate boxes fit fully inside the placement mask; " + "draw a larger mask or reduce box size" + ) + + points: list[dict[str, Any]] = [] + + for pick_i in range(k): + if not np.any(residual > 0): + break + i_star = int(np.argmax(residual)) + score = float(residual[i_star]) + if score <= 0: + break + cx_i = int(centers_x[i_star]) + cy_i = int(centers_y[i_star]) + mu = means[i_star] + mu_norm = float(np.linalg.norm(mu)) + x0 = max(0, cx_i - b) + y0 = max(0, cy_i - b) + # Exclusive max so Konva/CSS width = x1 - x0 equals 2*b when unclipped + x1 = min(w, cx_i + b) + y1 = min(h, cy_i + b) + points.append( + { + "x": cx_i, + "y": cy_i, + "cluster": pick_i, + "score": score, + "radius": float(r), + "box_size": int(2 * b), + "box": {"x0": x0, "y0": y0, "x1": x1, "y1": y1}, + } + ) + residual[i_star] = 0.0 + + # Spatial (Chebyshev) + feature suppress. Exclude with ≥ side so + # axis-aligned boxes of width `side` do not share interior area. + for j in range(n_win): + if residual[j] <= 0: + continue + dx = abs(float(centers_x[j] - cx_i)) + dy = abs(float(centers_y[j] - cy_i)) + if max(dx, dy) < r: + residual[j] = 0.0 + continue + mj = means[j] + mj_norm = float(np.linalg.norm(mj)) + if mu_norm < 1e-12 or mj_norm < 1e-12: + continue + cos = float(np.dot(mu, mj) / (mu_norm * mj_norm)) + cos = max(0.0, cos) + if cos > _COS_HARD: + residual[j] = 0.0 + else: + residual[j] *= 1.0 - (cos ** _COS_ALPHA) + + # Hard guarantee: drop any pick whose AABB still overlaps an earlier one + # (protects against hop / clip / float edge cases). + points = _dedupe_overlapping_boxes(points) + + heatmap = _residual_heatmap( + h, w, centers_y, centers_x, residual, hop=hop, half=b + ) + + meta = { + "k": k, + "n_picked": len(points), + "n_windows": n_win, + "n_windows_in_mask": n_windows_in_mask, + "n_subsample": int(n), + "pca_dims": d, + "explained_variance": explained, + "stride": hop, + "radius": float(r), + "box_half": b, + "box_size": int(2 * b), + "mask_pixels": mask_pixels, + "has_mask": place_mask is not None, + } + return ManifoldSample(job_id=job.job_id, points=points, heatmap=heatmap, meta=meta) + + +def _boxes_overlap(a: dict[str, int], b: dict[str, int]) -> bool: + """True if two exclusive-end AABBs share interior area.""" + return a["x0"] < b["x1"] and b["x0"] < a["x1"] and a["y0"] < b["y1"] and b["y0"] < a["y1"] + + +def _dedupe_overlapping_boxes(points: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Keep greedy order; drop later picks whose box overlaps an earlier one.""" + kept: list[dict[str, Any]] = [] + for p in points: + box = p.get("box") + if not isinstance(box, dict): + kept.append(p) + continue + if any(_boxes_overlap(box, k["box"]) for k in kept if isinstance(k.get("box"), dict)): + continue + kept.append(p) + for i, p in enumerate(kept): + p["cluster"] = i + return kept + + +def _residual_heatmap( + h: int, + w: int, + centers_y: np.ndarray, + centers_x: np.ndarray, + residual: np.ndarray, + *, + hop: int, + half: int, +) -> np.ndarray: + """Paint residual scores onto a coarse grid and upsample to HxW.""" + out = np.zeros((h, w), dtype=np.float32) + max_s = float(np.max(residual)) if residual.size else 0.0 + if max_s < 1e-12: + return out + for i in range(residual.size): + if residual[i] <= 0: + continue + val = float(residual[i] / max_s) + cy = int(centers_y[i]) + cx = int(centers_x[i]) + y0 = max(0, cy - half) + y1 = min(h, cy + half) + x0 = max(0, cx - half) + x1 = min(w, cx + half) + # Take max so overlapping windows keep strongest residual + patch = out[y0:y1, x0:x1] + np.maximum(patch, val, out=patch) + return out + + +def encode_heatmap_png(heatmap: np.ndarray) -> bytes: + """Encode HxW float [0,1] residual map as grayscale PNG (0–255).""" + u8 = np.clip(np.asarray(heatmap, dtype=np.float32) * 255.0, 0, 255).astype(np.uint8) + img = PILImage.fromarray(u8, mode="L") + buf = BytesIO() + img.save(buf, format="PNG") + return buf.getvalue() + + +def store_sample(result: ManifoldSample) -> CachedManifoldSample: + """Cache heatmap PNG + points; return handle for GET.""" + sample_id = uuid.uuid4().hex + cached = CachedManifoldSample( + sample_id=sample_id, + heatmap_png=encode_heatmap_png(result.heatmap), + points=list(result.points), + meta={ + "sample_id": sample_id, + "job_id": result.job_id, + **result.meta, + "points": list(result.points), + }, + ) + _sample_cache.set(sample_id, cached) + return cached + + +def get_sample(sample_id: str) -> CachedManifoldSample | None: + """Look up a cached manifold sample.""" + s = _sample_cache.get(sample_id) + return s if isinstance(s, CachedManifoldSample) else None diff --git a/backend/images.py b/backend/images.py index 8e1d8e4..1ee0723 100644 --- a/backend/images.py +++ b/backend/images.py @@ -43,6 +43,8 @@ def _sample_global_stats(node: Any, meta: dict[str, Any]) -> tuple[float, float] Returns: ``(vmin, vmax)`` floats across all sampled slices. """ + from concurrent.futures import ThreadPoolExecutor + from arrays import read_slice cache_key = ("global_stats", id(node)) @@ -50,14 +52,21 @@ def _sample_global_stats(node: Any, meta: dict[str, Any]) -> tuple[float, float] if cached is not None: return cached + # Sample fewer slices, read them concurrently, and spatially subsample each + # (every 4th pixel) — min/max are robust to this and the cost drops sharply. + # Result still spans the whole volume, so it matches the viewer's contrast. n = meta["n_slices"] - indices = list(range(0, n, max(1, n // 64)))[:64] - values: list[float] = [] - for i in indices: - sl = read_slice(node, meta, i).astype(np.float64) - values.extend([float(np.nanmin(sl)), float(np.nanmax(sl))]) + samples = 24 + indices = list(range(0, n, max(1, n // samples)))[:samples] or [0] + + def _minmax(i: int) -> tuple[float, float]: + sl = np.asarray(read_slice(node, meta, i))[::4, ::4].astype(np.float64) + return float(np.nanmin(sl)), float(np.nanmax(sl)) + + with ThreadPoolExecutor(max_workers=min(8, len(indices))) as ex: + pairs = list(ex.map(_minmax, indices)) - result: tuple[float, float] = (min(values), max(values)) + result: tuple[float, float] = (min(p[0] for p in pairs), max(p[1] for p in pairs)) _stats_cache.set(cache_key, result) return result diff --git a/backend/ingest.py b/backend/ingest.py new file mode 100644 index 0000000..b0fb5ea --- /dev/null +++ b/backend/ingest.py @@ -0,0 +1,218 @@ +"""Background ingest of uploaded image files into a Tiled container. + +The Connect page lets the user drag-and-drop a folder of images; those bytes +are streamed to temp files by the API layer, then this module's worker copies +each file into the connected Tiled server via ``write_array`` (works for remote +servers too — Tiled uploads the array over HTTP). + +Each file becomes its OWN node with metadata so the result is browsable in the +metadata-driven Browse UI (which needs fields with >=2 distinct values): + +* ``image_number`` — zero-padded STRING index parsed from the filename, so it + stays a string filter (see ``browse_helpers._typed_query_value``). +* ``size`` — ``"H x W"`` from the array shape. +* ``original_filename`` — the uploaded filename. +* ``sample_name`` — the filename stem (the node key); a friendlier label. +* ``description`` — optional user-supplied keyword(s) entered on Connect; the + same value is written to every node in the batch so a single drop is filterable + (these keys are made facet-eligible with one distinct value, see + ``browse_helpers._SINGLE_VALUE_FACET_RAW_KEYS``). + +Job state is held in-memory (lost on restart) — acceptable for a localhost tool. +""" + +from __future__ import annotations + +import logging +import re +import threading +import uuid +from pathlib import Path +from typing import Any + +import numpy as np + +from tiled_clients import api_key_for_uri, get_tiled_client + +logger = logging.getLogger("ingest") + +IMAGE_EXTS: frozenset[str] = frozenset({".tif", ".tiff", ".npy", ".png", ".jpg", ".jpeg"}) +# Trailing digits of the filename stem (e.g. "..._petiole22_00042" -> "00042"). +# Anchored at the end so a leading date like "20260221_..." is not mistaken for +# the frame index. +_FRAME_RE = re.compile(r"(\d+)$") +_MIN_PAD = 5 + +# job_id -> {state, total, done, failed, errors[], container_path, server_uri} +_jobs: dict[str, dict[str, Any]] = {} +_jobs_lock = threading.Lock() + + +def new_job(total: int, server_uri: str | None, container_path: str) -> str: + """Register a new ingest job and return its id.""" + jid = uuid.uuid4().hex + with _jobs_lock: + _jobs[jid] = { + "state": "pending", + "total": total, + "done": 0, + "failed": 0, + "errors": [], + "container_path": container_path, + "server_uri": server_uri, + } + return jid + + +def get_job(jid: str) -> dict | None: + """Return a snapshot copy of the job, or ``None`` if unknown.""" + with _jobs_lock: + job = _jobs.get(jid) + return dict(job) if job else None + + +def _update(jid: str, **kw: Any) -> None: + with _jobs_lock: + if jid in _jobs: + _jobs[jid].update(kw) + + +def _bump(jid: str, *, done: int = 0, failed: int = 0, error: str | None = None) -> None: + with _jobs_lock: + job = _jobs.get(jid) + if not job: + return + job["done"] += done + job["failed"] += failed + if error: + job["errors"].append(error) + + +def _read_array(path: Path) -> np.ndarray: + """Read a supported image file into a NumPy array.""" + import tifffile + from PIL import Image as PILImage + + suffix = path.suffix.lower() + if suffix in (".tif", ".tiff"): + return tifffile.imread(str(path)) + if suffix == ".npy": + return np.load(str(path), allow_pickle=False) + if suffix in (".png", ".jpg", ".jpeg"): + return np.asarray(PILImage.open(str(path))) + raise ValueError(f"unsupported extension {suffix!r}") + + +def _ensure_container(client: Any, parts: list[str]) -> Any: + """Navigate to ``client[parts...]``, creating containers as needed.""" + node = client + for key in parts: + try: + node = node[key] + except Exception: # noqa: BLE001 — KeyError or transport error → create + node = node.create_container(key=key, metadata={}) + return node + + +def _size_str(arr: np.ndarray) -> str: + """Return pixel dimensions as ``"H x W"`` (first two dims).""" + return " x ".join(str(d) for d in arr.shape[:2]) + + +def _pad_width(filenames: list[str]) -> int: + """Return a consistent zero-pad width for image_number across the batch.""" + widths = [ + len(m.group(1)) + for m in (_FRAME_RE.search(Path(f).stem) for f in filenames) + if m + ] + return max([*widths, _MIN_PAD]) if widths else _MIN_PAD + + +def _image_number(stem: str, width: int, fallback_index: int) -> str: + m = _FRAME_RE.search(stem) + if m: + return m.group(1).zfill(width) + return str(fallback_index).zfill(width) + + +def run_ingest_job( + jid: str, + server_uri: str | None, + container_path: str, + temp_files: list[tuple[str, Path]], + description: str = "", +) -> None: + """Copy each temp file into the target Tiled container. + + Args: + jid: Job id from :func:`new_job`. + server_uri: Connected Tiled server URI. + container_path: Slash-separated target container (e.g. ``browse/testset``). + temp_files: list of ``(original_filename, temp_path)``. + description: Optional user-supplied keyword(s) stored on every node so the + batch is identifiable/filterable in Browse (empty string → omitted). + """ + description = (description or "").strip() + _update(jid, state="running") + try: + api_key = api_key_for_uri(server_uri) + client = get_tiled_client(server_uri, api_key) + parts = [p for p in container_path.strip("/").split("/") if p] + target = _ensure_container(client, parts) + width = _pad_width([orig for orig, _ in temp_files]) + + # Describe the dataset at the CONTAINER level too. Browse treats each + # child of the browse root as a "sample" and reads its container + # metadata (not the per-array metadata written below), so this is what + # makes the upload identifiable/filterable there. + container_meta: dict[str, Any] = { + "sample_name": parts[-1] if parts else "", + "n_images": len(temp_files), + } + if description: + container_meta["description"] = description + try: + target.update_metadata(metadata=container_meta) + except Exception as exc: # noqa: BLE001 — best-effort; per-array meta still set + logger.warning("could not set container metadata on %s: %s", container_path, exc) + + for idx, (orig_name, tmp) in enumerate(temp_files): + try: + arr = _read_array(tmp) + stem = Path(orig_name).stem + meta = { + "image_number": _image_number(stem, width, idx), + "size": _size_str(arr), + "original_filename": orig_name, + "sample_name": stem, + } + if description: + meta["description"] = description + if arr.ndim == 3 and arr.shape[2] in (3, 4): + dims = ["y", "x", "channel"] + elif arr.ndim == 2: + dims = ["y", "x"] + else: + dims = None + target.write_array(arr, key=stem, metadata=meta, dims=dims) + _bump(jid, done=1) + except Exception as exc: # noqa: BLE001 — isolate per-file failures + logger.warning("ingest %s failed: %s", orig_name, exc) + _bump(jid, failed=1, error=f"{orig_name}: {exc}") + finally: + try: + tmp.unlink(missing_ok=True) + except Exception: # noqa: BLE001 + pass + _update(jid, state="done") + except Exception as exc: # noqa: BLE001 — fatal (e.g. cannot reach server) + logger.error("ingest job %s fatal: %s", jid, exc) + _bump(jid, error=str(exc)) + _update(jid, state="error") + finally: + for _, tmp in temp_files: + try: + tmp.unlink(missing_ok=True) + except Exception: # noqa: BLE001 + pass diff --git a/backend/ipred_client.py b/backend/ipred_client.py new file mode 100644 index 0000000..abf2750 --- /dev/null +++ b/backend/ipred_client.py @@ -0,0 +1,291 @@ +"""HTTP client for the disjoint ipred backend (no Python imports of ``ipred``).""" + +from __future__ import annotations + +import logging +import os +from typing import Any + +import httpx + +logger = logging.getLogger(__name__) + +DEFAULT_IPRED_URL = "http://127.0.0.1:8003" + + +def ipred_url() -> str: + """Base URL for the iterative prediction backend.""" + # Prefer IPRED_URL; accept legacy CLF_ENGINE_URL during transition. + return os.getenv( + "IPRED_URL", + os.getenv("CLF_ENGINE_URL", DEFAULT_IPRED_URL), + ).rstrip("/") + + +def _client(timeout: float = 300.0) -> httpx.Client: + return httpx.Client(base_url=ipred_url(), timeout=timeout) + + +def health() -> dict[str, Any]: + """GET /health.""" + with _client(timeout=5.0) as client: + r = client.get("/health") + r.raise_for_status() + return r.json() + + +def open_session( + *, + kind: str, + source: str, + server_uri: str | None = None, + root: str | None = None, +) -> dict[str, Any]: + """POST /sessions.""" + with _client() as client: + r = client.post( + "/sessions", + json={ + "kind": kind, + "source": source, + "server_uri": server_uri, + "root": root, + }, + ) + r.raise_for_status() + return r.json() + + +def list_setups() -> list[dict[str, Any]]: + """GET /setups.""" + with _client() as client: + r = client.get("/setups") + r.raise_for_status() + return list(r.json().get("setups") or []) + + +def get_setup(setup_id: str) -> dict[str, Any]: + """GET /setups/{id}.""" + with _client() as client: + r = client.get(f"/setups/{setup_id}") + r.raise_for_status() + return r.json() + + +def upsert_setup(payload: dict[str, Any]) -> dict[str, Any]: + """POST /setups.""" + with _client() as client: + r = client.post("/setups", json=payload) + r.raise_for_status() + return r.json() + + +def list_trainers() -> list[str]: + """GET /trainers.""" + with _client() as client: + r = client.get("/trainers") + r.raise_for_status() + return list(r.json().get("trainers") or []) + + +def list_modules() -> list[dict[str, Any]]: + """GET /modules.""" + with _client() as client: + r = client.get("/modules") + r.raise_for_status() + return list(r.json().get("modules") or []) + + +def list_compositions() -> list[dict[str, Any]]: + """GET /compositions.""" + with _client() as client: + r = client.get("/compositions") + r.raise_for_status() + return list(r.json().get("compositions") or []) + + +def get_composition(composition_id: str) -> dict[str, Any]: + """GET /compositions/{id}.""" + with _client() as client: + r = client.get(f"/compositions/{composition_id}") + r.raise_for_status() + return r.json() + + +def upsert_composition(payload: dict[str, Any]) -> dict[str, Any]: + """POST /compositions.""" + with _client() as client: + r = client.post("/compositions", json=payload) + r.raise_for_status() + return r.json() + + +def preview_composition(payload: dict[str, Any]) -> dict[str, Any]: + """POST /compositions/preview.""" + with _client() as client: + r = client.post("/compositions/preview", json=payload) + r.raise_for_status() + return r.json() + + +def upload_session_array(session_id: str, payload: dict[str, Any]) -> dict[str, Any]: + """POST /sessions/{id}/arrays.""" + with _client() as client: + r = client.post(f"/sessions/{session_id}/arrays", json=payload) + r.raise_for_status() + return r.json() + + +def preprocess( + *, + session_id: str, + feature_setup_id: str | None = None, + composition_id: str | None = None, + slice_index: int = 0, + array_ref: str | None = None, +) -> dict[str, Any]: + """POST /preprocess.""" + body: dict[str, Any] = { + "session_id": session_id, + "slice_index": slice_index, + } + if composition_id: + body["composition_id"] = composition_id + if feature_setup_id: + body["feature_setup_id"] = feature_setup_id + if array_ref: + body["array_ref"] = array_ref + with _client() as client: + r = client.post("/preprocess", json=body) + r.raise_for_status() + return r.json() + + +def feature_channel_bytes(feature_id: str, index: int) -> bytes: + """GET /features/{id}/channels/{index}.""" + with _client() as client: + r = client.get(f"/features/{feature_id}/channels/{index}") + r.raise_for_status() + return r.content + + +def train( + *, + session_id: str, + shapes: list[dict[str, Any]], + feature_id: str | None = None, + trainer_id: str = "catboost", + config: dict[str, Any] | None = None, +) -> dict[str, Any]: + """POST /train.""" + with _client() as client: + r = client.post( + "/train", + json={ + "session_id": session_id, + "shapes": shapes, + "feature_id": feature_id, + "trainer_id": trainer_id, + "config": config or {}, + }, + ) + r.raise_for_status() + return r.json() + + +def infer( + *, + session_id: str, + model_id: str | None = None, + feature_id: str | None = None, + alpha: float = 0.05, +) -> dict[str, Any]: + """POST /infer.""" + with _client() as client: + r = client.post( + "/infer", + json={ + "session_id": session_id, + "model_id": model_id, + "feature_id": feature_id, + "alpha": alpha, + }, + ) + r.raise_for_status() + return r.json() + + +def rethreshold( + *, + session_id: str, + alpha: float, + run_id: str | None = None, +) -> dict[str, Any]: + """POST /rethreshold.""" + with _client() as client: + r = client.post( + "/rethreshold", + json={ + "session_id": session_id, + "alpha": alpha, + "run_id": run_id, + }, + ) + r.raise_for_status() + return r.json() + + +def run_commit_png(run_id: str) -> bytes: + """GET /runs/{id}/commit.png.""" + with _client() as client: + r = client.get(f"/runs/{run_id}/commit.png") + r.raise_for_status() + return r.content + + +def run_status_png(run_id: str) -> bytes: + """GET /runs/{id}/status.png.""" + with _client() as client: + r = client.get(f"/runs/{run_id}/status.png") + r.raise_for_status() + return r.content + + +def run_proba_png(run_id: str, class_index: int) -> bytes: + """GET /runs/{id}/proba/{class_index}.png.""" + with _client() as client: + r = client.get(f"/runs/{run_id}/proba/{int(class_index)}.png") + r.raise_for_status() + return r.content + + +def threshold_class_map( + run_id: str, + *, + class_id: int, + threshold: float, +) -> dict[str, Any]: + """POST /runs/{id}/threshold-class.""" + with _client() as client: + r = client.post( + f"/runs/{run_id}/threshold-class", + json={"class_id": int(class_id), "threshold": float(threshold)}, + ) + r.raise_for_status() + return r.json() + + +def manifold_sample(payload: dict[str, Any]) -> dict[str, Any]: + """POST /manifold/sample.""" + with _client() as client: + r = client.post("/manifold/sample", json=payload) + r.raise_for_status() + return r.json() + + +def manifold_heatmap_png(sample_id: str) -> bytes: + """GET /manifold/{id}/heatmap.png.""" + with _client() as client: + r = client.get(f"/manifold/{sample_id}/heatmap.png") + r.raise_for_status() + return r.content diff --git a/backend/label_sets.py b/backend/label_sets.py new file mode 100644 index 0000000..6edfe47 --- /dev/null +++ b/backend/label_sets.py @@ -0,0 +1,138 @@ +"""# REMOVE THIS AND USE YOUR OWN STUFF + +Reusable annotation label sets (class id + label + color). + +TEMPORARY scaffold storage under ``$LOCAL_DATA_ROOT/.label_sets//meta.json`` +for Connect/Browse. Replace with your own label taxonomy backend, then delete +this module and the ``/api/label-sets`` routes. +""" + +from __future__ import annotations + +import json +import logging +import os +import uuid +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +logger = logging.getLogger(__name__) + +# ############################################################################## +# # REMOVE THIS AND USE YOUR OWN STUFF — disk label-set catalog +# ############################################################################## + + +def _root() -> Path: + return Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve() / ".label_sets" + + +@dataclass(frozen=True) +class LabelSetMeta: + """Indexed label-set summary.""" + + id: str + name: str + n_classes: int + created_at: str + + +def _dir_for(set_id: str) -> Path: + return _root() / set_id + + +def _validate_classes(classes: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Normalize and validate class entries.""" + out: list[dict[str, Any]] = [] + seen: set[int] = set() + for raw in classes: + if not isinstance(raw, dict): + raise ValueError("each class must be an object") + cid = int(raw.get("classId") or raw.get("class_id") or 0) + if cid <= 0: + raise ValueError("classId must be a positive integer") + if cid in seen: + raise ValueError(f"duplicate classId {cid}") + seen.add(cid) + label = str(raw.get("label") or f"class_{cid}").strip() or f"class_{cid}" + color = str(raw.get("color") or "#1f77b4") + visible = bool(raw.get("isVisible", raw.get("is_visible", True))) + out.append( + { + "classId": cid, + "label": label, + "color": color, + "isVisible": visible, + } + ) + if not out: + raise ValueError("label set must include at least one class") + return out + + +def save_label_set(*, name: str, classes: list[dict[str, Any]]) -> dict[str, Any]: + """Persist a named label set; return full record.""" + cleaned = _validate_classes(classes) + set_id = uuid.uuid4().hex + dest = _dir_for(set_id) + dest.mkdir(parents=True, exist_ok=True) + record = { + "id": set_id, + "name": (name or "Untitled labels").strip() or "Untitled labels", + "classes": cleaned, + "created_at": datetime.now(timezone.utc).isoformat(), + } + (dest / "meta.json").write_text(json.dumps(record, indent=2), encoding="utf-8") + logger.info("Saved label set %s (%s, n=%d)", set_id, record["name"], len(cleaned)) + return record + + +def list_label_sets() -> list[dict[str, Any]]: + """Return summaries for all saved label sets (newest first).""" + root = _root() + if not root.is_dir(): + return [] + items: list[dict[str, Any]] = [] + for child in root.iterdir(): + meta_path = child / "meta.json" + if not meta_path.is_file(): + continue + try: + data = json.loads(meta_path.read_text(encoding="utf-8")) + items.append( + { + "id": data["id"], + "name": data.get("name", data["id"]), + "n_classes": len(data.get("classes") or []), + "created_at": data.get("created_at", ""), + "classes": data.get("classes") or [], + } + ) + except (OSError, json.JSONDecodeError, KeyError, TypeError) as exc: + logger.warning("Skipping corrupt label set %s: %s", child, exc) + items.sort(key=lambda m: m.get("created_at") or "", reverse=True) + return items + + +def get_label_set(set_id: str) -> dict[str, Any] | None: + """Load one label set by id.""" + meta_path = _dir_for(set_id) / "meta.json" + if not meta_path.is_file(): + return None + try: + return json.loads(meta_path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + return None + + +def delete_label_set(set_id: str) -> bool: + """Delete a label set directory. Returns False if missing.""" + import shutil + + dest = _dir_for(set_id) + if not dest.is_dir(): + return False + shutil.rmtree(dest) + return True diff --git a/backend/local_fs.py b/backend/local_fs.py index 79a2939..14e47ff 100644 --- a/backend/local_fs.py +++ b/backend/local_fs.py @@ -25,33 +25,75 @@ logger = logging.getLogger(__name__) -_ROOT: Path = Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve() +_DEFAULT_ROOT: Path = Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve() -def _safe(rel: str) -> Path: - """Resolve *rel* under ``LOCAL_DATA_ROOT``; raise 403 on traversal. +def _resolve_root(root: str | None) -> Path: + """Return the granted browse root as an absolute, resolved Path. Args: - rel: Relative path string supplied by the caller. + root: Absolute path the user granted access to, or ``None`` to fall + back to ``LOCAL_DATA_ROOT``. Returns: - Absolute :class:`pathlib.Path` guaranteed to be inside ``_ROOT``. + Absolute :class:`pathlib.Path`. + """ + if root: + return Path(root).expanduser().resolve() + return _DEFAULT_ROOT + + +def _within(base: Path, resolved: Path) -> bool: + """Return True if *resolved* is *base* itself or a descendant of it. + + Parent containment avoids the string-prefix footgun where ``/data2`` would + slip past a ``/data`` root. + """ + return base == resolved or base in resolved.parents + + +def _safe(rel: str, root: str | None = None) -> Path: + """Resolve *rel* to an absolute path with traversal protection. + + Three cases: + + * *root* given → join under the granted root and enforce containment + (the sandboxed Browse flow). + * *root* omitted and *rel* absolute → an explicit file identity (used when + re-opening an already-chosen file by its absolute path, e.g. annotate / + thumbnail rendering); returned as-is with no sandbox. + * *root* omitted and *rel* relative → legacy behaviour under + ``LOCAL_DATA_ROOT`` with containment enforced. Raises: - HTTPException: 403 if the resolved path escapes ``_ROOT``. + HTTPException: 403 if a sandboxed path escapes its root. """ - resolved = (_ROOT / rel).resolve() - if not str(resolved).startswith(str(_ROOT)): - logger.warning("Path traversal attempt: %r", rel) + if root: + base = _resolve_root(root) + resolved = (base / rel).resolve() + if not _within(base, resolved): + logger.warning("Path traversal attempt: rel=%r root=%r", rel, root) + raise HTTPException(403, "Path traversal not allowed") + return resolved + + candidate = Path(rel).expanduser() + if candidate.is_absolute(): + return candidate.resolve() + + base = _DEFAULT_ROOT + resolved = (base / rel).resolve() + if not _within(base, resolved): + logger.warning("Path traversal attempt: rel=%r", rel) raise HTTPException(403, "Path traversal not allowed") return resolved -def list_dir(rel: str = "") -> list[dict[str, Any]]: - """List directory entries under ``LOCAL_DATA_ROOT``. +def list_dir(rel: str = "", root: str | None = None) -> list[dict[str, Any]]: + """List directory entries under the granted *root*. Args: rel: Relative path to the directory to list (empty → root). + root: Granted absolute root (defaults to ``LOCAL_DATA_ROOT``). Returns: List of dicts with keys ``name``, ``path``, ``is_dir``, ``size``. @@ -59,13 +101,14 @@ def list_dir(rel: str = "") -> list[dict[str, Any]]: Raises: HTTPException: 404 if path does not exist; 400 if not a directory. """ - path = _safe(rel) + base = _resolve_root(root) + path = _safe(rel, root) if not path.exists(): # A missing root directory is treated as empty rather than an error, # so the file browser can still render (and the user can fix the - # LOCAL_DATA_ROOT configuration) instead of seeing a 404. + # granted path) instead of seeing a 404. if rel in ("", "."): - logger.warning("LOCAL_DATA_ROOT does not exist: %s", _ROOT) + logger.warning("Browse root does not exist: %s", base) return [] raise HTTPException(404, f"Path not found: {rel!r}") if not path.is_dir(): @@ -75,7 +118,7 @@ def list_dir(rel: str = "") -> list[dict[str, Any]]: entries.append( { "name": child.name, - "path": str(child.relative_to(_ROOT)), + "path": str(child.relative_to(base)), "is_dir": child.is_dir(), "size": child.stat().st_size if child.is_file() else None, } @@ -83,26 +126,28 @@ def list_dir(rel: str = "") -> list[dict[str, Any]]: return entries -def count_image_files(rel: str) -> int: - """Return a recursive count of image files under ``LOCAL_DATA_ROOT/rel``. +def count_image_files(rel: str, root: str | None = None) -> int: + """Return a recursive count of image files under ``/rel``. Args: - rel: Relative path to a directory under ``LOCAL_DATA_ROOT``. + rel: Relative path to a directory under the granted root. + root: Granted absolute root (defaults to ``LOCAL_DATA_ROOT``). Returns: Number of files with a supported image extension. """ - path = _safe(rel) + path = _safe(rel, root) if not path.exists() or not path.is_dir(): return 0 return sum(1 for f in path.rglob("*") if f.is_file() and f.suffix.lower() in IMAGE_EXTS) -def list_image_files(rel: str) -> list[dict[str, Any]]: - """Return a flat, sorted list of image files under ``LOCAL_DATA_ROOT/rel``. +def list_image_files(rel: str, root: str | None = None) -> list[dict[str, Any]]: + """Return a flat, sorted list of image files under ``/rel``. Args: - rel: Relative path to a directory under ``LOCAL_DATA_ROOT``. + rel: Relative path to a directory under the granted root. + root: Granted absolute root (defaults to ``LOCAL_DATA_ROOT``). Returns: List of ``{"name", "path"}`` dicts sorted by path. @@ -110,7 +155,8 @@ def list_image_files(rel: str) -> list[dict[str, Any]]: Raises: HTTPException: 404 if path does not exist; 400 if not a directory. """ - path = _safe(rel) + base = _resolve_root(root) + path = _safe(rel, root) if not path.exists(): raise HTTPException(404, f"Path not found: {rel!r}") if not path.is_dir(): @@ -119,7 +165,7 @@ def list_image_files(rel: str) -> list[dict[str, Any]]: ( { "name": f.name, - "path": str(f.relative_to(_ROOT)), + "path": str(f.relative_to(base)), } for f in path.rglob("*") if f.is_file() and f.suffix.lower() in IMAGE_EXTS @@ -129,14 +175,15 @@ def list_image_files(rel: str) -> list[dict[str, Any]]: return entries -def open_array(rel: str) -> Any: +def open_array(rel: str, root: str | None = None) -> Any: """Open a local array file and return a lazily-sliceable array. Supported extensions: ``.tif``, ``.tiff``, ``.npy``, ``.png``, ``.jpg``, ``.jpeg``. Args: - rel: Relative path to the file under ``LOCAL_DATA_ROOT``. + rel: Relative path to the file under the granted root. + root: Granted absolute root (defaults to ``LOCAL_DATA_ROOT``). Returns: A memory-mapped or fully-loaded NumPy array. @@ -148,7 +195,7 @@ def open_array(rel: str) -> Any: import tifffile from PIL import Image as PILImage - path = _safe(rel) + path = _safe(rel, root) if not path.exists(): raise HTTPException(404, f"File not found: {rel!r}") suffix = path.suffix.lower() diff --git a/backend/multiscale_features.py b/backend/multiscale_features.py new file mode 100644 index 0000000..e35f083 --- /dev/null +++ b/backend/multiscale_features.py @@ -0,0 +1,292 @@ +"""Multiscale scale-space features via skimage (intensity / edges / texture). + +Used by the Annotate UI to compute a feature bank on the current slice, CLAHE- +normalize each channel, and serve channels as gray PNGs for display / tools. +Also keeps a float stack for pixel classifiers (CatBoost), optionally plus a +SlimSAM embedding grid (never full-res). +""" + +from __future__ import annotations + +import logging +import uuid +from dataclasses import dataclass +from io import BytesIO + +import numpy as np +from PIL import Image as PILImage +from skimage.exposure import equalize_adapthist +from skimage.feature import multiscale_basic_features + +from cache import TTLCache + +logger = logging.getLogger(__name__) + +_job_cache: TTLCache = TTLCache(ttl_seconds=600.0, max_entries=32) + + +@dataclass(frozen=True) +class FeatureJob: + """Cached multiscale feature computation result.""" + + job_id: str + labels: tuple[str, ...] + stack: np.ndarray # HxWxC uint8 (display) + float_stack: np.ndarray # HxWxC float32 (classifier) + sam_emb: np.ndarray | None = None # eh×ew×C float32 (optional) + sam_orig_hw: tuple[int, int] | None = None # (H, W) of source slice + sam_reshaped_hw: tuple[int, int] | None = None # resize before pad + + +def list_sigmas(sigma_min: float, sigma_max: float, num_sigma: int | None = None) -> np.ndarray: + """Return the σ grid used by ``multiscale_basic_features`` (powers of 2 by default).""" + if sigma_min <= 0 or sigma_max < sigma_min: + raise ValueError(f"invalid sigma range [{sigma_min}, {sigma_max}]") + if num_sigma is None: + num_sigma = int(np.log2(sigma_max) - np.log2(sigma_min) + 1) + return np.logspace( + np.log2(sigma_min), + np.log2(sigma_max), + num=int(num_sigma), + base=2, + endpoint=True, + ) + + +def feature_channel_labels( + *, + sigma_min: float = 1.0, + sigma_max: float = 8.0, + intensity: bool = True, + edges: bool = True, + texture: bool = True, + num_sigma: int | None = None, +) -> list[str]: + """Human-readable labels matching skimage's per-σ feature order. + + Order per sigma: intensity (optional), edges (optional), texture λ− / λ+ + (optional, two eigenvalues). + """ + if not any((intensity, edges, texture)): + raise ValueError("at least one of intensity, edges, texture must be True") + labels: list[str] = [] + for sigma in list_sigmas(sigma_min, sigma_max, num_sigma): + s = _fmt_sigma(float(sigma)) + if intensity: + labels.append(f"intensity σ={s}") + if edges: + labels.append(f"edges σ={s}") + if texture: + labels.append(f"texture λ− σ={s}") + labels.append(f"texture λ+ σ={s}") + return labels + + +def _fmt_sigma(sigma: float) -> str: + if abs(sigma - round(sigma)) < 1e-6: + return str(int(round(sigma))) + return f"{sigma:g}" + + +def to_grayscale(arr: np.ndarray) -> np.ndarray: + """Convert a slice to float grayscale in [0, 1] (approx).""" + a = np.asarray(arr) + if a.ndim == 3 and a.shape[-1] in (3, 4): + rgb = a[..., :3].astype(np.float64) + gray = 0.299 * rgb[..., 0] + 0.587 * rgb[..., 1] + 0.114 * rgb[..., 2] + elif a.ndim == 2: + gray = a.astype(np.float64) + else: + raise ValueError(f"unsupported array shape {a.shape}") + finite = gray[np.isfinite(gray)] + if finite.size == 0: + return np.zeros_like(gray, dtype=np.float64) + lo, hi = float(np.min(finite)), float(np.max(finite)) + if hi <= lo: + return np.zeros_like(gray, dtype=np.float64) + out = (gray - lo) / (hi - lo) + return np.clip(out, 0.0, 1.0) + + +def _float_feats_to_uint8(feats: np.ndarray, *, clahe: bool) -> np.ndarray: + """Convert float HxWxC features to display uint8 (optional per-channel CLAHE).""" + h, w, c = feats.shape + out = np.empty((h, w, c), dtype=np.uint8) + for i in range(c): + ch = feats[..., i].astype(np.float64) + lo, hi = float(np.nanmin(ch)), float(np.nanmax(ch)) + if hi > lo: + norm = (ch - lo) / (hi - lo) + else: + norm = np.zeros_like(ch) + if clahe: + norm = equalize_adapthist(np.clip(norm, 0.0, 1.0), clip_limit=0.01) + out[..., i] = np.clip(np.round(norm * 255.0), 0, 255).astype(np.uint8) + return out + + +def compute_feature_stacks( + gray: np.ndarray, + *, + sigma_min: float = 1.0, + sigma_max: float = 8.0, + intensity: bool = True, + edges: bool = True, + texture: bool = True, + clahe: bool = True, + num_sigma: int | None = None, + workers: int | None = 1, +) -> tuple[np.ndarray, np.ndarray, list[str]]: + """Compute display uint8 + float feature banks. + + Returns: + ``(uint8_stack, float_stack, labels)`` both ``HxWxC``. + """ + labels = feature_channel_labels( + sigma_min=sigma_min, + sigma_max=sigma_max, + intensity=intensity, + edges=edges, + texture=texture, + num_sigma=num_sigma, + ) + feats = multiscale_basic_features( + np.asarray(gray, dtype=np.float32), + intensity=intensity, + edges=edges, + texture=texture, + sigma_min=sigma_min, + sigma_max=sigma_max, + num_sigma=num_sigma, + workers=workers, + ) + if feats.shape[-1] != len(labels): + raise RuntimeError( + f"feature count mismatch: got {feats.shape[-1]} channels, " + f"expected {len(labels)} labels" + ) + float_stack = np.nan_to_num(feats.astype(np.float32), nan=0.0, posinf=0.0, neginf=0.0) + uint8_stack = _float_feats_to_uint8(float_stack, clahe=clahe) + return uint8_stack, float_stack, labels + + +def compute_feature_stack( + gray: np.ndarray, + *, + sigma_min: float = 1.0, + sigma_max: float = 8.0, + intensity: bool = True, + edges: bool = True, + texture: bool = True, + clahe: bool = True, + num_sigma: int | None = None, + workers: int | None = 1, +) -> tuple[np.ndarray, list[str]]: + """Compute the multiscale feature bank and optionally CLAHE each channel. + + Returns: + ``(stack, labels)`` where ``stack`` is ``HxWxC`` uint8. + """ + stack, _, labels = compute_feature_stacks( + gray, + sigma_min=sigma_min, + sigma_max=sigma_max, + intensity=intensity, + edges=edges, + texture=texture, + clahe=clahe, + num_sigma=num_sigma, + workers=workers, + ) + return stack, labels + + +def encode_channel_png(stack: np.ndarray, index: int) -> bytes: + """Encode one feature channel as a grayscale PNG.""" + if index < 0 or index >= stack.shape[-1]: + raise IndexError(f"channel index {index} out of range 0..{stack.shape[-1] - 1}") + img = PILImage.fromarray(stack[..., index], mode="L") + buf = BytesIO() + img.save(buf, format="PNG") + return buf.getvalue() + + +def store_job( + stack: np.ndarray, + labels: list[str], + *, + float_stack: np.ndarray, + sam_emb: np.ndarray | None = None, + sam_orig_hw: tuple[int, int] | None = None, + sam_reshaped_hw: tuple[int, int] | None = None, +) -> FeatureJob: + """Cache a computed stack and return a job handle.""" + if stack.shape != float_stack.shape: + raise ValueError("stack and float_stack shapes must match") + job_id = uuid.uuid4().hex + job = FeatureJob( + job_id=job_id, + labels=tuple(labels), + stack=stack, + float_stack=float_stack, + sam_emb=sam_emb, + sam_orig_hw=sam_orig_hw, + sam_reshaped_hw=sam_reshaped_hw, + ) + _job_cache.set(job_id, job) + return job + + +def get_job(job_id: str) -> FeatureJob | None: + """Look up a cached feature job (or ``None`` if missing/expired).""" + job = _job_cache.get(job_id) + return job if isinstance(job, FeatureJob) else None + + +def compute_and_store( + arr: np.ndarray, + *, + sigma_min: float = 1.0, + sigma_max: float = 8.0, + intensity: bool = True, + edges: bool = True, + texture: bool = True, + clahe: bool = True, + include_sam: bool = False, +) -> FeatureJob: + """Grayscale convert *arr*, compute features (+ optional SlimSAM), cache.""" + gray = to_grayscale(arr) + stack, float_stack, labels = compute_feature_stacks( + gray, + sigma_min=sigma_min, + sigma_max=sigma_max, + intensity=intensity, + edges=edges, + texture=texture, + clahe=clahe, + workers=1, + ) + sam_emb = None + sam_orig_hw = None + sam_reshaped_hw = None + if include_sam: + from sam_embed import encode_image_embeddings, sam_available + + if not sam_available(): + logger.warning("include_sam requested but vision_encoder.onnx missing") + else: + sam_emb, sam_orig_hw, sam_reshaped_hw = encode_image_embeddings(arr) + logger.info( + "SlimSAM embedding %s for slice %s (reshaped %s)", + sam_emb.shape, + sam_orig_hw, + sam_reshaped_hw, + ) + return store_job( + stack, + labels, + float_stack=float_stack, + sam_emb=sam_emb, + sam_orig_hw=sam_orig_hw, + sam_reshaped_hw=sam_reshaped_hw, + ) diff --git a/backend/pixel_clf.py b/backend/pixel_clf.py new file mode 100644 index 0000000..6848eba --- /dev/null +++ b/backend/pixel_clf.py @@ -0,0 +1,633 @@ +"""CatBoost pixel classifier on multiscale (+ optional SlimSAM) features. + +Trains on an 80% stratified split of annotated pixels; holds out 20% for +Mondrian (class-conditional) split-conformal calibration. Predict returns +per-pixel prediction sets at user-chosen alpha: commit map = singletons only. +""" + +from __future__ import annotations + +import json +import logging +import tempfile +import uuid +from dataclasses import dataclass, field +from io import BytesIO +from pathlib import Path +from typing import Any + +import numpy as np +from catboost import CatBoostClassifier +from PIL import Image as PILImage + +from cache import TTLCache +from coco_export import shape_to_mask +from multiscale_features import FeatureJob +from sam_embed import bilinear_sample_emb, fit_pca, transform_pca + +logger = logging.getLogger(__name__) + +_model_cache: TTLCache = TTLCache(ttl_seconds=1800.0, max_entries=16) +_pred_cache: TTLCache = TTLCache(ttl_seconds=600.0, max_entries=32) +_SAM_PCA_DIMS = 32 + +# status_map values +STATUS_ABSTAIN = 0 +STATUS_SINGLETON = 1 +STATUS_MULTI = 2 + + +@dataclass(frozen=True) +class ClfModel: + """Cached CatBoost model tied to a feature job (+ conformal calibration).""" + + model_id: str + feature_job_id: str + class_ids: tuple[int, ...] + n_samples: int + n_train: int + n_cal: int + model: CatBoostClassifier + feature_labels: tuple[str, ...] + train_accuracy: float + feature_importances: tuple[tuple[str, float], ...] + params: dict[str, Any] + n_trees: int + # Sorted nonconformity scores per class (calibration), for any-alpha quantiles + cal_scores_by_class: dict[int, np.ndarray] + _model_json: dict[str, Any] = field(repr=False, compare=False) + sam_pca_mean: np.ndarray | None = None + sam_pca_components: np.ndarray | None = None + uses_sam: bool = False + + +@dataclass(frozen=True) +class ConformalPredictResult: + """Commit map + set status + meta for one predict call.""" + + commit_map: np.ndarray # HxW uint8 classId or 0 + status_map: np.ndarray # HxW uint8 abstain/singleton/multi + alpha: float + q_by_class: dict[int, float] + counts: dict[str, int] + class_ids: tuple[int, ...] + + +@dataclass(frozen=True) +class CachedPrediction: + """Server-side predict artifacts for PNG GETs.""" + + pred_id: str + commit_png: bytes + status_png: bytes + meta: dict[str, Any] + + +def build_label_map(shapes: list[dict[str, Any]], height: int, width: int) -> np.ndarray: + """Compose a sparse int label map from shapes (0 = unlabeled). Last shape wins.""" + labels = np.zeros((height, width), dtype=np.uint8) + for shape in shapes: + class_id = int(shape.get("classId") or shape.get("class_id") or 0) + if class_id <= 0 or class_id > 255: + continue + mask = shape_to_mask(shape, height, width) + labels[mask] = class_id + return labels + + +def stratified_train_cal_split( + ys: np.ndarray, + *, + train_frac: float = 0.8, + rng: np.random.Generator, +) -> tuple[np.ndarray, np.ndarray]: + """Stratified indices into train / calibration (disjoint, cover all).""" + if not 0.0 < train_frac < 1.0: + raise ValueError("train_frac must be in (0, 1)") + train_parts: list[np.ndarray] = [] + cal_parts: list[np.ndarray] = [] + for cls in np.unique(ys): + idx = np.flatnonzero(ys == cls) + rng.shuffle(idx) + n = idx.size + # Ensure at least one cal sample when class has >=2 pixels + if n == 1: + train_parts.append(idx) + continue + n_train = max(1, min(n - 1, int(round(n * train_frac)))) + train_parts.append(idx[:n_train]) + cal_parts.append(idx[n_train:]) + train_idx = np.concatenate(train_parts) if train_parts else np.array([], dtype=np.int64) + cal_idx = np.concatenate(cal_parts) if cal_parts else np.array([], dtype=np.int64) + if cal_idx.size == 0: + raise ValueError("calibration split empty — need more labeled pixels per class") + return train_idx, cal_idx + + +def _stratified_sample( + ys: np.ndarray, + max_samples: int, + rng: np.random.Generator, +) -> np.ndarray: + """Return indices into *ys* capped at *max_samples*, roughly per-class balanced.""" + n = ys.shape[0] + if n <= max_samples: + return np.arange(n) + classes, counts = np.unique(ys, return_counts=True) + alloc = np.maximum(1, np.floor(counts / n * max_samples).astype(int)) + while alloc.sum() < max_samples: + alloc[int(np.argmax(counts - alloc))] += 1 + while alloc.sum() > max_samples: + i = int(np.argmax(alloc)) + if alloc[i] > 1: + alloc[i] -= 1 + else: + break + chosen: list[np.ndarray] = [] + for cls, k in zip(classes, alloc): + idx = np.flatnonzero(ys == cls) + take = min(int(k), idx.size) + chosen.append(rng.choice(idx, size=take, replace=False)) + return np.concatenate(chosen) if chosen else np.arange(0) + + +def conformal_quantile(scores: np.ndarray, alpha: float) -> float: + """Finite-sample split-conformal quantile of nonconformity scores. + + Uses level ``ceil((n+1)(1-alpha))/n`` clipped to [1/n, 1]. + """ + s = np.sort(np.asarray(scores, dtype=np.float64).ravel()) + n = s.size + if n == 0: + return 1.0 + if not 0.0 < alpha < 1.0: + raise ValueError("alpha must be in (0, 1)") + # Standard: q_level = ceil((n+1)(1-alpha)) / n + k = int(np.ceil((n + 1) * (1.0 - alpha))) + k = min(max(k, 1), n) + return float(s[k - 1]) + + +def mondrian_thresholds( + cal_scores_by_class: dict[int, np.ndarray], + alpha: float, +) -> dict[int, float]: + """Per-class conformal thresholds q_y(alpha).""" + return {int(c): conformal_quantile(scores, alpha) for c, scores in cal_scores_by_class.items()} + + +def _dump_model_json(clf: CatBoostClassifier) -> dict[str, Any]: + """Serialize model to CatBoost JSON (oblivious trees).""" + with tempfile.TemporaryDirectory() as td: + path = Path(td) / "model.json" + clf.save_model(str(path), format="json") + return json.loads(path.read_text(encoding="utf-8")) + + +def tree_view( + model: ClfModel, + tree_index: int = 0, +) -> dict[str, Any]: + """Human-readable view of one oblivious tree (splits + leaf values).""" + trees = model._model_json.get("oblivious_trees") or [] + n = len(trees) + if n == 0: + raise ValueError("model has no trees") + if tree_index < 0 or tree_index >= n: + raise IndexError(f"tree_index {tree_index} out of range 0..{n - 1}") + tree = trees[tree_index] + splits_out: list[dict[str, Any]] = [] + for sp in tree.get("splits") or []: + fi = int(sp.get("float_feature_index", 0)) + label = ( + model.feature_labels[fi] + if 0 <= fi < len(model.feature_labels) + else f"feature_{fi}" + ) + splits_out.append( + { + "feature_index": fi, + "feature_label": label, + "threshold": float(sp.get("border", 0.0)), + "split_type": sp.get("split_type", "FloatFeature"), + } + ) + leaf_values = [float(v) for v in (tree.get("leaf_values") or [])] + return { + "tree_index": tree_index, + "n_trees": n, + "depth": len(splits_out), + "splits": splits_out, + "n_leaves": len(leaf_values), + "leaf_values": leaf_values[:64], + "leaf_values_truncated": len(leaf_values) > 64, + } + + +def _job_has_sam(job: FeatureJob) -> bool: + return ( + job.sam_emb is not None + and job.sam_orig_hw is not None + and job.sam_reshaped_hw is not None + ) + + +def _sample_sk_and_sam( + job: FeatureJob, + ys: np.ndarray, + xs: np.ndarray, + *, + sam_pca_mean: np.ndarray | None = None, + sam_pca_components: np.ndarray | None = None, +) -> tuple[np.ndarray, np.ndarray | None]: + """Build skimage rows and optional raw or PCA-projected SAM rows.""" + h, w, c = job.float_stack.shape + ys_i = np.clip(ys.astype(np.int64), 0, h - 1) + xs_i = np.clip(xs.astype(np.int64), 0, w - 1) + x_sk = job.float_stack[ys_i, xs_i].astype(np.float32) + if not _job_has_sam(job): + return x_sk, None + assert job.sam_emb is not None and job.sam_orig_hw and job.sam_reshaped_hw + oh, ow = job.sam_orig_hw + rh, rw = job.sam_reshaped_hw + x_sam = bilinear_sample_emb( + job.sam_emb, ys_i.astype(np.float64), xs_i.astype(np.float64), + orig_h=oh, orig_w=ow, reshaped_h=rh, reshaped_w=rw, + ) + if sam_pca_mean is not None and sam_pca_components is not None: + x_sam = transform_pca(x_sam, sam_pca_mean, sam_pca_components) + return x_sk, x_sam + + +def _build_feature_matrix( + job: FeatureJob, + yy: np.ndarray, + xx: np.ndarray, + *, + uses_sam: bool, + sam_pca_mean: np.ndarray | None, + sam_pca_components: np.ndarray | None, + sam_pca_dims: int, + fit_pca_now: bool, +) -> tuple[np.ndarray, tuple[str, ...], np.ndarray | None, np.ndarray | None]: + """Assemble X and feature labels; optionally fit SAM PCA.""" + c = job.float_stack.shape[-1] + # Always sample raw (no PCA inside); apply PCA once here. + x_sk, x_sam_raw = _sample_sk_and_sam(job, yy, xx) + pca_mean = sam_pca_mean + pca_comp = sam_pca_components + if uses_sam and x_sam_raw is not None: + if fit_pca_now: + pca_mean, pca_comp = fit_pca(x_sam_raw, n_components=sam_pca_dims) + if pca_mean is not None and pca_comp is not None: + x_sam = transform_pca(x_sam_raw, pca_mean, pca_comp) + else: + x_sam = x_sam_raw + X = np.concatenate([x_sk, x_sam], axis=1) + feat_labels = tuple(job.labels) + tuple(f"sam{i}" for i in range(x_sam.shape[1])) + else: + X = x_sk + feat_labels = tuple(job.labels) if len(job.labels) == c else tuple(f"f{i}" for i in range(c)) + return X, feat_labels, pca_mean, pca_comp + + +def train_classifier( + job: FeatureJob, + shapes: list[dict[str, Any]], + *, + iterations: int = 200, + depth: int = 6, + learning_rate: float = 0.1, + max_samples: int = 200_000, + random_seed: int = 0, + sam_pca_dims: int = _SAM_PCA_DIMS, + train_frac: float = 0.8, +) -> ClfModel: + """Fit CatBoost on 80% of labels; calibrate Mondrian scores on 20%.""" + h, w, c = job.float_stack.shape + label_map = build_label_map(shapes, h, w) + if not np.any(label_map > 0): + raise ValueError("no labeled pixels — annotate at least one shape") + + yy, xx = np.nonzero(label_map) + y_all = label_map[yy, xx].astype(np.int32) + class_ids = tuple(int(x) for x in sorted(np.unique(y_all).tolist())) + if len(class_ids) < 2: + raise ValueError("need at least two classes with labeled pixels") + + rng = np.random.default_rng(random_seed) + # Cap then split + cap_idx = _stratified_sample(y_all, max_samples, rng) + yy_c = yy[cap_idx] + xx_c = xx[cap_idx] + y_c = y_all[cap_idx] + + train_idx, cal_idx = stratified_train_cal_split(y_c, train_frac=train_frac, rng=rng) + yy_tr, xx_tr, y_tr = yy_c[train_idx], xx_c[train_idx], y_c[train_idx] + yy_cal, xx_cal, y_cal = yy_c[cal_idx], xx_c[cal_idx], y_c[cal_idx] + + uses_sam = _job_has_sam(job) + X_tr, feat_labels, sam_pca_mean, sam_pca_components = _build_feature_matrix( + job, + yy_tr, + xx_tr, + uses_sam=uses_sam, + sam_pca_mean=None, + sam_pca_components=None, + sam_pca_dims=sam_pca_dims, + fit_pca_now=uses_sam, + ) + + params = { + "iterations": int(iterations), + "depth": int(depth), + "learning_rate": float(learning_rate), + "loss_function": "MultiClass", + "random_seed": int(random_seed), + "uses_sam": uses_sam, + "sam_pca_dims": int(sam_pca_components.shape[0]) if sam_pca_components is not None else 0, + "train_frac": float(train_frac), + } + clf = CatBoostClassifier( + iterations=params["iterations"], + depth=params["depth"], + learning_rate=params["learning_rate"], + loss_function="MultiClass", + verbose=False, + allow_writing_files=False, + random_seed=params["random_seed"], + thread_count=-1, + ) + clf.fit(X_tr, y_tr) + + pred_train = np.asarray(clf.predict(X_tr)).reshape(-1).astype(np.int32) + train_accuracy = float(np.mean(pred_train == y_tr)) + + # Calibration nonconformity: s = 1 - p_true + X_cal, _, _, _ = _build_feature_matrix( + job, + yy_cal, + xx_cal, + uses_sam=uses_sam, + sam_pca_mean=sam_pca_mean, + sam_pca_components=sam_pca_components, + sam_pca_dims=sam_pca_dims, + fit_pca_now=False, + ) + proba_cal = np.asarray(clf.predict_proba(X_cal), dtype=np.float64) + classes = np.asarray(clf.classes_) + class_to_col = {int(c): i for i, c in enumerate(classes.tolist())} + cal_scores_by_class: dict[int, np.ndarray] = {} + for cid in class_ids: + mask = y_cal == cid + if not np.any(mask): + continue + col = class_to_col[cid] + scores = 1.0 - proba_cal[mask, col] + cal_scores_by_class[cid] = np.sort(scores.astype(np.float64)) + + if not cal_scores_by_class: + raise ValueError("no calibration scores — need labeled pixels in every class for cal split") + + raw_imp = np.asarray(clf.get_feature_importance(), dtype=np.float64) + if len(feat_labels) != raw_imp.size: + feat_labels = tuple(f"f{i}" for i in range(raw_imp.size)) + order = np.argsort(-raw_imp) + feature_importances = tuple( + (feat_labels[int(i)], float(raw_imp[int(i)])) for i in order + ) + + model_json = _dump_model_json(clf) + n_trees = len(model_json.get("oblivious_trees") or []) + + model_id = uuid.uuid4().hex + entry = ClfModel( + model_id=model_id, + feature_job_id=job.job_id, + class_ids=class_ids, + n_samples=int(y_c.shape[0]), + n_train=int(y_tr.shape[0]), + n_cal=int(y_cal.shape[0]), + model=clf, + feature_labels=feat_labels, + train_accuracy=train_accuracy, + feature_importances=feature_importances, + params=params, + n_trees=n_trees, + cal_scores_by_class=cal_scores_by_class, + _model_json=model_json, + sam_pca_mean=sam_pca_mean, + sam_pca_components=sam_pca_components, + uses_sam=uses_sam, + ) + _model_cache.set(model_id, entry) + return entry + + +def get_model(model_id: str) -> ClfModel | None: + """Look up a cached classifier model.""" + m = _model_cache.get(model_id) + return m if isinstance(m, ClfModel) else None + + +def store_prediction(result: ConformalPredictResult) -> CachedPrediction: + """Cache commit/status PNGs; return handle for GET endpoints.""" + pred_id = uuid.uuid4().hex + meta = { + "pred_id": pred_id, + "alpha": result.alpha, + "class_ids": list(result.class_ids), + "q_by_class": {str(k): v for k, v in result.q_by_class.items()}, + "counts": result.counts, + } + cached = CachedPrediction( + pred_id=pred_id, + commit_png=encode_label_png(result.commit_map), + status_png=encode_label_png(result.status_map), + meta=meta, + ) + _pred_cache.set(pred_id, cached) + return cached + + +def get_prediction(pred_id: str) -> CachedPrediction | None: + """Look up a cached conformal prediction.""" + p = _pred_cache.get(pred_id) + return p if isinstance(p, CachedPrediction) else None + + +def _features_for_coords( + model: ClfModel, + job: FeatureJob, + ys: np.ndarray, + xs: np.ndarray, +) -> np.ndarray: + x_sk, x_sam = _sample_sk_and_sam( + job, + ys, + xs, + sam_pca_mean=model.sam_pca_mean, + sam_pca_components=model.sam_pca_components, + ) + if model.uses_sam: + if x_sam is None: + raise ValueError("model was trained with SlimSAM but job has no sam_emb") + return np.concatenate([x_sk, x_sam], axis=1) + return x_sk + + +def predict_conformal( + model: ClfModel, + job: FeatureJob, + *, + alpha: float = 0.05, + row_chunk: int = 128, + preserve_shapes: list[dict[str, Any]] | None = None, +) -> ConformalPredictResult: + """Mondrian conformal sets → commit map (singletons) + status map.""" + shelf = model.feature_job_id.startswith("shelf:") + if not shelf and job.job_id != model.feature_job_id: + raise ValueError("model was trained on a different feature job") + if model.uses_sam and not _job_has_sam(job): + raise ValueError("model expects SlimSAM embeddings on the feature job") + if not 0.0 < alpha < 1.0: + raise ValueError("alpha must be in (0, 1)") + # Shelf models: sanity-check feature width via a 1-pixel probe + if shelf and model.feature_labels: + probe = _features_for_coords( + model, job, np.array([0], dtype=np.int32), np.array([0], dtype=np.int32) + ) + expected = len(model.feature_labels) + if probe.shape[1] != expected: + raise ValueError( + f"feature width mismatch: job has {probe.shape[1]}, model expects {expected}" + ) + + q_by_class = mondrian_thresholds(model.cal_scores_by_class, alpha) + # p_y >= 1 - q_y ↔ included + thr_prob = {cid: 1.0 - q for cid, q in q_by_class.items()} + + h, w, _ = job.float_stack.shape + classes = np.asarray(model.model.classes_, dtype=np.int32) + class_list = [int(c) for c in classes.tolist()] + commit = np.zeros((h, w), dtype=np.uint8) + status = np.zeros((h, w), dtype=np.uint8) + + for y0 in range(0, h, row_chunk): + y1 = min(h, y0 + row_chunk) + yy = np.repeat(np.arange(y0, y1), w) + xx = np.tile(np.arange(w), y1 - y0) + block = _features_for_coords(model, job, yy, xx) + proba = np.asarray(model.model.predict_proba(block), dtype=np.float64) + if proba.ndim == 1: + proba = np.stack([1.0 - proba, proba], axis=1) + + n = proba.shape[0] + in_set = np.zeros((n, len(class_list)), dtype=bool) + for j, cid in enumerate(class_list): + t = thr_prob.get(cid) + if t is None: + continue + in_set[:, j] = proba[:, j] >= t + + set_sizes = in_set.sum(axis=1) + # Singleton: exactly one class; commit that classId + singleton = set_sizes == 1 + multi = set_sizes > 1 + abstain = set_sizes == 0 + + st = np.full(n, STATUS_ABSTAIN, dtype=np.uint8) + st[singleton] = STATUS_SINGLETON + st[multi] = STATUS_MULTI + + cm = np.zeros(n, dtype=np.uint8) + if np.any(singleton): + # argmax among members = the only True column + cols = np.argmax(in_set[singleton], axis=1) + cm[singleton] = classes[cols].astype(np.uint8) + + commit[y0:y1] = cm.reshape(y1 - y0, w) + status[y0:y1] = st.reshape(y1 - y0, w) + + if preserve_shapes: + preserve = build_label_map(preserve_shapes, h, w) > 0 + commit[preserve] = 0 + # Keep status for overlay elsewhere; still mark preserved as abstain-ish + status[preserve] = STATUS_ABSTAIN + + counts = { + "singleton": int(np.sum(status == STATUS_SINGLETON)), + "multi": int(np.sum(status == STATUS_MULTI)), + "abstain": int(np.sum(status == STATUS_ABSTAIN)), + } + return ConformalPredictResult( + commit_map=commit, + status_map=status, + alpha=float(alpha), + q_by_class=q_by_class, + counts=counts, + class_ids=model.class_ids, + ) + + +def predict_labels( + model: ClfModel, + job: FeatureJob, + *, + row_chunk: int = 128, + preserve_shapes: list[dict[str, Any]] | None = None, +) -> np.ndarray: + """Backward-compat argmax map (no conformal). Prefer ``predict_conformal``.""" + if job.job_id != model.feature_job_id: + raise ValueError("model was trained on a different feature job") + if model.uses_sam and not _job_has_sam(job): + raise ValueError("model expects SlimSAM embeddings on the feature job") + h, w, _ = job.float_stack.shape + classes = np.asarray(model.model.classes_, dtype=np.int32) + out = np.empty((h, w), dtype=np.uint8) + for y0 in range(0, h, row_chunk): + y1 = min(h, y0 + row_chunk) + yy = np.repeat(np.arange(y0, y1), w) + xx = np.tile(np.arange(w), y1 - y0) + block = _features_for_coords(model, job, yy, xx) + proba = np.asarray(model.model.predict_proba(block), dtype=np.float64) + if proba.ndim == 1: + pred_idx = (proba >= 0.5).astype(np.int32) + else: + pred_idx = np.argmax(proba, axis=1).astype(np.int32) + pred = classes[pred_idx] + out[y0:y1] = pred.astype(np.uint8).reshape(y1 - y0, w) + if preserve_shapes: + preserve = build_label_map(preserve_shapes, h, w) > 0 + out[preserve] = 0 + return out + + +def encode_label_png(label_map: np.ndarray) -> bytes: + """Encode HxW uint8 classId/status map as a grayscale PNG.""" + img = PILImage.fromarray(np.asarray(label_map, dtype=np.uint8), mode="L") + buf = BytesIO() + img.save(buf, format="PNG") + return buf.getvalue() + + +def train_result_dict(model: ClfModel) -> dict[str, Any]: + """JSON payload for the train endpoint / UI results panel.""" + preview = tree_view(model, 0) if model.n_trees else None + q_default = mondrian_thresholds(model.cal_scores_by_class, 0.05) + return { + "model_id": model.model_id, + "n_samples": model.n_samples, + "n_train": model.n_train, + "n_cal": model.n_cal, + "class_ids": list(model.class_ids), + "train_accuracy": model.train_accuracy, + "params": model.params, + "n_trees": model.n_trees, + "uses_sam": model.uses_sam, + "q_by_class_alpha_05": {str(k): v for k, v in q_default.items()}, + "feature_importances": [ + {"label": lab, "importance": imp} for lab, imp in model.feature_importances + ], + "tree_preview": preview, + } diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 5658ed0..3e29aeb 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -9,13 +9,20 @@ requires-python = ">=3.11" dependencies = [ "fastapi>=0.115", "uvicorn[standard]>=0.30", - "tiled[all]>=0.1", + # Client only — the app talks to an EXTERNAL Tiled (tiled.client / tiled.queries). + # The Tiled *server* is not a runtime dependency and is intentionally not shipped + # in the Docker image. Local dev runs its own Tiled server via start_all.sh, which + # installs tiled[all] into .venv independently of this file. + "tiled[client]>=0.1", "numpy>=1.26", "pillow>=10.3", "python-dotenv>=1.0", "matplotlib>=3.8", "pycocotools>=2.0.7", "scikit-image>=0.22", + "scikit-learn>=1.4", + "catboost>=1.2", + "onnxruntime>=1.17", "tifffile>=2024.0", "imagecodecs", ] diff --git a/backend/sam_embed.py b/backend/sam_embed.py new file mode 100644 index 0000000..3ab926d --- /dev/null +++ b/backend/sam_embed.py @@ -0,0 +1,184 @@ +"""Backend SlimSAM vision-encoder embeddings for CatBoost pixel features. + +Uses the same vendored ONNX as the frontend Magic/SAM tool when available +(``frontend/public/models/slimsam-77-uniform``), or ``backend/models/...``. +Embeddings stay at ``64×64×256`` — never upsampled to full HxW. +""" + +from __future__ import annotations + +import logging +import threading +from functools import lru_cache +from pathlib import Path + +import numpy as np +from PIL import Image as PILImage + +logger = logging.getLogger(__name__) + +_REPO_ROOT = Path(__file__).resolve().parent.parent +_BACKEND_DIR = Path(__file__).resolve().parent + +# ImageNet mean/std matching SamImageProcessor / preprocessor_config.json +_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(3, 1, 1) +_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(3, 1, 1) +_LONG_EDGE = 1024 +_PAD = 1024 +_EMB_SIDE = 64 # encoder spatial grid for 1024 input + + +def resolve_vision_encoder_path() -> Path | None: + """Locate ``vision_encoder.onnx`` (backend models or frontend public).""" + candidates = [ + _BACKEND_DIR / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx", + _REPO_ROOT / "frontend" / "public" / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx", + ] + for p in candidates: + if p.is_file(): + return p + return None + + +def sam_available() -> bool: + """True when the SlimSAM vision encoder ONNX file is present.""" + return resolve_vision_encoder_path() is not None + + +@lru_cache(maxsize=1) +def _session(): + """Lazy CPU InferenceSession (thread-safe via lock below).""" + import onnxruntime as ort + + path = resolve_vision_encoder_path() + if path is None: + raise FileNotFoundError( + "SlimSAM vision_encoder.onnx not found. Vendor via " + "`node frontend/scripts/fetch-sam-model.mjs` or place under " + "backend/models/slimsam-77-uniform/onnx/." + ) + return ort.InferenceSession(str(path), providers=["CPUExecutionProvider"]) + + +_session_lock = threading.Lock() + + +def rgb_uint8_from_array(arr: np.ndarray) -> np.ndarray: + """Convert a slice array to HxWx3 uint8 RGB (grayscale → triple).""" + a = np.asarray(arr) + if a.ndim == 3 and a.shape[-1] in (3, 4): + rgb = a[..., :3].astype(np.float64) + elif a.ndim == 2: + g = a.astype(np.float64) + rgb = np.stack([g, g, g], axis=-1) + else: + raise ValueError(f"unsupported array shape {a.shape}") + finite = rgb[np.isfinite(rgb)] + if finite.size == 0: + return np.zeros((*rgb.shape[:2], 3), dtype=np.uint8) + lo, hi = float(np.min(finite)), float(np.max(finite)) + if hi <= lo: + return np.zeros((*rgb.shape[:2], 3), dtype=np.uint8) + scaled = (rgb - lo) / (hi - lo) * 255.0 + return np.clip(np.round(scaled), 0, 255).astype(np.uint8) + + +def preprocess_sam_rgb(rgb_u8: np.ndarray) -> tuple[np.ndarray, tuple[int, int], tuple[int, int]]: + """Resize longest edge to 1024, pad to 1024², ImageNet-normalize NCHW float32. + + Returns: + ``(pixel_values [1,3,1024,1024], (orig_h, orig_w), (reshaped_h, reshaped_w))``. + """ + img = PILImage.fromarray(rgb_u8, mode="RGB") + ow, oh = img.size # PIL: width, height + scale = _LONG_EDGE / float(max(oh, ow)) + nw = max(1, int(round(ow * scale))) + nh = max(1, int(round(oh * scale))) + # resample=2 → BILINEAR in HF preprocessor_config + resized = img.resize((nw, nh), resample=PILImage.BILINEAR) + canvas = PILImage.new("RGB", (_PAD, _PAD), (0, 0, 0)) + canvas.paste(resized, (0, 0)) # top-left pad (SAM processor default) + arr = np.asarray(canvas, dtype=np.float32) * (1.0 / 255.0) + nchw = np.transpose(arr, (2, 0, 1)) # 3,H,W + nchw = (nchw - _MEAN) / _STD + return nchw[np.newaxis, ...].astype(np.float32), (oh, ow), (nh, nw) + + +def encode_image_embeddings(arr: np.ndarray) -> tuple[np.ndarray, tuple[int, int], tuple[int, int]]: + """Run SlimSAM vision encoder; return ``(eh,ew,C) float32`` + size metadata. + + Output layout is ``(64, 64, 256)`` channels-last for bilinear sampling. + """ + rgb = rgb_uint8_from_array(arr) + pixel_values, orig_hw, reshaped_hw = preprocess_sam_rgb(rgb) + with _session_lock: + sess = _session() + outs = sess.run(None, {sess.get_inputs()[0].name: pixel_values}) + # First output: image_embeddings [1, 256, 64, 64] + emb = np.asarray(outs[0], dtype=np.float32) + if emb.ndim != 4 or emb.shape[0] != 1: + raise RuntimeError(f"unexpected embedding shape {emb.shape}") + # → 64,64,256 + spatial = np.transpose(emb[0], (1, 2, 0)) + return np.ascontiguousarray(spatial), orig_hw, reshaped_hw + + +def bilinear_sample_emb( + emb: np.ndarray, + ys: np.ndarray, + xs: np.ndarray, + *, + orig_h: int, + orig_w: int, + reshaped_h: int, + reshaped_w: int, +) -> np.ndarray: + """Sample embedding channels at original-image pixel coords (bilinear). + + Maps ``(x,y)`` through the longest-edge resize into the 1024 pad, then into + the ``64×64`` embedding grid. + """ + eh, ew, c = emb.shape + # resized coords in pad space (top-left paste) + scale_y = reshaped_h / float(orig_h) + scale_x = reshaped_w / float(orig_w) + py = ys.astype(np.float64) * scale_y + px = xs.astype(np.float64) * scale_x + # pad → emb (1024 / 64 = 16) + cell = _PAD / float(eh) + fy = (py + 0.5) / cell - 0.5 + fx = (px + 0.5) / cell - 0.5 + fy = np.clip(fy, 0.0, eh - 1.001) + fx = np.clip(fx, 0.0, ew - 1.001) + y0 = np.floor(fy).astype(np.int32) + x0 = np.floor(fx).astype(np.int32) + y1 = np.minimum(y0 + 1, eh - 1) + x1 = np.minimum(x0 + 1, ew - 1) + wy = (fy - y0).astype(np.float32)[:, None] + wx = (fx - x0).astype(np.float32)[:, None] + v00 = emb[y0, x0] + v01 = emb[y0, x1] + v10 = emb[y1, x0] + v11 = emb[y1, x1] + top = v00 * (1.0 - wx) + v01 * wx + bot = v10 * (1.0 - wx) + v11 * wx + return top * (1.0 - wy) + bot * wy + + +def fit_pca(x: np.ndarray, n_components: int = 32) -> tuple[np.ndarray, np.ndarray]: + """Fit a simple PCA; return ``(mean [C], components [k,C])``.""" + if x.ndim != 2: + raise ValueError("x must be (n, c)") + n, c = x.shape + k = int(min(n_components, c, max(1, n - 1))) + mean = x.mean(axis=0) + xc = x - mean + # Economy SVD on n×c (prefer when n >= c use XtX path via svd of xc) + _, _, vt = np.linalg.svd(xc, full_matrices=False) + components = vt[:k].astype(np.float32) + return mean.astype(np.float32), components + + +def transform_pca(x: np.ndarray, mean: np.ndarray, components: np.ndarray) -> np.ndarray: + """Project rows of *x* with a fitted PCA.""" + return ((x - mean) @ components.T).astype(np.float32) diff --git a/backend/schemas.py b/backend/schemas.py index 5a5d099..58c761f 100644 --- a/backend/schemas.py +++ b/backend/schemas.py @@ -198,6 +198,9 @@ class ExportRequest(BaseModel): sources: list[ExportSourceItem] = Field(default_factory=list) mode: Literal["fail", "overwrite", "merge"] = "merge" dry_run: bool = False + # Include the (costly) polygon copy in COCO segmentation_poly. RLE is always + # written and is exact; polygons are opt-in for external viewers. + include_polygons: bool = False render: RenderOpts = Field(default_factory=RenderOpts) classes: list[AnnotationClass] = Field(default_factory=list) slices: dict[str, list[dict[str, Any]]] = Field(default_factory=dict) @@ -216,12 +219,14 @@ class DraftPayload(BaseModel): slices: Mapping of slice key → list of serialised shape dicts. split_by_slice: Mapping of slice key → dataset split name. negative_slices: Slice keys marked as negative examples. + mask_sets: Optional Cleanup Studio named mask sets. """ classes: list[AnnotationClass] = Field(default_factory=list) slices: dict[str, list[dict[str, Any]]] = Field(default_factory=dict) split_by_slice: dict[str, str] = Field(default_factory=dict) negative_slices: list[str] = Field(default_factory=list) + mask_sets: list[dict[str, Any]] = Field(default_factory=list) class SaveVersionRequest(BaseModel): diff --git a/backend/scripts/add_sample_metadata.py b/backend/scripts/add_sample_metadata.py new file mode 100644 index 0000000..00cfe6e --- /dev/null +++ b/backend/scripts/add_sample_metadata.py @@ -0,0 +1,113 @@ +"""Add browsable metadata to each TIFF sample node. + +The Browse UI is metadata-driven: it can only list/filter samples that have a +metadata field with >=2 distinct values. Freshly-registered TIFF nodes have +empty metadata ({}), so the browser shows nothing to add as a column. + +This script writes per node: + image_number : frame index parsed from the trailing digits of the key, + zero-padded to match the filenames (e.g. "00000".."00689"). + size : pixel dimensions from the node's array structure, + e.g. "2560 x 2560". + +Padding keeps the browser column in natural order (lexical sort == numeric). + +Run from the REPO ROOT: + python backend/scripts/add_sample_metadata.py +Refresh the Browse UI (or restart Tiled) afterwards. +""" + +from __future__ import annotations + +import json +import re +import sqlite3 +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[2] +CATALOG_DB = REPO_ROOT / ".tiled" / "catalog.db" + +# Trailing digits of the node key, e.g. "..._petiole22_00042" -> "00042". +_FRAME_RE = re.compile(r"(\d+)$") + + +def main(dry_run: bool = False) -> None: + if not CATALOG_DB.exists(): + print(f"ERROR: catalog.db not found at {CATALOG_DB}") + sys.exit(1) + + conn = sqlite3.connect(str(CATALOG_DB)) + conn.row_factory = sqlite3.Row + c = conn.cursor() + + # Sample nodes live directly under the root (parent=0). Join through the + # data_source to the structure so we can read each node's pixel shape. + rows = c.execute( + """ + SELECT n.id, n.key, n.metadata, s.structure + FROM nodes n + LEFT JOIN data_sources ds ON ds.node_id = n.id + LEFT JOIN structures s ON s.id = ds.structure_id + WHERE n.structure_family = 'array' AND n.parent = 0 + """ + ).fetchall() + + print(f"Found {len(rows)} sample nodes.") + if not rows: + print("Nothing to update.") + conn.close() + return + + # Use a consistent pad width across the whole set. + width = max( + (len(m.group(1)) for m in (_FRAME_RE.search(r["key"]) for r in rows) if m), + default=1, + ) + + updated = 0 + for row in rows: + m = _FRAME_RE.search(row["key"]) + if not m: + continue + new_fields = {"image_number": m.group(1).zfill(width)} + + # Pixel dimensions from the array structure, e.g. "2560 x 2560". + if row["structure"]: + try: + shape = json.loads(row["structure"]).get("shape") or [] + if shape: + new_fields["size"] = " x ".join(str(d) for d in shape) + except (TypeError, json.JSONDecodeError): + pass + + try: + existing = json.loads(row["metadata"]) if row["metadata"] else {} + except (TypeError, json.JSONDecodeError): + existing = {} + + merged = {**existing, **new_fields} + if merged == existing: + continue + + if dry_run: + if updated < 3: + print(f" {row['key']} -> {new_fields}") + else: + c.execute( + "UPDATE nodes SET metadata = ? WHERE id = ?", + (json.dumps(merged), row["id"]), + ) + updated += 1 + + if dry_run: + print(f"Would update {updated} node(s) (dry run).") + else: + conn.commit() + print(f"Set image_number on {updated} node(s).") + print("Refresh the Browse UI (or restart Tiled) to see the 'image_number' column.") + conn.close() + + +if __name__ == "__main__": + main(dry_run="--dry-run" in sys.argv) diff --git a/backend/scripts/reingest_tiffs_as_individual.py b/backend/scripts/reingest_tiffs_as_individual.py new file mode 100644 index 0000000..c191cf2 --- /dev/null +++ b/backend/scripts/reingest_tiffs_as_individual.py @@ -0,0 +1,165 @@ +"""Re-ingest stacked TIFF array as 690 individual sample nodes. + +The 690 TIFFs were previously registered as one stacked multi-chunk array. +This script splits them back into individual nodes so the Browse UI can +treat each TIFF as a separate sample. + +Run from the REPO ROOT (not from backend/scripts): + python backend/scripts/reingest_tiffs_as_individual.py + +Tiled must NOT be running when you run this (it modifies the catalog DB +directly). Restart Tiled after running. +""" + +from __future__ import annotations + +import hashlib +import json +import sqlite3 +import sys +from pathlib import Path + +try: + import canonicaljson +except ImportError: + print("ERROR: canonicaljson not installed. Run: pip install canonicaljson") + sys.exit(1) + +REPO_ROOT = Path(__file__).resolve().parents[2] +CATALOG_DB = REPO_ROOT / ".tiled" / "catalog.db" + +STACKED_KEY = "20260221_135217_petiole22_" + +# Structure for a single 2560×2560 float32 TIFF frame. +SINGLE_TIFF_STRUCTURE = { + "data_type": {"endianness": "little", "itemsize": 4, "kind": "f", "dt_units": None}, + "chunks": [[2560], [2560]], + "shape": [2560, 2560], + "dims": None, + "resizable": False, +} + + +def compute_structure_id(structure: dict) -> str: + canonical = canonicaljson.encode_canonical_json(structure) + return hashlib.md5(canonical).hexdigest() + + +def main(dry_run: bool = False) -> None: + if not CATALOG_DB.exists(): + print(f"ERROR: catalog.db not found at {CATALOG_DB}") + sys.exit(1) + + conn = sqlite3.connect(str(CATALOG_DB)) + conn.execute("PRAGMA foreign_keys = ON") + conn.row_factory = sqlite3.Row + c = conn.cursor() + + # --- find the stacked node --- + c.execute("SELECT id, parent FROM nodes WHERE key = ?", (STACKED_KEY,)) + stacked_row = c.fetchone() + if stacked_row is None: + print(f"No node with key '{STACKED_KEY}' found — nothing to do.") + conn.close() + return + + stacked_node_id = stacked_row["id"] + root_node_id = stacked_row["parent"] + print(f"Found stacked node id={stacked_node_id}, parent={root_node_id}") + + # --- collect assets (TIFFs) in order --- + c.execute( + """ + SELECT a.id AS asset_id, a.data_uri, dsaa.num + FROM data_source_asset_association dsaa + JOIN data_sources ds ON dsaa.data_source_id = ds.id + JOIN assets a ON dsaa.asset_id = a.id + WHERE ds.node_id = ? + ORDER BY dsaa.num + """, + (stacked_node_id,), + ) + tiff_rows = c.fetchall() + print(f"Found {len(tiff_rows)} TIFF assets to re-register.") + + if len(tiff_rows) == 0: + print("No assets found for the stacked node. Aborting.") + conn.close() + return + + if dry_run: + for r in tiff_rows[:5]: + print(f" [{r['num']}] {r['data_uri']}") + print(" ... (dry run, not modifying DB)") + conn.close() + return + + struct_id = compute_structure_id(SINGLE_TIFF_STRUCTURE) + struct_json = json.dumps(SINGLE_TIFF_STRUCTURE) + + # --- delete stacked node (cascades to data_sources and associations) --- + print(f"Deleting stacked node id={stacked_node_id} ...") + c.execute("DELETE FROM nodes WHERE id = ?", (stacked_node_id,)) + + # --- ensure single-TIFF structure exists --- + c.execute("SELECT id FROM structures WHERE id = ?", (struct_id,)) + if c.fetchone() is None: + c.execute("INSERT INTO structures (id, structure) VALUES (?, ?)", (struct_id, struct_json)) + print(f"Inserted structure id={struct_id}") + + # --- insert 690 individual nodes --- + print("Inserting individual nodes ...") + inserted = 0 + for row in tiff_rows: + uri: str = row["data_uri"] + asset_id: int = row["asset_id"] + + # Derive key from the filename stem + fname = uri.split("/")[-1] + stem = fname.rsplit(".", 1)[0] if "." in fname else fname + node_key = stem + + # Insert node + c.execute( + """ + INSERT INTO nodes (parent, key, structure_family, metadata, specs, access_blob) + VALUES (?, ?, 'array', '{}', '[]', '{}') + """, + (root_node_id, node_key), + ) + node_id = c.lastrowid + + # Insert data_source + c.execute( + """ + INSERT INTO data_sources + (node_id, structure_id, mimetype, parameters, properties, management, structure_family) + VALUES (?, ?, 'image/tiff', '{}', '{}', 'external', 'array') + """, + (node_id, struct_id), + ) + ds_id = c.lastrowid + + # Insert association (single file → parameter=data_uri, num=NULL) + c.execute( + """ + INSERT INTO data_source_asset_association + (data_source_id, asset_id, parameter, num) + VALUES (?, ?, 'data_uri', NULL) + """, + (ds_id, asset_id), + ) + + inserted += 1 + if inserted % 100 == 0: + print(f" {inserted}/{len(tiff_rows)} ...") + + conn.commit() + print(f"Done. Inserted {inserted} individual sample nodes.") + print("Restart Tiled to pick up the changes.") + conn.close() + + +if __name__ == "__main__": + dry_run = "--dry-run" in sys.argv + main(dry_run=dry_run) diff --git a/backend/scripts/repair_catalog_paths.py b/backend/scripts/repair_catalog_paths.py index 08ebbd9..14a9905 100644 --- a/backend/scripts/repair_catalog_paths.py +++ b/backend/scripts/repair_catalog_paths.py @@ -29,19 +29,33 @@ def repair(dry_run: bool = False) -> int: conn = sqlite3.connect(str(CATALOG_DB)) c = conn.cursor() + + # Only repair managed assets (those stored under .tiled/data/). + # External assets (management='external') live at user-supplied paths and + # should never be rewritten here — they're intentionally outside the repo. + managed_asset_ids: set[int] = set( + row[0] + for row in c.execute( + """ + SELECT DISTINCT a.id + FROM assets a + JOIN data_source_asset_association dsaa ON dsaa.asset_id = a.id + JOIN data_sources ds ON ds.id = dsaa.data_source_id + WHERE ds.management != 'external' + """ + ).fetchall() + ) + rows = c.execute("SELECT id, data_uri FROM assets").fetchall() updated = 0 for row_id, uri in rows: + if row_id not in managed_asset_ids: + continue # external asset — leave it alone if not uri.startswith("file://localhost"): continue # Extract the path portion after the scheme+host file_path = Path(uri[len("file://localhost"):]) - # The part we care about is everything from .tiled/data/ onward - try: - rel = file_path.relative_to(file_path.parts[0] + "/.tiled/data" if False else "") - except ValueError: - rel = None # Find .tiled/data/ anchor anywhere in the path parts = file_path.parts @@ -52,7 +66,7 @@ def repair(dry_run: bool = False) -> int: ) rel_parts = parts[idx + 1:] except StopIteration: - print(f" [{row_id}] cannot parse URI, skipping: {uri}") + print(f" [{row_id}] cannot parse managed URI, skipping: {uri}") continue new_path = DATA_DIR.joinpath(*rel_parts) diff --git a/backend/tests/test_browse_container.py b/backend/tests/test_browse_container.py new file mode 100644 index 0000000..15ac253 --- /dev/null +++ b/backend/tests/test_browse_container.py @@ -0,0 +1,68 @@ +"""Tests for browse-container auto-discovery. + +Discovery must prefer the fullest sample collection (e.g. ``petioles/`` with +dozens of children) over a thin ``browse/`` leftover with a single ingest. +""" + +from __future__ import annotations + +from typing import Any, Iterator + +import pytest + +from tiled_clients import get_browse_container + + +class _FakeNode: + """Minimal mapping that supports ``node[key]`` and ``len(node)``.""" + + def __init__(self, children: dict[str, Any] | None = None) -> None: + self._children = children or {} + + def __getitem__(self, key: str) -> Any: + return self._children[key] + + def __iter__(self) -> Iterator[str]: + return iter(self._children) + + def __len__(self) -> int: + return len(self._children) + + def keys(self) -> Any: + return self._children.keys() + + +def test_prefers_largest_top_level_over_small_browse( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """A populated ``petioles`` container must beat a 1-item ``browse``.""" + monkeypatch.delenv("TILED_BROWSE_PATH", raising=False) + petioles = _FakeNode({f"s{i}": object() for i in range(20)}) + browse = _FakeNode({"only_one": object()}) + root = _FakeNode({"browse": browse, "petioles": petioles}) + + node, prefix = get_browse_container(root) + assert prefix == "petioles" + assert node is petioles + assert len(node) == 20 + + +def test_env_path_wins(monkeypatch: pytest.MonkeyPatch) -> None: + """``TILED_BROWSE_PATH`` overrides size-based discovery.""" + monkeypatch.setenv("TILED_BROWSE_PATH", "browse") + petioles = _FakeNode({f"s{i}": object() for i in range(20)}) + browse = _FakeNode({"a": object(), "b": object()}) + root = _FakeNode({"browse": browse, "petioles": petioles}) + + node, prefix = get_browse_container(root) + assert prefix == "browse" + assert node is browse + + +def test_falls_back_to_root_when_empty(monkeypatch: pytest.MonkeyPatch) -> None: + """Empty catalog → browse at the client root.""" + monkeypatch.delenv("TILED_BROWSE_PATH", raising=False) + root = _FakeNode({}) + node, prefix = get_browse_container(root) + assert prefix == "" + assert node is root diff --git a/backend/tests/test_clf_api.py b/backend/tests/test_clf_api.py new file mode 100644 index 0000000..646045d --- /dev/null +++ b/backend/tests/test_clf_api.py @@ -0,0 +1,83 @@ +"""API smoke tests for CatBoost train/predict endpoints.""" + +from __future__ import annotations + +import io + +import numpy as np +from fastapi.testclient import TestClient +from PIL import Image + +import multiscale_features as features_mod +from annotation_server import app + + +def test_clf_train_predict_roundtrip() -> None: + gray = np.zeros((48, 48), dtype=np.float64) + gray[:, 24:] = 1.0 + stack, float_stack, labels = features_mod.compute_feature_stacks( + gray, + sigma_min=1.0, + sigma_max=2.0, + intensity=True, + edges=True, + texture=False, + clahe=False, + ) + job = features_mod.store_job(stack, labels, float_stack=float_stack) + client = TestClient(app) + + shapes = [ + {"kind": "rectangle", "classId": 1, "x": 2, "y": 4, "w": 16, "h": 40}, + {"kind": "rectangle", "classId": 2, "x": 30, "y": 4, "w": 16, "h": 40}, + ] + train = client.post( + "/api/image/features/clf/train", + json={ + "job_id": job.job_id, + "shapes": shapes, + "iterations": 40, + "depth": 4, + "learning_rate": 0.2, + }, + ) + assert train.status_code == 200, train.text + body = train.json() + assert body["n_samples"] > 0 + assert body["n_train"] > 0 + assert body["n_cal"] > 0 + assert set(body["class_ids"]) == {1, 2} + + pred = client.post( + "/api/image/features/clf/predict", + json={"model_id": body["model_id"], "job_id": job.job_id, "alpha": 0.1}, + ) + assert pred.status_code == 200, pred.text + meta = pred.json() + assert "pred_id" in meta + assert "counts" in meta + assert set(meta["counts"]) >= {"singleton", "multi", "abstain"} + + commit = client.get(f"/api/image/features/clf/predict/{meta['pred_id']}/commit.png") + status = client.get(f"/api/image/features/clf/predict/{meta['pred_id']}/status.png") + assert commit.status_code == 200 + assert status.status_code == 200 + carr = np.array(Image.open(io.BytesIO(commit.content))) + sarr = np.array(Image.open(io.BytesIO(status.content))) + assert carr.shape == (48, 48) + assert sarr.shape == (48, 48) + assert np.all(carr[sarr != 1] == 0) + + +def test_clf_train_empty_shapes_400() -> None: + gray = np.linspace(0, 1, 16 * 16).reshape(16, 16) + stack, float_stack, labels = features_mod.compute_feature_stacks( + gray, sigma_min=1.0, sigma_max=1.0, intensity=True, edges=False, texture=False + ) + job = features_mod.store_job(stack, labels, float_stack=float_stack) + client = TestClient(app) + res = client.post( + "/api/image/features/clf/train", + json={"job_id": job.job_id, "shapes": []}, + ) + assert res.status_code == 400 diff --git a/backend/tests/test_clf_shelf.py b/backend/tests/test_clf_shelf.py new file mode 100644 index 0000000..c21b500 --- /dev/null +++ b/backend/tests/test_clf_shelf.py @@ -0,0 +1,62 @@ +"""Tests for persistent CatBoost model shelf.""" + +from __future__ import annotations + +import os +from unittest import mock + +import numpy as np + +from multiscale_features import compute_feature_stacks, store_job +from pixel_clf import predict_conformal, train_classifier +import clf_shelf + + +def _two_blob(h: int = 48, w: int = 48) -> np.ndarray: + img = np.zeros((h, w), dtype=np.float64) + img[:, : w // 2] = 0.15 + img[:, w // 2 :] = 0.85 + return img + + +def test_shelf_save_load_predict_roundtrip(tmp_path) -> None: + with mock.patch.dict(os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}): + gray = _two_blob() + u8, fl, labs = compute_feature_stacks( + gray, + sigma_min=1.0, + sigma_max=2.0, + intensity=True, + edges=True, + texture=False, + clahe=False, + ) + job = store_job(u8, labs, float_stack=fl) + shapes = [ + {"kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 16, "h": 40}, + {"kind": "rectangle", "classId": 2, "x": 30, "y": 2, "w": 16, "h": 40}, + ] + model = train_classifier(job, shapes, iterations=30, depth=4, learning_rate=0.2) + recipe = { + "sigma_min": 1.0, + "sigma_max": 2.0, + "intensity": True, + "edges": True, + "texture": False, + "clahe": False, + "include_sam": False, + } + meta = clf_shelf.save_model(model, name="blob-clf", feature_recipe=recipe) + listed = clf_shelf.list_models() + assert any(m["id"] == meta.id for m in listed) + + loaded = clf_shelf.load_into_cache(meta.id) + assert loaded.feature_job_id.startswith("shelf:") + # Apply on a fresh feature job (same recipe dims) + job2 = store_job(u8, labs, float_stack=fl) + result = predict_conformal(loaded, job2, alpha=0.1, row_chunk=24) + assert result.commit_map.shape == (48, 48) + assert result.counts["singleton"] + result.counts["multi"] + result.counts["abstain"] == 48 * 48 + + assert clf_shelf.delete_model(meta.id) is True + assert clf_shelf.list_models() == [] diff --git a/backend/tests/test_coco_export.py b/backend/tests/test_coco_export.py index 9b644c3..6bd583f 100644 --- a/backend/tests/test_coco_export.py +++ b/backend/tests/test_coco_export.py @@ -115,6 +115,93 @@ def test_rle_json_serializable() -> None: assert isinstance(restored["segmentation"]["counts"], str) +def test_mask_export_writes_semantic_and_per_class(tmp_path) -> None: + """build_export_plan emits label-map + per-class masks; write_coco_split lays them out.""" + import numpy as np + from PIL import Image + from coco_export import build_export_plan, write_coco_split + from schemas import ExportRequest, AnnotationClass, RenderOpts + + h, w = 40, 40 + poly = {"id": "s1", "kind": "polygon", "classId": 7, "points": [5, 5, 25, 5, 25, 25, 5, 25]} + payload = ExportRequest( + kind="local", source="vol", server_uri=None, + slices={"0": [poly]}, split_by_slice={"0": "train"}, negative_slices=[], + classes=[AnnotationClass(classId=7, label="air", color="#ff0000")], + render=RenderOpts(norm="slice"), + auto_split={"ratios": [1, 0, 0], "seed": 1}, + ) + meta = {"height": h, "width": w, "n_slices": 1} + plan = build_export_plan( + object(), payload, + render_slice_fn=lambda arr, opts, gr: np.zeros((h, w, 3), dtype=np.uint8), + array_shape_meta_fn=lambda n: meta, + read_slice_fn=lambda n, m, i: np.zeros((h, w), dtype=np.uint8), + sample_global_stats_fn=lambda n, m: (0.0, 1.0), + ) + img = plan["splits"]["train"]["images"][0] + fname = img["file_name"] + assert img.get("label_png_bytes") + assert "air" in img["class_masks"] + + # Polygons are opt-in: default off → segmentation_poly empty, RLE always set. + ann0 = plan["splits"]["train"]["annotations"][0] + assert ann0["segmentation_poly"] == [] + assert isinstance(ann0["segmentation"]["counts"], str) and ann0["segmentation"]["counts"] + + import zipfile + zip_path = tmp_path / "ds.zip" + with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_STORED) as zf: + write_coco_split( + tmp_path / "train", + images=plan["splits"]["train"]["images"], + categories=plan["categories"], + annotations=plan["splits"]["train"]["annotations"], + mode="overwrite", info=plan["info"], + zf=zf, arc_prefix="train/", + ) + assert (tmp_path / "train" / fname).exists() + assert (tmp_path / "train" / "masks" / "semantic" / fname).exists() + assert (tmp_path / "train" / "masks" / "air" / fname).exists() + assert (tmp_path / "train" / "masks" / "legend.json").exists() + sem = np.array(Image.open(tmp_path / "train" / "masks" / "semantic" / fname)) + assert int(sem.max()) == 1 # single class → category id 1 in the label map + + # The zip mirrors the tree (STORED) and carries images, masks, and COCO. + with zipfile.ZipFile(zip_path) as zf: + names = set(zf.namelist()) + assert f"train/{fname}" in names + assert f"train/masks/semantic/{fname}" in names + assert f"train/masks/air/{fname}" in names + assert "train/_annotations.coco.json" in names + assert all(zi.compress_type == zipfile.ZIP_STORED for zi in zf.infolist()) + + +def test_export_polygons_opt_in() -> None: + """include_polygons=True populates segmentation_poly (reusing polygon points).""" + import numpy as np + from coco_export import build_export_plan + from schemas import ExportRequest, AnnotationClass, RenderOpts + + h, w = 30, 30 + poly = {"id": "s1", "kind": "polygon", "classId": 1, "points": [4, 4, 20, 4, 20, 20, 4, 20]} + payload = ExportRequest( + kind="local", source="vol", slices={"0": [poly]}, split_by_slice={"0": "train"}, + classes=[AnnotationClass(classId=1, label="air", color="#fff")], + render=RenderOpts(norm="slice"), include_polygons=True, + ) + plan = build_export_plan( + object(), payload, + render_slice_fn=lambda a, o, g: np.zeros((h, w, 3), np.uint8), + array_shape_meta_fn=lambda n: {"height": h, "width": w, "n_slices": 1}, + read_slice_fn=lambda n, m, i: np.zeros((h, w), np.uint8), + sample_global_stats_fn=lambda n, m: (0.0, 1.0), + include_polygons=True, + ) + ann0 = plan["splits"]["train"]["annotations"][0] + assert ann0["segmentation_poly"] == [poly["points"]] # reused, not re-traced + + def test_zero_area_polygon_skipped() -> None: """A 1-px degenerate polygon produces zero area.""" from coco_export import _polygon_mask @@ -125,3 +212,20 @@ def test_zero_area_polygon_skipped() -> None: rle = mask_utils.encode(np.asfortranarray(mask.astype(np.uint8))) area = float(mask_utils.area(rle)) assert area < 2, "Degenerate polygon should produce near-zero area" + + +def test_polygon_hole_carved_out() -> None: + """A polygon with an inner ring (invert-shape) leaves the hole empty.""" + from coco_export import shape_to_mask + + h, w = 40, 40 + shape = { + "id": "inv", "classId": 1, "kind": "polygon", + "points": [0, 0, 40, 0, 40, 40, 0, 40], + "holes": [[10, 10, 30, 10, 30, 30, 10, 30]], + } + mask = shape_to_mask(shape, h, w) + assert not mask[20, 20], "hole interior must be empty" + assert mask[2, 2], "frame corner must be filled" + # Area ~= 40*40 - 20*20 = 1200, allow boundary slack. + assert 1050 < int(mask.sum()) < 1350 diff --git a/backend/tests/test_conformal_clf.py b/backend/tests/test_conformal_clf.py new file mode 100644 index 0000000..1bed50f --- /dev/null +++ b/backend/tests/test_conformal_clf.py @@ -0,0 +1,92 @@ +"""Tests for Mondrian split-conformal CatBoost pixel prediction.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from multiscale_features import compute_feature_stacks, store_job +from pixel_clf import ( + conformal_quantile, + mondrian_thresholds, + predict_conformal, + stratified_train_cal_split, + train_classifier, +) + + +def _two_blob(h: int = 64, w: int = 64) -> np.ndarray: + img = np.zeros((h, w), dtype=np.float64) + img[:, : w // 2] = 0.15 + img[:, w // 2 :] = 0.85 + return img + + +def test_stratified_train_cal_split_sizes() -> None: + y = np.array([1] * 50 + [2] * 50) + train_idx, cal_idx = stratified_train_cal_split(y, train_frac=0.8, rng=np.random.default_rng(0)) + assert len(train_idx) + len(cal_idx) == 100 + assert abs(len(train_idx) / 100 - 0.8) < 0.05 + # Both classes in both splits + assert set(y[train_idx].tolist()) == {1, 2} + assert set(y[cal_idx].tolist()) == {1, 2} + + +def test_conformal_quantile_finite_sample() -> None: + scores = np.array([0.1, 0.2, 0.3, 0.4, 0.5], dtype=np.float64) + # (1-alpha)(n+1)/n level — for alpha=0.2, n=5 → ceiling index + q = conformal_quantile(scores, alpha=0.2) + assert 0.1 <= q <= 0.5 + + +def test_train_stores_cal_scores_and_split_sizes() -> None: + gray = _two_blob() + u8, fl, labs = compute_feature_stacks( + gray, sigma_min=1.0, sigma_max=2.0, intensity=True, edges=True, texture=False, clahe=False + ) + job = store_job(u8, labs, float_stack=fl) + # Dense enough scribbles for 80/20 + shapes = [ + {"kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 20, "h": 50}, + {"kind": "rectangle", "classId": 2, "x": 42, "y": 2, "w": 20, "h": 50}, + ] + model = train_classifier(job, shapes, iterations=40, depth=4, learning_rate=0.2, train_frac=0.8) + assert model.n_train > 0 + assert model.n_cal > 0 + assert model.n_train + model.n_cal == model.n_samples + assert set(model.cal_scores_by_class.keys()) == {1, 2} + for cid, scores in model.cal_scores_by_class.items(): + assert scores.ndim == 1 and scores.size >= 1 + + +def test_predict_conformal_commit_only_singletons() -> None: + gray = _two_blob() + u8, fl, labs = compute_feature_stacks( + gray, sigma_min=1.0, sigma_max=2.0, intensity=True, edges=True, texture=False, clahe=False + ) + job = store_job(u8, labs, float_stack=fl) + shapes = [ + {"kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 24, "h": 56}, + {"kind": "rectangle", "classId": 2, "x": 38, "y": 2, "w": 24, "h": 56}, + ] + model = train_classifier(job, shapes, iterations=50, depth=4, learning_rate=0.2) + result = predict_conformal(model, job, alpha=0.05, row_chunk=32) + commit = result.commit_map + status = result.status_map + assert commit.shape == status.shape == (64, 64) + # Where status != singleton, commit must be 0 + assert np.all(commit[status != 1] == 0) + # Singleton pixels have a class id + assert np.all(commit[status == 1] > 0) + assert result.counts["singleton"] + result.counts["multi"] + result.counts["abstain"] == 64 * 64 + + loose = predict_conformal(model, job, alpha=0.25, row_chunk=32) + # Larger alpha → lower score quantile → more abstain (emptier sets) + assert result.counts["abstain"] <= loose.counts["abstain"] + 500 + + +def test_mondrian_thresholds_change_with_alpha() -> None: + scores = {1: np.linspace(0.0, 1.0, 40), 2: np.linspace(0.0, 0.5, 40)} + q_tight = mondrian_thresholds(scores, alpha=0.05) # high coverage → high q + q_loose = mondrian_thresholds(scores, alpha=0.30) + assert q_tight[1] >= q_loose[1] - 1e-9 diff --git a/backend/tests/test_feature_api.py b/backend/tests/test_feature_api.py new file mode 100644 index 0000000..8976241 --- /dev/null +++ b/backend/tests/test_feature_api.py @@ -0,0 +1,32 @@ +"""API smoke tests for multiscale feature endpoints (in-process, no Tiled).""" + +from __future__ import annotations + +import numpy as np +from fastapi.testclient import TestClient + +import multiscale_features as features_mod +from annotation_server import app + + +def test_get_feature_channel_roundtrip(monkeypatch) -> None: + """POST is skipped; exercise GET against a pre-stored job.""" + gray = np.linspace(0, 1, 16 * 16, dtype=np.float64).reshape(16, 16) + stack, float_stack, labels = features_mod.compute_feature_stacks( + gray, + sigma_min=1.0, + sigma_max=2.0, + intensity=True, + edges=False, + texture=False, + clahe=False, + ) + job = features_mod.store_job(stack, labels, float_stack=float_stack) + client = TestClient(app) + res = client.get(f"/api/image/features/{job.job_id}/0") + assert res.status_code == 200 + assert res.headers["content-type"].startswith("image/png") + assert len(res.content) > 50 + + missing = client.get("/api/image/features/does-not-exist/0") + assert missing.status_code == 404 diff --git a/backend/tests/test_feature_manifold.py b/backend/tests/test_feature_manifold.py new file mode 100644 index 0000000..abcf795 --- /dev/null +++ b/backend/tests/test_feature_manifold.py @@ -0,0 +1,178 @@ +"""Tests for greedy feature-variance box sampling on FeatureJob float_stack.""" + +from __future__ import annotations + +import io + +import numpy as np +from PIL import Image +from fastapi.testclient import TestClient + +from annotation_server import app +from multiscale_features import compute_feature_stacks, store_job +from feature_manifold import ( + derived_exclusion_radius, + encode_heatmap_png, + sample_inducing_points, + store_sample, +) + + +def _two_blob(h: int = 64, w: int = 64) -> np.ndarray: + img = np.zeros((h, w), dtype=np.float64) + img[:, : w // 2] = 0.1 + img[:, w // 2 :] = 0.9 + return img + + +def _checker(h: int = 64, w: int = 64, tile: int = 8) -> np.ndarray: + """Tiled high-variance checker pattern (identical patches across image).""" + yy, xx = np.mgrid[0:h, 0:w] + return (((xx // tile) + (yy // tile)) % 2).astype(np.float64) + + +def _job(gray: np.ndarray): + u8, fl, labs = compute_feature_stacks( + gray, + sigma_min=1.0, + sigma_max=2.0, + intensity=True, + edges=True, + texture=False, + clahe=False, + ) + return store_job(u8, labs, float_stack=fl) + + +def test_derived_radius_shrinks_with_k() -> None: + r8 = derived_exclusion_radius(64, 64, 8) + r32 = derived_exclusion_radius(64, 64, 32) + assert r8 >= r32 + assert r8 >= 8 + + +def test_manual_box_size_controls_window() -> None: + job = _job(_two_blob(128, 128)) + small = sample_inducing_points(job, k=8, box_size=24, seed=0) + large = sample_inducing_points(job, k=8, box_size=64, seed=0) + assert small.meta["box_size"] == 24 + assert large.meta["box_size"] == 64 + # Drawn box extent matches + sb = small.points[0]["box"] + assert (sb["x1"] - sb["x0"]) == 24 or abs((sb["x1"] - sb["x0"]) - 24) <= 1 + lb = large.points[0]["box"] + assert abs((lb["x1"] - lb["x0"]) - 64) <= 1 + + +def test_sample_returns_k_points_with_boxes() -> None: + job = _job(_two_blob()) + k = 12 + result = sample_inducing_points(job, k=k, seed=0) + assert 1 <= len(result.points) <= k + h, w = job.float_stack.shape[:2] + assert result.heatmap.shape == (h, w) + assert 0.0 <= float(result.heatmap.min()) + assert float(result.heatmap.max()) <= 1.0 + 1e-6 + r = result.meta["radius"] + for p in result.points: + assert 0 <= p["x"] < w and 0 <= p["y"] < h + assert "box" in p and "radius" in p and "score" in p + assert p["radius"] == r + assert p.get("box_size") == result.meta["box_size"] + box = p["box"] + assert box["x0"] <= p["x"] < box["x1"] or box["x0"] <= p["x"] <= box["x1"] + assert box["y0"] <= p["y"] < box["y1"] or box["y0"] <= p["y"] <= box["y1"] + assert abs((box["x1"] - box["x0"]) - result.meta["box_size"]) <= 1 + assert abs((box["y1"] - box["y0"]) - result.meta["box_size"]) <= 1 + + +def test_picks_spread_on_two_blob_and_respect_exclusion() -> None: + job = _job(_two_blob(96, 96)) + result = sample_inducing_points(job, k=10, seed=1) + assert len(result.points) >= 2 + r = float(result.meta["radius"]) + # Pairwise Chebyshev distance must be ≥ exclusion (non-overlapping squares) + for i, a in enumerate(result.points): + for b in result.points[i + 1 :]: + d_inf = max(abs(a["x"] - b["x"]), abs(a["y"] - b["y"])) + assert d_inf >= r - 1e-6 + # First pick near the vertical boundary (high variance) + first = result.points[0] + assert 96 * 0.25 <= first["x"] <= 96 * 0.75 + + +def test_large_boxes_do_not_overlap_after_sample() -> None: + """Growing box size must re-space centers so AABBs do not overlap.""" + job = _job(_two_blob(256, 256)) + result = sample_inducing_points(job, k=16, box_size=96, seed=0) + side = int(result.meta["box_size"]) + assert result.meta["radius"] >= side - 1e-6 + assert len(result.points) >= 2 + for i, a in enumerate(result.points): + ba = a["box"] + for b in result.points[i + 1 :]: + bb = b["box"] + # Axis-aligned rectangles: no area overlap (edges may touch) + overlap_x = ba["x0"] < bb["x1"] and bb["x0"] < ba["x1"] + overlap_y = ba["y0"] < bb["y1"] and bb["y0"] < ba["y1"] + assert not (overlap_x and overlap_y), (a["box"], b["box"]) + + +def test_placement_mask_keeps_boxes_fully_inside() -> None: + """Only windows whose full box lies inside the mask are eligible.""" + job = _job(_two_blob(160, 160)) + h, w = job.float_stack.shape[:2] + mask = np.zeros((h, w), dtype=bool) + mask[:, : w // 2] = True # left half only + result = sample_inducing_points(job, k=12, box_size=32, seed=0, mask=mask) + assert result.meta["has_mask"] is True + assert result.meta["mask_pixels"] == int(mask.sum()) + assert result.meta["n_windows_in_mask"] >= 1 + assert len(result.points) >= 1 + for p in result.points: + box = p["box"] + assert box["x1"] <= w // 2 + 1 # exclusive end at or before half (+tol) + assert mask[box["y0"] : box["y1"], box["x0"] : box["x1"]].all() + + +def test_placement_mask_empty_raises() -> None: + job = _job(_two_blob(64, 64)) + mask = np.zeros(job.float_stack.shape[:2], dtype=bool) + try: + sample_inducing_points(job, k=4, box_size=16, seed=0, mask=mask) + assert False, "expected ValueError" + except ValueError as exc: + assert "empty" in str(exc).lower() + + +def test_feature_suppress_avoids_duplicate_textures() -> None: + job = _job(_checker(80, 80, tile=8)) + result = sample_inducing_points(job, k=8, seed=0) + # With pure spatial packing on 80x80 we could get many; feature suppress + # should leave fewer than a dense spatial pack of tiny r. + assert len(result.points) >= 1 + assert len(result.points) <= 8 + + +def test_encode_and_cache_api_roundtrip() -> None: + job = _job(_two_blob()) + result = sample_inducing_points(job, k=8, seed=0) + cached = store_sample(result) + png = encode_heatmap_png(result.heatmap) + arr = np.array(Image.open(io.BytesIO(png))) + assert arr.shape == result.heatmap.shape + + client = TestClient(app) + res = client.post( + "/api/image/features/manifold/sample", + json={"job_id": job.job_id, "k": 8}, + ) + assert res.status_code == 200, res.text + body = res.json() + assert body["k"] == 8 + assert len(body["points"]) >= 1 + assert "radius" in body["points"][0] + assert "box" in body["points"][0] + heat = client.get(f"/api/image/features/manifold/{body['sample_id']}/heatmap.png") + assert heat.status_code == 200 + assert cached.sample_id diff --git a/backend/tests/test_label_sets.py b/backend/tests/test_label_sets.py new file mode 100644 index 0000000..f2714e0 --- /dev/null +++ b/backend/tests/test_label_sets.py @@ -0,0 +1,72 @@ +"""Tests for reusable annotation label sets.""" + +from __future__ import annotations + +import os +from unittest import mock + +import pytest +from fastapi.testclient import TestClient + +from annotation_server import app +import label_sets as label_sets_mod + + +@pytest.fixture() +def label_root(tmp_path): + with mock.patch.dict(os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}): + yield tmp_path + + +def test_save_list_get_delete_label_set(label_root) -> None: + classes = [ + {"classId": 1, "label": "sample", "color": "#1f77b4", "isVisible": True}, + {"classId": 2, "label": "void", "color": "#ff7f0e", "isVisible": True}, + ] + saved = label_sets_mod.save_label_set(name="Petiole", classes=classes) + assert saved["id"] + assert saved["name"] == "Petiole" + assert len(saved["classes"]) == 2 + + listed = label_sets_mod.list_label_sets() + assert len(listed) == 1 + assert listed[0]["n_classes"] == 2 + + got = label_sets_mod.get_label_set(saved["id"]) + assert got is not None + assert got["classes"][0]["label"] == "sample" + + assert label_sets_mod.delete_label_set(saved["id"]) is True + assert label_sets_mod.get_label_set(saved["id"]) is None + + +def test_empty_classes_rejected(label_root) -> None: + with pytest.raises(ValueError): + label_sets_mod.save_label_set(name="x", classes=[]) + + +def test_label_sets_api_roundtrip(label_root) -> None: + client = TestClient(app) + res = client.post( + "/api/label-sets", + json={ + "name": "Default", + "classes": [ + {"classId": 1, "label": "air", "color": "#aaaaaa", "isVisible": True}, + ], + }, + ) + assert res.status_code == 200, res.text + body = res.json() + set_id = body["id"] + + listed = client.get("/api/label-sets") + assert listed.status_code == 200 + assert any(x["id"] == set_id for x in listed.json()["sets"]) + + got = client.get(f"/api/label-sets/{set_id}") + assert got.status_code == 200 + assert got.json()["classes"][0]["label"] == "air" + + deleted = client.delete(f"/api/label-sets/{set_id}") + assert deleted.status_code == 200 diff --git a/backend/tests/test_multiscale_features.py b/backend/tests/test_multiscale_features.py new file mode 100644 index 0000000..c78f743 --- /dev/null +++ b/backend/tests/test_multiscale_features.py @@ -0,0 +1,64 @@ +"""Tests for multiscale feature labels + CLAHE stack (skimage).""" + +from __future__ import annotations + +import numpy as np +import pytest + +from multiscale_features import ( + compute_feature_stack, + feature_channel_labels, + list_sigmas, +) + + +def test_list_sigmas_powers_of_two() -> None: + assert list(list_sigmas(1.0, 8.0)) == pytest.approx([1.0, 2.0, 4.0, 8.0]) + + +def test_label_count_matches_feature_kinds() -> None: + sigmas = list_sigmas(1.0, 8.0) + labels = feature_channel_labels( + sigma_min=1.0, sigma_max=8.0, intensity=True, edges=True, texture=True + ) + # Per σ: intensity + edges + 2 texture eigenvalues + assert len(labels) == len(sigmas) * 4 + assert labels[0] == "intensity σ=1" + assert labels[1] == "edges σ=1" + assert "texture λ− σ=1" in labels[2] + assert labels[4].startswith("intensity σ=2") + + +def test_compute_stack_shape_and_uint8_range() -> None: + rng = np.random.default_rng(0) + gray = rng.random((48, 48), dtype=np.float64) + stack, labels = compute_feature_stack( + gray, + sigma_min=1.0, + sigma_max=4.0, + intensity=True, + edges=True, + texture=True, + clahe=True, + ) + assert stack.dtype == np.uint8 + assert stack.shape == (48, 48, len(labels)) + assert stack.min() >= 0 + assert stack.max() <= 255 + # CLAHE should use most of the dynamic range on structured noise + assert int(stack.max()) - int(stack.min()) > 50 + + +def test_intensity_only_fewer_channels() -> None: + gray = np.linspace(0, 1, 32 * 32, dtype=np.float64).reshape(32, 32) + stack, labels = compute_feature_stack( + gray, + sigma_min=1.0, + sigma_max=2.0, + intensity=True, + edges=False, + texture=False, + clahe=False, + ) + assert len(labels) == 2 + assert stack.shape[-1] == 2 diff --git a/backend/tests/test_pixel_clf.py b/backend/tests/test_pixel_clf.py new file mode 100644 index 0000000..64e6466 --- /dev/null +++ b/backend/tests/test_pixel_clf.py @@ -0,0 +1,177 @@ +"""Tests for CatBoost pixel classifier (sparse labels → predict).""" + +from __future__ import annotations + +import numpy as np +import pytest + +from multiscale_features import compute_feature_stacks, store_job +from pixel_clf import ( + build_label_map, + encode_label_png, + predict_labels, + train_classifier, + train_result_dict, + tree_view, +) + + +def _two_blob_image(h: int = 48, w: int = 48) -> np.ndarray: + """Synthetic gray with left dark / right bright regions.""" + img = np.zeros((h, w), dtype=np.float64) + img[:, : w // 2] = 0.2 + img[:, w // 2 :] = 0.8 + return img + + +def test_build_label_map_sparse_and_last_wins() -> None: + shapes = [ + { + "kind": "rectangle", + "classId": 1, + "x": 0, + "y": 0, + "w": 10, + "h": 10, + }, + { + "kind": "rectangle", + "classId": 2, + "x": 5, + "y": 5, + "w": 10, + "h": 10, + }, + ] + labels = build_label_map(shapes, 20, 20) + assert labels.dtype == np.uint8 + assert labels[0, 0] == 1 + assert labels[7, 7] == 2 # overlap: second wins + assert labels[19, 19] == 0 # unlabeled + + +def test_train_requires_labeled_pixels() -> None: + gray = _two_blob_image() + u8, fl, labs = compute_feature_stacks( + gray, + sigma_min=1.0, + sigma_max=2.0, + intensity=True, + edges=False, + texture=False, + clahe=False, + ) + job = store_job(u8, labs, float_stack=fl) + with pytest.raises(ValueError, match="labeled"): + train_classifier(job, shapes=[]) + + +def test_train_predict_two_class_blobs() -> None: + gray = _two_blob_image(64, 64) + u8, fl, labs = compute_feature_stacks( + gray, + sigma_min=1.0, + sigma_max=2.0, + intensity=True, + edges=True, + texture=False, + clahe=False, + ) + job = store_job(u8, labs, float_stack=fl) + + # Sparse scribbles only (ilastik): left → class 1, right → class 2 + shapes = [ + {"kind": "rectangle", "classId": 1, "x": 4, "y": 28, "w": 8, "h": 8}, + {"kind": "rectangle", "classId": 2, "x": 52, "y": 28, "w": 8, "h": 8}, + ] + model = train_classifier( + job, + shapes, + iterations=50, + depth=4, + learning_rate=0.2, + max_samples=50_000, + ) + assert set(model.class_ids) == {1, 2} + assert model.n_samples >= 50 + + pred = predict_labels(model, job) + assert pred.shape == (64, 64) + # Exactly one class id per pixel (argmax) + assert set(np.unique(pred).tolist()).issubset({1, 2}) + left = pred[:, :32] + right = pred[:, 32:] + assert np.mean(left == 1) > 0.7 + assert np.mean(right == 2) > 0.7 + + preserved = predict_labels(model, job, preserve_shapes=shapes) + # Scribbles zeroed out + assert preserved[28:36, 4:12].sum() == 0 + assert preserved[28:36, 52:60].sum() == 0 + + png = encode_label_png(pred) + assert png[:8] == b"\x89PNG\r\n\x1a\n" + + result = train_result_dict(model) + assert 0.0 <= result["train_accuracy"] <= 1.0 + assert result["n_trees"] >= 1 + assert result["feature_importances"] + assert result["tree_preview"]["splits"] is not None + tv = tree_view(model, 0) + assert tv["tree_index"] == 0 + assert "splits" in tv + + +def test_train_with_fake_sam_emb() -> None: + """CatBoost concat path with a synthetic SlimSAM grid (no ONNX).""" + gray = _two_blob_image(48, 48) + u8, fl, labs = compute_feature_stacks( + gray, + sigma_min=1.0, + sigma_max=2.0, + intensity=True, + edges=False, + texture=False, + clahe=False, + ) + rng = np.random.default_rng(1) + # Fake emb: left vs right distinguishable + emb = np.zeros((64, 64, 16), dtype=np.float32) + emb[:, :32] = 1.0 + emb[:, 32:] = -1.0 + emb += 0.01 * rng.standard_normal(emb.shape).astype(np.float32) + job = store_job( + u8, + labs, + float_stack=fl, + sam_emb=emb, + sam_orig_hw=(48, 48), + sam_reshaped_hw=(1024, 1024), + ) + shapes = [ + {"kind": "rectangle", "classId": 1, "x": 2, "y": 20, "w": 8, "h": 8}, + {"kind": "rectangle", "classId": 2, "x": 38, "y": 20, "w": 8, "h": 8}, + ] + model = train_classifier(job, shapes, iterations=30, depth=3, learning_rate=0.3, sam_pca_dims=8) + assert model.uses_sam + assert any(lab.startswith("sam") for lab, _ in model.feature_importances) + pred = predict_labels(model, job, row_chunk=16) + assert np.mean(pred[:, :24] == 1) > 0.6 + assert np.mean(pred[:, 24:] == 2) > 0.6 + + +def test_compute_stack_returns_float() -> None: + gray = np.random.default_rng(0).random((32, 32)) + u8, fl, labs = compute_feature_stacks( + gray, + sigma_min=1.0, + sigma_max=2.0, + intensity=True, + edges=False, + texture=False, + clahe=True, + ) + assert u8.dtype == np.uint8 + assert fl.dtype == np.float32 + assert u8.shape == fl.shape + assert fl.shape[-1] == len(labs) diff --git a/backend/tests/test_sam_embed.py b/backend/tests/test_sam_embed.py new file mode 100644 index 0000000..78f8831 --- /dev/null +++ b/backend/tests/test_sam_embed.py @@ -0,0 +1,44 @@ +"""Tests for SlimSAM embedding helpers (no full ONNX required for core math).""" + +from __future__ import annotations + +import numpy as np + +from sam_embed import ( + bilinear_sample_emb, + fit_pca, + preprocess_sam_rgb, + rgb_uint8_from_array, + transform_pca, +) + + +def test_rgb_uint8_from_gray() -> None: + g = np.linspace(0, 1, 16 * 16, dtype=np.float64).reshape(16, 16) + rgb = rgb_uint8_from_array(g) + assert rgb.shape == (16, 16, 3) + assert rgb.dtype == np.uint8 + + +def test_preprocess_shape() -> None: + rgb = np.zeros((40, 80, 3), dtype=np.uint8) + rgb[:, 40:] = 255 + pv, orig, reshaped = preprocess_sam_rgb(rgb) + assert pv.shape == (1, 3, 1024, 1024) + assert orig == (40, 80) + assert reshaped[1] == 1024 # width is long edge + assert reshaped[0] < 1024 + + +def test_bilinear_sample_and_pca() -> None: + rng = np.random.default_rng(0) + emb = rng.standard_normal((64, 64, 8)).astype(np.float32) + ys = np.linspace(0, 39, 20) + xs = np.linspace(0, 79, 20) + samples = bilinear_sample_emb( + emb, ys, xs, orig_h=40, orig_w=80, reshaped_h=512, reshaped_w=1024 + ) + assert samples.shape == (20, 8) + mean, comps = fit_pca(samples, n_components=4) + proj = transform_pca(samples, mean, comps) + assert proj.shape == (20, 4) diff --git a/backend/tests/test_schemas.py b/backend/tests/test_schemas.py index ed6db98..8dbf913 100644 --- a/backend/tests/test_schemas.py +++ b/backend/tests/test_schemas.py @@ -2,8 +2,6 @@ from __future__ import annotations -import pytest - from schemas import BrushShape, BrushStroke, EllipseShape, PolygonShape, RectShape, RenderOpts diff --git a/backend/tests/test_tiled_config.py b/backend/tests/test_tiled_config.py new file mode 100644 index 0000000..9cd4e7f --- /dev/null +++ b/backend/tests/test_tiled_config.py @@ -0,0 +1,69 @@ +"""Tests for Tiled env config — empty API keys and local single-user key fallback.""" + +from __future__ import annotations + +import os +from pathlib import Path + +import pytest +import yaml + + +@pytest.fixture(autouse=True) +def _restore_tiled_api_key_env() -> None: + """Preserve caller env around mutations of TILED_* API key vars.""" + names = ("TILED_API_KEY", "TILED_LOCAL_API_KEY") + prior = {name: os.environ.get(name) for name in names} + yield + for name, value in prior.items(): + if value is None: + os.environ.pop(name, None) + else: + os.environ[name] = value + + +def test_empty_tiled_api_key_is_cleared_from_environ() -> None: + """Blank TILED_API_KEY must be unset so tiled client never sends ``Apikey ``.""" + os.environ["TILED_API_KEY"] = "" + os.environ["TILED_LOCAL_API_KEY"] = " " + + import tiled_config + + tiled_config.clear_empty_tiled_api_key_env() + + assert "TILED_API_KEY" not in os.environ + assert "TILED_LOCAL_API_KEY" not in os.environ + + +def test_nonempty_tiled_api_key_is_preserved() -> None: + """A real API key must survive the empty-key cleanup.""" + os.environ["TILED_API_KEY"] = "real-key-value" + + import tiled_config + + tiled_config.clear_empty_tiled_api_key_env() + + assert os.environ.get("TILED_API_KEY") == "real-key-value" + assert tiled_config.get_tiled_api_key() == "real-key-value" + + +def test_local_api_key_falls_back_to_tiled_config_yml() -> None: + """When env keys are blank, use single_user_api_key from tiled/config.yml. + + Tiled anonymous access is read-only; writes need this key (create:node, etc.). + """ + os.environ.pop("TILED_API_KEY", None) + os.environ.pop("TILED_LOCAL_API_KEY", None) + + import tiled_config + + config_path = Path(tiled_config.__file__).resolve().parent.parent / "tiled" / "config.yml" + assert config_path.is_file(), f"missing {config_path}" + with config_path.open(encoding="utf-8") as fh: + raw = yaml.safe_load(fh) + expected = (raw.get("authentication") or {}).get("single_user_api_key") + assert expected, "tiled/config.yml must define authentication.single_user_api_key for local writes" + + assert tiled_config.get_tiled_api_key() == expected + local = tiled_config.get_tiled_servers()["Local Data (port 8010)"] + assert local["api_key"] == expected diff --git a/backend/tests/test_tiled_mask_sync.py b/backend/tests/test_tiled_mask_sync.py new file mode 100644 index 0000000..2a6e8bd --- /dev/null +++ b/backend/tests/test_tiled_mask_sync.py @@ -0,0 +1,110 @@ +"""Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization → volumes).""" +import numpy as np + +from schemas import AnnotationClass, ExportSourceItem +from tiled_mask_sync import build_mask_volumes, merge_mask_volumes + +H = W = 32 + + +def _classes(): + return [ + AnnotationClass(classId=10, label="Cell", color="#ff0000"), + AnnotationClass(classId=20, label="Wall", color="#00ff00"), + ] + + +def test_build_mask_volumes_stacks_and_labels(): + slices = { + "5": [{"id": "b", "kind": "polygon", "classId": 20, + "points": [10, 10, 20, 10, 20, 20, 10, 20]}], + "2": [{"id": "a", "kind": "rectangle", "classId": 10, + "x": 2, "y": 2, "w": 6, "h": 6}], + } + item = ExportSourceItem(kind="tiled", source="browse/ds/img", server_uri=None, slices=slices) + vols = build_mask_volumes(item, _classes(), {"height": H, "width": W}) + + assert vols is not None + # Sorted numeric slice order. + assert vols["slice_indices"] == [2, 5] + assert vols["semantic"].shape == (2, H, W) + assert vols["semantic"].dtype == np.uint8 + + # Slice 0 (key "2") = the rectangle → class id 1 (Cell); slice 1 = polygon → id 2 (Wall). + assert vols["semantic"][0].max() == 1 + assert vols["semantic"][1].max() == 2 + assert vols["semantic"][0, 4, 4] == 1 # inside the rect + assert vols["semantic"][1, 15, 15] == 2 # inside the polygon + + # One binary volume per class, 0/255, present only on its slice. + assert set(vols["class_vols"]) == {"Cell", "Wall"} + cell, wall = vols["class_vols"]["Cell"], vols["class_vols"]["Wall"] + assert cell.shape == (2, H, W) and wall.shape == (2, H, W) + assert set(np.unique(cell)).issubset({0, 255}) + assert cell[0].sum() > 0 and cell[1].sum() == 0 # Cell only on slice 0 + assert wall[1].sum() > 0 and wall[0].sum() == 0 # Wall only on slice 1 + + # Legend mirrors the zip's legend shape. + assert vols["legend"] == [ + {"id": 1, "name": "Cell", "color": "#ff0000"}, + {"id": 2, "name": "Wall", "color": "#00ff00"}, + ] + + +def test_negative_slices_emitted_as_zero_frames(): + slices = {"1": [{"id": "a", "kind": "rectangle", "classId": 10, "x": 2, "y": 2, "w": 4, "h": 4}]} + item = ExportSourceItem(kind="tiled", source="browse/ds/img", slices=slices, negative_slices=["3"]) + vols = build_mask_volumes(item, _classes(), {"height": H, "width": W}) + + assert vols is not None + assert vols["slice_indices"] == [1, 3] + # Negative slice 3 (index 1 in the stack) is all background. + assert vols["semantic"][1].sum() == 0 + + +def test_returns_none_without_slices(): + item = ExportSourceItem(kind="tiled", source="browse/ds/img", slices={}) + assert build_mask_volumes(item, _classes(), {"height": H, "width": W}) is None + + +def _volumes(slices, negatives=None): + item = ExportSourceItem( + kind="tiled", source="browse/ds/img", slices=slices, negative_slices=negatives or [], + ) + return build_mask_volumes(item, _classes(), {"height": H, "width": W}) + + +def test_merge_updates_pushed_slice_and_keeps_others(): + # Existing container holds slices 2 (Cell) and 5 (Wall). + existing_vols = _volumes({ + "2": [{"id": "a", "kind": "rectangle", "classId": 10, "x": 2, "y": 2, "w": 6, "h": 6}], + "5": [{"id": "b", "kind": "rectangle", "classId": 20, "x": 2, "y": 2, "w": 6, "h": 6}], + }) + existing = { + "slice_indices": existing_vols["slice_indices"], + "semantic": existing_vols["semantic"], + "class_arrays": existing_vols["class_vols"], + "legend": existing_vols["legend"], + } + + # Re-push ONLY slice 2, now as Wall (class changed on that slice). + new = _volumes({"2": [{"id": "c", "kind": "rectangle", "classId": 20, "x": 2, "y": 2, "w": 6, "h": 6}]}) + merged = merge_mask_volumes(existing, new) + + # Both slices survive; only slice 2 reported as updated. + assert merged["slice_indices"] == [2, 5] + assert merged["updated_indices"] == [2] + # Slice 2 (index 0) is now Wall (id 2), slice 5 (index 1) still Wall. + assert merged["semantic"][0, 4, 4] == 2 # updated slice → Wall + assert merged["semantic"][1, 4, 4] == 2 # untouched slice preserved + # Slice 2's Cell mask was replaced (now empty there); Wall present on both. + assert merged["class_vols"]["Cell"][0].sum() == 0 + assert merged["class_vols"]["Wall"][0].sum() > 0 + + +def test_merge_fresh_when_no_existing(): + new = _volumes({"3": [{"id": "a", "kind": "rectangle", "classId": 10, "x": 1, "y": 1, "w": 4, "h": 4}]}) + merged = merge_mask_volumes(None, new) + assert merged["slice_indices"] == [3] + assert merged["updated_indices"] == [3] + assert merged["semantic"].shape == (1, H, W) diff --git a/backend/tiled_clients.py b/backend/tiled_clients.py index 9c19879..7483a22 100644 --- a/backend/tiled_clients.py +++ b/backend/tiled_clients.py @@ -7,6 +7,7 @@ from __future__ import annotations +import os from typing import Any from tiled_config import get_tiled_api_key, get_tiled_base, get_tiled_servers @@ -45,30 +46,98 @@ def get_tiled_client(server_uri: str | None = None, server_api_key: str | None = return client -# Paths checked in order when looking for the Browse root container. The first -# non-empty path wins. Falling back to the root container is valid but usually -# means the user hasn't seeded any data yet. +# Well-known demo paths. Used as candidates alongside every top-level child of +# the catalog root; the auto-discoverer then picks the *largest* non-empty one +# so a leftover single ingest under ``browse/`` does not hide a fuller tree +# such as ``petioles/``. _BROWSE_CANDIDATES: tuple[tuple[tuple[str, ...], str], ...] = ( (("browse", "generated_data"), "browse/generated_data"), (("beamlines", "bl733", "projects", "10k"), "beamlines/bl733/projects/10k"), (("beamlines", "bl733"), "beamlines/bl733"), (("beamlines", "bl901"), "beamlines/bl901"), + (("petioles",), "petioles"), + # Plain drag-and-drop ingest writes samples under `browse/`. + (("browse",), "browse"), ) +def _try_navigate(client: Any, keys: tuple[str, ...]) -> Any | None: + """Return ``client[keys...]`` or ``None`` if missing / not a container.""" + try: + node: Any = client + for k in keys: + node = node[k] + if len(node) > 0: + return node + except (KeyError, TypeError, AttributeError): + return None + return None + + def get_browse_container(client: Any) -> tuple[Any, str]: """Return ``(container_node, path_prefix)`` for the Metadata Browser root. - Walks known beamline-ish paths in priority order and returns the first - container that exists and is non-empty. Falls back to the client's root. + Resolution order: + + 1. ``TILED_BROWSE_PATH`` env var (explicit override). + 2. Among known candidate paths **and** every non-empty top-level child of + the root, pick the container with the most children (most samples). + 3. Fall back to the client's root. """ - for keys, prefix in _BROWSE_CANDIDATES: + browse_path = (os.getenv("TILED_BROWSE_PATH") or "").strip().strip("/") + if browse_path: try: node: Any = client - for k in keys: + for k in browse_path.split("/"): node = node[k] if len(node) > 0: - return node, prefix - except (KeyError, TypeError): + return node, browse_path + except (KeyError, TypeError, AttributeError): + pass + + scored: list[tuple[int, str, Any]] = [] + seen_prefixes: set[str] = set() + + for keys, prefix in _BROWSE_CANDIDATES: + node = _try_navigate(client, keys) + if node is None: continue + scored.append((len(node), prefix, node)) + seen_prefixes.add(prefix.split("/")[0]) + + # Top-level containers (e.g. ``petioles``) not already covered by a candidate. + try: + for key in list(client): + name = str(key) + if name in seen_prefixes: + continue + node = _try_navigate(client, (name,)) + if node is None: + continue + scored.append((len(node), name, node)) + except (TypeError, AttributeError): + pass + + if scored: + # Most samples first; stable tie-break by path for determinism. + scored.sort(key=lambda t: (-t[0], t[1])) + count, prefix, node = scored[0] + return node, prefix + return client, "" + + +def get_browse_container_for(client: Any, container_path: str | None) -> tuple[Any, str]: + """Return ``(container_node, path_prefix)`` for a specific browse target. + + When *container_path* is given (slash-separated, e.g. ``browse/testset``), + navigate directly to that node. Otherwise fall back to the heuristic + discovery in :func:`get_browse_container`. + """ + path = (container_path or "").strip().strip("/") + if not path: + return get_browse_container(client) + node: Any = client + for k in path.split("/"): + node = node[k] + return node, path diff --git a/backend/tiled_config.py b/backend/tiled_config.py index 336b0f2..25807bc 100644 --- a/backend/tiled_config.py +++ b/backend/tiled_config.py @@ -11,25 +11,89 @@ * ``TILED_LOCAL_API_KEY`` — override API key for the local (8010) server * ``TILED_SERVER_{N}_NAME`` / ``..._URI`` / ``..._API_KEY`` — additional named servers (contiguous ``N = 1, 2, ...``; stop at first missing slot) + +When env keys are blank, the local server falls back to +``authentication.single_user_api_key`` in ``tiled/config.yml``. Tiled anonymous +access is read-only; that key is required for write scopes (ingest, create node). """ from __future__ import annotations +import logging import os +from pathlib import Path from dotenv import load_dotenv load_dotenv() _LOCAL_URI = "http://127.0.0.1:8010" +_REPO_ROOT = Path(__file__).resolve().parent.parent +_TILED_CONFIG_PATH = _REPO_ROOT / "tiled" / "config.yml" +_logger = logging.getLogger("tiled_config") + +# Env var names the tiled Python client (and this backend) may treat as API keys. +# An *empty* value is worse than unset: tiled builds ``Authorization: Apikey `` +# which httpx rejects as an illegal header value. +_API_KEY_ENV_NAMES: tuple[str, ...] = ( + "TILED_API_KEY", + "TILED_LOCAL_API_KEY", +) + + +def clear_empty_tiled_api_key_env() -> None: + """Remove blank Tiled API key env vars so clients do not send ``Apikey ``. + + ``backend/.env.example`` ships ``TILED_API_KEY=`` for documentation. When that + file is sourced (`set -a; source .env`) or loaded via dotenv, the empty string + stays in ``os.environ``. Tiled's client then does ``os.getenv("TILED_API_KEY")`` + and sets an illegal Authorization header. + """ + for name in _API_KEY_ENV_NAMES: + if name in os.environ and not os.environ[name].strip(): + del os.environ[name] + + +clear_empty_tiled_api_key_env() def _stripped(name: str) -> str: return (os.getenv(name) or "").strip() +def single_user_api_key_from_tiled_config( + config_path: Path | None = None, +) -> str | None: + """Return ``authentication.single_user_api_key`` from the local Tiled config. + + Args: + config_path: Optional override path (defaults to repo ``tiled/config.yml``). + + Returns: + The key string, or ``None`` if missing/unreadable. + """ + path = config_path or _TILED_CONFIG_PATH + try: + import yaml + + with path.open(encoding="utf-8") as fh: + raw = yaml.safe_load(fh) or {} + key = (raw.get("authentication") or {}).get("single_user_api_key") + if isinstance(key, str) and key.strip(): + return key.strip() + except FileNotFoundError: + _logger.debug("tiled config not found at %s", path) + except Exception as exc: # noqa: BLE001 — best-effort fallback + _logger.warning("could not read single_user_api_key from %s: %s", path, exc) + return None + + def _local_api_key() -> str | None: - return _stripped("TILED_LOCAL_API_KEY") or _stripped("TILED_API_KEY") or None + return ( + _stripped("TILED_LOCAL_API_KEY") + or _stripped("TILED_API_KEY") + or single_user_api_key_from_tiled_config() + ) def get_tiled_servers() -> dict[str, dict[str, str | None]]: diff --git a/backend/tiled_mask_sync.py b/backend/tiled_mask_sync.py new file mode 100644 index 0000000..35c71b2 --- /dev/null +++ b/backend/tiled_mask_sync.py @@ -0,0 +1,347 @@ +"""Write rasterized annotation masks into Tiled as stacked volumes. + +Standalone counterpart to the COCO .zip export: instead of writing per-slice PNG +files to disk, this rasterizes the same shapes (via ``coco_export.shape_to_mask``) +and stores them as compact **stacked uint8 arrays** in a sibling Tiled container +``__masks`` next to the source dataset — so a downstream app +(SAM3 / DINOv3 fine-tuning) can grab every mask in one request. + +Layout (all annotated + explicit negative slices, stacked in sorted order): + + __masks/ (container; metadata carries legend + slice_indices) + semantic uint8 (n, H, W) class-index per pixel, 0 = background + uint8 (n, H, W) 0/255 binary volume, one per class + +Tiled serves these back as PNG/TIFF via format negotiation. Only Tiled sources +are handled; local sources are skipped (nothing to write back to). +""" +from __future__ import annotations + +import logging +from datetime import datetime, timezone +from typing import Any + +import numpy as np + +import arrays as arrays_mod +import export_jobs +from coco_export import _safe_name, shape_to_mask +from tiled_clients import api_key_for_uri, get_tiled_client + +logger = logging.getLogger(__name__) + + +def _category_maps(classes: list[Any]) -> tuple[dict[int, int], dict[int, str], list[dict[str, Any]]]: + """1-based COCO ids + legend, matching ``build_export_plan``'s convention.""" + cat_id_map: dict[int, int] = {} + cat_id_to_name: dict[int, str] = {} + legend: list[dict[str, Any]] = [] + for i, cls in enumerate(classes, 1): + d = cls if isinstance(cls, dict) else cls.model_dump() + cat_id_map[int(d["classId"])] = i + cat_id_to_name[i] = d["label"] + legend.append({"id": i, "name": d["label"], "color": d.get("color")}) + return cat_id_map, cat_id_to_name, legend + + +def build_mask_volumes( + item: Any, + classes: list[Any], + meta: dict[str, Any], + progress_cb: Any = None, +) -> dict[str, Any] | None: + """Rasterize an item's shapes into stacked mask volumes. + + Returns ``{semantic, class_vols, slice_indices, legend}`` or ``None`` when + there are no slices to write. ``semantic`` is ``(n,H,W)`` uint8 (class index, + 0=bg); ``class_vols`` maps class label → ``(n,H,W)`` uint8 (0/255). Slices are + every annotated key plus any ``negative_slices`` (emitted as all-zero frames + for hard negatives), in sorted numeric order. + """ + h, w = int(meta["height"]), int(meta["width"]) + cat_id_map, cat_id_to_name, legend = _category_maps(classes) + + slices: dict[str, list[dict[str, Any]]] = item.slices or {} + neg = {str(k) for k in (item.negative_slices or [])} + keys = sorted( + {k for k, shapes in slices.items() if shapes} | neg, + key=lambda k: int(k), + ) + if not keys: + return None + + class_names = [cat_id_to_name[i] for i in sorted(cat_id_to_name)] + sem_list: list[np.ndarray] = [] + class_lists: dict[str, list[np.ndarray]] = {name: [] for name in class_names} + + for key in keys: + label = np.zeros((h, w), dtype=np.uint8) + acc: dict[str, np.ndarray] = {name: np.zeros((h, w), dtype=bool) for name in class_names} + for shape in slices.get(key, []): + shape_dict = shape if isinstance(shape, dict) else shape.model_dump() + mask = shape_to_mask(shape_dict, h, w) + if float(mask.sum()) < 1: + continue + cat_id = cat_id_map.get(int(shape_dict.get("classId", 1)), 1) + label[mask] = cat_id + acc[cat_id_to_name.get(cat_id, "")] |= mask + sem_list.append(label) + for name in class_names: + class_lists[name].append((acc[name] * 255).astype(np.uint8)) + if progress_cb is not None: + progress_cb(f"slice {key}: rasterized") + + return { + "semantic": np.stack(sem_list, axis=0), + "class_vols": {name: np.stack(lst, axis=0) for name, lst in class_lists.items()}, + "slice_indices": [int(k) for k in keys], + "legend": legend, + } + + +def _legend_id_to_name(legend: list[dict[str, Any]]) -> dict[int, str]: + return {int(e["id"]): e["name"] for e in legend} + + +def _remap_semantic(frame: np.ndarray, id_to_name: dict[int, str], name_to_uid: dict[str, int]) -> np.ndarray: + """Recode a semantic frame from one legend's class ids to unified ids (by name).""" + out = np.zeros_like(frame) + for old_id in np.unique(frame): + if old_id == 0: + continue + name = id_to_name.get(int(old_id)) + if name is None: + continue + uid = name_to_uid.get(name) + if uid: + out[frame == old_id] = uid + return out + + +def merge_mask_volumes(existing: dict[str, Any] | None, new: dict[str, Any]) -> dict[str, Any]: + """Merge freshly-rasterized ``new`` volumes onto ``existing`` ones, per slice. + + Slices present in ``new`` overwrite the same index; other existing slices are + kept. Classes are unioned by NAME and given a single unified id scheme, so the + stored ``semantic`` label map stays consistent even if the class set changed + between pushes. Pure (no I/O) so it can be unit-tested. + + ``existing`` is ``None`` (fresh) or ``{slice_indices, semantic (k,H,W), + class_arrays {name:(k,H,W)}, legend}``. Returns + ``{semantic, class_vols, slice_indices, legend, updated_indices}``. + """ + old_legend = (existing or {}).get("legend") or [] + old_indices = [int(i) for i in (existing or {}).get("slice_indices", [])] + old_sem = (existing or {}).get("semantic") + old_cls = (existing or {}).get("class_arrays", {}) or {} + new_indices = [int(i) for i in new["slice_indices"]] + + # Unified class list: existing names first (stable ids), then new-only names. + unified_names: list[str] = [e["name"] for e in old_legend] + for e in new["legend"]: + if e["name"] not in unified_names: + unified_names.append(e["name"]) + name_to_uid = {name: i + 1 for i, name in enumerate(unified_names)} + color_by_name: dict[str, Any] = {} + for e in [*old_legend, *new["legend"]]: # new overrides old for colour + color_by_name[e["name"]] = e.get("color") + + old_id_to_name = _legend_id_to_name(old_legend) + new_id_to_name = _legend_id_to_name(new["legend"]) + + merged_sem: dict[int, np.ndarray] = {} + merged_cls: dict[str, dict[int, np.ndarray]] = {name: {} for name in unified_names} + + if old_sem is not None: + for pos, idx in enumerate(old_indices): + merged_sem[idx] = _remap_semantic(old_sem[pos], old_id_to_name, name_to_uid) + for name, arr in old_cls.items(): + for pos, idx in enumerate(old_indices): + merged_cls.setdefault(name, {})[idx] = arr[pos] + + for i, idx in enumerate(new_indices): # new overrides same index + merged_sem[idx] = _remap_semantic(new["semantic"][i], new_id_to_name, name_to_uid) + for name, vol in new["class_vols"].items(): + merged_cls.setdefault(name, {})[idx] = vol[i] + + all_indices = sorted(merged_sem) + h, w = new["semantic"].shape[1], new["semantic"].shape[2] + zero = np.zeros((h, w), dtype=np.uint8) + semantic = np.stack([merged_sem[i] for i in all_indices], axis=0).astype(np.uint8) + class_vols = { + name: np.stack([merged_cls.get(name, {}).get(i, zero) for i in all_indices], axis=0).astype(np.uint8) + for name in unified_names + } + legend = [{"id": name_to_uid[n], "name": n, "color": color_by_name.get(n)} for n in unified_names] + return { + "semantic": semantic, + "class_vols": class_vols, + "slice_indices": all_indices, + "legend": legend, + "updated_indices": sorted(new_indices), + } + + +def _read_existing_masks(container: Any) -> dict[str, Any] | None: + """Read a prior masks container into ``merge_mask_volumes`` shape, or None.""" + try: + meta = dict(container.metadata) + legend = meta.get("legend") or meta.get("classes") or [] + indices = [int(i) for i in (meta.get("slice_indices") or [])] + semantic = np.asarray(container["semantic"][...]) + safe_to_name = {_safe_name(e["name"]): e["name"] for e in legend} + class_arrays: dict[str, np.ndarray] = {} + for key in list(container): + if key == "semantic": + continue + name = safe_to_name.get(key) + if name is not None: + class_arrays[name] = np.asarray(container[key][...]) + return { + "slice_indices": indices, + "semantic": semantic, + "class_arrays": class_arrays, + "legend": legend, + "slice_updated_at": dict(meta.get("slice_updated_at") or {}), + } + except Exception as exc: # noqa: BLE001 — unreadable/legacy → treat as fresh + logger.warning("mask merge: could not read existing masks (%s) — replacing", exc) + return None + + +def write_masks_to_tiled( + source: str, + server_uri: str | None, + volumes: dict[str, Any], + classes: list[Any], +) -> dict[str, Any]: + """Merge stacked mask volumes into a ``__masks`` sibling container. + + Slices in this push overwrite the same index; previously-pushed slices are + kept (merge). Metadata records ``updated_at`` and a per-slice + ``slice_updated_at`` map plus ``last_updated_slices`` so the latest version of + each slice is explicit. Returns ``{path, n_slices, updated, n_classes}``. + """ + api_key = api_key_for_uri(server_uri) + client = get_tiled_client(server_uri, api_key) + + parts = [p for p in source.strip("/").split("/") if p] + stem = parts[-1] + parent: Any = client + for part in parts[:-1]: + parent = parent[part] + + container_key = f"{stem}__masks" + try: + container: Any = parent[container_key] + except KeyError: + container = None + + existing = _read_existing_masks(container) if container is not None else None + # H/W mismatch → can't merge; replace instead. + if existing is not None and existing["semantic"].shape[1:] != volumes["semantic"].shape[1:]: + logger.warning("mask merge: shape changed for %s — replacing existing masks", source) + existing = None + + merged = merge_mask_volumes(existing, volumes) + + now_iso = datetime.now(timezone.utc).isoformat() + slice_ts: dict[str, str] = dict((existing or {}).get("slice_updated_at", {})) if existing else {} + for idx in merged["updated_indices"]: + slice_ts[str(idx)] = now_iso + + container_meta = { + "studio_type": "segmentation_masks", + "source": source, + "updated_at": now_iso, + "n_slices": len(merged["slice_indices"]), + "slice_indices": merged["slice_indices"], + "slice_updated_at": slice_ts, + "last_updated_slices": merged["updated_indices"], + "classes": merged["legend"], + "legend": merged["legend"], + } + + if container is not None: + # Clear prior arrays (external_only=False → also internally-managed data). + container.delete_contents(recursive=True, external_only=False) + container.update_metadata(container_meta) + else: + container = parent.create_container(key=container_key, metadata=container_meta) + + dims = ["slice", "y", "x"] + container.write_array( + merged["semantic"], key="semantic", dims=dims, + metadata={"studio_type": "segmentation_semantic"}, + ) + for name, vol in merged["class_vols"].items(): + container.write_array( + vol, key=_safe_name(name), dims=dims, + metadata={"studio_type": "segmentation_class", "class_name": name}, + ) + + path = "/".join(parts[:-1] + [container_key]) + logger.info( + "tiled_mask_sync: merged masks into %s (%d total, %d updated)", + path, len(merged["slice_indices"]), len(merged["updated_indices"]), + ) + return { + "path": path, + "n_slices": len(merged["slice_indices"]), + "updated": len(merged["updated_indices"]), + "n_classes": len(merged["class_vols"]), + } + + +def run_mask_sync_job(jid: str, source_items: list[Any], payload: Any) -> None: + """Background worker: rasterize each Tiled source's masks and write them back.""" + try: + export_jobs.update(jid, state="running", phase="reading") + tiled_items = [it for it in source_items if it.kind == "tiled"] + total = 0 + for it in tiled_items: + total += len({k for k, s in (it.slices or {}).items() if s} | {str(k) for k in (it.negative_slices or [])}) + export_jobs.set_total(jid, total) + + if not tiled_items: + export_jobs.update( + jid, state="done", phase="done", + result={"written": [], "note": "skipped — no Tiled sources (masks only write back to Tiled)"}, + ) + export_jobs.log(jid, "No Tiled sources — nothing to write.") + return + + written: list[dict[str, Any]] = [] + for item in tiled_items: + export_jobs.log(jid, f"Rasterizing {item.source} …") + node = arrays_mod.resolve_array(item.source, item.kind, item.server_uri) + meta = arrays_mod.array_shape_meta(node) + + def _cb(message: str, _jid: str = jid) -> None: + export_jobs.bump(_jid, 1) + export_jobs.log(_jid, message) + + volumes = build_mask_volumes(item, payload.classes, meta, progress_cb=_cb) + if volumes is None: + export_jobs.log(jid, f"{item.source}: no annotated slices — skipped.") + continue + + export_jobs.update(jid, phase="writing") + info = write_masks_to_tiled(item.source, item.server_uri, volumes, payload.classes) + written.append({ + "source": item.source, + "container": info["path"], + "n_slices": info["n_slices"], + "updated": info["updated"], + "n_classes": info["n_classes"], + }) + export_jobs.log( + jid, + f"Wrote {info['path']} ({info['n_slices']} slices total, {info['updated']} updated).", + ) + + export_jobs.update(jid, state="done", phase="done", result={"written": written}) + export_jobs.log(jid, "Mask sync complete.") + except Exception as exc: # noqa: BLE001 + logger.error("Mask sync job failed: %s", exc) + export_jobs.update(jid, state="error", phase="error", error=str(exc)) diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..c1fa1b7 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,26 @@ +# Production app container (frontend + backend). Tiled runs separately — point +# TILED_URI/TILED_API_KEY at your external Tiled server. +# +# docker compose up --build +# +services: + app: + build: . + ports: + - "8002:8002" + environment: + # External Tiled server (required): + TILED_URI: "${TILED_URI:-http://host.docker.internal:8010}" + TILED_API_KEY: "${TILED_API_KEY:-}" + # Persisted annotation drafts/versions/exports live under this path: + LOCAL_DATA_ROOT: "/data" + # Same-origin SPA → CORS can be empty; set if you split origins: + BROWSE_ALLOWED_ORIGINS: "${BROWSE_ALLOWED_ORIGINS:-}" + volumes: + - annotation-data:/data + # host.docker.internal lets the container reach a Tiled on the host (Linux): + extra_hosts: + - "host.docker.internal:host-gateway" + +volumes: + annotation-data: diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 119d67c..59697b9 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -9,6 +9,7 @@ "version": "0.1.0", "dependencies": { "@blueskyproject/finch": 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"@protobufjs/path": "^1.1.2", + "@protobufjs/pool": "^1.1.0", + "@protobufjs/utf8": "^1.1.1", + "@types/node": ">=13.7.0", + "long": "^5.3.2" + }, + "engines": { + "node": ">=12.0.0" + } + }, "node_modules/punycode": { "version": "2.3.1", "resolved": "https://registry.npmjs.org/punycode/-/punycode-2.3.1.tgz", @@ -5946,6 +6733,23 @@ "node": ">=0.10.0" } }, + "node_modules/roarr": { + "version": "2.15.4", + "resolved": "https://registry.npmjs.org/roarr/-/roarr-2.15.4.tgz", + "integrity": "sha512-CHhPh+UNHD2GTXNYhPWLnU8ONHdI+5DI+4EYIAOaiD63rHeYlZvyh8P+in5999TTSFgUYuKUAjzRI4mdh/p+2A==", + "license": "BSD-3-Clause", + "dependencies": { + "boolean": "^3.0.1", + "detect-node": "^2.0.4", + "globalthis": "^1.0.1", + "json-stringify-safe": "^5.0.1", + "semver-compare": "^1.0.0", + "sprintf-js": "^1.1.2" + }, + "engines": { + "node": ">=8.0" + } + }, "node_modules/robust-predicates": { "version": "3.0.3", "resolved": "https://registry.npmjs.org/robust-predicates/-/robust-predicates-3.0.3.tgz", @@ -6084,6 +6888,39 @@ "semver": "bin/semver.js" } }, + "node_modules/semver-compare": { + "version": "1.0.0", + "resolved": "https://registry.npmjs.org/semver-compare/-/semver-compare-1.0.0.tgz", + "integrity": "sha512-YM3/ITh2MJ5MtzaM429anh+x2jiLVjqILF4m4oyQB18W7Ggea7BfqdH/wGMK7dDiMghv/6WG7znWMwUDzJiXow==", + "license": "MIT" + }, + "node_modules/serialize-error": { + "version": "7.0.1", + "resolved": "https://registry.npmjs.org/serialize-error/-/serialize-error-7.0.1.tgz", + "integrity": "sha512-8I8TjW5KMOKsZQTvoxjuSIa7foAwPWGOts+6o7sgjz41/qMD9VQHEDxi6PBvK2l0MXUmqZyNpUK+T2tQaaElvw==", + "license": "MIT", + "dependencies": { + "type-fest": "^0.13.1" + }, + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/serialize-error/node_modules/type-fest": { + "version": "0.13.1", + "resolved": "https://registry.npmjs.org/type-fest/-/type-fest-0.13.1.tgz", + "integrity": "sha512-34R7HTnG0XIJcBSn5XhDd7nNFPRcXYRZrBB2O2jdKqYODldSzBAqzsWoZYYvduky73toYS/ESqxPvkDf/F0XMg==", + "license": "(MIT OR CC0-1.0)", + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/set-cookie-parser": { "version": "2.7.2", "resolved": "https://registry.npmjs.org/set-cookie-parser/-/set-cookie-parser-2.7.2.tgz", @@ -6108,6 +6945,62 @@ "node": ">= 0.4" } }, + "node_modules/sharp": { + "version": "0.34.5", + "resolved": "https://registry.npmjs.org/sharp/-/sharp-0.34.5.tgz", + "integrity": "sha512-Ou9I5Ft9WNcCbXrU9cMgPBcCK8LiwLqcbywW3t4oDV37n1pzpuNLsYiAV8eODnjbtQlSDwZ2cUEeQz4E54Hltg==", + "hasInstallScript": true, + "license": "Apache-2.0", + "dependencies": { + "@img/colour": "^1.0.0", + "detect-libc": "^2.1.2", + "semver": "^7.7.3" + }, + "engines": { + "node": "^18.17.0 || ^20.3.0 || >=21.0.0" + }, + "funding": { + "url": "https://opencollective.com/libvips" + }, + "optionalDependencies": { + "@img/sharp-darwin-arm64": "0.34.5", + "@img/sharp-darwin-x64": "0.34.5", + "@img/sharp-libvips-darwin-arm64": "1.2.4", + "@img/sharp-libvips-darwin-x64": "1.2.4", + "@img/sharp-libvips-linux-arm": "1.2.4", + "@img/sharp-libvips-linux-arm64": "1.2.4", + "@img/sharp-libvips-linux-ppc64": "1.2.4", + "@img/sharp-libvips-linux-riscv64": "1.2.4", + "@img/sharp-libvips-linux-s390x": "1.2.4", + "@img/sharp-libvips-linux-x64": "1.2.4", + "@img/sharp-libvips-linuxmusl-arm64": "1.2.4", + "@img/sharp-libvips-linuxmusl-x64": "1.2.4", + "@img/sharp-linux-arm": "0.34.5", + "@img/sharp-linux-arm64": "0.34.5", + "@img/sharp-linux-ppc64": "0.34.5", + "@img/sharp-linux-riscv64": "0.34.5", + "@img/sharp-linux-s390x": "0.34.5", + "@img/sharp-linux-x64": "0.34.5", + "@img/sharp-linuxmusl-arm64": "0.34.5", + "@img/sharp-linuxmusl-x64": "0.34.5", + "@img/sharp-wasm32": "0.34.5", + "@img/sharp-win32-arm64": "0.34.5", + "@img/sharp-win32-ia32": "0.34.5", + "@img/sharp-win32-x64": "0.34.5" + } + }, + "node_modules/sharp/node_modules/semver": { + "version": "7.8.5", + "resolved": "https://registry.npmjs.org/semver/-/semver-7.8.5.tgz", + "integrity": "sha512-Y7/KDsb8LjooZpwaqGyulO6DQlksgCncchHGk+sZIY4SBvUocMBEFH5Ur1fI4dV+Jvl0w6cjvucaIi40puRioA==", + "license": "ISC", + "bin": { + "semver": "bin/semver.js" + }, + "engines": { + "node": ">=10" + } + }, "node_modules/side-channel": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/side-channel/-/side-channel-1.1.1.tgz", @@ -6206,6 +7099,12 @@ "node": ">=0.10.0" } }, + "node_modules/sprintf-js": { + "version": "1.1.3", + "resolved": "https://registry.npmjs.org/sprintf-js/-/sprintf-js-1.1.3.tgz", + "integrity": "sha512-Oo+0REFV59/rz3gfJNKQiBlwfHaSESl1pcGyABQsnnIfWOFt6JNj5gCog2U6MLZ//IGYD+nA8nI+mTShREReaA==", + "license": "BSD-3-Clause" + }, "node_modules/stackback": { "version": "0.0.2", "resolved": "https://registry.npmjs.org/stackback/-/stackback-0.0.2.tgz", diff --git a/frontend/package.json b/frontend/package.json index 43e1ea2..87d4cad 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -5,14 +5,15 @@ "type": "module", "scripts": { "dev": "vite", - "build": "tsc && vite build", + "build": "tsc -b && vite build", "preview": "vite preview", - "typecheck": "tsc --noEmit", + "typecheck": "tsc -b", "test": "vitest run", "test:watch": "vitest" }, "dependencies": { "@blueskyproject/finch": "^0.1.7", + "@huggingface/transformers": "^4.2.0", "@phosphor-icons/react": "^2.1.7", "@tanstack/react-query": "^5.99.0", "clsx": "^2.1.1", diff --git a/frontend/scripts/fetch-sam-model.mjs b/frontend/scripts/fetch-sam-model.mjs new file mode 100644 index 0000000..202daa2 --- /dev/null +++ b/frontend/scripts/fetch-sam-model.mjs @@ -0,0 +1,63 @@ +#!/usr/bin/env node +/** + * Vendor the SlimSAM model files for offline / CORS-safe use. + * + * Hugging Face's ONNX CDN (Xet) often returns 403 for bare browser/curl + * downloads, which greys out Smart (AI). This script uses huggingface_hub + * (Python) to pull weights into frontend/public/models/slimsam-77-uniform/. + * + * uv pip install --python .venv/bin/python huggingface_hub # once + * node frontend/scripts/fetch-sam-model.mjs + * + * Ensure USE_LOCAL_MODEL = true in src/lib/sam/samWorker.ts (default). + */ +import { spawnSync } from 'node:child_process'; +import { existsSync } from 'node:fs'; +import { dirname, join } from 'node:path'; +import { fileURLToPath } from 'node:url'; + +const ROOT = join(dirname(fileURLToPath(import.meta.url)), '..'); +const OUT = join(ROOT, 'public', 'models', 'slimsam-77-uniform'); +const REPO = 'Xenova/slimsam-77-uniform'; + +function pythonWithHub() { + const candidates = [ + join(ROOT, '..', '.venv', 'bin', 'python'), + 'python3', + 'python', + ]; + for (const p of candidates) { + if ((p.includes('/') || p.startsWith('.')) && !existsSync(p)) continue; + const r = spawnSync(p, ['-c', 'import huggingface_hub'], { encoding: 'utf8' }); + if (r.status === 0) return p; + } + return null; +} + +const python = pythonWithHub(); +if (!python) { + console.error('Need Python with huggingface_hub. Try:'); + console.error(' uv pip install --python .venv/bin/python huggingface_hub'); + process.exit(1); +} + +const script = ` +from pathlib import Path +import shutil +from huggingface_hub import snapshot_download + +out = Path(${JSON.stringify(OUT)}) +out.mkdir(parents=True, exist_ok=True) +path = snapshot_download(${JSON.stringify(REPO)}, local_dir=str(out)) +cache = out / ".cache" +if cache.exists(): + shutil.rmtree(cache) +print("Downloaded", ${JSON.stringify(REPO)}, "→", path) +for p in sorted(Path(path).rglob("*")): + if p.is_file(): + print(f" {p.relative_to(path)} {(p.stat().st_size / 1e6):.1f} MB") +`; + +const result = spawnSync(python, ['-c', script], { stdio: 'inherit' }); +if (result.status !== 0) process.exit(result.status ?? 1); +console.log('Done. USE_LOCAL_MODEL should be true in src/lib/sam/samWorker.ts.'); diff --git a/frontend/src/app/App.tsx b/frontend/src/app/App.tsx index e47ee8a..2b94c2c 100644 --- a/frontend/src/app/App.tsx +++ b/frontend/src/app/App.tsx @@ -1,13 +1,26 @@ -import { useState, useEffect } from 'react'; +/** + * App — Hub routes: Connect → Browse → Ipred → Preprocess → Draw → Train. + */ +import { useEffect } from 'react'; import { useNavigate, useLocation, Navigate } from 'react-router'; import './App.css'; import { RouteItem } from '@/types/navigationRouterTypes'; import HubAppLayout from '@/components/HubAppLayout'; import { useHubSelectedTabs } from '@/hooks/useHubSelectedTabs'; -import { PlugsConnected, PencilSimple, MagnifyingGlass } from '@phosphor-icons/react'; +import { + PlugsConnected, + MagnifyingGlass, + Cpu, + Faders, + PencilSimple, + TreeStructure, +} from '@phosphor-icons/react'; import ConnectPage from './pages/ConnectPage'; -import AnnotatePage from './pages/AnnotatePage'; import BrowsePage from './pages/BrowsePage'; +import IpredPage from './pages/IpredPage'; +import PreprocessPage from './pages/PreprocessPage'; +import DrawPage from './pages/DrawPage'; +import TrainPage from './pages/TrainPage'; import CustomizePages from '@/components/CustomizePages'; const allRoutes: RouteItem[] = [ @@ -25,23 +38,45 @@ const allRoutes: RouteItem[] = [ isBackgroundTransparent: true, }, { - path: '/annotate', - label: 'Annotate', + path: '/ipred', + label: 'Ipred', + icon: , + element: , + }, + { + path: '/preprocess', + label: 'Preprocess', + icon: , + element: , + isBackgroundTransparent: true, + }, + { + path: '/draw', + label: 'Draw', icon: , - element: , + element: , + isBackgroundTransparent: true, + }, + { + path: '/train', + label: 'Train & Predict', + icon: , + element: , isBackgroundTransparent: true, }, ]; const DEFAULT_PATHS = allRoutes.map((r) => r.path); +/** + * App — root component: validates persisted tab selection against known routes, redirects on + * first load, and renders the hub layout plus the tab-customisation modal. + */ function App() { const { selectedPaths, setSelectedPaths } = useHubSelectedTabs(); const navigate = useNavigate(); const location = useLocation(); - const [showTabSelector, setShowTabSelector] = useState(false); - // Validate stored paths — discard unknown paths and merge in any newly added tabs. const storedValid = selectedPaths?.filter((p) => DEFAULT_PATHS.includes(p)) ?? null; const validPaths = storedValid === null @@ -49,7 +84,6 @@ function App() { : [...storedValid, ...DEFAULT_PATHS.filter((p) => !storedValid.includes(p))]; const needsInit = validPaths === null || validPaths.length === 0; - // Navigate on first mount if no valid paths stored; persist merged tab list. useEffect(() => { if (needsInit) { setSelectedPaths(DEFAULT_PATHS); @@ -68,6 +102,14 @@ function App() { ? allRoutes : allRoutes.filter((r) => validPaths!.includes(r.path)); + if (location.pathname === '/annotate') { + return ; + } + + if (location.pathname === '/cleanup') { + return ; + } + if (needsInit) { return null; } @@ -80,7 +122,7 @@ function App() { <> (null); - const [activeBrushShapeId, setActiveBrushShapeId] = useState(null); - - const handleActivateClass = useCallback((classId: number) => { - setActiveClassId(classId); - setActiveBrushShapeId(null); - }, []); - - const handleClassDeleted = useCallback((_deletedClassId: number) => { - setActiveBrushShapeId(null); - }, []); - - useEffect(() => { - if (activeClassId !== null) return; - if (classes.length > 0) setActiveClassId(classes[0].classId); - }, [classes, activeClassId]); - - const [brightness, setBrightness] = useState(0); - const [contrast, setContrast] = useState(0); - const [showDownload, setShowDownload] = useState(false); - const [showVersionHistory, setShowVersionHistory] = useState(false); - const [showSaveModal, setShowSaveModal] = useState(false); - const [saveModalPayload, setSaveModalPayload] = useState(null); - - // Version preview ("time-travel") state — non-destructive. - const [previewVersion, setPreviewVersion] = useState(null); - const [previewPayload, setPreviewPayload] = useState(null); - const [previewLoading, setPreviewLoading] = useState(false); - - const sourceKey = source && kind - ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri) - : null; - - // Crash-recovery autosave (local draft only, no Tiled sync) - useDraftSync(sourceKey); - - // Explicit versioned save - const { isDirty, isSaving, lastSavedAt, save, buildSavePayload, saveSummary, versions, fetchVersionPayload, restoreVersion } = useSave(sourceKey); - - const { currentSlice } = useDatasetStore(); - - // Load the previewed version's payload (cached) whenever the slider moves. - useEffect(() => { - if (previewVersion === null) { - setPreviewPayload(null); - return; - } - let cancelled = false; - setPreviewLoading(true); - fetchVersionPayload(previewVersion).then((payload) => { - if (cancelled) return; - setPreviewPayload(payload); - setPreviewLoading(false); - }); - return () => { cancelled = true; }; - }, [previewVersion, fetchVersionPayload]); - - // Exit preview if the source changes out from under us. - useEffect(() => { - setPreviewVersion(null); - }, [sourceKey]); - - const handleRestoreFromPreview = useCallback((version: number) => { - restoreVersion(version); - setPreviewVersion(null); - }, [restoreVersion]); - - // Shapes for the current slice from the previewed version (read-only on canvas). - const previewShapes = previewPayload - ? (previewPayload.slices[String(currentSlice)] ?? []) - : null; - - const handleDeleteSelected = () => { - if (!sourceKey || !selectedShapeId) return; - removeShape(sourceKey, currentSlice, selectedShapeId); - setSelectedShapeId(null); - }; - - const handleOpenSaveModal = () => { - const payload = buildSavePayload(); - if (!payload) return; - setSaveModalPayload(payload); - setShowSaveModal(true); - }; - - const handleConfirmSave = async (opts: { annotatedBy: string; notes: string; thumbnailBase64?: string }) => { - const ok = await save(opts); - if (ok) { - setShowSaveModal(false); - setSaveModalPayload(null); - } - }; - - const handleCancelDraft = () => { - setActiveBrushShapeId(null); - }; - - useKeybinds( - activeClassId, - handleActivateClass, - () => setActiveBrushShapeId(null), - handleDeleteSelected, - handleCancelDraft - ); - - if (!meta) { - return ( -
-

No sample loaded. Go to Browse to pick a sample.

-
- ); - } - - const savedLabel = lastSavedAt - ? `Saved ${new Date(lastSavedAt).toLocaleTimeString(undefined, { hour: '2-digit', minute: '2-digit' })}` - : versions.length > 0 - ? `v${versions[versions.length - 1].version} saved` - : null; - - return ( - <> -
- {/* Sidebar */} -
- -
- { setBrightness(0); setContrast(0); }} - /> -
- -
- -
- - {/* Save button + status */} -
- - - {/* Dirty / saved status line */} -
- {isDirty ? 'Unsaved changes' : (savedLabel ?? 'No saves yet')} - {versions.length > 0 && ( - - )} -
-
- - {/* Download / export */} - -
- - {/* Canvas */} -
- - {previewVersion !== null && ( - setPreviewVersion(null)} - onRestore={handleRestoreFromPreview} - /> - )} -
-
- - {showDownload && setShowDownload(false)} />} - {showSaveModal && saveModalPayload && sourceKey && ( - { - if (!isSaving) { - setShowSaveModal(false); - setSaveModalPayload(null); - } - }} - /> - )} - {showVersionHistory && ( - { setPreviewVersion(v); setShowVersionHistory(false); }} - onRestore={restoreVersion} - onClose={() => setShowVersionHistory(false)} - /> - )} - - ); + return ; } diff --git a/frontend/src/app/pages/AnnotateWorkspace.tsx b/frontend/src/app/pages/AnnotateWorkspace.tsx new file mode 100644 index 0000000..46278cd --- /dev/null +++ b/frontend/src/app/pages/AnnotateWorkspace.tsx @@ -0,0 +1,566 @@ +/** + * AnnotateWorkspace — shared canvas + Hub-stage-driven sidebar. + */ +import { useState, useEffect, useCallback } from 'react'; +import { useNavigate, useLocation } from 'react-router'; +import { DownloadSimple, FloppyDisk, ClockCounterClockwise, CircleDashed } from '@phosphor-icons/react'; +import { useDatasetStore } from '@/stores/datasetStore'; +import { useAnnotationStore } from '@/stores/annotationStore'; +import { useToolStore } from '@/stores/toolStore'; +import { useClassStore } from '@/stores/classStore'; +import { useMaskSetStore } from '@/stores/maskSetStore'; +import { useDraftSync } from '@/hooks/useDraftSync'; +import { useSave, type VersionPayload } from '@/hooks/useSave'; +import { buildSourceKey } from '@/lib/sourceKey'; +import { stageFromPath } from '@/lib/annotateStage'; +import { useKeybinds } from '@/hooks/useKeybinds'; +import Toolbar from '@/components/annotate/Toolbar'; +import ClassManager from '@/components/annotate/ClassManager'; +import DisplayControls from '@/components/annotate/DisplayControls'; +import FeatureChannelsPanel from '@/components/annotate/FeatureChannelsPanel'; +import PixelClassifierPanel from '@/components/annotate/PixelClassifierPanel'; +import ManifoldSuggestPanel from '@/components/annotate/ManifoldSuggestPanel'; +import SliceNavigator from '@/components/annotate/SliceNavigator'; +import MaskToolsPanel from '@/components/annotate/MaskToolsPanel'; +import ModelShelfPanel from '@/components/annotate/ModelShelfPanel'; +import LayersPanel from '@/components/annotate/LayersPanel'; +import AnnotationCanvas from '@/components/annotate/AnnotationCanvas'; +import DownloadModal from '@/components/annotate/DownloadModal'; +import VersionHistoryModal from '@/components/annotate/VersionHistoryModal'; +import VersionPreviewBar from '@/components/annotate/VersionPreviewBar'; +import SaveModal from '@/components/annotate/SaveModal'; +import type { SaveDraftPayload } from '@/hooks/useSave'; +import { useFeatureChannels } from '@/hooks/useFeatureChannels'; +import { usePixelClassifier } from '@/hooks/usePixelClassifier'; +import { useFeatureManifold } from '@/hooks/useFeatureManifold'; +import { classProbaMaskSetName, labelMapToPolygonShapes, loadLabelPng } from '@/lib/pixelClf'; +import { labelMapFromBase64 } from '@/lib/labelMap'; +import { featureParamsFromRecipe } from '@/lib/featureRecipe'; +import { useConnectionStore } from '@/stores/connectionStore'; +import type { Tool } from '@/stores/toolStore'; + +const PREPROCESS_TOOLS: Tool[] = ['pan', 'select', 'polygon', 'rectangle', 'ellipse', 'brush']; + +/** Shared annotate shell used by Preprocess / Draw / Train Hub stages. */ +export default function AnnotateWorkspace() { + const navigate = useNavigate(); + const location = useLocation(); + const stage = stageFromPath(location.pathname); + const { source, kind, serverUri, meta } = useDatasetStore(); + const { removeShapes, byImage, addShapes } = useAnnotationStore(); + const { selectedShapeIds, setSelectedShapeId } = useToolStore(); + const { classes } = useClassStore(); + const addMaskSet = useMaskSetStore((s) => s.addSet); + + const [activeClassId, setActiveClassId] = useState(null); + const [activeBrushShapeId, setActiveBrushShapeId] = useState(null); + + /** Sets the active class and clears any in-progress brush instance. */ + const handleActivateClass = useCallback((classId: number) => { + setActiveClassId(classId); + setActiveBrushShapeId(null); + }, []); + + /** Resets the active brush instance after a class is deleted. */ + const handleClassDeleted = useCallback((_deletedClassId: number) => { + setActiveBrushShapeId(null); + }, []); + + useEffect(() => { + if (activeClassId !== null) return; + if (classes.length > 0) setActiveClassId(classes[0].classId); + }, [classes, activeClassId]); + + const [brightness, setBrightness] = useState(0); + const [contrast, setContrast] = useState(0); + const [clahe, setClahe] = useState(false); + const [sharpen, setSharpen] = useState(false); + // Min/max levels window (0–255) + histogram of the current slice (client-side). + const [levelsLo, setLevelsLo] = useState(0); + const [levelsHi, setLevelsHi] = useState(255); + const [histogramBins, setHistogramBins] = useState(null); + const [showDownload, setShowDownload] = useState(false); + const [showVersionHistory, setShowVersionHistory] = useState(false); + const [showSaveModal, setShowSaveModal] = useState(false); + const [saveModalPayload, setSaveModalPayload] = useState(null); + + // Version preview ("time-travel") state — non-destructive. + const [previewVersion, setPreviewVersion] = useState(null); + const [previewPayload, setPreviewPayload] = useState(null); + const [previewLoading, setPreviewLoading] = useState(false); + + const sourceKey = source && kind + ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri) + : null; + + // Crash-recovery autosave (local draft only, no Tiled sync) + useDraftSync(sourceKey); + + // Explicit versioned save + const { isDirty, isSaving, lastSavedAt, save, buildSavePayload, saveSummary, versions, fetchVersionPayload, restoreVersion } = useSave(sourceKey); + + const { currentSlice } = useDatasetStore(); + + const features = useFeatureChannels({ + source, + kind, + sliceIndex: currentSlice, + serverUri, + }); + + // Prefill Preprocess feature params from the session's preferred model recipe. + const preferredFeatureRecipe = useConnectionStore((s) => s.preferredFeatureRecipe); + useEffect(() => { + if (!preferredFeatureRecipe) return; + features.setParams(featureParamsFromRecipe(preferredFeatureRecipe)); + // Intentionally only when the preferred recipe identity changes. + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [preferredFeatureRecipe]); + + const sliceShapes = sourceKey + ? (byImage[sourceKey]?.[String(currentSlice)] ?? []) + : []; + + const clf = usePixelClassifier({ + featureJobId: features.job?.jobId ?? null, + resetKey: sourceKey ? `${sourceKey}:${currentSlice}` : null, + source, + kind, + serverUri, + sliceIndex: currentSlice, + onFeatureJobExpired: features.invalidateJob, + onFeatureReady: (info) => { + features.adoptFeatureBank(info); + }, + }); + + const manifold = useFeatureManifold({ + featureJobId: features.job?.jobId ?? null, + onFeatureJobExpired: features.invalidateJob, + }); + + const handleClfTrain = useCallback(() => { + void clf.train(sliceShapes); + }, [clf, sliceShapes]); + + const handleClfPredict = useCallback(() => { + void clf.predict(sliceShapes); + }, [clf, sliceShapes]); + + const handleClfCommit = useCallback(async () => { + if (!sourceKey || !clf.commitUrl || !clf.model) return; + try { + const { data, width, height } = await loadLabelPng(clf.commitUrl); + const shapes = labelMapToPolygonShapes(data, width, height, clf.model.classIds, { + minRegion: 64, + preserveShapes: sliceShapes, + }); + if (shapes.length) addShapes(sourceKey, currentSlice, shapes); + clf.dismiss(); + } catch { + /* keep overlay; error surface via clf if needed */ + } + }, [sourceKey, clf, addShapes, currentSlice, sliceShapes]); + + const classLabelForId = useCallback( + (classId: number) => { + const c = classes.find((x) => x.classId === classId); + return c?.label?.trim() || `class ${classId}`; + }, + [classes], + ); + + const handleSaveClassMap = useCallback(async () => { + if (!sourceKey || clf.activeProbaClassId === null) return; + const result = await clf.saveThresholdedClass(); + if (!result) return; + const labelMap = labelMapFromBase64(result.label_map_b64, result.width * result.height); + addMaskSet({ + name: classProbaMaskSetName( + result.class_id, + result.threshold, + classLabelForId(result.class_id), + ), + sourceKey, + slice: currentSlice, + origin: 'clf', + labelMap, + width: result.width, + height: result.height, + shapes: [], + }); + }, [sourceKey, clf, addMaskSet, currentSlice, classLabelForId]); + + const maskSetOverlayShapes = null; + const maskSetLabelOverlay = null; + + // Load the previewed version's payload (cached) whenever the slider moves. + useEffect(() => { + if (previewVersion === null) { + setPreviewPayload(null); + return; + } + let cancelled = false; + setPreviewLoading(true); + fetchVersionPayload(previewVersion).then((payload) => { + if (cancelled) return; + setPreviewPayload(payload); + setPreviewLoading(false); + }); + return () => { cancelled = true; }; + }, [previewVersion, fetchVersionPayload]); + + // Exit preview if the source changes out from under us. + useEffect(() => { + setPreviewVersion(null); + }, [sourceKey]); + + /** Restores the given version into the editor and exits preview mode. */ + const handleRestoreFromPreview = useCallback((version: number) => { + restoreVersion(version); + setPreviewVersion(null); + }, [restoreVersion]); + + // Shapes for the current slice from the previewed version (read-only on canvas). + const previewShapes = previewPayload + ? (previewPayload.slices[String(currentSlice)] ?? []) + : null; + + /** Removes the currently selected shapes from the active slice and clears the selection. */ + const handleDeleteSelected = () => { + if (!sourceKey || selectedShapeIds.length === 0) return; + removeShapes(sourceKey, currentSlice, selectedShapeIds); + setSelectedShapeId(null); + }; + + /** Builds the save payload and opens the save modal (no-op if nothing to save). */ + const handleOpenSaveModal = () => { + const payload = buildSavePayload(); + if (!payload) return; + setSaveModalPayload(payload); + setShowSaveModal(true); + }; + + /** Saves the version and closes the modal on success. */ + const handleConfirmSave = async (opts: { annotatedBy: string; notes: string; thumbnailBase64?: string }) => { + const ok = await save(opts); + if (ok) { + setShowSaveModal(false); + setSaveModalPayload(null); + } + }; + + /** Cancels the in-progress draft by clearing the active brush instance. */ + const handleCancelDraft = () => { + setActiveBrushShapeId(null); + }; + + useKeybinds( + activeClassId, + handleActivateClass, + () => setActiveBrushShapeId(null), + handleDeleteSelected, + handleCancelDraft + ); + + if (!meta) { + return ( +
+

No sample loaded. Pick a sample to annotate.

+ +
+ ); + } + + const savedLabel = lastSavedAt + ? `Saved ${new Date(lastSavedAt).toLocaleTimeString(undefined, { hour: '2-digit', minute: '2-digit' })}` + : versions.length > 0 + ? `v${versions[versions.length - 1].version} saved` + : null; + + return ( + <> +
+ {/* Sidebar */} +
+ {stage === 'draw' && ( + <> + +
+ +
+ +
+ + )} + + {stage === 'preprocess' && ( + <> + +
+ { + setBrightness(0); + setContrast(0); + setClahe(false); + setSharpen(false); + setLevelsLo(0); + setLevelsHi(255); + }} + histogramBins={histogramBins} + levelsLo={levelsLo} + levelsHi={levelsHi} + onLevelsChange={(lo, hi) => { setLevelsLo(lo); setLevelsHi(hi); }} + onLevelsReset={() => { setLevelsLo(0); setLevelsHi(255); }} + clahe={clahe} + onClaheChange={setClahe} + sharpen={sharpen} + onSharpenChange={setSharpen} + /> +
+ { void manifold.sample(); }} + onDismiss={manifold.dismiss} + roiShapeCount={manifold.placementMask?.length ?? 0} + canCaptureRoi={selectedShapeIds.length > 0} + onCaptureRoi={() => { + const selected = sliceShapes.filter((s) => + selectedShapeIds.includes(s.id), + ); + manifold.setPlacementMaskFromShapes(selected); + }} + onClearRoi={manifold.clearPlacementMask} + /> + )} + /> +
+ + )} + + {stage === 'train' && ( + <> + 0} + training={clf.training} + predicting={clf.predicting} + params={clf.params} + onParamsChange={clf.setParams} + model={clf.model} + treeView={clf.treeView} + treeIndex={clf.treeIndex} + onTreeIndexChange={(i) => { void clf.loadTree(i); }} + hasPrediction={!!clf.commitUrl} + predictCounts={clf.predictCounts} + probaClassIndex={clf.probaClassIndex} + activeProbaClassId={clf.activeProbaClassId} + activeProbaThreshold={clf.activeProbaThreshold} + classLabelForId={classLabelForId} + onCycleProbaClass={clf.cycleProbaClass} + onProbaThresholdChange={clf.setProbaThreshold} + onSaveClassMap={() => { void handleSaveClassMap(); }} + savingClass={clf.savingClass} + error={clf.error} + onTrain={handleClfTrain} + onPredict={handleClfPredict} + onCommit={() => { void handleClfCommit(); }} + onDismiss={clf.dismiss} + commitLabel="Commit singletons" + featureSetupId={clf.featureSetupId} + trainerId={clf.trainerId} + /> +
+ +
+ + )} + + +
+ + +
+ + {/* Save button + status */} +
+ + + {/* Dirty / saved status line */} +
+ {isDirty ? 'Unsaved changes' : (savedLabel ?? 'No saves yet')} + {versions.length > 0 && ( + + )} +
+
+ + {/* Download / export */} + +
+ + {/* Canvas */} +
+ + {previewVersion !== null && ( + setPreviewVersion(null)} + onRestore={handleRestoreFromPreview} + /> + )} +
+
+ + {showDownload && setShowDownload(false)} />} + {showSaveModal && saveModalPayload && sourceKey && ( + { + if (!isSaving) { + setShowSaveModal(false); + setSaveModalPayload(null); + } + }} + /> + )} + {showVersionHistory && ( + { setPreviewVersion(v); setShowVersionHistory(false); }} + onRestore={restoreVersion} + onClose={() => setShowVersionHistory(false)} + /> + )} + + ); +} diff --git a/frontend/src/app/pages/BrowsePage.tsx b/frontend/src/app/pages/BrowsePage.tsx index 5d66f8f..dd9f562 100644 --- a/frontend/src/app/pages/BrowsePage.tsx +++ b/frontend/src/app/pages/BrowsePage.tsx @@ -13,10 +13,12 @@ import { useOpenInAnnotate } from '@/hooks/useOpenInAnnotate'; import { API_BASE } from '@/config'; import type { ServerInfo } from '@/types/server'; import type { AnnotationFilter } from '@/types/annotationFilter'; +import SessionSetupPanel from '@/components/SessionSetupPanel'; export default function BrowsePage() { const navigate = useNavigate(); - const { kind, serverUri, localRoot, label, sampleCount, setConnection } = useConnectionStore(); + const { kind, serverUri, browseContainerPath, localRoot, localRel, label, sampleCount, setConnection } = + useConnectionStore(); const { openLocalFile } = useOpenInAnnotate(); const [annotationFilter, setAnnotationFilter] = useState('all'); @@ -32,6 +34,7 @@ export default function BrowsePage() { enabled: kind === 'tiled', }); + /** Switch the active Tiled connection to the chosen server URI. */ const handleServerChange = (uri: string) => { setConnection({ kind: 'tiled', @@ -77,26 +80,35 @@ export default function BrowsePage() { - {/* Main browser */} - {kind === 'tiled' && serverUri && ( - - )} - {kind === 'local' && ( - - )} +
+ +
+ + {/* Main browser — flex-1 so it fills the space under the banner and its + own internal scroll areas are bounded (otherwise the bottom is clipped). */} +
+ {kind === 'tiled' && serverUri && ( + + )} + {kind === 'local' && ( + + )} +
); } diff --git a/frontend/src/app/pages/CleanupPage.tsx b/frontend/src/app/pages/CleanupPage.tsx new file mode 100644 index 0000000..86021d1 --- /dev/null +++ b/frontend/src/app/pages/CleanupPage.tsx @@ -0,0 +1,6 @@ +/** Cleanup & Merge Hub stage — dense mask sets and merge. */ +import AnnotateWorkspace from './AnnotateWorkspace'; + +export default function CleanupPage() { + return ; +} diff --git a/frontend/src/app/pages/ConnectPage.tsx b/frontend/src/app/pages/ConnectPage.tsx index 070edd3..5fc110a 100644 --- a/frontend/src/app/pages/ConnectPage.tsx +++ b/frontend/src/app/pages/ConnectPage.tsx @@ -1,13 +1,20 @@ /** * ConnectPage — choose a Tiled server or local folder, establish the connection, * see the sample count, then navigate to Browse to pick individual samples. + * + * Tiled mode also supports picking a specific browse container and ingesting + * new data via drag-and-drop. Local mode lets the user grant access to any + * absolute folder on the backend machine. */ import { useEffect, useState } from 'react'; import { useNavigate } from 'react-router'; import { useQuery } from '@tanstack/react-query'; -import { PlugsConnected, Folder, HardDrives } from '@phosphor-icons/react'; +import { PlugsConnected, Folder, HardDrives, Stack } from '@phosphor-icons/react'; import { API_BASE } from '@/config'; import { useConnectionStore } from '@/stores/connectionStore'; +import { useOpenInAnnotate } from '@/hooks/useOpenInAnnotate'; +import IngestDropzone from '@/components/Ingest/IngestDropzone'; +import SessionSetupPanel from '@/components/SessionSetupPanel'; interface ServerInfo { name: string; @@ -22,16 +29,28 @@ interface LocalEntry { size: number | null; } +interface TiledEntry { + name: string; + path: string; + is_dir: boolean; + is_array: boolean; +} + export default function ConnectPage() { const navigate = useNavigate(); const { setConnection } = useConnectionStore(); + const { openTiledArray } = useOpenInAnnotate(); const [mode, setMode] = useState<'tiled' | 'local'>('tiled'); - // Tiled: just pick the server + // Tiled: pick the server + optionally a browse container const [selectedServerUri, setSelectedServerUri] = useState(''); + const [containerDir, setContainerDir] = useState(''); // currently-browsed container node + const [selectedContainer, setSelectedContainer] = useState(''); // chosen browse target - // Local: browse directories until you pick a folder + // Local: grant an absolute root, then browse subfolders until you pick a folder + const [grantedRoot, setGrantedRoot] = useState(''); + const [rootInput, setRootInput] = useState(''); const [browseDir, setBrowseDir] = useState(''); const [selectedFolder, setSelectedFolder] = useState(''); @@ -48,43 +67,82 @@ export default function ConnectPage() { }, }); - // Default to the first server useEffect(() => { if (!selectedServerUri && servers.length > 0) setSelectedServerUri(servers[0].uri); }, [servers, selectedServerUri]); + // --- Tiled container listing (for the optional browse-target picker) --- + const { data: tiledEntries = [] } = useQuery({ + queryKey: ['tiledList', selectedServerUri, containerDir], + queryFn: async () => { + const res = await fetch( + `${API_BASE}/api/tiled/list?server_uri=${encodeURIComponent(selectedServerUri)}&path=${encodeURIComponent(containerDir)}`, + ); + if (!res.ok) throw new Error(await res.text()); + return res.json(); + }, + enabled: mode === 'tiled' && !!selectedServerUri, + }); + // --- Local directory listing --- const { data: localEntries = [], isLoading: localLoading } = useQuery({ - queryKey: ['localList', browseDir], + queryKey: ['localList', grantedRoot, browseDir], queryFn: async () => { - const res = await fetch(`${API_BASE}/api/local/list?rel=${encodeURIComponent(browseDir)}`); + const res = await fetch( + `${API_BASE}/api/local/list?root=${encodeURIComponent(grantedRoot)}&rel=${encodeURIComponent(browseDir)}`, + ); if (!res.ok) throw new Error(await res.text()); return res.json(); }, - enabled: mode === 'local', + enabled: mode === 'local' && !!grantedRoot, }); - const canConnect = - mode === 'tiled' ? !!selectedServerUri : !!selectedFolder; + const canConnect = mode === 'tiled' ? !!selectedServerUri : !!grantedRoot && !!selectedFolder; + /** Set the Tiled connection (optional browse container) and, by default, navigate to Browse. */ + const connectTiled = (containerPath: string | null, gotoBrowse = true) => { + setConnection({ + kind: 'tiled', + serverUri: selectedServerUri, + browseContainerPath: containerPath, + label: servers.find((s) => s.uri === selectedServerUri)?.name ?? selectedServerUri, + sampleCount: 0, + }); + if (gotoBrowse) navigate('/browse'); + }; + + // Jump straight from ingest to the Annotate tab for the first uploaded sample. + const annotateIngested = (containerPath: string, firstKey: string) => { + connectTiled(containerPath, false); // set connection context, don't navigate to Browse + void openTiledArray(`${containerPath}/${firstKey}`, selectedServerUri); // → Preprocess + }; + + /** Fetch a connection summary for the chosen Tiled server or local folder, store it, then go to Browse. */ const handleConnect = async () => { setStatus('Connecting…'); setConnecting(true); try { - let summaryUrl: string; + const params = new URLSearchParams({ kind: mode }); if (mode === 'tiled') { - summaryUrl = `${API_BASE}/api/connect/summary?kind=tiled&server_uri=${encodeURIComponent(selectedServerUri)}`; + params.set('server_uri', selectedServerUri); + if (selectedContainer) params.set('container_path', selectedContainer); } else { - summaryUrl = `${API_BASE}/api/connect/summary?kind=local&rel=${encodeURIComponent(selectedFolder)}`; + params.set('root', grantedRoot); + params.set('rel', selectedFolder); } - const res = await fetch(summaryUrl); + const res = await fetch(`${API_BASE}/api/connect/summary?${params}`); if (!res.ok) throw new Error(await res.text()); const summary = await res.json(); setConnection({ kind: summary.kind, serverUri: summary.server_uri ?? null, - localRoot: mode === 'local' ? selectedFolder : null, + browseContainerPath: + mode === 'tiled' + ? (selectedContainer || summary.container_path || null) + : null, + localRoot: mode === 'local' ? grantedRoot : null, + localRel: mode === 'local' ? selectedFolder : null, label: summary.label, sampleCount: summary.sample_count, }); @@ -99,8 +157,8 @@ export default function ConnectPage() { }; return ( -
-
+
+

Connect to Dataset

@@ -127,112 +185,206 @@ export default function ConnectPage() { ))}
- {/* Tiled: server dropdown only */} + {/* Tiled: server dropdown + optional container picker + ingest */} {mode === 'tiled' && ( -
- - {servers.length === 0 ? ( -

Loading servers…

- ) : ( - +
+
+ + {servers.length === 0 ? ( +

Loading servers…

+ ) : ( + + )} +
+ + {/* Optional browse-target container picker */} + {selectedServerUri && ( +
+ +

+ Which collection on the server the Browse tab will show. + Most people can leave this blank — it auto-detects, and ingesting below fills it in for you. +

+
+ + {containerDir.split('/').filter(Boolean).map((seg, i, arr) => { + const target = arr.slice(0, i + 1).join('/'); + return ( + + / + + + ); + })} +
+
+ {containerDir && ( + + )} + {tiledEntries.filter((e) => e.is_dir).map((e) => ( + + ))} + {tiledEntries.filter((e) => e.is_dir).length === 0 && ( +
+ No sub-containers. Leave unset to auto-discover, or pick a parent. +
+ )} +
+ {selectedContainer && ( +

+ Browse will show {selectedContainer} +

+ )} +
+ )} + + {/* Drag-and-drop ingest */} + {selectedServerUri && ( +
+ connectTiled(containerPath)} + onAnnotate={annotateIngested} + /> +
)} -

- After connecting, use Browse to filter and select individual samples. -

)} - {/* Local: directory browser — pick a folder */} + {/* Local: grant a root, then browse subfolders */} {mode === 'local' && ( -
+
- - {/* Breadcrumbs */} -
+
+ setRootInput(e.target.value)} + onKeyDown={(e) => { if (e.key === 'Enter') { setGrantedRoot(rootInput.trim()); setBrowseDir(''); setSelectedFolder(''); } }} + placeholder="/absolute/path/to/data" + className="flex-1 border border-white/20 rounded-md px-3 py-2 text-sm font-mono bg-white/10 text-white focus:outline-none focus:ring-2 focus:ring-sky-500" + /> - {browseDir.split('/').filter(Boolean).map((seg, i, arr) => { - const target = arr.slice(0, i + 1).join('/'); - return ( - - / - - - ); - })}
- {/* Folder listing */} -
- {/* "Use this folder" button */} - + {grantedRoot && ( + <> +
+ + {browseDir.split('/').filter(Boolean).map((seg, i, arr) => { + const target = arr.slice(0, i + 1).join('/'); + return ( + + / + + + ); + })} +
- {browseDir && ( - - )} +
+ - {localLoading &&
Loading…
} - {!localLoading && - localEntries - .filter((e) => e.is_dir) - .map((e) => ( + {browseDir && ( - ))} + )} - {!localLoading && localEntries.filter((e) => e.is_dir).length === 0 && ( -
No sub-folders here.
- )} -
+ {localLoading &&
Loading…
} + {!localLoading && + localEntries + .filter((e) => e.is_dir) + .map((e) => ( + + ))} + + {!localLoading && localEntries.filter((e) => e.is_dir).length === 0 && ( +
No sub-folders here.
+ )} +
- {selectedFolder !== '' && ( -

- Selected folder:{' '} - {selectedFolder || 'root'} -

+ {selectedFolder !== '' && ( +

+ Selected folder:{' '} + {selectedFolder || 'root'} +

+ )} + )}
)} @@ -243,6 +395,8 @@ export default function ConnectPage() {

)} + +
{status &&

{status}

} @@ -184,10 +201,43 @@ export default function ExportPage() {
)} - {result && ( -
-

Export complete

- 
{JSON.stringify(result, null, 2)}
+ {/* Live export progress (phase + bar + backend log) */} + {(job.status === 'running' || job.status === 'done' || job.status === 'error') && ( +
+
+ + {job.status === 'error' ? 'Failed' : job.status === 'done' ? 'Export complete' : `${job.phase || 'working'}…`} + + {job.total > 0 && {job.done}/{job.total} slices} +
+ {job.status !== 'error' && ( +
+
+
+ )} + {job.error &&

{job.error}

} + {job.log.length > 0 && ( +
+ {job.log.slice(-15).map((line, i) =>
{line}
)} +
+ )} + {job.status === 'done' && Array.isArray(job.result?.written) ? ( +

+ Wrote masks into Tiled:{' '} + {(job.result.written as Array<{ container?: string; n_slices?: number }>).length === 0 + ? 'nothing (no Tiled sources / no annotated slices).' + : (job.result.written as Array<{ container?: string; n_slices?: number }>) + .map((w) => `${w.container} (${w.n_slices} slices)`) + .join(', ')} +

+ ) : job.status === 'done' && ( +

+ Saved to {String(job.result?.dataset_path ?? 'server')} (Tiled). Download includes images + masks (semantic + per-class) + COCO. +

+ )}
)} @@ -198,14 +248,36 @@ export default function ExportPage() { > Dry run preview - {preview && ( + {preview && job.status !== 'done' && ( )} + {job.status === 'done' && downloadUrl && ( + + Download .zip + + )} +
); diff --git a/frontend/src/app/pages/IpredPage.tsx b/frontend/src/app/pages/IpredPage.tsx new file mode 100644 index 0000000..8325ac7 --- /dev/null +++ b/frontend/src/app/pages/IpredPage.tsx @@ -0,0 +1,45 @@ +/** + * IpredPage — configure feature compositions and trainer defaults. + */ +import { useNavigate } from 'react-router'; +import { Cpu } from '@phosphor-icons/react'; +import { useConnectionStore } from '@/stores/connectionStore'; +import CompositionPanel from '@/components/CompositionPanel'; +import IpredConfigPanel from '@/components/IpredConfigPanel'; + +export default function IpredPage() { + const navigate = useNavigate(); + const kind = useConnectionStore((s) => s.kind); + + if (!kind) { + return ( +
+ +

Connect a dataset before configuring ipred.

+ +
+ ); + } + + return ( +
+
+
+

Ipred

+

+ Compose feature modules (skimage, CLAHE, SlimSAM, TomoJEPA, PCA). Preprocess + Compute uses the active composition. +

+
+ + +
+
+ ); +} diff --git a/frontend/src/app/pages/PreprocessPage.tsx b/frontend/src/app/pages/PreprocessPage.tsx new file mode 100644 index 0000000..151caf5 --- /dev/null +++ b/frontend/src/app/pages/PreprocessPage.tsx @@ -0,0 +1,6 @@ +/** Preprocess Hub stage — features, Suggest Labels, placement mask. */ +import AnnotateWorkspace from './AnnotateWorkspace'; + +export default function PreprocessPage() { + return ; +} diff --git a/frontend/src/app/pages/TrainPage.tsx b/frontend/src/app/pages/TrainPage.tsx new file mode 100644 index 0000000..17e279c --- /dev/null +++ b/frontend/src/app/pages/TrainPage.tsx @@ -0,0 +1,6 @@ +/** Train & Predict Hub stage — CatBoost + model shelf. */ +import AnnotateWorkspace from './AnnotateWorkspace'; + +export default function TrainPage() { + return ; +} diff --git a/frontend/src/components/Browse/BrowseColumn.tsx b/frontend/src/components/Browse/BrowseColumn.tsx index 77b463a..fec0434 100644 --- a/frontend/src/components/Browse/BrowseColumn.tsx +++ b/frontend/src/components/Browse/BrowseColumn.tsx @@ -14,6 +14,10 @@ interface BrowseColumnProps { isLast: boolean; } +/** + * BrowseColumn — one facet column: a field picker plus its distinct values with counts. + * Selecting a value (or re-clicking to clear) drives the filter chain via onSelect. + */ export default function BrowseColumn({ colIndex, column, diff --git a/frontend/src/components/Browse/BrowseDetailPanel.tsx b/frontend/src/components/Browse/BrowseDetailPanel.tsx index c153cf5..10a0ae8 100644 --- a/frontend/src/components/Browse/BrowseDetailPanel.tsx +++ b/frontend/src/components/Browse/BrowseDetailPanel.tsx @@ -8,6 +8,8 @@ interface BrowseDetailPanelProps { onClose: () => void; serverUri?: string; onOpenInAnnotate?: () => void; + /** Panel width in px; the preview image scales with it. */ + width?: number; } const SECTION_ORDER = [ @@ -19,6 +21,7 @@ const SECTION_ORDER = [ { label: 'Chemistry', keys: ['OrganicSalt', 'OrganicSalt_Abbrev', 'MetalSalt', 'MetalSalt_Abbrev', 'Solvent', 'Concentration_M', 'MixingRatio', 'TargetStoichiometry', 'Stoichiometry'] }, ]; +/** Render a metadata value for display: arrays/objects are summarised and ISO dates are localised. */ function formatValue(v: unknown): string { if (v === null || v === undefined) return '—'; if (Array.isArray(v)) return `[${v.length} items]`; @@ -36,6 +39,7 @@ function formatValue(v: unknown): string { return text; } +/** Map a raw metadata key to a friendlier label, falling back to the key itself. */ function displayKey(key: string): string { const aliases: Record = { studio_annotated: 'Annotated', @@ -46,9 +50,19 @@ function displayKey(key: string): string { return aliases[key] ?? key; } -export default function BrowseDetailPanel({ item, onClose, serverUri, onOpenInAnnotate }: BrowseDetailPanelProps) { +/** + * BrowseDetailPanel — side panel showing a sample's thumbnail and grouped metadata. + * Fetches a backend thumbnail (size quantised to the panel width) and offers Open in Annotate. + */ +export default function BrowseDetailPanel({ item, onClose, serverUri, onOpenInAnnotate, width = 340 }: BrowseDetailPanelProps) { const meta = item.metadata; + // Preview scales with the panel width. The fetched thumbnail size is + // quantised to 128px steps so resizing doesn't spam the backend. + const previewHeight = Math.round(Math.min(Math.max(width * 0.9, 180), 760)); + // Backend caps thumbnail size at 512; request in 128px steps to limit refetches. + const requestedSize = Math.min(512, Math.max(256, Math.ceil(width / 128) * 128)); + // Thumbnail state const [thumbSrc, setThumbSrc] = useState(null); const [thumbStatus, setThumbStatus] = useState<'loading' | 'ok' | 'error'>('loading'); @@ -56,7 +70,7 @@ export default function BrowseDetailPanel({ item, onClose, serverUri, onOpenInAn useEffect(() => { setThumbSrc(null); setThumbStatus('loading'); - const params = new URLSearchParams({ tiled_path: item.path, size: '320' }); + const params = new URLSearchParams({ tiled_path: item.path, size: String(requestedSize) }); if (serverUri) params.set('server_uri', serverUri); const url = `${API_BASE}/api/browse/thumbnail?${params}`; const img = new window.Image(); @@ -64,7 +78,7 @@ export default function BrowseDetailPanel({ item, onClose, serverUri, onOpenInAn img.onerror = () => setThumbStatus('error'); img.src = url; return () => { img.onload = null; img.onerror = null; }; - }, [item.path, serverUri]); + }, [item.path, serverUri, requestedSize]); // Build sections, collecting remaining keys for "Other" const shown = new Set(); @@ -120,10 +134,10 @@ export default function BrowseDetailPanel({ item, onClose, serverUri, onOpenInAn
- {/* Thumbnail */} + {/* Thumbnail — scales with the panel width */}
{thumbStatus === 'loading' && (
@@ -135,7 +149,7 @@ export default function BrowseDetailPanel({ item, onClose, serverUri, onOpenInAn Array preview )} {thumbStatus === 'error' && null} diff --git a/frontend/src/components/Browse/ColumnBrowser.tsx b/frontend/src/components/Browse/ColumnBrowser.tsx index 86dee1c..fd159c5 100644 --- a/frontend/src/components/Browse/ColumnBrowser.tsx +++ b/frontend/src/components/Browse/ColumnBrowser.tsx @@ -1,8 +1,9 @@ import React, { useCallback, useEffect, useMemo, useRef, useState } from 'react'; -import { ArrowsClockwise, PencilSimple, Plus } from '@phosphor-icons/react'; +import { ArrowsClockwise, PencilSimple, Plus, Stack } from '@phosphor-icons/react'; import BrowseColumn from './BrowseColumn'; import BrowseDetailPanel from './BrowseDetailPanel'; import ItemsColumn from './ItemsColumn'; +import SlicesColumn from './SlicesColumn'; import ResizeDivider from './ResizeDivider'; import { useBrowseData, type BrowseItem } from './hooks/useBrowseData'; import { useOpenInAnnotate } from '@/hooks/useOpenInAnnotate'; @@ -11,6 +12,7 @@ import { ANNOTATION_FILTER_OPTIONS, type AnnotationFilter } from '@/types/annota interface ColumnBrowserProps { serverUri: string; + containerPath?: string | null; servers: ServerInfo[]; selectedServerUri: string; onServerChange: (uri: string) => void; @@ -20,11 +22,11 @@ interface ColumnBrowserProps { const DEFAULT_COLUMN_WIDTH = 220; const DEFAULT_ITEMS_WIDTH = 260; -const INITIAL_COLUMN_COUNT = 4; - -/** Studio facets are useful as filters but should not fill the initial column set. */ -const STUDIO_FACETS = new Set(['Annotated', 'Annotated at', 'Shape count', 'Class count']); +const DEFAULT_DETAIL_WIDTH = 340; +const MIN_DETAIL_WIDTH = 280; +const MAX_DETAIL_WIDTH = 900; +/** Pick a sensible default column width for a facet field based on its name/length. */ function columnWidthForField(field: string): number { if (field === 'sample_name') return 200; if (field === 'Annotated') return 160; @@ -34,6 +36,7 @@ function columnWidthForField(field: string): number { return Math.min(360, Math.max(DEFAULT_COLUMN_WIDTH, field.length * 7 + 48)); } +/** Format a column value for display (e.g. localise the "Annotated at" timestamp). */ function formatColumnValue(field: string, value: string): string { if (field === 'Annotated at') { const parsed = Date.parse(value); @@ -50,20 +53,28 @@ function formatColumnValue(field: string, value: string): string { return value; } +/** + * ColumnBrowser — Miller-column metadata browser for a Tiled server: a chain of facet + * columns narrows to a matching sample list, with a resizable detail panel. Manages column + * widths, auto-scroll to new columns, and opening selected samples in Annotate. + */ export default function ColumnBrowser({ serverUri, + containerPath, servers, selectedServerUri, onServerChange, annotationFilter, onAnnotationFilterChange, }: ColumnBrowserProps) { - const { state, actions } = useBrowseData(serverUri, 'All'); + const { state, actions } = useBrowseData(serverUri, 'All', undefined, containerPath); const { openTiledArray } = useOpenInAnnotate(); const scrollRef = useRef(null); const [columnWidths, setColumnWidths] = useState([]); const [itemsColumnWidth, setItemsColumnWidth] = useState(DEFAULT_ITEMS_WIDTH); + const [sliceColumnWidth, setSliceColumnWidth] = useState(DEFAULT_ITEMS_WIDTH); + const [detailWidth, setDetailWidth] = useState(DEFAULT_DETAIL_WIDTH); const [openStatus, setOpenStatus] = useState(null); // Keep columnWidths in sync with the number of columns. @@ -101,18 +112,22 @@ export default function ColumnBrowser({ prevColumnCount.current = state.columns.length; }, [state.columns.length]); - // First-load: populate with the first few discovered facets. + // First-load: show every sample immediately (low-friction default) so data + // already in Tiled appears without the user having to add filter columns. + // Reset when the server/container changes (which resets the browse state). const initialised = useRef(false); useEffect(() => { - if (initialised.current || state.facets.length === 0) return; + initialised.current = false; + }, [serverUri, containerPath]); + + useEffect(() => { + if (initialised.current || state.connectionStatus !== 'connected') return; initialised.current = true; skipNextAutoScroll.current = true; - const preferred = state.facets.filter((f) => !STUDIO_FACETS.has(f)); - const pool = preferred.length >= INITIAL_COLUMN_COUNT ? preferred : state.facets; - const n = Math.min(INITIAL_COLUMN_COUNT, pool.length); - pool.slice(0, n).forEach((f) => actions.addColumn(f)); - }, [state.facets, actions]); + actions.showAll(); + }, [state.connectionStatus, actions]); + /** Update the stored width for the facet column at the given index. */ const handleResizeColumn = useCallback((index: number, newWidth: number) => { setColumnWidths((prev) => { if (index < 0 || index >= prev.length) return prev; @@ -122,22 +137,47 @@ export default function ColumnBrowser({ }); }, []); + /** Append a column for the first facet not already in use. */ const handleAddColumn = useCallback(() => { const used = new Set(state.columns.map((c) => c.field)); const next = state.facets.find((f) => !used.has(f)); if (next) actions.addColumn(next); }, [state.columns, state.facets, actions]); + /** Open a sample in the Annotate tab (navigates), surfacing progress/errors via openStatus. + * A slice of the drilled-in volume opens the whole volume at that slice index, so its + * annotations share the volume's sourceKey rather than a standalone single-array key. */ const handleOpenInAnnotate = useCallback( async (item: BrowseItem) => { setOpenStatus(`Opening ${item.sample}…`); try { - await openTiledArray(item.path, serverUri); + const sliceIdx = state.expandedSample + ? state.slices.findIndex((s) => s.path === item.path) + : -1; + if (state.expandedSample && sliceIdx >= 0) { + await openTiledArray(state.expandedSample.path, serverUri, sliceIdx); + } else { + await openTiledArray(item.path, serverUri); + } } catch (err) { setOpenStatus(`Failed to open: ${err}`); } }, - [openTiledArray, serverUri], + [openTiledArray, serverUri, state.expandedSample, state.slices], + ); + + /** Sample-row click: drill multi-slice volumes into a Slices column; select + * single images into the detail panel (collapsing any open drill-in). */ + const handleSelectItem = useCallback( + (item: BrowseItem | null) => { + if (item && (item.n_slices ?? 1) > 1) { + actions.expandSample(state.expandedSample?.path === item.path ? null : item); + return; + } + if (state.expandedSample) actions.expandSample(null); + actions.selectItem(item); + }, + [actions, state.expandedSample], ); const activeFilters = useMemo(() => { @@ -150,7 +190,7 @@ export default function ColumnBrowser({ const activeFilterCount = Object.keys(activeFilters).length; const lastColumn = state.columns[state.columns.length - 1]; - const showItems = state.columns.length > 0 && lastColumn?.selected !== null; + const showItems = state.showingAll || (state.columns.length > 0 && lastColumn?.selected !== null); return (
@@ -158,10 +198,12 @@ export default function ColumnBrowser({ facetsLoading={state.facetsLoading} facetCount={state.facets.length} activeFilterCount={activeFilterCount} + showingAll={state.showingAll} selectedItem={state.selectedItem} onOpenInAnnotate={handleOpenInAnnotate} onRefresh={actions.refresh} onAddColumn={handleAddColumn} + onShowAll={actions.showAll} servers={servers} selectedServerUri={selectedServerUri} onServerChange={onServerChange} @@ -186,12 +228,14 @@ export default function ColumnBrowser({ ref={scrollRef} className="relative z-0 flex min-w-0 flex-1 overflow-x-auto overflow-y-hidden" > - {state.columns.length === 0 && !state.facetsLoading && ( + {state.columns.length === 0 && !state.showingAll && !state.facetsLoading && (
-

+

{state.connectionStatus === 'disconnected' ? 'Connect to a Tiled server to browse.' - : 'Click "Add column" to start browsing.'} + : state.facets.length === 0 + ? 'No metadata fields to filter by. Click "All samples" to view every dataset.' + : 'Click "Add column" to filter, or "All samples" to view everything.'}

)} @@ -229,23 +273,57 @@ export default function ColumnBrowser({ total={state.itemsTotal} loading={state.itemsLoading} selectedItem={state.selectedItem} - onSelect={actions.selectItem} + onSelect={handleSelectItem} onOpenInAnnotate={handleOpenInAnnotate} width={itemsColumnWidth} serverUri={serverUri} annotationFilter={annotationFilter} + expandedPath={state.expandedSample?.path ?? null} + /> + + )} + + {state.expandedSample && ( + <> + + actions.expandSample(null)} + width={sliceColumnWidth} + serverUri={serverUri} /> )}
{state.selectedItem && ( - actions.selectItem(null)} - serverUri={serverUri} - onOpenInAnnotate={handleOpenInAnnotate} - /> + <> + + actions.selectItem(null)} + serverUri={serverUri} + onOpenInAnnotate={handleOpenInAnnotate} + /> + )}
@@ -258,10 +336,12 @@ interface ToolbarProps { facetsLoading: boolean; facetCount: number; activeFilterCount: number; + showingAll: boolean; selectedItem: BrowseItem | null; onOpenInAnnotate: (item: BrowseItem) => void; onRefresh: () => void; onAddColumn: () => void; + onShowAll: () => void; servers: ServerInfo[]; selectedServerUri: string; onServerChange: (uri: string) => void; @@ -269,14 +349,17 @@ interface ToolbarProps { onAnnotationFilterChange: (filter: AnnotationFilter) => void; } +/** Toolbar — top bar with server/annotation selectors and refresh / show-all / add-column actions. */ function Toolbar({ facetsLoading, facetCount, activeFilterCount, + showingAll, selectedItem, onOpenInAnnotate, onRefresh, onAddColumn, + onShowAll, servers, selectedServerUri, onServerChange, @@ -347,6 +430,19 @@ function Toolbar({ > + +
+ {isVolume && ( + + )}
); } diff --git a/frontend/src/components/Browse/LocalSampleBrowser.tsx b/frontend/src/components/Browse/LocalSampleBrowser.tsx index 42eeaed..cd0d90c 100644 --- a/frontend/src/components/Browse/LocalSampleBrowser.tsx +++ b/frontend/src/components/Browse/LocalSampleBrowser.tsx @@ -18,14 +18,25 @@ interface LocalSample { } interface LocalSampleBrowserProps { - localRoot: string; - onOpenInAnnotate: (relPath: string) => void; + /** Granted absolute browse root. */ + root: string; + /** Chosen subfolder relative to root. */ + rel: string; + /** Called with the ABSOLUTE file path to open in Annotate. */ + onOpenInAnnotate: (absPath: string) => void; annotationFilter: AnnotationFilter; onAnnotationFilterChange: (filter: AnnotationFilter) => void; } +/** Join an absolute root with a relative subpath into an absolute path. */ +export function joinPath(root: string, rel: string): string { + if (!rel) return root; + return `${root.replace(/\/$/, '')}/${rel.replace(/^\//, '')}`; +} + export default function LocalSampleBrowser({ - localRoot, + root, + rel, onOpenInAnnotate, annotationFilter, onAnnotationFilterChange, @@ -36,9 +47,11 @@ export default function LocalSampleBrowser({ const annotatedKeys = useAnnotatedSourceKeys(); const { data, isLoading, error } = useQuery<{ items: LocalSample[]; total: number }>({ - queryKey: ['localSamples', localRoot], + queryKey: ['localSamples', root, rel], queryFn: async () => { - const res = await fetch(`${API_BASE}/api/local/samples?rel=${encodeURIComponent(localRoot)}`); + const res = await fetch( + `${API_BASE}/api/local/samples?root=${encodeURIComponent(root)}&rel=${encodeURIComponent(rel)}`, + ); if (!res.ok) throw new Error(await res.text()); return res.json(); }, @@ -47,7 +60,7 @@ export default function LocalSampleBrowser({ const allSamples = data?.items ?? []; const filtered = allSamples.filter((s) => { - const sk = buildSourceKey('local', s.path); + const sk = buildSourceKey('local', joinPath(root, s.path)); const isAnnotated = annotatedKeys.has(sk); if (annotationFilter === 'annotated' && !isAnnotated) return false; @@ -78,7 +91,7 @@ export default function LocalSampleBrowser({
Local Samples - {localRoot || 'root'} + {joinPath(root, rel) || 'root'} + +
+
+ + Modules + + {modules.map((m) => ( + + ))} +
+ +
+ + +

+ Graph: {chainSummary || 'empty'} +

+ +
+ {nodes.map((n) => { + const mod = moduleById.get(n.module); + return ( +
+
+ + {n.id} · {mod?.name ?? n.module} + + +
+ {mod?.accepts_input_from && ( + + )} + {n.module === 'tomojepa' && ( + + )} + {(n.module === 'tomojepa' || n.module === 'pca' || n.module === 'clahe') && ( +
+ {n.module === 'tomojepa' && ( + <> + + + + )} + {n.module === 'pca' && ( + + )} + {n.module === 'clahe' && ( + + )} +
+ )} +
+ + {outputs.includes(n.id) && ( + + + + + )} +
+
+ ); + })} +
+
+
+ +
+ + Bank will concatenate + +

+ {previewLabels.length + ? previewLabels.join(' · ') + : '— add channel-producing nodes and mark them as outputs —'} +

+

+ Outputs order: {outputs.join(' → ') || 'none'} +

+
+ +
+ + +
+ + {error && ( +

+ {error} +

+ )} + + ); +} diff --git a/frontend/src/components/CustomizePages.tsx b/frontend/src/components/CustomizePages.tsx index e72c015..2ad88b4 100644 --- a/frontend/src/components/CustomizePages.tsx +++ b/frontend/src/components/CustomizePages.tsx @@ -9,23 +9,30 @@ export type CustomizePagesProps = { onSelectionChange: (selectedPaths: string[]) => void; }; -export default function CustomizePages({ - routes, - selectedPaths, +/** + * CustomizePages — floating button + full-screen modal for choosing which route tabs are visible. + * Edits a temporary selection and commits it via onSelectionChange on Apply. + */ +export default function CustomizePages({ + routes, + selectedPaths, onSelectionChange }: CustomizePagesProps) { const [isModalOpen, setIsModalOpen] = useState(false); const [tempSelected, setTempSelected] = useState>(() => new Set(selectedPaths)); + /** Open the modal, seeding the temp selection from the current selection. */ const openModal = () => { setTempSelected(new Set(selectedPaths)); setIsModalOpen(true); }; + /** Close the modal without committing changes. */ const closeModal = () => { setIsModalOpen(false); }; + /** Add or remove a route path from the temporary selection. */ const toggleTab = (path: string) => { setTempSelected(prev => { const next = new Set(prev); @@ -38,12 +45,14 @@ export default function CustomizePages({ }); }; + /** Commit the temporary selection (ignored if empty) and close. */ const handleApply = () => { if (tempSelected.size === 0) return; onSelectionChange(Array.from(tempSelected)); closeModal(); }; + /** Discard changes, reset the temp selection, and close. */ const handleCancel = () => { setTempSelected(new Set(selectedPaths)); closeModal(); diff --git a/frontend/src/components/HubAppLayout.tsx b/frontend/src/components/HubAppLayout.tsx index 4fa0b33..25f7b47 100644 --- a/frontend/src/components/HubAppLayout.tsx +++ b/frontend/src/components/HubAppLayout.tsx @@ -17,6 +17,7 @@ export type HubAppLayoutProps = { sidebarInactiveLinkClassName?: string; onOpenTabSelector?: () => void; } +/** HubAppLayout — overall app shell wiring the fixed sidebar, header, and routed main content. */ export default function HubAppLayout ( { routes, headerTitle, diff --git a/frontend/src/components/HubHeader.tsx b/frontend/src/components/HubHeader.tsx index fa2983b..1b0a6c7 100644 --- a/frontend/src/components/HubHeader.tsx +++ b/frontend/src/components/HubHeader.tsx @@ -9,6 +9,7 @@ export type HubHeaderProps = { titleClassName?: string; onOpenTabSelector?: () => void; } +/** HubHeader — top app bar with logo, title, and an optional "Change Tabs" button. */ export default function HubHeader({title="ALS COMPUTING HUB", logoUrl=alsLogo, className, titleClassName, onOpenTabSelector}: HubHeaderProps) { return (
diff --git a/frontend/src/components/HubMainContent.tsx b/frontend/src/components/HubMainContent.tsx index 848c205..ee884a5 100644 --- a/frontend/src/components/HubMainContent.tsx +++ b/frontend/src/components/HubMainContent.tsx @@ -1,66 +1,110 @@ -import { Routes, Route, useNavigate, useLocation } from "react-router"; -import { cn } from "@/lib/utils"; +import { useEffect, useState } from 'react'; +import { Routes, Route, useNavigate, useLocation } from 'react-router'; +import { cn } from '@/lib/utils'; +import { RouteItem } from '@/types/navigationRouterTypes'; +import AnnotateWorkspace from '@/app/pages/AnnotateWorkspace'; +import { stageFromPath, type AnnotateStage } from '@/lib/annotateStage'; -import { RouteItem } from "@/types/navigationRouterTypes"; +const WORK_PATHS = new Set(['/preprocess', '/draw', '/train']); +function isWorkPath(pathname: string): boolean { + return WORK_PATHS.has(pathname); +} + +/** Fallback shown for unmatched routes; offers a button back to the first available page. */ function NotFoundFallback({ routes }: { routes: RouteItem[] }) { const navigate = useNavigate(); const location = useLocation(); - const firstPath = routes.length > 0 ? routes[0].path : "/"; + const firstPath = routes.length > 0 ? routes[0].path : '/'; return (

Page not found

- {location.pathname} doesn’t match any page. + {location.pathname} doesn't match any + page.

); } export type HubMainContentProps = { - routes: RouteItem[]; - className?: string; -} + routes: RouteItem[]; + className?: string; +}; +/** + * HubMainContent — renders the active route inside the main area, applying full-bleed + * layout for routes flagged isBackgroundTransparent and a 404 fallback otherwise. + * + * Annotate Hub stages share one persistent {@link AnnotateWorkspace} so feature / + * suggest / classifier state survives stage switches. + */ export default function HubMainContent({ routes, className }: HubMainContentProps) { - const location = useLocation(); - const fullBleed = routes.some( - (r) => r.isBackgroundTransparent && r.path === location.pathname, - ); + const location = useLocation(); + const onWork = isWorkPath(location.pathname); + const [workspaceArmed, setWorkspaceArmed] = useState(onWork); + + useEffect(() => { + if (onWork) setWorkspaceArmed(true); + }, [onWork]); + + const fullBleed = + onWork || + routes.some((r) => r.isBackgroundTransparent && r.path === location.pathname); + + const otherRoutes = routes.filter((r) => !isWorkPath(r.path)); + const workRoutes = routes.filter((r) => isWorkPath(r.path)); + + return ( +
+
+ {workspaceArmed && ( +
+ +
+ )} + + {otherRoutes.map((route) => ( + + ))} + {workRoutes.map((route) => ( + + } + /> + ))} + } /> + +
+
+ ); +} - return ( -
-
- - {routes.map((route) => ( - - ))} - } /> - -
-
- ); +/** Keeps React Router matched for work stages; workspace reads location itself. */ +function WorkStageSync({ expected }: { expected: AnnotateStage }) { + void expected; + return null; } diff --git a/frontend/src/components/HubSidebar.tsx b/frontend/src/components/HubSidebar.tsx index 3faea02..a841d9c 100644 --- a/frontend/src/components/HubSidebar.tsx +++ b/frontend/src/components/HubSidebar.tsx @@ -12,6 +12,7 @@ export type HubSidebarProps = { onOpenTabSelector?: () => void; }; +/** HubSidebar — vertical nav with one link per route plus an optional tab-selector button. */ export default function HubSidebar({ routes, className, activeLinkClassName, inactiveLinkClassName, onOpenTabSelector }: HubSidebarProps) { const baseNavStyles = cn( "flex flex-col items-center justify-start gap-1 min-h-[5rem] w-full py-3 px-2 rounded-lg", diff --git a/frontend/src/components/Ingest/IngestDropzone.tsx b/frontend/src/components/Ingest/IngestDropzone.tsx new file mode 100644 index 0000000..9c4bad7 --- /dev/null +++ b/frontend/src/components/Ingest/IngestDropzone.tsx @@ -0,0 +1,380 @@ +/** + * IngestDropzone — drag-and-drop (or pick) image files/folders and copy them + * into the connected Tiled server as individually-browsable samples. + * + * Shown on the Connect page once a Tiled server is selected. On success it can + * point the Browse view at the freshly-created container. + */ +import { useCallback, useEffect, useRef, useState } from 'react'; +import { UploadSimple, Warning, CheckCircle } from '@phosphor-icons/react'; +import { API_BASE } from '@/config'; + +const SUPPORTED_EXTS = ['tif', 'tiff', 'npy', 'png', 'jpg', 'jpeg']; + +interface JobStatus { + state: 'pending' | 'running' | 'done' | 'error'; + total: number; + done: number; + failed: number; + errors: string[]; + container_path: string; +} + +interface IngestDropzoneProps { + serverUri: string; + /** Called when the user wants to browse the freshly-ingested container. */ + onBrowse?: (containerPath: string, sampleCount: number) => void; + /** Called to open the first ingested sample directly in the Annotate tab. */ + onAnnotate?: (containerPath: string, firstKey: string) => void; +} + +function isSupported(name: string): boolean { + const ext = name.toLowerCase().split('.').pop() ?? ''; + return SUPPORTED_EXTS.includes(ext); +} + +/** Tiled node key the backend assigns to a file = its filename stem. */ +function fileStem(name: string): string { + return name.replace(/\.[^.]+$/, ''); +} + +/** Turn a name into a filesystem/Tiled-safe slug, defaulting to 'dataset' if empty. */ +function sanitizeName(name: string): string { + return name.replace(/[^A-Za-z0-9_-]+/g, '_').replace(/^_+|_+$/g, '') || 'dataset'; +} + +/** Recursively read a dropped directory entry into a flat list of Files. */ +async function readEntry(entry: any): Promise { + if (entry.isFile) { + return new Promise((resolve) => entry.file((f: File) => resolve([f]), () => resolve([]))); + } + if (entry.isDirectory) { + const reader = entry.createReader(); + const all: File[] = []; + // readEntries returns batches; keep calling until empty. + const readBatch = (): Promise => + new Promise((resolve) => { + reader.readEntries(async (entries: any[]) => { + if (!entries.length) return resolve(); + for (const e of entries) all.push(...(await readEntry(e))); + await readBatch(); + resolve(); + }, () => resolve()); + }); + await readBatch(); + return all; + } + return []; +} + +/** Gather all dropped files (recursing into directories) and the top-level folder name. */ +async function collectFromDrop(dt: DataTransfer): Promise<{ files: File[]; folderName: string }> { + const items = Array.from(dt.items); + const entries = items + .map((it) => (it.webkitGetAsEntry ? it.webkitGetAsEntry() : null)) + .filter(Boolean) as any[]; + + let folderName = ''; + if (entries.length) { + const dir = entries.find((e) => e.isDirectory); + if (dir) folderName = dir.name; + const nested = await Promise.all(entries.map((e) => readEntry(e))); + return { files: nested.flat(), folderName }; + } + // Fallback: plain file list (no directory structure). + return { files: Array.from(dt.files), folderName }; +} + +export default function IngestDropzone({ serverUri, onBrowse, onAnnotate }: IngestDropzoneProps) { + const [dragging, setDragging] = useState(false); + const [containerPath, setContainerPath] = useState('browse/'); + const [description, setDescription] = useState(''); + const [jobId, setJobId] = useState(null); + const [status, setStatus] = useState(null); + const [error, setError] = useState(null); + const [uploading, setUploading] = useState(false); + // Node key of the first ingested sample (for "Open in Annotate"). + const [firstKey, setFirstKey] = useState(null); + const pollRef = useRef(null); + + /** Clear the active status-polling interval, if any. */ + const stopPolling = useCallback(() => { + if (pollRef.current !== null) { + window.clearInterval(pollRef.current); + pollRef.current = null; + } + }, []); + + useEffect(() => stopPolling, [stopPolling]); + + /** Poll the ingest job status every second until it reports done or error. */ + const poll = useCallback( + (id: string) => { + stopPolling(); + pollRef.current = window.setInterval(async () => { + try { + const res = await fetch(`${API_BASE}/api/ingest/status/${id}`); + if (!res.ok) throw new Error(await res.text()); + const job: JobStatus = await res.json(); + setStatus(job); + if (job.state === 'done' || job.state === 'error') stopPolling(); + } catch (e) { + setError(String(e)); + stopPolling(); + } + }, 1000); + }, + [stopPolling], + ); + + /** + * Filter to supported files, derive a destination container name, POST the upload, + * then start polling the resulting job. Side-effects: sets status/error/firstKey state. + */ + const startUpload = useCallback( + async (files: File[], suggestedName: string) => { + const supported = files.filter((f) => isSupported(f.name)); + if (supported.length === 0) { + setError('No supported image files (TIFF, PNG, JPG, NPY).'); + return; + } + const sortedNames = [...supported].map((f) => f.name).sort(); + + // Default the destination name: dropped-folder name → single file's stem → + // generic 'dataset'. The user can still override the field before/after. + let target = containerPath.trim(); + if (!target || target === 'browse/') { + const base = + suggestedName || (supported.length === 1 ? fileStem(sortedNames[0]) : 'dataset'); + target = `browse/${sanitizeName(base)}`; + setContainerPath(target); + } + + // Remember the first sample (by name) so we can jump straight to Annotate. + setFirstKey(fileStem(sortedNames[0])); + + setError(null); + setStatus(null); + setUploading(true); + try { + const fd = new FormData(); + fd.append('container_path', target); + if (description.trim()) fd.append('description', description.trim()); + for (const f of supported) fd.append('files', f, f.name); + + const res = await fetch( + `${API_BASE}/api/ingest/upload?server_uri=${encodeURIComponent(serverUri)}`, + { method: 'POST', body: fd }, + ); + if (!res.ok) throw new Error(await res.text()); + const { job_id } = await res.json(); + setJobId(job_id); + setStatus({ state: 'running', total: supported.length, done: 0, failed: 0, errors: [], container_path: target }); + poll(job_id); + } catch (e) { + setError(String(e)); + } finally { + setUploading(false); + } + }, + [containerPath, description, serverUri, poll], + ); + + const fileInputRef = useRef(null); + const dirInputRef = useRef(null); + + /** Drop handler: collect dropped files/folder and kick off the upload. */ + const onDrop = useCallback( + async (e: React.DragEvent) => { + e.preventDefault(); + e.stopPropagation(); + setDragging(false); + const { files, folderName } = await collectFromDrop(e.dataTransfer); + await startUpload(files, folderName); + }, + [startUpload], + ); + + /** File/folder input change handler: start the upload from the chosen files. */ + const onPick = useCallback( + (e: React.ChangeEvent) => { + const files = Array.from(e.target.files ?? []); + const folderName = files[0]?.webkitRelativePath?.split('/')[0] ?? ''; + startUpload(files, folderName); + }, + [startUpload], + ); + + const done = status?.state === 'done'; + const failedFatally = status?.state === 'error'; + const processed = status ? status.done + status.failed : 0; + const pct = status && status.total ? Math.round((processed / status.total) * 100) : 0; + + return ( +
+ + +
!uploading && fileInputRef.current?.click()} + onDragEnter={(e) => { + e.preventDefault(); + e.stopPropagation(); + setDragging(true); + }} + onDragOver={(e) => { + e.preventDefault(); + e.stopPropagation(); + if (!dragging) setDragging(true); + }} + onDragLeave={(e) => { + e.preventDefault(); + e.stopPropagation(); + setDragging(false); + }} + onDrop={onDrop} + className={`border-2 border-dashed rounded-lg p-6 text-center transition-colors cursor-pointer ${ + dragging ? 'border-sky-400 bg-sky-400/10' : 'border-white/20 bg-white/5 hover:border-white/40' + }`} + > + {/* One picker for individual files, one for a whole folder. */} + + +
+ +

+ {uploading ? 'Uploading…' : 'Drag an image file or folder of images here'} +

+ {!uploading && ( +

+ or{' '} + + {' · '} + +

+ )} +

TIFF, PNG, JPG, NPY

+
+
+ + + + + + {status && ( +
+
+
+
+

+ {done ? ( + + Ingested {status.done} of {status.total} + {status.failed > 0 && · {status.failed} failed} + + ) : failedFatally ? ( + Ingest failed + ) : ( + + {processed} / {status.total} processed… + + )} +

+ {status.errors.length > 0 && ( +
    + {status.errors.slice(0, 5).map((er, i) => ( +
  • {er}
    • + ))} +
+ )} + {done && status.done > 0 && (onBrowse || onAnnotate) && ( +
+ {onBrowse && ( + + )} + {onAnnotate && firstKey && ( + + )} +
+ )} +
+ )} + + {error && ( +

+ {error} +

+ )} +
+ ); +} diff --git a/frontend/src/components/IpredConfigPanel/IpredConfigPanel.test.tsx b/frontend/src/components/IpredConfigPanel/IpredConfigPanel.test.tsx new file mode 100644 index 0000000..4a379ce --- /dev/null +++ b/frontend/src/components/IpredConfigPanel/IpredConfigPanel.test.tsx @@ -0,0 +1,51 @@ +/** + * IpredConfigPanel — health + trainer. + */ +import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest'; +import { cleanup, render, screen, waitFor } from '@testing-library/react'; +import IpredConfigPanel from './index'; +import { useConnectionStore } from '@/stores/connectionStore'; + +vi.mock('@/lib/ipredApi', () => ({ + ipredHealth: vi.fn(async () => ({ status: 'ok', service: 'ipred' })), + listIpredTrainers: vi.fn(async () => ['catboost']), +})); + +describe('IpredConfigPanel', () => { + beforeEach(() => { + useConnectionStore.setState({ + preferredCompositionId: 'comp-skimage-slimsam', + preferredFeatureSetupId: 'comp-skimage-slimsam', + preferredTrainerId: 'catboost', + preferredTrainerConfig: { iterations: 200, depth: 6, learning_rate: 0.1 }, + }); + }); + + afterEach(() => { + cleanup(); + vi.clearAllMocks(); + }); + + it('shows reachable health when ipred responds ok', async () => { + render(); + await waitFor(() => { + expect(screen.getByTestId('ipred-health-status')).toHaveTextContent('ipred reachable'); + }); + }); + + it('shows active composition id', async () => { + render(); + await waitFor(() => { + expect(screen.getByText(/comp-skimage-slimsam/)).toBeTruthy(); + }); + }); + + it('shows down health when health check fails', async () => { + const { ipredHealth } = await import('@/lib/ipredApi'); + vi.mocked(ipredHealth).mockRejectedValueOnce(new Error('offline')); + render(); + await waitFor(() => { + expect(screen.getByTestId('ipred-health-status')).toHaveTextContent('unreachable'); + }); + }); +}); diff --git a/frontend/src/components/IpredConfigPanel/index.tsx b/frontend/src/components/IpredConfigPanel/index.tsx new file mode 100644 index 0000000..35f887e --- /dev/null +++ b/frontend/src/components/IpredConfigPanel/index.tsx @@ -0,0 +1,170 @@ +/** + * IpredConfigPanel — service health + trainer defaults (compositions are separate). + */ +import { useCallback, useEffect, useState } from 'react'; +import { ArrowsClockwise, CircleNotch } from '@phosphor-icons/react'; +import { useConnectionStore } from '@/stores/connectionStore'; +import { ipredHealth, listIpredTrainers } from '@/lib/ipredApi'; +import { cn } from '@/lib/utils'; + +const btn = + 'px-2 py-1 rounded border text-[11px] transition-colors disabled:opacity-40 ' + + 'bg-white text-gray-700 border-gray-200 hover:bg-sky-50 hover:border-sky-300'; + +const section = + 'rounded-md border border-gray-200 bg-white p-4 flex flex-col gap-3'; + +type HealthState = 'unknown' | 'ok' | 'down' | 'checking'; + +/** Configure ipred service health and trainer defaults. */ +export default function IpredConfigPanel() { + const preferredTrainerId = useConnectionStore((s) => s.preferredTrainerId); + const preferredTrainerConfig = useConnectionStore((s) => s.preferredTrainerConfig); + const setPreferredTrainer = useConnectionStore((s) => s.setPreferredTrainer); + const preferredCompositionId = useConnectionStore((s) => s.preferredCompositionId); + + const [health, setHealth] = useState('unknown'); + const [trainers, setTrainers] = useState(['catboost']); + const [error, setError] = useState(null); + + const refreshHealth = useCallback(async () => { + setHealth('checking'); + try { + const h = await ipredHealth(); + setHealth(h.status === 'ok' ? 'ok' : 'down'); + } catch { + setHealth('down'); + } + }, []); + + const refreshTrainers = useCallback(async () => { + setError(null); + try { + const trainerIds = await listIpredTrainers().catch(() => ['catboost']); + setTrainers(trainerIds.length ? trainerIds : ['catboost']); + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + } + }, []); + + useEffect(() => { + void refreshHealth(); + void refreshTrainers(); + }, [refreshHealth, refreshTrainers]); + + return ( +
+
+
+

Service

+ +
+

+ {health === 'ok' && 'ipred reachable'} + {health === 'down' && 'ipred unreachable — start via ./start_all.sh'} + {health === 'checking' && 'Checking…'} + {health === 'unknown' && 'Status unknown'} +

+ {preferredCompositionId && ( +

+ Active composition{' '} + {preferredCompositionId} +

+ )} +
+ +
+

Trainer defaults

+ + {preferredTrainerId === 'catboost' && ( +
+ + + +
+ )} +
+ + {error && ( +

+ {error} +

+ )} +
+ ); +} diff --git a/frontend/src/components/KillSwitchBanner.tsx b/frontend/src/components/KillSwitchBanner.tsx new file mode 100644 index 0000000..cdc3dd9 --- /dev/null +++ b/frontend/src/components/KillSwitchBanner.tsx @@ -0,0 +1,39 @@ +/** + * # REMOVE THIS AND USE YOUR OWN STUFF + * + * Loud temporary markers for Finch-scaffold label/model pickers. + * Delete this module once you wire your own taxonomy + model UX. + */ + +/** Exact flag string — keep identical in comments + UI. */ +export const REMOVE_THIS_AND_USE_YOUR_OWN_STUFF = + '# REMOVE THIS AND USE YOUR OWN STUFF'; + +export interface KillSwitchBannerProps { + /** One-line hint: what this block is (labels, models, …). */ + detail?: string; + className?: string; +} + +/** Big orange strip so scaffold panels are impossible to miss. */ +export function KillSwitchBanner({ detail, className = '' }: KillSwitchBannerProps) { + return ( +
+

+ {REMOVE_THIS_AND_USE_YOUR_OWN_STUFF} +

+ {detail ? ( +

{detail}

+ ) : null} +
+ ); +} diff --git a/frontend/src/components/SessionSetupPanel/index.tsx b/frontend/src/components/SessionSetupPanel/index.tsx new file mode 100644 index 0000000..dda1fdb --- /dev/null +++ b/frontend/src/components/SessionSetupPanel/index.tsx @@ -0,0 +1,339 @@ +/** + * # REMOVE THIS AND USE YOUR OWN STUFF + * + * SessionSetupPanel — TEMPORARY scaffold: reusable label name/color defs + + * optional past CatBoost shelf model. Replace with your own label taxonomy + * loader and model-selection UX, then delete this panel. + */ +import { useCallback, useEffect, useState } from 'react'; +import { BookmarkSimple, FloppyDisk, Trash } from '@phosphor-icons/react'; +import { API_BASE } from '@/config'; +import { KillSwitchBanner } from '@/components/KillSwitchBanner'; +import { useConnectionStore } from '@/stores/connectionStore'; +import { + DEFAULT_COLORS, + useClassStore, + type AnnotationClass, +} from '@/stores/classStore'; +import { formatFeatureRecipe } from '@/lib/featureRecipe'; +import { cn } from '@/lib/utils'; + +interface LabelSetSummary { + id: string; + name: string; + n_classes: number; + created_at: string; + classes?: AnnotationClass[]; +} + +interface ShelfModelSummary { + id: string; + name: string; + class_ids: number[]; + n_train: number; + n_cal: number; + uses_sam?: boolean; + feature_recipe?: Record; + n_features?: number; +} + +const btn = + 'px-2 py-1 rounded border text-[11px] transition-colors disabled:opacity-40 ' + + 'bg-white text-gray-700 border-gray-200 hover:bg-sky-50 hover:border-sky-300'; + +////////////////////////////////////////////////////////////////////////////// +// # REMOVE THIS AND USE YOUR OWN STUFF +// Hardcoded starter classes (sample / void / air) — not production taxonomy. +////////////////////////////////////////////////////////////////////////////// +const STARTER: AnnotationClass[] = [ + { classId: 1, label: 'sample', color: DEFAULT_COLORS[0], isVisible: true }, + { classId: 2, label: 'void', color: DEFAULT_COLORS[1], isVisible: true }, + { classId: 3, label: 'air', color: DEFAULT_COLORS[2], isVisible: true }, +]; + +interface SessionSetupPanelProps { + compact?: boolean; +} + +/** + * # REMOVE THIS AND USE YOUR OWN STUFF + * Pick reusable label names/colors and an optional shelf model (+ feature recipe). + */ +export default function SessionSetupPanel({ compact = false }: SessionSetupPanelProps) { + const preferredLabelSetId = useConnectionStore((s) => s.preferredLabelSetId); + const preferredClasses = useConnectionStore((s) => s.preferredClasses); + const preferredShelfModelId = useConnectionStore((s) => s.preferredShelfModelId); + const preferredShelfModelName = useConnectionStore((s) => s.preferredShelfModelName); + const preferredFeatureRecipe = useConnectionStore((s) => s.preferredFeatureRecipe); + const preferredFeatureSetupId = useConnectionStore((s) => s.preferredFeatureSetupId); + const setPreferredLabelSet = useConnectionStore((s) => s.setPreferredLabelSet); + const setPreferredShelfModel = useConnectionStore((s) => s.setPreferredShelfModel); + const sessionClasses = useClassStore((s) => s.classes); + const setClasses = useClassStore((s) => s.setClasses); + + const [sets, setSets] = useState([]); + const [models, setModels] = useState([]); + const [newName, setNewName] = useState('My labels'); + const [error, setError] = useState(null); + const [busy, setBusy] = useState(false); + + const refresh = useCallback(async () => { + try { + const [lsRes, mRes] = await Promise.all([ + fetch(`${API_BASE}/api/label-sets`), + fetch(`${API_BASE}/api/clf/models`), + ]); + if (lsRes.ok) { + const body = (await lsRes.json()) as { sets: LabelSetSummary[] }; + setSets(body.sets ?? []); + } + if (mRes.ok) { + const body = (await mRes.json()) as ShelfModelSummary[]; + setModels(Array.isArray(body) ? body : []); + } + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + } + }, []); + + useEffect(() => { + void refresh(); + }, [refresh]); + + const applyClassesNow = (classes: AnnotationClass[]) => { + setClasses(classes.map((c) => ({ ...c }))); + }; + + const selectSet = async (id: string | null) => { + setError(null); + if (!id) { + setPreferredLabelSet({ id: null, classes: null }); + return; + } + setBusy(true); + try { + const res = await fetch(`${API_BASE}/api/label-sets/${id}`); + if (!res.ok) throw new Error(await res.text()); + const body = (await res.json()) as { id: string; classes: AnnotationClass[] }; + const classes = body.classes ?? []; + setPreferredLabelSet({ id: body.id, classes }); + applyClassesNow(classes); + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + } finally { + setBusy(false); + } + }; + + const saveCurrentAsSet = async () => { + setError(null); + const classes = sessionClasses.length > 0 ? sessionClasses : STARTER; + if (!sessionClasses.length) applyClassesNow(STARTER); + setBusy(true); + try { + const res = await fetch(`${API_BASE}/api/label-sets`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ name: newName, classes }), + }); + if (!res.ok) throw new Error(await res.text()); + const body = (await res.json()) as { id: string; classes: AnnotationClass[] }; + setPreferredLabelSet({ id: body.id, classes: body.classes }); + applyClassesNow(body.classes); + await refresh(); + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + } finally { + setBusy(false); + } + }; + + const deleteSet = async (id: string) => { + setBusy(true); + try { + await fetch(`${API_BASE}/api/label-sets/${id}`, { method: 'DELETE' }); + if (preferredLabelSetId === id) setPreferredLabelSet({ id: null, classes: null }); + await refresh(); + } finally { + setBusy(false); + } + }; + + const selectedModel = models.find((m) => m.id === preferredShelfModelId) ?? null; + const recipeShown = preferredFeatureRecipe ?? selectedModel?.feature_recipe ?? null; + + return ( +
+ + +
+ + + Labels & model + + + TEMP / REMOVE + +
+

+ Save label names and colors so you don’t redefine them + each sample. Optionally pick a past model — its required features are shown below. +

+ + {/* # REMOVE THIS AND USE YOUR OWN STUFF — label set load/save */} +
+ + + {preferredClasses && preferredClasses.length > 0 && ( +
+ {preferredClasses.map((c) => ( + + + {c.label} + + ))} +
+ )} +
+ setNewName(e.target.value)} + placeholder="Set name" + /> + +
+ {sets.length > 0 && !compact && ( +
    + {sets.map((s) => ( +
  • + + +
    • + ))} +
+ )} +
+ + {/* # REMOVE THIS AND USE YOUR OWN STUFF — shelf model selection */} +
+ + + {preferredShelfModelId && ( +
+

{preferredShelfModelName ?? 'Selected model'}

+

+ Features in: {formatFeatureRecipe(recipeShown)} + {preferredFeatureSetupId + ? ` · ipred setup ${preferredFeatureSetupId}` + : ''} +

+ {selectedModel?.class_ids?.length ? ( +

+ Class ids: {selectedModel.class_ids.join(', ')} +

+ ) : null} +

+ Preprocess will use these feature settings; Apply on Train. +

+
+ )} + {models.length === 0 && ( +

No saved models yet — train & save on Train.

+ )} +
+ + {error &&

{error}

} +
+ ); +} diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.tsx index 8f45432..ff08d55 100644 --- a/frontend/src/components/annotate/AnnotationCanvas/index.tsx +++ b/frontend/src/components/annotate/AnnotationCanvas/index.tsx @@ -11,23 +11,96 @@ * committed to the Zustand store ONCE on mouseup, eliminating the per-frame * store updates + zundo history snapshots that caused cursor lag. */ -import { useRef, useState, useEffect, useCallback } from 'react'; +import { useRef, useState, useEffect, useCallback, useMemo, useId } from 'react'; import { - Stage, Layer, Image as KonvaImage, Line, Rect, Ellipse, Group, Circle, + Stage, Layer, Image as KonvaImage, Line, Rect, Ellipse, Group, Circle, Transformer, + Shape as KonvaShape, } from 'react-konva'; +import { Trash } from '@phosphor-icons/react'; import type Konva from 'konva'; import { v4 as uuidv4 } from 'uuid'; import { useDatasetStore } from '@/stores/datasetStore'; -import { useAnnotationStore, type Shape, type BrushStroke } from '@/stores/annotationStore'; +import { useAnnotationStore, type Shape, type BrushStroke, type EraseStroke } from '@/stores/annotationStore'; import { useToolStore } from '@/stores/toolStore'; import { useClassStore, type AnnotationClass } from '@/stores/classStore'; import { toImage, normalizeRect, normalizeEllipse } from '@/lib/geometry'; +import { buildCostMap, dijkstra, tracePath, imageToGrid, simplifyPath, type CostMap } from '@/lib/livewire'; import { useImageSlice } from '@/hooks/useImageSlice'; import { buildSourceKey } from '@/lib/sourceKey'; +import { buildField, magicSelect, maskToPolygons, maskToPolygonsWithHoles, type GrayField } from '@/lib/magicwand'; +import { useSam } from '@/hooks/useSam'; +import { renderAdjusted, renderPreprocessOnly } from '@/lib/sam/adjust'; +import { gridFor, rasterizeShapes, rasterizeUnion } from '@/lib/rasterize'; +import { computeRegionOps, type RegionOp } from '@/lib/regionOps'; +import { clipShapesToOthers, hasOtherClass } from '@/lib/clipToClasses'; +import { useClipboardStore } from '@/stores/clipboardStore'; +import { colorizeConformalOverlay, colorizeLabelMap, loadLabelPng } from '@/lib/pixelClf'; +import { loadManifoldHeatmapCanvas, manifoldMarkerRect } from '@/lib/featureManifold'; +import { + isPredictionClassVisible, + useLayerVisibilityStore, +} from '@/stores/layerVisibilityStore'; + +// macOS labels the Alt key "Option" (⌥). e.altKey is true for it either way, +// so only the on-screen label needs to differ. +const IS_MAC = typeof navigator !== 'undefined' && /mac/i.test(navigator.userAgent); +const REMOVE_KEY_LABEL = IS_MAC ? 'Option' : 'Alt'; + +/** Shared stable empty shape list — see `storeShapes` for why identity matters. */ +const EMPTY_SHAPES: Shape[] = []; interface AnnotationCanvasProps { brightness: number; contrast: number; + /** When true, apply CLAHE before brightness/contrast/levels (display + tools). */ + clahe?: boolean; + /** When true, apply classic 3×3 Laplacian sharpen (display + tools). */ + sharpen?: boolean; + /** + * When set, this object URL is the display base (feature channel PNG). + * Client CLAHE is skipped; sharpen still applies. Tools (SAM/magic) follow it. + */ + featureChannelUrl?: string | null; + /** Job/channel identity for SAM + magic field cache keys. */ + featureCacheKey?: string | null; + /** + * Softmax probability heatmap PNG — rendered as a translucent overlay (does not + * replace the base image). Controlled by the Layers panel. + */ + probaOverlayUrl?: string | null; + /** CatBoost commit map PNG (singleton classId; 0 elsewhere). */ + clfCommitUrl?: string | null; + /** CatBoost status PNG (0=abstain, 1=singleton, 2=multi). */ + clfStatusUrl?: string | null; + /** Ghost overlay for visible mask sets (Cleanup mode) — shape fallback. */ + maskSetOverlayShapes?: Shape[] | null; + /** Dense label-map overlay for mask sets (preferred, pixel-accurate). */ + maskSetLabelOverlay?: { data: Uint8Array; width: number; height: number } | null; + /** Manifold coverage heatmap grayscale URL (Suggest Labels). */ + manifoldHeatmapUrl?: string | null; + /** Manifold heatmap opacity 0–1. */ + manifoldHeatmapOpacity?: number; + /** Whether to show the manifold heatmap layer. */ + manifoldShowHeatmap?: boolean; + /** Inducing-point markers / boxes (image coords). */ + manifoldMarkers?: Array<{ + x: number; + y: number; + radius?: number; + box_size?: number; + box?: { x0: number; y0: number; x1: number; y1: number }; + }> | null; + /** Live Box size (px) from the slider — used for marker side length. */ + manifoldBoxSize?: number; + /** Whether to show inducing-point rings / boxes. */ + manifoldShowMarkers?: boolean; + /** Placement-mask shapes for Suggest Labels (outline only). */ + placementMaskShapes?: Shape[] | null; + /** Client-side levels window (0–255) applied to the displayed image. */ + levelsLo: number; + levelsHi: number; + /** Emits the current slice's 256-bin luminance histogram when it loads. */ + onHistogram?: (bins: number[]) => void; activeClassId: number | null; activeBrushShapeId: string | null; onNewBrushInstance: (id: string) => void; @@ -37,9 +110,230 @@ interface AnnotationCanvasProps { previewClasses?: AnnotationClass[] | null; } +// ---- Point-in-shape hit testing (used by the eraser to pick a target) ---- + +/** Build an even-odd path (outer + hole rings) on a Konva context. */ +function buildRingsPath(ctx: Konva.Context, rings: number[][]): void { + ctx.beginPath(); + for (const r of rings) { + if (r.length < 6) continue; + ctx.moveTo(r[0], r[1]); + for (let i = 2; i < r.length; i += 2) ctx.lineTo(r[i], r[i + 1]); + ctx.closePath(); + } +} + +/** Translate a shape by (dx,dy) in image coords (used for paste offset). */ +function offsetShape(shape: Shape, dx: number, dy: number): Shape { + const shiftFlat = (pts: number[]) => pts.map((v, i) => (i % 2 === 0 ? v + dx : v + dy)); + if (shape.kind === 'polygon') { + return { ...shape, points: shiftFlat(shape.points), holes: shape.holes?.map(shiftFlat) }; + } + if (shape.kind === 'rectangle') return { ...shape, x: shape.x + dx, y: shape.y + dy }; + if (shape.kind === 'ellipse') return { ...shape, cx: shape.cx + dx, cy: shape.cy + dy }; + // brush + return { ...shape, strokes: shape.strokes.map((st) => ({ ...st, points: shiftFlat(st.points) })) }; +} + +/** Even-odd ray cast: true if point (px,py) is inside the flat [x,y,…] polygon. */ +function pointInPolygon(px: number, py: number, pts: number[]): boolean { + let inside = false; + for (let i = 0, j = pts.length - 2; i < pts.length; j = i, i += 2) { + const xi = pts[i], yi = pts[i + 1]; + const xj = pts[j], yj = pts[j + 1]; + if ((yi > py) !== (yj > py) && px < ((xj - xi) * (py - yi)) / (yj - yi) + xi) { + inside = !inside; + } + } + return inside; +} + +/** Shortest distance from point (px,py) to segment AB. */ +function distToSegment(px: number, py: number, ax: number, ay: number, bx: number, by: number): number { + const dx = bx - ax, dy = by - ay; + const len2 = dx * dx + dy * dy; + let t = len2 ? ((px - ax) * dx + (py - ay) * dy) / len2 : 0; + t = Math.max(0, Math.min(1, t)); + const cx = ax + t * dx, cy = ay + t * dy; + return Math.hypot(px - cx, py - cy); +} + +/** True if image-coord point (x,y) lies inside any shape kind; brush strokes + * count as filled within `radius` of their segments (erase strokes ignored). */ +function shapeContainsPoint(shape: Shape, x: number, y: number): boolean { + if (shape.kind === 'polygon') return pointInPolygon(x, y, shape.points); + if (shape.kind === 'rectangle') { + return x >= shape.x && x <= shape.x + shape.w && y >= shape.y && y <= shape.y + shape.h; + } + if (shape.kind === 'ellipse') { + const nx = (x - shape.cx) / (shape.rx || 1); + const ny = (y - shape.cy) / (shape.ry || 1); + return nx * nx + ny * ny <= 1; + } + if (shape.kind === 'brush') { + for (const st of shape.strokes) { + if (st.mode === 'erase') continue; + const p = st.points; + for (let i = 0; i + 3 < p.length; i += 2) { + if (distToSegment(x, y, p[i], p[i + 1], p[i + 2], p[i + 3]) <= st.radius) return true; + } + if (p.length >= 2 && distToSegment(x, y, p[0], p[1], p[0], p[1]) <= st.radius) return true; + } + } + return false; +} + +/** A representative point that lies inside *shape* (best-effort), in image + * coords — used to seed SAM with negative ("not") prompts for other-class + * regions. Returns null when no interior point can be derived. */ +function interiorPoint(shape: Shape): { x: number; y: number } | null { + if (shape.kind === 'rectangle') return { x: shape.x + shape.w / 2, y: shape.y + shape.h / 2 }; + if (shape.kind === 'ellipse') return { x: shape.cx, y: shape.cy }; + if (shape.kind === 'polygon') { + const p = shape.points; + if (p.length < 6) return null; + let cx = 0, cy = 0; + const n = p.length / 2; + let minx = Infinity, miny = Infinity, maxx = -Infinity, maxy = -Infinity; + for (let i = 0; i < p.length; i += 2) { + cx += p[i]; cy += p[i + 1]; + minx = Math.min(minx, p[i]); maxx = Math.max(maxx, p[i]); + miny = Math.min(miny, p[i + 1]); maxy = Math.max(maxy, p[i + 1]); + } + cx /= n; cy /= n; + if (pointInPolygon(cx, cy, p)) return { x: cx, y: cy }; + // Concave polygon: centroid may fall outside — scan a grid for an inside point. + const steps = 8; + for (let gy = 1; gy < steps; gy++) { + for (let gx = 1; gx < steps; gx++) { + const x = minx + ((maxx - minx) * gx) / steps; + const y = miny + ((maxy - miny) * gy) / steps; + if (pointInPolygon(x, y, p)) return { x, y }; + } + } + return { x: cx, y: cy }; + } + if (shape.kind === 'brush') { + for (const st of shape.strokes) { + if (st.mode === 'erase') continue; + const pp = st.points; + if (pp.length >= 2) { + const mid = Math.min(pp.length - 2, Math.floor(pp.length / 4) * 2); + return { x: pp[mid], y: pp[mid + 1] }; + } + } + return null; + } + return null; +} + +interface BBox { x: number; y: number; w: number; h: number; } + +/** True if two AABBs overlap. */ +function bboxIntersects(a: BBox, b: BBox): boolean { + return a.x < b.x + b.w && a.x + a.w > b.x && a.y < b.y + b.h && a.y + a.h > b.y; +} + +/** True if segments AB and CD intersect. */ +function segIntersects( + ax: number, ay: number, bx: number, by: number, + cx: number, cy: number, dx: number, dy: number, +): boolean { + const d = (bx - ax) * (dy - cy) - (by - ay) * (dx - cx); + if (d === 0) return false; // parallel/collinear — endpoint cases caught elsewhere + const t = ((cx - ax) * (dy - cy) - (cy - ay) * (dx - cx)) / d; + const u = ((cx - ax) * (by - ay) - (cy - ay) * (bx - ax)) / d; + return t >= 0 && t <= 1 && u >= 0 && u <= 1; +} + +/** + * True if a shape's actual geometry overlaps the marquee rect — NOT just its + * bounding box. A bbox test wrongly selects a concave/edge-hugging shape (e.g. a + * C-shape or perimeter stroke) when the marquee is drawn in its empty middle. + */ +function shapeIntersectsRect(shape: Shape, r: BBox): boolean { + const corners: Array<[number, number]> = [ + [r.x, r.y], [r.x + r.w, r.y], [r.x + r.w, r.y + r.h], [r.x, r.y + r.h], + ]; + const inRect = (x: number, y: number) => + x >= r.x && x <= r.x + r.w && y >= r.y && y <= r.y + r.h; + + if (shape.kind === 'rectangle') { + return bboxIntersects({ x: shape.x, y: shape.y, w: shape.w, h: shape.h }, r); + } + + if (shape.kind === 'ellipse') { + const rx = shape.rx || 1, ry = shape.ry || 1; + // Closest point on the rect to the ellipse center, normalised into the unit + // circle — handles overlap, rect-inside-ellipse, and center-in-rect. + const clx = Math.max(r.x, Math.min(shape.cx, r.x + r.w)); + const cly = Math.max(r.y, Math.min(shape.cy, r.y + r.h)); + if (((clx - shape.cx) / rx) ** 2 + ((cly - shape.cy) / ry) ** 2 <= 1) return true; + for (const [x, y] of corners) { + if (((x - shape.cx) / rx) ** 2 + ((y - shape.cy) / ry) ** 2 <= 1) return true; + } + return false; + } + + if (shape.kind === 'polygon') { + const p = shape.points; + if (p.length < 6) return false; + for (let i = 0; i < p.length; i += 2) if (inRect(p[i], p[i + 1])) return true; + for (const [x, y] of corners) if (pointInPolygon(x, y, p)) return true; + for (let i = 0; i < p.length; i += 2) { + const ax = p[i], ay = p[i + 1]; + const bx = p[(i + 2) % p.length], by = p[(i + 3) % p.length]; + for (let k = 0; k < 4; k++) { + const [c1x, c1y] = corners[k]; + const [c2x, c2y] = corners[(k + 1) % 4]; + if (segIntersects(ax, ay, bx, by, c1x, c1y, c2x, c2y)) return true; + } + } + return false; + } + + if (shape.kind === 'brush') { + for (const st of shape.strokes) { + if (st.mode === 'erase') continue; + const pts = st.points; + for (let i = 0; i + 1 < pts.length; i += 2) if (inRect(pts[i], pts[i + 1])) return true; + // A stroke has width: a rect corner within `radius` of a segment counts. + for (let i = 0; i + 3 < pts.length; i += 2) { + for (const [x, y] of corners) { + if (distToSegment(x, y, pts[i], pts[i + 1], pts[i + 2], pts[i + 3]) <= st.radius) return true; + } + } + } + return false; + } + + return false; +} + +/** The annotation viewport: image + shape layers and all drawing/editing tools. + * Shapes are stored in IMAGE pixel coords; `transform` is display-only (pan/zoom). */ export default function AnnotationCanvas({ brightness, contrast, + clahe = false, + sharpen = false, + featureChannelUrl = null, + featureCacheKey = null, + probaOverlayUrl = null, + clfCommitUrl = null, + clfStatusUrl = null, + maskSetOverlayShapes = null, + maskSetLabelOverlay = null, + manifoldHeatmapUrl = null, + manifoldHeatmapOpacity = 0.45, + manifoldShowHeatmap = true, + manifoldMarkers = null, + manifoldBoxSize, + manifoldShowMarkers = true, + placementMaskShapes = null, + levelsLo, + levelsHi, + onHistogram, activeClassId, activeBrushShapeId, onNewBrushInstance, @@ -48,8 +342,11 @@ export default function AnnotationCanvas({ }: AnnotationCanvasProps) { const containerRef = useRef(null); const imageRef = useRef(null); + const imageLayerRef = useRef(null); const stageRef = useRef(null); const shapesLayerRef = useRef(null); + // Resize handles for the selected rect/ellipse (select tool). + const transformerRef = useRef(null); // Imperative refs for lag-free brush drawing const draftStrokeLayerRef = useRef(null); @@ -64,15 +361,31 @@ export default function AnnotationCanvas({ mode: 'paint' | 'erase'; points: number[]; radius: number; + /** For erase: whether the target is a brush shape or a vector shape. */ + eraseTargetKind?: 'brush' | 'vector'; } | null>(null); const { kind, source, serverUri, meta, currentSlice, renderOpts } = useDatasetStore(); const sourceKey = source && kind ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri) : null; - const { byImage, addShape, appendBrushStroke } = useAnnotationStore(); - const { tool, brushSize, fillOpacity, selectedShapeId, setSelectedShapeId } = useToolStore(); + const { byImage, addShape, addShapes, appendBrushStroke, appendEraseStroke, updateShape, removeShapes, setShapes, setClassForShapes } = useAnnotationStore(); + const clipboard = useClipboardStore(); + const { tool, brushSize, fillOpacity, selectedShapeIds, setSelectedShapeId, setSelectedShapeIds } = useToolStore(); + // Single-selection id — drives move/resize/vertex editing (those need exactly one). + const selectedId = selectedShapeIds.length === 1 ? selectedShapeIds[0] : null; + const magicTolerance = useToolStore((s) => s.magicTolerance); + const magicMode = useToolStore((s) => s.magicMode); + const magicSigma = useToolStore((s) => s.magicSigma); + const magicEdgeStop = useToolStore((s) => s.magicEdgeStop); + const magicEngine = useToolStore((s) => s.magicEngine); + const setMagicEngine = useToolStore((s) => s.setMagicEngine); + const samDetail = useToolStore((s) => s.samDetail); + const samThreshold = useToolStore((s) => s.samThreshold); + const samAvoidLabeled = useToolStore((s) => s.samAvoidLabeled); const fitRequestId = useToolStore((s) => s.fitRequestId); + const clipToOtherClasses = useToolStore((s) => s.clipToOtherClasses); + const fillThreshold = useToolStore((s) => s.fillThreshold); const { classes } = useClassStore(); const [stageSize, setStageSize] = useState({ width: 800, height: 600 }); @@ -83,12 +396,51 @@ export default function AnnotationCanvas({ // Draft rect/ellipse start const [dragStart, setDragStart] = useState<{ x: number; y: number } | null>(null); const [dragCurrent, setDragCurrent] = useState<{ x: number; y: number } | null>(null); + // Live polygon vertex edit (select tool): { id, points } while dragging a handle. + const [editPoly, setEditPoly] = useState<{ id: string; points: number[] } | null>(null); + + // Magic-wand selection: seed click + preview polygons (image coords) before commit. + // Marquee rubber-band (select tool): drag a box to select multiple shapes. + const [marqueeStart, setMarqueeStart] = useState<{ x: number; y: number } | null>(null); + const [marqueeRect, setMarqueeRect] = useState<{ x: number; y: number; w: number; h: number } | null>(null); + // True when the marquee drag began with Shift held — its result is added to + // (unioned with) the existing selection instead of replacing it. + const marqueeShiftRef = useRef(false); + + // Magic seeds: each click is a prompt point. label 1 = positive (grow the + // object / region), 0 = negative (carve back out — SAM only). The classic + // wand ignores the label and treats each as a fresh seed. + const [magicSeeds, setMagicSeeds] = useState>([]); + const [magicPreview, setMagicPreview] = useState([]); + const [magicLoading, setMagicLoading] = useState(false); + // SAM box prompt (image coords): committed box + live drag preview. + const [magicBox, setMagicBox] = useState<{ x: number; y: number; w: number; h: number } | null>(null); + const [magicBoxDraft, setMagicBoxDraft] = useState<{ x: number; y: number; w: number; h: number } | null>(null); + const magicDragStartRef = useRef<{ x: number; y: number } | null>(null); + + // Magnetic lasso (livewire) state. Committed = locked path; preview = live + // least-cost path from the current seed to the cursor. + const [magneticCommitted, setMagneticCommitted] = useState([]); + const [magneticPreview, setMagneticPreview] = useState([]); + const magneticCostRef = useRef(null); + const magneticBuiltForRef = useRef(null); + const magneticPrevRef = useRef(null); + const magneticSeedRef = useRef<{ x: number; y: number } | null>(null); // True while a brush/eraser stroke is actively being drawn — suppresses the // (expensive) layer re-cache so we don't rebuild the shapes bitmap mid-stroke. const [isDrawing, setIsDrawing] = useState(false); const { data: sliceUrl } = useImageSlice(source, kind, currentSlice, renderOpts, serverUri); const [imageEl, setImageEl] = useState(null); + const [featureEl, setFeatureEl] = useState(null); + const [probaEl, setProbaEl] = useState(null); + + const layerGroups = useLayerVisibilityStore((s) => s.groups); + const probaOpacity = useLayerVisibilityStore((s) => s.probaOpacity); + const predictionsOpacity = useLayerVisibilityStore((s) => s.predictionsOpacity); + const predictionClassVisible = useLayerVisibilityStore((s) => s.predictionClassVisible); + const showPredictionMulti = useLayerVisibilityStore((s) => s.showPredictionMulti); + const showPredictionAbstain = useLayerVisibilityStore((s) => s.showPredictionAbstain); useEffect(() => { if (!sliceUrl) return; @@ -98,16 +450,209 @@ export default function AnnotationCanvas({ }, [sliceUrl]); useEffect(() => { - if (!imageRef.current) return; - imageRef.current.cache(); - imageRef.current.filters([ - (window as any).Konva?.Filters?.Brighten ?? (() => {}), - (window as any).Konva?.Filters?.Contrast ?? (() => {}), - ]); - imageRef.current.brightness(brightness); - imageRef.current.contrast(contrast); - imageRef.current.getLayer()?.batchDraw(); - }, [brightness, contrast, imageEl]); + if (!featureChannelUrl || !layerGroups.features) { + setFeatureEl(null); + return; + } + const img = new window.Image(); + img.onload = () => setFeatureEl(img); + img.onerror = () => setFeatureEl(null); + img.src = featureChannelUrl; + }, [featureChannelUrl, layerGroups.features]); + + useEffect(() => { + if (!probaOverlayUrl || !layerGroups.proba) { + setProbaEl(null); + return; + } + const img = new window.Image(); + img.onload = () => setProbaEl(img); + img.onerror = () => setProbaEl(null); + img.src = probaOverlayUrl; + }, [probaOverlayUrl, layerGroups.proba]); + + // CatBoost conformal overlay (singleton color / multi hatch / abstain dark). + const [clfOverlay, setClfOverlay] = useState(null); + useEffect(() => { + if (!clfCommitUrl || !clfStatusUrl || !layerGroups.predictions) { + setClfOverlay(null); + return; + } + let cancelled = false; + const colorByClass = new Map(classes.map((c) => [c.classId, c.color])); + Promise.all([loadLabelPng(clfCommitUrl), loadLabelPng(clfStatusUrl)]) + .then(([commit, status]) => { + if (cancelled) return; + if (commit.width !== status.width || commit.height !== status.height) { + setClfOverlay(null); + return; + } + setClfOverlay( + colorizeConformalOverlay( + commit.data, + status.data, + commit.width, + commit.height, + colorByClass, + { + classVisible: (cid) => isPredictionClassVisible(predictionClassVisible, cid), + showMulti: showPredictionMulti, + showAbstain: showPredictionAbstain, + }, + ), + ); + }) + .catch(() => { + if (!cancelled) setClfOverlay(null); + }); + return () => { + cancelled = true; + }; + }, [ + clfCommitUrl, + clfStatusUrl, + classes, + layerGroups.predictions, + predictionClassVisible, + showPredictionMulti, + showPredictionAbstain, + ]); + + // Manifold coverage heatmap (Suggest Labels). + const [manifoldOverlay, setManifoldOverlay] = useState(null); + useEffect(() => { + if (!manifoldHeatmapUrl || !manifoldShowHeatmap) { + setManifoldOverlay(null); + return; + } + let cancelled = false; + loadManifoldHeatmapCanvas(manifoldHeatmapUrl, manifoldHeatmapOpacity) + .then((canvas) => { + if (!cancelled) setManifoldOverlay(canvas); + }) + .catch(() => { + if (!cancelled) setManifoldOverlay(null); + }); + return () => { + cancelled = true; + }; + }, [manifoldHeatmapUrl, manifoldShowHeatmap, manifoldHeatmapOpacity]); + + // Dense mask-set label map overlay (Cleanup Studio). + const [maskSetLabelCanvas, setMaskSetLabelCanvas] = useState(null); + useEffect(() => { + if (!maskSetLabelOverlay) { + setMaskSetLabelCanvas(null); + return; + } + const colorByClass = new Map(classes.map((c) => [c.classId, c.color])); + setMaskSetLabelCanvas( + colorizeLabelMap( + maskSetLabelOverlay.data, + maskSetLabelOverlay.width, + maskSetLabelOverlay.height, + colorByClass, + 120, + ), + ); + }, [maskSetLabelOverlay, classes]); + + // Base for display + tools: feature channel when selected, else slice PNG. + // Skip client CLAHE when features are active (bank already CLAHE'd server-side). + const displaySource = useMemo(() => { + const base = featureEl ?? imageEl; + if (!base || !meta) return null; + const applyClahe = clahe && !featureEl; + if (!applyClahe && !sharpen) return base; + return renderPreprocessOnly(base, meta.width, meta.height, { + clahe: applyClahe, + sharpen, + }); + }, [featureEl, imageEl, meta, clahe, sharpen]); + + // SAM (in-browser Segment Anything) — the smart magic engine. Only spun up + // while the magic tool is active and the SAM engine is selected. + const samActive = tool === 'magic' && magicEngine === 'sam'; + const sam = useSam(samActive); + + // If SAM can't load or encode at all (no WebGPU/WASM, model fetch failed), + // silently fall back to the classic wand so the tool always works. + useEffect(() => { + if ((sam.status === 'unsupported' || sam.error) && magicEngine === 'sam') { + setMagicEngine('classic'); + } + }, [sam.status, sam.error, magicEngine, setMagicEngine]); + + // SAM sees the brightness/contrast-adjusted image (windowing a low-contrast + // slice greatly helps), so the encode is keyed on those — adjusting them + // re-encodes. Building the source is deferred so it only runs on a real encode. + const samEncodeKey = displaySource && meta + ? `${sourceKey}|${currentSlice}|feat${featureCacheKey ?? 'none'}|clahe${clahe && !featureEl ? 1 : 0}|sh${sharpen ? 1 : 0}|b${brightness}|c${contrast}|l${levelsLo}-${levelsHi}` + : null; + /** Lazily render the display-adjusted slice (brightness/contrast/levels) that SAM encodes. */ + const makeSamSource = useCallback( + () => renderAdjusted(displaySource!, meta!.width, meta!.height, brightness, contrast, levelsLo, levelsHi, false), + [displaySource, meta, brightness, contrast, levelsLo, levelsHi], + ); + + // Proactively encode the slice when SAM is active so the first click is fast. + // Deliberately NOT keyed on sam.status — ensureEncoded is idempotent and + // keying on status would re-fire every encoding→ready transition. + useEffect(() => { + if (samActive && samEncodeKey) { + sam.ensureEncoded(samEncodeKey, makeSamSource).catch(() => { /* fallback handled by sam.error */ }); + } + }, [samActive, samEncodeKey, makeSamSource, sam.ensureEncoded]); // eslint-disable-line react-hooks/exhaustive-deps + + // Display adjustments (brightness + contrast + levels) applied as a single + // GPU-composited SVG filter on the image layer's canvas — NOT Konva CPU pixel + // filters. Folds all three into one per-channel linear transform + // (out = slope*in + intercept), so dragging the sliders re-composites on the + // GPU with zero per-pixel JS and no re-cache → lag-free at any image size. + // Math mirrors `renderAdjusted` exactly, so the tools (SAM/wand/Fill) stay in + // sync with what's displayed. + const displayFilterId = 'display-adjust-' + useId().replace(/[^a-zA-Z0-9]/g, ''); + const displayAffine = useMemo(() => { + const b255 = brightness * 255; + const adjust = Math.pow((contrast + 100) / 100, 2); + const range = Math.max(1, levelsHi - levelsLo); + const A = adjust * (255 / range); + const Bconst = (255 / range) * (adjust * b255 + 127.5 * (1 - adjust)) - (255 * levelsLo) / range; + const identity = brightness === 0 && contrast === 0 && levelsLo <= 0 && levelsHi >= 255; + return { slope: A, intercept: Bconst / 255, identity }; + }, [brightness, contrast, levelsLo, levelsHi]); + + useEffect(() => { + const layer = imageLayerRef.current; + if (!layer) return; + const canvas = + (layer as unknown as { getNativeCanvasElement?: () => HTMLCanvasElement }).getNativeCanvasElement?.() ?? + (layer.getCanvas() as unknown as { _canvas: HTMLCanvasElement })._canvas; + if (canvas) canvas.style.filter = displayAffine.identity ? 'none' : `url(#${displayFilterId})`; + }, [displayAffine.identity, displayFilterId, displaySource, stageSize]); + + // Compute a 256-bin luminance histogram of the current slice (downsampled) for + // the levels control. Runs once per loaded image / CLAHE toggle. + useEffect(() => { + if (!displaySource || !onHistogram || !meta) return; + const maxDim = 512; + const scale = Math.max(1, Math.ceil(Math.max(meta.width, meta.height) / maxDim)); + const w = Math.max(1, Math.floor(meta.width / scale)); + const h = Math.max(1, Math.floor(meta.height / scale)); + const canvas = document.createElement('canvas'); + canvas.width = w; canvas.height = h; + const ctx = canvas.getContext('2d', { willReadFrequently: true }); + if (!ctx) return; + ctx.drawImage(displaySource, 0, 0, w, h); + let data: Uint8ClampedArray; + try { data = ctx.getImageData(0, 0, w, h).data; } catch { return; } + const bins = new Array(256).fill(0); + for (let i = 0; i < data.length; i += 4) { + const lum = (0.299 * data[i] + 0.587 * data[i + 1] + 0.114 * data[i + 2]) | 0; + bins[lum < 0 ? 0 : lum > 255 ? 255 : lum]++; + } + onHistogram(bins); + }, [displaySource, meta, onHistogram]); useEffect(() => { const el = containerRef.current; @@ -148,12 +693,22 @@ export default function AnnotationCanvas({ }, [fitRequestId, fitToScreen]); const isPreviewing = previewShapes !== null; - const storeShapes = sourceKey ? (byImage[sourceKey]?.[String(currentSlice)] ?? []) : []; + // Stable empty fallback: a fresh `[]` here changes identity every render, which + // would make the `autoNegPoints` memo (and the SAM preview effect that depends + // on it) re-run in a loop — leaving `magicLoading` stuck true so "Add"/Enter + // never enable. Reuse one constant instead. + const storeShapes = sourceKey ? (byImage[sourceKey]?.[String(currentSlice)] ?? EMPTY_SHAPES) : EMPTY_SHAPES; // Shapes actually drawn on Layer 1: previewed version when previewing, else the live store. const displayShapes = isPreviewing ? previewShapes! : storeShapes; // Classes used for color/visibility lookups when rendering Layer 1. const renderClasses = isPreviewing && previewClasses ? previewClasses : classes; + // Clip stroke/shape layers to the image frame (layer coords == image coords, + // since pan/zoom lives on the Stage) so painting past the edge is cropped. + const imageClip = meta + ? { clipX: 0, clipY: 0, clipWidth: meta.width, clipHeight: meta.height } + : {}; + // Cache the shapes layer for uniform opacity compositing — skipped mid-stroke // so the cache rebuild doesn't stall painting. useEffect(() => { @@ -166,16 +721,30 @@ export default function AnnotationCanvas({ layer.cache({ pixelRatio: pr }); } layer.batchDraw(); - }, [displayShapes, renderClasses, fillOpacity, meta, transform.scaleX, isDrawing]); + }, [displayShapes, renderClasses, fillOpacity, meta, transform.scaleX, isDrawing, layerGroups.annotations]); const colorForClass = (classId: number) => renderClasses.find((c) => c.classId === classId)?.color ?? '#ff0000'; + /** Commit new shapes, clipping them against other classes when the toggle is on + * (neighbor classes act as a hard boundary). One undo step. */ + const commitShapes = useCallback((newShapes: Shape[]) => { + if (!sourceKey || newShapes.length === 0) return; + const slice = useDatasetStore.getState().currentSlice; + const sliceShapes = useAnnotationStore.getState().byImage[sourceKey]?.[String(slice)] ?? []; + let toAdd = newShapes; + if (clipToOtherClasses && meta && newShapes.some((s) => hasOtherClass(sliceShapes, s.classId))) { + toAdd = clipShapesToOthers(newShapes, sliceShapes, meta.width, meta.height); + } + if (toAdd.length) addShapes(sourceKey, slice, toAdd); + }, [sourceKey, clipToOtherClasses, meta, addShapes]); + const activeColor = activeClassId !== null ? colorForClass(activeClassId) : '#4090ff'; const showBrushCursor = (tool === 'brush' || tool === 'eraser') && !!meta && !isPreviewing; + /** Current pointer position mapped from stage/display coords to image pixels. */ const getPointerImagePos = () => { const stage = stageRef.current; if (!stage) return null; @@ -184,11 +753,214 @@ export default function AnnotationCanvas({ return toImage(pos, transform); }; + // Build (and cache, per rendered image) the live-wire edge cost map. + const ensureCostMap = (): CostMap | null => { + if (!displaySource || !meta) return null; + if (magneticCostRef.current && magneticBuiltForRef.current === displaySource) { + return magneticCostRef.current; + } + const cm = buildCostMap(displaySource, meta.width, meta.height); + magneticCostRef.current = cm; + magneticBuiltForRef.current = displaySource; + return cm; + }; + + /** Clear the magnetic-lasso trace and its dijkstra/seed refs. */ + const resetMagnetic = useCallback(() => { + magneticPrevRef.current = null; + magneticSeedRef.current = null; + setMagneticCommitted([]); + setMagneticPreview([]); + }, []); + + /** Clear all magic-wand/SAM seeds, box, and preview state. */ + const resetMagic = useCallback(() => { + setMagicSeeds([]); + setMagicPreview([]); + setMagicLoading(false); + setMagicBox(null); + setMagicBoxDraft(null); + magicDragStartRef.current = null; + }, []); + + // Build (and cache) the grayscale field used by the wand + Fill. Built from the + // DISPLAY-adjusted image (brightness/contrast/levels) so these tools operate on + // what the user sees, exactly like SAM. Cache key includes the display settings. + const magicFieldRef = useRef(null); + const magicFieldForRef = useRef(null); + const ensureMagicField = useCallback((): GrayField | null => { + if (!displaySource || !meta) return null; + const key = `${sourceKey}|${currentSlice}|feat${featureCacheKey ?? 'none'}|clahe${clahe && !featureEl ? 1 : 0}|sh${sharpen ? 1 : 0}|b${brightness}|c${contrast}|l${levelsLo}-${levelsHi}`; + if (magicFieldRef.current && magicFieldForRef.current === key) return magicFieldRef.current; + const src = renderAdjusted(displaySource, meta.width, meta.height, brightness, contrast, levelsLo, levelsHi, false); + const f = buildField(src, meta.width, meta.height); + magicFieldRef.current = f; + magicFieldForRef.current = key; + return f; + }, [displaySource, meta, sourceKey, currentSlice, featureCacheKey, featureEl, clahe, sharpen, brightness, contrast, levelsLo, levelsHi]); + + // Auto negative ("not") prompts for SAM: interior points of nearby other-class + // regions, so a new selection won't bleed into already-labeled areas. Anchored + // to the current click (last positive seed, else the box center), capped to the + // 12 nearest, and skipping any region a positive click sits inside. + const autoNegPoints = useMemo>(() => { + if (magicEngine !== 'sam' || !samAvoidLabeled || activeClassId === null) return []; + const positives = magicSeeds.filter((s) => s.label === 1); + const ref = + positives.length > 0 + ? positives[positives.length - 1] + : magicBox + ? { x: magicBox.x + magicBox.w / 2, y: magicBox.y + magicBox.h / 2 } + : null; + if (!ref) return []; + + const isClassVisible = (s: Shape) => + classes.find((c) => c.classId === s.classId)?.isVisible !== false; + + const cands: Array<{ x: number; y: number }> = []; + for (const shape of storeShapes) { + if (shape.classId === activeClassId || !isClassVisible(shape)) continue; + // Don't fight the user's own target: skip a region a positive click is in. + if (positives.some((p) => shapeContainsPoint(shape, p.x, p.y))) continue; + const ip = interiorPoint(shape); + if (ip) cands.push(ip); + } + cands.sort( + (a, b) => + (a.x - ref.x) ** 2 + (a.y - ref.y) ** 2 - ((b.x - ref.x) ** 2 + (b.y - ref.y) ** 2), + ); + return cands.slice(0, 12); + }, [magicEngine, samAvoidLabeled, activeClassId, magicSeeds, magicBox, storeShapes, classes]); + + // (Re)compute the preview whenever the seeds or params change. + // SAM engine: encode the slice (cached) then decode the point prompts into a + // mask → polygons. Classic engine: client-side flood/threshold via magicSelect. + // Magic sliders already debounce their store commits (DebouncedSlider). + useEffect(() => { + if (tool === 'magic' && magicEngine === 'sam') { + if ((magicSeeds.length === 0 && !magicBox) || !displaySource || !meta) { setMagicPreview([]); return; } + let cancelled = false; + setMagicLoading(true); + (async () => { + const ready = samEncodeKey ? await sam.ensureEncoded(samEncodeKey, makeSamSource) : false; + if (cancelled) return; + if (!ready) { setMagicLoading(false); return; } // status flips → classic + const points = [ + ...magicSeeds.map((s) => ({ + x: s.x / meta.width, y: s.y / meta.height, label: s.label, + })), + // Auto "not" prompts from nearby other-class regions (label 0). + ...autoNegPoints.map((p) => ({ + x: p.x / meta.width, y: p.y / meta.height, label: 0 as const, + })), + ]; + const box = magicBox + ? { + x0: magicBox.x / meta.width, y0: magicBox.y / meta.height, + x1: (magicBox.x + magicBox.w) / meta.width, y1: (magicBox.y + magicBox.h) / meta.height, + } + : null; + const res = await sam.segment(points, box, samDetail, samThreshold); + if (cancelled) return; + if (res) { + const scale = res.width > 0 ? meta.width / res.width : 1; + setMagicPreview( + maskToPolygons(res.mask, res.width, res.height, { + smooth: magicSigma, scale, minRegion: 25, + }), + ); + } else { + setMagicPreview([]); + } + setMagicLoading(false); + })(); + return () => { cancelled = true; }; + } + + if (magicSeeds.length === 0) { setMagicPreview([]); return; } + // Classic flood — serves both the classic magic wand and the Fill tool + // (Fill = contiguous flood by `fillThreshold`). Synchronous, one run per frame. + const isFill = tool === 'fill'; + const field = ensureMagicField(); + if (!field) { setMagicPreview([]); return; } + setMagicLoading(true); + const id = requestAnimationFrame(() => { + const polys: number[][] = []; + for (const seed of magicSeeds) { + polys.push( + ...magicSelect(field, seed.x, seed.y, { + toleranceFrac: isFill ? fillThreshold : magicTolerance, + mode: isFill ? 'contiguous' : magicMode, + smooth: isFill ? 1 : magicSigma, + edgeStop: isFill ? 0 : magicEdgeStop, + }), + ); + } + setMagicPreview(polys); + setMagicLoading(false); + }); + return () => cancelAnimationFrame(id); + }, [tool, magicSeeds, magicBox, autoNegPoints, samDetail, samThreshold, samEncodeKey, makeSamSource, magicEngine, magicTolerance, magicMode, magicSigma, magicEdgeStop, fillThreshold, ensureMagicField, imageEl, meta, sam.ensureEncoded, sam.segment]); // eslint-disable-line react-hooks/exhaustive-deps + + /** Commit the magic preview polygons as new shapes (one batched undo step). */ + const commitMagic = useCallback(() => { + if (!sourceKey || activeClassId === null) return; + // One batched add = one undo step for the whole magic selection. + const shapes = magicPreview + .filter((pts) => pts.length >= 6) + .map((pts) => ({ id: uuidv4(), classId: activeClassId, kind: 'polygon' as const, points: pts })); + if (shapes.length) commitShapes(shapes); + resetMagic(); + }, [sourceKey, activeClassId, magicPreview, commitShapes, resetMagic]); + + // Preserve in-progress polygon/lasso/magic drafts across a transient hold-Space + // pan (tool flips to 'pan' then back), but abandon them on a real tool switch. + const prevToolRef = useRef(tool); + useEffect(() => { + const prev = prevToolRef.current; + prevToolRef.current = tool; + if (tool === 'pan' || prev === 'pan') return; // entering/leaving pan keeps the draft + resetMagnetic(); + resetMagic(); + setDraftPoly([]); + }, [tool, resetMagnetic, resetMagic]); + + // A new image/slice always invalidates any in-progress draft. + useEffect(() => { + resetMagnetic(); + resetMagic(); + setDraftPoly([]); + }, [currentSlice, sourceKey, resetMagnetic, resetMagic]); + + // Escape cancels the entire in-progress shape (polygon vertices, magnetic + // trace, magic selection, or rect/ellipse drag) without committing anything. + // Enter accepts the current magic-tool selection (same as the "Add" button). + useEffect(() => { + const onKey = (e: KeyboardEvent) => { + const tag = (e.target as HTMLElement | null)?.tagName; + if (tag === 'INPUT' || tag === 'TEXTAREA' || tag === 'SELECT') return; + if (e.key === 'Escape') { + setDraftPoly([]); + setDragStart(null); + setDragCurrent(null); + setMarqueeStart(null); + setMarqueeRect(null); + resetMagnetic(); + resetMagic(); + } else if (e.key === 'Enter' && (tool === 'magic' || tool === 'fill') && !magicLoading && magicPreview.length > 0) { + e.preventDefault(); + commitMagic(); + } + }; + window.addEventListener('keydown', onKey); + return () => window.removeEventListener('keydown', onKey); + }, [resetMagnetic, resetMagic, tool, magicLoading, magicPreview, commitMagic]); + /** Flush the buffered draft stroke to the Zustand store (one write per stroke). */ const commitDraftStroke = () => { const draft = draftStrokeRef.current; if (!draft || !sourceKey) return; - const { shapeId, mode, points, radius } = draft; + const { shapeId, mode, points, radius, eraseTargetKind } = draft; draftStrokeRef.current = null; // Hide the draft line imperatively @@ -200,17 +972,136 @@ export default function AnnotationCanvas({ if (points.length < 2) return; // Duplicate single point so Konva renders it as a dot const finalPoints = points.length === 2 ? [...points, ...points] : points; + if (mode === 'erase' && eraseTargetKind === 'vector') { + appendEraseStroke(sourceKey, currentSlice, shapeId, { points: finalPoints, radius }); + return; + } + + // Clip-to-other-classes (paint only): if this stroke makes the brush overlap + // another class, convert the whole brush instance to a clipped polygon. + if (mode === 'paint' && clipToOtherClasses && meta) { + const sliceShapes = useAnnotationStore.getState().byImage[sourceKey]?.[String(currentSlice)] ?? []; + const brush = sliceShapes.find((s) => s.id === shapeId); + const others = brush ? sliceShapes.filter((s) => s.classId !== brush.classId) : []; + if (brush && brush.kind === 'brush' && others.length > 0) { + const { gw, gh, scale } = gridFor(meta.width, meta.height); + const prospective = { ...brush, strokes: [...brush.strokes, { points: finalPoints, radius, mode: 'paint' as const }] }; + const mine = rasterizeShapes([prospective], gw, gh, scale); + const otherMask = rasterizeUnion(others, gw, gh, scale); + let overlap = false; + for (let i = 0; i < mine.length; i++) { if (mine[i] && otherMask[i]) { overlap = true; break; } } + if (overlap) { + for (let i = 0; i < mine.length; i++) if (otherMask[i]) mine[i] = 0; + const polys = maskToPolygonsWithHoles(mine, gw, gh, { minRegion: 4, scale }) + .filter((p) => p.points.length >= 6) + .map((p) => ({ + id: uuidv4(), classId: brush.classId, kind: 'polygon' as const, + points: p.points, ...(p.holes.length ? { holes: p.holes } : {}), + })); + const kept = sliceShapes.filter((s) => s.id !== shapeId); + setShapes(sourceKey, currentSlice, [...kept, ...polys]); + onNewBrushInstance(''); // this brush instance no longer exists + return; + } + } + } appendBrushStroke(sourceKey, currentSlice, shapeId, { points: finalPoints, radius, mode }); }; + /** Tool-dispatched press: starts a marquee, seeds magic/magnetic, adds a polygon + * vertex, begins a rect/ellipse drag, or starts a buffered brush/erase stroke. */ const handleStageMouseDown = (e: Konva.KonvaEventObject) => { if (isPreviewing) return; + + // Select tool: pressing on empty canvas begins a marquee (rubber-band). + // Whether it becomes a box-select or a deselect is decided on mouse-up. + // Clicks on a shape are handled by that shape's own onClick (selectShape). + if (tool === 'select') { + if (e.target === e.target.getStage()) { + const p = getPointerImagePos(); + if (p) { + setMarqueeStart(p); + setMarqueeRect(null); + // Shift-drag accumulates onto the existing selection (decided on mouse-up). + marqueeShiftRef.current = e.evt.shiftKey; + } + } + return; + } + if (!sourceKey || !meta || activeClassId === null) return; const pos = getPointerImagePos(); if (!pos) return; if (tool === 'polygon') { setDraftPoly((prev) => [...prev, pos.x, pos.y]); + } else if (tool === 'magic') { + // Seed the magic selection (the effect computes the preview). + // SAM engine: + // • plain click → new positive point (start the object) + // • Shift-click → add a positive point (extend the object) + // • Alt/Option-click → add a negative point (carve a leak back out) + // Classic wand keeps its original meaning (Alt removes the nearest seed). + if (magicEngine === 'sam') { + // Shift/Alt-click refine with points; a plain press starts a box drag + // (resolved to a box prompt or a single point on mouse-up). + if (e.evt.altKey) { + setMagicSeeds((prev) => (prev.length === 0 && !magicBox ? prev : [...prev, { x: pos.x, y: pos.y, label: 0 }])); + } else if (e.evt.shiftKey) { + setMagicSeeds((prev) => [...prev, { x: pos.x, y: pos.y, label: 1 }]); + } else { + magicDragStartRef.current = { x: pos.x, y: pos.y }; + setMagicBoxDraft(null); + } + } else if (e.evt.altKey) { + setMagicSeeds((prev) => { + if (prev.length === 0) return prev; + let bestI = 0, bestD = Infinity; + prev.forEach((s, i) => { + const d = (s.x - pos.x) ** 2 + (s.y - pos.y) ** 2; + if (d < bestD) { bestD = d; bestI = i; } + }); + return prev.filter((_, i) => i !== bestI); + }); + } else if (e.evt.shiftKey) { + setMagicSeeds((prev) => [...prev, { x: pos.x, y: pos.y, label: 1 }]); + } else { + setMagicSeeds([{ x: pos.x, y: pos.y, label: 1 }]); + } + } else if (tool === 'magnetic') { + const cm = ensureCostMap(); + if (cm && magneticSeedRef.current && magneticPrevRef.current) { + // Lock in the least-cost path from the previous seed to this click. + const path = tracePath(cm, magneticPrevRef.current, imageToGrid(cm, pos.x, pos.y)); + setMagneticCommitted((prev) => [...prev, ...path.slice(2)]); + } else { + // First click (or no edge map) — straight-segment fallback. + setMagneticCommitted((prev) => [...prev, pos.x, pos.y]); + } + // Re-seed at the click point. + magneticSeedRef.current = pos; + magneticPrevRef.current = cm ? dijkstra(cm, imageToGrid(cm, pos.x, pos.y)) : null; + setMagneticPreview([]); + } else if (tool === 'fill') { + // Paint bucket: a plain click seeds a single contiguous flood (within + // `fillThreshold`); Shift-click adds another region; Alt-click removes the + // nearest seed. The region(s) preview, then Add/Enter commits (clip-aware). + if (e.evt.altKey) { + setMagicSeeds((prev) => { + if (prev.length === 0) return prev; + let bestI = 0, bestD = Infinity; + prev.forEach((s, i) => { + const d = (s.x - pos.x) ** 2 + (s.y - pos.y) ** 2; + if (d < bestD) { bestD = d; bestI = i; } + }); + return prev.filter((_, i) => i !== bestI); + }); + } else if (e.evt.shiftKey) { + setMagicSeeds((prev) => [...prev, { x: pos.x, y: pos.y, label: 1 }]); + } else { + // Plain click replaces the current selection with this single region. + setMagicSeeds([{ x: pos.x, y: pos.y, label: 1 }]); + } } else if (tool === 'rectangle' || tool === 'ellipse') { setDragStart(pos); setDragCurrent(pos); @@ -251,13 +1142,29 @@ export default function AnnotationCanvas({ } } else if (tool === 'eraser') { setIsDrawing(true); - const targetId = - activeBrushShapeId ?? - shapes.filter((s) => s.kind === 'brush' && s.classId === activeClassId).slice(-1)[0]?.id ?? - null; - if (targetId) { - draftStrokeRef.current = { shapeId: targetId, mode: 'erase', points: [pos.x, pos.y], radius: brushSize }; - // Show a white dash to indicate erasing (destination-out not feasible in uncached layer) + const sliceShapes = byImage[sourceKey]?.[String(currentSlice)] ?? []; + // Erase carves only from the topmost shape of the ACTIVE class under the + // cursor, so it never eats into another layer. Falls back to the active + // brush instance if the click isn't over a shape. + const visible = (s: Shape) => + classes.find((c) => c.classId === s.classId)?.isVisible !== false; + let target: Shape | undefined; + for (let i = sliceShapes.length - 1; i >= 0; i--) { + const s = sliceShapes[i]; + if (s.classId === activeClassId && visible(s) && shapeContainsPoint(s, pos.x, pos.y)) { target = s; break; } + } + if (!target && activeBrushShapeId) { + target = sliceShapes.find((s) => s.id === activeBrushShapeId && s.kind === 'brush'); + } + if (target) { + draftStrokeRef.current = { + shapeId: target.id, + mode: 'erase', + points: [pos.x, pos.y], + radius: brushSize, + eraseTargetKind: target.kind === 'brush' ? 'brush' : 'vector', + }; + // Show a white dash to indicate erasing. if (draftLineRef.current) { draftLineRef.current.stroke('#ffffff'); draftLineRef.current.strokeWidth(brushSize * 2); @@ -269,6 +1176,8 @@ export default function AnnotationCanvas({ } }; + /** Drives all live previews while dragging: brush cursor, marquee, SAM box, + * magnetic path, rect/ellipse draft, and buffering brush/erase points. */ const handleStageMouseMove = (e: Konva.KonvaEventObject) => { if (isPreviewing) return; const pos = getPointerImagePos(); @@ -280,8 +1189,33 @@ export default function AnnotationCanvas({ brushCursorLayerRef.current?.batchDraw(); } + // Marquee rubber-band: update the box while dragging on empty canvas. + if (tool === 'select' && marqueeStart && e.evt.buttons === 1) { + setMarqueeRect(normalizeRect(marqueeStart.x, marqueeStart.y, pos.x - marqueeStart.x, pos.y - marqueeStart.y)); + return; + } + if (!sourceKey || !meta) return; + // SAM box prompt: live rubber-band while dragging. + if (tool === 'magic' && magicEngine === 'sam' && magicDragStartRef.current && e.evt.buttons === 1) { + const s = magicDragStartRef.current; + setMagicBoxDraft(normalizeRect(s.x, s.y, pos.x - s.x, pos.y - s.y)); + return; + } + + // Magnetic lasso: live least-cost path from the seed to the cursor. + if (tool === 'magnetic' && magneticSeedRef.current) { + const cm = magneticCostRef.current; + if (cm && magneticPrevRef.current) { + setMagneticPreview(tracePath(cm, magneticPrevRef.current, imageToGrid(cm, pos.x, pos.y))); + } else { + const seed = magneticSeedRef.current; + setMagneticPreview([seed.x, seed.y, pos.x, pos.y]); + } + return; + } + if ((tool === 'rectangle' || tool === 'ellipse') && dragStart && e.evt.buttons === 1) { setDragCurrent(pos); return; @@ -298,8 +1232,49 @@ export default function AnnotationCanvas({ } }; + /** Finalizes the press: box-select/deselect marquee, resolve SAM box-vs-point, + * flush a brush/erase stroke, or commit a dragged rect/ellipse. */ const handleStageMouseUp = () => { if (isPreviewing) return; + + // Finish a marquee. A real drag box-selects intersecting shapes; with Shift + // those are added to the current selection (drag several boxes to build it + // up). A bare non-Shift click clears; a bare Shift-click keeps the selection. + if (tool === 'select' && marqueeStart) { + const rect = marqueeRect; + const additive = marqueeShiftRef.current; + if (rect && rect.w > 3 && rect.h > 3 && sourceKey) { + const ids = storeShapes + .filter((s) => classes.find((c) => c.classId === s.classId)?.isVisible !== false) + .filter((s) => shapeIntersectsRect(s, rect)) + .map((s) => s.id); + setSelectedShapeIds(additive ? Array.from(new Set([...selectedShapeIds, ...ids])) : ids); + } else if (!additive) { + setSelectedShapeIds([]); + } + setMarqueeStart(null); + setMarqueeRect(null); + marqueeShiftRef.current = false; + return; + } + + // SAM magic: resolve the press into a box prompt (real drag) or a point. + if (tool === 'magic' && magicEngine === 'sam' && magicDragStartRef.current) { + const start = magicDragStartRef.current; + magicDragStartRef.current = null; + const up = getPointerImagePos() ?? start; + const box = normalizeRect(start.x, start.y, up.x - start.x, up.y - start.y); + setMagicBoxDraft(null); + if (box.w > 4 && box.h > 4) { + setMagicBox(box); + setMagicSeeds([]); // a box replaces any accumulated points + } else { + setMagicSeeds([{ x: start.x, y: start.y, label: 1 }]); + setMagicBox(null); + } + return; + } + if (isDrawing) { commitDraftStroke(); setIsDrawing(false); @@ -313,7 +1288,7 @@ export default function AnnotationCanvas({ const id = uuidv4(); if (tool === 'rectangle') { const { x, y, w, h } = normalizeRect(dragStart.x, dragStart.y, dx, dy); - addShape(sourceKey, currentSlice, { id, classId: activeClassId, kind: 'rectangle', x, y, w, h }); + commitShapes([{ id, classId: activeClassId, kind: 'rectangle', x, y, w, h }]); } else { const { cx, cy, rx, ry } = normalizeEllipse( (dragStart.x + dragCurrent.x) / 2, @@ -321,7 +1296,7 @@ export default function AnnotationCanvas({ Math.abs(dx) / 2, Math.abs(dy) / 2 ); - addShape(sourceKey, currentSlice, { id, classId: activeClassId, kind: 'ellipse', cx, cy, rx, ry }); + commitShapes([{ id, classId: activeClassId, kind: 'ellipse', cx, cy, rx, ry }]); } } setDragStart(null); @@ -329,15 +1304,30 @@ export default function AnnotationCanvas({ } }; + /** Closes the in-progress polygon, or finishes & simplifies a magnetic trace, into a committed shape. */ const handleStageDblClick = () => { if (isPreviewing) return; if (tool === 'polygon' && draftPoly.length >= 6 && sourceKey && activeClassId !== null) { const id = uuidv4(); - addShape(sourceKey, currentSlice, { id, classId: activeClassId, kind: 'polygon', points: draftPoly }); + commitShapes([{ id, classId: activeClassId, kind: 'polygon', points: draftPoly }]); setDraftPoly([]); + } else if (tool === 'magnetic' && sourceKey && activeClassId !== null) { + // Commit the final hovered segment, then close the traced polygon. + let pts = magneticCommitted; + const cm = magneticCostRef.current; + const pos = getPointerImagePos(); + if (cm && magneticPrevRef.current && pos) { + pts = [...pts, ...tracePath(cm, magneticPrevRef.current, imageToGrid(cm, pos.x, pos.y)).slice(2)]; + } + const simplified = simplifyPath(pts, 3); + if (simplified.length >= 6) { + commitShapes([{ id: uuidv4(), classId: activeClassId, kind: 'polygon', points: simplified }]); + } + resetMagnetic(); } }; + /** Hides the brush cursor and commits any in-progress stroke/drag on exit. */ const handleStageMouseLeave = () => { // Hide brush cursor if (brushCursorRef.current) { @@ -353,6 +1343,7 @@ export default function AnnotationCanvas({ setDragCurrent(null); }; + /** Re-shows the brush/eraser size cursor when the pointer re-enters the stage. */ const handleStageMouseEnter = () => { if (showBrushCursor && brushCursorRef.current) { brushCursorRef.current.visible(true); @@ -368,6 +1359,7 @@ export default function AnnotationCanvas({ } }, [showBrushCursor]); + /** Zoom in/out toward the cursor, keeping the point under the pointer fixed. */ const handleWheel = (e: Konva.KonvaEventObject) => { e.evt.preventDefault(); const stage = stageRef.current; @@ -384,6 +1376,7 @@ export default function AnnotationCanvas({ }); }; + /** Live (uncommitted) rect/ellipse preview while dragging it out. */ const renderDraftShape = () => { if (!dragStart || !dragCurrent) return null; const dx = dragCurrent.x - dragStart.x; @@ -413,50 +1406,99 @@ export default function AnnotationCanvas({ return null; }; + /** Destination-out lines that carve erase strokes out of a vector shape. */ + const renderErased = (erased?: EraseStroke[]) => + (erased ?? []).map((st, i) => ( + + )); + + /** Render a polygon that may have holes via an even-odd fill (outer path minus + * hole subpaths). Even-odd — not destination-out — so a hole reveals whatever + * is *beneath* it (e.g. another class) instead of erasing it off the layer. */ + const renderPolygonWithHoles = (points: number[], holes: number[][], color: string, strokeW: number) => ( + { + buildRingsPath(ctx, [points, ...holes]); + const raw = (ctx as unknown as { _context: CanvasRenderingContext2D })._context; + raw.fillStyle = color; + raw.fill('evenodd'); + ctx.strokeShape(node); + }} + /> + ); + + /** Render a committed shape (any kind) on the cached display layer, with the + * active brush instance recolored to the active class and erase strokes carved out. */ const renderShape = (shape: Shape) => { const color = shape.id === activeBrushShapeId && activeClassId !== null ? colorForClass(activeClassId) : colorForClass(shape.classId); - const isSelected = shape.id === selectedShapeId; + const isSelected = selectedShapeIds.includes(shape.id); const strokeW = (isSelected ? 2 : 1) / transform.scaleX; if (shape.kind === 'polygon') { return ( - setSelectedShapeId(shape.id)} - /> + + {shape.holes?.length + ? renderPolygonWithHoles(shape.points, shape.holes, color, strokeW) + : ( + + )} + {renderErased(shape.erased)} + ); } if (shape.kind === 'rectangle') { return ( - setSelectedShapeId(shape.id)} - /> + + + {renderErased(shape.erased)} + ); } if (shape.kind === 'ellipse') { return ( - setSelectedShapeId(shape.id)} - /> + + + {renderErased(shape.erased)} + ); } if (shape.kind === 'brush') { @@ -497,11 +1539,394 @@ export default function AnnotationCanvas({ return null; }; + // ----- Interactive selection / move / vertex-edit (select tool only) ----- + + /** Select a clicked shape; shift-click toggles it in the multi-selection. */ + const selectShape = (e: Konva.KonvaEventObject, id: string) => { + e.cancelBubble = true; + // Shift-click toggles the shape in/out of the current multi-selection. + if (e.evt.shiftKey) { + setSelectedShapeIds( + selectedShapeIds.includes(id) + ? selectedShapeIds.filter((sid) => sid !== id) + : [...selectedShapeIds, id], + ); + } else { + setSelectedShapeId(id); + } + }; + + /** Render a shape as an interactive, draggable hit target with a selection outline. */ + const renderInteractive = (shape: Shape) => { + if (!sourceKey) return null; + const selected = selectedShapeIds.includes(shape.id); + // Move/resize/vertex editing require exactly one selected shape. + const single = selectedId === shape.id; + const baseColor = colorForClass(shape.classId); + const outline = selected ? '#ffffff' : baseColor; + const sw = (selected ? 2.5 : 1.5) / transform.scaleX; + const dash = selected ? [6 / transform.scaleX, 4 / transform.scaleX] : undefined; + // Near-transparent fill still registers hit detection without obscuring Layer 1. + const hitFill = 'rgba(0,0,0,0.001)'; + const handlers = { + onClick: (e: Konva.KonvaEventObject) => selectShape(e, shape.id), + onTap: (e: Konva.KonvaEventObject) => selectShape(e, shape.id), + }; + + if (shape.kind === 'polygon') { + const livePoints = editPoly?.id === shape.id ? editPoly.points : shape.points; + const holes = shape.holes ?? []; + const hasHoles = holes.length > 0; + return ( + { + // Only the Group itself moving = a whole-shape move; vertex-circle + // drags set their own target and are handled below. + if (e.target !== e.currentTarget) return; + const dx = e.target.x(); + const dy = e.target.y(); + e.target.position({ x: 0, y: 0 }); + if (dx === 0 && dy === 0) return; + const shift = (r: number[]) => r.map((v, i) => (i % 2 === 0 ? v + dx : v + dy)); + updateShape(sourceKey, currentSlice, shape.id, (s) => + s.kind === 'polygon' + ? { ...s, points: shift(s.points), holes: s.holes?.map(shift) } + : s, + ); + }} + > + {hasHoles ? ( + // Even-odd hit so the hole is click-through (the enclosed class stays + // selectable) and the outline traces both the outer ring and holes. + { + buildRingsPath(ctx, [livePoints, ...holes]); + const raw = (ctx as unknown as { _context: CanvasRenderingContext2D })._context; + raw.fillStyle = 'rgba(0,0,0,0.001)'; + raw.fill('evenodd'); + ctx.strokeShape(node); + }} + hitFunc={(ctx: Konva.Context, node: Konva.Shape) => { + buildRingsPath(ctx, [livePoints, ...holes]); + const raw = (ctx as unknown as { _context: CanvasRenderingContext2D })._context; + raw.fillStyle = (node as unknown as { colorKey: string }).colorKey; + raw.fill('evenodd'); + }} + /> + ) : ( + + )} + {single && + Array.from({ length: livePoints.length / 2 }, (_, vi) => ( + { e.cancelBubble = true; }} + onDragMove={(e) => { + const np = [...livePoints]; + np[vi * 2] = e.target.x(); + np[vi * 2 + 1] = e.target.y(); + setEditPoly({ id: shape.id, points: np }); + }} + onDragEnd={(e) => { + const np = [...livePoints]; + np[vi * 2] = e.target.x(); + np[vi * 2 + 1] = e.target.y(); + updateShape(sourceKey, currentSlice, shape.id, (s) => + s.kind === 'polygon' ? { ...s, points: np } : s, + ); + setEditPoly(null); + }} + /> + ))} + {/* Hole (inner-ring) vertices — amber to distinguish from the outer ring. */} + {single && holes.map((ring, hi) => + Array.from({ length: ring.length / 2 }, (_, vi) => ( + { e.cancelBubble = true; }} + onDragEnd={(e) => { + const nx = e.target.x(), ny = e.target.y(); + updateShape(sourceKey, currentSlice, shape.id, (s) => { + if (s.kind !== 'polygon' || !s.holes) return s; + const hc = s.holes.map((r) => r.slice()); + hc[hi][vi * 2] = nx; + hc[hi][vi * 2 + 1] = ny; + return { ...s, holes: hc }; + }); + }} + /> + )), + )} + + ); + } + + if (shape.kind === 'rectangle') { + return ( + { + const nx = e.target.x(); + const ny = e.target.y(); + updateShape(sourceKey, currentSlice, shape.id, (s) => + s.kind === 'rectangle' ? { ...s, x: nx, y: ny } : s, + ); + }} + onTransformEnd={(e) => { + // Transformer applies a scale; bake it into width/height and reset. + const node = e.target as Konva.Rect; + const nw = Math.max(1, node.width() * node.scaleX()); + const nh = Math.max(1, node.height() * node.scaleY()); + node.scaleX(1); node.scaleY(1); + node.width(nw); node.height(nh); + const nx = node.x(); + const ny = node.y(); + updateShape(sourceKey, currentSlice, shape.id, (s) => + s.kind === 'rectangle' ? { ...s, x: nx, y: ny, w: nw, h: nh } : s, + ); + }} + /> + ); + } + + if (shape.kind === 'ellipse') { + return ( + { + const nx = e.target.x(); + const ny = e.target.y(); + updateShape(sourceKey, currentSlice, shape.id, (s) => + s.kind === 'ellipse' ? { ...s, cx: nx, cy: ny } : s, + ); + }} + onTransformEnd={(e) => { + const node = e.target as Konva.Ellipse; + const nrx = Math.max(1, node.radiusX() * node.scaleX()); + const nry = Math.max(1, node.radiusY() * node.scaleY()); + node.scaleX(1); node.scaleY(1); + node.radiusX(nrx); node.radiusY(nry); + const ncx = node.x(); + const ncy = node.y(); + updateShape(sourceKey, currentSlice, shape.id, (s) => + s.kind === 'ellipse' ? { ...s, cx: ncx, cy: ncy, rx: nrx, ry: nry } : s, + ); + }} + /> + ); + } + + if (shape.kind === 'brush') { + return ( + { + const dx = e.target.x(); + const dy = e.target.y(); + e.target.position({ x: 0, y: 0 }); + if (dx === 0 && dy === 0) return; + updateShape(sourceKey, currentSlice, shape.id, (s) => + s.kind === 'brush' + ? { + ...s, + strokes: s.strokes.map((st) => ({ + ...st, + points: st.points.map((v, i) => (i % 2 === 0 ? v + dx : v + dy)), + })), + } + : s, + ); + }} + > + {shape.strokes.map((st, i) => ( + + ))} + + ); + } + return null; + }; + + const showInteractive = tool === 'select' && !isPreviewing; + // The single selected shape (resize/transform only applies to one). + const selectedShape = sourceKey && selectedId + ? storeShapes.find((s) => s.id === selectedId) ?? null + : null; + + // Attach the resize Transformer to the selected rect/ellipse (by Konva id). + const selectedResizable = + selectedShape?.kind === 'rectangle' || selectedShape?.kind === 'ellipse'; + useEffect(() => { + const tr = transformerRef.current; + const stage = stageRef.current; + if (!tr || !stage) return; + if (showInteractive && selectedShape && selectedResizable) { + const node = stage.findOne('#' + selectedShape.id); + tr.nodes(node ? [node] : []); + } else { + tr.nodes([]); + } + tr.getLayer()?.batchDraw(); + }, [showInteractive, selectedShape, selectedResizable, displayShapes, transform]); + + /** Remove all currently selected shapes from the store and clear the selection. */ + const handleDeleteSelected = () => { + if (!sourceKey || selectedShapeIds.length === 0) return; + removeShapes(sourceKey, currentSlice, selectedShapeIds); + setSelectedShapeId(null); + setEditPoly(null); + }; + + /** Reassign every selected shape to *classId* (one undo step). */ + const handleReassignClass = (classId: number) => { + if (!sourceKey || selectedShapeIds.length === 0) return; + setClassForShapes(sourceKey, currentSlice, selectedShapeIds, classId); + }; + + // The class shared by all selected shapes, or null if they differ ("mixed"). + const selectedClassIds = new Set( + storeShapes.filter((s) => selectedShapeIds.includes(s.id)).map((s) => s.classId), + ); + const commonClassId = selectedClassIds.size === 1 ? [...selectedClassIds][0] : null; + + // ----- Selection editing: copy/paste, invert, region ops, brush thickness ----- + const selectedShapes = useMemo( + () => storeShapes.filter((s) => selectedShapeIds.includes(s.id)), + [storeShapes, selectedShapeIds], + ); + const selectedBrush = selectedShape?.kind === 'brush' ? selectedShape : null; + + const [regionOp, setRegionOp] = useState(null); + const [regionParam, setRegionParam] = useState(4); + + // Live region-op preview polygons (per class), recomputed as op/param/selection change. + const regionPreview = useMemo(() => { + if (!regionOp || !meta || selectedShapes.length === 0) return []; + return computeRegionOps(selectedShapes, meta.width, meta.height, regionOp, regionParam); + }, [regionOp, regionParam, selectedShapes, meta]); + + // Cancel a pending region op whenever the selection empties or the tool changes. + useEffect(() => { + if (selectedShapeIds.length === 0 || tool !== 'select') setRegionOp(null); + }, [selectedShapeIds, tool]); + + const handleCopy = useCallback(() => { + if (selectedShapes.length) clipboard.copy(selectedShapes); + }, [selectedShapes, clipboard]); + + const handlePaste = useCallback(() => { + if (!sourceKey || clipboard.shapes.length === 0) return; + const pasted = clipboard.shapes.map((s) => offsetShape({ ...structuredClone(s), id: uuidv4() }, 12, 12)); + const sliceShapes = useAnnotationStore.getState().byImage[sourceKey]?.[String(currentSlice)] ?? []; + const toAdd = clipToOtherClasses && meta && pasted.some((s) => hasOtherClass(sliceShapes, s.classId)) + ? clipShapesToOthers(pasted, sliceShapes, meta.width, meta.height) + : pasted; + if (toAdd.length === 0) return; + addShapes(sourceKey, currentSlice, toAdd); + setSelectedShapeIds(toAdd.map((s) => s.id)); + }, [sourceKey, clipboard.shapes, addShapes, currentSlice, setSelectedShapeIds, clipToOtherClasses, meta]); + + /** Replace the single selected shape with its complement (frame minus shape). */ + const handleInvert = useCallback(() => { + if (!sourceKey || !meta || !selectedShape) return; + const { gw, gh, scale } = gridFor(meta.width, meta.height); + const mask = rasterizeShapes([selectedShape], gw, gh, scale); + const holes = maskToPolygons(mask, gw, gh, { minRegion: 4, scale }); + const outer = [0, 0, meta.width, 0, meta.width, meta.height, 0, meta.height]; + updateShape(sourceKey, currentSlice, selectedShape.id, (s) => ({ + id: s.id, classId: s.classId, kind: 'polygon', points: outer, holes, + })); + }, [sourceKey, meta, selectedShape, updateShape, currentSlice]); + + /** Commit the region-op preview: replace the selected shapes with the result. */ + const handleApplyRegion = useCallback(() => { + if (!sourceKey || regionPreview.length === 0) return; + const selSet = new Set(selectedShapeIds); + const kept = storeShapes.filter((s) => !selSet.has(s.id)); + const created: Shape[] = regionPreview.map((r) => ({ + id: uuidv4(), classId: r.classId, kind: 'polygon' as const, points: r.points, + })); + const finalCreated = clipToOtherClasses && meta + ? clipShapesToOthers(created, kept, meta.width, meta.height) + : created; + setShapes(sourceKey, currentSlice, [...kept, ...finalCreated]); + setSelectedShapeIds(finalCreated.map((s) => s.id)); + setRegionOp(null); + }, [sourceKey, regionPreview, selectedShapeIds, storeShapes, setShapes, currentSlice, setSelectedShapeIds, clipToOtherClasses, meta]); + + /** Set every stroke's radius on the selected brush shape (post-draw re-thickness). */ + const handleBrushThickness = useCallback((radius: number) => { + if (!sourceKey || !selectedBrush) return; + updateShape(sourceKey, currentSlice, selectedBrush.id, (s) => + s.kind === 'brush' ? { ...s, strokes: s.strokes.map((st) => ({ ...st, radius })) } : s, + ); + }, [sourceKey, selectedBrush, updateShape, currentSlice]); + + // Keyboard: copy/paste/invert (select tool) + Enter to apply a region op. + useEffect(() => { + const onKey = (e: KeyboardEvent) => { + if (tool !== 'select') return; + const t = e.target as HTMLElement | null; + if (t && (t.tagName === 'INPUT' || t.tagName === 'TEXTAREA' || t.isContentEditable)) return; + const mod = e.metaKey || e.ctrlKey; + const k = e.key.toLowerCase(); + if (mod && k === 'c' && selectedShapes.length) { e.preventDefault(); handleCopy(); } + else if (mod && k === 'v' && clipboard.shapes.length) { e.preventDefault(); handlePaste(); } + else if (!mod && k === 'i' && selectedShape) { e.preventDefault(); handleInvert(); } + else if (e.key === 'Enter' && regionOp && regionPreview.length) { e.preventDefault(); handleApplyRegion(); } + }; + window.addEventListener('keydown', onKey); + return () => window.removeEventListener('keydown', onKey); + }, [tool, selectedShapes, clipboard.shapes, selectedShape, regionOp, regionPreview, + handleCopy, handlePaste, handleInvert, handleApplyRegion]); + return (
{ + // Drag events bubble: a vertex/shape drag also fires this. Only the + // stage itself being dragged (pan) should update the pan transform — + // otherwise the image jumps to the dragged node's coordinates. + if (e.target !== e.target.getStage()) return; setTransform((t) => ({ ...t, x: e.target.x(), y: e.target.y() })); }} > - {/* Layer 0: image */} - - {imageEl && meta && ( + {/* Layer 0: image (display adjustments applied via the GPU SVG filter below) */} + + {displaySource && meta && (featureEl ? layerGroups.features : layerGroups.image) && ( + {/* Softmax probability overlay — does not replace the base image. */} + {probaEl && meta && layerGroups.proba && ( + + + + )} + + {/* CatBoost prediction overlay — outside the display filter so class colors stay true. */} + {clfOverlay && meta && layerGroups.predictions && ( + + + + )} + + {/* Manifold coverage heatmap + inducing-point rings (guidance only). */} + {manifoldOverlay && meta && manifoldShowHeatmap && layerGroups.manifold && ( + + + + )} + {manifoldShowMarkers && manifoldMarkers && manifoldMarkers.length > 0 && meta && layerGroups.manifold && ( + + {manifoldMarkers.map((p, i) => { + // Use Box size (slider / point.box_size) — never exclusion radius. + const rect = manifoldMarkerRect(p, { + side: manifoldBoxSize ?? p.box_size, + width: meta.width, + height: meta.height, + }); + return ( + + + + + ); + })} + + )} + + {/* Suggest Labels placement mask outline (Preprocess). */} + {placementMaskShapes && placementMaskShapes.length > 0 && ( + + {placementMaskShapes.map((s) => { + const sw = 2 / transform.scaleX; + const stroke = '#0ea5e9'; + const dash = [8 / transform.scaleX, 5 / transform.scaleX]; + if (s.kind === 'polygon') { + return ( + + ); + } + if (s.kind === 'rectangle') { + return ( + + ); + } + if (s.kind === 'ellipse') { + return ( + + ); + } + // Brush: outline painted strokes as polylines + if (s.kind === 'brush') { + return ( + + {s.strokes + .filter((st) => st.mode !== 'erase') + .map((st, i) => ( + + ))} + + ); + } + return null; + })} + + )} + {/* Layer 1: committed shapes — cached + opacity applied once at the - layer so overlapping same-class shapes render a uniform class color. */} - + layer so overlapping same-class shapes render a uniform class color. + Clipped to the image frame so strokes never render past the edges. */} + {layerGroups.annotations && ( + {displayShapes .filter((s) => renderClasses.find((c) => c.classId === s.classId)?.isVisible !== false) .map(renderShape)} + )} + + {/* Mask-set dense label overlay (preferred) or shape ghost fallback. */} + {maskSetLabelCanvas && meta && ( + + + + )} + {!maskSetLabelCanvas && maskSetOverlayShapes && maskSetOverlayShapes.length > 0 && ( + + {maskSetOverlayShapes + .filter((s) => renderClasses.find((c) => c.classId === s.classId)?.isVisible !== false) + .map((s) => renderShape({ ...s, id: `ms-${s.id}` }))} + + )} + + {/* Interactive layer: hit targets for select/move/edit (select tool only). */} + {showInteractive && layerGroups.annotations && ( + + {storeShapes + .filter((s) => classes.find((c) => c.classId === s.classId)?.isVisible !== false) + .map(renderInteractive)} + + newBox.width < 5 || newBox.height < 5 ? oldBox : newBox + } + /> + {marqueeRect && ( + + )} + + )} {/* Layer 2: draft polygon + drag preview */} @@ -570,10 +2207,120 @@ export default function AnnotationCanvas({ )} {renderDraftShape()} + + {/* Magnetic lasso: committed path (solid) + live edge-traced preview (dashed). + Kept visible while panning (tool flips to 'pan') so the trace survives. */} + {(tool === 'magnetic' || tool === 'pan') && magneticCommitted.length >= 2 && ( + + )} + {tool === 'magnetic' && magneticPreview.length >= 2 && ( + + )} + {(tool === 'magnetic' || tool === 'pan') && magneticSeedRef.current && ( + + )} + + {/* SAM box prompt: committed box (solid) + live drag (dashed). */} + {(tool === 'magic' || tool === 'pan') && (magicBox || magicBoxDraft) && (() => { + const b = magicBoxDraft ?? magicBox!; + return ( + + ); + })()} + + {/* Magic-wand preview regions (kept visible while panning). */} + {(tool === 'magic' || tool === 'fill' || tool === 'pan') && + magicPreview.map((pts, i) => ( + + ))} + {/* Markers for each accumulated magic prompt: white dot = positive + (include), red ring = negative (exclude, SAM only). */} + {(tool === 'magic' || tool === 'fill' || tool === 'pan') && + magicSeeds.map((s, i) => ( + + ))} + {/* Auto "not" anchors from other-class regions: faint hollow red rings, + distinct from the user's solid-red manual negatives. */} + {(tool === 'magic' || tool === 'pan') && magicEngine === 'sam' && samAvoidLabeled && + autoNegPoints.map((p, i) => ( + + ))} + + {/* Region-op preview (select tool): dashed outline of the pending result. */} + {regionOp && regionPreview.map((r, i) => ( + + ))} {/* Layer 3: in-progress brush stroke (imperatively updated, no React re-renders per move) */} - + + {/* Selection toolbar (select tool) */} + {showInteractive && selectedShapeIds.length > 0 && ( +
+
+ + {selectedShapeIds.length > 1 + ? `${selectedShapeIds.length} selected` + : selectedShape?.kind === 'polygon' + ? 'Drag a vertex to edit, or drag to move' + : 'Drag to move or resize'} + + {/* Reassign the selected shape(s) to a different class. */} + + + + + +
+ + {/* Brush thickness (single brush shape) */} + {selectedBrush && ( + + )} + + {/* Region ops on the selection (mask-level), with a magic-wand-style confirm. */} +
+ Region: + {(['merge', 'grow', 'shrink', 'islands'] as RegionOp[]).map((op) => ( + + ))} + {regionOp && regionOp !== 'merge' && ( + + )} + {regionOp && ( + <> + + + + )} +
+
+ )} + + {showInteractive && selectedShapeIds.length === 0 && ( +
+ Click a shape · drag a box to select many · Shift-drag to add more · Shift-click to toggle +
+ )} + + {tool === 'magnetic' && !isPreviewing && ( +
+ Click along an edge to trace it · double-click to finish +
+ )} + + {/* Magic: hint before a click, Add/Cancel panel after. */} + {tool === 'magic' && !isPreviewing && magicSeeds.length === 0 && !magicBox && ( +
+ {magicEngine === 'sam' + ? sam.status === 'loading-model' + ? 'Loading SAM model…' + : sam.status === 'encoding' + ? 'Encoding slice…' + : `Drag a box or click an object · Shift-click to add to it · ${REMOVE_KEY_LABEL}-click to exclude a region` + : `Click a region · Shift-click to add more · ${REMOVE_KEY_LABEL}-click to remove one`} +
+ )} + {tool === 'magic' && !isPreviewing && (magicSeeds.length > 0 || magicBox) && ( +
+ + {magicEngine === 'sam' && sam.status === 'loading-model' + ? 'Loading SAM model…' + : magicEngine === 'sam' && sam.status === 'encoding' + ? 'Encoding slice…' + : magicLoading + ? magicEngine === 'sam' ? 'Segmenting…' : 'Selecting…' + : magicPreview.length > 0 + ? magicEngine === 'sam' + ? `${magicPreview.length} region${magicPreview.length === 1 ? '' : 's'} · Shift-click adds · ${REMOVE_KEY_LABEL}-click marks "not" (red) to exclude` + : `${magicPreview.length} region${magicPreview.length === 1 ? '' : 's'} · shift-add · ${REMOVE_KEY_LABEL.toLowerCase()}-remove` + : magicEngine === 'sam' + ? 'No object — try another point' + : 'No match — raise tolerance'} + + + {magicSeeds.length > 1 && ( + + )} + +
+ )} + + {/* Fill: hint before a click, Add/Cancel panel after (preview + confirm). */} + {tool === 'fill' && !isPreviewing && magicSeeds.length === 0 && ( +
+ {`Click a region to flood-fill it · Shift-click adds more · ${REMOVE_KEY_LABEL}-click removes one`} +
+ )} + {tool === 'fill' && !isPreviewing && magicSeeds.length > 0 && ( +
+ + {magicLoading + ? 'Filling…' + : magicPreview.length > 0 + ? `${magicPreview.length} region${magicPreview.length === 1 ? '' : 's'} · Shift-click adds · ${REMOVE_KEY_LABEL}-click removes` + : 'No match — raise the fill threshold'} + + + {magicSeeds.length > 1 && ( + + )} + +
+ )} + {/* Zoom overlay */}
{Math.round(transform.scaleX * 100)}%
+ + {/* GPU display-adjust filter (brightness/contrast/levels as one linear + transform). Referenced by the image layer canvas via CSS filter:url(). */} +
); } diff --git a/frontend/src/components/annotate/ClassManager/index.tsx b/frontend/src/components/annotate/ClassManager/index.tsx index 7a1df56..2596148 100644 --- a/frontend/src/components/annotate/ClassManager/index.tsx +++ b/frontend/src/components/annotate/ClassManager/index.tsx @@ -10,6 +10,7 @@ import { useAnnotationStore } from '@/stores/annotationStore'; import { useToolStore } from '@/stores/toolStore'; import { cn } from '@/lib/utils'; +/** Counts all shapes assigned to a class across every image/slice in the annotation store. */ function countShapesForClass(classId: number): number { const { byImage } = useAnnotationStore.getState(); let total = 0; @@ -28,6 +29,7 @@ interface ClassRowProps { onClassDeleted: (deletedClassId: number) => void; } +/** Renders a single class row with inline rename, visibility toggle, and delete. */ function ClassRow({ cls, isActive, onActivate, onClassDeleted }: ClassRowProps) { const { updateClass, deleteClass, toggleVisibility } = useClassStore(); const removeShapesByClassId = useAnnotationStore((s) => s.removeShapesByClassId); @@ -35,12 +37,14 @@ function ClassRow({ cls, isActive, onActivate, onClassDeleted }: ClassRowProps) const [editing, setEditing] = useState(false); const [labelInput, setLabelInput] = useState(cls.label); + /** Saves the edited label (if non-empty) to the class store and exits edit mode. */ const commitLabel = () => { const trimmed = labelInput.trim(); if (trimmed) updateClass(cls.classId, { label: trimmed }); setEditing(false); }; + /** Confirms with the user, then removes the class and all of its shapes from the stores. */ const handleDelete = (e: React.MouseEvent) => { e.stopPropagation(); const shapeCount = countShapesForClass(cls.classId); @@ -119,12 +123,17 @@ export interface ClassManagerProps { onClassDeleted?: (deletedClassId: number) => void; } +/** Common segmentation classes offered as one-click chips. */ +const SUGGESTED_CLASSES = ['air', 'sample', 'void', 'pore', 'background', 'substrate']; + +/** Renders the class list, add form, and quick-add suggestion chips. */ export default function ClassManager({ activeClassId, onActivate, onClassDeleted }: ClassManagerProps) { const { classes, addClass } = useClassStore(); const [showAdd, setShowAdd] = useState(false); const [newLabel, setNewLabel] = useState(''); const [newColor, setNewColor] = useState(''); + /** Notifies the parent of a deletion and re-activates the first remaining class if the active one was removed. */ const handleClassDeleted = (deletedClassId: number) => { onClassDeleted?.(deletedClassId); if (activeClassId === deletedClassId) { @@ -135,11 +144,13 @@ export default function ClassManager({ activeClassId, onActivate, onClassDeleted } }; + /** Returns the first unused default color, or cycles back if all are taken. */ const nextColor = () => { const used = new Set(classes.map((c) => c.color)); return DEFAULT_COLORS.find((c) => !used.has(c)) ?? DEFAULT_COLORS[classes.length % DEFAULT_COLORS.length]; }; + /** Validates the new-class form (rejecting duplicate labels), adds the class, and activates it. */ const handleAdd = () => { const label = newLabel.trim(); if (!label) return; @@ -157,6 +168,18 @@ export default function ClassManager({ activeClassId, onActivate, onClassDeleted setShowAdd(false); }; + /** One-click add (or re-activate) a suggested class. */ + const handleQuickAdd = (label: string) => { + const existing = classes.find((c) => c.label.toLowerCase() === label.toLowerCase()); + if (existing) { onActivate(existing.classId); return; } + const classId = addClass(label, nextColor()); + onActivate(classId); + }; + + const suggestions = SUGGESTED_CLASSES.filter( + (label) => !classes.some((c) => c.label.toLowerCase() === label.toLowerCase()), + ); + return (
@@ -196,6 +219,23 @@ export default function ClassManager({ activeClassId, onActivate, onClassDeleted
)} + {suggestions.length > 0 && ( +
+ Quick add + {suggestions.map((label) => ( + + ))} +
+ )} +
{classes.length === 0 && (

No classes yet. Click + to add one.

diff --git a/frontend/src/components/annotate/CleanupStudioPanel/index.tsx b/frontend/src/components/annotate/CleanupStudioPanel/index.tsx new file mode 100644 index 0000000..ad76fb0 --- /dev/null +++ b/frontend/src/components/annotate/CleanupStudioPanel/index.tsx @@ -0,0 +1,365 @@ +/** + * CleanupStudioPanel — size/class filter, ROI keep/delete, morphology, mask sets. + */ +import { useMemo, useState } from 'react'; +import { + Broom, + Copy, + Eye, + EyeSlash, + Stack, + Trash, +} from '@phosphor-icons/react'; +import { useClassStore } from '@/stores/classStore'; +import { useToolStore } from '@/stores/toolStore'; +import { + useCleanupStudio, + type CleanupTarget, +} from '@/hooks/useCleanupStudio'; +import type { MorphCleanupOp, RoiMode, SizeFilterMode } from '@/lib/cleanupOps'; +import type { MergePolicy } from '@/lib/maskSetMerge'; +import type { Shape } from '@/stores/annotationStore'; +import { cn } from '@/lib/utils'; + +interface CleanupStudioPanelProps { + sourceKey: string | null; + activeClassId: number | null; +} + +const btn = + 'px-2 py-1 rounded border text-[11px] transition-colors disabled:opacity-40 ' + + 'bg-white text-gray-700 border-gray-200 hover:bg-sky-50 hover:border-sky-300'; + +/** Sidebar Cleanup Studio controls. */ +export default function CleanupStudioPanel({ + sourceKey, + activeClassId, +}: CleanupStudioPanelProps) { + const classes = useClassStore((s) => s.classes); + const selectedShapeIds = useToolStore((s) => s.selectedShapeIds); + const studio = useCleanupStudio(sourceKey); + + const [target, setTarget] = useState('mask_set'); + const [classFilter, setClassFilter] = useState<'all' | number>( + activeClassId ?? 'all', + ); + const [minArea, setMinArea] = useState(64); + const [maxArea, setMaxArea] = useState(1_000_000); + const [sizeMode, setSizeMode] = useState('keep'); + const [roiMode, setRoiMode] = useState('keep_inside'); + const [allClassesRoi, setAllClassesRoi] = useState(false); + const [morphOp, setMorphOp] = useState('islands'); + const [morphParam, setMorphParam] = useState(50); + const [mergePolicy, setMergePolicy] = useState('union'); + const [status, setStatus] = useState(null); + const [roiShape, setRoiShape] = useState(null); + + const classId = classFilter === 'all' ? null : classFilter; + const activePx = studio.activeSet?.labelMap + ? studio.labelMapPixelCount(studio.activeSet.labelMap) + : studio.activeSet?.shapes.length ?? 0; + const workingOk = + target === 'annotations' + ? studio.sliceShapes.length > 0 + : !!studio.activeSet && (activePx > 0 || (studio.activeSet.shapes?.length ?? 0) > 0); + + const stats = useMemo( + () => studio.sizeStats(target, classId), + // eslint-disable-next-line react-hooks/exhaustive-deps -- intentional refresh on shapes + [studio, target, classId, studio.sliceShapes, studio.activeSet], + ); + + const captureRoiFromSelection = () => { + const shapes = + target === 'mask_set' ? studio.activeSet?.shapes ?? [] : studio.sliceShapes; + const selected = shapes.filter((s) => selectedShapeIds.includes(s.id)); + if (!selected.length) { + setStatus('Select a polygon / rect / ellipse first (Select tool)'); + return; + } + setRoiShape(selected[0]); + setStatus(`ROI captured: ${selected[0].kind}`); + }; + + const resolveClassForRoi = () => (allClassesRoi ? null : (activeClassId ?? classId)); + + return ( +
+ + Cleanup Studio + +

+ Edits dense pixel label maps (not polygons). Commit classifier → Active set, clean, + then Merge → slice (vectorizes once). +

+ + {/* Target */} +
+ {(['annotations', 'mask_set'] as const).map((t) => ( + + ))} +
+ + {/* Mask sets */} +
+
+ + Mask sets + + +
+ {studio.sliceSets.length === 0 && ( +

No sets on this slice yet.

+ )} +
    + {studio.sliceSets.map((m) => ( +
  • + + + +
    • + ))} +
+ {studio.activeSet && ( + studio.renameSet(studio.activeSet!.id, e.target.value)} + /> + )} +
+ + +
+
+ + {/* Class + size */} +
+ By size (connected components) + +
+ + +
+

+ {stats.count} CCs · area {Math.round(stats.min)}–{Math.round(stats.max)} px² +

+
+ + +
+
+ + {/* ROI */} +
+ By region (ROI) +

+ Draw or select a polygon / rect / ellipse, capture it as ROI, then apply. +

+ + {roiShape && ( +

ROI: {roiShape.kind} ready

+ )} + + + +
+ + {/* Morphology */} +
+ Morphology +
+ + setMorphParam(Math.max(1, Number(e.target.value) || 1))} + className="rounded border border-gray-200 px-1 py-0.5 text-[10px]" + /> +
+ +
+ + {status &&

{status}

} +
+ ); +} diff --git a/frontend/src/components/annotate/DisplayControls/index.tsx b/frontend/src/components/annotate/DisplayControls/index.tsx index 359bfd6..f7a2690 100644 --- a/frontend/src/components/annotate/DisplayControls/index.tsx +++ b/frontend/src/components/annotate/DisplayControls/index.tsx @@ -1,8 +1,11 @@ /** - * DisplayControls — brightness/contrast sliders (display-only via Konva filters). - * Does NOT affect exported pixel values — that is governed by RenderOpts. + * DisplayControls — brightness/contrast, CLAHE / Sharpen toggles, levels histogram. + * Display-only. Does NOT affect exported pixel values (RenderOpts). */ -import { ArrowCounterClockwise } from '@phosphor-icons/react'; +import { ArrowCounterClockwise, CircleHalf, Sparkle } from '@phosphor-icons/react'; +import DebouncedSlider from '@/components/common/DebouncedSlider'; +import HistogramControl from '@/components/annotate/HistogramControl'; +import { cn } from '@/lib/utils'; export interface DisplayControlsProps { brightness: number; @@ -10,21 +13,49 @@ export interface DisplayControlsProps { onBrightnessChange: (v: number) => void; onContrastChange: (v: number) => void; onReset: () => void; + histogramBins: number[] | null; + levelsLo: number; + levelsHi: number; + onLevelsChange: (lo: number, hi: number) => void; + onLevelsReset: () => void; + clahe: boolean; + onClaheChange: (on: boolean) => void; + sharpen: boolean; + onSharpenChange: (on: boolean) => void; } +/** Renders display preprocessor toggles + brightness/contrast + levels. */ export default function DisplayControls({ brightness, contrast, onBrightnessChange, onContrastChange, onReset, + histogramBins, + levelsLo, + levelsHi, + onLevelsChange, + onLevelsReset, + clahe, + onClaheChange, + sharpen, + onSharpenChange, }: DisplayControlsProps) { + const toggleCls = (on: boolean) => + cn( + 'flex items-center justify-center gap-1.5 flex-1 py-1.5 rounded-md text-xs border transition-colors', + on + ? 'bg-sky-600 text-white border-sky-700' + : 'bg-white text-gray-700 border-gray-200 hover:bg-sky-50 hover:border-sky-300', + ); + return (
Display
+
+ + +
+
- - `${v > 0 ? '+' : ''}${v}`} min={-1} max={1} step={0.05} value={brightness} - onChange={(e) => onBrightnessChange(Number(e.target.value))} - className="w-full" + onChange={onBrightnessChange} + debounceMs={0} />
- - `${v > 0 ? '+' : ''}${v}`} min={-100} max={100} step={1} value={contrast} - onChange={(e) => onContrastChange(Number(e.target.value))} - className="w-full" + onChange={onContrastChange} + debounceMs={0} />
+ +
); } diff --git a/frontend/src/components/annotate/DownloadModal.tsx b/frontend/src/components/annotate/DownloadModal.tsx index f6ae960..4f3c0b1 100644 --- a/frontend/src/components/annotate/DownloadModal.tsx +++ b/frontend/src/components/annotate/DownloadModal.tsx @@ -3,18 +3,18 @@ */ import { useMemo, useState } from 'react'; import { DownloadSimple, X, CheckCircle, WarningCircle } from '@phosphor-icons/react'; -import { API_BASE } from '@/config'; import { useDatasetStore } from '@/stores/datasetStore'; import { useAnnotationStore } from '@/stores/annotationStore'; import { useClassStore } from '@/stores/classStore'; import { useRatingStore } from '@/stores/ratingStore'; import { buildSourceKey } from '@/lib/sourceKey'; +import { useExportJob } from '@/hooks/useExportJob'; interface DownloadModalProps { onClose: () => void; } -type Scope = 'current' | 'all' | 'stars1' | 'stars2' | 'stars3'; +type Scope = 'slice' | 'current' | 'all' | 'stars1' | 'stars2' | 'stars3'; /** Parse a canonical sourceKey back to { kind, source, serverUri }. */ function parseSourceKey(sk: string) { @@ -27,6 +27,11 @@ function parseSourceKey(sk: string) { } const SCOPE_OPTIONS: { value: Scope; label: string; desc: string; stars?: string }[] = [ + { + value: 'slice', + label: 'Current slice only', + desc: 'Export just the slice currently open in the viewer.', + }, { value: 'current', label: 'Current sample only', @@ -57,19 +62,22 @@ const SCOPE_OPTIONS: { value: Scope; label: string; desc: string; stars?: string }, ]; +/** Renders the COCO download dialog and drives the export job for the chosen scope. */ export default function DownloadModal({ onClose }: DownloadModalProps) { - const { source, kind, serverUri } = useDatasetStore(); + const { source, kind, serverUri, currentSlice } = useDatasetStore(); const { byImage, splitBySlice, negativeSlices } = useAnnotationStore(); const { classes } = useClassStore(); const ratings = useRatingStore((s) => s.ratings); const [scope, setScope] = useState('current'); - const [status, setStatus] = useState<'idle' | 'running' | 'done' | 'error'>('idle'); - const [message, setMessage] = useState(''); + const [includePolygons, setIncludePolygons] = useState(false); + const { state: job, start, startMaskSync, downloadUrl } = useExportJob(); + const status = job.status; + const maskResult = Array.isArray(job.result?.written) ? (job.result.written as Array<{ container?: string; n_slices?: number; updated?: number }>) : null; /** Preview count of samples that would be exported for the selected scope. */ const previewCount = useMemo(() => { - if (scope === 'current') return source ? 1 : 0; + if (scope === 'slice' || scope === 'current') return source ? 1 : 0; const minStars = scope === 'stars1' ? 1 : scope === 'stars2' ? 2 : scope === 'stars3' ? 3 : 0; return Object.keys(byImage).filter((sk) => { const hasShapes = Object.values(byImage[sk]).some((s) => s.length > 0); @@ -79,17 +87,28 @@ export default function DownloadModal({ onClose }: DownloadModalProps) { }).length; }, [scope, source, byImage, ratings]); + /** Builds the per-sample export source list for the selected scope; throws if no current sample. */ const buildSources = () => { - if (scope === 'current') { + if (scope === 'slice' || scope === 'current') { if (!source || !kind) throw new Error('No active sample loaded.'); const sk = buildSourceKey(kind as 'tiled' | 'local', source, serverUri); + const allSlices = byImage[sk] ?? {}; + const allSplits = splitBySlice[sk] ?? {}; + const allNeg = negativeSlices[sk] ?? []; + + // "Current slice only": keep just the slice open in the viewer. + const cur = String(currentSlice); + const slices = scope === 'slice' ? (allSlices[cur] ? { [cur]: allSlices[cur] } : {}) : allSlices; + const split_by_slice = scope === 'slice' ? (allSplits[cur] ? { [cur]: allSplits[cur] } : {}) : allSplits; + const negative_slices = scope === 'slice' ? allNeg.filter((k) => String(k) === cur) : allNeg; + return [{ kind, source, server_uri: serverUri ?? null, - slices: byImage[sk] ?? {}, - split_by_slice: splitBySlice[sk] ?? {}, - negative_slices: negativeSlices[sk] ?? [], + slices, + split_by_slice, + negative_slices, }]; } @@ -115,37 +134,41 @@ export default function DownloadModal({ onClose }: DownloadModalProps) { }); }; - const handleExport = async () => { - setStatus('running'); - setMessage(''); + /** Builds the sources (alerting on error/empty) and starts the COCO export job. */ + const handleExport = () => { + let sources; try { - const sources = buildSources(); - if (sources.length === 0) throw new Error('No samples match the selected scope.'); + sources = buildSources(); + } catch (e) { + // buildSources throws only for an empty/invalid scope; surface inline. + window.alert(String(e)); + return; + } + if (sources.length === 0) { window.alert('No samples match the selected scope.'); return; } + start({ sources, classes, mode: 'merge', include_polygons: includePolygons }); + }; - const res = await fetch(`${API_BASE}/api/export/coco`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ sources, classes, mode: 'merge' }), - }); + /** True when the chosen scope can actually write back to Tiled. */ + const canMaskSync = + previewCount > 0 && !((scope === 'slice' || scope === 'current') && kind !== 'tiled'); - if (!res.ok) throw new Error(await res.text()); - const result = await res.json(); - - setStatus('done'); - setMessage( - `Exported ${sources.length} sample${sources.length !== 1 ? 's' : ''} to ${result.dataset_path ?? 'disk'}.` + - (result.written - ? ' ' + Object.entries(result.written) - .map(([split, r]: [string, any]) => `${split}: ${r.n_images ?? 0} images`) - .join(', ') - : ''), - ); + /** Rasterizes the selected sources' masks and writes them into Tiled (no zip). */ + const handleMaskSync = () => { + let sources; + try { + sources = buildSources(); } catch (e) { - setStatus('error'); - setMessage(String(e)); + window.alert(String(e)); + return; } + // Masks only write back to Tiled sources; the backend skips any local ones. + sources = sources.filter((s) => s.kind === 'tiled'); + if (sources.length === 0) { window.alert('Masks can only be written to Tiled sources.'); return; } + startMaskSync({ sources, classes }); }; + const pct = job.total > 0 ? Math.round((job.done / job.total) * 100) : 0; + return (
@@ -196,6 +219,21 @@ export default function DownloadModal({ onClose }: DownloadModalProps) {
+ {/* Options */} + {status === 'idle' && ( + + )} + {/* Preview count */} {status === 'idle' && (

@@ -205,17 +243,50 @@ export default function DownloadModal({ onClose }: DownloadModalProps) {

)} - {/* Status messages */} - {status === 'done' && ( + {/* Live progress: phase + bar + scrolling backend log */} + {(status === 'running' || status === 'done') && ( +
+
+ {job.phase || 'working'}… + {job.total > 0 && {job.done}/{job.total} slices} +
+
+
+
+ {job.log.length > 0 && ( +
+ {job.log.slice(-12).map((line, i) =>
{line}
)} +
+ )} +
+ )} + + {status === 'done' && maskResult && (
- {message} + + {maskResult.length === 0 + ? 'No masks written (no Tiled sources or no annotated slices).' + : <>Masks merged into Tiled: {maskResult.map((w) => `${w.container} (${w.n_slices} slices total, ${w.updated ?? 0} updated)`).join(', ')}.} + +
+ )} + {status === 'done' && !maskResult && ( +
+ + + Saved to {String(job.result?.dataset_path ?? 'server')} (Tiled). + {' '}Use Download .zip to save images + masks to your computer. +
)} {status === 'error' && (
- {message} + {job.error ?? 'Export failed.'}
)} @@ -229,6 +300,30 @@ export default function DownloadModal({ onClose }: DownloadModalProps) { {status === 'done' ? 'Close' : 'Cancel'} {status !== 'done' && ( + + )} + {status === 'done' && downloadUrl ? ( + + + Download .zip + + ) : status !== 'done' && ( + + + + {showLegacy && ( + <> +
+ + +
+
+ {( + [ + ['intensity', 'Intensity'], + ['edges', 'Edges'], + ['texture', 'Texture'], + ['clahe', 'CLAHE'], + ['includeSam', 'SlimSAM'], + ] as const + ).map(([key, label]) => ( + + ))} +
+

+ Legacy knobs no longer drive Compute — edit the Feature Setup on the Ipred tab. +

+ + )} + + {error && ( +

{error}

+ )} + + {job && n > 0 && ( +
+
+ + + +
+ {activeLabel && ( +

+ {activeLabel} +

+ )} + +
+ )} + + {manifoldSlot} + {classifierSlot} +
+ ); +} diff --git a/frontend/src/components/annotate/HistogramControl/index.tsx b/frontend/src/components/annotate/HistogramControl/index.tsx new file mode 100644 index 0000000..d2a1eb0 --- /dev/null +++ b/frontend/src/components/annotate/HistogramControl/index.tsx @@ -0,0 +1,189 @@ +/** + * HistogramControl — a compact intensity histogram (2D plot) with a single + * dual-knob range slider laid over the plot: drag the left/right handles (or the + * band between them) to set the min/max window that drives a client-side levels + * remap of the displayed image (no refetch). Knobs align with the 256-bin plot. + */ +import { useEffect, useMemo, useRef } from 'react'; +import { ArrowCounterClockwise } from '@phosphor-icons/react'; + +export interface HistogramControlProps { + /** 256-bin luminance histogram of the current slice, or null before load. */ + bins: number[] | null; + lo: number; + hi: number; + onChange: (lo: number, hi: number) => void; + onReset: () => void; +} + +const VBW = 256; +const VBH = 60; +const MAXV = 255; + +export default function HistogramControl({ bins, lo, hi, onChange, onReset }: HistogramControlProps) { + const trackRef = useRef(null); + // Active drag target + the window state captured at press (for band drags). + const drag = useRef<{ target: 'lo' | 'hi' | 'band'; startV: number; startLo: number; startHi: number } | null>(null); + + // Log-scaled bar heights so sparse bright tails stay visible. + const path = useMemo(() => { + if (!bins || bins.length === 0) return ''; + const max = Math.max(1, ...bins.map((b) => Math.log1p(b))); + return bins + .map((b, i) => { + const h = (Math.log1p(b) / max) * VBH; + return `M${i},${VBH} L${i},${VBH - h}`; + }) + .join(' '); + }, [bins]); + + const clampLo = (v: number) => Math.max(0, Math.min(v, hi - 1)); + const clampHi = (v: number) => Math.min(MAXV, Math.max(v, lo + 1)); + + // Coalesce drag updates to one per animation frame (throttle, not debounce): + // pointermove can fire faster than the display refresh, so we collapse bursts + // into a single onChange per frame to keep React updates minimal + smooth. + const rafRef = useRef(null); + const pendingRef = useRef<[number, number] | null>(null); + const scheduleChange = (nlo: number, nhi: number) => { + pendingRef.current = [nlo, nhi]; + if (rafRef.current == null) { + rafRef.current = requestAnimationFrame(() => { + rafRef.current = null; + if (pendingRef.current) onChange(pendingRef.current[0], pendingRef.current[1]); + }); + } + }; + useEffect(() => () => { if (rafRef.current != null) cancelAnimationFrame(rafRef.current); }, []); + + /** Pointer clientX → intensity value (0–255) across the track width. */ + const xToValue = (clientX: number): number => { + const el = trackRef.current; + if (!el) return lo; + const rect = el.getBoundingClientRect(); + if (rect.width <= 0) return lo; + const t = (clientX - rect.left) / rect.width; + return Math.round(Math.max(0, Math.min(1, t)) * MAXV); + }; + + const applyDrag = (clientX: number) => { + if (!drag.current) return; + const v = xToValue(clientX); + if (drag.current.target === 'lo') { + scheduleChange(clampLo(v), hi); + } else if (drag.current.target === 'hi') { + scheduleChange(lo, clampHi(v)); + } else { + // Band: translate the whole window, preserving its width. + const width = drag.current.startHi - drag.current.startLo; + let nlo = drag.current.startLo + (v - drag.current.startV); + nlo = Math.max(0, Math.min(nlo, MAXV - width)); + scheduleChange(nlo, nlo + width); + } + }; + + const onPointerDown = (e: React.PointerEvent) => { + const v = xToValue(e.clientX); + const dLo = Math.abs(v - lo); + const dHi = Math.abs(v - hi); + // Inside the band and clearly away from both knobs → drag the window. + let target: 'lo' | 'hi' | 'band'; + if (v > lo && v < hi && Math.min(dLo, dHi) > 6) target = 'band'; + else target = dLo <= dHi ? 'lo' : 'hi'; + drag.current = { target, startV: v, startLo: lo, startHi: hi }; + (e.currentTarget as HTMLElement).setPointerCapture(e.pointerId); + applyDrag(e.clientX); + }; + + const onPointerMove = (e: React.PointerEvent) => { + if (drag.current) applyDrag(e.clientX); + }; + + const onPointerUp = (e: React.PointerEvent) => { + drag.current = null; + try { (e.currentTarget as HTMLElement).releasePointerCapture(e.pointerId); } catch { /* noop */ } + }; + + /** Keyboard nudge for a focused knob (±1, Shift ±10). */ + const onKnobKey = (which: 'lo' | 'hi') => (e: React.KeyboardEvent) => { + const step = e.shiftKey ? 10 : 1; + let delta = 0; + if (e.key === 'ArrowLeft' || e.key === 'ArrowDown') delta = -step; + else if (e.key === 'ArrowRight' || e.key === 'ArrowUp') delta = step; + else return; + e.preventDefault(); + if (which === 'lo') onChange(clampLo(lo + delta), hi); + else onChange(lo, clampHi(hi + delta)); + }; + + const pct = (v: number) => `${(v / MAXV) * 100}%`; + const knobCls = + 'absolute top-0 h-full w-3 -translate-x-1/2 flex items-center justify-center cursor-ew-resize'; + const knobGrip = 'w-1.5 h-5 rounded-sm bg-sky-600 border border-white shadow'; + + return ( +
+
+ + Levels {lo} – {hi} + + +
+ + {/* Track: histogram plot + dual-knob range slider overlaid on it. */} +
+ + + + + + + + {/* Knob grips (crisp HTML overlay, aligned to the plot by percentage). */} +
+ +
+
+ +
+
+
+ ); +} diff --git a/frontend/src/components/annotate/LayersPanel/index.tsx b/frontend/src/components/annotate/LayersPanel/index.tsx new file mode 100644 index 0000000..a1c4181 --- /dev/null +++ b/frontend/src/components/annotate/LayersPanel/index.tsx @@ -0,0 +1,253 @@ +/** + * LayersPanel — always-on toggles for canvas function groups + per-class visibility. + */ +import { useEffect } from 'react'; +import { Eye, EyeSlash, Stack } from '@phosphor-icons/react'; +import { useClassStore } from '@/stores/classStore'; +import { + LAYER_GROUP_META, + type LayerGroupId, + useLayerVisibilityStore, + isPredictionClassVisible, +} from '@/stores/layerVisibilityStore'; +import { cn } from '@/lib/utils'; + +const GROUP_ORDER: LayerGroupId[] = [ + 'image', + 'features', + 'proba', + 'predictions', + 'annotations', + 'manifold', +]; + +export interface LayersPanelProps { + /** Feature channel available (preprocess job + selection). */ + hasFeatures?: boolean; + /** Softmax probability URL loaded. */ + hasProba?: boolean; + /** Conformal prediction overlays loaded. */ + hasPredictions?: boolean; + /** Prediction class ids (from trained model). */ + predictionClassIds?: number[]; + /** Manifold sample active. */ + hasManifold?: boolean; +} + +function GroupToggle({ + id, + disabled, +}: { + id: LayerGroupId; + disabled?: boolean; +}) { + const on = useLayerVisibilityStore((s) => s.groups[id]); + const toggle = useLayerVisibilityStore((s) => s.toggleGroup); + const meta = LAYER_GROUP_META[id]; + return ( + + ); +} + +/** Sidebar / floating layer stack controls. */ +export default function LayersPanel({ + hasFeatures = false, + hasProba = false, + hasPredictions = false, + predictionClassIds = [], + hasManifold = false, +}: LayersPanelProps) { + const classes = useClassStore((s) => s.classes); + const toggleClassVis = useClassStore((s) => s.toggleVisibility); + const groups = useLayerVisibilityStore((s) => s.groups); + const probaOpacity = useLayerVisibilityStore((s) => s.probaOpacity); + const setProbaOpacity = useLayerVisibilityStore((s) => s.setProbaOpacity); + const predictionsOpacity = useLayerVisibilityStore((s) => s.predictionsOpacity); + const setPredictionsOpacity = useLayerVisibilityStore((s) => s.setPredictionsOpacity); + const predictionClassVisible = useLayerVisibilityStore((s) => s.predictionClassVisible); + const togglePredictionClass = useLayerVisibilityStore((s) => s.togglePredictionClass); + const showPredictionMulti = useLayerVisibilityStore((s) => s.showPredictionMulti); + const showPredictionAbstain = useLayerVisibilityStore((s) => s.showPredictionAbstain); + const setShowPredictionMulti = useLayerVisibilityStore((s) => s.setShowPredictionMulti); + const setShowPredictionAbstain = useLayerVisibilityStore((s) => s.setShowPredictionAbstain); + const ensurePredictionClasses = useLayerVisibilityStore((s) => s.ensurePredictionClasses); + + useEffect(() => { + if (predictionClassIds.length) ensurePredictionClasses(predictionClassIds); + }, [predictionClassIds, ensurePredictionClasses]); + + + const classLabel = (id: number) => + classes.find((c) => c.classId === id)?.label?.trim() || `class ${id}`; + const classColor = (id: number) => + classes.find((c) => c.classId === id)?.color ?? '#64748b'; + + const disabledFor = (id: LayerGroupId): boolean => { + if (id === 'features') return !hasFeatures; + if (id === 'proba') return !hasProba; + if (id === 'predictions') return !hasPredictions; + if (id === 'manifold') return !hasManifold; + if (id === 'annotations') return classes.length === 0; + return false; + }; + + return ( +
+
+ Layers +
+

+ Toggle function groups and per-class visibility. Probability stays an overlay so + annotations and predictions can show together. +

+ +
+ {GROUP_ORDER.map((id) => ( + + ))} +
+ + {groups.proba && hasProba && ( + + )} + + {groups.predictions && hasPredictions && ( +
+ +
+ + +
+ + Prediction classes + + {(predictionClassIds.length ? predictionClassIds : classes.map((c) => c.classId)).map( + (cid) => { + const on = isPredictionClassVisible(predictionClassVisible, cid); + return ( + + ); + }, + )} +
+ )} + + {groups.annotations && classes.length > 0 && ( +
+ + Annotation classes + + {classes.map((c) => ( + + ))} +
+ )} +
+ ); +} diff --git a/frontend/src/components/annotate/ManifoldSuggestPanel/index.tsx b/frontend/src/components/annotate/ManifoldSuggestPanel/index.tsx new file mode 100644 index 0000000..d99eb11 --- /dev/null +++ b/frontend/src/components/annotate/ManifoldSuggestPanel/index.tsx @@ -0,0 +1,251 @@ +/** + * ManifoldSuggestPanel — Suggest Labels (variance boxes + ROI). + */ +import { CircleNotch, Crosshair, MapTrifold, Selection } from '@phosphor-icons/react'; +import type { ManifoldParams } from '@/hooks/useFeatureManifold'; +import { cn } from '@/lib/utils'; + +export interface ManifoldSuggestPanelProps { + hasFeatureJob: boolean; + params: ManifoldParams; + onParamsChange: (p: ManifoldParams) => void; + sampling: boolean; + hasSample: boolean; + showHeatmap: boolean; + onShowHeatmapChange: (v: boolean) => void; + showMarkers: boolean; + onShowMarkersChange: (v: boolean) => void; + heatmapOpacity: number; + onHeatmapOpacityChange: (v: number) => void; + meta: { + k: number; + nPicked: number; + nSubsample: number; + explainedVariance: number; + radius: number; + boxSize: number; + hasMask?: boolean; + maskPixels?: number; + } | null; + error: string | null; + onSample: () => void; + onDismiss: () => void; + /** Number of shapes in the active suggest ROI (0 = full image). */ + roiShapeCount: number; + canCaptureRoi: boolean; + onCaptureRoi: () => void; + onClearRoi: () => void; +} + +const btn = + 'flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border transition-colors'; + +/** Controls for greedy variance-box sampling overlay. */ +export default function ManifoldSuggestPanel({ + hasFeatureJob, + params, + onParamsChange, + sampling, + hasSample, + showHeatmap, + onShowHeatmapChange, + showMarkers, + onShowMarkersChange, + heatmapOpacity, + onHeatmapOpacityChange, + meta, + error, + onSample, + onDismiss, + roiShapeCount, + canCaptureRoi, + onCaptureRoi, + onClearRoi, +}: ManifoldSuggestPanelProps) { + const canSample = hasFeatureJob && !sampling; + + return ( +
+
+ + Suggest labels + + + variance boxes + +
+

+ Optionally select a region of interest, then Suggest. Boxes are placed only fully inside + the ROI. Guidance only — not saved. +

+ +
+ +
+ + {roiShapeCount > 0 + ? `ROI: ${roiShapeCount} shape${roiShapeCount === 1 ? '' : 's'}` + : 'ROI: none (full image)'} + + {roiShapeCount > 0 && ( + + )} +
+
+ + + + + + + + {hasSample && ( +
+ {meta && ( +

+ {meta.nPicked}/{meta.k} boxes · {meta.boxSize}px side · exclude r≈ + {Math.round(meta.radius)}px + {meta.hasMask ? ` · ROI` : ''} · PCA{' '} + {(meta.explainedVariance * 100).toFixed(0)}% var +

+ )} + {sampling && ( +

Re-placing boxes…

+ )} + {meta && (meta.boxSize !== params.boxSize || meta.k !== params.k) && !sampling && ( +

+ Params changed — waiting to re-suggest… +

+ )} + + + {showHeatmap && ( + + )} + +
+ )} + + {error && ( +

{error}

+ )} +
+ ); +} diff --git a/frontend/src/components/annotate/MaskToolsPanel/index.tsx b/frontend/src/components/annotate/MaskToolsPanel/index.tsx new file mode 100644 index 0000000..4918519 --- /dev/null +++ b/frontend/src/components/annotate/MaskToolsPanel/index.tsx @@ -0,0 +1,66 @@ +/** + * MaskToolsPanel — sidebar controls for cross-slice operations (copy / + * interpolate) on the ACTIVE class. Ops round-trip the class's shapes through a + * binary mask; see `useMaskOps`. + */ +import { useMemo, useState } from 'react'; +import { Stack } from '@phosphor-icons/react'; +import { useAnnotationStore } from '@/stores/annotationStore'; +import { useDatasetStore } from '@/stores/datasetStore'; +import { useClassStore } from '@/stores/classStore'; +import { useMaskOps } from '@/hooks/useMaskOps'; + +interface MaskToolsPanelProps { + sourceKey: string | null; + activeClassId: number | null; +} + +export default function MaskToolsPanel({ sourceKey, activeClassId }: MaskToolsPanelProps) { + const byImage = useAnnotationStore((s) => s.byImage); + const classes = useClassStore((s) => s.classes); + const { meta, currentSlice } = useDatasetStore(); + const { copyToNext } = useMaskOps(sourceKey, activeClassId); + + const [status, setStatus] = useState(null); + + const slices = sourceKey ? byImage[sourceKey] ?? {} : {}; + const className = classes.find((c) => c.classId === activeClassId)?.label ?? 'the active class'; + + // Active-class shape count on the current slice (enables copy). + const hasClassHere = useMemo( + () => + activeClassId !== null && + (slices[String(currentSlice)] ?? []).some((sh) => sh.classId === activeClassId), + [slices, currentSlice, activeClassId], + ); + + const isVolume = (meta?.nSlices ?? 1) > 1; + const disabled = !sourceKey || activeClassId === null; + + const btn = 'px-2 py-1 rounded border text-[11px] transition-colors disabled:opacity-40 ' + + 'bg-white text-gray-700 border-gray-200 hover:bg-sky-50 hover:border-sky-300'; + + if (!isVolume) return null; + + return ( +
+ + Cross-slice + +

Acts on ‘{className}’.

+ + + + {status &&

{status}

} +
+ ); +} diff --git a/frontend/src/components/annotate/ModelShelfPanel/index.tsx b/frontend/src/components/annotate/ModelShelfPanel/index.tsx new file mode 100644 index 0000000..c99a132 --- /dev/null +++ b/frontend/src/components/annotate/ModelShelfPanel/index.tsx @@ -0,0 +1,170 @@ +/** + * # REMOVE THIS AND USE YOUR OWN STUFF + * + * ModelShelfPanel — TEMPORARY scaffold to save / list / apply CatBoost models. + * Replace with your own model registry UX, then delete this panel. + */ +import { useEffect, useState } from 'react'; +import { FloppyDisk, Lightning, Trash } from '@phosphor-icons/react'; +import { KillSwitchBanner } from '@/components/KillSwitchBanner'; +import { useModelShelf } from '@/hooks/useModelShelf'; +import type { FeatureParams } from '@/hooks/useFeatureChannels'; +import { useAnnotationStore } from '@/stores/annotationStore'; +import { useDatasetStore } from '@/stores/datasetStore'; +import { useConnectionStore } from '@/stores/connectionStore'; +import { formatFeatureRecipe } from '@/lib/featureRecipe'; +import { cn } from '@/lib/utils'; + +interface ModelShelfPanelProps { + sourceKey: string | null; + trainedModelId: string | null; + alpha: number; + /** Current feature params used when saving a freshly trained model. */ + featureParams: FeatureParams; +} + +const btn = + 'px-2 py-1 rounded border text-[11px] transition-colors disabled:opacity-40 ' + + 'bg-white text-gray-700 border-gray-200 hover:bg-sky-50 hover:border-sky-300'; + +/** + * # REMOVE THIS AND USE YOUR OWN STUFF + * Persist classifiers and re-apply them on the current image into a mask set. + */ +export default function ModelShelfPanel({ + sourceKey, + trainedModelId, + alpha, + featureParams, +}: ModelShelfPanelProps) { + const shelf = useModelShelf(); + const preferredShelfModelId = useConnectionStore((s) => s.preferredShelfModelId); + const preferredShelfModelName = useConnectionStore((s) => s.preferredShelfModelName); + const [name, setName] = useState('my classifier'); + const [status, setStatus] = useState(null); + const { currentSlice } = useDatasetStore(); + const byImage = useAnnotationStore((s) => s.byImage); + const sliceShapes = sourceKey + ? (byImage[sourceKey]?.[String(currentSlice)] ?? []) + : []; + + useEffect(() => { + if (preferredShelfModelName) { + setStatus(`Session model ready: “${preferredShelfModelName}” — click Apply`); + } + }, [preferredShelfModelName]); + + const sortedModels = [...shelf.models].sort((a, b) => { + if (a.id === preferredShelfModelId) return -1; + if (b.id === preferredShelfModelId) return 1; + return 0; + }); + + return ( +
+ + + Model shelf + + TEMP / REMOVE + + +

+ Save a trained classifier, then Apply on other images (recomputes its feature recipe). + {preferredShelfModelId + ? ' A model was selected on Connect/Browse (highlighted).' + : ''} +

+ +
+ setName(e.target.value)} + placeholder="Model name" + /> + +
+ +
    + {sortedModels.length === 0 && ( +
  • No saved models yet.
    • + )} + {sortedModels.map((m) => ( +
  • +
    +

    + {m.name} + {m.id === preferredShelfModelId ? ' · session' : ''} +

    +

    + train {m.n_train} / cal {m.n_cal} + {m.uses_sam ? ' · SAM' : ''} · classes {(m.class_ids ?? []).join(',')} +

    +

    + {formatFeatureRecipe(m.feature_recipe)} +

    +
    + + +
    • + ))} +
+ + {(status || shelf.error) && ( +

+ {shelf.error ?? status} +

+ )} +
+ ); +} diff --git a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx new file mode 100644 index 0000000..2a61d52 --- /dev/null +++ b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx @@ -0,0 +1,395 @@ +/** + * PixelClassifierPanel — CatBoost train / conformal predict / commit under Features. + */ +import { CaretLeft, CaretRight, CircleNotch, FloppyDisk, TreeStructure } from '@phosphor-icons/react'; +import type { + ClfParams, + ClfPredictCounts, + ClfTrainResult, + ClfTreeView, +} from '@/hooks/usePixelClassifier'; +import { cn } from '@/lib/utils'; + +export interface PixelClassifierPanelProps { + hasFeatureJob: boolean; + /** True when Train can auto-preprocess via preferred Feature Setup. */ + canAutoPreprocess?: boolean; + hasShapes: boolean; + training: boolean; + predicting: boolean; + params: ClfParams; + onParamsChange: (p: ClfParams) => void; + model: ClfTrainResult | null; + treeView: ClfTreeView | null; + treeIndex: number; + onTreeIndexChange: (index: number) => void; + hasPrediction: boolean; + predictCounts: ClfPredictCounts | null; + /** Softmax class heatmap browsing (after Predict). */ + probaClassIndex?: number; + activeProbaClassId?: number | null; + activeProbaThreshold?: number; + classLabelForId?: (classId: number) => string; + onCycleProbaClass?: (delta: number) => void; + onProbaThresholdChange?: (threshold: number) => void; + onSaveClassMap?: () => void; + savingClass?: boolean; + error: string | null; + onTrain: () => void; + onPredict: () => void; + onCommit: () => void; + onDismiss: () => void; + /** Button label for Commit (differs in Cleanup vs Draw mode). */ + commitLabel?: string; + /** Active ipred Feature Setup id (from Ipred tab). */ + featureSetupId?: string | null; + /** Active trainer plugin id. */ + trainerId?: string | null; +} + +/** Sidebar controls for ilastik-style CatBoost + split-conformal sets. */ +export default function PixelClassifierPanel({ + hasFeatureJob, + canAutoPreprocess = false, + hasShapes, + training, + predicting, + params, + onParamsChange, + model, + treeView, + treeIndex, + onTreeIndexChange, + hasPrediction, + predictCounts, + probaClassIndex = 0, + activeProbaClassId = null, + activeProbaThreshold = 0.5, + classLabelForId, + onCycleProbaClass, + onProbaThresholdChange, + onSaveClassMap, + savingClass = false, + error, + onTrain, + onPredict, + onCommit, + onDismiss, + commitLabel = 'Commit singletons', + featureSetupId = null, + trainerId = null, +}: PixelClassifierPanelProps) { + const busy = training || predicting || savingClass; + const canTrain = (hasFeatureJob || canAutoPreprocess) && hasShapes && !busy; + const canPredict = !!model && (hasFeatureJob || !!model.featureId) && !busy; + const maxImp = model?.featureImportances[0]?.importance ?? 1; + const alphaPct = Math.round(params.alpha * 100); + const nClasses = model?.classIds.length ?? 0; + const threshPct = Math.round(activeProbaThreshold * 100); + const probaLabel = + activeProbaClassId !== null + ? (classLabelForId?.(activeProbaClassId) ?? `class ${activeProbaClassId}`) + : '—'; + + return ( +
+
+ + Classifier + + + {trainerId ?? 'catboost'} · conformal + +
+ +

+ Setup:{' '} + + {featureSetupId ?? 'none — pick on Ipred tab'} + + {!hasFeatureJob && canAutoPreprocess ? ( + · Train will compute features + ) : null} +

+ +

+ Sparse labels. Conformal sets at α: solid = singleton (commit), hatch = multi, + dark = abstain. Commit keeps scribbles and only adds singletons. Browse softmax + class maps below to threshold each class (live preview) and optionally + cache a mask set. +

+ +
+ + + +
+ + + + + + + + {model && ( +
+

+ Acc {(model.trainAccuracy * 100).toFixed(1)}% · train {model.nTrain.toLocaleString()} / + cal {model.nCal.toLocaleString()} · {model.nTrees} trees · classes{' '} + {model.classIds.join(', ')} + {model.usesSam ? ' · +SlimSAM' : ''} +

+ + {model.featureImportances.length > 0 && ( +
+ Feature importance + {model.featureImportances.slice(0, 8).map((fi) => ( +
+ {fi.label} +
+
+
+ {fi.importance.toFixed(1)} +
+ ))} +
+ )} + + {model.nTrees > 0 && ( +
+
+ Tree + onTreeIndexChange(Number(e.target.value) || 0)} + className="w-14 rounded border border-gray-200 px-1 py-0.5 text-[10px]" + /> +
+ {treeView && ( +
+

depth {treeView.depth} · {treeView.n_leaves} leaves

+ {treeView.splits.map((sp, i) => ( +

+ L{i}: {sp.feature_label} ≤ {sp.threshold.toPrecision(3)} +

+ ))} +
+ )} +
+ )} +
+ )} + + {hasPrediction && predictCounts && ( +

+ Sets · singleton {predictCounts.singleton.toLocaleString()} · multi{' '} + {predictCounts.multi.toLocaleString()} · abstain {predictCounts.abstain.toLocaleString()} +

+ )} + + {hasPrediction && nClasses > 0 && onCycleProbaClass && onProbaThresholdChange && ( +
+
+ Class probability + + {probaClassIndex + 1}/{nClasses} + +
+
+ + + {probaLabel} + + +
+ + {onSaveClassMap && ( + + )} +
+ )} + + {hasPrediction && ( +
+ + +
+ )} + + {error && ( +

{error}

+ )} +
+ ); +} diff --git a/frontend/src/components/annotate/SaveModal.tsx b/frontend/src/components/annotate/SaveModal.tsx index 08c56c5..2e1605c 100644 --- a/frontend/src/components/annotate/SaveModal.tsx +++ b/frontend/src/components/annotate/SaveModal.tsx @@ -18,6 +18,7 @@ export interface SaveModalProps { onClose: () => void; } +/** POSTs the draft to the backend to render a preview thumbnail; returns null on failure. */ async function fetchPreviewBlob(sourceKey: string, payload: SaveDraftPayload): Promise { try { const res = await fetch( @@ -35,6 +36,7 @@ async function fetchPreviewBlob(sourceKey: string, payload: SaveDraftPayload): P } } +/** Reads a Blob into a base64 string (without the data-URL prefix). */ function blobToBase64(blob: Blob): Promise { return new Promise((resolve, reject) => { const reader = new FileReader(); @@ -51,6 +53,7 @@ function blobToBase64(blob: Blob): Promise { }); } +/** Renders the save-version dialog and fetches a live thumbnail preview of the draft. */ export default function SaveModal({ sourceKey, payload, @@ -95,6 +98,7 @@ export default function SaveModal({ }; }, [sourceKey, payload]); + /** Persists the annotator name to localStorage, encodes the thumbnail, and invokes onSave. */ const handleSubmit = async (e: React.FormEvent) => { e.preventDefault(); try { diff --git a/frontend/src/components/annotate/SliceNavigator/index.tsx b/frontend/src/components/annotate/SliceNavigator/index.tsx index ba7a6b2..f201fbe 100644 --- a/frontend/src/components/annotate/SliceNavigator/index.tsx +++ b/frontend/src/components/annotate/SliceNavigator/index.tsx @@ -5,7 +5,9 @@ import { CaretLeft, CaretRight, WarningCircle } from '@phosphor-icons/react'; import { useAnnotationStore } from '@/stores/annotationStore'; import { useDatasetStore } from '@/stores/datasetStore'; import { buildSourceKey } from '@/lib/sourceKey'; +import DebouncedSlider from '@/components/common/DebouncedSlider'; +/** Renders slice navigation controls bound to the dataset and annotation stores. */ export default function SliceNavigator() { const { meta, currentSlice, source, kind, serverUri, setSlice } = useDatasetStore(); const { byImage, negativeSlices, toggleNegativeSlice } = useAnnotationStore(); @@ -27,7 +29,9 @@ export default function SliceNavigator() { const isNegative = (negativeSlices[sourceKey] ?? []).includes(String(currentSlice)); + /** Steps back one slice (clamped at 0); writes to the dataset store. */ const prev = () => setSlice(Math.max(0, currentSlice - 1)); + /** Steps forward one slice (clamped at the last slice); writes to the dataset store. */ const next = () => setSlice(Math.min(n - 1, currentSlice + 1)); return ( @@ -36,14 +40,13 @@ export default function SliceNavigator() { Slice {currentSlice + 1} / {n} - setSlice(Number(e.target.value))} - className="w-full" - aria-label="Slice index" + onChange={setSlice} + debounceMs={120} + ariaLabel="Slice index" />
diff --git a/frontend/src/components/annotate/Toolbar/index.tsx b/frontend/src/components/annotate/Toolbar/index.tsx index 51a926e..9cee336 100644 --- a/frontend/src/components/annotate/Toolbar/index.tsx +++ b/frontend/src/components/annotate/Toolbar/index.tsx @@ -1,13 +1,19 @@ /** * Toolbar — tool selector (radiogroup), brush size, opacity, undo/redo. - * Keybinds: a=polygon, w=ellipse, e=rectangle, q=eraser, b=brush, s=select, - * Space=pan (hold), x=next slice, f=fit to screen, z=undo + * Keybinds: p=polygon, l=ellipse, e=rectangle, r=eraser, b=brush, f=fill, s=select, + * g=magic, m=magnetic, Space=pan (hold), x=next slice, t=fit to screen, Ctrl/Cmd+Z=undo */ -import { Hand, Cursor, Polygon, Rectangle, Circle, PaintBucket, Eraser, ArrowBendUpLeft, ArrowBendUpRight } from '@phosphor-icons/react'; +import { Hand, Cursor, Polygon, MagnetStraight, MagicWand, Rectangle, Circle, PaintBrush, PaintBucket, Eraser, ArrowBendUpLeft, ArrowBendUpRight } from '@phosphor-icons/react'; import { useStore } from 'zustand'; import { useToolStore, type Tool } from '@/stores/toolStore'; import { useAnnotationStore } from '@/stores/annotationStore'; import { cn } from '@/lib/utils'; +import DebouncedSlider from '@/components/common/DebouncedSlider'; +import { useSam } from '@/hooks/useSam'; + +// macOS labels the Alt key "Option" (⌥); the key name only differs on screen. +const IS_MAC = typeof navigator !== 'undefined' && /mac/i.test(navigator.userAgent); +const REMOVE_KEY_LABEL = IS_MAC ? 'Option' : 'Alt'; interface ToolButtonProps { tool: Tool; @@ -15,28 +21,46 @@ interface ToolButtonProps { icon: React.ReactNode; keybind?: string; activeTool: Tool; + disabled?: boolean; onSelect: (t: Tool) => void; } -function ToolButton({ tool, label, icon, keybind, activeTool, onSelect }: ToolButtonProps) { - const isActive = tool === activeTool; +/** Renders one tool as a radio button showing its icon, label, and keybind badge. */ +function ToolButton({ tool, label, icon, keybind, activeTool, disabled, onSelect }: ToolButtonProps) { + const isActive = tool === activeTool && !disabled; + // Underline the hotkey letter within the label (first case-insensitive match). + const renderLabel = () => { + if (!keybind || keybind.length !== 1) return label; + const idx = label.toLowerCase().indexOf(keybind.toLowerCase()); + if (idx < 0) return label; + return ( + <> + {label.slice(0, idx)} + {label[idx]} + {label.slice(idx + 1)} + + ); + }; return ( + ))} +
+ + {magicEngine === 'sam' ? ( + <> +

+ {sam.status === 'loading-model' + ? 'Loading model… (first time only)' + : sam.status === 'encoding' + ? 'Encoding slice…' + : sam.status === 'ready' + ? `SAM ready · ${sam.backend === 'webgpu' ? 'WebGPU' : 'CPU'}` + : sam.webgpu + ? 'Drag a box around an object, or click it.' + : 'No WebGPU — SAM will run on CPU (slower).'} +

+ +
+ {(['auto', 'fine', 'medium', 'coarse'] as const).map((d) => ( + + ))} +
+ (v === 0 ? '0' : `${v > 0 ? '+' : ''}${v.toFixed(1)}`)} + min={-6} + max={6} + step={0.5} + value={samThreshold} + onChange={setSamThreshold} + /> + v.toFixed(1)} + min={0} + max={5} + step={0.5} + value={magicSigma} + onChange={setMagicSigma} + /> + +

+ Drag a box around the object (most reliable), or click it. Shift-click adds to the + object; {REMOVE_KEY_LABEL}-click drops a "not" point (red) to remove an area + SAM grabbed by mistake. Grabs too much? Lower Detail or raise Tightness. + Adjust Display brightness/contrast to re-encode what SAM sees. +

+ + ) : ( + <> +
+ {(['contiguous', 'global'] as const).map((m) => ( + + ))} +
+ `${v}%`} + min={1} + max={60} + value={Math.round(magicTolerance * 100)} + onChange={(v) => setMagicTolerance(v / 100)} + /> + {magicMode === 'contiguous' && ( + `${v}%`} + min={0} + max={100} + value={Math.round(magicEdgeStop * 100)} + onChange={(v) => setMagicEdgeStop(v / 100)} + /> + )} + v.toFixed(1)} + min={0} + max={5} + step={0.5} + value={magicSigma} + onChange={setMagicSigma} + /> +

+ Click a region on the image. For voids that leak, raise Edge stop; for + low-contrast scans, adjust contrast (Display) first. +

+ + )} +
+ )} +
- - `${v}%`} min={0} max={100} value={Math.round(fillOpacity * 100)} - onChange={(e) => setFillOpacity(Number(e.target.value) / 100)} - className="w-full" + onChange={(v) => setFillOpacity(v / 100)} />
+ +
); } diff --git a/frontend/src/components/annotate/VersionHistoryModal.tsx b/frontend/src/components/annotate/VersionHistoryModal.tsx index 717fd30..745dc83 100644 --- a/frontend/src/components/annotate/VersionHistoryModal.tsx +++ b/frontend/src/components/annotate/VersionHistoryModal.tsx @@ -15,6 +15,7 @@ interface Props { onClose: () => void; } +/** Formats an ISO timestamp as a short local date/time; falls back to the raw string. */ function formatDate(iso: string): string { try { return new Date(iso).toLocaleString(undefined, { @@ -28,10 +29,12 @@ function formatDate(iso: string): string { } } +/** Builds the backend URL for a version's rendered thumbnail. */ function thumbnailUrl(sourceKey: string, version: number): string { return `${API_BASE}/api/annotations/versions/${version}/thumbnail?source_key=${encodeURIComponent(sourceKey)}`; } +/** Lazy-loading thumbnail image with a placeholder icon on load failure. */ function VersionThumbnail({ src, alt }: { src: string; alt: string }) { const [status, setStatus] = useState<'loading' | 'ok' | 'error'>('loading'); return ( @@ -51,6 +54,7 @@ function VersionThumbnail({ src, alt }: { src: string; alt: string }) { ); } +/** Renders the modal listing saved versions with preview/restore actions. */ export default function VersionHistoryModal({ versions, sourceKey, onPreview, onRestore, onClose }: Props) { return (
diff --git a/frontend/src/components/annotate/VersionPreviewBar.tsx b/frontend/src/components/annotate/VersionPreviewBar.tsx index 4d6b715..7ccc5cb 100644 --- a/frontend/src/components/annotate/VersionPreviewBar.tsx +++ b/frontend/src/components/annotate/VersionPreviewBar.tsx @@ -7,6 +7,7 @@ */ import { ArrowCounterClockwise, X, CaretLeft, CaretRight } from '@phosphor-icons/react'; import type { VersionMeta } from '@/hooks/useSave'; +import DebouncedSlider from '@/components/common/DebouncedSlider'; interface Props { versions: VersionMeta[]; @@ -19,6 +20,7 @@ interface Props { onRestore: (version: number) => void; } +/** Formats an ISO timestamp as a short local date/time; falls back to the raw string. */ function formatDate(iso: string): string { try { return new Date(iso).toLocaleString(undefined, { @@ -32,6 +34,7 @@ function formatDate(iso: string): string { } } +/** Renders the version time-travel overlay (scrub/restore/exit). */ export default function VersionPreviewBar({ versions, current, @@ -49,6 +52,7 @@ export default function VersionPreviewBar({ const meta = sorted.find((v) => v.version === current) ?? sorted[sorted.length - 1]; const isLatest = current === max; + /** Moves the preview one version older/newer, clamped to the available range. */ const step = (dir: -1 | 1) => { const next = current + dir; if (next >= min && next <= max) onChange(next); @@ -106,14 +110,15 @@ export default function VersionPreviewBar({ v{min} - onChange(Number(e.target.value))} + onChange={onChange} + debounceMs={150} className="flex-1 accent-sky-500 cursor-pointer" + ariaLabel="Preview version" /> v{max} diff --git a/frontend/src/components/common/DebouncedSlider.tsx b/frontend/src/components/common/DebouncedSlider.tsx new file mode 100644 index 0000000..193e34e --- /dev/null +++ b/frontend/src/components/common/DebouncedSlider.tsx @@ -0,0 +1,78 @@ +/** + * DebouncedSlider — a range input whose thumb and label update instantly, but + * whose committed `onChange` is debounced. Use it for sliders that trigger + * expensive work (image re-render, layer re-cache, network fetch, segmentation) + * so dragging stays smooth and the heavy work runs once the user pauses. + */ +import { useEffect, useRef, useState, type ReactNode } from 'react'; + +interface DebouncedSliderProps { + value: number; + min: number; + max: number; + step?: number; + onChange: (value: number) => void; + debounceMs?: number; + /** Static label text shown before the live value. */ + label?: ReactNode; + /** Formats the live (local) value for the label, e.g. (v) => `${v}%`. */ + format?: (value: number) => ReactNode; + className?: string; + labelClassName?: string; + ariaLabel?: string; +} + +/** Range input with instant local feedback and a debounced `onChange` commit. */ +export default function DebouncedSlider({ + value, + min, + max, + step = 1, + onChange, + debounceMs = 120, + label, + format, + className = 'w-full', + labelClassName = 'text-xs text-gray-500', + ariaLabel, +}: DebouncedSliderProps) { + const [local, setLocal] = useState(value); + const timer = useRef(null); + + // Track external changes (reset buttons, keyboard nav, etc.) when not dragging. + useEffect(() => { + setLocal(value); + }, [value]); + + useEffect(() => () => { if (timer.current !== null) window.clearTimeout(timer.current); }, []); + + /** Update the local value immediately and (re)arm the debounced onChange commit. */ + const handle = (next: number) => { + setLocal(next); // instant thumb + label feedback + // debounceMs <= 0 → commit synchronously (no debounce), for cheap live updates. + if (debounceMs <= 0) { onChange(next); return; } + if (timer.current !== null) window.clearTimeout(timer.current); + timer.current = window.setTimeout(() => onChange(next), debounceMs); + }; + + return ( + <> + {(label !== undefined || format) && ( + + )} + handle(Number(e.target.value))} + className={className} + aria-label={ariaLabel} + /> + + ); +} diff --git a/frontend/src/config.ts b/frontend/src/config.ts index 868a59f..0556772 100644 --- a/frontend/src/config.ts +++ b/frontend/src/config.ts @@ -1,4 +1,8 @@ -/** In dev, empty base uses same origin so Vite's proxy applies. */ -export const API_BASE = - import.meta.env.VITE_API_BASE?.trim() || - (import.meta.env.DEV ? '' : 'http://127.0.0.1:8002'); +/** + * Base URL for the backend API. + * - Default is '' (same origin): dev uses Vite's `/api` proxy; the production + * container serves the SPA from FastAPI itself, so `/api` is same-origin too. + * - Set `VITE_API_BASE` at build time only for split deployments where the API + * lives on a different origin (e.g. `https://api.example.com`). + */ +export const API_BASE = import.meta.env.VITE_API_BASE?.trim() || ''; diff --git a/frontend/src/hooks/useAnnotatedSourceKeys.ts b/frontend/src/hooks/useAnnotatedSourceKeys.ts index ccb3ffe..744d304 100644 --- a/frontend/src/hooks/useAnnotatedSourceKeys.ts +++ b/frontend/src/hooks/useAnnotatedSourceKeys.ts @@ -12,6 +12,10 @@ interface DraftSummary { has_annotations?: boolean; } +/** + * Returns a Set of sourceKeys that have at least one annotation, merging + * in-session store state with backend drafts (queried, 30s stale time). + */ export function useAnnotatedSourceKeys() { const byImage = useAnnotationStore((s) => s.byImage); diff --git a/frontend/src/hooks/useCleanupStudio.ts b/frontend/src/hooks/useCleanupStudio.ts new file mode 100644 index 0000000..9cc5c1d --- /dev/null +++ b/frontend/src/hooks/useCleanupStudio.ts @@ -0,0 +1,230 @@ +/** + * Cleanup Studio — size / ROI / morph ops on dense label maps (pixel-accurate). + */ +import { useCallback, useMemo } from 'react'; +import { useAnnotationStore, type Shape } from '@/stores/annotationStore'; +import { useDatasetStore } from '@/stores/datasetStore'; +import { useMaskSetStore } from '@/stores/maskSetStore'; +import { + applyMorphToLabelMap, + applyRoiToLabelMap, + componentSizeStatsLabelMap, + filterLabelMapBySize, + type MorphCleanupOp, + type RoiMode, + type SizeFilterMode, +} from '@/lib/cleanupOps'; +import { mergeMaskSetIntoAnnotations, type MergePolicy, labelMapPixelCount } from '@/lib/maskSetMerge'; +import { shapesToLabelMap } from '@/lib/labelMap'; + +export type CleanupTarget = 'annotations' | 'mask_set'; + +export function useCleanupStudio(sourceKey: string | null) { + const { meta, currentSlice } = useDatasetStore(); + const byImage = useAnnotationStore((s) => s.byImage); + const setShapes = useAnnotationStore((s) => s.setShapes); + const { + sets, + activeSetId, + updateSetLabelMap, + addSet, + deleteSet, + renameSet, + duplicateSet, + setActiveSetId, + setVisible, + } = useMaskSetStore(); + + const sliceShapes = useMemo(() => { + if (!sourceKey) return [] as Shape[]; + return byImage[sourceKey]?.[String(currentSlice)] ?? []; + }, [sourceKey, byImage, currentSlice]); + + const sliceSets = useMemo(() => { + if (!sourceKey) return []; + return sets.filter((m) => m.sourceKey === sourceKey && m.slice === currentSlice); + }, [sets, sourceKey, currentSlice]); + + const activeSet = sliceSets.find((m) => m.id === activeSetId) ?? null; + + /** Resolve a dense label map for the working target (full image resolution). */ + const readLabelMap = useCallback( + (target: CleanupTarget): { labels: Uint8Array; width: number; height: number } | null => { + if (!meta) return null; + const { width, height } = meta; + if (target === 'mask_set') { + if (!activeSet) return null; + if (activeSet.labelMap && activeSet.width === width && activeSet.height === height) { + return { labels: activeSet.labelMap, width, height }; + } + // Legacy set with only shapes — rasterize once at full res + if (activeSet.shapes.length) { + return { + labels: shapesToLabelMap(activeSet.shapes, width, height), + width, + height, + }; + } + return null; + } + if (!sliceShapes.length) return null; + return { labels: shapesToLabelMap(sliceShapes, width, height), width, height }; + }, + [meta, activeSet, sliceShapes], + ); + + const writeLabelMap = useCallback( + (target: CleanupTarget, labels: Uint8Array, width: number, height: number) => { + if (!sourceKey) return false; + if (target === 'mask_set') { + if (!activeSet) return false; + updateSetLabelMap(activeSet.id, labels, width, height); + return true; + } + // Annotations target: promote edits into a new/updated working mask set + // so we never force polygon round-trips mid-cleanup. Also write polygons + // would lose fidelity — create a set instead. + addSet({ + name: `annotations edit slice ${currentSlice + 1}`, + sourceKey, + slice: currentSlice, + origin: 'manual', + labelMap: labels, + width, + height, + shapes: [], + }); + return true; + }, + [sourceKey, activeSet, updateSetLabelMap, addSet, currentSlice], + ); + + const sizeStats = useCallback( + (target: CleanupTarget, classId: number | null) => { + const buf = readLabelMap(target); + if (!buf) return { areas: [], min: 0, max: 0, count: 0 }; + return componentSizeStatsLabelMap(buf.labels, buf.width, buf.height, classId); + }, + [readLabelMap], + ); + + const applySizeFilter = useCallback( + ( + target: CleanupTarget, + opts: { + classId: number | null; + minArea: number; + maxArea: number; + mode: SizeFilterMode; + }, + ) => { + const buf = readLabelMap(target); + if (!buf) return false; + const next = filterLabelMapBySize(buf.labels, buf.width, buf.height, opts); + return writeLabelMap(target, next, buf.width, buf.height); + }, + [readLabelMap, writeLabelMap], + ); + + const applyRoi = useCallback( + ( + target: CleanupTarget, + opts: { roi: Shape; mode: RoiMode; classId: number | null }, + ) => { + const buf = readLabelMap(target); + if (!buf) return false; + const next = applyRoiToLabelMap(buf.labels, buf.width, buf.height, opts); + return writeLabelMap(target, next, buf.width, buf.height); + }, + [readLabelMap, writeLabelMap], + ); + + const applyMorph = useCallback( + ( + target: CleanupTarget, + opts: { op: MorphCleanupOp; param: number; classId: number | null }, + ) => { + const buf = readLabelMap(target); + if (!buf) return false; + const next = applyMorphToLabelMap(buf.labels, buf.width, buf.height, opts); + return writeLabelMap(target, next, buf.width, buf.height); + }, + [readLabelMap, writeLabelMap], + ); + + const createSetFromLabelMap = useCallback( + ( + name: string, + labelMap: Uint8Array, + width: number, + height: number, + origin: 'clf' | 'manual' | 'merge' | 'import' | 'shelf', + ) => { + if (!sourceKey) return null; + return addSet({ + name, + sourceKey, + slice: currentSlice, + origin, + labelMap, + width, + height, + shapes: [], + }); + }, + [sourceKey, currentSlice, addSet], + ); + + const copyAnnotationsToSet = useCallback( + (name?: string) => { + if (!sourceKey || !meta || !sliceShapes.length) return null; + const labels = shapesToLabelMap(sliceShapes, meta.width, meta.height); + return createSetFromLabelMap( + name ?? `annotations slice ${currentSlice + 1}`, + labels, + meta.width, + meta.height, + 'manual', + ); + }, + [sourceKey, meta, sliceShapes, currentSlice, createSetFromLabelMap], + ); + + const mergeActiveIntoAnnotations = useCallback( + (policy: MergePolicy) => { + if (!sourceKey || !meta || !activeSet) return false; + const next = mergeMaskSetIntoAnnotations( + sliceShapes, + activeSet.shapes, + meta.width, + meta.height, + policy, + activeSet.labelMap, + ); + setShapes(sourceKey, currentSlice, next); + return true; + }, + [sourceKey, meta, activeSet, sliceShapes, setShapes, currentSlice], + ); + + return { + sliceShapes, + sliceSets, + activeSet, + activeSetId, + setActiveSetId, + setVisible, + renameSet, + deleteSet, + duplicateSet, + sizeStats, + applySizeFilter, + applyRoi, + applyMorph, + createSetFromLabelMap, + copyAnnotationsToSet, + mergeActiveIntoAnnotations, + labelMapPixelCount, + meta, + }; +} diff --git a/frontend/src/hooks/useDraftSync.ts b/frontend/src/hooks/useDraftSync.ts index c6fec85..8b9c396 100644 --- a/frontend/src/hooks/useDraftSync.ts +++ b/frontend/src/hooks/useDraftSync.ts @@ -5,15 +5,22 @@ import { useEffect, useRef } from 'react'; import { API_BASE } from '@/config'; import { useAnnotationStore } from '@/stores/annotationStore'; import { useClassStore } from '@/stores/classStore'; +import { serializeMaskSet, useMaskSetStore } from '@/stores/maskSetStore'; const DEBOUNCE_MS = 1500; +/** + * Autosaves the current annotation stores for `sourceKey` to the draft endpoint, + * debounced 1.5s on change, and flushes via sendBeacon on page unload. No-op when + * sourceKey is null. + */ export function useDraftSync(sourceKey: string | null) { const timerRef = useRef | null>(null); const byImage = useAnnotationStore((s) => s.byImage); const splitBySlice = useAnnotationStore((s) => s.splitBySlice); const negativeSlices = useAnnotationStore((s) => s.negativeSlices); const classes = useClassStore((s) => s.classes); + const maskSets = useMaskSetStore((s) => s.sets); useEffect(() => { if (!sourceKey) return; @@ -25,6 +32,9 @@ export function useDraftSync(sourceKey: string | null) { slices: byImage[sourceKey] ?? {}, split_by_slice: splitBySlice[sourceKey] ?? {}, negative_slices: negativeSlices[sourceKey] ?? [], + mask_sets: maskSets + .filter((m) => m.sourceKey === sourceKey) + .map(serializeMaskSet), }; fetch(`${API_BASE}/api/annotations/draft?source_key=${encodeURIComponent(sourceKey)}`, { method: 'PUT', @@ -36,7 +46,7 @@ export function useDraftSync(sourceKey: string | null) { return () => { if (timerRef.current) clearTimeout(timerRef.current); }; - }, [sourceKey, byImage, splitBySlice, negativeSlices, classes]); + }, [sourceKey, byImage, splitBySlice, negativeSlices, classes, maskSets]); // Flush on page unload useEffect(() => { @@ -47,6 +57,9 @@ export function useDraftSync(sourceKey: string | null) { slices: byImage[sourceKey] ?? {}, split_by_slice: splitBySlice[sourceKey] ?? {}, negative_slices: negativeSlices[sourceKey] ?? [], + mask_sets: maskSets + .filter((m) => m.sourceKey === sourceKey) + .map(serializeMaskSet), }; navigator.sendBeacon( `${API_BASE}/api/annotations/draft?source_key=${encodeURIComponent(sourceKey)}`, @@ -55,7 +68,7 @@ export function useDraftSync(sourceKey: string | null) { }; window.addEventListener('beforeunload', flush); return () => window.removeEventListener('beforeunload', flush); - }, [sourceKey, byImage, splitBySlice, negativeSlices, classes]); + }, [sourceKey, byImage, splitBySlice, negativeSlices, classes, maskSets]); } /** Load a draft from the server for sourceKey. Returns null if none exists. */ diff --git a/frontend/src/hooks/useExportJob.ts b/frontend/src/hooks/useExportJob.ts new file mode 100644 index 0000000..392f983 --- /dev/null +++ b/frontend/src/hooks/useExportJob.ts @@ -0,0 +1,104 @@ +/** + * useExportJob — drive a background COCO/mask export and stream its progress. + * + * POSTs to /api/export/coco (which returns a job_id), then polls + * /api/export/status/{job_id} until done/error, exposing phase, done/total, and + * a live log for the UI. When finished, `downloadUrl` points at the .zip so the + * browser's save dialog can write it to the user's machine. The server-side + * (Tiled/EXPORT_ROOT) copy is always written regardless. + */ +import { useCallback, useEffect, useRef, useState } from 'react'; +import { API_BASE } from '@/config'; + +export interface ExportJobState { + status: 'idle' | 'running' | 'done' | 'error'; + phase: string; + done: number; + total: number; + log: string[]; + result: Record | null; + error: string | null; + jobId: string | null; +} + +const IDLE: ExportJobState = { + status: 'idle', phase: '', done: 0, total: 0, log: [], result: null, error: null, jobId: null, +}; + +/** + * Drives a background COCO export: returns the live job `state`, a `start` action, + * `reset`, and a `downloadUrl` for the result zip when available. + */ +export function useExportJob() { + const [state, setState] = useState(IDLE); + const timer = useRef(null); + + /** Cancel any pending poll timeout. */ + const clearTimer = () => { + if (timer.current !== null) { window.clearTimeout(timer.current); timer.current = null; } + }; + useEffect(() => clearTimer, []); + + /** Stop polling and return state to idle. */ + const reset = useCallback(() => { clearTimer(); setState(IDLE); }, []); + + /** Recursively poll /api/export/status/{jobId} every 500ms until done/error. */ + const poll = useCallback((jobId: string) => { + const tick = async () => { + try { + const r = await fetch(`${API_BASE}/api/export/status/${jobId}`); + if (!r.ok) { setState((s) => ({ ...s, status: 'error', error: `Status ${r.status}` })); return; } + const j = await r.json(); + const status: ExportJobState['status'] = + j.state === 'done' ? 'done' : j.state === 'error' ? 'error' : 'running'; + setState((s) => ({ + ...s, status, phase: j.phase ?? '', done: j.done ?? 0, total: j.total ?? 0, + log: j.log ?? [], result: j.result ?? null, error: j.error ?? null, + })); + if (j.state === 'done' || j.state === 'error') return; // stop polling + } catch (e) { + setState((s) => ({ ...s, status: 'error', error: String(e) })); + return; + } + timer.current = window.setTimeout(tick, 500); + }; + tick(); + }, []); + + /** POST a job-starting request to `path`, then poll the shared status route. */ + const run = useCallback(async (path: string, payload: unknown) => { + clearTimer(); + setState({ ...IDLE, status: 'running', phase: 'queued' }); + try { + const res = await fetch(`${API_BASE}${path}`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + }); + if (!res.ok) { const msg = await res.text(); setState((s) => ({ ...s, status: 'error', error: msg })); return; } + const data = await res.json(); + if (data.job_id) { + setState((s) => ({ ...s, jobId: data.job_id })); + poll(data.job_id); + } else { + // Synchronous response (e.g. dry-run) — treat as immediately done. + setState((s) => ({ ...s, status: 'done', result: data })); + } + } catch (e) { + setState((s) => ({ ...s, status: 'error', error: String(e) })); + } + }, [poll]); + + /** Start a real COCO export. Returns once the job is queued (progress via state). */ + const start = useCallback((payload: unknown) => run('/api/export/coco', payload), [run]); + + /** Write rasterized masks into Tiled (standalone). Shares the status polling. */ + const startMaskSync = useCallback((payload: unknown) => run('/api/masks/to-tiled', payload), [run]); + + const downloadUrl = + state.jobId && (state.result as { zip_available?: boolean } | null)?.zip_available + ? `${API_BASE}/api/export/download/${state.jobId}` + : null; + + return { state, start, startMaskSync, reset, downloadUrl }; +} diff --git a/frontend/src/hooks/useFeatureChannels.test.ts b/frontend/src/hooks/useFeatureChannels.test.ts new file mode 100644 index 0000000..47b0a14 --- /dev/null +++ b/frontend/src/hooks/useFeatureChannels.test.ts @@ -0,0 +1,16 @@ +import { describe, expect, it } from 'vitest'; +import { DEFAULT_FEATURE_PARAMS } from './useFeatureChannels'; + +describe('DEFAULT_FEATURE_PARAMS', () => { + it('matches locked plan defaults including SlimSAM', () => { + expect(DEFAULT_FEATURE_PARAMS).toEqual({ + sigmaMin: 1, + sigmaMax: 8, + intensity: true, + edges: true, + texture: true, + clahe: true, + includeSam: true, + }); + }); +}); diff --git a/frontend/src/hooks/useFeatureChannels.ts b/frontend/src/hooks/useFeatureChannels.ts new file mode 100644 index 0000000..452bea7 --- /dev/null +++ b/frontend/src/hooks/useFeatureChannels.ts @@ -0,0 +1,299 @@ +/** + * useFeatureChannels — compute feature bank via ipred and load channel PNGs. + */ +import { useCallback, useEffect, useRef, useState } from 'react'; +import { API_BASE } from '@/config'; +import { useConnectionStore } from '@/stores/connectionStore'; +import { + ipredChannelUrl, + ipredPreprocess, + openIpredSession, +} from '@/lib/ipredApi'; + +export interface FeatureChannelInfo { + index: number; + label: string; +} + +export interface FeatureJobInfo { + jobId: string; + width: number; + height: number; + channels: FeatureChannelInfo[]; + hasSam: boolean; + setupId?: string; + cacheHit?: boolean; +} + +export interface FeatureParams { + sigmaMin: number; + sigmaMax: number; + intensity: boolean; + edges: boolean; + texture: boolean; + clahe: boolean; + /** Concat SlimSAM embeddings into CatBoost (legacy in-proc path). */ + includeSam: boolean; +} + +export const DEFAULT_FEATURE_PARAMS: FeatureParams = { + sigmaMin: 1, + sigmaMax: 8, + intensity: true, + edges: true, + texture: true, + clahe: true, + includeSam: true, +}; + +export interface UseFeatureChannelsArgs { + source: string | null; + kind: string | null; + sliceIndex: number; + serverUri: string | null; +} + +/** + * Owns feature compute + channel blob URL lifecycle via ipred. + * `channelIndex === null` means show the original rendered slice. + */ +export function useFeatureChannels({ + source, + kind, + sliceIndex, + serverUri, +}: UseFeatureChannelsArgs) { + const [params, setParams] = useState(DEFAULT_FEATURE_PARAMS); + const [job, setJob] = useState(null); + const [channelIndex, setChannelIndex] = useState(null); + const [channelUrl, setChannelUrl] = useState(null); + const [computing, setComputing] = useState(false); + const [error, setError] = useState(null); + const [samAvailable, setSamAvailable] = useState(false); + const channelUrlRef = useRef(null); + + const preferredFeatureSetupId = useConnectionStore((s) => s.preferredFeatureSetupId); + const preferredCompositionId = useConnectionStore((s) => s.preferredCompositionId); + const ipredSessionId = useConnectionStore((s) => s.ipredSessionId); + const localRoot = useConnectionStore((s) => s.localRoot); + const setIpredSession = useConnectionStore((s) => s.setIpredSession); + + useEffect(() => { + let cancelled = false; + fetch(`${API_BASE}/api/image/features/sam-status`) + .then((r) => (r.ok ? r.json() : null)) + .then((data: { available?: boolean } | null) => { + if (!cancelled && data) setSamAvailable(!!data.available); + }) + .catch(() => { + if (!cancelled) setSamAvailable(false); + }); + return () => { + cancelled = true; + }; + }, []); + + const revokeChannelUrl = useCallback(() => { + if (channelUrlRef.current) { + URL.revokeObjectURL(channelUrlRef.current); + channelUrlRef.current = null; + } + setChannelUrl(null); + }, []); + + useEffect(() => { + setJob(null); + setChannelIndex(null); + revokeChannelUrl(); + setError(null); + }, [source, kind, sliceIndex, serverUri, revokeChannelUrl]); + + useEffect(() => { + if (!job || channelIndex === null) { + revokeChannelUrl(); + return; + } + let cancelled = false; + const url = ipredChannelUrl(job.jobId, channelIndex); + (async () => { + try { + const res = await fetch(url); + if (!res.ok) throw new Error(`Channel fetch failed: ${res.status}`); + const blob = await res.blob(); + if (cancelled) return; + const objUrl = URL.createObjectURL(blob); + if (channelUrlRef.current) URL.revokeObjectURL(channelUrlRef.current); + channelUrlRef.current = objUrl; + setChannelUrl(objUrl); + } catch (e) { + if (!cancelled) { + setError(e instanceof Error ? e.message : String(e)); + revokeChannelUrl(); + } + } + })(); + return () => { + cancelled = true; + }; + }, [job, channelIndex, revokeChannelUrl]); + + useEffect( + () => () => { + if (channelUrlRef.current) URL.revokeObjectURL(channelUrlRef.current); + }, + [], + ); + + const ensureSession = useCallback(async (): Promise => { + if (ipredSessionId) return ipredSessionId; + if (!source || !kind) throw new Error('No sample open'); + const session = await openIpredSession({ + kind, + source, + server_uri: serverUri, + root: kind === 'local' ? localRoot : null, + }); + setIpredSession({ + sessionId: session.session_id, + projectId: session.project_id, + }); + return session.session_id; + }, [ipredSessionId, source, kind, serverUri, localRoot, setIpredSession]); + + const compute = useCallback(async () => { + if (!source || !kind || computing) return; + const compositionId = preferredCompositionId || preferredFeatureSetupId; + if (!compositionId) { + setError('Select a composition on the Ipred tab first.'); + return; + } + setComputing(true); + setError(null); + try { + const sessionId = await ensureSession(); + const data = await ipredPreprocess({ + session_id: sessionId, + composition_id: compositionId, + slice_index: sliceIndex, + }); + const channels: FeatureChannelInfo[] = (data.labels ?? []).map((label, index) => ({ + index, + label, + })); + if (channels.length === 0) { + for (let i = 0; i < data.n_channels; i += 1) { + channels.push({ index: i, label: `channel ${i}` }); + } + } + setJob({ + jobId: data.feature_id, + width: data.width, + height: data.height, + channels, + hasSam: + compositionId.includes('slimsam') || + compositionId.includes('sam') || + compositionId.includes('mark'), + setupId: data.setup_id, + cacheHit: data.cache_hit, + }); + setChannelIndex(0); + if (data.cache_hit) { + // Soft notice — not an error. + } + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + setJob(null); + setChannelIndex(null); + } finally { + setComputing(false); + } + }, [ + source, + kind, + sliceIndex, + computing, + preferredCompositionId, + preferredFeatureSetupId, + ensureSession, + ]); + + const selectChannel = useCallback((index: number | null) => { + setChannelIndex(index); + }, []); + + const cycleChannel = useCallback( + (delta: number) => { + if (!job || job.channels.length === 0) return; + setChannelIndex((prev) => { + const cur = prev ?? 0; + const n = job.channels.length; + return (((cur + delta) % n) + n) % n; + }); + }, + [job], + ); + + const clearSelection = useCallback(() => { + setChannelIndex(null); + }, []); + + const invalidateJob = useCallback(() => { + setJob(null); + setChannelIndex(null); + revokeChannelUrl(); + }, [revokeChannelUrl]); + + /** Adopt a feature bank produced elsewhere (e.g. Train auto-preprocess). */ + const adoptFeatureBank = useCallback( + (payload: { + featureId: string; + width: number; + height: number; + labels?: string[]; + nChannels?: number; + setupId?: string; + cacheHit?: boolean; + }) => { + const n = payload.nChannels ?? payload.labels?.length ?? 0; + const channels: FeatureChannelInfo[] = (payload.labels ?? []).map((label, index) => ({ + index, + label, + })); + if (channels.length === 0) { + for (let i = 0; i < n; i += 1) { + channels.push({ index: i, label: `channel ${i}` }); + } + } + setJob({ + jobId: payload.featureId, + width: payload.width, + height: payload.height, + channels, + hasSam: (payload.setupId ?? '').includes('sam'), + setupId: payload.setupId, + cacheHit: payload.cacheHit, + }); + }, + [], + ); + + return { + params, + setParams, + job, + channelIndex, + channelUrl, + computing, + error, + samAvailable, + preferredFeatureSetupId, + preferredCompositionId, + compute, + selectChannel, + cycleChannel, + clearSelection, + invalidateJob, + adoptFeatureBank, + }; +} diff --git a/frontend/src/hooks/useFeatureManifold.ts b/frontend/src/hooks/useFeatureManifold.ts new file mode 100644 index 0000000..12cb253 --- /dev/null +++ b/frontend/src/hooks/useFeatureManifold.ts @@ -0,0 +1,207 @@ +/** + * useFeatureManifold — greedy variance-box suggestions + residual heatmap. + */ +import { useCallback, useEffect, useRef, useState } from 'react'; +import { API_BASE } from '@/config'; +import type { ManifoldPoint } from '@/lib/featureManifold'; +import type { Shape } from '@/stores/annotationStore'; + +export interface ManifoldParams { + k: number; + /** Full square box side length in image pixels. */ + boxSize: number; +} + +export const DEFAULT_MANIFOLD_PARAMS: ManifoldParams = { + k: 24, + boxSize: 64, +}; + +export interface UseFeatureManifoldArgs { + featureJobId: string | null; + onFeatureJobExpired?: () => void; +} + +async function readErrorDetail(res: Response): Promise { + const text = await res.text(); + try { + const parsed = JSON.parse(text) as { detail?: unknown }; + if (typeof parsed.detail === 'string') return parsed.detail; + } catch { + /* plain */ + } + return text || `Request failed: ${res.status}`; +} + +export function useFeatureManifold({ + featureJobId, + onFeatureJobExpired, +}: UseFeatureManifoldArgs) { + const [params, setParams] = useState(DEFAULT_MANIFOLD_PARAMS); + const [points, setPoints] = useState([]); + const [heatmapUrl, setHeatmapUrl] = useState(null); + const [showHeatmap, setShowHeatmap] = useState(true); + const [showMarkers, setShowMarkers] = useState(true); + const [heatmapOpacity, setHeatmapOpacity] = useState(0.45); + const [sampling, setSampling] = useState(false); + const [error, setError] = useState(null); + const [placementMask, setPlacementMask] = useState(null); + const [meta, setMeta] = useState<{ + k: number; + nPicked: number; + nSubsample: number; + explainedVariance: number; + radius: number; + boxSize: number; + hasMask?: boolean; + maskPixels?: number; + } | null>(null); + const heatmapUrlRef = useRef(null); + const samplingRef = useRef(false); + const hasSampleRef = useRef(false); + const paramsRef = useRef(params); + paramsRef.current = params; + const placementMaskRef = useRef(placementMask); + placementMaskRef.current = placementMask; + const onExpiredRef = useRef(onFeatureJobExpired); + onExpiredRef.current = onFeatureJobExpired; + + const revoke = useCallback(() => { + if (heatmapUrlRef.current) { + URL.revokeObjectURL(heatmapUrlRef.current); + heatmapUrlRef.current = null; + } + setHeatmapUrl(null); + setPoints([]); + setMeta(null); + hasSampleRef.current = false; + }, []); + + useEffect(() => { + revoke(); + setError(null); + setPlacementMask(null); + }, [featureJobId, revoke]); + + useEffect( + () => () => { + if (heatmapUrlRef.current) URL.revokeObjectURL(heatmapUrlRef.current); + }, + [], + ); + + const sample = useCallback(async () => { + if (!featureJobId || samplingRef.current) return; + samplingRef.current = true; + setSampling(true); + setError(null); + const { k, boxSize } = paramsRef.current; + const maskShapes = placementMaskRef.current; + try { + const res = await fetch(`${API_BASE}/api/ipred/manifold/sample`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + feature_id: featureJobId, + k, + box_size: boxSize, + ...(maskShapes && maskShapes.length > 0 ? { shapes: maskShapes } : {}), + }), + }); + if (!res.ok) { + const detail = await readErrorDetail(res); + if (/not found|unknown feature/i.test(detail)) { + onExpiredRef.current?.(); + throw new Error('Feature bank missing. Click Compute again, then Suggest labels.'); + } + throw new Error(detail); + } + const body = (await res.json()) as { + sample_id: string; + points: ManifoldPoint[]; + k: number; + n_picked?: number; + n_subsample: number; + explained_variance: number; + radius?: number; + box_size?: number; + has_mask?: boolean; + mask_pixels?: number; + }; + const heatRes = await fetch( + `${API_BASE}/api/ipred/manifold/${body.sample_id}/heatmap.png`, + ); + if (!heatRes.ok) throw new Error('Failed to fetch manifold heatmap'); + const blob = await heatRes.blob(); + const url = URL.createObjectURL(blob); + if (heatmapUrlRef.current) URL.revokeObjectURL(heatmapUrlRef.current); + heatmapUrlRef.current = url; + setHeatmapUrl(url); + setPoints(body.points ?? []); + hasSampleRef.current = (body.points?.length ?? 0) > 0 || !!url; + setMeta({ + k: body.k, + nPicked: body.n_picked ?? (body.points?.length ?? 0), + nSubsample: body.n_subsample, + explainedVariance: body.explained_variance, + radius: body.radius ?? body.points?.[0]?.radius ?? 0, + boxSize: body.box_size ?? boxSize, + hasMask: body.has_mask, + maskPixels: body.mask_pixels, + }); + } catch (e) { + revoke(); + setError(e instanceof Error ? e.message : String(e)); + } finally { + samplingRef.current = false; + setSampling(false); + } + }, [featureJobId, revoke]); + + const sampleRef = useRef(sample); + sampleRef.current = sample; + + // Re-place boxes when K / box size / mask change after an existing suggestion. + useEffect(() => { + if (!featureJobId || !hasSampleRef.current) return; + const t = window.setTimeout(() => { + void sampleRef.current(); + }, 350); + return () => window.clearTimeout(t); + }, [featureJobId, params.k, params.boxSize, placementMask]); + + const setPlacementMaskFromShapes = useCallback((shapes: Shape[]) => { + setPlacementMask(shapes.length ? shapes.map((s) => ({ ...s })) : null); + }, []); + + const clearPlacementMask = useCallback(() => { + setPlacementMask(null); + }, []); + + const dismiss = useCallback(() => { + revoke(); + setError(null); + }, [revoke]); + + return { + params, + setParams, + points, + heatmapUrl, + showHeatmap, + setShowHeatmap, + showMarkers, + setShowMarkers, + heatmapOpacity, + setHeatmapOpacity, + sampling, + error, + meta, + sample, + dismiss, + placementMask, + setPlacementMaskFromShapes, + clearPlacementMask, + hasSample: points.length > 0 || !!heatmapUrl, + }; +} diff --git a/frontend/src/hooks/useHubSelectedTabs.ts b/frontend/src/hooks/useHubSelectedTabs.ts index 48cb87f..7f6da10 100644 --- a/frontend/src/hooks/useHubSelectedTabs.ts +++ b/frontend/src/hooks/useHubSelectedTabs.ts @@ -2,6 +2,10 @@ import { useState, useEffect } from 'react'; const STORAGE_KEY = 'sam3_hub_selected_tab_paths'; +/** + * useHubSelectedTabs — persists the set of selected hub tab paths in localStorage. + * Returns the stored paths (null if none) and a setter that writes through to storage. + */ export function useHubSelectedTabs() { const [selectedPaths, setSelectedPathsState] = useState(() => { try { @@ -18,6 +22,7 @@ export function useHubSelectedTabs() { return null; }); + /** Persist `paths` to localStorage and update state; logs on storage failure. */ const setSelectedPaths = (paths: string[]) => { try { localStorage.setItem(STORAGE_KEY, JSON.stringify(paths)); diff --git a/frontend/src/hooks/useImageSlice.ts b/frontend/src/hooks/useImageSlice.ts index b54f087..8547aac 100644 --- a/frontend/src/hooks/useImageSlice.ts +++ b/frontend/src/hooks/useImageSlice.ts @@ -9,7 +9,8 @@ export interface SliceResult { url: string; } -function buildSliceUrl( +/** Build the /api/image/slice URL encoding source, slice index, and render options. */ +export function buildSliceUrl( source: string, kind: string, sliceIndex: number, @@ -30,6 +31,10 @@ function buildSliceUrl( return `${API_BASE}/api/image/slice?${params.toString()}`; } +/** + * Fetches a PNG slice as a blob object URL (5min stale time). Disabled until + * both source and kind are set. Use for displaying a rendered image slice. + */ export function useImageSlice( source: string | null, kind: string | null, diff --git a/frontend/src/hooks/useKeybinds.ts b/frontend/src/hooks/useKeybinds.ts index b859900..1fbe0c1 100644 --- a/frontend/src/hooks/useKeybinds.ts +++ b/frontend/src/hooks/useKeybinds.ts @@ -1,10 +1,11 @@ /** * useKeybinds — keyboard shortcuts for the annotation workspace. * - * Tools: a=polygon w=ellipse e=rectangle q=eraser b=brush s=select + * Tools: p=polygon l=ellipse e=rectangle r=eraser b=brush f=fill g=magic + * m=magnetic s=select (each key is a letter in the tool's label) * Pan: hold Space (reverts to the previous tool on release) - * View: x=next slice (forward) f=fit image to screen arrows=prev/next slice - * Edit: z (or Ctrl/Cmd+Z)=undo Ctrl/Cmd+Shift+Z / Ctrl+Y=redo + * View: x=next slice (forward) t=fit image to screen arrows=prev/next slice + * Edit: Ctrl/Cmd+Z=undo Ctrl/Cmd+Shift+Z / Ctrl+Y=redo * Other: 1-9=class n=new brush instance Del/Back=delete Esc=cancel */ import { useEffect, useRef } from 'react'; @@ -14,15 +15,24 @@ import { useDatasetStore } from '@/stores/datasetStore'; import { useAnnotationStore } from '@/stores/annotationStore'; import { useClassStore } from '@/stores/classStore'; +// Each tool's key is a letter in its label (shown underlined in the Toolbar). const TOOL_KEYBINDS: Record = { s: 'select', - a: 'polygon', - e: 'rectangle', - w: 'ellipse', + p: 'polygon', + m: 'magnetic', + g: 'magic', // maGic (m is taken by magnetic) + e: 'rectangle', // Rect + l: 'ellipse', // eLlipse (e is taken by rect) b: 'brush', - q: 'eraser', + f: 'fill', + r: 'eraser', // eRaser }; +/** + * Installs global keydown/keyup listeners for the annotation workspace (tools, + * slice nav, undo/redo, class select). Ignores keys typed in form fields. + * Invokes the supplied callbacks for class activation, brush, delete, and cancel. + */ export function useKeybinds( activeClassId: number | null, onActivateClass: (id: number) => void, @@ -76,15 +86,9 @@ export function useKeybinds( temporalStore.redo(); return; } - // Plain Z = undo - if (key === 'z' && !e.ctrlKey && !e.metaKey && !e.shiftKey) { - e.preventDefault(); - temporalStore.undo(); - return; - } - // Fit image to screen - if (key === 'f') { + // Fit image to screen (moved off 'f', which is now the Fill tool) + if (key === 't') { requestFit(); return; } diff --git a/frontend/src/hooks/useMaskOps.ts b/frontend/src/hooks/useMaskOps.ts new file mode 100644 index 0000000..87dc3cb --- /dev/null +++ b/frontend/src/hooks/useMaskOps.ts @@ -0,0 +1,76 @@ +/** + * Mask operations that work on the ACTIVE class of the current sample by + * round-tripping its shapes through a binary mask: cleanup (morphology) on the + * current slice, and cross-slice copy across a volume. + * + * Everything reads global store state at call time (fresh, no stale closures) + * and writes back via one-undo-step store actions. + */ +import { useCallback } from 'react'; +import { v4 as uuidv4 } from 'uuid'; +import { useAnnotationStore, type Shape, type PolygonShape } from '@/stores/annotationStore'; +import { useDatasetStore } from '@/stores/datasetStore'; +import { rasterizeShapes, gridFor } from '@/lib/rasterize'; +import { maskToPolygons } from '@/lib/magicwand'; +import { fillHoles, removeSmallComponents, dilate, erode, smooth } from '@/lib/morphology'; + +export type CleanupOp = 'fill' | 'islands' | 'smooth' | 'grow' | 'shrink'; + +/** Polygons (image coords) → polygon shapes for a class. */ +function polysToShapes(polys: number[][], classId: number): PolygonShape[] { + return polys + .filter((pts) => pts.length >= 6) + .map((pts) => ({ id: uuidv4(), classId, kind: 'polygon', points: pts })); +} + +export function useMaskOps(sourceKey: string | null, activeClassId: number | null) { + const replaceClassShapesOnSlice = useAnnotationStore((s) => s.replaceClassShapesOnSlice); + const copySliceShapes = useAnnotationStore((s) => s.copySliceShapes); + + /** Shapes of the active class on a given slice (fresh from the store). */ + const classShapesOn = useCallback( + (slice: number): Shape[] => { + if (!sourceKey || activeClassId === null) return []; + const { byImage } = useAnnotationStore.getState(); + return (byImage[sourceKey]?.[String(slice)] ?? []).filter((sh) => sh.classId === activeClassId); + }, + [sourceKey, activeClassId], + ); + + /** Apply a morphological cleanup op to the active class on the current slice. */ + const applyCleanup = useCallback( + (op: CleanupOp, param = 2): boolean => { + const meta = useDatasetStore.getState().meta; + if (!sourceKey || activeClassId === null || !meta) return false; + const slice = useDatasetStore.getState().currentSlice; + const shapes = classShapesOn(slice); + if (shapes.length === 0) return false; + + const { gw, gh, scale } = gridFor(meta.width, meta.height); + let mask = rasterizeShapes(shapes, gw, gh, scale); + switch (op) { + case 'fill': mask = fillHoles(mask, gw, gh); break; + case 'islands': mask = removeSmallComponents(mask, gw, gh, Math.max(1, param)); break; + case 'smooth': mask = smooth(mask, gw, gh, Math.max(1, param)); break; + case 'grow': mask = dilate(mask, gw, gh, Math.max(1, param)); break; + case 'shrink': mask = erode(mask, gw, gh, Math.max(1, param)); break; + } + const polys = maskToPolygons(mask, gw, gh, { minRegion: 4, scale }); + replaceClassShapesOnSlice(sourceKey, slice, activeClassId, polysToShapes(polys, activeClassId)); + return true; + }, + [sourceKey, activeClassId, classShapesOn, replaceClassShapesOnSlice], + ); + + /** Copy the active class's shapes from the current slice to the next slice. */ + const copyToNext = useCallback((): boolean => { + const { meta, currentSlice } = useDatasetStore.getState(); + if (!sourceKey || activeClassId === null || !meta) return false; + if (currentSlice >= meta.nSlices - 1) return false; + if (classShapesOn(currentSlice).length === 0) return false; + copySliceShapes(sourceKey, currentSlice, [currentSlice + 1], activeClassId); + return true; + }, [sourceKey, activeClassId, classShapesOn, copySliceShapes]); + + return { applyCleanup, copyToNext }; +} diff --git a/frontend/src/hooks/useModelShelf.ts b/frontend/src/hooks/useModelShelf.ts new file mode 100644 index 0000000..68ae523 --- /dev/null +++ b/frontend/src/hooks/useModelShelf.ts @@ -0,0 +1,203 @@ +/** + * # REMOVE THIS AND USE YOUR OWN STUFF + * + * useModelShelf — TEMPORARY scaffold to persist / list / apply CatBoost models. + * Replace with your model registry client, then delete this hook (+ ModelShelfPanel). + */ +import { useCallback, useEffect, useState } from 'react'; +import { API_BASE } from '@/config'; +import type { FeatureParams } from '@/hooks/useFeatureChannels'; +import { loadLabelPng } from '@/lib/pixelClf'; +import { useMaskSetStore } from '@/stores/maskSetStore'; +import { useDatasetStore } from '@/stores/datasetStore'; +import type { Shape } from '@/stores/annotationStore'; + +export interface ShelfModelMeta { + id: string; + name: string; + class_ids: number[]; + n_train: number; + n_cal: number; + train_accuracy: number; + uses_sam: boolean; + feature_recipe: Record; + created_at: string; + n_features?: number; +} + +function recipeFromParams(p: FeatureParams): Record { + return { + sigma_min: p.sigmaMin, + sigma_max: p.sigmaMax, + intensity: p.intensity, + edges: p.edges, + texture: p.texture, + clahe: p.clahe, + include_sam: p.includeSam, + }; +} + +export function useModelShelf() { + const [models, setModels] = useState([]); + const [busy, setBusy] = useState(false); + const [error, setError] = useState(null); + const addSet = useMaskSetStore((s) => s.addSet); + const { source, kind, serverUri, currentSlice } = useDatasetStore(); + + const refresh = useCallback(async () => { + try { + const res = await fetch(`${API_BASE}/api/clf/models`); + if (!res.ok) throw new Error(`List failed: ${res.status}`); + const data = (await res.json()) as ShelfModelMeta[]; + setModels(data); + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + } + }, []); + + useEffect(() => { + void refresh(); + }, [refresh]); + + const saveModel = useCallback( + async (modelId: string, name: string, featureParams: FeatureParams) => { + setBusy(true); + setError(null); + try { + const res = await fetch(`${API_BASE}/api/clf/models/save`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + model_id: modelId, + name, + feature_recipe: recipeFromParams(featureParams), + }), + }); + if (!res.ok) { + const t = await res.text(); + throw new Error(t || `Save failed: ${res.status}`); + } + await refresh(); + return true; + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + return false; + } finally { + setBusy(false); + } + }, + [refresh], + ); + + const deleteModel = useCallback( + async (shelfId: string) => { + setBusy(true); + setError(null); + try { + const res = await fetch(`${API_BASE}/api/clf/models/${shelfId}`, { method: 'DELETE' }); + if (!res.ok) throw new Error(`Delete failed: ${res.status}`); + await refresh(); + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + } finally { + setBusy(false); + } + }, + [refresh], + ); + + const applyToCurrent = useCallback( + async ( + shelfId: string, + { + sourceKey, + alpha, + preserveShapes, + name, + }: { + sourceKey: string; + alpha: number; + preserveShapes: Shape[]; + name?: string; + }, + ) => { + if (!source || !kind) { + setError('No image open'); + return null; + } + setBusy(true); + setError(null); + try { + const res = await fetch(`${API_BASE}/api/clf/models/${shelfId}/predict`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + source, + kind, + slice_index: currentSlice, + server_uri: serverUri, + alpha, + shapes: preserveShapes, + }), + }); + if (!res.ok) { + const t = await res.text(); + throw new Error(t || `Predict failed: ${res.status}`); + } + const meta = (await res.json()) as { + pred_id: string; + class_ids: number[]; + alpha: number; + }; + const commitRes = await fetch( + `${API_BASE}/api/image/features/clf/predict/${meta.pred_id}/commit.png`, + ); + if (!commitRes.ok) throw new Error('Failed to fetch commit PNG'); + const blob = await commitRes.blob(); + const url = URL.createObjectURL(blob); + try { + const { data, width, height } = await loadLabelPng(url); + // Keep dense pixels for Cleanup; do not polygonize early. + let any = false; + for (let i = 0; i < data.length; i++) { + if (data[i]) { + any = true; + break; + } + } + if (!any) return null; + const alphaPct = Math.round((meta.alpha ?? alpha) * 100); + const id = addSet({ + name: name ?? `shelf α=${alphaPct}%`, + sourceKey, + slice: currentSlice, + origin: 'shelf', + labelMap: data, + width, + height, + shapes: [], + }); + return id; + } finally { + URL.revokeObjectURL(url); + } + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + return null; + } finally { + setBusy(false); + } + }, + [source, kind, serverUri, currentSlice, addSet], + ); + + return { + models, + busy, + error, + refresh, + saveModel, + deleteModel, + applyToCurrent, + }; +} diff --git a/frontend/src/hooks/useOpenInAnnotate.ts b/frontend/src/hooks/useOpenInAnnotate.ts index ac4ddda..cba1cc6 100644 --- a/frontend/src/hooks/useOpenInAnnotate.ts +++ b/frontend/src/hooks/useOpenInAnnotate.ts @@ -1,5 +1,5 @@ /** - * Open any sample (Tiled or local) in the Annotate tab — load meta + restore draft. + * Open any sample (Tiled or local) — load meta + restore draft + session label prefs. */ import { useCallback } from 'react'; import { useNavigate } from 'react-router'; @@ -7,36 +7,88 @@ import { API_BASE } from '@/config'; import { useDatasetStore } from '@/stores/datasetStore'; import { useClassStore } from '@/stores/classStore'; import { useAnnotationStore } from '@/stores/annotationStore'; +import { useConnectionStore } from '@/stores/connectionStore'; +import { + deserializeMaskSet, + useMaskSetStore, + type MaskSetSerialized, +} from '@/stores/maskSetStore'; import { loadDraft } from '@/hooks/useDraftSync'; +import { openIpredSession } from '@/lib/ipredApi'; import { buildSourceKey } from '@/lib/sourceKey'; import type { Shape } from '@/stores/annotationStore'; -/** Merge a loaded draft into the stores for the given sourceKey. */ +/** Best-effort: bind opened sample to an ipred session (project). */ +async function bindIpredSession(payload: { + kind: string; + source: string; + server_uri?: string | null; + root?: string | null; +}): Promise { + try { + const session = await openIpredSession(payload); + useConnectionStore.getState().setIpredSession({ + sessionId: session.session_id, + projectId: session.project_id, + }); + } catch { + // ipred may be down in some dev setups; Preprocess can retry later. + useConnectionStore.getState().setIpredSession({ sessionId: null, projectId: null }); + } +} + +/** Merge a loaded draft into the stores. Returns true if draft supplied classes. */ async function applyDraft( sourceKey: string, - setClasses: (classes: Parameters['setClasses']>[0]) => void, - mergeSourceDraft: ReturnType['mergeSourceDraft'], -) { + setClasses: ReturnType['setClasses'], + mergeSourceDraft: ReturnType['mergeSourceDraft'], +): Promise { const draft = await loadDraft(sourceKey); - if (!draft?.payload) return; + if (!draft?.payload) return false; const payload = draft.payload as Record; - if (Array.isArray(payload.classes)) { + let hadClasses = false; + if (Array.isArray(payload.classes) && payload.classes.length > 0) { setClasses(payload.classes as Parameters[0]); + hadClasses = true; } const slices = (payload.slices ?? {}) as Record; const splitMap = (payload.split_by_slice ?? {}) as Record; const negSlices = (payload.negative_slices ?? []) as string[]; mergeSourceDraft(sourceKey, slices, splitMap, negSlices); + const maskSets = Array.isArray(payload.mask_sets) + ? (payload.mask_sets as MaskSetSerialized[]).map(deserializeMaskSet) + : []; + const others = useMaskSetStore.getState().sets.filter((m) => m.sourceKey !== sourceKey); + useMaskSetStore.getState().replaceAll([...others, ...maskSets]); + return hadClasses; } +////////////////////////////////////////////////////////////////////////////// +// # REMOVE THIS AND USE YOUR OWN STUFF +// Seeds classes from the Connect/Browse label-set picker when a sample has no +// draft classes. Replace with your own label bootstrap. +////////////////////////////////////////////////////////////////////////////// +/** Seed preferred session label set when the sample has no draft classes. */ +function applyPreferredClassesIfNeeded( + hadDraftClasses: boolean, + setClasses: ReturnType['setClasses'], +) { + if (hadDraftClasses) return; + const preferred = useConnectionStore.getState().preferredClasses; + if (preferred && preferred.length > 0) { + setClasses(preferred.map((c) => ({ ...c }))); + } +} + +/** Returns openers that load meta, restore draft, apply session prefs, go to Preprocess. */ export function useOpenInAnnotate() { const navigate = useNavigate(); - const { setDataset } = useDatasetStore(); + const { setDataset, setSlice } = useDatasetStore(); const { setClasses } = useClassStore(); const { mergeSourceDraft } = useAnnotationStore(); const openTiledArray = useCallback( - async (tiledPath: string, serverUri: string) => { + async (tiledPath: string, serverUri: string, initialSlice = 0) => { const params = new URLSearchParams({ source: tiledPath, kind: 'tiled' }); if (serverUri) params.set('server_uri', serverUri); @@ -52,12 +104,19 @@ export function useOpenInAnnotate() { isRgb: meta.is_rgb, valueRange: meta.value_range, }); + if (initialSlice > 0) setSlice(Math.min(initialSlice, Math.max(0, meta.n_slices - 1))); const sourceKey = buildSourceKey('tiled', tiledPath, serverUri); - await applyDraft(sourceKey, setClasses, mergeSourceDraft); - navigate('/annotate'); + const hadClasses = await applyDraft(sourceKey, setClasses, mergeSourceDraft); + applyPreferredClassesIfNeeded(hadClasses, setClasses); + await bindIpredSession({ + kind: 'tiled', + source: tiledPath, + server_uri: serverUri || null, + }); + navigate('/preprocess'); }, - [navigate, setDataset, setClasses, mergeSourceDraft], + [navigate, setDataset, setSlice, setClasses, mergeSourceDraft], ); const openLocalFile = useCallback( @@ -78,8 +137,15 @@ export function useOpenInAnnotate() { }); const sourceKey = buildSourceKey('local', relPath); - await applyDraft(sourceKey, setClasses, mergeSourceDraft); - navigate('/annotate'); + const hadClasses = await applyDraft(sourceKey, setClasses, mergeSourceDraft); + applyPreferredClassesIfNeeded(hadClasses, setClasses); + const localRoot = useConnectionStore.getState().localRoot; + await bindIpredSession({ + kind: 'local', + source: relPath, + root: localRoot, + }); + navigate('/preprocess'); }, [navigate, setDataset, setClasses, mergeSourceDraft], ); diff --git a/frontend/src/hooks/usePixelClassifier.ts b/frontend/src/hooks/usePixelClassifier.ts new file mode 100644 index 0000000..848c0b2 --- /dev/null +++ b/frontend/src/hooks/usePixelClassifier.ts @@ -0,0 +1,519 @@ +/** + * usePixelClassifier — train / conformal predict via ipred. + */ +import { useCallback, useEffect, useRef, useState } from 'react'; +import { useConnectionStore } from '@/stores/connectionStore'; +import type { Shape } from '@/stores/annotationStore'; +import { + ipredInfer, + ipredPreprocess, + ipredRunCommitUrl, + ipredRunProbaUrl, + ipredRunStatusUrl, + ipredThresholdClass, + ipredTrain, + openIpredSession, + type IpredThresholdClassResult, +} from '@/lib/ipredApi'; +import { thresholdProbaPngBlob } from '@/lib/pixelClf'; + +export interface ClfParams { + iterations: number; + depth: number; + learningRate: number; + /** Misfire level α for conformal sets (e.g. 0.05 = 5%). */ + alpha: number; +} + +export const DEFAULT_CLF_PARAMS: ClfParams = { + iterations: 200, + depth: 6, + learningRate: 0.1, + alpha: 0.05, +}; + +export interface ClfFeatureImportance { + label: string; + importance: number; +} + +export interface ClfTreeSplit { + feature_index: number; + feature_label: string; + threshold: number; + split_type: string; +} + +export interface ClfTreeView { + tree_index: number; + n_trees: number; + depth: number; + splits: ClfTreeSplit[]; + n_leaves: number; + leaf_values: number[]; + leaf_values_truncated: boolean; +} + +export interface ClfPredictCounts { + singleton: number; + multi: number; + abstain: number; +} + +export interface ClfTrainResult { + modelId: string; + featureId: string; + nSamples: number; + nTrain: number; + nCal: number; + classIds: number[]; + trainAccuracy: number; + params: ClfParams; + nTrees: number; + usesSam: boolean; + featureImportances: ClfFeatureImportance[]; + treePreview: ClfTreeView | null; + trainerId: string; + featureSetupId: string | null; +} + +export interface UsePixelClassifierArgs { + /** Current ipred feature bank id (from Preprocess compute), if any. */ + featureJobId: string | null; + resetKey: string | null; + source: string | null; + kind: string | null; + serverUri: string | null; + sliceIndex: number; + onFeatureJobExpired?: () => void; + /** Called when train auto-runs preprocess and gets a new feature bank. */ + onFeatureReady?: (info: { + featureId: string; + width: number; + height: number; + labels: string[]; + nChannels: number; + setupId: string; + cacheHit: boolean; + }) => void; +} + +const DEFAULT_PROBA_THRESHOLD = 0.5; + +export function usePixelClassifier({ + featureJobId, + resetKey, + source, + kind, + serverUri, + sliceIndex, + onFeatureJobExpired, + onFeatureReady, +}: UsePixelClassifierArgs) { + const preferredFeatureSetupId = useConnectionStore((s) => s.preferredFeatureSetupId); + const preferredCompositionId = useConnectionStore((s) => s.preferredCompositionId); + const preferredTrainerId = useConnectionStore((s) => s.preferredTrainerId); + const preferredTrainerConfig = useConnectionStore((s) => s.preferredTrainerConfig); + const ipredSessionId = useConnectionStore((s) => s.ipredSessionId); + const localRoot = useConnectionStore((s) => s.localRoot); + const setIpredSession = useConnectionStore((s) => s.setIpredSession); + + const [params, setParams] = useState(() => ({ + ...DEFAULT_CLF_PARAMS, + iterations: preferredTrainerConfig.iterations, + depth: preferredTrainerConfig.depth, + learningRate: preferredTrainerConfig.learning_rate, + })); + const [model, setModel] = useState(null); + const [treeView, setTreeView] = useState(null); + const [treeIndex, setTreeIndex] = useState(0); + const [commitUrl, setCommitUrl] = useState(null); + const [statusUrl, setStatusUrl] = useState(null); + const [predictCounts, setPredictCounts] = useState(null); + const [runId, setRunId] = useState(null); + const [probaClassIndex, setProbaClassIndex] = useState(0); + const [probaThresholds, setProbaThresholds] = useState>({}); + const [probaUrl, setProbaUrl] = useState(null); + const [training, setTraining] = useState(false); + const [predicting, setPredicting] = useState(false); + const [savingClass, setSavingClass] = useState(false); + const [error, setError] = useState(null); + const commitUrlRef = useRef(null); + const statusUrlRef = useRef(null); + const probaUrlRef = useRef(null); + /** Raw softmax PNG per class (before threshold preview). */ + const rawProbaBlobRef = useRef(null); + const probaThresholdsRef = useRef(probaThresholds); + probaThresholdsRef.current = probaThresholds; + const modelRef = useRef(model); + modelRef.current = model; + const onExpiredRef = useRef(onFeatureJobExpired); + onExpiredRef.current = onFeatureJobExpired; + const onFeatureReadyRef = useRef(onFeatureReady); + onFeatureReadyRef.current = onFeatureReady; + + // Keep Train knobs aligned with Ipred trainer defaults when they change. + useEffect(() => { + setParams((p) => ({ + ...p, + iterations: preferredTrainerConfig.iterations, + depth: preferredTrainerConfig.depth, + learningRate: preferredTrainerConfig.learning_rate, + })); + }, [preferredTrainerConfig]); + + const revokeProba = useCallback(() => { + if (probaUrlRef.current) { + URL.revokeObjectURL(probaUrlRef.current); + probaUrlRef.current = null; + } + setProbaUrl(null); + rawProbaBlobRef.current = null; + }, []); + + const publishProbaPreview = useCallback( + async (raw: Blob, threshold: number) => { + const preview = await thresholdProbaPngBlob(raw, threshold); + const url = URL.createObjectURL(preview); + if (probaUrlRef.current) URL.revokeObjectURL(probaUrlRef.current); + probaUrlRef.current = url; + setProbaUrl(url); + }, + [], + ); + + const revokePredict = useCallback(() => { + if (commitUrlRef.current) { + URL.revokeObjectURL(commitUrlRef.current); + commitUrlRef.current = null; + } + if (statusUrlRef.current) { + URL.revokeObjectURL(statusUrlRef.current); + statusUrlRef.current = null; + } + setCommitUrl(null); + setStatusUrl(null); + setPredictCounts(null); + setRunId(null); + setProbaClassIndex(0); + setProbaThresholds({}); + revokeProba(); + }, [revokeProba]); + + useEffect(() => { + setModel(null); + setTreeView(null); + setTreeIndex(0); + revokePredict(); + setError(null); + }, [featureJobId, resetKey, preferredFeatureSetupId, preferredCompositionId, revokePredict]); + + useEffect( + () => () => { + if (commitUrlRef.current) URL.revokeObjectURL(commitUrlRef.current); + if (statusUrlRef.current) URL.revokeObjectURL(statusUrlRef.current); + if (probaUrlRef.current) URL.revokeObjectURL(probaUrlRef.current); + }, + [], + ); + + const ensureSession = useCallback(async (): Promise => { + if (ipredSessionId) return ipredSessionId; + if (!source || !kind) throw new Error('No sample open'); + const session = await openIpredSession({ + kind, + source, + server_uri: serverUri, + root: kind === 'local' ? localRoot : null, + }); + setIpredSession({ + sessionId: session.session_id, + projectId: session.project_id, + }); + return session.session_id; + }, [ipredSessionId, source, kind, serverUri, localRoot, setIpredSession]); + + const ensureFeatureBank = useCallback(async (): Promise => { + if (featureJobId) return featureJobId; + const compositionId = preferredCompositionId || preferredFeatureSetupId; + if (!compositionId) { + throw new Error('Select a composition on the Ipred tab first.'); + } + const sessionId = await ensureSession(); + const bank = await ipredPreprocess({ + session_id: sessionId, + composition_id: compositionId, + slice_index: sliceIndex, + }); + onFeatureReadyRef.current?.({ + featureId: bank.feature_id, + width: bank.width, + height: bank.height, + labels: bank.labels ?? [], + nChannels: bank.n_channels, + setupId: bank.setup_id, + cacheHit: bank.cache_hit, + }); + return bank.feature_id; + }, [ + featureJobId, + preferredCompositionId, + preferredFeatureSetupId, + ensureSession, + sliceIndex, + ]); + + const loadProbaChannel = useCallback( + async (run: string, classIndex: number) => { + const res = await fetch(ipredRunProbaUrl(run, classIndex)); + if (!res.ok) throw new Error(`Failed to load class ${classIndex} probability map`); + const blob = await res.blob(); + rawProbaBlobRef.current = blob; + setProbaClassIndex(classIndex); + const m = modelRef.current; + const cid = m?.classIds[classIndex]; + const t = + cid !== undefined + ? (probaThresholdsRef.current[cid] ?? DEFAULT_PROBA_THRESHOLD) + : DEFAULT_PROBA_THRESHOLD; + await publishProbaPreview(blob, t); + }, + [publishProbaPreview], + ); + + const train = useCallback( + async (shapes: Shape[]) => { + if (training) return; + if (!(preferredCompositionId || preferredFeatureSetupId)) { + setError('Select a composition on the Ipred tab first.'); + return; + } + setTraining(true); + setError(null); + revokePredict(); + try { + const sessionId = await ensureSession(); + const featureId = await ensureFeatureBank(); + const data = await ipredTrain({ + session_id: sessionId, + shapes, + feature_id: featureId, + trainer_id: preferredTrainerId, + config: { + iterations: params.iterations, + depth: params.depth, + learning_rate: params.learningRate, + }, + }); + const result: ClfTrainResult = { + modelId: data.model_id, + featureId: data.feature_id, + nSamples: data.n_samples, + nTrain: data.n_train, + nCal: data.n_cal, + classIds: data.class_ids, + trainAccuracy: data.train_accuracy, + params: { ...params }, + nTrees: Number(data.params?.iterations ?? params.iterations), + usesSam: !!(data.params as { uses_sam?: boolean } | undefined)?.uses_sam, + featureImportances: (data.feature_importances ?? []).map((fi) => ({ + label: fi.label, + importance: fi.importance, + })), + treePreview: null, + trainerId: data.trainer_id, + featureSetupId: preferredCompositionId || preferredFeatureSetupId, + }; + setModel(result); + setTreeView(null); + setTreeIndex(0); + } catch (e) { + setModel(null); + setTreeView(null); + const msg = e instanceof Error ? e.message : String(e); + if (/unknown feature|not found/i.test(msg)) { + onExpiredRef.current?.(); + setError('Feature bank missing. Compute or Train again (auto-preprocesses).'); + } else { + setError(msg); + } + } finally { + setTraining(false); + } + }, + [ + training, + preferredCompositionId, + preferredFeatureSetupId, + preferredTrainerId, + params, + revokePredict, + ensureSession, + ensureFeatureBank, + ], + ); + + const loadTree = useCallback(async (index: number) => { + // Tree inspection not yet exposed on ipred; keep index for UI. + setTreeIndex(index); + }, []); + + const predict = useCallback( + async (_shapes: Shape[]) => { + if (!model || predicting) return; + setPredicting(true); + setError(null); + try { + const sessionId = await ensureSession(); + const featureId = model.featureId || (await ensureFeatureBank()); + const run = await ipredInfer({ + session_id: sessionId, + model_id: model.modelId, + feature_id: featureId, + alpha: params.alpha, + }); + const [commitRes, statusRes] = await Promise.all([ + fetch(ipredRunCommitUrl(run.run_id)), + fetch(ipredRunStatusUrl(run.run_id)), + ]); + if (!commitRes.ok || !statusRes.ok) { + throw new Error('Failed to fetch conformal prediction PNGs'); + } + const [commitBlob, statusBlob] = await Promise.all([ + commitRes.blob(), + statusRes.blob(), + ]); + const cUrl = URL.createObjectURL(commitBlob); + const sUrl = URL.createObjectURL(statusBlob); + if (commitUrlRef.current) URL.revokeObjectURL(commitUrlRef.current); + if (statusUrlRef.current) URL.revokeObjectURL(statusUrlRef.current); + commitUrlRef.current = cUrl; + statusUrlRef.current = sUrl; + setCommitUrl(cUrl); + setStatusUrl(sUrl); + setPredictCounts(run.counts); + setRunId(run.run_id); + const classIds = run.class_ids?.length ? run.class_ids : model.classIds; + setModel((m) => (m ? { ...m, classIds } : m)); + const thresholds: Record = {}; + for (const cid of classIds) thresholds[cid] = DEFAULT_PROBA_THRESHOLD; + setProbaThresholds(thresholds); + await loadProbaChannel(run.run_id, 0); + } catch (e) { + revokePredict(); + setError(e instanceof Error ? e.message : String(e)); + } finally { + setPredicting(false); + } + }, + [ + model, + predicting, + params.alpha, + ensureSession, + ensureFeatureBank, + revokePredict, + loadProbaChannel, + ], + ); + + const selectProbaClass = useCallback( + async (index: number) => { + if (!runId || !model) return; + const n = model.classIds.length; + if (n < 1) return; + const next = ((index % n) + n) % n; + setError(null); + try { + await loadProbaChannel(runId, next); + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + } + }, + [runId, model, loadProbaChannel], + ); + + const cycleProbaClass = useCallback( + (delta: number) => { + void selectProbaClass(probaClassIndex + delta); + }, + [selectProbaClass, probaClassIndex], + ); + + const setProbaThreshold = useCallback( + (threshold: number) => { + if (!model) return; + const classId = model.classIds[probaClassIndex]; + if (classId === undefined) return; + const t = Math.min(1, Math.max(0, threshold)); + setProbaThresholds((prev) => ({ ...prev, [classId]: t })); + const raw = rawProbaBlobRef.current; + if (raw) { + void publishProbaPreview(raw, t).catch((e) => { + setError(e instanceof Error ? e.message : String(e)); + }); + } + }, + [model, probaClassIndex, publishProbaPreview], + ); + + const activeProbaClassId = model?.classIds[probaClassIndex] ?? null; + const activeProbaThreshold = + activeProbaClassId !== null + ? (probaThresholds[activeProbaClassId] ?? DEFAULT_PROBA_THRESHOLD) + : DEFAULT_PROBA_THRESHOLD; + + const saveThresholdedClass = useCallback(async (): Promise => { + if (!runId || activeProbaClassId === null || savingClass) return null; + setSavingClass(true); + setError(null); + try { + return await ipredThresholdClass(runId, { + class_id: activeProbaClassId, + threshold: activeProbaThreshold, + }); + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + return null; + } finally { + setSavingClass(false); + } + }, [runId, activeProbaClassId, activeProbaThreshold, savingClass]); + + const dismiss = useCallback(() => { + revokePredict(); + }, [revokePredict]); + + return { + params, + setParams, + model, + treeView, + treeIndex, + loadTree, + commitUrl, + statusUrl, + predictUrl: commitUrl, + predictCounts, + runId, + probaUrl, + probaClassIndex, + activeProbaClassId, + activeProbaThreshold, + selectProbaClass, + cycleProbaClass, + setProbaThreshold, + saveThresholdedClass, + savingClass, + training, + predicting, + error, + train, + predict, + dismiss, + featureSetupId: preferredCompositionId || preferredFeatureSetupId, + trainerId: preferredTrainerId, + canTrainWithoutJob: + !!(preferredCompositionId || preferredFeatureSetupId) && !!source && !!kind, + }; +} diff --git a/frontend/src/hooks/useSam.ts b/frontend/src/hooks/useSam.ts new file mode 100644 index 0000000..7711758 --- /dev/null +++ b/frontend/src/hooks/useSam.ts @@ -0,0 +1,82 @@ +/** + * useSam — React wrapper around the SAM worker singleton. + * + * Exposes the load/encode status, an `ensureEncoded(image)` that encodes a + * slice at most once (cached per HTMLImageElement), and `segment(points)` for + * per-click decoding. Used by the AnnotationCanvas magic tool. + */ +import { useCallback, useEffect, useRef, useState } from 'react'; +import { samClient, webgpuAvailable, type SamStatus, type PromptPoint, type PromptBox, type Granularity, type SamMask } from '@/lib/sam/samClient'; + +/** + * Subscribes to the SAM worker and, when `enabled`, kicks off model load. + * Returns status/error/backend info plus `ensureEncoded` and `segment`. + */ +export function useSam(enabled: boolean) { + const [status, setStatus] = useState(samClient.getStatus()); + const [error, setError] = useState(null); + // The encode is keyed by a string so that a brightness/contrast change (which + // alters the pixels SAM sees) invalidates it and forces a re-encode. + const encodedKeyRef = useRef(null); + const encodePromiseRef = useRef | null>(null); + const failedRef = useRef>(new Set()); // keys whose encode threw + + useEffect(() => samClient.subscribe(setStatus), []); + + // Kick off model load as soon as the tool is active. + useEffect(() => { + if (enabled && samClient.getStatus() === 'idle') { + samClient.init().catch(() => { /* status flips to 'unsupported' */ }); + } + }, [enabled]); + + /** Encode the source for `key` unless already encoded. `makeSource` is only + * called when an encode is actually needed (it can be expensive to build). */ + const ensureEncoded = useCallback(async (key: string, makeSource: () => CanvasImageSource): Promise => { + if (encodedKeyRef.current === key) return true; + if (failedRef.current.has(key)) return false; // known-bad; don't retry + if (encodePromiseRef.current) { + try { await encodePromiseRef.current; } catch { /* fall through to re-encode */ } + if (encodedKeyRef.current === key) return true; + } + const p = samClient.encode(makeSource()).then(() => { encodedKeyRef.current = key; }); + encodePromiseRef.current = p; + try { + await p; + setError(null); + return true; + } catch (e) { + failedRef.current.add(key); + setError(e instanceof Error ? e.message : String(e)); + return false; + } finally { + if (encodePromiseRef.current === p) encodePromiseRef.current = null; + } + }, []); + + /** Decode a mask from prompt points/box at the given granularity/threshold. + * Returns null when there is no prompt or the decode fails. */ + const segment = useCallback(async ( + points: PromptPoint[], + box: PromptBox | null, + granularity: Granularity, + threshold: number, + ): Promise => { + if (points.length === 0 && !box) return null; + try { + return await samClient.decode(points, box, granularity, threshold); + } catch { + return null; + } + }, []); + + return { + status, + error, + backend: samClient.getBackend(), + webgpu: webgpuAvailable(), + supported: status !== 'unsupported', + ensureEncoded, + segment, + }; +} diff --git a/frontend/src/hooks/useSave.ts b/frontend/src/hooks/useSave.ts index 3687a44..091096d 100644 --- a/frontend/src/hooks/useSave.ts +++ b/frontend/src/hooks/useSave.ts @@ -9,6 +9,13 @@ import { useCallback, useEffect, useMemo, useRef, useState } from 'react'; import { API_BASE } from '@/config'; import { useAnnotationStore, type Shape } from '@/stores/annotationStore'; import { useClassStore, type AnnotationClass } from '@/stores/classStore'; +import { + deserializeMaskSet, + serializeMaskSet, + useMaskSetStore, + type MaskSet, + type MaskSetSerialized, +} from '@/stores/maskSetStore'; export interface VersionMeta { version: number; @@ -26,6 +33,7 @@ export interface SaveDraftPayload { slices: Record; split_by_slice: Record; negative_slices: string[]; + mask_sets?: Array; } /** Full annotation payload for one saved version (used for preview + restore). */ @@ -63,6 +71,11 @@ export interface UseSaveReturn { markClean: () => void; } +/** + * Explicit versioned-save controller for one `sourceKey`: tracks dirty state and + * exposes save/restore/version-history actions (see UseSaveReturn). Resets when + * sourceKey changes. + */ export function useSave(sourceKey: string | null): UseSaveReturn { const byImage = useAnnotationStore((s) => s.byImage); const splitBySlice = useAnnotationStore((s) => s.splitBySlice); @@ -70,6 +83,8 @@ export function useSave(sourceKey: string | null): UseSaveReturn { const mergeSourceDraft = useAnnotationStore((s) => s.mergeSourceDraft); const classes = useClassStore((s) => s.classes); const setClasses = useClassStore((s) => s.setClasses); + const maskSets = useMaskSetStore((s) => s.sets); + const replaceAllMaskSets = useMaskSetStore((s) => s.replaceAll); const [isDirty, setIsDirty] = useState(false); const [isSaving, setIsSaving] = useState(false); @@ -86,7 +101,7 @@ export function useSave(sourceKey: string | null): UseSaveReturn { } setIsDirty(true); // eslint-disable-next-line react-hooks/exhaustive-deps - }, [byImage, splitBySlice, negativeSlices, classes]); + }, [byImage, splitBySlice, negativeSlices, classes, maskSets]); useEffect(() => { cleanRef.current = true; @@ -96,6 +111,7 @@ export function useSave(sourceKey: string | null): UseSaveReturn { payloadCacheRef.current.clear(); }, [sourceKey]); + /** Assemble the current stores into a draft payload, or null if no sourceKey. */ const buildSavePayload = useCallback((): SaveDraftPayload | null => { if (!sourceKey) return null; return { @@ -103,8 +119,11 @@ export function useSave(sourceKey: string | null): UseSaveReturn { slices: byImage[sourceKey] ?? {}, split_by_slice: splitBySlice[sourceKey] ?? {}, negative_slices: negativeSlices[sourceKey] ?? [], + mask_sets: maskSets + .filter((m) => m.sourceKey === sourceKey) + .map(serializeMaskSet), }; - }, [sourceKey, byImage, splitBySlice, negativeSlices, classes]); + }, [sourceKey, byImage, splitBySlice, negativeSlices, classes, maskSets]); const saveSummary = useMemo(() => { const payload = buildSavePayload(); @@ -113,6 +132,7 @@ export function useSave(sourceKey: string | null): UseSaveReturn { return { shapeCount, classCount: payload.classes.length }; }, [buildSavePayload, classes.length]); + /** Fetch the version list from the server into state; non-fatal on failure. */ const refreshVersions = useCallback(async () => { if (!sourceKey) return; try { @@ -127,6 +147,8 @@ export function useSave(sourceKey: string | null): UseSaveReturn { } }, [sourceKey]); + /** POST the current payload as a new version, then refresh versions and clear + * dirty. Returns true on success, false (logged) on failure. */ const save = useCallback( async (options: SaveOptions = {}): Promise => { if (!sourceKey) return false; @@ -165,6 +187,7 @@ export function useSave(sourceKey: string | null): UseSaveReturn { [sourceKey, buildSavePayload, refreshVersions] ); + /** Fetch a version's full payload, caching by version number; null on failure. */ const fetchVersionPayload = useCallback( async (version: number): Promise => { if (!sourceKey) return null; @@ -182,6 +205,9 @@ export function useSave(sourceKey: string | null): UseSaveReturn { slices: (raw.slices ?? {}) as Record, split_by_slice: (raw.split_by_slice ?? {}) as Record, negative_slices: (raw.negative_slices ?? []) as string[], + mask_sets: Array.isArray(raw.mask_sets) + ? (raw.mask_sets as MaskSetSerialized[]).map(deserializeMaskSet) + : [], }; payloadCacheRef.current.set(version, payload); return payload; @@ -193,6 +219,7 @@ export function useSave(sourceKey: string | null): UseSaveReturn { [sourceKey] ); + /** Load a version's payload back into the stores and mark state dirty. */ const restoreVersion = useCallback( async (version: number) => { if (!sourceKey) return; @@ -200,12 +227,20 @@ export function useSave(sourceKey: string | null): UseSaveReturn { if (!payload) return; setClasses(payload.classes); mergeSourceDraft(sourceKey, payload.slices, payload.split_by_slice, payload.negative_slices); + const others = useMaskSetStore.getState().sets.filter((m) => m.sourceKey !== sourceKey); + const restored = (payload.mask_sets ?? []).map((m) => + m instanceof Object && 'labelMap' in m && (m as MaskSet).labelMap instanceof Uint8Array + ? (m as MaskSet) + : deserializeMaskSet(m as MaskSetSerialized), + ); + replaceAllMaskSets([...others, ...restored]); cleanRef.current = false; setIsDirty(true); }, - [sourceKey, setClasses, mergeSourceDraft, fetchVersionPayload] + [sourceKey, setClasses, mergeSourceDraft, fetchVersionPayload, replaceAllMaskSets] ); + /** Mark current state as clean (e.g. after restoring a draft on open). */ const markClean = useCallback(() => { cleanRef.current = true; setIsDirty(false); diff --git a/frontend/src/lib/annotateStage.test.ts b/frontend/src/lib/annotateStage.test.ts new file mode 100644 index 0000000..b56a98a --- /dev/null +++ b/frontend/src/lib/annotateStage.test.ts @@ -0,0 +1,17 @@ +import { describe, expect, it } from 'vitest'; +import { pathForStage, stageFromPath } from './annotateStage'; + +describe('annotateStage', () => { + it('maps hub paths to stages', () => { + expect(stageFromPath('/preprocess')).toBe('preprocess'); + expect(stageFromPath('/draw')).toBe('draw'); + expect(stageFromPath('/train')).toBe('train'); + expect(stageFromPath('/cleanup')).toBe('train'); + expect(stageFromPath('/annotate')).toBe('preprocess'); + }); + + it('returns hub paths for stages', () => { + expect(pathForStage('preprocess')).toBe('/preprocess'); + expect(pathForStage('train')).toBe('/train'); + }); +}); diff --git a/frontend/src/lib/annotateStage.ts b/frontend/src/lib/annotateStage.ts new file mode 100644 index 0000000..21095bd --- /dev/null +++ b/frontend/src/lib/annotateStage.ts @@ -0,0 +1,22 @@ +/** + * Annotate Hub stages (sidebar routes after Connect / Browse). + */ +export type AnnotateStage = 'preprocess' | 'draw' | 'train'; + +const STAGE_PATHS: Record = { + preprocess: '/preprocess', + draw: '/draw', + train: '/train', +}; + +/** Map a Hub pathname to the annotate work stage. */ +export function stageFromPath(pathname: string): AnnotateStage { + if (pathname.startsWith('/train') || pathname.startsWith('/cleanup')) return 'train'; + if (pathname.startsWith('/draw')) return 'draw'; + return 'preprocess'; +} + +/** Hub path for a stage. */ +export function pathForStage(stage: AnnotateStage): string { + return STAGE_PATHS[stage]; +} diff --git a/frontend/src/lib/clahe.test.ts b/frontend/src/lib/clahe.test.ts new file mode 100644 index 0000000..c6bd7db --- /dev/null +++ b/frontend/src/lib/clahe.test.ts @@ -0,0 +1,90 @@ +/** + * CLAHE unit tests — contrast-limited adaptive histogram equalization. + */ +import { describe, expect, it } from 'vitest'; +import { applyClaheGray, claheRgba } from './clahe'; + +/** Build a gray gradient (left dark → right bright) as Uint8Array luminance. */ +function gradient(w: number, h: number): Uint8Array { + const out = new Uint8Array(w * h); + for (let y = 0; y < h; y++) { + for (let x = 0; x < w; x++) { + out[y * w + x] = Math.round((x / Math.max(1, w - 1)) * 255); + } + } + return out; +} + +/** Soft-edged dark blob on mid-gray — local contrast should rise under CLAHE. */ +function blobOnGray(w: number, h: number): Uint8Array { + const out = new Uint8Array(w * h); + out.fill(128); + const cx = w / 2; + const cy = h / 2; + const r = Math.min(w, h) * 0.2; + for (let y = 0; y < h; y++) { + for (let x = 0; x < w; x++) { + const d = Math.hypot(x - cx, y - cy); + if (d < r) out[y * w + x] = Math.round(40 + (d / r) * 40); + } + } + return out; +} + +describe('applyClaheGray', () => { + it('keeps values in 0–255', () => { + const src = blobOnGray(64, 64); + const out = applyClaheGray(src, 64, 64, { clipLimit: 2, tilesX: 8, tilesY: 8 }); + expect(out.length).toBe(src.length); + for (let i = 0; i < out.length; i++) { + expect(out[i]).toBeGreaterThanOrEqual(0); + expect(out[i]).toBeLessThanOrEqual(255); + } + }); + + it('is a no-op on a flat field', () => { + const src = new Uint8Array(32 * 32).fill(90); + const out = applyClaheGray(src, 32, 32, { clipLimit: 2, tilesX: 4, tilesY: 4 }); + // Flat images stay flat (all equal, near the original level). + const first = out[0]; + expect(out.every((v) => v === first)).toBe(true); + }); + + it('increases local contrast on a soft blob', () => { + const src = blobOnGray(128, 128); + const out = applyClaheGray(src, 128, 128, { clipLimit: 3, tilesX: 8, tilesY: 8 }); + const srcRange = Math.max(...src) - Math.min(...src); + const outRange = Math.max(...out) - Math.min(...out); + expect(outRange).toBeGreaterThanOrEqual(srcRange); + }); + + it('leaves a full-range gradient roughly ordered', () => { + const src = gradient(64, 16); + const out = applyClaheGray(src, 64, 16, { clipLimit: 2, tilesX: 8, tilesY: 2 }); + // Left third still darker than right third after CLAHE. + const left = out.slice(0, 8).reduce((a, b) => a + b, 0); + const right = out.slice(56, 64).reduce((a, b) => a + b, 0); + expect(left).toBeLessThan(right); + }); +}); + +describe('claheRgba', () => { + it('writes equal RGB for a grayscale source', () => { + const w = 32; + const h = 32; + const rgba = new Uint8ClampedArray(w * h * 4); + for (let i = 0; i < w * h; i++) { + const g = (i * 7) % 256; + rgba[i * 4] = g; + rgba[i * 4 + 1] = g; + rgba[i * 4 + 2] = g; + rgba[i * 4 + 3] = 255; + } + claheRgba(rgba, w, h, { clipLimit: 2, tilesX: 4, tilesY: 4 }); + for (let i = 0; i < w * h; i++) { + expect(rgba[i * 4]).toBe(rgba[i * 4 + 1]); + expect(rgba[i * 4 + 1]).toBe(rgba[i * 4 + 2]); + expect(rgba[i * 4 + 3]).toBe(255); + } + }); +}); diff --git a/frontend/src/lib/clahe.ts b/frontend/src/lib/clahe.ts new file mode 100644 index 0000000..2b911b1 --- /dev/null +++ b/frontend/src/lib/clahe.ts @@ -0,0 +1,174 @@ +/** + * CLAHE — Contrast Limited Adaptive Histogram Equalization (grayscale). + * + * Tile the image, equalize each tile's histogram with a clip limit, then + * bilinear-interpolate the per-tile CDFs so the result has no tile seams. + * Used as a display preprocessor for low-contrast slices (does not change + * exported pixel values — same posture as brightness/contrast). + */ + +export interface ClaheOptions { + /** Histogram clip limit relative to tile average bin height (OpenCV-style). Default 2. */ + clipLimit?: number; + /** Number of tiles along width. Default 8. */ + tilesX?: number; + /** Number of tiles along height. Default 8. */ + tilesY?: number; +} + +const NUM_BINS = 256; + +/** + * Apply CLAHE to a packed grayscale buffer (row-major, one byte per pixel). + * + * Args: + * src: Length ``width * height`` luminance bytes. + * width / height: Image size in pixels. + * options: Clip limit and tile grid. + * + * Returns: + * New ``Uint8Array`` of the same length with CLAHE applied. + */ +export function applyClaheGray( + src: Uint8Array, + width: number, + height: number, + options: ClaheOptions = {}, +): Uint8Array { + const clipLimit = Math.max(0, options.clipLimit ?? 2); + const tilesX = Math.max(1, Math.min(options.tilesX ?? 8, width)); + const tilesY = Math.max(1, Math.min(options.tilesY ?? 8, height)); + const out = new Uint8Array(src.length); + + // Tile geometry (last tiles absorb remainders). + const tileW = Math.floor(width / tilesX); + const tileH = Math.floor(height / tilesY); + if (tileW < 1 || tileH < 1) { + out.set(src); + return out; + } + + // Per-tile LUTs: map input gray → equalized gray via clipped CDF. + const luts = new Array(tilesX * tilesY); + for (let ty = 0; ty < tilesY; ty++) { + for (let tx = 0; tx < tilesX; tx++) { + const x0 = tx * tileW; + const y0 = ty * tileH; + const x1 = tx === tilesX - 1 ? width : x0 + tileW; + const y1 = ty === tilesY - 1 ? height : y0 + tileH; + luts[ty * tilesX + tx] = buildTileLut(src, width, x0, y0, x1, y1, clipLimit); + } + } + + // Bilinear blend of the 4 surrounding tile LUTs at each pixel. + for (let y = 0; y < height; y++) { + const tyF = (y + 0.5) / tileH - 0.5; + const ty0 = Math.max(0, Math.min(tilesY - 1, Math.floor(tyF))); + const ty1 = Math.max(0, Math.min(tilesY - 1, ty0 + 1)); + const fy = ty0 === ty1 ? 0 : tyF - ty0; + + for (let x = 0; x < width; x++) { + const txF = (x + 0.5) / tileW - 0.5; + const tx0 = Math.max(0, Math.min(tilesX - 1, Math.floor(txF))); + const tx1 = Math.max(0, Math.min(tilesX - 1, tx0 + 1)); + const fx = tx0 === tx1 ? 0 : txF - tx0; + + const v = src[y * width + x]; + const a = luts[ty0 * tilesX + tx0][v]; + const b = luts[ty0 * tilesX + tx1][v]; + const c = luts[ty1 * tilesX + tx0][v]; + const d = luts[ty1 * tilesX + tx1][v]; + const top = a + (b - a) * fx; + const bot = c + (d - c) * fx; + out[y * width + x] = Math.round(top + (bot - top) * fy); + } + } + return out; +} + +/** + * In-place CLAHE on RGBA ImageData bytes via luminance, then rescale RGB. + * + * Grayscale slices keep R=G=B. Color (e.g. viridis) keeps hue by scaling + * channels with ``Y'/Y``. + */ +export function claheRgba( + rgba: Uint8ClampedArray, + width: number, + height: number, + options: ClaheOptions = {}, +): void { + const n = width * height; + const gray = new Uint8Array(n); + for (let i = 0, p = 0; i < n; i++, p += 4) { + gray[i] = Math.round(0.299 * rgba[p] + 0.587 * rgba[p + 1] + 0.114 * rgba[p + 2]); + } + const eq = applyClaheGray(gray, width, height, options); + for (let i = 0, p = 0; i < n; i++, p += 4) { + const y0 = gray[i]; + const y1 = eq[i]; + if (y0 === 0) { + rgba[p] = y1; + rgba[p + 1] = y1; + rgba[p + 2] = y1; + } else { + const s = y1 / y0; + rgba[p] = Math.round(rgba[p] * s); + rgba[p + 1] = Math.round(rgba[p + 1] * s); + rgba[p + 2] = Math.round(rgba[p + 2] * s); + } + // alpha unchanged + } +} + +/** Build a 256-entry LUT for one tile using a clip-limited histogram CDF. */ +function buildTileLut( + src: Uint8Array, + width: number, + x0: number, + y0: number, + x1: number, + y1: number, + clipLimit: number, +): Uint8Array { + const hist = new Float64Array(NUM_BINS); + let area = 0; + for (let y = y0; y < y1; y++) { + const row = y * width; + for (let x = x0; x < x1; x++) { + hist[src[row + x]] += 1; + area += 1; + } + } + if (area === 0) return identityLut(); + + // OpenCV-style clip: max bin height = clipLimit * (area / bins). + const limit = Math.max(1, (clipLimit * area) / NUM_BINS); + let clipped = 0; + for (let i = 0; i < NUM_BINS; i++) { + if (hist[i] > limit) { + clipped += hist[i] - limit; + hist[i] = limit; + } + } + const redistrib = clipped / NUM_BINS; + for (let i = 0; i < NUM_BINS; i++) hist[i] += redistrib; + + // CDF → LUT mapped to [0, 255]. + const lut = new Uint8Array(NUM_BINS); + let cdf = 0; + let cdfMin = 0; + for (let i = 0; i < NUM_BINS; i++) { + cdf += hist[i]; + if (cdfMin === 0 && cdf > 0) cdfMin = cdf; + const denom = area - cdfMin; + lut[i] = denom <= 0 ? 0 : Math.round(((cdf - cdfMin) / denom) * 255); + } + return lut; +} + +function identityLut(): Uint8Array { + const lut = new Uint8Array(NUM_BINS); + for (let i = 0; i < NUM_BINS; i++) lut[i] = i; + return lut; +} diff --git a/frontend/src/lib/cleanupOps.test.ts b/frontend/src/lib/cleanupOps.test.ts new file mode 100644 index 0000000..b7c47fc --- /dev/null +++ b/frontend/src/lib/cleanupOps.test.ts @@ -0,0 +1,157 @@ +import { describe, expect, it } from 'vitest'; +import { + applyMorphToLabelMap, + applyRoiToLabelMap, + applyRoiToShapes, + filterLabelMapBySize, + filterShapesByComponentSize, + filterBySize, + labelComponents, +} from './cleanupOps'; +import type { Shape } from '@/stores/annotationStore'; + +describe('labelComponents', () => { + it('labels separate 4-connected blobs with their sizes', () => { + const gw = 8; + const gh = 8; + const mask = new Uint8Array(gw * gh); + // 2×2 blob + mask[1 * gw + 1] = 1; + mask[1 * gw + 2] = 1; + mask[2 * gw + 1] = 1; + mask[2 * gw + 2] = 1; + // 1px island + mask[6 * gw + 6] = 1; + const { labels, sizesByLabel, count } = labelComponents(mask, gw, gh); + expect(count).toBe(2); + const sizes = Object.values(sizesByLabel).sort((a, b) => a - b); + expect(sizes).toEqual([1, 4]); + expect(labels[1 * gw + 1]).toBe(labels[2 * gw + 2]); + expect(labels[6 * gw + 6]).not.toBe(labels[1 * gw + 1]); + }); +}); + +describe('filterBySize', () => { + it('keeps only components in [min, max]', () => { + const gw = 10; + const gh = 10; + const mask = new Uint8Array(gw * gh); + for (let y = 1; y < 5; y++) for (let x = 1; x < 5; x++) mask[y * gw + x] = 1; // 16 + mask[8 * gw + 8] = 1; + const kept = filterBySize(mask, gw, gh, { minArea: 4, maxArea: 20, mode: 'keep' }); + expect(kept[8 * gw + 8]).toBe(0); + expect(kept[2 * gw + 2]).toBe(1); + const deleted = filterBySize(mask, gw, gh, { minArea: 4, maxArea: 20, mode: 'delete' }); + expect(deleted[8 * gw + 8]).toBe(1); + expect(deleted[2 * gw + 2]).toBe(0); + }); +}); + +describe('filterShapesByComponentSize', () => { + it('removes small islands for a class', () => { + const shapes: Shape[] = [ + { id: 'a', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 8, h: 8 }, + { id: 'b', classId: 1, kind: 'rectangle', x: 14, y: 14, w: 1, h: 1 }, + { id: 'c', classId: 2, kind: 'rectangle', x: 0, y: 0, w: 4, h: 4 }, + ]; + const out = filterShapesByComponentSize(shapes, 16, 16, { + classId: 1, + minArea: 10, + maxArea: Infinity, + mode: 'keep', + }); + const c1 = out.filter((s) => s.classId === 1); + const c2 = out.filter((s) => s.classId === 2); + expect(c2).toHaveLength(1); + expect(c1.length).toBeGreaterThanOrEqual(1); + // Small island at (14,14) should be gone after re-vectorize + const bigOnly = filterShapesByComponentSize(shapes, 16, 16, { + classId: 1, + minArea: 10, + maxArea: Infinity, + mode: 'keep', + }); + // Class 2 untouched + expect(bigOnly.some((s) => s.classId === 2)).toBe(true); + }); +}); + +describe('filterLabelMapBySize', () => { + it('edits pixels without polygon round-trip', () => { + const w = 16; + const h = 16; + const labels = new Uint8Array(w * h); + for (let y = 0; y < 8; y++) for (let x = 0; x < 8; x++) labels[y * w + x] = 1; // 64 + labels[15 * w + 15] = 1; // 1px island + const kept = filterLabelMapBySize(labels, w, h, { + classId: 1, + minArea: 10, + maxArea: Infinity, + mode: 'keep', + }); + expect(kept[15 * w + 15]).toBe(0); + expect(kept[2 * w + 2]).toBe(1); + }); +}); + +describe('applyMorphToLabelMap', () => { + it('fills a hole in a solid class blob', () => { + const w = 20; + const h = 20; + const labels = new Uint8Array(w * h); + for (let y = 4; y < 16; y++) for (let x = 4; x < 16; x++) labels[y * w + x] = 2; + labels[10 * w + 10] = 0; + const filled = applyMorphToLabelMap(labels, w, h, { + op: 'fill', + param: 1, + classId: 2, + }); + expect(filled[10 * w + 10]).toBe(2); + }); +}); + +describe('applyRoiToLabelMap', () => { + it('keep_inside zeros exterior class pixels', () => { + const w = 16; + const h = 16; + const labels = new Uint8Array(w * h).fill(1); + const roi: Shape = { id: 'r', classId: 0, kind: 'rectangle', x: 4, y: 4, w: 4, h: 4 }; + const out = applyRoiToLabelMap(labels, w, h, { + roi, + mode: 'keep_inside', + classId: 1, + }); + expect(out[0]).toBe(0); + expect(out[6 * w + 6]).toBe(1); + }); +}); + +describe('applyRoiToShapes', () => { + it('keep-inside leaves exterior untouched for that class', () => { + const shapes: Shape[] = [ + { id: 'a', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 16, h: 16 }, + ]; + const roi: Shape = { id: 'r', classId: 0, kind: 'rectangle', x: 4, y: 4, w: 4, h: 4 }; + const out = applyRoiToShapes(shapes, 16, 16, { + roi, + mode: 'keep_inside', + classId: 1, + }); + // Should only keep the center 4×4 ≈ 16 px → few polygons + expect(out.filter((s) => s.classId === 1).length).toBeGreaterThanOrEqual(1); + // Outside class untouched if we only pass class 1 + }); + + it('delete-inside clears ROI pixels but keeps exterior', () => { + const shapes: Shape[] = [ + { id: 'a', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 12, h: 12 }, + ]; + const roi: Shape = { id: 'r', classId: 0, kind: 'rectangle', x: 2, y: 2, w: 4, h: 4 }; + const out = applyRoiToShapes(shapes, 16, 16, { + roi, + mode: 'delete_inside', + classId: 1, + }); + expect(out.some((s) => s.classId === 1)).toBe(true); + }); +}); diff --git a/frontend/src/lib/cleanupOps.ts b/frontend/src/lib/cleanupOps.ts new file mode 100644 index 0000000..51a5226 --- /dev/null +++ b/frontend/src/lib/cleanupOps.ts @@ -0,0 +1,396 @@ +/** + * Cleanup Studio ops: connected-component size filters and ROI keep/delete. + * Prefer *LabelMap* variants (full-res classId pixels). Shape variants remain + * for legacy tests; Cleanup Studio uses label maps so we never round-trip + * polygons mid-edit. + */ +import { v4 as uuidv4 } from 'uuid'; +import type { PolygonShape, Shape } from '@/stores/annotationStore'; +import { gridFor, rasterizeShapes } from '@/lib/rasterize'; +import { maskToPolygonsWithHoles } from '@/lib/magicwand'; +import { + dilate, + erode, + fillHoles, + removeSmallComponents, + smooth, +} from '@/lib/morphology'; +import { + classBinary, + classIdsInLabelMap, + paintClass, +} from '@/lib/labelMap'; + +export type SizeFilterMode = 'keep' | 'delete'; +export type RoiMode = 'keep_inside' | 'delete_inside' | 'keep_outside' | 'delete_outside'; +export type MorphCleanupOp = 'fill' | 'islands' | 'smooth' | 'grow' | 'shrink'; + +export interface LabelComponentsResult { + labels: Int32Array; + sizesByLabel: Record; + count: number; +} + +/** 4-connected CC labels (1..N) and per-label pixel counts. */ +export function labelComponents( + mask: Uint8Array, + gw: number, + gh: number, +): LabelComponentsResult { + const labels = new Int32Array(gw * gh); + const sizesByLabel: Record = {}; + let next = 1; + for (let p = 0; p < mask.length; p++) { + if (!mask[p] || labels[p]) continue; + const lbl = next++; + let size = 0; + const stack = [p]; + labels[p] = lbl; + while (stack.length) { + const idx = stack.pop()!; + size++; + const x = idx % gw; + const y = (idx / gw) | 0; + const push = (j: number) => { + if (mask[j] && !labels[j]) { + labels[j] = lbl; + stack.push(j); + } + }; + if (x > 0) push(idx - 1); + if (x < gw - 1) push(idx + 1); + if (y > 0) push(idx - gw); + if (y < gh - 1) push(idx + gw); + } + sizesByLabel[lbl] = size; + } + return { labels, sizesByLabel, count: next - 1 }; +} + +/** + * Keep or delete CCs whose area is in [minArea, maxArea] (grid pixels). + * mode=keep: only in-range survive; mode=delete: in-range are removed. + */ +export function filterBySize( + mask: Uint8Array, + gw: number, + gh: number, + { + minArea, + maxArea, + mode, + }: { minArea: number; maxArea: number; mode: SizeFilterMode }, +): Uint8Array { + const { labels, sizesByLabel } = labelComponents(mask, gw, gh); + const keepLabel = new Set(); + for (const [lblStr, size] of Object.entries(sizesByLabel)) { + const lbl = Number(lblStr); + const inRange = size >= minArea && size <= maxArea; + if (mode === 'keep' ? inRange : !inRange) keepLabel.add(lbl); + } + const out = new Uint8Array(gw * gh); + for (let i = 0; i < out.length; i++) { + if (labels[i] && keepLabel.has(labels[i])) out[i] = 1; + } + return out; +} + +function maskToShapes(mask: Uint8Array, gw: number, gh: number, scale: number, classId: number): PolygonShape[] { + const polys = maskToPolygonsWithHoles(mask, gw, gh, { minRegion: 4, scale }); + return polys + .filter((p) => p.points.length >= 6) + .map((p) => ({ + id: uuidv4(), + classId, + kind: 'polygon' as const, + points: p.points, + ...(p.holes.length ? { holes: p.holes } : {}), + })); +} + +/** + * Filter CCs by image-pixel area for one class (or all if classId null). + * Other classes are preserved unchanged. + */ +export function filterShapesByComponentSize( + shapes: Shape[], + width: number, + height: number, + { + classId, + minArea, + maxArea, + mode, + }: { + classId: number | null; + minArea: number; + maxArea: number; + mode: SizeFilterMode; + }, +): Shape[] { + const { gw, gh, scale } = gridFor(width, height); + const minG = Math.max(1, Math.round(minArea / (scale * scale))); + const maxG = + maxArea === Infinity + ? Infinity + : Math.max(minG, Math.round(maxArea / (scale * scale))); + + const classIds = new Set( + shapes.map((s) => s.classId).filter((id) => classId === null || id === classId), + ); + const kept: Shape[] = shapes.filter((s) => classId !== null && s.classId !== classId); + for (const cid of classIds) { + const group = shapes.filter((s) => s.classId === cid); + if (!group.length) continue; + let mask = rasterizeShapes(group, gw, gh, scale); + mask = filterBySize(mask, gw, gh, { minArea: minG, maxArea: maxG, mode }); + kept.push(...maskToShapes(mask, gw, gh, scale, cid)); + } + return kept; +} + +/** Apply ROI gate to a binary mask. */ +export function applyRoiMask( + mask: Uint8Array, + roi: Uint8Array, + mode: RoiMode, +): Uint8Array { + const out = new Uint8Array(mask.length); + for (let i = 0; i < mask.length; i++) { + const inRoi = !!roi[i]; + const on = !!mask[i]; + switch (mode) { + case 'keep_inside': + out[i] = on && inRoi ? 1 : 0; + break; + case 'delete_inside': + out[i] = on && !inRoi ? 1 : 0; + break; + case 'keep_outside': + out[i] = on && !inRoi ? 1 : 0; + break; + case 'delete_outside': + out[i] = on && inRoi ? 1 : 0; + break; + } + } + return out; +} + +/** + * Keep/delete mask pixels relative to an ROI shape. + * classId null = all classes. + */ +export function applyRoiToShapes( + shapes: Shape[], + width: number, + height: number, + { + roi, + mode, + classId, + }: { roi: Shape; mode: RoiMode; classId: number | null }, +): Shape[] { + const { gw, gh, scale } = gridFor(width, height); + const roiMask = rasterizeShapes([{ ...roi, classId: roi.classId || 1 }], gw, gh, scale); + const classIds = new Set( + shapes.map((s) => s.classId).filter((id) => classId === null || id === classId), + ); + const kept: Shape[] = shapes.filter((s) => classId !== null && s.classId !== classId); + for (const cid of classIds) { + const group = shapes.filter((s) => s.classId === cid); + if (!group.length) continue; + let mask = rasterizeShapes(group, gw, gh, scale); + mask = applyRoiMask(mask, roiMask, mode); + kept.push(...maskToShapes(mask, gw, gh, scale, cid)); + } + return kept; +} + +/** Morphology on one/all classes; returns full replacement shape list. */ +export function applyMorphToShapes( + shapes: Shape[], + width: number, + height: number, + { + op, + param, + classId, + }: { op: MorphCleanupOp; param: number; classId: number | null }, +): Shape[] { + const { gw, gh, scale } = gridFor(width, height); + const classIds = new Set( + shapes.map((s) => s.classId).filter((id) => classId === null || id === classId), + ); + const kept: Shape[] = shapes.filter((s) => classId !== null && s.classId !== classId); + for (const cid of classIds) { + const group = shapes.filter((s) => s.classId === cid); + if (!group.length) continue; + let mask = rasterizeShapes(group, gw, gh, scale); + switch (op) { + case 'fill': + mask = fillHoles(mask, gw, gh); + break; + case 'islands': + mask = removeSmallComponents( + mask, + gw, + gh, + Math.max(1, Math.round(param / (scale * scale))), + ); + break; + case 'smooth': + mask = smooth(mask, gw, gh, Math.max(1, Math.round(param))); + break; + case 'grow': + mask = dilate(mask, gw, gh, Math.max(1, Math.round(param / scale))); + break; + case 'shrink': + mask = erode(mask, gw, gh, Math.max(1, Math.round(param / scale))); + break; + } + kept.push(...maskToShapes(mask, gw, gh, scale, cid)); + } + return kept; +} + +/** Size histogram (grid px buckets) for CC areas of a class mask. */ +export function componentSizeStats( + shapes: Shape[], + width: number, + height: number, + classId: number | null, +): { areas: number[]; min: number; max: number; count: number } { + const { gw, gh, scale } = gridFor(width, height); + const group = + classId === null + ? shapes + : shapes.filter((s) => s.classId === classId); + if (!group.length) return { areas: [], min: 0, max: 0, count: 0 }; + const mask = rasterizeShapes(group, gw, gh, scale); + const { sizesByLabel } = labelComponents(mask, gw, gh); + const areas = Object.values(sizesByLabel) + .map((a) => a * scale * scale) + .sort((a, b) => a - b); + return { + areas, + min: areas[0] ?? 0, + max: areas[areas.length - 1] ?? 0, + count: areas.length, + }; +} + +/* ---------- Full-resolution classId label-map ops (Cleanup Studio) ---------- */ + +function targetClassIds(labels: Uint8Array, classId: number | null): number[] { + const all = classIdsInLabelMap(labels); + if (classId === null) return all; + return all.includes(classId) ? [classId] : []; +} + +/** Filter CCs on a dense classId map (image pixels, no downsample). */ +export function filterLabelMapBySize( + labels: Uint8Array, + width: number, + height: number, + { + classId, + minArea, + maxArea, + mode, + }: { + classId: number | null; + minArea: number; + maxArea: number; + mode: SizeFilterMode; + }, +): Uint8Array { + let out = new Uint8Array(labels); + const minA = Math.max(1, Math.round(minArea)); + const maxA = maxArea === Infinity ? Infinity : Math.max(minA, Math.round(maxArea)); + for (const cid of targetClassIds(labels, classId)) { + let mask = classBinary(out, cid); + mask = filterBySize(mask, width, height, { minArea: minA, maxArea: maxA, mode }); + out = paintClass(out, mask, cid, { protectOthers: true }); + } + return out; +} + +/** ROI keep/delete on a dense classId map. */ +export function applyRoiToLabelMap( + labels: Uint8Array, + width: number, + height: number, + { + roi, + mode, + classId, + }: { roi: Shape; mode: RoiMode; classId: number | null }, +): Uint8Array { + const roiMask = rasterizeShapes([{ ...roi, classId: 1 }], width, height, 1); + let out = new Uint8Array(labels); + for (const cid of targetClassIds(labels, classId)) { + let mask = classBinary(out, cid); + mask = applyRoiMask(mask, roiMask, mode); + out = paintClass(out, mask, cid, { protectOthers: true }); + } + return out; +} + +/** Morphology on a dense classId map (full resolution). */ +export function applyMorphToLabelMap( + labels: Uint8Array, + width: number, + height: number, + { + op, + param, + classId, + }: { op: MorphCleanupOp; param: number; classId: number | null }, +): Uint8Array { + let out = new Uint8Array(labels); + for (const cid of targetClassIds(labels, classId)) { + let mask = classBinary(out, cid); + switch (op) { + case 'fill': + mask = fillHoles(mask, width, height); + break; + case 'islands': + mask = removeSmallComponents(mask, width, height, Math.max(1, Math.round(param))); + break; + case 'smooth': + mask = smooth(mask, width, height, Math.max(1, Math.round(param))); + break; + case 'grow': + mask = dilate(mask, width, height, Math.max(1, Math.round(param))); + break; + case 'shrink': + mask = erode(mask, width, height, Math.max(1, Math.round(param))); + break; + } + out = paintClass(out, mask, cid, { protectOthers: true }); + } + return out; +} + +/** CC size stats from a dense classId map. */ +export function componentSizeStatsLabelMap( + labels: Uint8Array, + width: number, + height: number, + classId: number | null, +): { areas: number[]; min: number; max: number; count: number } { + const areas: number[] = []; + for (const cid of targetClassIds(labels, classId)) { + const mask = classBinary(labels, cid); + const { sizesByLabel } = labelComponents(mask, width, height); + for (const a of Object.values(sizesByLabel)) areas.push(a); + } + areas.sort((a, b) => a - b); + return { + areas, + min: areas[0] ?? 0, + max: areas[areas.length - 1] ?? 0, + count: areas.length, + }; +} diff --git a/frontend/src/lib/clipToClasses.test.ts b/frontend/src/lib/clipToClasses.test.ts new file mode 100644 index 0000000..c5d7972 --- /dev/null +++ b/frontend/src/lib/clipToClasses.test.ts @@ -0,0 +1,98 @@ +import { describe, it, expect } from 'vitest'; +import { clipShapesToOthers } from './clipToClasses'; +import { rasterizeShapes, gridFor } from './rasterize'; +import type { Shape } from '@/stores/annotationStore'; + +const W = 200, H = 200; + +function rect(id: string, classId: number, x: number, y: number, w: number, h: number): Shape { + return { id, classId, kind: 'rectangle', x, y, w, h }; +} + +/** Foreground pixel count of a shape list on the standard grid. */ +function area(shapes: Shape[]): number { + const { gw, gh, scale } = gridFor(W, H); + const m = rasterizeShapes(shapes, gw, gh, scale); + let n = 0; + for (let i = 0; i < m.length; i++) if (m[i]) n++; + return n; +} + +describe('clipShapesToOthers', () => { + it('removes the part of a shape that overlaps another class', () => { + const classA = rect('a', 1, 0, 0, 100, 100); // existing class A + const newB = rect('b', 2, 50, 0, 100, 100); // class B overlaps A in x∈[50,100] + const clipped = clipShapesToOthers([newB], [classA], W, H); + expect(clipped.length).toBeGreaterThanOrEqual(1); + // No clipped-B pixel may fall inside A. + const { gw, gh, scale } = gridFor(W, H); + const aMask = rasterizeShapes([classA], gw, gh, scale); + const bMask = rasterizeShapes(clipped, gw, gh, scale); + let overlap = 0; + for (let i = 0; i < aMask.length; i++) if (aMask[i] && bMask[i]) overlap++; + expect(overlap).toBe(0); + // Area is roughly halved (the non-overlapping right half remains). + expect(area(clipped)).toBeLessThan(area([newB]) * 0.7); + expect(area(clipped)).toBeGreaterThan(0); + }); + + it('leaves a non-overlapping shape essentially unchanged', () => { + const classA = rect('a', 1, 0, 0, 40, 40); + const newB = rect('b', 2, 120, 120, 40, 40); // far from A + const clipped = clipShapesToOthers([newB], [classA], W, H); + expect(clipped.length).toBe(1); + expect(area(clipped)).toBeGreaterThan(area([newB]) * 0.8); + }); + + it('drops a shape fully enclosed by another class', () => { + const classA = rect('a', 1, 0, 0, 200, 200); // A covers everything + const newB = rect('b', 2, 60, 60, 40, 40); // entirely inside A + const clipped = clipShapesToOthers([newB], [classA], W, H); + expect(clipped.length).toBe(0); + }); + + it('drops a fragment fully redundant with the SAME class (spurious SAM blob)', () => { + const existingB = rect('b0', 2, 40, 40, 60, 60); // already-labeled class B + const blob = rect('b1', 2, 55, 55, 20, 20); // new B blob entirely inside existing B + const clipped = clipShapesToOthers([blob], [existingB], W, H); + expect(clipped.length).toBe(0); + }); + + it('keeps a same-class fragment that extends beyond existing labels', () => { + const existingB = rect('b0', 2, 40, 40, 40, 40); // existing B + const extend = rect('b1', 2, 60, 60, 60, 60); // overlaps + extends past B + const clipped = clipShapesToOthers([extend], [existingB], W, H); + expect(clipped.length).toBe(1); + }); + + it('avoids a class enclosed by a holed shape of another class (union bug)', () => { + // classC (1) is a big ring with a HOLE where classA (3) sits — the exact + // result of an earlier clip. classA (3) fills that hole. A new class-B (2) + // region over the center must still avoid BOTH: classA must not be erased + // from the "other" union by classC's hole carve. + const classC: Shape = { + id: 'c', classId: 1, kind: 'polygon', + points: [40, 40, 160, 40, 160, 160, 40, 160], + holes: [[80, 80, 120, 80, 120, 120, 80, 120]], + }; + const classA = rect('a', 3, 80, 80, 40, 40); // fills classC's hole + const newB = rect('b', 2, 85, 85, 30, 30); // fully inside classA + // classA listed before classC: the old shared-mask union let classC's hole + // carve classA away, so newB wasn't clipped against it. + const clipped = clipShapesToOthers([newB], [classA, classC], W, H); + expect(clipped.length).toBe(0); // fully over classA → dropped + }); + + it('carves a hole when the new region encloses another class', () => { + const classA = rect('a', 1, 90, 90, 20, 20); // small A blob in the middle + const newB = rect('b', 2, 40, 40, 120, 120); // large B surrounds A + const clipped = clipShapesToOthers([newB], [classA], W, H); + expect(clipped.length).toBe(1); + expect(clipped[0].holes && clipped[0].holes.length).toBeGreaterThanOrEqual(1); + // Rasterizing the clipped B leaves A's center empty (hole carved). + const { gw, gh, scale } = gridFor(W, H); + const bMask = rasterizeShapes(clipped, gw, gh, scale); + expect(bMask[Math.floor((100 / scale)) * gw + Math.floor(100 / scale)]).toBe(0); // A center → empty + expect(bMask[Math.floor((50 / scale)) * gw + Math.floor(50 / scale)]).toBe(1); // B body → filled + }); +}); diff --git a/frontend/src/lib/clipToClasses.ts b/frontend/src/lib/clipToClasses.ts new file mode 100644 index 0000000..7ac8708 --- /dev/null +++ b/frontend/src/lib/clipToClasses.ts @@ -0,0 +1,87 @@ +/** + * clipToClasses — clip newly-drawn shapes so they can't overlap OTHER classes' + * regions on the same slice (neighbor classes act as a hard boundary). + * + * Each new shape is rasterized, the union of other-class shapes is subtracted, + * and the remainder is re-vectorized to polygon(s). Pure (no store / React) so + * it's unit-testable and reusable across every commit site. Adjacent boundaries + * clip exactly; a fully-enclosed neighbor would need a hole (maskToPolygons only + * traces outer contours) — acceptable for v1. + */ +import { v4 as uuidv4 } from 'uuid'; +import type { PolygonShape, Shape } from '@/stores/annotationStore'; +import { gridFor, rasterizeShapes, rasterizeUnion } from '@/lib/rasterize'; +import { maskToPolygonsWithHoles } from '@/lib/magicwand'; + +/** Does the slice hold any shape of a different class than `classId`? */ +export function hasOtherClass(sliceShapes: Shape[], classId: number): boolean { + return sliceShapes.some((s) => s.classId !== classId); +} + +/** + * Clip `newShapes` against every same-slice shape of a *different* class, + * returning replacement polygon shapes (image coords). Fragments left with no + * new area — fully inside another class (subtracted away) OR entirely redundant + * with the same class's existing labels (e.g. a spurious little SAM blob on top + * of an already-labeled region) — are dropped. Fragments that add any new area + * are kept whole (so legitimate extensions/merges are preserved). + */ +export function clipShapesToOthers( + newShapes: Shape[], + sliceShapes: Shape[], + width: number, + height: number, +): PolygonShape[] { + const { gw, gh, scale } = gridFor(width, height); + const out: PolygonShape[] = []; + + // Cached per classId: other = union of DIFFERENT-class shapes (subtracted); + // same = union of SAME-class shapes (used to drop fully-redundant fragments). + const otherMaskByClass = new Map(); + const sameMaskByClass = new Map(); + const otherMaskFor = (classId: number): Uint8Array => { + let m = otherMaskByClass.get(classId); + if (!m) { + m = rasterizeUnion(sliceShapes.filter((s) => s.classId !== classId), gw, gh, scale); + otherMaskByClass.set(classId, m); + } + return m; + }; + const sameMaskFor = (classId: number): Uint8Array => { + let m = sameMaskByClass.get(classId); + if (!m) { + m = rasterizeUnion(sliceShapes.filter((s) => s.classId === classId), gw, gh, scale); + sameMaskByClass.set(classId, m); + } + return m; + }; + + for (const shape of newShapes) { + const other = otherMaskFor(shape.classId); + const same = sameMaskFor(shape.classId); + const mine = rasterizeShapes([shape], gw, gh, scale); + // Subtract other classes; track whether any surviving pixel adds NEW area + // (outside both other and same-class existing labels). + let hasNew = false; + for (let i = 0; i < mine.length; i++) { + if (other[i]) { mine[i] = 0; continue; } // mine AND NOT other + if (mine[i] && !same[i]) hasNew = true; + } + // Fully covered by existing labels (other or same) → redundant fragment, drop. + if (!hasNew) continue; + const polys = maskToPolygonsWithHoles(mine, gw, gh, { minRegion: 4, scale }); + for (let k = 0; k < polys.length; k++) { + if (polys[k].points.length >= 6) { + out.push({ + // Reuse the id when the shape maps to a single polygon; fresh ids on split. + id: k === 0 ? shape.id : uuidv4(), + classId: shape.classId, + kind: 'polygon', + points: polys[k].points, + ...(polys[k].holes.length ? { holes: polys[k].holes } : {}), + }); + } + } + } + return out; +} diff --git a/frontend/src/lib/featureManifold.test.ts b/frontend/src/lib/featureManifold.test.ts new file mode 100644 index 0000000..e354150 --- /dev/null +++ b/frontend/src/lib/featureManifold.test.ts @@ -0,0 +1,36 @@ +import { describe, expect, it } from 'vitest'; +import { colorizeManifoldHeatmap, manifoldMarkerRect } from './featureManifold'; + +describe('colorizeManifoldHeatmap', () => { + it('returns a canvas matching the grayscale map size', () => { + const w = 4; + const h = 3; + const gray = new Uint8Array(w * h); + gray[0] = 255; + gray[1] = 0; + const canvas = colorizeManifoldHeatmap(gray, w, h, 0.5); + expect(canvas.width).toBe(w); + expect(canvas.height).toBe(h); + }); +}); + +describe('manifoldMarkerRect', () => { + it('uses server box coordinates when present (no edge shift)', () => { + const r = manifoldMarkerRect( + { x: 10, y: 10, box_size: 64, box: { x0: 0, y0: 0, x1: 32, y1: 32 } }, + { side: 64, width: 400, height: 300 }, + ); + expect(r).toEqual({ x: 0, y: 0, width: 32, height: 32 }); + }); + + it('clips reconstructed squares instead of shifting them', () => { + const r = manifoldMarkerRect( + { x: 10, y: 50 }, + { side: 64, width: 200, height: 200 }, + ); + expect(r.x).toBe(0); + expect(r.width).toBe(42); // 10 + 32, clipped — not shifted to 64 wide + expect(r.y).toBe(18); + expect(r.height).toBe(64); + }); +}); diff --git a/frontend/src/lib/featureManifold.ts b/frontend/src/lib/featureManifold.ts new file mode 100644 index 0000000..fdf2f0e --- /dev/null +++ b/frontend/src/lib/featureManifold.ts @@ -0,0 +1,92 @@ +/** + * Feature-manifold heatmap helpers for Suggest Labels overlay. + */ +import { loadLabelPng } from '@/lib/pixelClf'; + +export interface ManifoldPoint { + x: number; + y: number; + cluster: number; + score?: number; + /** Exclusion / packing radius — spacing only, not box half-size. */ + radius?: number; + dist?: number; + /** Full square side in image pixels (preferred for drawing). */ + box_size?: number; + /** Axis-aligned box; x1/y1 are exclusive max coords (width = x1 − x0). */ + box?: { x0: number; y0: number; x1: number; y1: number }; +} + +/** + * Axis-aligned marker rect in image pixels for a suggest-label center. + * + * Prefer the server `box` (already clipped, non-overlapping). Never use + * exclusion `radius` for geometry. When reconstructing, **clip** — do not + * shift to preserve side length (shifting causes overlays to overlap). + */ +export function manifoldMarkerRect( + point: Pick, + opts: { side?: number; width: number; height: number }, +): { x: number; y: number; width: number; height: number } { + if (point.box) { + const { x0, y0, x1, y1 } = point.box; + return { + x: x0, + y: y0, + width: Math.max(1, x1 - x0), + height: Math.max(1, y1 - y0), + }; + } + const side = Math.max(8, opts.side ?? point.box_size ?? 64); + const half = side / 2; + const x0 = Math.max(0, point.x - half); + const y0 = Math.max(0, point.y - half); + const x1 = Math.min(opts.width, point.x + half); + const y1 = Math.min(opts.height, point.y + half); + return { + x: x0, + y: y0, + width: Math.max(1, x1 - x0), + height: Math.max(1, y1 - y0), + }; +} + +/** Cyan→magenta coverage colormap; alpha scales with score. */ +export function colorizeManifoldHeatmap( + gray: Uint8Array, + width: number, + height: number, + opacity = 0.45, +): HTMLCanvasElement { + const canvas = document.createElement('canvas'); + canvas.width = width; + canvas.height = height; + const ctx = canvas.getContext('2d'); + if (!ctx) return canvas; + const img = ctx.createImageData(width, height); + const out = img.data; + const aScale = Math.round(Math.min(1, Math.max(0, opacity)) * 255); + for (let i = 0; i < gray.length; i++) { + const t = gray[i] / 255; + // low residual (dark) → cool; high residual interestingness → warm + const r = Math.round(40 + 200 * t); + const g = Math.round(180 * (1 - t) + 40 * t); + const b = Math.round(220 * (1 - 0.3 * t)); + const p = i * 4; + out[p] = r; + out[p + 1] = g; + out[p + 2] = b; + out[p + 3] = Math.round(aScale * (0.15 + 0.85 * t)); + } + ctx.putImageData(img, 0, 0); + return canvas; +} + +/** Load grayscale coverage PNG and colorize. */ +export async function loadManifoldHeatmapCanvas( + url: string, + opacity = 0.45, +): Promise { + const { data, width, height } = await loadLabelPng(url); + return colorizeManifoldHeatmap(data, width, height, opacity); +} diff --git a/frontend/src/lib/featureRecipe.test.ts b/frontend/src/lib/featureRecipe.test.ts new file mode 100644 index 0000000..024a4e2 --- /dev/null +++ b/frontend/src/lib/featureRecipe.test.ts @@ -0,0 +1,36 @@ +import { describe, expect, it } from 'vitest'; +import { featureParamsFromRecipe, formatFeatureRecipe } from './featureRecipe'; + +describe('formatFeatureRecipe', () => { + it('summarizes sigma and toggles', () => { + expect( + formatFeatureRecipe({ + sigma_min: 1, + sigma_max: 8, + intensity: true, + edges: true, + texture: false, + clahe: true, + include_sam: true, + }), + ).toBe('σ 1–8 · intensity · edges · CLAHE · SlimSAM'); + }); +}); + +describe('featureParamsFromRecipe', () => { + it('maps snake_case recipe into FeatureParams', () => { + const p = featureParamsFromRecipe({ + sigma_min: 2, + sigma_max: 16, + intensity: true, + edges: false, + texture: true, + clahe: false, + include_sam: true, + }); + expect(p.sigmaMin).toBe(2); + expect(p.sigmaMax).toBe(16); + expect(p.edges).toBe(false); + expect(p.includeSam).toBe(true); + }); +}); diff --git a/frontend/src/lib/featureRecipe.ts b/frontend/src/lib/featureRecipe.ts new file mode 100644 index 0000000..b4503c4 --- /dev/null +++ b/frontend/src/lib/featureRecipe.ts @@ -0,0 +1,37 @@ +/** + * Feature recipe helpers — describe shelf-model required features. + */ +import type { FeatureParams } from '@/hooks/useFeatureChannels'; +import { DEFAULT_FEATURE_PARAMS } from '@/hooks/useFeatureChannels'; + +/** Convert a shelf feature_recipe dict into FeatureParams. */ +export function featureParamsFromRecipe( + recipe: Record | null | undefined, +): FeatureParams { + if (!recipe) return { ...DEFAULT_FEATURE_PARAMS }; + return { + sigmaMin: Number(recipe.sigma_min ?? DEFAULT_FEATURE_PARAMS.sigmaMin), + sigmaMax: Number(recipe.sigma_max ?? DEFAULT_FEATURE_PARAMS.sigmaMax), + intensity: recipe.intensity !== false, + edges: recipe.edges !== false, + texture: recipe.texture !== false, + clahe: recipe.clahe !== false, + includeSam: Boolean(recipe.include_sam ?? recipe.includeSam), + }; +} + +/** Short human-readable feature list for Connect/Browse/Train. */ +export function formatFeatureRecipe( + recipe: Record | null | undefined, +): string { + if (!recipe) return '—'; + const lo = recipe.sigma_min ?? '?'; + const hi = recipe.sigma_max ?? '?'; + const bits: string[] = [`σ ${lo}–${hi}`]; + if (recipe.intensity !== false) bits.push('intensity'); + if (recipe.edges !== false) bits.push('edges'); + if (recipe.texture !== false) bits.push('texture'); + if (recipe.clahe) bits.push('CLAHE'); + if (recipe.include_sam || recipe.includeSam) bits.push('SlimSAM'); + return bits.join(' · '); +} diff --git a/frontend/src/lib/ipredApi.ts b/frontend/src/lib/ipredApi.ts new file mode 100644 index 0000000..240b99d --- /dev/null +++ b/frontend/src/lib/ipredApi.ts @@ -0,0 +1,313 @@ +/** + * Thin client for Annotate → ipred proxy (`/api/ipred/*`). + * Frontend never talks to the ipred port directly. + */ +import { API_BASE } from '@/config'; + +export interface IpredSession { + session_id: string; + project_id: string; + current_feature_id?: string | null; + current_model_id?: string | null; + current_run_id?: string | null; +} + +export interface FeatureSetup { + id: string; + name: string; + kind: 'procedure' | 'weights' | string; + builtin?: boolean; + procedure_id?: string; + params?: Record; + encoder_setup_id?: string; + weights_path?: string; + weights_format?: string; + inference?: Record; + content_hash?: string; +} + +export interface IpredPreprocessResult { + feature_id: string; + project_id: string; + setup_id: string; + slice_index: number; + n_channels: number; + height: number; + width: number; + labels: string[]; + cache_hit: boolean; + blob_dir?: string; +} + +async function parseError(res: Response): Promise { + const text = await res.text(); + try { + const j = JSON.parse(text) as { detail?: unknown }; + if (typeof j.detail === 'string') return new Error(j.detail); + return new Error(text || res.statusText); + } catch { + return new Error(text || res.statusText); + } +} + +export async function ipredHealth(): Promise<{ status: string; service?: string }> { + const res = await fetch(`${API_BASE}/api/ipred/health`); + if (!res.ok) throw await parseError(res); + return res.json() as Promise<{ status: string; service?: string }>; +} + +export async function openIpredSession(payload: { + kind: string; + source: string; + server_uri?: string | null; + root?: string | null; +}): Promise { + const res = await fetch(`${API_BASE}/api/ipred/sessions`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + }); + if (!res.ok) throw await parseError(res); + return res.json() as Promise; +} + +export async function listIpredSetups(): Promise { + const res = await fetch(`${API_BASE}/api/ipred/setups`); + if (!res.ok) throw await parseError(res); + const body = (await res.json()) as { setups: FeatureSetup[] }; + return body.setups ?? []; +} + +export async function getIpredSetup(setupId: string): Promise { + const res = await fetch(`${API_BASE}/api/ipred/setups/${encodeURIComponent(setupId)}`); + if (!res.ok) throw await parseError(res); + return res.json() as Promise; +} + +export async function upsertIpredSetup(payload: { + name: string; + kind: string; + procedure_id?: string; + params?: Record; + encoder_setup_id?: string | null; + weights_path?: string; + weights_format?: string; + inference?: Record; + setup_id?: string; +}): Promise { + const res = await fetch(`${API_BASE}/api/ipred/setups`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + }); + if (!res.ok) throw await parseError(res); + return res.json() as Promise; +} + +export async function listIpredTrainers(): Promise { + const res = await fetch(`${API_BASE}/api/ipred/trainers`); + if (!res.ok) throw await parseError(res); + const body = (await res.json()) as { trainers: string[] }; + return body.trainers ?? []; +} + +export interface FeatureModuleInfo { + id: string; + name: string; + description: string; + runtime: string; + ready: boolean; + accepts_input_from: boolean; + produces_channels: boolean; + produces_embedding: boolean; + params_schema: Record; +} + +export interface CompositionNode { + id: string; + module: string; + params?: Record; + input_from?: string; +} + +export interface CompositionDoc { + id: string; + name: string; + kind?: string; + builtin?: boolean; + nodes: CompositionNode[]; + outputs: string[]; + content_hash?: string; + preview_labels?: string[]; +} + +export async function listIpredModules(): Promise { + const res = await fetch(`${API_BASE}/api/ipred/modules`); + if (!res.ok) throw await parseError(res); + const body = (await res.json()) as { modules: FeatureModuleInfo[] }; + return body.modules ?? []; +} + +export async function listIpredCompositions(): Promise { + const res = await fetch(`${API_BASE}/api/ipred/compositions`); + if (!res.ok) throw await parseError(res); + const body = (await res.json()) as { compositions: CompositionDoc[] }; + return body.compositions ?? []; +} + +export async function getIpredComposition(id: string): Promise { + const res = await fetch( + `${API_BASE}/api/ipred/compositions/${encodeURIComponent(id)}`, + ); + if (!res.ok) throw await parseError(res); + return res.json() as Promise; +} + +export async function upsertIpredComposition(payload: { + name: string; + nodes: CompositionNode[]; + outputs: string[]; + composition_id?: string; + builtin?: boolean; +}): Promise { + const res = await fetch(`${API_BASE}/api/ipred/compositions`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + }); + if (!res.ok) throw await parseError(res); + return res.json() as Promise; +} + +export async function previewIpredComposition(payload: { + name?: string; + nodes: CompositionNode[]; + outputs: string[]; +}): Promise<{ preview_labels: string[] }> { + const res = await fetch(`${API_BASE}/api/ipred/compositions/preview`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ name: payload.name ?? 'preview', ...payload }), + }); + if (!res.ok) throw await parseError(res); + return res.json() as Promise<{ preview_labels: string[] }>; +} + +export async function ipredPreprocess(payload: { + session_id: string; + feature_setup_id?: string; + composition_id?: string; + slice_index?: number; + array_ref?: string; +}): Promise { + const res = await fetch(`${API_BASE}/api/ipred/preprocess`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + }); + if (!res.ok) throw await parseError(res); + return res.json() as Promise; +} + +export function ipredChannelUrl(featureId: string, index: number): string { + return `${API_BASE}/api/ipred/features/${encodeURIComponent(featureId)}/channels/${index}`; +} + +export interface IpredFeatureImportance { + label: string; + importance: number; +} + +export interface IpredTrainResult { + model_id: string; + feature_id: string; + trainer_id: string; + class_ids: number[]; + train_accuracy: number; + n_train: number; + n_cal: number; + n_samples: number; + params: Record; + /** Trainer-specific (CatBoost); omit or empty when unavailable. */ + feature_importances?: IpredFeatureImportance[]; +} + +export interface IpredInferResult { + run_id: string; + model_id: string; + feature_id: string; + alpha: number; + class_ids: number[]; + counts: { singleton: number; multi: number; abstain: number }; + q_by_class?: Record; +} + +export async function ipredTrain(payload: { + session_id: string; + shapes: unknown[]; + feature_id?: string | null; + trainer_id?: string; + config?: Record; +}): Promise { + const res = await fetch(`${API_BASE}/api/ipred/train`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + }); + if (!res.ok) throw await parseError(res); + return res.json() as Promise; +} + +export async function ipredInfer(payload: { + session_id: string; + model_id?: string | null; + feature_id?: string | null; + alpha?: number; +}): Promise { + const res = await fetch(`${API_BASE}/api/ipred/infer`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + }); + if (!res.ok) throw await parseError(res); + return res.json() as Promise; +} + +export function ipredRunCommitUrl(runId: string): string { + return `${API_BASE}/api/ipred/runs/${encodeURIComponent(runId)}/commit.png`; +} + +export function ipredRunStatusUrl(runId: string): string { + return `${API_BASE}/api/ipred/runs/${encodeURIComponent(runId)}/status.png`; +} + +export function ipredRunProbaUrl(runId: string, classIndex: number): string { + return `${API_BASE}/api/ipred/runs/${encodeURIComponent(runId)}/proba/${classIndex}.png`; +} + +export interface IpredThresholdClassResult { + run_id: string; + class_id: number; + class_index: number; + threshold: number; + width: number; + height: number; + n_positive: number; + label_map_b64: string; +} + +export async function ipredThresholdClass( + runId: string, + payload: { class_id: number; threshold: number }, +): Promise { + const res = await fetch( + `${API_BASE}/api/ipred/runs/${encodeURIComponent(runId)}/threshold-class`, + { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + }, + ); + if (!res.ok) throw await parseError(res); + return res.json() as Promise; +} diff --git a/frontend/src/lib/labelMap.ts b/frontend/src/lib/labelMap.ts new file mode 100644 index 0000000..1ddd9de --- /dev/null +++ b/frontend/src/lib/labelMap.ts @@ -0,0 +1,126 @@ +/** + * Full-resolution classId label maps for Cleanup Studio (pixel-accurate buffer). + */ +import { v4 as uuidv4 } from 'uuid'; +import type { Shape } from '@/stores/annotationStore'; +import { rasterizeShapes } from '@/lib/rasterize'; +import { maskToPolygonsWithHoles } from '@/lib/magicwand'; +import { colorizeLabelMap } from '@/lib/pixelClf'; + +/** Rasterize shapes into an HxW classId map (scale=1, last class wins on overlap). */ +export function shapesToLabelMap( + shapes: Shape[], + width: number, + height: number, +): Uint8Array { + const out = new Uint8Array(width * height); + const byClass = new Map(); + for (const s of shapes) { + const arr = byClass.get(s.classId); + if (arr) arr.push(s); + else byClass.set(s.classId, [s]); + } + for (const [cid, group] of byClass) { + const mask = rasterizeShapes(group, width, height, 1); + for (let i = 0; i < mask.length; i++) { + if (mask[i]) out[i] = cid; + } + } + return out; +} + +/** Class ids present in a label map (non-zero). */ +export function classIdsInLabelMap(labels: Uint8Array): number[] { + const seen = new Set(); + for (let i = 0; i < labels.length; i++) { + const v = labels[i]; + if (v) seen.add(v); + } + return [...seen].sort((a, b) => a - b); +} + +/** Extract binary mask for one classId. */ +export function classBinary(labels: Uint8Array, classId: number): Uint8Array { + const out = new Uint8Array(labels.length); + for (let i = 0; i < labels.length; i++) if (labels[i] === classId) out[i] = 1; + return out; +} + +/** Write a binary mask back as classId (optionally without overwriting other classes). */ +export function paintClass( + labels: Uint8Array, + mask: Uint8Array, + classId: number, + { protectOthers = true }: { protectOthers?: boolean } = {}, +): Uint8Array { + const out = new Uint8Array(labels); + for (let i = 0; i < out.length; i++) { + if (out[i] === classId) out[i] = 0; + } + for (let i = 0; i < mask.length; i++) { + if (!mask[i]) continue; + if (protectOthers && out[i] !== 0 && out[i] !== classId) continue; + out[i] = classId; + } + return out; +} + +/** Vectorize a label map to polygon shapes (only at Merge). */ +export function labelMapToShapes( + labels: Uint8Array, + width: number, + height: number, + { minRegion = 4 }: { minRegion?: number } = {}, +): Shape[] { + const shapes: Shape[] = []; + for (const cid of classIdsInLabelMap(labels)) { + const mask = classBinary(labels, cid); + const polys = maskToPolygonsWithHoles(mask, width, height, { + minRegion, + scale: 1, + }); + for (const p of polys) { + if (p.points.length < 6) continue; + shapes.push({ + id: uuidv4(), + classId: cid, + kind: 'polygon', + points: p.points, + ...(p.holes.length ? { holes: p.holes } : {}), + }); + } + } + return shapes; +} + +/** Colorize label map for canvas overlay. */ +export function labelMapOverlayCanvas( + labels: Uint8Array, + width: number, + height: number, + colorByClass: Map, + alpha = 110, +): HTMLCanvasElement { + return colorizeLabelMap(labels, width, height, colorByClass, alpha); +} + +/** base64 encode for draft JSON. */ +export function labelMapToBase64(labels: Uint8Array): string { + let binary = ''; + const chunk = 0x8000; + for (let i = 0; i < labels.length; i += chunk) { + binary += String.fromCharCode(...labels.subarray(i, i + chunk)); + } + return btoa(binary); +} + +/** Decode draft base64 label map. */ +export function labelMapFromBase64(b64: string, expectLen?: number): Uint8Array { + const binary = atob(b64); + const out = new Uint8Array(binary.length); + for (let i = 0; i < binary.length; i++) out[i] = binary.charCodeAt(i); + if (expectLen !== undefined && out.length !== expectLen) { + throw new Error(`label map length ${out.length} != expected ${expectLen}`); + } + return out; +} diff --git a/frontend/src/lib/livewire.test.ts b/frontend/src/lib/livewire.test.ts new file mode 100644 index 0000000..58cc6b2 --- /dev/null +++ b/frontend/src/lib/livewire.test.ts @@ -0,0 +1,42 @@ +import { describe, it, expect } from 'vitest'; +import { dijkstra, tracePath, imageToGrid, simplifyPath, type CostMap } from './livewire'; + +/** 5x5 grid, scale 1, with a zero-cost "edge" corridor along row y=2. */ +function corridorMap(): CostMap { + const gw = 5, gh = 5; + const cost = new Float32Array(gw * gh).fill(1); + for (let x = 0; x < gw; x++) cost[2 * gw + x] = 0; // strong edge on row 2 + return { gw, gh, scale: 1, cost }; +} + +describe('livewire', () => { + it('imageToGrid clamps and maps to the right cell', () => { + const cm = corridorMap(); + expect(imageToGrid(cm, 0, 0)).toBe(0); + expect(imageToGrid(cm, 4, 2)).toBe(2 * 5 + 4); + expect(imageToGrid(cm, 999, 999)).toBe(5 * 5 - 1); // clamped + }); + + it('traces the least-cost path along the edge corridor', () => { + const cm = corridorMap(); + const seed = imageToGrid(cm, 0, 2); + const prev = dijkstra(cm, seed); + const path = tracePath(cm, prev, imageToGrid(cm, 4, 2)); + // Path is flat [x,y,...]; every vertex should sit on the corridor (y center = 2.5). + expect(path.length).toBeGreaterThanOrEqual(4); + for (let i = 1; i < path.length; i += 2) { + expect(path[i]).toBeCloseTo(2.5, 5); + } + // Starts at the seed, ends at the target. + expect(path[0]).toBeCloseTo(0.5, 5); + expect(path[path.length - 2]).toBeCloseTo(4.5, 5); + }); + + it('simplifyPath keeps endpoints and thins the middle', () => { + const dense = [0, 0, 1, 0, 2, 0, 3, 0, 4, 0]; // 5 points + const simplified = simplifyPath(dense, 2); + expect(simplified.slice(0, 2)).toEqual([0, 0]); + expect(simplified.slice(-2)).toEqual([4, 0]); + expect(simplified.length).toBeLessThan(dense.length); + }); +}); diff --git a/frontend/src/lib/livewire.ts b/frontend/src/lib/livewire.ts new file mode 100644 index 0000000..2b6adee --- /dev/null +++ b/frontend/src/lib/livewire.ts @@ -0,0 +1,204 @@ +/** + * Live-wire / "magnetic lasso" edge tracing. + * + * Builds a downsampled edge-cost map from the rendered slice (low cost on + * strong image gradients), runs Dijkstra from a seed point, then backtracks + * the least-cost path to the cursor — so the traced boundary hugs feature + * edges. Coordinates are in/out in full-resolution IMAGE pixels. + */ + +export interface CostMap { + gw: number; + gh: number; + /** Image pixels per grid cell (downsample factor). */ + scale: number; + /** Per-node traversal cost in [~0, 1]; ~0 on strong edges. */ + cost: Float32Array; +} + +/** Build an edge-cost map from an image, downsampled so the long side <= maxDim. */ +export function buildCostMap( + image: CanvasImageSource, + imgW: number, + imgH: number, + maxDim = 512, +): CostMap | null { + const scale = Math.max(1, Math.ceil(Math.max(imgW, imgH) / maxDim)); + const gw = Math.max(1, Math.floor(imgW / scale)); + const gh = Math.max(1, Math.floor(imgH / scale)); + + const canvas = document.createElement('canvas'); + canvas.width = gw; + canvas.height = gh; + const ctx = canvas.getContext('2d', { willReadFrequently: true }); + if (!ctx) return null; + ctx.drawImage(image, 0, 0, gw, gh); + let data: Uint8ClampedArray; + try { + data = ctx.getImageData(0, 0, gw, gh).data; + } catch { + return null; // tainted canvas (shouldn't happen for same-origin slices) + } + + const gray = new Float32Array(gw * gh); + for (let i = 0; i < gw * gh; i++) { + gray[i] = 0.299 * data[i * 4] + 0.587 * data[i * 4 + 1] + 0.114 * data[i * 4 + 2]; + } + + // Sobel gradient magnitude. + const grad = new Float32Array(gw * gh); + let maxG = 0; + for (let y = 1; y < gh - 1; y++) { + for (let x = 1; x < gw - 1; x++) { + const i = y * gw + x; + const gx = + -gray[i - gw - 1] - 2 * gray[i - 1] - gray[i + gw - 1] + + gray[i - gw + 1] + 2 * gray[i + 1] + gray[i + gw + 1]; + const gy = + -gray[i - gw - 1] - 2 * gray[i - gw] - gray[i - gw + 1] + + gray[i + gw - 1] + 2 * gray[i + gw] + gray[i + gw + 1]; + const m = Math.hypot(gx, gy); + grad[i] = m; + if (m > maxG) maxG = m; + } + } + + const cost = new Float32Array(gw * gh); + const inv = maxG > 0 ? 1 / maxG : 0; + for (let i = 0; i < gw * gh; i++) { + cost[i] = 1 - grad[i] * inv; // strong edge -> ~0 cost + } + return { gw, gh, scale, cost }; +} + +/** Map image-pixel (x,y) to a clamped flat grid index into the cost map. */ +export function imageToGrid(cm: CostMap, x: number, y: number): number { + const gx = Math.max(0, Math.min(cm.gw - 1, Math.floor(x / cm.scale))); + const gy = Math.max(0, Math.min(cm.gh - 1, Math.floor(y / cm.scale))); + return gy * cm.gw + gx; +} + +/** Minimal binary min-heap keyed by float priority. */ +class MinHeap { + private ids: number[] = []; + private keys: number[] = []; + /** Number of entries in the heap. */ + get size() { return this.ids.length; } + /** Insert `id` with priority `key`, sifting up to restore the min-heap. */ + push(id: number, key: number) { + this.ids.push(id); + this.keys.push(key); + let i = this.ids.length - 1; + while (i > 0) { + const p = (i - 1) >> 1; + if (this.keys[p] <= this.keys[i]) break; + this.swap(i, p); + i = p; + } + } + /** Remove and return the id with the smallest key, sifting down to repair. */ + pop(): number { + const n = this.ids.length; + const top = this.ids[0]; + const lastId = this.ids.pop()!; + const lastKey = this.keys.pop()!; + if (n > 1) { + this.ids[0] = lastId; + this.keys[0] = lastKey; + let i = 0; + for (;;) { + const l = 2 * i + 1, r = 2 * i + 2; + let s = i; + if (l < this.ids.length && this.keys[l] < this.keys[s]) s = l; + if (r < this.ids.length && this.keys[r] < this.keys[s]) s = r; + if (s === i) break; + this.swap(i, s); + i = s; + } + } + return top; + } + /** Swap the entries at heap positions `a` and `b`. */ + private swap(a: number, b: number) { + [this.ids[a], this.ids[b]] = [this.ids[b], this.ids[a]]; + [this.keys[a], this.keys[b]] = [this.keys[b], this.keys[a]]; + } +} + +/** Dijkstra over the cost map from *seedIdx*; returns a back-pointer array. */ +export function dijkstra(cm: CostMap, seedIdx: number): Int32Array { + const { gw, gh, cost } = cm; + const n = gw * gh; + const dist = new Float32Array(n).fill(Infinity); + const prev = new Int32Array(n).fill(-1); + const done = new Uint8Array(n); + dist[seedIdx] = 0; + const heap = new MinHeap(); + heap.push(seedIdx, 0); + + while (heap.size > 0) { + const u = heap.pop(); + if (done[u]) continue; + done[u] = 1; + const ux = u % gw; + const uy = (u / gw) | 0; + const du = dist[u]; + for (let dy = -1; dy <= 1; dy++) { + const ny = uy + dy; + if (ny < 0 || ny >= gh) continue; + for (let dx = -1; dx <= 1; dx++) { + if (dx === 0 && dy === 0) continue; + const nx = ux + dx; + if (nx < 0 || nx >= gw) continue; + const v = ny * gw + nx; + if (done[v]) continue; + const diag = dx !== 0 && dy !== 0 ? Math.SQRT2 : 1; + const w = (cost[v] + 0.02) * diag; // +base keeps weak edges traversable + const nd = du + w; + if (nd < dist[v]) { + dist[v] = nd; + prev[v] = u; + heap.push(v, nd); + } + } + } + } + return prev; +} + +/** + * Backtrack the least-cost path from *targetIdx* to the seed used to build + * *prev*; returns flat [x0,y0,x1,y1,...] in image pixels (seed-first order). + */ +export function tracePath(cm: CostMap, prev: Int32Array, targetIdx: number): number[] { + const grid: number[] = []; + let cur = targetIdx; + let guard = 0; + const maxSteps = cm.gw * cm.gh; + while (cur !== -1 && guard++ < maxSteps) { + grid.push(cur); + cur = prev[cur]; + } + grid.reverse(); // seed -> target + const half = cm.scale / 2; + const out: number[] = []; + for (const idx of grid) { + const gx = idx % cm.gw; + const gy = (idx / cm.gw) | 0; + out.push(gx * cm.scale + half, gy * cm.scale + half); + } + return out; +} + +/** Reduce a dense path to fewer vertices (keep every Nth point + endpoints). */ +export function simplifyPath(points: number[], stride = 4): number[] { + if (points.length <= 4) return points; + const out: number[] = []; + const nPts = points.length / 2; + for (let i = 0; i < nPts; i++) { + if (i % stride === 0 || i === nPts - 1) { + out.push(points[i * 2], points[i * 2 + 1]); + } + } + return out; +} diff --git a/frontend/src/lib/magicwand.test.ts b/frontend/src/lib/magicwand.test.ts new file mode 100644 index 0000000..25c34b9 --- /dev/null +++ b/frontend/src/lib/magicwand.test.ts @@ -0,0 +1,171 @@ +import { describe, it, expect } from 'vitest'; +import { magicSelect, maskToPolygons, maskToPolygonsWithHoles, gradientField, type GrayField } from './magicwand'; + +/** 40x40 grid (scale 1): background 0 with two value-200 blocks. */ +function twoBlocks(): GrayField { + const gw = 40, gh = 40; + const gray = new Float32Array(gw * gh); + const fill = (x0: number, y0: number, s: number) => { + for (let y = y0; y < y0 + s; y++) for (let x = x0; x < x0 + s; x++) gray[y * gw + x] = 200; + }; + fill(4, 4, 10); // block A (top-left) + fill(26, 26, 10); // block B (bottom-right) + return { gw, gh, scale: 1, gray }; +} + +describe('magicwand', () => { + it('global mode selects all similar regions at once', () => { + const field = twoBlocks(); + const polys = magicSelect(field, 9, 9, { toleranceFrac: 0.2, mode: 'global', smooth: 0, minRegion: 8 }); + expect(polys.length).toBe(2); + }); + + it('contiguous mode selects only the clicked region', () => { + const field = twoBlocks(); + const polys = magicSelect(field, 9, 9, { toleranceFrac: 0.2, mode: 'contiguous', smooth: 0, minRegion: 8 }); + expect(polys.length).toBe(1); + const xs = polys[0].filter((_, i) => i % 2 === 0); + const ys = polys[0].filter((_, i) => i % 2 === 1); + expect(Math.max(...xs)).toBeLessThan(20); // stayed in the top-left block + expect(Math.max(...ys)).toBeLessThan(20); + }); + + it('minRegion drops specks', () => { + const gw = 40, gh = 40; + const gray = new Float32Array(gw * gh); + for (let y = 4; y < 16; y++) for (let x = 4; x < 16; x++) gray[y * gw + x] = 200; // real region + gray[35 * gw + 35] = 200; // 1px speck + const polys = magicSelect({ gw, gh, scale: 1, gray }, 8, 8, { + toleranceFrac: 0.3, mode: 'global', smooth: 0, minRegion: 12, + }); + expect(polys.length).toBe(1); + }); + + it('edgeStop keeps the flood from crossing a gradient wall', () => { + const gw = 40, gh = 40; + const gray = new Float32Array(gw * gh).fill(50); // uniform intensity + const grad = new Float32Array(gw * gh); + for (let y = 0; y < gh; y++) grad[y * gw + 20] = 1; // strong vertical edge at x=20 + const field: GrayField = { gw, gh, scale: 1, gray, grad }; + + // Without an edge barrier the whole uniform field floods (crosses x=20). + const open = magicSelect(field, 5, 5, { toleranceFrac: 0.5, mode: 'contiguous', smooth: 0, edgeStop: 0, minRegion: 8 }); + expect(open.length).toBe(1); + expect(Math.max(...open[0].filter((_, i) => i % 2 === 0))).toBeGreaterThan(25); + + // With the barrier the flood stops at the wall (stays left of x=20). + const walled = magicSelect(field, 5, 5, { toleranceFrac: 0.5, mode: 'contiguous', smooth: 0, edgeStop: 0.5, minRegion: 8 }); + expect(walled.length).toBe(1); + expect(Math.max(...walled[0].filter((_, i) => i % 2 === 0))).toBeLessThan(21); + }); + + it('fills a uniform region with edgeStop on (noise must not wall the flood)', () => { + // Left half uniform (100, with faint noise), right half 200, sharp edge at x=30. + const gw = 60, gh = 40; + const gray = new Float32Array(gw * gh); + for (let y = 0; y < gh; y++) { + for (let x = 0; x < gw; x++) gray[y * gw + x] = x < 30 ? 100 : 200; + } + gray[20 * gw + 10] = 104; // a faint interior "noise" pixel + gray[15 * gw + 18] = 97; + const field: GrayField = { gw, gh, scale: 1, gray, grad: gradientField(gray, gw, gh) }; + + // Seed in the uniform left region with the edge barrier active. + const polys = magicSelect(field, 10, 20, { + toleranceFrac: 0.2, mode: 'contiguous', smooth: 0, edgeStop: 0.3, minRegion: 8, + }); + expect(polys.length).toBe(1); + const xs = polys[0].filter((_, i) => i % 2 === 0); + // Should fill most of the left region (not get trapped at the seed) and not cross x=30. + expect(Math.max(...xs)).toBeGreaterThan(20); + expect(Math.max(...xs)).toBeLessThan(31); + }); + + it('scales polygon coordinates back to image space', () => { + const field = twoBlocks(); + field.scale = 4; // pretend the field was downsampled 4x + const polys = magicSelect(field, 36, 36, { toleranceFrac: 0.2, mode: 'contiguous', smooth: 0, minRegion: 8 }); + expect(polys.length).toBe(1); + // block A spans grid x∈[4,14) → image x∈[~16,~58); coords should exceed grid range. + const xs = polys[0].filter((_, i) => i % 2 === 0); + expect(Math.max(...xs)).toBeGreaterThan(20); + }); +}); + +/** Build a w×h binary mask with `fill` setting rectangular blocks to 1. */ +function maskWith(w: number, h: number, blocks: Array<[number, number, number, number]>): Uint8Array { + const m = new Uint8Array(w * h); + for (const [x0, y0, bw, bh] of blocks) { + for (let y = y0; y < y0 + bh; y++) for (let x = x0; x < x0 + bw; x++) m[y * w + x] = 1; + } + return m; +} + +describe('maskToPolygons (shared by SAM + classic)', () => { + it('returns one polygon for one blob', () => { + const mask = maskWith(40, 40, [[5, 5, 12, 12]]); + const polys = maskToPolygons(mask, 40, 40, { minRegion: 8 }); + expect(polys.length).toBe(1); + expect(polys[0].length).toBeGreaterThanOrEqual(6); + }); + + it('returns two polygons for two disjoint blobs', () => { + const mask = maskWith(40, 40, [[4, 4, 10, 10], [26, 26, 10, 10]]); + const polys = maskToPolygons(mask, 40, 40, { minRegion: 8 }); + expect(polys.length).toBe(2); + }); + + it('drops components smaller than minRegion', () => { + const mask = maskWith(40, 40, [[4, 4, 12, 12]]); + mask[38 * 40 + 38] = 1; // 1px speck + const polys = maskToPolygons(mask, 40, 40, { minRegion: 12 }); + expect(polys.length).toBe(1); + }); + + it('maps coordinates to image space via scale', () => { + const mask = maskWith(40, 40, [[4, 4, 10, 10]]); + const polys = maskToPolygons(mask, 40, 40, { minRegion: 8, scale: 4 }); + const xs = polys[0].filter((_, i) => i % 2 === 0); + // grid x∈[4,14) → image x∈[~18,~58): max should clear the raw grid range. + expect(Math.max(...xs)).toBeGreaterThan(40); + }); +}); + +describe('maskToPolygonsWithHoles', () => { + const W = 60, H = 60; + /** 60x60 mask: filled outer square minus a centered square = a donut. */ + function donut(): Uint8Array { + const m = new Uint8Array(W * H); + for (let y = 10; y < 50; y++) for (let x = 10; x < 50; x++) m[y * W + x] = 1; // outer + for (let y = 22; y < 38; y++) for (let x = 22; x < 38; x++) m[y * W + x] = 0; // hole + return m; + } + + it('returns one outer ring and one hole for a donut', () => { + const out = maskToPolygonsWithHoles(donut(), W, H, { minRegion: 8 }); + expect(out.length).toBe(1); + expect(out[0].holes.length).toBe(1); + expect(out[0].points.length).toBeGreaterThanOrEqual(6); + }); + + it('attaches the hole even when the outer region is concave (magic-like)', () => { + // A "C"/notched block (concave outer) with an enclosed square hole. The hole's + // centroid can fall in the notch (outside the outer), so this exercises the + // single-region direct-assign path rather than the centroid test. + const m = new Uint8Array(W * H); + for (let y = 8; y < 52; y++) for (let x = 8; x < 52; x++) m[y * W + x] = 1; // block + for (let y = 8; y < 30; y++) for (let x = 40; x < 52; x++) m[y * W + x] = 0; // notch (concavity) + for (let y = 34; y < 46; y++) for (let x = 16; x < 28; x++) m[y * W + x] = 0; // enclosed hole + const out = maskToPolygonsWithHoles(m, W, H, { minRegion: 8 }); + expect(out.length).toBe(1); + expect(out[0].holes.length).toBe(1); + }); + + it('returns no holes for a solid region', () => { + const m = new Uint8Array(W * H); + for (let y = 10; y < 50; y++) for (let x = 10; x < 50; x++) m[y * W + x] = 1; + const out = maskToPolygonsWithHoles(m, W, H, { minRegion: 8 }); + expect(out.length).toBe(1); + expect(out[0].holes.length).toBe(0); + }); +}); diff --git a/frontend/src/lib/magicwand.ts b/frontend/src/lib/magicwand.ts new file mode 100644 index 0000000..f193f52 --- /dev/null +++ b/frontend/src/lib/magicwand.ts @@ -0,0 +1,440 @@ +/** + * Client-side "magic wand" selection — runs entirely in the browser on the + * rendered slice, so it's instant (no backend round-trip) and tolerance changes + * update live. + * + * Pipeline: rendered image → downsampled grayscale field → flood-fill + * (contiguous) or intensity-band (global) mask → connected components → + * Moore-neighbor boundary trace → Douglas–Peucker simplify → polygons in + * full-resolution IMAGE pixel coordinates. + */ +import { fillHoles } from '@/lib/morphology'; + +export interface GrayField { + gw: number; + gh: number; + /** Image pixels per grid cell (downsample factor). */ + scale: number; + gray: Float32Array; + /** Sobel gradient magnitude normalised to ~[0,1] (edge barrier for flood). */ + grad?: Float32Array; +} + +const MAX_POLYGONS = 300; + +/** Sobel gradient magnitude, normalised to ~[0,1] so the edge threshold is + * dataset-independent. + * + * Normalisation matters a lot for uniform (fully white/black) regions: a naive + * sampled percentile lands near 0 there (edges are sparse), which would blow up + * faint interior rendering noise into "walls" and trap the flood at the seed. + * We instead use a full-array histogram percentile with a floor relative to the + * strongest edge, so interior noise stays ~0 and real boundaries stay ~1. */ +export function gradientField(gray: Float32Array, gw: number, gh: number): Float32Array { + const grad = new Float32Array(gw * gh); + let maxRaw = 0; + for (let y = 1; y < gh - 1; y++) { + for (let x = 1; x < gw - 1; x++) { + const i = y * gw + x; + const gx = + -gray[i - gw - 1] - 2 * gray[i - 1] - gray[i + gw - 1] + + gray[i - gw + 1] + 2 * gray[i + 1] + gray[i + gw + 1]; + const gy = + -gray[i - gw - 1] - 2 * gray[i - gw] - gray[i - gw + 1] + + gray[i + gw - 1] + 2 * gray[i + gw] + gray[i + gw + 1]; + const m = Math.hypot(gx, gy); + grad[i] = m; + if (m > maxRaw) maxRaw = m; + } + } + if (maxRaw < 1e-6) return grad; // perfectly uniform → no edges, no walls + + // 99th-percentile via a 256-bin histogram over ALL pixels (robust + O(n)). + const n = grad.length; + const bins = new Int32Array(256); + const toBin = 255 / maxRaw; + for (let i = 0; i < n; i++) bins[(grad[i] * toBin) | 0]++; + const target = 0.99 * n; + let cum = 0; + let p99 = maxRaw; + for (let b = 0; b < 256; b++) { + cum += bins[b]; + if (cum >= target) { p99 = (b / 255) * maxRaw; break; } + } + // Floor at 20% of the max edge so a uniform region (p99≈0) doesn't over-wall. + const denom = Math.max(p99, 0.2 * maxRaw); + const inv = 1 / denom; + for (let i = 0; i < n; i++) grad[i] = Math.min(1, grad[i] * inv); + return grad; +} + +/** Build a grayscale + gradient field from an image (long side ≤ maxDim). + * + * 1600 keeps a 2560px slice at half-resolution (scale 2) — plenty of detail for + * the edge-aware flood while keeping the per-click work ~4x cheaper than full res. */ +export function buildField( + image: CanvasImageSource, + imgW: number, + imgH: number, + maxDim = 1600, +): GrayField | null { + const scale = Math.max(1, Math.ceil(Math.max(imgW, imgH) / maxDim)); + const gw = Math.max(1, Math.floor(imgW / scale)); + const gh = Math.max(1, Math.floor(imgH / scale)); + const canvas = document.createElement('canvas'); + canvas.width = gw; + canvas.height = gh; + const ctx = canvas.getContext('2d', { willReadFrequently: true }); + if (!ctx) return null; + ctx.drawImage(image, 0, 0, gw, gh); + let data: Uint8ClampedArray; + try { + data = ctx.getImageData(0, 0, gw, gh).data; + } catch { + return null; // tainted canvas + } + const gray = new Float32Array(gw * gh); + for (let i = 0; i < gw * gh; i++) { + gray[i] = 0.299 * data[i * 4] + 0.587 * data[i * 4 + 1] + 0.114 * data[i * 4 + 2]; + } + return { gw, gh, scale, gray, grad: gradientField(gray, gw, gh) }; +} + +/** Robust intensity spread (2nd–98th percentile) for tolerance scaling. */ +function spread(gray: Float32Array): number { + const n = gray.length; + const step = Math.max(1, Math.floor(n / 10000)); + const s: number[] = []; + for (let i = 0; i < n; i += step) s.push(gray[i]); + s.sort((a, b) => a - b); + const p2 = s[Math.floor(s.length * 0.02)] ?? s[0] ?? 0; + const p98 = s[Math.floor(s.length * 0.98)] ?? s[s.length - 1] ?? 1; + return Math.max(1, p98 - p2); +} + +/** Cheap separable box blur (approx Gaussian) applied in-place-ish; returns new array. */ +function boxBlur(gray: Float32Array, gw: number, gh: number, radius: number): Float32Array { + if (radius < 1) return gray; + const r = Math.round(radius); + const tmp = new Float32Array(gray.length); + const out = new Float32Array(gray.length); + // horizontal + for (let y = 0; y < gh; y++) { + for (let x = 0; x < gw; x++) { + let sum = 0, cnt = 0; + for (let dx = -r; dx <= r; dx++) { + const xx = x + dx; + if (xx >= 0 && xx < gw) { sum += gray[y * gw + xx]; cnt++; } + } + tmp[y * gw + x] = sum / cnt; + } + } + // vertical + for (let y = 0; y < gh; y++) { + for (let x = 0; x < gw; x++) { + let sum = 0, cnt = 0; + for (let dy = -r; dy <= r; dy++) { + const yy = y + dy; + if (yy >= 0 && yy < gh) { sum += tmp[yy * gw + x]; cnt++; } + } + out[y * gw + x] = sum / cnt; + } + } + return out; +} + +/** Perpendicular-distance polyline simplification (Douglas–Peucker). */ +function simplify(pts: Array<[number, number]>, tol: number): Array<[number, number]> { + if (pts.length < 3 || tol <= 0) return pts; + const keep = new Uint8Array(pts.length); + keep[0] = 1; + keep[pts.length - 1] = 1; + const stack: Array<[number, number]> = [[0, pts.length - 1]]; + while (stack.length) { + const [a, b] = stack.pop()!; + let maxD = -1; + let idx = -1; + const [ax, ay] = pts[a]; + const [bx, by] = pts[b]; + const dx = bx - ax, dy = by - ay; + const len = Math.hypot(dx, dy) || 1; + for (let i = a + 1; i < b; i++) { + const [px, py] = pts[i]; + const d = Math.abs((px - ax) * dy - (py - ay) * dx) / len; + if (d > maxD) { maxD = d; idx = i; } + } + if (maxD > tol && idx > 0) { + keep[idx] = 1; + stack.push([a, idx], [idx, b]); + } + } + return pts.filter((_, i) => keep[i]); +} + +/** Chaikin corner-cutting: rounds a closed polygon (more iterations = smoother). */ +function chaikin(pts: Array<[number, number]>, iterations: number): Array<[number, number]> { + let p = pts; + for (let it = 0; it < iterations; it++) { + if (p.length < 3) break; + const out: Array<[number, number]> = []; + const n = p.length; + for (let i = 0; i < n; i++) { + const [x0, y0] = p[i]; + const [x1, y1] = p[(i + 1) % n]; + out.push([0.75 * x0 + 0.25 * x1, 0.75 * y0 + 0.25 * y1]); + out.push([0.25 * x0 + 0.75 * x1, 0.25 * y0 + 0.75 * y1]); + } + p = out; + } + return p; +} + +const NBR: Array<[number, number]> = [ + [-1, 0], [-1, -1], [0, -1], [1, -1], [1, 0], [1, 1], [0, 1], [-1, 1], +]; // clockwise starting from West + +/** Moore-neighbor boundary trace of the component with `labels[idx] === lbl`. */ +function traceContour( + labels: Int32Array, gw: number, gh: number, lbl: number, startIdx: number, +): Array<[number, number]> { + const inside = (x: number, y: number) => + x >= 0 && x < gw && y >= 0 && y < gh && labels[y * gw + x] === lbl; + + const sx = startIdx % gw; + const sy = (startIdx / gw) | 0; + const contour: Array<[number, number]> = [[sx, sy]]; + let px = sx, py = sy; + let bx = sx - 1, by = sy; // backtrack (came from the west background cell) + const maxSteps = gw * gh * 8; + + for (let step = 0; step < maxSteps; step++) { + let di = NBR.findIndex((d) => px + d[0] === bx && py + d[1] === by); + if (di < 0) di = 0; + let found = false; + for (let k = 1; k <= 8; k++) { + const idx = (di + k) % 8; + const nx = px + NBR[idx][0]; + const ny = py + NBR[idx][1]; + if (inside(nx, ny)) { + // new backtrack = the (background) cell examined just before this one + bx = px + NBR[(idx + 7) % 8][0]; + by = py + NBR[(idx + 7) % 8][1]; + px = nx; py = ny; + found = true; + break; + } + } + if (!found) break; // isolated pixel + if (px === sx && py === sy) break; // returned to start + contour.push([px, py]); + } + return contour; +} + +interface SelectOpts { + toleranceFrac: number; + mode: 'contiguous' | 'global'; + /** 0–~5 edge smoothing: denoises the field, then rounds the contour. */ + smooth?: number; + /** 0–1 edge barrier (contiguous only): higher = flood stops at weaker edges. */ + edgeStop?: number; + minRegion?: number; // min component size in grid pixels +} + +interface MaskPolyOpts { + /** Min component size, in mask pixels, to keep. */ + minRegion?: number; + /** 0–~5: Douglas–Peucker tolerance + Chaikin rounding strength on the outline. */ + smooth?: number; + /** Mask pixels per output image pixel (1 if the mask is already image-resolution). */ + scale?: number; +} + +/** + * Convert a binary mask into simplified, smoothed polygon contours in IMAGE + * coordinates. Shared by the classic wand (`magicSelect`) and the SAM engine, + * which both produce a `Uint8Array` mask and want editable polygon shapes. + * + * Labels 4-connected components, Moore-neighbor traces each, simplifies the + * staircase with Douglas–Peucker, then rounds corners with Chaikin. Components + * smaller than `minRegion` are dropped; output is capped at `MAX_POLYGONS` + * (largest first). + */ +export function maskToPolygons( + mask: Uint8Array, + gw: number, + gh: number, + { minRegion = 12, smooth = 0, scale = 1 }: MaskPolyOpts = {}, +): number[][] { + const chaikinIters = Math.min(Math.round(smooth), 4); + const dpTol = 1 + smooth * 0.6; + + // Label 4-connected components; remember each one's first (topmost-left) pixel. + const labels = new Int32Array(gw * gh).fill(0); + const starts: number[] = []; + const sizes: number[] = []; + let next = 1; + for (let p = 0; p < mask.length; p++) { + if (!mask[p] || labels[p]) continue; + const lbl = next++; + starts[lbl] = p; + let size = 0; + const stack = [p]; + labels[p] = lbl; + while (stack.length) { + const idx = stack.pop()!; + size++; + const x = idx % gw, y = (idx / gw) | 0; + const push = (j: number) => { if (mask[j] && !labels[j]) { labels[j] = lbl; stack.push(j); } }; + if (x > 0) push(idx - 1); + if (x < gw - 1) push(idx + 1); + if (y > 0) push(idx - gw); + if (y < gh - 1) push(idx + gw); + } + sizes[lbl] = size; + } + + const polys: Array<{ area: number; flat: number[] }> = []; + for (let lbl = 1; lbl < next; lbl++) { + if (sizes[lbl] < minRegion) continue; + let contour = traceContour(labels, gw, gh, lbl, starts[lbl]); + contour = simplify(contour, dpTol); // drop staircase collinear points + contour = chaikin(contour, chaikinIters); // round the remaining corners + if (contour.length < 3) continue; + const flat: number[] = []; + for (const [x, y] of contour) { + flat.push(Math.round((x + 0.5) * scale * 100) / 100, Math.round((y + 0.5) * scale * 100) / 100); + } + polys.push({ area: sizes[lbl], flat }); + } + + polys.sort((a, b) => b.area - a.area); + return polys.slice(0, MAX_POLYGONS).map((p) => p.flat); +} + +/** Even-odd point-in-polygon test on a flat [x,y,…] ring. */ +function pointInPolygonFlat(px: number, py: number, pts: number[]): boolean { + let inside = false; + for (let i = 0, j = pts.length - 2; i < pts.length; j = i, i += 2) { + const xi = pts[i], yi = pts[i + 1]; + const xj = pts[j], yj = pts[j + 1]; + if ((yi > py) !== (yj > py) && px < ((xj - xi) * (py - yi)) / (yj - yi) + xi) inside = !inside; + } + return inside; +} + +/** + * Like `maskToPolygons`, but also returns each region's enclosed voids as inner + * rings ("holes"). Enclosed background (not connected to the image border) is + * detected via `fillHoles`, vectorized, and each void assigned to the outer + * polygon that contains it. Outer + hole rings are in IMAGE coordinates. + */ +export function maskToPolygonsWithHoles( + mask: Uint8Array, + gw: number, + gh: number, + opts: MaskPolyOpts = {}, +): Array<{ points: number[]; holes: number[][] }> { + const outers = maskToPolygons(mask, gw, gh, opts); + const result = outers.map((points) => ({ points, holes: [] as number[][] })); + if (result.length === 0) return result; + + // Enclosed background = pixels fillHoles would fill (interior voids only). + const filled = fillHoles(mask, gw, gh); + const holeMask = new Uint8Array(mask.length); + let anyHole = false; + for (let i = 0; i < mask.length; i++) { + if (filled[i] && !mask[i]) { holeMask[i] = 1; anyHole = true; } + } + if (!anyHole) return result; + + const holeRings = maskToPolygons(holeMask, gw, gh, opts); + for (const ring of holeRings) { + // Single region (the common clip case): the hole is by construction inside + // the only outer, so assign directly — a centroid test can misfire on a + // concave outer (e.g. an irregular SAM/magic outline) and wrongly drop it. + let owner = result.length === 1 ? result[0] : undefined; + if (!owner) { + let cx = 0, cy = 0; + const n = ring.length / 2; + for (let i = 0; i < ring.length; i += 2) { cx += ring[i]; cy += ring[i + 1]; } + cx /= n; cy /= n; + owner = result.find((r) => pointInPolygonFlat(cx, cy, r.points)) + // Fallback for concave outers: the outer whose bbox contains the ring. + ?? result.find((r) => bboxContainsRing(r.points, ring)); + } + if (owner) owner.holes.push(ring); + } + return result; +} + +/** True if `outer`'s bounding box fully contains `ring`'s bounding box. */ +function bboxContainsRing(outer: number[], ring: number[]): boolean { + let ox0 = Infinity, oy0 = Infinity, ox1 = -Infinity, oy1 = -Infinity; + for (let i = 0; i < outer.length; i += 2) { + if (outer[i] < ox0) ox0 = outer[i]; + if (outer[i] > ox1) ox1 = outer[i]; + if (outer[i + 1] < oy0) oy0 = outer[i + 1]; + if (outer[i + 1] > oy1) oy1 = outer[i + 1]; + } + let rx0 = Infinity, ry0 = Infinity, rx1 = -Infinity, ry1 = -Infinity; + for (let i = 0; i < ring.length; i += 2) { + if (ring[i] < rx0) rx0 = ring[i]; + if (ring[i] > rx1) rx1 = ring[i]; + if (ring[i + 1] < ry0) ry0 = ring[i + 1]; + if (ring[i + 1] > ry1) ry1 = ring[i + 1]; + } + return rx0 >= ox0 && ry0 >= oy0 && rx1 <= ox1 && ry1 <= oy1; +} + +/** + * Select similar pixels from `seed` and return polygons in IMAGE coordinates. + */ +export function magicSelect( + field: GrayField, + seedXimg: number, + seedYimg: number, + { toleranceFrac, mode, smooth = 0, edgeStop = 0, minRegion = 12 }: SelectOpts, +): number[][] { + const { gw, gh, scale, grad } = field; + // Smoothing drives a pre-blur (denoise so the boundary is less ragged) here; + // the Douglas–Peucker + Chaikin contour smoothing happens in maskToPolygons. + const blurR = Math.min(Math.round(smooth), 3); + const gray = blurR > 0 ? boxBlur(field.gray, gw, gh, blurR) : field.gray; + const sx = Math.max(0, Math.min(gw - 1, Math.floor(seedXimg / scale))); + const sy = Math.max(0, Math.min(gh - 1, Math.floor(seedYimg / scale))); + const seedVal = gray[sy * gw + sx]; + const tolAbs = Math.max(0, toleranceFrac) * spread(field.gray); + + // Edge barrier: pixels whose normalised gradient exceeds this are walls the + // flood won't cross — keeps a void's selection bounded by its rim instead of + // leaking across a soft/ringy edge. Disabled when edgeStop is 0 or no grad. + const wallLimit = edgeStop > 0 ? 1 - edgeStop : Infinity; + const isWall = (i: number) => grad !== undefined && grad[i] >= wallLimit; + + const mask = new Uint8Array(gw * gh); + if (mode === 'global') { + for (let i = 0; i < mask.length; i++) { + if (Math.abs(gray[i] - seedVal) <= tolAbs) mask[i] = 1; + } + } else { + // Flood fill from the seed (4-connected): stay within tol of the seed value + // and don't expand into edge (wall) pixels. The seed itself is always kept. + const seedIdx = sy * gw + sx; + const stack = [seedIdx]; + while (stack.length) { + const idx = stack.pop()!; + if (mask[idx]) continue; + if (Math.abs(gray[idx] - seedVal) > tolAbs) continue; + if (idx !== seedIdx && isWall(idx)) continue; + mask[idx] = 1; + const x = idx % gw, y = (idx / gw) | 0; + if (x > 0) stack.push(idx - 1); + if (x < gw - 1) stack.push(idx + 1); + if (y > 0) stack.push(idx - gw); + if (y < gh - 1) stack.push(idx + gw); + } + } + + return maskToPolygons(mask, gw, gh, { minRegion, smooth, scale }); +} diff --git a/frontend/src/lib/maskSetMerge.test.ts b/frontend/src/lib/maskSetMerge.test.ts new file mode 100644 index 0000000..689cdb4 --- /dev/null +++ b/frontend/src/lib/maskSetMerge.test.ts @@ -0,0 +1,43 @@ +import { describe, expect, it } from 'vitest'; +import { mergeMaskSetIntoAnnotations } from './maskSetMerge'; +import type { Shape } from '@/stores/annotationStore'; + +describe('mergeMaskSetIntoAnnotations', () => { + it('replace_class swaps that class and keeps others', () => { + const ann: Shape[] = [ + { id: 'a', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 8, h: 8 }, + { id: 'b', classId: 2, kind: 'rectangle', x: 0, y: 0, w: 4, h: 4 }, + ]; + const set: Shape[] = [ + { id: 'c', classId: 1, kind: 'rectangle', x: 10, y: 10, w: 4, h: 4 }, + ]; + const out = mergeMaskSetIntoAnnotations(ann, set, 16, 16, 'replace_class'); + expect(out.filter((s) => s.classId === 2)).toHaveLength(1); + expect(out.some((s) => s.classId === 1)).toBe(true); + // Old class-1 rect at origin should be gone (replaced) + const c1 = out.filter((s) => s.classId === 1); + expect(c1.every((s) => s.id !== 'a')).toBe(true); + }); + + it('union enlarges class coverage', () => { + const ann: Shape[] = [ + { id: 'a', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 4, h: 4 }, + ]; + const set: Shape[] = [ + { id: 'c', classId: 1, kind: 'rectangle', x: 8, y: 8, w: 4, h: 4 }, + ]; + const out = mergeMaskSetIntoAnnotations(ann, set, 16, 16, 'union'); + expect(out.filter((s) => s.classId === 1).length).toBeGreaterThanOrEqual(1); + }); + + it('delete_where_set punches holes / removes overlap', () => { + const ann: Shape[] = [ + { id: 'a', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 12, h: 12 }, + ]; + const set: Shape[] = [ + { id: 'c', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 12, h: 12 }, + ]; + const out = mergeMaskSetIntoAnnotations(ann, set, 16, 16, 'delete_where_set'); + expect(out.filter((s) => s.classId === 1)).toHaveLength(0); + }); +}); diff --git a/frontend/src/lib/maskSetMerge.ts b/frontend/src/lib/maskSetMerge.ts new file mode 100644 index 0000000..ef8e61a --- /dev/null +++ b/frontend/src/lib/maskSetMerge.ts @@ -0,0 +1,113 @@ +/** + * Merge mask-set label maps / shapes into slice annotations. + */ +import { v4 as uuidv4 } from 'uuid'; +import type { Shape } from '@/stores/annotationStore'; +import { gridFor, rasterizeShapes } from '@/lib/rasterize'; +import { maskToPolygonsWithHoles } from '@/lib/magicwand'; +import { classIdsInLabelMap, labelMapToShapes, shapesToLabelMap } from '@/lib/labelMap'; + +export type MergePolicy = 'union' | 'replace_class' | 'delete_where_set'; + +function shapesFromMask( + mask: Uint8Array, + gw: number, + gh: number, + scale: number, + classId: number, +): Shape[] { + return maskToPolygonsWithHoles(mask, gw, gh, { minRegion: 4, scale }) + .filter((p) => p.points.length >= 6) + .map((p) => ({ + id: uuidv4(), + classId, + kind: 'polygon' as const, + points: p.points, + ...(p.holes.length ? { holes: p.holes } : {}), + })); +} + +/** + * Merge set into annotations under *policy*. + * Prefer dense labelMap when present (pixel-accurate). + */ +export function mergeMaskSetIntoAnnotations( + annotations: Shape[], + setShapes: Shape[], + width: number, + height: number, + policy: MergePolicy, + setLabelMap?: Uint8Array | null, +): Shape[] { + const setMap = + setLabelMap && setLabelMap.length === width * height + ? setLabelMap + : setShapes.length + ? shapesToLabelMap(setShapes, width, height) + : null; + + if (setMap) { + const setClasses = classIdsInLabelMap(setMap); + if (!setClasses.length) return annotations; + + if (policy === 'replace_class') { + const kept = annotations.filter((s) => !setClasses.includes(s.classId)); + return [...kept, ...labelMapToShapes(setMap, width, height)]; + } + + const annMap = shapesToLabelMap(annotations, width, height); + const out = new Uint8Array(annMap); + if (policy === 'union') { + for (let i = 0; i < out.length; i++) { + if (setMap[i]) out[i] = setMap[i]; + } + } else { + // delete_where_set: clear annotation pixels where set has that class + for (let i = 0; i < out.length; i++) { + const s = setMap[i]; + if (s && out[i] === s) out[i] = 0; + } + } + // Preserve annotation classes not in set for union/delete semantics already handled + return labelMapToShapes(out, width, height); + } + + // Legacy shape-only path + const setClasses = [...new Set(setShapes.map((s) => s.classId))]; + if (!setClasses.length) return annotations; + + if (policy === 'replace_class') { + const kept = annotations.filter((s) => !setClasses.includes(s.classId)); + return [ + ...kept, + ...setShapes.map((s) => ({ ...s, id: uuidv4() })), + ]; + } + + const { gw, gh, scale } = gridFor(width, height); + const out: Shape[] = annotations.filter((s) => !setClasses.includes(s.classId)); + for (const cid of setClasses) { + const annGroup = annotations.filter((s) => s.classId === cid); + const setGroup = setShapes.filter((s) => s.classId === cid); + const annMask = annGroup.length + ? rasterizeShapes(annGroup, gw, gh, scale) + : new Uint8Array(gw * gh); + const setMask = rasterizeShapes(setGroup, gw, gh, scale); + const merged = new Uint8Array(gw * gh); + if (policy === 'union') { + for (let i = 0; i < merged.length; i++) merged[i] = annMask[i] || setMask[i] ? 1 : 0; + } else { + for (let i = 0; i < merged.length; i++) merged[i] = annMask[i] && !setMask[i] ? 1 : 0; + } + out.push(...shapesFromMask(merged, gw, gh, scale, cid)); + } + return out; +} + +/** Count non-zero pixels (for UI). */ +export function labelMapPixelCount(labels: Uint8Array | null): number { + if (!labels) return 0; + let n = 0; + for (let i = 0; i < labels.length; i++) if (labels[i]) n++; + return n; +} diff --git a/frontend/src/lib/morphology.test.ts b/frontend/src/lib/morphology.test.ts new file mode 100644 index 0000000..e61a92a --- /dev/null +++ b/frontend/src/lib/morphology.test.ts @@ -0,0 +1,51 @@ +import { describe, it, expect } from 'vitest'; +import { fillHoles, removeSmallComponents, dilate, erode, smooth } from './morphology'; + +const gw = 20, gh = 20; + +/** Solid filled square [x0,x0+s) × [y0,y0+s). */ +function square(x0: number, y0: number, s: number): Uint8Array { + const m = new Uint8Array(gw * gh); + for (let y = y0; y < y0 + s; y++) for (let x = x0; x < x0 + s; x++) m[y * gw + x] = 1; + return m; +} + +function area(m: Uint8Array): number { + let n = 0; + for (let i = 0; i < m.length; i++) if (m[i]) n++; + return n; +} + +describe('morphology', () => { + it('fillHoles fills an interior hole but leaves the border background alone', () => { + const m = square(5, 5, 10); + m[10 * gw + 10] = 0; // punch a hole + const filled = fillHoles(m, gw, gh); + expect(filled[10 * gw + 10]).toBe(1); + expect(filled[0]).toBe(0); // outside untouched + expect(area(filled)).toBe(100); + }); + + it('removeSmallComponents drops islands below the threshold', () => { + const m = square(2, 2, 8); // area 64 + m[18 * gw + 18] = 1; // a 1px island + const cleaned = removeSmallComponents(m, gw, gh, 10); + expect(cleaned[18 * gw + 18]).toBe(0); + expect(area(cleaned)).toBe(64); + }); + + it('dilate grows and erode shrinks (roughly inverse)', () => { + const m = square(8, 8, 4); // area 16 + const grown = dilate(m, gw, gh, 1); + expect(area(grown)).toBeGreaterThan(area(m)); + const shrunk = erode(grown, gw, gh, 1); + expect(area(shrunk)).toBe(area(m)); // dilate then erode restores a solid square + }); + + it('smooth removes an isolated speck', () => { + const m = new Uint8Array(gw * gh); + m[10 * gw + 10] = 1; // lone pixel + const s = smooth(m, gw, gh, 1); + expect(area(s)).toBe(0); + }); +}); diff --git a/frontend/src/lib/morphology.ts b/frontend/src/lib/morphology.ts new file mode 100644 index 0000000..e34c59f --- /dev/null +++ b/frontend/src/lib/morphology.ts @@ -0,0 +1,135 @@ +/** + * Binary-mask morphology for user-facing mask cleanup (fill holes, remove small + * islands, smooth, grow/shrink). Operates on `Uint8Array` 0/1 masks of size + * `gw × gh`; pairs with `rasterizeShapes` (shapes → mask) and `maskToPolygons` + * (mask → shapes). Out-of-bounds neighbors count as background. + */ + +/** Fill interior holes: any background pixel not connected to the border → 1. */ +export function fillHoles(mask: Uint8Array, gw: number, gh: number): Uint8Array { + const outside = new Uint8Array(gw * gh); + const stack: number[] = []; + const pushBg = (i: number) => { if (!mask[i] && !outside[i]) { outside[i] = 1; stack.push(i); } }; + for (let x = 0; x < gw; x++) { pushBg(x); pushBg((gh - 1) * gw + x); } + for (let y = 0; y < gh; y++) { pushBg(y * gw); pushBg(y * gw + gw - 1); } + while (stack.length) { + const idx = stack.pop()!; + const x = idx % gw, y = (idx / gw) | 0; + if (x > 0) pushBg(idx - 1); + if (x < gw - 1) pushBg(idx + 1); + if (y > 0) pushBg(idx - gw); + if (y < gh - 1) pushBg(idx + gw); + } + const out = new Uint8Array(gw * gh); + for (let i = 0; i < out.length; i++) out[i] = mask[i] || !outside[i] ? 1 : 0; + return out; +} + +/** Zero out 4-connected foreground components smaller than *minPx* pixels. */ +export function removeSmallComponents(mask: Uint8Array, gw: number, gh: number, minPx: number): Uint8Array { + const out = new Uint8Array(mask); // copy + const labels = new Int32Array(gw * gh); + let next = 1; + for (let p = 0; p < mask.length; p++) { + if (!mask[p] || labels[p]) continue; + const lbl = next++; + const comp: number[] = []; + const stack = [p]; + labels[p] = lbl; + while (stack.length) { + const idx = stack.pop()!; + comp.push(idx); + const x = idx % gw, y = (idx / gw) | 0; + const push = (j: number) => { if (mask[j] && !labels[j]) { labels[j] = lbl; stack.push(j); } }; + if (x > 0) push(idx - 1); + if (x < gw - 1) push(idx + 1); + if (y > 0) push(idx - gw); + if (y < gh - 1) push(idx + gw); + } + if (comp.length < minPx) for (const idx of comp) out[idx] = 0; + } + return out; +} + +/** One 8-connected dilation pass (a pixel is set if any neighbor is set). */ +function dilateOnce(mask: Uint8Array, gw: number, gh: number): Uint8Array { + const out = new Uint8Array(gw * gh); + for (let y = 0; y < gh; y++) { + for (let x = 0; x < gw; x++) { + let on = 0; + for (let dy = -1; dy <= 1 && !on; dy++) { + const yy = y + dy; + if (yy < 0 || yy >= gh) continue; + for (let dx = -1; dx <= 1; dx++) { + const xx = x + dx; + if (xx < 0 || xx >= gw) continue; + if (mask[yy * gw + xx]) { on = 1; break; } + } + } + out[y * gw + x] = on; + } + } + return out; +} + +/** One 8-connected erosion pass (a pixel stays set only if all neighbors are set). */ +function erodeOnce(mask: Uint8Array, gw: number, gh: number): Uint8Array { + const out = new Uint8Array(gw * gh); + for (let y = 0; y < gh; y++) { + for (let x = 0; x < gw; x++) { + let all = 1; + for (let dy = -1; dy <= 1 && all; dy++) { + const yy = y + dy; + for (let dx = -1; dx <= 1; dx++) { + const xx = x + dx; + if (xx < 0 || xx >= gw || yy < 0 || yy >= gh || !mask[yy * gw + xx]) { all = 0; break; } + } + } + out[y * gw + x] = all; + } + } + return out; +} + +/** Grow the mask by *iters* pixels (8-connected dilation). */ +export function dilate(mask: Uint8Array, gw: number, gh: number, iters = 1): Uint8Array { + let m = mask; + for (let i = 0; i < iters; i++) m = dilateOnce(m, gw, gh); + return m; +} + +/** Shrink the mask by *iters* pixels (8-connected erosion). */ +export function erode(mask: Uint8Array, gw: number, gh: number, iters = 1): Uint8Array { + let m = mask; + for (let i = 0; i < iters; i++) m = erodeOnce(m, gw, gh); + return m; +} + +/** + * Smooth the boundary via a majority filter (a pixel is set when ≥5 of its 3×3 + * neighborhood are set), repeated *iters* times. Removes single-pixel specks + * and jagged edges without changing overall size much. + */ +export function smooth(mask: Uint8Array, gw: number, gh: number, iters = 1): Uint8Array { + let m = mask; + for (let it = 0; it < iters; it++) { + const out = new Uint8Array(gw * gh); + for (let y = 0; y < gh; y++) { + for (let x = 0; x < gw; x++) { + let c = 0; + for (let dy = -1; dy <= 1; dy++) { + const yy = y + dy; + if (yy < 0 || yy >= gh) continue; + for (let dx = -1; dx <= 1; dx++) { + const xx = x + dx; + if (xx < 0 || xx >= gw) continue; + c += m[yy * gw + xx]; + } + } + out[y * gw + x] = c >= 5 ? 1 : 0; + } + } + m = out; + } + return m; +} diff --git a/frontend/src/lib/pixelClf.overlay.test.ts b/frontend/src/lib/pixelClf.overlay.test.ts new file mode 100644 index 0000000..cb55a76 --- /dev/null +++ b/frontend/src/lib/pixelClf.overlay.test.ts @@ -0,0 +1,23 @@ +import { describe, expect, it } from 'vitest'; +import { colorizeConformalOverlay } from './pixelClf'; + +describe('colorizeConformalOverlay filters', () => { + it('builds a canvas with class / multi / abstain filters without throwing', () => { + const w = 2; + const h = 2; + const commit = new Uint8Array([1, 2, 0, 0]); + const status = new Uint8Array([1, 1, 2, 0]); + const colors = new Map([ + [1, '#ff0000'], + [2, '#00ff00'], + ]); + + const canvas = colorizeConformalOverlay(commit, status, w, h, colors, { + classVisible: (cid) => cid === 1, + showMulti: false, + showAbstain: false, + }); + expect(canvas.width).toBe(2); + expect(canvas.height).toBe(2); + }); +}); diff --git a/frontend/src/lib/pixelClf.test.ts b/frontend/src/lib/pixelClf.test.ts new file mode 100644 index 0000000..8275bd3 --- /dev/null +++ b/frontend/src/lib/pixelClf.test.ts @@ -0,0 +1,100 @@ +import { describe, expect, it } from 'vitest'; +import { + applyProbaThresholdRgba, + classProbaMaskSetName, + labelMapToPolygonShapes, + parseHexColor, +} from './pixelClf'; + +describe('parseHexColor', () => { + it('parses 6-digit hex', () => { + expect(parseHexColor('#ff0000')).toEqual([255, 0, 0]); + }); +}); + +describe('classProbaMaskSetName', () => { + it('formats label and percent threshold', () => { + expect(classProbaMaskSetName(2, 0.65, 'membrane')).toBe('membrane p≥65%'); + }); + + it('falls back to class id when label missing', () => { + expect(classProbaMaskSetName(3, 0.5, null)).toBe('class 3 p≥50%'); + }); +}); + +describe('applyProbaThresholdRgba', () => { + it('makes pixels below the cut transparent and colors the rest viridis', () => { + const rgba = new Uint8ClampedArray([ + 100, 100, 100, 255, + 255, 255, 255, 255, + ]); + applyProbaThresholdRgba(rgba, 0.5); // cut = 0.5 + expect(rgba[3]).toBe(0); // below: transparent + expect(rgba[7]).toBe(255); // above: opaque + // p=1 remaps to viridis end (yellowish) + expect(rgba[4]).toBeGreaterThan(200); + expect(rgba[5]).toBeGreaterThan(200); + }); +}); + +describe('labelMapToPolygonShapes', () => { + it('emits a polygon for a solid class blob', () => { + const w = 16; + const h = 16; + const labels = new Uint8Array(w * h); + for (let y = 4; y < 12; y++) { + for (let x = 4; x < 12; x++) labels[y * w + x] = 1; + } + const shapes = labelMapToPolygonShapes(labels, w, h, [1], { minRegion: 4, smooth: 0 }); + expect(shapes.length).toBeGreaterThanOrEqual(1); + expect(shapes[0].classId).toBe(1); + expect(shapes[0].kind).toBe('polygon'); + expect(shapes[0].points.length).toBeGreaterThanOrEqual(6); + }); + + it('skips pixels covered by preserveShapes', () => { + const w = 16; + const h = 16; + const labels = new Uint8Array(w * h).fill(1); + const preserve = [ + { + id: 'a', + classId: 1, + kind: 'rectangle' as const, + x: 0, + y: 0, + w: 16, + h: 16, + }, + ]; + const shapes = labelMapToPolygonShapes(labels, w, h, [1], { + minRegion: 4, + preserveShapes: preserve, + smooth: 0, + }); + expect(shapes).toHaveLength(0); + }); + + it('carves holes so nested class regions do not fill through each other', () => { + const w = 32; + const h = 32; + const labels = new Uint8Array(w * h); + // Outer class 1 border; inner 10×10 is class 2 + for (let y = 2; y < 30; y++) { + for (let x = 2; x < 30; x++) labels[y * w + x] = 1; + } + for (let y = 11; y < 21; y++) { + for (let x = 11; x < 21; x++) labels[y * w + x] = 2; + } + const shapes = labelMapToPolygonShapes(labels, w, h, [1, 2], { + minRegion: 4, + smooth: 0, + }); + const c1 = shapes.filter((s) => s.classId === 1); + const c2 = shapes.filter((s) => s.classId === 2); + expect(c1.length).toBeGreaterThanOrEqual(1); + expect(c2.length).toBeGreaterThanOrEqual(1); + // Class 1 must have a hole for the class 2 island (pixel-accurate commit). + expect(c1.some((s) => (s.holes?.length ?? 0) > 0)).toBe(true); + }); +}); diff --git a/frontend/src/lib/pixelClf.ts b/frontend/src/lib/pixelClf.ts new file mode 100644 index 0000000..9f89f2a --- /dev/null +++ b/frontend/src/lib/pixelClf.ts @@ -0,0 +1,280 @@ +/** + * Pixel-classifier helpers: label PNG → colorized overlay / polygon shapes. + */ +import { v4 as uuidv4 } from 'uuid'; +import { maskToPolygonsWithHoles } from '@/lib/magicwand'; +import { rasterizeUnion } from '@/lib/rasterize'; +import type { PolygonShape, Shape } from '@/stores/annotationStore'; +import { viridisRgb } from '@/lib/viridis'; + +/** Parse #rgb / #rrggbb into [r,g,b]. */ +export function parseHexColor(hex: string): [number, number, number] { + const h = hex.replace('#', ''); + if (h.length === 3) { + return [ + parseInt(h[0] + h[0], 16), + parseInt(h[1] + h[1], 16), + parseInt(h[2] + h[2], 16), + ]; + } + return [ + parseInt(h.slice(0, 2), 16) || 0, + parseInt(h.slice(2, 4), 16) || 0, + parseInt(h.slice(4, 6), 16) || 0, + ]; +} + +/** Mask-set name for a thresholded softmax class cache. */ +export function classProbaMaskSetName( + classId: number, + threshold: number, + classLabel?: string | null, +): string { + const t = Math.round(Math.min(1, Math.max(0, threshold)) * 100); + const label = (classLabel ?? '').trim() || `class ${classId}`; + return `${label} p≥${t}%`; +} + +/** + * Colorize grayscale probability RGBA with viridis; pixels below ``threshold`` + * are transparent. Values at/above the cut are remapped to [0,1] so the full + * colormap spans the visible range. + */ +export function applyProbaThresholdRgba( + rgba: Uint8ClampedArray, + threshold: number, +): void { + const t = Math.min(1, Math.max(0, threshold)); + const span = Math.max(1e-6, 1 - t); + for (let i = 0; i < rgba.length; i += 4) { + const p = rgba[i] / 255; + if (p < t) { + rgba[i] = 0; + rgba[i + 1] = 0; + rgba[i + 2] = 0; + rgba[i + 3] = 0; + continue; + } + const [r, g, b] = viridisRgb((p - t) / span); + rgba[i] = r; + rgba[i + 1] = g; + rgba[i + 2] = b; + rgba[i + 3] = 255; + } +} + +/** Render a softmax grayscale PNG as viridis, clipped below ``threshold``. */ +export async function thresholdProbaPngBlob( + sourceBlob: Blob, + threshold: number, +): Promise { + const url = URL.createObjectURL(sourceBlob); + try { + const img = await new Promise((resolve, reject) => { + const el = new Image(); + el.onload = () => resolve(el); + el.onerror = () => reject(new Error('Failed to decode probability PNG')); + el.src = url; + }); + const width = img.naturalWidth; + const height = img.naturalHeight; + const canvas = document.createElement('canvas'); + canvas.width = width; + canvas.height = height; + const ctx = canvas.getContext('2d'); + if (!ctx) throw new Error('2D context unavailable'); + ctx.drawImage(img, 0, 0); + const imageData = ctx.getImageData(0, 0, width, height); + applyProbaThresholdRgba(imageData.data, threshold); + ctx.putImageData(imageData, 0, 0); + const out = await new Promise((resolve, reject) => { + canvas.toBlob( + (b) => (b ? resolve(b) : reject(new Error('toBlob failed'))), + 'image/png', + ); + }); + return out; + } finally { + URL.revokeObjectURL(url); + } +} + +/** Load a grayscale classId PNG into Uint8Array + dimensions. */ +export async function loadLabelPng( + url: string, +): Promise<{ data: Uint8Array; width: number; height: number }> { + const img = await new Promise((resolve, reject) => { + const el = new Image(); + el.onload = () => resolve(el); + el.onerror = () => reject(new Error('Failed to load prediction PNG')); + el.src = url; + }); + const width = img.naturalWidth; + const height = img.naturalHeight; + const canvas = document.createElement('canvas'); + canvas.width = width; + canvas.height = height; + const ctx = canvas.getContext('2d'); + if (!ctx) throw new Error('2D context unavailable'); + ctx.drawImage(img, 0, 0); + const rgba = ctx.getImageData(0, 0, width, height).data; + const data = new Uint8Array(width * height); + for (let i = 0, p = 0; i < data.length; i++, p += 4) data[i] = rgba[p]; + return { data, width, height }; +} + +/** + * Build a translucent RGBA canvas (one pixel per classId with known color). + * Unknown / 0 stays transparent. + */ +export function colorizeLabelMap( + labels: Uint8Array, + width: number, + height: number, + colorByClass: Map, + alpha = 110, +): HTMLCanvasElement { + const canvas = document.createElement('canvas'); + canvas.width = width; + canvas.height = height; + const ctx = canvas.getContext('2d'); + if (!ctx) return canvas; + const img = ctx.createImageData(width, height); + const out = img.data; + const rgbCache = new Map(); + for (const [cid, hex] of colorByClass) rgbCache.set(cid, parseHexColor(hex)); + + for (let i = 0; i < labels.length; i++) { + const cid = labels[i]; + if (!cid) continue; + const rgb = rgbCache.get(cid); + if (!rgb) continue; + const p = i * 4; + out[p] = rgb[0]; + out[p + 1] = rgb[1]; + out[p + 2] = rgb[2]; + out[p + 3] = alpha; + } + ctx.putImageData(img, 0, 0); + return canvas; +} + +/** + * Build conformal overlay: singleton = class color, multi = amber hatch, + * abstain = dark translucent. + * + * Optional filters hide prediction classes / multi / abstain for the Layers panel. + */ +export function colorizeConformalOverlay( + commit: Uint8Array, + status: Uint8Array, + width: number, + height: number, + colorByClass: Map, + opts?: { + classVisible?: (classId: number) => boolean; + showMulti?: boolean; + showAbstain?: boolean; + }, +): HTMLCanvasElement { + const canvas = document.createElement('canvas'); + canvas.width = width; + canvas.height = height; + const ctx = canvas.getContext('2d'); + if (!ctx) return canvas; + const img = ctx.createImageData(width, height); + const out = img.data; + const rgbCache = new Map(); + for (const [cid, hex] of colorByClass) rgbCache.set(cid, parseHexColor(hex)); + const classVisible = opts?.classVisible ?? (() => true); + const showMulti = opts?.showMulti !== false; + const showAbstain = opts?.showAbstain !== false; + + for (let i = 0; i < commit.length; i++) { + const st = status[i]; + const p = i * 4; + const x = i % width; + const y = (i / width) | 0; + if (st === 1) { + // singleton + const cid = commit[i]; + if (!classVisible(cid)) continue; + const rgb = rgbCache.get(cid); + if (!rgb) continue; + out[p] = rgb[0]; + out[p + 1] = rgb[1]; + out[p + 2] = rgb[2]; + out[p + 3] = 120; + } else if (st === 2) { + if (!showMulti) continue; + // multi — diagonal hatch in amber + const on = ((x + y) & 3) < 2; + out[p] = on ? 245 : 180; + out[p + 1] = on ? 158 : 120; + out[p + 2] = on ? 11 : 40; + out[p + 3] = on ? 140 : 70; + } else if (showAbstain) { + // abstain + out[p] = 30; + out[p + 1] = 30; + out[p + 2] = 40; + out[p + 3] = 90; + } + } + ctx.putImageData(img, 0, 0); + return canvas; +} + +/** + * Convert a classId label map into polygon shapes. + * Each pixel contributes to at most one class (labels are already argmax). + * Uses outer+hole rings so one class does not fill through another (or through + * preserved scribble gaps) — matching the pixel overlay. + * Pixels covered by *preserveShapes* are skipped so Commit keeps annotations. + */ +export function labelMapToPolygonShapes( + labels: Uint8Array, + width: number, + height: number, + classIds: number[], + { + minRegion = 64, + preserveShapes, + smooth = 1, + }: { minRegion?: number; preserveShapes?: Shape[]; smooth?: number } = {}, +): PolygonShape[] { + const preserve = + preserveShapes && preserveShapes.length > 0 + ? rasterizeUnion(preserveShapes, width, height, 1) + : null; + + const shapes: PolygonShape[] = []; + for (const classId of classIds) { + const mask = new Uint8Array(width * height); + let any = false; + for (let i = 0; i < labels.length; i++) { + if (preserve && preserve[i]) continue; + if (labels[i] === classId) { + mask[i] = 1; + any = true; + } + } + if (!any) continue; + const polys = maskToPolygonsWithHoles(mask, width, height, { + minRegion, + scale: 1, + smooth, + }); + for (const { points, holes } of polys) { + if (points.length < 6) continue; + shapes.push({ + id: uuidv4(), + classId, + kind: 'polygon', + points, + ...(holes.length ? { holes } : {}), + }); + } + } + return shapes; +} diff --git a/frontend/src/lib/rasterize.test.ts b/frontend/src/lib/rasterize.test.ts new file mode 100644 index 0000000..381e014 --- /dev/null +++ b/frontend/src/lib/rasterize.test.ts @@ -0,0 +1,71 @@ +import { describe, it, expect } from 'vitest'; +import { rasterizeShapes, gridFor } from './rasterize'; +import { maskToPolygons } from './magicwand'; +import type { Shape } from '@/stores/annotationStore'; + +/** Count set pixels in a binary mask. */ +function area(mask: Uint8Array): number { + let n = 0; + for (let i = 0; i < mask.length; i++) if (mask[i]) n++; + return n; +} + +describe('rasterizeShapes', () => { + it('fills a rectangle with the expected area', () => { + const rect: Shape = { id: 'r', classId: 0, kind: 'rectangle', x: 10, y: 20, w: 30, h: 40 }; + const mask = rasterizeShapes([rect], 100, 100, 1); + // ~30x40 = 1200 px, allow small boundary rounding. + expect(area(mask)).toBeGreaterThan(1100); + expect(area(mask)).toBeLessThan(1400); + expect(mask[25 * 100 + 25]).toBe(1); // interior set + expect(mask[5 * 100 + 5]).toBe(0); // outside clear + }); + + it('fills a polygon (triangle ~ half the bounding box)', () => { + const tri: Shape = { id: 't', classId: 0, kind: 'polygon', points: [0, 0, 40, 0, 0, 40] }; + const mask = rasterizeShapes([tri], 40, 40, 1); + const a = area(mask); + expect(a).toBeGreaterThan(600); // ~800 (half of 1600), leave slack + expect(a).toBeLessThan(900); + }); + + it('carves an erase stroke back out of a rectangle', () => { + const rect: Shape = { + id: 'r', classId: 0, kind: 'rectangle', x: 0, y: 0, w: 40, h: 40, + erased: [{ points: [20, 20, 20, 20], radius: 5 }], + }; + const mask = rasterizeShapes([rect], 40, 40, 1); + expect(mask[20 * 40 + 20]).toBe(0); // erased center + expect(mask[2 * 40 + 2]).toBe(1); // corner still set + }); + + it('leaves a polygon hole empty (invert-shape complement)', () => { + // Outer = full 40x40 frame; hole = centered 20x20 square. + const inverted: Shape = { + id: 'inv', classId: 0, kind: 'polygon', + points: [0, 0, 40, 0, 40, 40, 0, 40], + holes: [[10, 10, 30, 10, 30, 30, 10, 30]], + }; + const mask = rasterizeShapes([inverted], 40, 40, 1); + expect(mask[20 * 40 + 20]).toBe(0); // inside the hole → empty + expect(mask[2 * 40 + 2]).toBe(1); // frame corner → filled + // Area ≈ 40² − 20² = 1200, allow boundary slack. + expect(area(mask)).toBeGreaterThan(1050); + expect(area(mask)).toBeLessThan(1350); + }); + + it('round-trips shape → mask → polygons preserving rough area', () => { + const rect: Shape = { id: 'r', classId: 0, kind: 'rectangle', x: 20, y: 20, w: 60, h: 60 }; + const { gw, gh, scale } = gridFor(200, 200); + const mask = rasterizeShapes([rect], gw, gh, scale); + const polys = maskToPolygons(mask, gw, gh, { minRegion: 4, scale }); + expect(polys.length).toBe(1); + const xs = polys[0].filter((_, i) => i % 2 === 0); + const ys = polys[0].filter((_, i) => i % 2 === 1); + // Vectorized polygon should sit within a few px of the original rect. + expect(Math.min(...xs)).toBeLessThan(26); + expect(Math.max(...xs)).toBeGreaterThan(74); + expect(Math.min(...ys)).toBeLessThan(26); + expect(Math.max(...ys)).toBeGreaterThan(74); + }); +}); diff --git a/frontend/src/lib/rasterize.ts b/frontend/src/lib/rasterize.ts new file mode 100644 index 0000000..9a9811d --- /dev/null +++ b/frontend/src/lib/rasterize.ts @@ -0,0 +1,158 @@ +/** + * Rasterize annotation shapes to a binary mask (pure JS — no canvas), the + * inverse of `maskToPolygons`. Used by threshold selection, mask cleanup + * (morphology), and cross-slice interpolation, which all need to move between + * the vector shape model and a pixel mask. + * + * Shapes are stored in IMAGE pixel coords; the mask is computed on a grid of + * `gw × gh` where image coord `p` maps to grid `p / scale` (matching the + * downsampling convention of `buildField` / `maskToPolygons`). + */ +import type { Shape } from '@/stores/annotationStore'; + +export interface MaskGrid { + gw: number; + gh: number; + scale: number; +} + +/** Grid dimensions for an image, downsampled so the long side is ≤ maxDim. */ +export function gridFor(width: number, height: number, maxDim = 1600): MaskGrid { + const scale = Math.max(1, Math.ceil(Math.max(width, height) / maxDim)); + return { gw: Math.max(1, Math.floor(width / scale)), gh: Math.max(1, Math.floor(height / scale)), scale }; +} + +/** + * Rasterize *shapes* into a `gw × gh` binary mask (Uint8Array of 0/1). Paint + * strokes and shape bodies set 1; brush erase strokes and vector `erased` + * carve-outs set 0, applied per-shape so a later shape can repaint. + */ +export function rasterizeShapes(shapes: Shape[], gw: number, gh: number, scale = 1): Uint8Array { + const mask = new Uint8Array(gw * gh); + const s = scale || 1; + for (const shape of shapes) { + if (shape.kind === 'polygon') { + fillPolygon(mask, gw, gh, shape.points, s, 1); + // Carve inner rings (holes) back out — e.g. from "invert shape". + for (const hole of shape.holes ?? []) fillPolygon(mask, gw, gh, hole, s, 0); + } else if (shape.kind === 'rectangle') { + fillRect(mask, gw, gh, shape.x / s, shape.y / s, shape.w / s, shape.h / s, 1); + } else if (shape.kind === 'ellipse') { + fillEllipse(mask, gw, gh, shape.cx / s, shape.cy / s, shape.rx / s, shape.ry / s, 1); + } else if (shape.kind === 'brush') { + for (const st of shape.strokes) { + if (st.mode === 'paint') stampStroke(mask, gw, gh, st.points, st.radius / s, s, 1); + } + for (const st of shape.strokes) { + if (st.mode === 'erase') stampStroke(mask, gw, gh, st.points, st.radius / s, s, 0); + } + } + for (const er of shape.erased ?? []) { + stampStroke(mask, gw, gh, er.points, er.radius / s, s, 0); + } + } + return mask; +} + +/** + * Union of independently-rasterized shapes. Unlike `rasterizeShapes([...])` + * (which fills all shapes onto one mask in order, so a later shape's holes/erase + * carve-outs can cut through an earlier shape's fill), each shape is rasterized + * on its own and OR-ed in — so carve-outs stay scoped to their own shape. Use + * this whenever combining DIFFERENT shapes (e.g. a class's region mask). + */ +export function rasterizeUnion(shapes: Shape[], gw: number, gh: number, scale = 1): Uint8Array { + const mask = new Uint8Array(gw * gh); + for (const shape of shapes) { + const m = rasterizeShapes([shape], gw, gh, scale); + for (let i = 0; i < m.length; i++) if (m[i]) mask[i] = 1; + } + return mask; +} + +// --------------------------------------------------------------------------- +// Primitives (grid coords unless noted; `scale` converts image → grid) +// --------------------------------------------------------------------------- + +/** Even-odd scanline fill of a flat [x,y,…] image-coord polygon. */ +function fillPolygon(mask: Uint8Array, gw: number, gh: number, imgPts: number[], scale: number, v: number): void { + const n = imgPts.length / 2; + if (n < 3) return; + const xs = new Array(n); + const ys = new Array(n); + let minY = Infinity, maxY = -Infinity; + for (let i = 0; i < n; i++) { + xs[i] = imgPts[i * 2] / scale; + ys[i] = imgPts[i * 2 + 1] / scale; + if (ys[i] < minY) minY = ys[i]; + if (ys[i] > maxY) maxY = ys[i]; + } + const y0 = Math.max(0, Math.ceil(minY)); + const y1 = Math.min(gh - 1, Math.floor(maxY)); + const xints: number[] = []; + for (let y = y0; y <= y1; y++) { + xints.length = 0; + for (let i = 0, j = n - 1; i < n; j = i++) { + const yi = ys[i], yj = ys[j]; + if ((yi > y) !== (yj > y)) { + xints.push(xs[i] + ((y - yi) / (yj - yi)) * (xs[j] - xs[i])); + } + } + xints.sort((a, b) => a - b); + for (let k = 0; k + 1 < xints.length; k += 2) { + const xa = Math.max(0, Math.ceil(xints[k])); + const xb = Math.min(gw - 1, Math.floor(xints[k + 1])); + for (let x = xa; x <= xb; x++) mask[y * gw + x] = v; + } + } +} + +function fillRect(mask: Uint8Array, gw: number, gh: number, x: number, y: number, w: number, h: number, v: number): void { + const x0 = Math.max(0, Math.floor(x)); + const y0 = Math.max(0, Math.floor(y)); + const x1 = Math.min(gw - 1, Math.ceil(x + w)); + const y1 = Math.min(gh - 1, Math.ceil(y + h)); + for (let yy = y0; yy <= y1; yy++) for (let xx = x0; xx <= x1; xx++) mask[yy * gw + xx] = v; +} + +function fillEllipse(mask: Uint8Array, gw: number, gh: number, cx: number, cy: number, rx: number, ry: number, v: number): void { + const rxi = rx || 1, ryi = ry || 1; + const x0 = Math.max(0, Math.floor(cx - rxi)); + const y0 = Math.max(0, Math.floor(cy - ryi)); + const x1 = Math.min(gw - 1, Math.ceil(cx + rxi)); + const y1 = Math.min(gh - 1, Math.ceil(cy + ryi)); + for (let yy = y0; yy <= y1; yy++) { + const ny = (yy - cy) / ryi; + for (let xx = x0; xx <= x1; xx++) { + const nx = (xx - cx) / rxi; + if (nx * nx + ny * ny <= 1) mask[yy * gw + xx] = v; + } + } +} + +/** Stamp a round-capped thick polyline (image-coord points) with value *v*. */ +function stampStroke(mask: Uint8Array, gw: number, gh: number, imgPts: number[], r: number, scale: number, v: number): void { + const rad = Math.max(0.5, r); + const r2 = rad * rad; + const pts: number[] = []; + for (let i = 0; i < imgPts.length; i++) pts.push(imgPts[i] / scale); + const stampSeg = (ax: number, ay: number, bx: number, by: number) => { + const minX = Math.max(0, Math.floor(Math.min(ax, bx) - rad)); + const maxX = Math.min(gw - 1, Math.ceil(Math.max(ax, bx) + rad)); + const minY = Math.max(0, Math.floor(Math.min(ay, by) - rad)); + const maxY = Math.min(gh - 1, Math.ceil(Math.max(ay, by) + rad)); + const dx = bx - ax, dy = by - ay; + const len2 = dx * dx + dy * dy; + for (let y = minY; y <= maxY; y++) { + for (let x = minX; x <= maxX; x++) { + let t = len2 ? ((x - ax) * dx + (y - ay) * dy) / len2 : 0; + t = t < 0 ? 0 : t > 1 ? 1 : t; + const cxp = ax + t * dx, cyp = ay + t * dy; + const ddx = x - cxp, ddy = y - cyp; + if (ddx * ddx + ddy * ddy <= r2) mask[y * gw + x] = v; + } + } + }; + if (pts.length === 2) { stampSeg(pts[0], pts[1], pts[0], pts[1]); return; } + for (let i = 0; i + 3 < pts.length; i += 2) stampSeg(pts[i], pts[i + 1], pts[i + 2], pts[i + 3]); +} diff --git a/frontend/src/lib/regionOps.test.ts b/frontend/src/lib/regionOps.test.ts new file mode 100644 index 0000000..df98edc --- /dev/null +++ b/frontend/src/lib/regionOps.test.ts @@ -0,0 +1,46 @@ +import { describe, it, expect } from 'vitest'; +import { computeRegionOps } from './regionOps'; +import type { Shape } from '@/stores/annotationStore'; + +const W = 200, H = 200; + +function rect(id: string, classId: number, x: number, y: number, w: number, h: number): Shape { + return { id, classId, kind: 'rectangle', x, y, w, h }; +} + +/** Rough area (px) of a returned polygon via its bounding box (adequate for sanity). */ +function bboxArea(points: number[]): number { + const xs = points.filter((_, i) => i % 2 === 0); + const ys = points.filter((_, i) => i % 2 === 1); + return (Math.max(...xs) - Math.min(...xs)) * (Math.max(...ys) - Math.min(...ys)); +} + +describe('computeRegionOps', () => { + it('merge unions two overlapping rects of a class into one region', () => { + const shapes = [rect('a', 1, 20, 20, 60, 60), rect('b', 1, 60, 60, 60, 60)]; + const out = computeRegionOps(shapes, W, H, 'merge', 0); + expect(out.length).toBe(1); + expect(out[0].classId).toBe(1); + }); + + it('grow enlarges, shrink reduces the bounding area', () => { + const shapes = [rect('a', 1, 60, 60, 40, 40)]; + const base = bboxArea(computeRegionOps(shapes, W, H, 'merge', 0)[0].points); + const grown = bboxArea(computeRegionOps(shapes, W, H, 'grow', 8)[0].points); + const shrunk = bboxArea(computeRegionOps(shapes, W, H, 'shrink', 8)[0].points); + expect(grown).toBeGreaterThan(base); + expect(shrunk).toBeLessThan(base); + }); + + it('remove-islands drops components smaller than the threshold', () => { + const shapes = [rect('big', 1, 40, 40, 80, 80), rect('speck', 1, 5, 5, 3, 3)]; + const out = computeRegionOps(shapes, W, H, 'islands', 500); // 500 px² min + expect(out.length).toBe(1); // the speck is removed + }); + + it('keeps classes separate', () => { + const shapes = [rect('a', 1, 20, 20, 30, 30), rect('b', 2, 120, 120, 30, 30)]; + const out = computeRegionOps(shapes, W, H, 'merge', 0); + expect(new Set(out.map((r) => r.classId))).toEqual(new Set([1, 2])); + }); +}); diff --git a/frontend/src/lib/regionOps.ts b/frontend/src/lib/regionOps.ts new file mode 100644 index 0000000..5ce2596 --- /dev/null +++ b/frontend/src/lib/regionOps.ts @@ -0,0 +1,61 @@ +/** + * regionOps — mask-level operations on a selection of shapes, grouped by class. + * + * Each op rasterizes the selected shapes of a class into one binary mask, applies + * a morphology transform, and re-vectorizes to polygons (image coords). Pure (no + * store / no React) so the select-tool "region ops" preview can be unit-tested. + */ +import type { Shape } from '@/stores/annotationStore'; +import { gridFor, rasterizeShapes } from '@/lib/rasterize'; +import { maskToPolygons } from '@/lib/magicwand'; +import { dilate, erode, removeSmallComponents, fillHoles } from '@/lib/morphology'; + +export type RegionOp = 'merge' | 'grow' | 'shrink' | 'islands'; + +export interface RegionResult { + classId: number; + points: number[]; +} + +/** + * Apply `op` to the selected shapes, grouped by class, returning replacement + * polygons (image coords). `param` is in image pixels: grow/shrink radius, or the + * min island area (px²) for `islands`. + */ +export function computeRegionOps( + shapes: Shape[], + width: number, + height: number, + op: RegionOp, + param: number, +): RegionResult[] { + const { gw, gh, scale } = gridFor(width, height); + const results: RegionResult[] = []; + const byClass = new Map(); + for (const s of shapes) { + const arr = byClass.get(s.classId); + if (arr) arr.push(s); else byClass.set(s.classId, [s]); + } + + for (const [classId, group] of byClass) { + let mask = rasterizeShapes(group, gw, gh, scale); + switch (op) { + case 'merge': + mask = fillHoles(mask, gw, gh); // union already implicit; close pinholes + break; + case 'grow': + mask = dilate(mask, gw, gh, Math.max(1, Math.round(param / scale))); + break; + case 'shrink': + mask = erode(mask, gw, gh, Math.max(1, Math.round(param / scale))); + break; + case 'islands': + mask = removeSmallComponents(mask, gw, gh, Math.max(1, Math.round(param / (scale * scale)))); + break; + } + for (const points of maskToPolygons(mask, gw, gh, { minRegion: 4, scale })) { + if (points.length >= 6) results.push({ classId, points }); + } + } + return results; +} diff --git a/frontend/src/lib/sam/adjust.ts b/frontend/src/lib/sam/adjust.ts new file mode 100644 index 0000000..dbd3c37 --- /dev/null +++ b/frontend/src/lib/sam/adjust.ts @@ -0,0 +1,89 @@ +/** + * Bake the same display adjustments the canvas shows into an offscreen canvas + * so tools (SAM encode, classic wand, Fill) operate on what the user sees. + * + * Pipeline: optional CLAHE → optional 3×3 sharpen → Brighten → Contrast → Levels. + */ +import { claheRgba } from '../clahe'; +import { sharpenRgba } from '../sharpen'; + +export interface DisplayPreprocess { + clahe?: boolean; + sharpen?: boolean; +} + +export function renderAdjusted( + image: CanvasImageSource, + width: number, + height: number, + brightness: number, + contrast: number, + levelsLo = 0, + levelsHi = 255, + preprocess: boolean | DisplayPreprocess = false, +): HTMLCanvasElement { + const opts: DisplayPreprocess = + typeof preprocess === 'boolean' ? { clahe: preprocess } : preprocess; + const doClahe = !!opts.clahe; + const doSharpen = !!opts.sharpen; + + const canvas = document.createElement('canvas'); + canvas.width = width; + canvas.height = height; + const ctx = canvas.getContext('2d', { willReadFrequently: true })!; + ctx.drawImage(image, 0, 0, width, height); + + const bcNoop = brightness === 0 && contrast === 0; + const levelsNoop = levelsLo <= 0 && levelsHi >= 255; + if (bcNoop && levelsNoop && !doClahe && !doSharpen) return canvas; + + const imageData = ctx.getImageData(0, 0, width, height); + if (doClahe) claheRgba(imageData.data, width, height); + if (doSharpen) sharpenRgba(imageData.data, width, height); + if (bcNoop && levelsNoop) { + ctx.putImageData(imageData, 0, 0); + return canvas; + } + + const d = imageData.data; + const b = brightness * 255; + const adjust = Math.pow((contrast + 100) / 100, 2); + const range = Math.max(1, levelsHi - levelsLo); + for (let i = 0; i < d.length; i += 4) { + for (let k = 0; k < 3; k++) { + let v = d[i + k]; + if (!bcNoop) { + v = v + b; + v = ((v / 255 - 0.5) * adjust + 0.5) * 255; + } + if (!levelsNoop) { + v = v <= levelsLo ? 0 : v >= levelsHi ? 255 : Math.round(((v - levelsLo) * 255) / range); + } + d[i + k] = v; + } + } + ctx.putImageData(imageData, 0, 0); + return canvas; +} + +/** + * Offscreen canvas with CLAHE and/or sharpen only (no brightness/contrast/levels). + * Used as the Konva image source when nonlinear preprocessors are on. + */ +export function renderPreprocessOnly( + image: CanvasImageSource, + width: number, + height: number, + preprocess: DisplayPreprocess, +): HTMLCanvasElement { + return renderAdjusted(image, width, height, 0, 0, 0, 255, preprocess); +} + +/** @deprecated Use renderPreprocessOnly({ clahe: true }). */ +export function renderClaheOnly( + image: CanvasImageSource, + width: number, + height: number, +): HTMLCanvasElement { + return renderPreprocessOnly(image, width, height, { clahe: true }); +} diff --git a/frontend/src/lib/sam/samClient.ts b/frontend/src/lib/sam/samClient.ts new file mode 100644 index 0000000..aa621b6 --- /dev/null +++ b/frontend/src/lib/sam/samClient.ts @@ -0,0 +1,130 @@ +/** + * samClient — main-thread singleton wrapping the SAM Web Worker. + * + * Owns one worker for the whole app: lazily spawns it on first use, tracks load + * status, and turns the worker's message protocol into promises. The canvas + * encodes the current slice once, then issues a decode per click. + */ +export type SamStatus = 'idle' | 'loading-model' | 'encoding' | 'ready' | 'unsupported'; +export interface PromptPoint { x: number; y: number; label: 0 | 1 } +export interface PromptBox { x0: number; y0: number; x1: number; y1: number } +export type Granularity = 'auto' | 'fine' | 'medium' | 'coarse'; +export interface SamMask { mask: Uint8Array; width: number; height: number; score: number } + +type Pending = { resolve: (v: unknown) => void; reject: (e: Error) => void }; + +class SamClient { + private worker: Worker | null = null; + private status: SamStatus = 'idle'; + private backend: string | null = null; + private nextId = 1; + private pending = new Map(); + private listeners = new Set<(s: SamStatus) => void>(); + private initPromise: Promise | null = null; + + /** Current worker status (model/encode lifecycle). */ + getStatus(): SamStatus { return this.status; } + /** The compute backend in use ('webgpu' | 'wasm'), or null before load. */ + getBackend(): string | null { return this.backend; } + + /** Subscribe to status changes; fires immediately with the current status. + * Returns an unsubscribe function. */ + subscribe(fn: (s: SamStatus) => void): () => void { + this.listeners.add(fn); + fn(this.status); + return () => { this.listeners.delete(fn); }; + } + + /** Update status and notify all subscribers. */ + private setStatus(s: SamStatus) { + this.status = s; + this.listeners.forEach((fn) => fn(s)); + } + + /** Spawn the worker and load the model (idempotent). */ + init(): Promise { + if (this.initPromise) return this.initPromise; + this.setStatus('loading-model'); + this.initPromise = new Promise((resolve, reject) => { + try { + this.worker = new Worker(new URL('./samWorker.ts', import.meta.url), { type: 'module' }); + } catch (err) { + this.initPromise = null; + this.setStatus('unsupported'); + reject(err instanceof Error ? err : new Error(String(err))); + return; + } + this.worker.onmessage = (e: MessageEvent) => { + const msg = e.data; + if (msg.type === 'status') { + this.backend = msg.backend ?? this.backend; + this.setStatus(msg.status); + if (msg.status === 'ready') resolve(); + if (msg.status === 'unsupported') { + this.initPromise = null; + reject(new Error(msg.message ?? 'unsupported')); + } + } else if (msg.type === 'encoded' || msg.type === 'decoded') { + this.pending.get(msg.id)?.resolve(msg); + this.pending.delete(msg.id); + } else if (msg.type === 'error') { + this.pending.get(msg.id)?.reject(new Error(msg.message)); + this.pending.delete(msg.id); + } + }; + this.worker.onerror = (e) => { + this.initPromise = null; + this.setStatus('unsupported'); + reject(new Error(e.message || 'SAM worker error')); + }; + this.worker.postMessage({ type: 'init' }); + }); + // Keep a dangling rejection from breaking the next caller; failures clear + // initPromise above so a later init() can retry (e.g. after vendoring models). + this.initPromise.catch(() => { /* status already updated */ }); + return this.initPromise; + } + + /** Post a tagged message to the worker and resolve when its matching reply + * (by auto-assigned id) arrives. `transfer` ownership-transfers buffers. */ + private request(payload: Record, transfer?: Transferable[]): Promise { + const id = this.nextId++; + return new Promise((resolve, reject) => { + this.pending.set(id, { resolve: resolve as (v: unknown) => void, reject }); + this.worker!.postMessage({ ...payload, id }, transfer ?? []); + }); + } + + /** Encode a slice image (any canvas source — bake brightness/contrast in to + * let SAM "see" what the user sees). Heavy; run once per slice/adjustment. */ + async encode(source: CanvasImageSource): Promise { + await this.init(); + this.setStatus('encoding'); + const bitmap = await createImageBitmap(source); + try { + await this.request<{ type: 'encoded' }>({ type: 'encode', bitmap }, [bitmap]); + } finally { + this.setStatus('ready'); + } + } + + /** Decode a mask for the given prompts (image-normalised [0,1]). */ + async decode( + points: PromptPoint[], + box: PromptBox | null, + granularity: Granularity, + threshold: number, + ): Promise { + const res = await this.request<{ mask: ArrayBuffer; width: number; height: number; score: number }>( + { type: 'decode', points, box, granularity, threshold }, + ); + return { mask: new Uint8Array(res.mask), width: res.width, height: res.height, score: res.score }; + } +} + +export const samClient = new SamClient(); + +/** WebGPU is the fast path; absence isn't fatal (WASM fallback) but signals it. */ +export function webgpuAvailable(): boolean { + return typeof navigator !== 'undefined' && 'gpu' in navigator; +} diff --git a/frontend/src/lib/sam/samWorker.ts b/frontend/src/lib/sam/samWorker.ts new file mode 100644 index 0000000..ae1afff --- /dev/null +++ b/frontend/src/lib/sam/samWorker.ts @@ -0,0 +1,256 @@ +/** + * SAM Web Worker — runs Segment Anything entirely in the browser via + * Transformers.js (onnxruntime-web). Kept off the UI thread because the image + * encoder is heavy (~1–4 s on WebGPU, slower on WASM). + * + * Lifecycle: + * init → load model + processor (WebGPU, falling back to WASM). Posts the + * backend it ended up on, or 'unsupported' if nothing loads. + * encode → run the vision encoder ONCE for the current slice; cache the + * embedding + the processor's reshaped/original sizes + the source + * pixels (so a WASM fallback can re-encode without a round-trip). + * decode → run the lightweight prompt decoder for point and/or box prompts + * and return the selected mask as a flat Uint8Array. + * + * Prompts arrive normalised to [0,1] (x/imageW, y/imageH); the worker scales + * them to the processor's reshaped input size. Positive points grow the object, + * negative points (label 0) carve regions out, and a box constrains the search + * to a region (the strongest quality lever on low-contrast scans). SAM returns + * 3 masks per prompt at increasing scale; `granularity` selects which. + * + * Robustness: WebGPU can load fine yet throw at session-run time, and the + * processor mutates its RawImage in place — so we keep the raw pixels (not the + * RawImage) and rebuild a fresh one per attempt, retrying once on WASM. + */ +import { + SamModel, + AutoProcessor, + RawImage, + Tensor, + env, + type PreTrainedModel, + type Processor, +} from '@huggingface/transformers'; + +// SlimSAM-77 (uniform) — vendored under public/models/ (HF CDN 403s browser +// downloads). Offline fetch: node scripts/fetch-sam-model.mjs +const USE_LOCAL_MODEL = true; +const MODEL_ID = USE_LOCAL_MODEL ? 'slimsam-77-uniform' : 'Xenova/slimsam-77-uniform'; + +if (USE_LOCAL_MODEL) { + env.allowRemoteModels = false; + env.allowLocalModels = true; + env.localModelPath = '/models/'; +} +if (env.backends?.onnx?.wasm) env.backends.onnx.wasm.numThreads = 1; + +interface PromptPoint { x: number; y: number; label: 0 | 1 } +type Box = { x0: number; y0: number; x1: number; y1: number }; +type Granularity = 'auto' | 'fine' | 'medium' | 'coarse'; +type Device = 'webgpu' | 'wasm'; + +let model: PreTrainedModel | null = null; +let processor: Processor | null = null; +let backend: Device = 'wasm'; +// Cached per-slice encoder state + the source pixels (kept as raw RGBA so we +// can rebuild a fresh RawImage per attempt — the processor mutates it). +let lastPixels: Uint8ClampedArray | null = null; +let lastW = 0; +let lastH = 0; +let imageEmbeddings: Record | null = null; +let reshaped: [number, number] | null = null; // [height, width] after processor resize +let originalSizes: [number, number][] | null = null; + +type InMsg = + | { type: 'init' } + | { type: 'encode'; id: number; bitmap: ImageBitmap } + | { type: 'decode'; id: number; points: PromptPoint[]; box: Box | null; granularity: Granularity; threshold: number }; + +/** Post a message back to the main thread, optionally transferring buffers. */ +function post(msg: unknown, transfer?: Transferable[]) { + (self as unknown as Worker).postMessage(msg, transfer ?? []); +} + +/** Load the SAM model + processor onto a specific device and record the backend. */ +async function loadOn(device: Device) { + // eslint-disable-next-line @typescript-eslint/no-explicit-any + model = await SamModel.from_pretrained(MODEL_ID, { device } as any); + processor = await AutoProcessor.from_pretrained(MODEL_ID); + backend = device; +} + +/** Load on the best available device, trying WebGPU first then WASM; returns the + * device that succeeded or throws if none load. */ +async function load(): Promise { + const hasGpu = typeof (navigator as Navigator & { gpu?: unknown }).gpu !== 'undefined'; + const devices: Device[] = hasGpu ? ['webgpu', 'wasm'] : ['wasm']; + let lastErr: unknown; + for (const device of devices) { + try { + await loadOn(device); + return device; + } catch (err) { + lastErr = err; + console.error(`[SAM] load on ${device} failed`, err); + } + } + throw lastErr ?? new Error('SAM model failed to load'); +} + +/** Reload the model on WASM after a WebGPU runtime failure and re-announce ready. */ +async function fallbackToWasm(reason: unknown) { + console.warn('[SAM] WebGPU runtime failure — falling back to WASM', reason); + await loadOn('wasm'); + post({ type: 'status', status: 'ready', backend: 'wasm' }); +} + +/** Fresh RawImage from the cached pixels (preprocessing mutates it). */ +function freshRaw(): RawImage { + return new RawImage(new Uint8ClampedArray(lastPixels!), lastW, lastH, 4).rgb(); +} + +/** Run the vision encoder on the cached pixels; caches the embedding plus the + * processor's reshaped and original sizes for later decode calls. */ +async function runEncoder() { + const inputs = await processor!(freshRaw()); + imageEmbeddings = await (model as PreTrainedModel & { + get_image_embeddings: (i: unknown) => Promise>; + }).get_image_embeddings(inputs); + const r = (inputs as { reshaped_input_sizes: number[][] }).reshaped_input_sizes[0]; + reshaped = [r[0], r[1]]; + originalSizes = (inputs as { original_sizes: [number, number][] }).original_sizes; +} + +/** Cache the bitmap's RGBA pixels and run the encoder, retrying once on WASM if + * a WebGPU run fails. Heavy; called once per slice. */ +async function encode(bitmap: ImageBitmap) { + if (!model || !processor) throw new Error('model not loaded'); + const canvas = new OffscreenCanvas(bitmap.width, bitmap.height); + const ctx = canvas.getContext('2d', { willReadFrequently: true })!; + ctx.drawImage(bitmap, 0, 0); + const { data } = ctx.getImageData(0, 0, bitmap.width, bitmap.height); + lastW = bitmap.width; + lastH = bitmap.height; + lastPixels = new Uint8ClampedArray(data); + bitmap.close(); + + try { + await runEncoder(); + } catch (err) { + if (backend === 'webgpu') { + await fallbackToWasm(err); + await runEncoder(); + } else { + throw err; + } + } +} + +/** Run the prompt decoder on the cached embedding for the given normalised + * points/box, threshold the masks, pick a channel per `granularity` (auto = + * highest IoU; else by area), and return the chosen mask as a flat Uint8Array. */ +async function runDecoder(points: PromptPoint[], box: Box | null, granularity: Granularity, threshold: number) { + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const inputs: any = { ...imageEmbeddings }; + const [rh, rw] = reshaped!; + + // SamModel.forward unconditionally dereferences input_points.dims, so we must + // always pass points. For a box with no clicks, send one padding point + // (label -10 = ignored by the prompt encoder) to satisfy that. + const flatPts: number[] = []; + const flatLabels: bigint[] = []; + for (const p of points) { + flatPts.push(p.x * rw, p.y * rh); + flatLabels.push(BigInt(p.label)); + } + if (flatLabels.length === 0) { + flatPts.push(0, 0); + flatLabels.push(BigInt(-10)); + } + inputs.input_points = new Tensor('float32', flatPts, [1, 1, flatLabels.length, 2]); + inputs.input_labels = new Tensor('int64', flatLabels, [1, 1, flatLabels.length]); + if (box) { + inputs.input_boxes = new Tensor( + 'float32', + [box.x0 * rw, box.y0 * rh, box.x1 * rw, box.y1 * rh], + [1, 1, 4], + ); + } + + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const outputs: any = await (model as PreTrainedModel)(inputs); + + const masks = await (processor as Processor & { + post_process_masks: (m: unknown, o: unknown, r: unknown, opts: unknown) => Promise; + }).post_process_masks(outputs.pred_masks, originalSizes, [reshaped], { mask_threshold: threshold }); + + const maskTensor = masks[0]; // dims [1, numMasks, H, W], bool + const [, numMasks, H, W] = maskTensor.dims as number[]; + const iou = outputs.iou_scores.data as Float32Array; // length numMasks + const src = maskTensor.data as Uint8Array; + + // Pick the mask channel. 'auto' → highest IoU. Otherwise order the channels by + // area (fine = smallest selection … coarse = largest) and pick accordingly. + let chosen = 0; + if (granularity === 'auto' || numMasks === 1) { + for (let i = 1; i < numMasks; i++) if (iou[i] > iou[chosen]) chosen = i; + } else { + const areas: Array<{ i: number; a: number }> = []; + for (let i = 0; i < numMasks; i++) { + let a = 0; + const off = i * H * W; + for (let j = 0; j < H * W; j++) a += src[off + j]; + areas.push({ i, a }); + } + areas.sort((p, q) => p.a - q.a); // ascending area + const idx = granularity === 'fine' ? 0 : granularity === 'coarse' ? areas.length - 1 : (areas.length >> 1); + chosen = areas[idx].i; + } + + const out = new Uint8Array(H * W); + const offset = chosen * H * W; + for (let i = 0; i < H * W; i++) out[i] = src[offset + i] ? 1 : 0; + return { mask: out, width: W, height: H, score: iou[chosen] }; +} + +/** Decode a mask for the current slice's embedding, retrying once on WASM + * (re-encoding first) if a WebGPU run fails. Requires a prior encode. */ +async function decode(points: PromptPoint[], box: Box | null, granularity: Granularity, threshold: number) { + if (!model || !processor || !imageEmbeddings || !reshaped || !originalSizes) { + throw new Error('encode must run before decode'); + } + try { + return await runDecoder(points, box, granularity, threshold); + } catch (err) { + if (backend === 'webgpu' && lastPixels) { + await fallbackToWasm(err); + await runEncoder(); + return await runDecoder(points, box, granularity, threshold); + } + throw err; + } +} + +/** Dispatch init/encode/decode messages from the main thread and post back the + * result (or an error/status reply tagged with the request id). */ +self.onmessage = async (e: MessageEvent) => { + const msg = e.data; + try { + if (msg.type === 'init') { + post({ type: 'status', status: 'loading-model' }); + const device = await load(); + post({ type: 'status', status: 'ready', backend: device }); + } else if (msg.type === 'encode') { + await encode(msg.bitmap); + post({ type: 'encoded', id: msg.id }); + } else if (msg.type === 'decode') { + const { mask, width, height, score } = await decode(msg.points, msg.box, msg.granularity, msg.threshold); + post({ type: 'decoded', id: msg.id, mask: mask.buffer, width, height, score }, [mask.buffer]); + } + } catch (err) { + const message = err instanceof Error ? err.message : String(err); + console.error('[SAM] worker error', err); + if (msg.type === 'init') post({ type: 'status', status: 'unsupported', message }); + else post({ type: 'error', id: (msg as { id: number }).id, message }); + } +}; diff --git a/frontend/src/lib/sharpen.test.ts b/frontend/src/lib/sharpen.test.ts new file mode 100644 index 0000000..da6d4bb --- /dev/null +++ b/frontend/src/lib/sharpen.test.ts @@ -0,0 +1,53 @@ +/** + * Classic 3×3 Laplacian sharpen tests. + */ +import { describe, expect, it } from 'vitest'; +import { sharpenRgba } from './sharpen'; + +function grayRgba(w: number, h: number, fill: (x: number, y: number) => number): Uint8ClampedArray { + const out = new Uint8ClampedArray(w * h * 4); + for (let y = 0; y < h; y++) { + for (let x = 0; x < w; x++) { + const g = fill(x, y); + const i = (y * w + x) * 4; + out[i] = g; + out[i + 1] = g; + out[i + 2] = g; + out[i + 3] = 255; + } + } + return out; +} + +describe('sharpenRgba', () => { + it('keeps a flat field flat', () => { + const rgba = grayRgba(16, 16, () => 100); + sharpenRgba(rgba, 16, 16, { amount: 1 }); + for (let i = 0; i < rgba.length; i += 4) { + expect(rgba[i]).toBe(100); + expect(rgba[i + 3]).toBe(255); + } + }); + + it('increases the jump across a vertical step edge', () => { + const w = 32; + const h = 16; + const rgba = grayRgba(w, h, (x) => (x < w / 2 ? 40 : 200)); + const beforeLeft = rgba[(8 * w + 14) * 4]; + const beforeRight = rgba[(8 * w + 17) * 4]; + const beforeJump = beforeRight - beforeLeft; + sharpenRgba(rgba, w, h, { amount: 1 }); + const afterLeft = rgba[(8 * w + 14) * 4]; + const afterRight = rgba[(8 * w + 17) * 4]; + expect(afterRight - afterLeft).toBeGreaterThanOrEqual(beforeJump); + }); + + it('clamps outputs to 0–255', () => { + const rgba = grayRgba(8, 8, (x, y) => (x === 4 && y === 4 ? 255 : 0)); + sharpenRgba(rgba, 8, 8, { amount: 2 }); + for (let i = 0; i < rgba.length; i += 4) { + expect(rgba[i]).toBeGreaterThanOrEqual(0); + expect(rgba[i]).toBeLessThanOrEqual(255); + } + }); +}); diff --git a/frontend/src/lib/sharpen.ts b/frontend/src/lib/sharpen.ts new file mode 100644 index 0000000..36b505a --- /dev/null +++ b/frontend/src/lib/sharpen.ts @@ -0,0 +1,55 @@ +/** + * Fast classic sharpening — 3×3 Laplacian high-boost convolution. + * + * Kernel (amount = 1): + * [ 0 -1 0 ] + * [ -1 5 -1 ] + * [ 0 -1 0 ] + * + * Equivalent to ``out = src + amount * (4·center − N − S − E − W)``. + * Much cheaper than unsharp-mask (no blur pass). Display-only preprocessor. + */ + +export interface SharpenOptions { + /** Edge boost strength; 1 ≈ classic high-boost kernel. Default 1. */ + amount?: number; +} + +/** + * In-place 3×3 Laplacian sharpen on RGBA ImageData (alpha unchanged). + * + * Args: + * rgba: Packed RGBA bytes (mutated in place). + * width / height: Image size in pixels. + * options: Sharpen strength. + */ +export function sharpenRgba( + rgba: Uint8ClampedArray, + width: number, + height: number, + options: SharpenOptions = {}, +): void { + const amount = options.amount ?? 1; + if (amount === 0 || width < 2 || height < 2) return; + + // Work from a copy so neighbouring reads stay original. + const src = new Uint8ClampedArray(rgba); + for (let y = 0; y < height; y++) { + const yN = y > 0 ? y - 1 : 0; + const yS = y < height - 1 ? y + 1 : height - 1; + for (let x = 0; x < width; x++) { + const xW = x > 0 ? x - 1 : 0; + const xE = x < width - 1 ? x + 1 : width - 1; + const iC = (y * width + x) * 4; + const iN = (yN * width + x) * 4; + const iS = (yS * width + x) * 4; + const iW = (y * width + xW) * 4; + const iE = (y * width + xE) * 4; + for (let k = 0; k < 3; k++) { + const c = src[iC + k]; + const lap = 4 * c - src[iN + k] - src[iS + k] - src[iW + k] - src[iE + k]; + rgba[iC + k] = c + amount * lap; // Uint8ClampedArray clamps + } + } + } +} diff --git a/frontend/src/lib/sourceKey.ts b/frontend/src/lib/sourceKey.ts index f3ef940..c039151 100644 --- a/frontend/src/lib/sourceKey.ts +++ b/frontend/src/lib/sourceKey.ts @@ -9,6 +9,7 @@ * local → "local:" */ +/** Builds the canonical sourceKey for a sample; tiled keys embed the server URI, local keys use the relative path. */ export function buildSourceKey( kind: 'tiled' | 'local', path: string, diff --git a/frontend/src/lib/viridis.ts b/frontend/src/lib/viridis.ts new file mode 100644 index 0000000..14ce9bd --- /dev/null +++ b/frontend/src/lib/viridis.ts @@ -0,0 +1,269 @@ +/** + * Viridis (Matplotlib) 256-stop RGB LUT for probability overlays. + */ +export const VIRIDIS_RGB: ReadonlyArray = [ + [68, 1, 84], + [68, 2, 86], + [69, 4, 87], + [69, 5, 89], + [70, 7, 90], + [70, 8, 92], + [70, 10, 93], + [70, 11, 94], + [71, 13, 96], + [71, 14, 97], + [71, 16, 99], + [71, 17, 100], + [71, 19, 101], + [72, 20, 103], + [72, 22, 104], + [72, 23, 105], + [72, 24, 106], + [72, 26, 108], + [72, 27, 109], + [72, 28, 110], + [72, 29, 111], + [72, 31, 112], + [72, 32, 113], + [72, 33, 115], + [72, 35, 116], + [72, 36, 117], + [72, 37, 118], + [72, 38, 119], + [72, 40, 120], + [72, 41, 121], + [71, 42, 122], + [71, 44, 122], + [71, 45, 123], + [71, 46, 124], + [71, 47, 125], + [70, 48, 126], + [70, 50, 126], + [70, 51, 127], + [70, 52, 128], + [69, 53, 129], + [69, 55, 129], + [69, 56, 130], + [68, 57, 131], + [68, 58, 131], + [68, 59, 132], + [67, 61, 132], + [67, 62, 133], + [66, 63, 133], + [66, 64, 134], + [66, 65, 134], + [65, 66, 135], + [65, 68, 135], + [64, 69, 136], + [64, 70, 136], + [63, 71, 136], + [63, 72, 137], + [62, 73, 137], + [62, 74, 137], + [62, 76, 138], + [61, 77, 138], + [61, 78, 138], + [60, 79, 138], + [60, 80, 139], + [59, 81, 139], + [59, 82, 139], + [58, 83, 139], + [58, 84, 140], + [57, 85, 140], + [57, 86, 140], + [56, 88, 140], + [56, 89, 140], + [55, 90, 140], + [55, 91, 141], + [54, 92, 141], + [54, 93, 141], + [53, 94, 141], + [53, 95, 141], + [52, 96, 141], + [52, 97, 141], + [51, 98, 141], + [51, 99, 141], + [50, 100, 142], + [50, 101, 142], + [49, 102, 142], + [49, 103, 142], + [49, 104, 142], + [48, 105, 142], + [48, 106, 142], + [47, 107, 142], + [47, 108, 142], + [46, 109, 142], + [46, 110, 142], + [46, 111, 142], + [45, 112, 142], + [45, 113, 142], + [44, 113, 142], + [44, 114, 142], + [44, 115, 142], + [43, 116, 142], + [43, 117, 142], + [42, 118, 142], + [42, 119, 142], + [42, 120, 142], + [41, 121, 142], + [41, 122, 142], + [41, 123, 142], + [40, 124, 142], + [40, 125, 142], + [39, 126, 142], + [39, 127, 142], + [39, 128, 142], + [38, 129, 142], + [38, 130, 142], + [38, 130, 142], + [37, 131, 142], + [37, 132, 142], + [37, 133, 142], + [36, 134, 142], + [36, 135, 142], + [35, 136, 142], + [35, 137, 142], + [35, 138, 141], + [34, 139, 141], + [34, 140, 141], + [34, 141, 141], + [33, 142, 141], + [33, 143, 141], + [33, 144, 141], + [33, 145, 140], + [32, 146, 140], + [32, 146, 140], + [32, 147, 140], + [31, 148, 140], + [31, 149, 139], + [31, 150, 139], + [31, 151, 139], + [31, 152, 139], + [31, 153, 138], + [31, 154, 138], + [30, 155, 138], + [30, 156, 137], + [30, 157, 137], + [31, 158, 137], + [31, 159, 136], + [31, 160, 136], + [31, 161, 136], + [31, 161, 135], + [31, 162, 135], + [32, 163, 134], + [32, 164, 134], + [33, 165, 133], + [33, 166, 133], + [34, 167, 133], + [34, 168, 132], + [35, 169, 131], + [36, 170, 131], + [37, 171, 130], + [37, 172, 130], + [38, 173, 129], + [39, 173, 129], + [40, 174, 128], + [41, 175, 127], + [42, 176, 127], + [44, 177, 126], + [45, 178, 125], + [46, 179, 124], + [47, 180, 124], + [49, 181, 123], + [50, 182, 122], + [52, 182, 121], + [53, 183, 121], + [55, 184, 120], + [56, 185, 119], + [58, 186, 118], + [59, 187, 117], + [61, 188, 116], + [63, 188, 115], + [64, 189, 114], + [66, 190, 113], + [68, 191, 112], + [70, 192, 111], + [72, 193, 110], + [74, 193, 109], + [76, 194, 108], + [78, 195, 107], + [80, 196, 106], + [82, 197, 105], + [84, 197, 104], + [86, 198, 103], + [88, 199, 101], + [90, 200, 100], + [92, 200, 99], + [94, 201, 98], + [96, 202, 96], + [99, 203, 95], + [101, 203, 94], + [103, 204, 92], + [105, 205, 91], + [108, 205, 90], + [110, 206, 88], + [112, 207, 87], + [115, 208, 86], + [117, 208, 84], + [119, 209, 83], + [122, 209, 81], + [124, 210, 80], + [127, 211, 78], + [129, 211, 77], + [132, 212, 75], + [134, 213, 73], + [137, 213, 72], + [139, 214, 70], + [142, 214, 69], + [144, 215, 67], + [147, 215, 65], + [149, 216, 64], + [152, 216, 62], + [155, 217, 60], + [157, 217, 59], + [160, 218, 57], + [162, 218, 55], + [165, 219, 54], + [168, 219, 52], + [170, 220, 50], + [173, 220, 48], + [176, 221, 47], + [178, 221, 45], + [181, 222, 43], + [184, 222, 41], + [186, 222, 40], + [189, 223, 38], + [192, 223, 37], + [194, 223, 35], + [197, 224, 33], + [200, 224, 32], + [202, 225, 31], + [205, 225, 29], + [208, 225, 28], + [210, 226, 27], + [213, 226, 26], + [216, 226, 25], + [218, 227, 25], + [221, 227, 24], + [223, 227, 24], + [226, 228, 24], + [229, 228, 25], + [231, 228, 25], + [234, 229, 26], + [236, 229, 27], + [239, 229, 28], + [241, 229, 29], + [244, 230, 30], + [246, 230, 32], + [248, 230, 33], + [251, 231, 35], + [253, 231, 37], +]; + +/** Sample viridis; ``t`` in [0, 1] → RGB. */ +export function viridisRgb(t: number): [number, number, number] { + const x = Math.min(1, Math.max(0, t)); + const i = Math.min(255, Math.max(0, Math.round(x * 255))); + const rgb = VIRIDIS_RGB[i]!; + return [rgb[0], rgb[1], rgb[2]]; +} diff --git a/frontend/src/stores/annotateModeStore.ts b/frontend/src/stores/annotateModeStore.ts new file mode 100644 index 0000000..a8909f5 --- /dev/null +++ b/frontend/src/stores/annotateModeStore.ts @@ -0,0 +1,19 @@ +/** + * AnnotateModeStore — thin compatibility shim; stage is driven by Hub routes. + * Prefer {@link stageFromPath} from annotateStage for new code. + */ +import { create } from 'zustand'; +import type { AnnotateStage } from '@/lib/annotateStage'; + +export type AnnotateMode = AnnotateStage; + +interface AnnotateModeState { + /** @deprecated Prefer route pathname; kept for any leftover consumers. */ + mode: AnnotateMode; + setMode: (mode: AnnotateMode) => void; +} + +export const useAnnotateModeStore = create((set) => ({ + mode: 'preprocess', + setMode: (mode) => set({ mode }), +})); diff --git a/frontend/src/stores/annotationStore.ts b/frontend/src/stores/annotationStore.ts index 0742a98..03453f5 100644 --- a/frontend/src/stores/annotationStore.ts +++ b/frontend/src/stores/annotationStore.ts @@ -4,6 +4,7 @@ */ import { create } from 'zustand'; import { temporal } from 'zundo'; +import { v4 as uuidv4 } from 'uuid'; // ---- Shape types ---- @@ -13,15 +14,26 @@ export interface BrushStroke { mode: 'paint' | 'erase'; } +/** An erase carve-out applied to any vector shape (polygon/rect/ellipse). */ +export interface EraseStroke { + points: number[]; + radius: number; +} + export interface BaseShape { id: string; classId: number; kind: Shape['kind']; + /** Optional erase carve-outs (rendered destination-out, subtracted on export). */ + erased?: EraseStroke[]; } export interface PolygonShape extends BaseShape { kind: 'polygon'; points: number[]; + /** Optional inner rings (flat [x,y,…] image coords) carved out of the outer + * polygon — e.g. produced by "invert shape". Rendered/rasterized even-odd. */ + holes?: number[][]; } export interface RectShape extends BaseShape { @@ -48,16 +60,37 @@ export interface BrushShape extends BaseShape { export type Shape = PolygonShape | RectShape | EllipseShape | BrushShape; export type Split = 'train' | 'valid' | 'test'; +/** Deep-clone a shape with a fresh id (used when copying across slices). */ +function cloneShapeWithNewId(shape: Shape): Shape { + return { ...structuredClone(shape), id: uuidv4() }; +} + export interface AnnotationState { byImage: Record>; splitBySlice: Record>; negativeSlices: Record; addShape: (sourceKey: string, sliceIdx: number, shape: Shape) => void; + /** Append several shapes in one update (one undo step) — used by magic-wand. */ + addShapes: (sourceKey: string, sliceIdx: number, shapes: Shape[]) => void; removeShape: (sourceKey: string, sliceIdx: number, shapeId: string) => void; + /** Remove several shapes in one update (one undo step). */ + removeShapes: (sourceKey: string, sliceIdx: number, shapeIds: string[]) => void; + /** Replace a single shape via an updater (used for move / vertex editing). */ + updateShape: (sourceKey: string, sliceIdx: number, shapeId: string, updater: (shape: Shape) => Shape) => void; + /** Reassign several shapes to a class in one update (one undo step). */ + setClassForShapes: (sourceKey: string, sliceIdx: number, shapeIds: string[], classId: number) => void; + /** Replace every shape of *classId* on a slice with *shapes* (one undo step). + * Used by threshold/cleanup/interpolate to write a recomputed region back. */ + replaceClassShapesOnSlice: (sourceKey: string, sliceIdx: number, classId: number, shapes: Shape[]) => void; + /** Clone shapes from *fromSlice* into each *toSlices* index (fresh ids, merged + * with existing), optionally limited to one class. One undo step. */ + copySliceShapes: (sourceKey: string, fromSlice: number, toSlices: number[], classId?: number | null) => void; /** Remove every shape with *classId* across all loaded samples (all slices). */ removeShapesByClassId: (classId: number) => void; appendBrushStroke: (sourceKey: string, sliceIdx: number, shapeId: string, stroke: BrushStroke) => void; + /** Append an erase carve-out to any shape (brush → erase stroke; vector → `erased`). */ + appendEraseStroke: (sourceKey: string, sliceIdx: number, shapeId: string, stroke: EraseStroke) => void; setShapes: (sourceKey: string, sliceIdx: number, shapes: Shape[]) => void; setSplitForSlice: (sourceKey: string, sliceIdx: number, split: Split | 'auto') => void; toggleNegativeSlice: (sourceKey: string, sliceIdx: number) => void; @@ -75,6 +108,7 @@ export const useAnnotationStore = create()( splitBySlice: {}, negativeSlices: {}, + /** Appends one shape to the given (sourceKey, slice); one undo step. */ addShape: (sourceKey, sliceIdx, shape) => set((s) => { const sliceKey = String(sliceIdx); @@ -90,6 +124,23 @@ export const useAnnotationStore = create()( }; }), + /** Appends several shapes in one update (one undo step); used by magic-wand. */ + addShapes: (sourceKey, sliceIdx, shapes) => + set((s) => { + const sliceKey = String(sliceIdx); + const prev = s.byImage[sourceKey]?.[sliceKey] ?? []; + return { + byImage: { + ...s.byImage, + [sourceKey]: { + ...(s.byImage[sourceKey] ?? {}), + [sliceKey]: [...prev, ...shapes], + }, + }, + }; + }), + + /** Removes the shape with the given id from the (sourceKey, slice). */ removeShape: (sourceKey, sliceIdx, shapeId) => set((s) => { const sliceKey = String(sliceIdx); @@ -105,6 +156,89 @@ export const useAnnotationStore = create()( }; }), + /** Removes several shapes by id in one update (one undo step). */ + removeShapes: (sourceKey, sliceIdx, shapeIds) => + set((s) => { + const sliceKey = String(sliceIdx); + const prev = s.byImage[sourceKey]?.[sliceKey] ?? []; + const drop = new Set(shapeIds); + return { + byImage: { + ...s.byImage, + [sourceKey]: { + ...(s.byImage[sourceKey] ?? {}), + [sliceKey]: prev.filter((sh) => !drop.has(sh.id)), + }, + }, + }; + }), + + /** Replaces a single shape via the updater fn (move / vertex edit). */ + updateShape: (sourceKey, sliceIdx, shapeId, updater) => + set((s) => { + const sliceKey = String(sliceIdx); + const prev = s.byImage[sourceKey]?.[sliceKey] ?? []; + return { + byImage: { + ...s.byImage, + [sourceKey]: { + ...(s.byImage[sourceKey] ?? {}), + [sliceKey]: prev.map((sh) => (sh.id === shapeId ? updater(sh) : sh)), + }, + }, + }; + }), + + /** Reassigns every shape in *shapeIds* to *classId* (one undo step). */ + setClassForShapes: (sourceKey, sliceIdx, shapeIds, classId) => + set((s) => { + const sliceKey = String(sliceIdx); + const prev = s.byImage[sourceKey]?.[sliceKey] ?? []; + const ids = new Set(shapeIds); + return { + byImage: { + ...s.byImage, + [sourceKey]: { + ...(s.byImage[sourceKey] ?? {}), + [sliceKey]: prev.map((sh) => (ids.has(sh.id) ? { ...sh, classId } : sh)), + }, + }, + }; + }), + + /** Replaces all shapes of *classId* on one slice with *shapes* (one undo step). */ + replaceClassShapesOnSlice: (sourceKey, sliceIdx, classId, shapes) => + set((s) => { + const sliceKey = String(sliceIdx); + const prev = s.byImage[sourceKey]?.[sliceKey] ?? []; + const kept = prev.filter((sh) => sh.classId !== classId); + return { + byImage: { + ...s.byImage, + [sourceKey]: { + ...(s.byImage[sourceKey] ?? {}), + [sliceKey]: [...kept, ...shapes], + }, + }, + }; + }), + + /** Clones shapes (fresh ids) from *fromSlice* into each *toSlices* index. */ + copySliceShapes: (sourceKey, fromSlice, toSlices, classId = null) => + set((s) => { + const src = s.byImage[sourceKey]?.[String(fromSlice)] ?? []; + const picked = classId == null ? src : src.filter((sh) => sh.classId === classId); + if (picked.length === 0) return {}; + const slices = { ...(s.byImage[sourceKey] ?? {}) }; + for (const t of toSlices) { + if (t === fromSlice) continue; + const key = String(t); + slices[key] = [...(slices[key] ?? []), ...picked.map(cloneShapeWithNewId)]; + } + return { byImage: { ...s.byImage, [sourceKey]: slices } }; + }), + + /** Removes every shape of *classId* across all samples/slices, pruning emptied slices and sources. */ removeShapesByClassId: (classId) => set((s) => { const nextByImage: Record> = {}; @@ -123,6 +257,7 @@ export const useAnnotationStore = create()( return { byImage: nextByImage }; }), + /** Appends a brush stroke to the named brush shape; no-op for non-brush shapes. */ appendBrushStroke: (sourceKey, sliceIdx, shapeId, stroke) => set((s) => { const sliceKey = String(sliceIdx); @@ -142,6 +277,29 @@ export const useAnnotationStore = create()( }; }), + /** Appends an erase carve-out: brush shapes get an erase stroke, vector shapes get an `erased` entry. */ + appendEraseStroke: (sourceKey, sliceIdx, shapeId, stroke) => + set((s) => { + const sliceKey = String(sliceIdx); + const shapes = s.byImage[sourceKey]?.[sliceKey] ?? []; + return { + byImage: { + ...s.byImage, + [sourceKey]: { + ...(s.byImage[sourceKey] ?? {}), + [sliceKey]: shapes.map((sh) => { + if (sh.id !== shapeId) return sh; + if (sh.kind === 'brush') { + return { ...sh, strokes: [...sh.strokes, { ...stroke, mode: 'erase' as const }] }; + } + return { ...sh, erased: [...(sh.erased ?? []), stroke] }; + }), + }, + }, + }; + }), + + /** Replaces all shapes for the given (sourceKey, slice). */ setShapes: (sourceKey, sliceIdx, shapes) => set((s) => ({ byImage: { @@ -153,6 +311,7 @@ export const useAnnotationStore = create()( }, })), + /** Sets the train/valid/test split (or 'auto') for one slice. */ setSplitForSlice: (sourceKey, sliceIdx, split) => set((s) => ({ splitBySlice: { @@ -164,6 +323,7 @@ export const useAnnotationStore = create()( }, })), + /** Toggles whether a slice is flagged as a negative (background-only) example. */ toggleNegativeSlice: (sourceKey, sliceIdx) => set((s) => { const sliceKey = String(sliceIdx); @@ -174,16 +334,21 @@ export const useAnnotationStore = create()( return { negativeSlices: { ...s.negativeSlices, [sourceKey]: next } }; }), + /** Replaces ALL annotation data wholesale; used only on initial session restore. */ loadFromDraft: (draft) => set({ byImage: draft.byImage, splitBySlice: draft.splitBySlice, negativeSlices: draft.negativeSlices }), + /** Merges one sample's data into the store, leaving other samples untouched. + * The draft's split map arrives loosely typed (JSON); its values are always + * 'train' | 'valid' | 'test' | 'auto', so we narrow it here. */ mergeSourceDraft: (sourceKey, slices, splitMap, negSlices) => set((s) => ({ byImage: { ...s.byImage, [sourceKey]: slices }, - splitBySlice: { ...s.splitBySlice, [sourceKey]: splitMap }, + splitBySlice: { ...s.splitBySlice, [sourceKey]: splitMap as Record }, negativeSlices: { ...s.negativeSlices, [sourceKey]: negSlices }, })), + /** Clears all shapes, splits, and negative-slice flags. */ reset: () => set({ byImage: {}, splitBySlice: {}, negativeSlices: {} }), }), { limit: 200, partialize: (s) => ({ byImage: s.byImage }) } diff --git a/frontend/src/stores/classStore.ts b/frontend/src/stores/classStore.ts index fa9ab07..dc75eaa 100644 --- a/frontend/src/stores/classStore.ts +++ b/frontend/src/stores/classStore.ts @@ -29,6 +29,7 @@ interface ClassStore { export const useClassStore = create((set, get) => ({ classes: [], + /** Appends a new visible class with an auto-assigned id (max existing + 1); returns the new classId. */ addClass: (label, color) => { const { classes } = get(); const existingIds = classes.map((c) => c.classId); @@ -36,19 +37,23 @@ export const useClassStore = create((set, get) => ({ set({ classes: [...classes, { classId, label, color, isVisible: true }] }); return classId; }, + /** Merges partial updates (label/color/visibility) into the matching class. */ updateClass: (classId, updates) => set((s) => ({ classes: s.classes.map((c) => c.classId === classId ? { ...c, ...updates } : c ), })), + /** Removes the class with the given id (does not touch existing shapes). */ deleteClass: (classId) => set((s) => ({ classes: s.classes.filter((c) => c.classId !== classId) })), + /** Flips the class's isVisible flag (controls whether its shapes render). */ toggleVisibility: (classId) => set((s) => ({ classes: s.classes.map((c) => c.classId === classId ? { ...c, isVisible: !c.isVisible } : c ), })), + /** Replaces the entire class list (used when loading a saved set). */ setClasses: (classes) => set({ classes }), })); diff --git a/frontend/src/stores/clipboardStore.ts b/frontend/src/stores/clipboardStore.ts new file mode 100644 index 0000000..8384260 --- /dev/null +++ b/frontend/src/stores/clipboardStore.ts @@ -0,0 +1,19 @@ +/** + * clipboardStore — in-memory shape clipboard for copy/paste within the editor. + * Holds a deep copy of the shapes that were copied (ids stripped on paste). + */ +import { create } from 'zustand'; +import type { Shape } from '@/stores/annotationStore'; + +interface ClipboardState { + shapes: Shape[]; + /** Store a deep copy of the given shapes as the clipboard contents. */ + copy: (shapes: Shape[]) => void; + clear: () => void; +} + +export const useClipboardStore = create((set) => ({ + shapes: [], + copy: (shapes) => set({ shapes: shapes.map((s) => structuredClone(s)) }), + clear: () => set({ shapes: [] }), +})); diff --git a/frontend/src/stores/connectionStore.ts b/frontend/src/stores/connectionStore.ts index e225190..3c308c5 100644 --- a/frontend/src/stores/connectionStore.ts +++ b/frontend/src/stores/connectionStore.ts @@ -1,41 +1,173 @@ /** - * connectionStore — active data-source connection (server or local folder). + * connectionStore — active data-source connection + session prefs. * - * Separate from datasetStore (which holds the *active sample* being annotated). - * Connect once; browse + annotate many samples. + * Separate from datasetStore (active sample). Connect once; browse + annotate many. + * Session prefs (label names/colors, preferred model + its feature recipe) apply when opening samples. */ import { create } from 'zustand'; +import type { AnnotationClass } from '@/stores/classStore'; + +export interface TrainerConfig { + iterations: number; + depth: number; + learning_rate: number; +} + +export const DEFAULT_TRAINER_CONFIG: TrainerConfig = { + iterations: 200, + depth: 6, + learning_rate: 0.1, +}; export interface ConnectionState { kind: 'tiled' | 'local' | null; /** Tiled server URI */ serverUri: string | null; - /** Relative folder path under LOCAL_DATA_ROOT (local mode) */ + /** Tiled container to browse (e.g. "browse/myset"); null = auto-discover */ + browseContainerPath: string | null; + /** Granted absolute browse root (local mode) */ localRoot: string | null; + /** Chosen subfolder relative to localRoot (local mode) */ + localRel: string | null; /** Human-readable display label */ label: string | null; /** Total number of samples reported by /api/connect/summary */ sampleCount: number | null; + // ########################################################################## + // # REMOVE THIS AND USE YOUR OWN STUFF + // Session prefs for scaffold label-sets + shelf models (Connect/Browse). + // ########################################################################## + /** Selected reusable label set id (disk). REMOVE — use your taxonomy. */ + preferredLabelSetId: string | null; + /** Label name + color defs from preferred set. REMOVE — use your taxonomy. */ + preferredClasses: AnnotationClass[] | null; + /** Optional CatBoost shelf model to prefer on Train. REMOVE — use your model UX. */ + preferredShelfModelId: string | null; + preferredShelfModelName: string | null; + /** Feature recipe required by the preferred model (for Preprocess compute). */ + preferredFeatureRecipe: Record | null; + /** Preferred ipred Feature Setup id (legacy procedure|weights|composition). */ + preferredFeatureSetupId: string | null; + /** Preferred composition document id (modular feature graph). */ + preferredCompositionId: string | null; + /** Preferred trainer plugin id (ipred). */ + preferredTrainerId: string; + preferredTrainerConfig: TrainerConfig; + /** Active ipred session for the opened sample (project). */ + ipredSessionId: string | null; + ipredProjectId: string | null; setConnection: (payload: { kind: 'tiled' | 'local'; serverUri?: string | null; + browseContainerPath?: string | null; localRoot?: string | null; + localRel?: string | null; label: string; sampleCount: number; }) => void; + setPreferredLabelSet: (payload: { + id: string | null; + classes: AnnotationClass[] | null; + }) => void; + /** # REMOVE THIS AND USE YOUR OWN STUFF — scaffold shelf model picker. */ + setPreferredShelfModel: (payload: { + id: string | null; + name?: string | null; + featureRecipe?: Record | null; + }) => void; + setPreferredFeatureSetupId: (id: string | null) => void; + setPreferredCompositionId: (id: string | null) => void; + setPreferredTrainer: (payload: { + id?: string; + config?: Partial; + }) => void; + setIpredSession: (payload: { + sessionId: string | null; + projectId?: string | null; + }) => void; clearConnection: () => void; } export const useConnectionStore = create((set) => ({ kind: null, serverUri: null, + browseContainerPath: null, localRoot: null, + localRel: null, label: null, sampleCount: null, + preferredLabelSetId: null, + preferredClasses: null, + preferredShelfModelId: null, + preferredShelfModelName: null, + preferredFeatureRecipe: null, + preferredFeatureSetupId: 'default-skimage-slimsam', + preferredCompositionId: 'comp-skimage-slimsam', + preferredTrainerId: 'catboost', + preferredTrainerConfig: { ...DEFAULT_TRAINER_CONFIG }, + ipredSessionId: null, + ipredProjectId: null, + + setConnection: ({ + kind, + serverUri = null, + browseContainerPath = null, + localRoot = null, + localRel = null, + label, + sampleCount, + }) => + set({ kind, serverUri, browseContainerPath, localRoot, localRel, label, sampleCount }), + + setPreferredLabelSet: ({ id, classes }) => + set({ preferredLabelSetId: id, preferredClasses: classes }), + + setPreferredShelfModel: ({ id, name = null, featureRecipe = null }) => + set({ + preferredShelfModelId: id, + preferredShelfModelName: name, + preferredFeatureRecipe: featureRecipe, + }), + + setPreferredFeatureSetupId: (id) => set({ preferredFeatureSetupId: id }), + + setPreferredCompositionId: (id) => + set({ + preferredCompositionId: id, + // Keep legacy key in sync when selecting a composition + preferredFeatureSetupId: id, + }), + + setPreferredTrainer: ({ id, config }) => + set((s) => ({ + preferredTrainerId: id ?? s.preferredTrainerId, + preferredTrainerConfig: config + ? { ...s.preferredTrainerConfig, ...config } + : s.preferredTrainerConfig, + })), - setConnection: ({ kind, serverUri = null, localRoot = null, label, sampleCount }) => - set({ kind, serverUri, localRoot, label, sampleCount }), + setIpredSession: ({ sessionId, projectId = null }) => + set({ ipredSessionId: sessionId, ipredProjectId: projectId }), clearConnection: () => - set({ kind: null, serverUri: null, localRoot: null, label: null, sampleCount: null }), + set({ + kind: null, + serverUri: null, + browseContainerPath: null, + localRoot: null, + localRel: null, + label: null, + sampleCount: null, + preferredLabelSetId: null, + preferredClasses: null, + preferredShelfModelId: null, + preferredShelfModelName: null, + preferredFeatureRecipe: null, + preferredFeatureSetupId: 'default-skimage-slimsam', + preferredCompositionId: 'comp-skimage-slimsam', + preferredTrainerId: 'catboost', + preferredTrainerConfig: { ...DEFAULT_TRAINER_CONFIG }, + ipredSessionId: null, + ipredProjectId: null, + }), })); diff --git a/frontend/src/stores/datasetStore.ts b/frontend/src/stores/datasetStore.ts index 6c8e9ed..ecdb445 100644 --- a/frontend/src/stores/datasetStore.ts +++ b/frontend/src/stores/datasetStore.ts @@ -50,11 +50,15 @@ export const useDatasetStore = create((set) => ({ meta: null, currentSlice: 0, renderOpts: { ...DEFAULT_RENDER }, + /** Activates a new image source and resets the current slice to 0. */ setDataset: (kind, source, serverUri, meta) => set({ kind, source, serverUri, meta, currentSlice: 0 }), + /** Sets the active slice index. */ setSlice: (idx) => set({ currentSlice: idx }), + /** Merges partial render options (normalization/scale/percentiles/cmap). */ setRenderOpts: (opts) => set((s) => ({ renderOpts: { ...s.renderOpts, ...opts } })), + /** Clears the active dataset and restores default render options. */ reset: () => set({ kind: null, source: null, serverUri: null, meta: null, currentSlice: 0, renderOpts: { ...DEFAULT_RENDER } }), })); diff --git a/frontend/src/stores/layerVisibilityStore.test.ts b/frontend/src/stores/layerVisibilityStore.test.ts new file mode 100644 index 0000000..482374b --- /dev/null +++ b/frontend/src/stores/layerVisibilityStore.test.ts @@ -0,0 +1,37 @@ +import { describe, expect, it, beforeEach } from 'vitest'; +import { + isPredictionClassVisible, + useLayerVisibilityStore, +} from '@/stores/layerVisibilityStore'; + +describe('layerVisibilityStore', () => { + beforeEach(() => { + useLayerVisibilityStore.setState({ + groups: { + image: true, + features: true, + proba: true, + predictions: true, + annotations: true, + manifold: true, + }, + predictionClassVisible: {}, + showPredictionMulti: true, + showPredictionAbstain: true, + }); + }); + + it('toggles groups and prediction classes', () => { + const s = useLayerVisibilityStore.getState(); + s.toggleGroup('proba'); + expect(useLayerVisibilityStore.getState().groups.proba).toBe(false); + s.ensurePredictionClasses([1, 2]); + expect(isPredictionClassVisible(useLayerVisibilityStore.getState().predictionClassVisible, 1)).toBe( + true, + ); + s.togglePredictionClass(1); + expect(isPredictionClassVisible(useLayerVisibilityStore.getState().predictionClassVisible, 1)).toBe( + false, + ); + }); +}); diff --git a/frontend/src/stores/layerVisibilityStore.ts b/frontend/src/stores/layerVisibilityStore.ts new file mode 100644 index 0000000..65711b9 --- /dev/null +++ b/frontend/src/stores/layerVisibilityStore.ts @@ -0,0 +1,120 @@ +/** + * Canvas layer visibility — function groups + per-class toggles. + * + * Groups control image / features / probability / predictions / annotations / + * manifold. Per-class keys under predictions and annotations (annotations also + * sync with classStore.isVisible). + */ +import { create } from 'zustand'; + +export type LayerGroupId = + | 'image' + | 'features' + | 'proba' + | 'predictions' + | 'annotations' + | 'manifold'; + +export const LAYER_GROUP_META: Record< + LayerGroupId, + { label: string; hint: string } +> = { + image: { label: 'Image', hint: 'Base slice (brightness/levels apply here)' }, + features: { label: 'Features', hint: 'Preprocess channel as display base' }, + proba: { label: 'Probability', hint: 'Softmax class heatmap overlay' }, + predictions: { label: 'Predictions', hint: 'Conformal singleton / multi / abstain' }, + annotations: { label: 'Annotations', hint: 'Drawn shapes / scribbles' }, + manifold: { label: 'Suggest', hint: 'Manifold heatmap + markers' }, +}; + +export interface LayerVisibilityState { + groups: Record; + /** Opacity of the probability overlay (0–1). */ + probaOpacity: number; + /** Opacity of the prediction overlay (0–1). */ + predictionsOpacity: number; + /** Per-class visibility for prediction singletons. Missing → visible. */ + predictionClassVisible: Record; + /** Show conformal multi-set hatch. */ + showPredictionMulti: boolean; + /** Show conformal abstain. */ + showPredictionAbstain: boolean; + setGroup: (id: LayerGroupId, visible: boolean) => void; + toggleGroup: (id: LayerGroupId) => void; + setProbaOpacity: (v: number) => void; + setPredictionsOpacity: (v: number) => void; + setPredictionClassVisible: (classId: number, visible: boolean) => void; + togglePredictionClass: (classId: number) => void; + setShowPredictionMulti: (v: boolean) => void; + setShowPredictionAbstain: (v: boolean) => void; + ensurePredictionClasses: (classIds: number[]) => void; +} + +const DEFAULT_GROUPS: Record = { + image: true, + features: true, + proba: true, + predictions: true, + annotations: true, + manifold: true, +}; + +export const useLayerVisibilityStore = create((set, get) => ({ + groups: { ...DEFAULT_GROUPS }, + probaOpacity: 0.55, + predictionsOpacity: 0.85, + predictionClassVisible: {}, + showPredictionMulti: true, + showPredictionAbstain: true, + + setGroup: (id, visible) => + set((s) => ({ groups: { ...s.groups, [id]: visible } })), + + toggleGroup: (id) => { + const cur = get().groups[id]; + set((s) => ({ groups: { ...s.groups, [id]: !cur } })); + }, + + setProbaOpacity: (v) => + set({ probaOpacity: Math.min(1, Math.max(0, v)) }), + + setPredictionsOpacity: (v) => + set({ predictionsOpacity: Math.min(1, Math.max(0, v)) }), + + setPredictionClassVisible: (classId, visible) => + set((s) => ({ + predictionClassVisible: { ...s.predictionClassVisible, [classId]: visible }, + })), + + togglePredictionClass: (classId) => { + const cur = get().predictionClassVisible[classId]; + const next = cur === undefined ? false : !cur; + set((s) => ({ + predictionClassVisible: { ...s.predictionClassVisible, [classId]: next }, + })); + }, + + setShowPredictionMulti: (v) => set({ showPredictionMulti: v }), + setShowPredictionAbstain: (v) => set({ showPredictionAbstain: v }), + + ensurePredictionClasses: (classIds) => + set((s) => { + const next = { ...s.predictionClassVisible }; + let changed = false; + for (const id of classIds) { + if (next[id] === undefined) { + next[id] = true; + changed = true; + } + } + return changed ? { predictionClassVisible: next } : s; + }), +})); + +/** True when a prediction class should draw (default visible). */ +export function isPredictionClassVisible( + map: Record, + classId: number, +): boolean { + return map[classId] !== false; +} diff --git a/frontend/src/stores/maskSetStore.ts b/frontend/src/stores/maskSetStore.ts new file mode 100644 index 0000000..4e5ee1d --- /dev/null +++ b/frontend/src/stores/maskSetStore.ts @@ -0,0 +1,199 @@ +/** + * Named mask sets for Cleanup Studio — dense pixel label maps (+ optional shapes). + */ +import { create } from 'zustand'; +import { v4 as uuidv4 } from 'uuid'; +import type { Shape } from '@/stores/annotationStore'; +import { labelMapFromBase64, labelMapToBase64 } from '@/lib/labelMap'; + +export type MaskSetOrigin = 'clf' | 'manual' | 'merge' | 'import' | 'shelf'; + +export interface MaskSet { + id: string; + name: string; + sourceKey: string; + slice: number; + origin: MaskSetOrigin; + /** Legacy / cached polygons — prefer labelMap for cleanup edits. */ + shapes: Shape[]; + /** Full-res classId map (HxW). Primary Cleanup buffer. */ + labelMap: Uint8Array | null; + width: number; + height: number; + createdAt: number; + visible: boolean; +} + +/** JSON-friendly draft form of a mask set. */ +export interface MaskSetSerialized { + id: string; + name: string; + sourceKey: string; + slice: number; + origin: MaskSetOrigin; + shapes: Shape[]; + label_map_b64?: string | null; + width: number; + height: number; + createdAt: number; + visible: boolean; +} + +export function serializeMaskSet(m: MaskSet): MaskSetSerialized { + return { + id: m.id, + name: m.name, + sourceKey: m.sourceKey, + slice: m.slice, + origin: m.origin, + shapes: m.shapes, + label_map_b64: m.labelMap ? labelMapToBase64(m.labelMap) : null, + width: m.width, + height: m.height, + createdAt: m.createdAt, + visible: m.visible, + }; +} + +export function deserializeMaskSet(raw: MaskSetSerialized | MaskSet): MaskSet { + const r = raw as MaskSetSerialized & Partial; + let labelMap: Uint8Array | null = null; + if (r.labelMap instanceof Uint8Array) { + labelMap = r.labelMap; + } else if (typeof r.label_map_b64 === 'string' && r.label_map_b64 && r.width && r.height) { + try { + labelMap = labelMapFromBase64(r.label_map_b64, r.width * r.height); + } catch { + labelMap = null; + } + } + return { + id: r.id, + name: r.name, + sourceKey: r.sourceKey, + slice: r.slice, + origin: r.origin, + shapes: Array.isArray(r.shapes) ? r.shapes : [], + labelMap, + width: Number(r.width) || 0, + height: Number(r.height) || 0, + createdAt: Number(r.createdAt) || Date.now(), + visible: r.visible !== false, + }; +} + +type AddSetInput = { + name: string; + sourceKey: string; + slice: number; + origin: MaskSetOrigin; + shapes?: Shape[]; + labelMap?: Uint8Array | null; + width?: number; + height?: number; + visible?: boolean; +}; + +interface MaskSetState { + sets: MaskSet[]; + activeSetId: string | null; + addSet: (input: AddSetInput) => string; + updateSetShapes: (id: string, shapes: Shape[]) => void; + updateSetLabelMap: (id: string, labelMap: Uint8Array, width: number, height: number) => void; + renameSet: (id: string, name: string) => void; + deleteSet: (id: string) => void; + duplicateSet: (id: string) => string | null; + setActiveSetId: (id: string | null) => void; + setVisible: (id: string, visible: boolean) => void; + replaceAll: (sets: MaskSet[]) => void; + clearSource: (sourceKey: string) => void; +} + +export const useMaskSetStore = create((set, get) => ({ + sets: [], + activeSetId: null, + + addSet: (input) => { + const id = uuidv4(); + const entry: MaskSet = { + id, + name: input.name, + sourceKey: input.sourceKey, + slice: input.slice, + origin: input.origin, + shapes: input.shapes ?? [], + labelMap: input.labelMap ?? null, + width: input.width ?? 0, + height: input.height ?? 0, + createdAt: Date.now(), + visible: input.visible ?? true, + }; + set((s) => ({ sets: [...s.sets, entry], activeSetId: id })); + return id; + }, + + updateSetShapes: (id, shapes) => + set((s) => ({ + sets: s.sets.map((m) => (m.id === id ? { ...m, shapes } : m)), + })), + + updateSetLabelMap: (id, labelMap, width, height) => + set((s) => ({ + sets: s.sets.map((m) => + m.id === id + ? { ...m, labelMap: new Uint8Array(labelMap), width, height, shapes: [] } + : m, + ), + })), + + renameSet: (id, name) => + set((s) => ({ + sets: s.sets.map((m) => (m.id === id ? { ...m, name } : m)), + })), + + deleteSet: (id) => + set((s) => ({ + sets: s.sets.filter((m) => m.id !== id), + activeSetId: s.activeSetId === id ? null : s.activeSetId, + })), + + duplicateSet: (id) => { + const src = get().sets.find((m) => m.id === id); + if (!src) return null; + return get().addSet({ + name: `${src.name} copy`, + sourceKey: src.sourceKey, + slice: src.slice, + origin: src.origin, + shapes: src.shapes.map((sh) => ({ ...sh, id: uuidv4() })), + labelMap: src.labelMap ? new Uint8Array(src.labelMap) : null, + width: src.width, + height: src.height, + }); + }, + + setActiveSetId: (id) => set({ activeSetId: id }), + + setVisible: (id, visible) => + set((s) => ({ + sets: s.sets.map((m) => (m.id === id ? { ...m, visible } : m)), + })), + + replaceAll: (sets) => + set({ + sets, + activeSetId: sets[0]?.id ?? null, + }), + + clearSource: (sourceKey) => + set((s) => { + const sets = s.sets.filter((m) => m.sourceKey !== sourceKey); + const activeStill = sets.some((m) => m.id === s.activeSetId); + return { sets, activeSetId: activeStill ? s.activeSetId : null }; + }), +})); + +/** Sets for a source + slice. */ +export function setsForSlice(sets: MaskSet[], sourceKey: string, slice: number): MaskSet[] { + return sets.filter((m) => m.sourceKey === sourceKey && m.slice === slice); +} diff --git a/frontend/src/stores/ratingStore.ts b/frontend/src/stores/ratingStore.ts index bb55db4..b1b4c06 100644 --- a/frontend/src/stores/ratingStore.ts +++ b/frontend/src/stores/ratingStore.ts @@ -20,9 +20,11 @@ export const useRatingStore = create()( (set, get) => ({ ratings: {}, + /** Stores the star rating for a sample, keyed by canonical sourceKey. */ setRating: (sourceKey, rating) => set((s) => ({ ratings: { ...s.ratings, [sourceKey]: rating } })), + /** Returns the sample's rating, or 0 (unrated) if none is stored. */ getRating: (sourceKey) => get().ratings[sourceKey] ?? 0, }), { name: 'sam3_ratings' }, diff --git a/frontend/src/stores/toolStore.ts b/frontend/src/stores/toolStore.ts index 4cb32da..a14e0b1 100644 --- a/frontend/src/stores/toolStore.ts +++ b/frontend/src/stores/toolStore.ts @@ -3,19 +3,58 @@ */ import { create } from 'zustand'; -export type Tool = 'pan' | 'select' | 'polygon' | 'rectangle' | 'ellipse' | 'brush' | 'eraser'; +export type Tool = 'pan' | 'select' | 'polygon' | 'magnetic' | 'magic' | 'rectangle' | 'ellipse' | 'brush' | 'fill' | 'eraser'; + +export type MagicMode = 'contiguous' | 'global'; +/** Magic-selection engine: SAM (learned object prior) or the classic wand. */ +export type MagicEngine = 'sam' | 'classic'; +/** SAM mask granularity — which of SAM's 3 multimask outputs to keep. */ +export type SamDetail = 'auto' | 'fine' | 'medium' | 'coarse'; export interface ToolState { tool: Tool; brushSize: number; fillOpacity: number; - selectedShapeId: string | null; + /** Fill (paint-bucket) similarity threshold (0–1): higher fills more. */ + fillThreshold: number; + /** Selected shape ids (multi-select via marquee/shift-click). */ + selectedShapeIds: string[]; + /** Magic-wand: similarity tolerance (0–1) and selection mode + denoise. */ + magicTolerance: number; + magicMode: MagicMode; + magicSigma: number; + /** Edge barrier strength (0–1) for the contiguous magic wand. */ + magicEdgeStop: number; + /** Active magic engine. Defaults to SAM; auto-falls back to 'classic'. */ + magicEngine: MagicEngine; + /** SAM mask granularity (which multimask output to keep). */ + samDetail: SamDetail; + /** SAM mask-logit threshold: >0 tightens the selection, <0 grows it. */ + samThreshold: number; + /** When true, feed interior points of other-class regions to SAM as negative + * ("not") prompts so a new selection won't bleed into already-labeled areas. */ + samAvoidLabeled: boolean; + /** When true, new annotations are clipped so they can't overlap other classes' + * regions on the current slice (neighbor classes act as a hard boundary). */ + clipToOtherClasses: boolean; /** Bumped to request the canvas re-fit the image to the viewport (F shortcut). */ fitRequestId: number; setTool: (tool: Tool) => void; setBrushSize: (size: number) => void; setFillOpacity: (opacity: number) => void; + setFillThreshold: (t: number) => void; + /** Convenience single-select (clears to [] when null). */ setSelectedShapeId: (id: string | null) => void; + setSelectedShapeIds: (ids: string[]) => void; + setMagicTolerance: (t: number) => void; + setMagicMode: (m: MagicMode) => void; + setMagicSigma: (s: number) => void; + setMagicEdgeStop: (e: number) => void; + setMagicEngine: (e: MagicEngine) => void; + setSamDetail: (d: SamDetail) => void; + setSamThreshold: (t: number) => void; + setSamAvoidLabeled: (v: boolean) => void; + setClipToOtherClasses: (v: boolean) => void; requestFit: () => void; } @@ -23,11 +62,48 @@ export const useToolStore = create((set) => ({ tool: 'pan', brushSize: 10, fillOpacity: 0.5, - selectedShapeId: null, + fillThreshold: 0.1, + selectedShapeIds: [], + magicTolerance: 0.08, + magicMode: 'contiguous', + magicSigma: 1, + magicEdgeStop: 0.3, + magicEngine: 'sam', + samDetail: 'auto', + samThreshold: 0, + samAvoidLabeled: true, + clipToOtherClasses: false, fitRequestId: 0, - setTool: (tool) => set({ tool, selectedShapeId: null }), + /** Switches the active tool and clears the current shape selection. */ + setTool: (tool) => set({ tool, selectedShapeIds: [] }), + /** Sets the brush/eraser radius. */ setBrushSize: (brushSize) => set({ brushSize }), + /** Sets the shape fill opacity (0–1). */ setFillOpacity: (fillOpacity) => set({ fillOpacity }), - setSelectedShapeId: (selectedShapeId) => set({ selectedShapeId }), + /** Sets the Fill (paint-bucket) similarity threshold (0–1). */ + setFillThreshold: (fillThreshold) => set({ fillThreshold }), + /** Single-selects a shape, or clears the selection when null. */ + setSelectedShapeId: (id) => set({ selectedShapeIds: id ? [id] : [] }), + /** Replaces the multi-selection with the given shape ids. */ + setSelectedShapeIds: (selectedShapeIds) => set({ selectedShapeIds }), + /** Sets the magic-wand similarity tolerance (0–1). */ + setMagicTolerance: (magicTolerance) => set({ magicTolerance }), + /** Sets the magic-wand selection mode (contiguous/global). */ + setMagicMode: (magicMode) => set({ magicMode }), + /** Sets the magic-wand pre-blur denoise sigma. */ + setMagicSigma: (magicSigma) => set({ magicSigma }), + /** Sets the contiguous magic-wand edge-barrier strength (0–1). */ + setMagicEdgeStop: (magicEdgeStop) => set({ magicEdgeStop }), + /** Selects the magic engine (SAM or classic wand). */ + setMagicEngine: (magicEngine) => set({ magicEngine }), + /** Sets the SAM multimask granularity to keep. */ + setSamDetail: (samDetail) => set({ samDetail }), + /** Sets the SAM mask-logit threshold (>0 tightens, <0 grows). */ + setSamThreshold: (samThreshold) => set({ samThreshold }), + /** Toggles using other-class regions as SAM negative ("not") prompts. */ + setSamAvoidLabeled: (samAvoidLabeled) => set({ samAvoidLabeled }), + /** Toggles clipping new annotations against other classes' regions. */ + setClipToOtherClasses: (clipToOtherClasses) => set({ clipToOtherClasses }), + /** Bumps fitRequestId to signal the canvas to re-fit the image to the viewport. */ requestFit: () => set((s) => ({ fitRequestId: s.fitRequestId + 1 })), })); diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json index d937f5f..223ceff 100644 --- a/frontend/tsconfig.app.json +++ b/frontend/tsconfig.app.json @@ -16,7 +16,6 @@ "noUnusedLocals": false, "noUnusedParameters": false, "noFallthroughCasesInSwitch": true, - "baseUrl": ".", "paths": { "@/*": ["./src/*"] } diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts index b69a38d..80d7eb0 100644 --- a/frontend/vite.config.ts +++ b/frontend/vite.config.ts @@ -1,9 +1,15 @@ -import { defineConfig } from 'vite'; +// `vitest/config` re-exports Vite's defineConfig with the `test` option typed. +import { defineConfig } from 'vitest/config'; import react from '@vitejs/plugin-react'; import tsconfigPaths from 'vite-tsconfig-paths'; export default defineConfig({ plugins: [react(), tsconfigPaths()], + // Transformers.js bundles onnxruntime-web (wasm/webgpu); let it load its own + // assets at runtime rather than having Vite pre-bundle/optimize it. + optimizeDeps: { exclude: ['@huggingface/transformers'] }, + // The SAM worker is an ES module worker (`new Worker(url, { type: 'module' })`). + worker: { format: 'es' }, server: { host: '127.0.0.1', port: 5173, diff --git a/ipred/pyproject.toml b/ipred/pyproject.toml new file mode 100644 index 0000000..f452ed6 --- /dev/null +++ b/ipred/pyproject.toml @@ -0,0 +1,38 @@ +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" + +[project] +name = "ipred" +version = "0.1.0" +description = "Generic iterative prediction backend (features, train plugins, conformal products)" +requires-python = ">=3.11" +dependencies = [ + "fastapi>=0.115", + "uvicorn[standard]>=0.30", + "numpy>=1.26", + "pillow>=10.3", + "python-dotenv>=1.0", + "scikit-image>=0.22", + "scikit-learn>=1.4", + "catboost>=1.2", + "onnxruntime>=1.17", + "tifffile>=2024.0", + "httpx>=0.27", + "tiled[client]>=0.1", +] + +[project.optional-dependencies] +test = ["pytest>=8", "pytest-asyncio>=0.23", "httpx>=0.27"] +torch = ["torch>=2.2", "timm>=1.0", "onnx>=1.16"] + +[project.scripts] +ipred = "ipred.cli:main" + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +testpaths = ["tests"] +pythonpath = ["src"] diff --git a/ipred/src/ipred/__init__.py b/ipred/src/ipred/__init__.py new file mode 100644 index 0000000..8fe81e6 --- /dev/null +++ b/ipred/src/ipred/__init__.py @@ -0,0 +1,3 @@ +"""ipred — generic iterative prediction backend.""" + +__version__ = "0.1.0" diff --git a/ipred/src/ipred/api.py b/ipred/src/ipred/api.py new file mode 100644 index 0000000..46662d4 --- /dev/null +++ b/ipred/src/ipred/api.py @@ -0,0 +1,506 @@ +"""FastAPI app for the ipred iterative prediction backend.""" + +from __future__ import annotations + +import logging +from contextlib import asynccontextmanager +from pathlib import Path +from typing import Any, Optional + +from fastapi import FastAPI, HTTPException +from fastapi.responses import FileResponse, Response +from pydantic import BaseModel, Field + +from ipred import compositions, feature_setups, manifold_jobs, preprocess, train_infer +from ipred.catalog import Catalog +from ipred.modules import list_module_catalog +from ipred.trainers import list_trainers + +logger = logging.getLogger(__name__) + +_catalog: Catalog | None = None + + +def get_catalog() -> Catalog: + """Return process-wide catalog.""" + global _catalog + if _catalog is None: + _catalog = Catalog() + feature_setups.ensure_default_setups(_catalog) + compositions.ensure_default_compositions(_catalog) + return _catalog + + +@asynccontextmanager +async def lifespan(_app: FastAPI): + """Ensure defaults on startup.""" + get_catalog() + yield + + +app = FastAPI(title="ipred", version="0.1.0", lifespan=lifespan) + + +class ProjectIdentity(BaseModel): + """Source identity for a project / session.""" + + kind: str + source: str + server_uri: Optional[str] = None + root: Optional[str] = None + + +class PreprocessBody(BaseModel): + """Featurize request.""" + + session_id: str + feature_setup_id: Optional[str] = None + composition_id: Optional[str] = None + slice_index: int = 0 + array_ref: Optional[str] = None + + +class CompositionUpsertBody(BaseModel): + """Create/update a composition document.""" + + name: str + nodes: list[dict[str, Any]] + outputs: list[str] + composition_id: Optional[str] = None + builtin: bool = False + + +class ArrayUploadBody(BaseModel): + """Upload a float array (base64 raw float32) for remote preprocess.""" + + session_id: str + shape: list[int] + dtype: str = "float32" + data_b64: str + array_ref: Optional[str] = None + + +class TrainBody(BaseModel): + """Train request.""" + + session_id: str + shapes: list[dict[str, Any]] + feature_id: Optional[str] = None + trainer_id: str = "catboost" + config: dict[str, Any] = Field(default_factory=dict) + + +class InferBody(BaseModel): + """Infer request.""" + + session_id: str + model_id: Optional[str] = None + feature_id: Optional[str] = None + alpha: float = 0.05 + + +class RethresholdBody(BaseModel): + """Rethreshold request.""" + + session_id: str + alpha: float + run_id: Optional[str] = None + + +class SetupUpsertBody(BaseModel): + """Create/update a Feature Setup.""" + + name: str + kind: str + procedure_id: Optional[str] = None + params: Optional[dict[str, Any]] = None + encoder_setup_id: Optional[str] = None + weights_path: Optional[str] = None + weights_format: Optional[str] = None + inference: Optional[dict[str, Any]] = None + setup_id: Optional[str] = None + + +class ManifoldSampleBody(BaseModel): + """Suggest Labels sample request.""" + + feature_id: str + k: int = 24 + box_size: Optional[int] = None + stride: Optional[int] = None + pca_dims: int = 16 + shapes: Optional[list[dict[str, Any]]] = None + + +@app.get("/health") +def health() -> dict[str, str]: + """Liveness probe.""" + return {"status": "ok", "service": "ipred"} + + +@app.post("/sessions") +def open_session(body: ProjectIdentity) -> dict[str, Any]: + """Open a session for a project (creates project if needed).""" + cat = get_catalog() + session = cat.open_session( + kind=body.kind, + source=body.source, + server_uri=body.server_uri, + root=body.root, + ) + return { + "session_id": session.session_id, + "project_id": session.project_id, + "current_feature_id": session.current_feature_id, + "current_model_id": session.current_model_id, + "current_run_id": session.current_run_id, + } + + +@app.get("/sessions/{session_id}") +def get_session(session_id: str) -> dict[str, Any]: + """Fetch session state.""" + session = get_catalog().get_session(session_id) + if session is None: + raise HTTPException(404, "session not found") + project = get_catalog().get_project(session.project_id) + return { + "session_id": session.session_id, + "project_id": session.project_id, + "project": { + "kind": project.kind if project else None, + "source": project.source if project else None, + "server_uri": project.server_uri if project else None, + "root": project.root if project else None, + }, + "current_feature_id": session.current_feature_id, + "current_model_id": session.current_model_id, + "current_run_id": session.current_run_id, + } + + +@app.get("/setups") +def api_list_setups() -> dict[str, Any]: + """List Feature Setups (legacy) + ensure compositions exist.""" + feature_setups.ensure_default_setups(get_catalog()) + compositions.ensure_default_compositions(get_catalog()) + return {"setups": feature_setups.list_setups()} + + +@app.get("/modules") +def api_list_modules() -> dict[str, Any]: + """List composable feature modules.""" + return {"modules": list_module_catalog()} + + +@app.get("/compositions") +def api_list_compositions() -> dict[str, Any]: + """List composition documents.""" + compositions.ensure_default_compositions(get_catalog()) + return {"compositions": compositions.list_compositions()} + + +@app.get("/compositions/{composition_id}") +def api_get_composition(composition_id: str) -> dict[str, Any]: + """Get one composition (+ concat preview labels).""" + try: + doc = compositions.resolve_composition(composition_id) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + doc = dict(doc) + doc["preview_labels"] = compositions.preview_concat_labels(doc) + return doc + + +@app.post("/compositions") +def api_upsert_composition(body: CompositionUpsertBody) -> dict[str, Any]: + """Create or update a composition.""" + try: + return compositions.save_composition( + name=body.name, + nodes=body.nodes, + outputs=body.outputs, + composition_id=body.composition_id, + builtin=body.builtin, + catalog=get_catalog(), + ) + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + + +@app.post("/compositions/preview") +def api_preview_composition(body: CompositionUpsertBody) -> dict[str, Any]: + """Return concat labels without saving.""" + try: + labels = compositions.preview_concat_labels( + {"nodes": body.nodes, "outputs": body.outputs} + ) + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + return {"preview_labels": labels} + + +@app.post("/sessions/{session_id}/arrays") +def api_upload_array(session_id: str, body: ArrayUploadBody) -> dict[str, Any]: + """Upload a content-addressed array blob for remote preprocess.""" + import base64 + + import numpy as np + + from ipred import array_blobs + + session = get_catalog().get_session(session_id) + if session is None: + raise HTTPException(404, "session not found") + if body.session_id and body.session_id != session_id: + raise HTTPException(422, "session_id mismatch") + try: + raw = base64.b64decode(body.data_b64) + shape = tuple(int(x) for x in body.shape) + arr = np.frombuffer(raw, dtype=np.dtype(body.dtype)).reshape(shape) + except Exception as exc: + raise HTTPException(422, f"invalid array payload: {exc}") from exc + return array_blobs.save_array_blob( + session.project_id, + arr, + array_ref=body.array_ref, + ) + + +@app.get("/setups/{setup_id}") +def api_get_setup(setup_id: str) -> dict[str, Any]: + """Get one Feature Setup.""" + try: + return feature_setups.resolve_setup(setup_id) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + + +@app.post("/setups") +def api_upsert_setup(body: SetupUpsertBody) -> dict[str, Any]: + """Create or update a Feature Setup.""" + try: + return feature_setups.save_setup( + name=body.name, + kind=body.kind, + procedure_id=body.procedure_id, + params=body.params, + encoder_setup_id=body.encoder_setup_id, + weights_path=body.weights_path, + weights_format=body.weights_format, + inference=body.inference, + setup_id=body.setup_id, + catalog=get_catalog(), + ) + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + + +@app.get("/trainers") +def api_list_trainers() -> dict[str, Any]: + """List trainer plugin ids.""" + return {"trainers": list_trainers()} + + +@app.post("/preprocess") +def api_preprocess(body: PreprocessBody) -> dict[str, Any]: + """Cache-aware featurize (composition_id preferred; setup_id still accepted).""" + try: + return preprocess.run_preprocess( + get_catalog(), + session_id=body.session_id, + feature_setup_id=body.feature_setup_id, + composition_id=body.composition_id, + slice_index=body.slice_index, + array_ref=body.array_ref, + ) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + except FileNotFoundError as exc: + raise HTTPException(404, str(exc)) from exc + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + except Exception as exc: + logger.exception("preprocess failed") + raise HTTPException(500, f"preprocess failed: {exc}") from exc + + +@app.get("/features/{feature_id}/channels/{index}") +def api_feature_channel(feature_id: str, index: int) -> Response: + """Return a channel PNG for a feature bank.""" + row = get_catalog().get_feature_bank(feature_id) + if row is None: + raise HTTPException(404, "feature bank not found") + path = preprocess.channel_png_path(row["blob_dir"], index) + if not path.is_file(): + raise HTTPException(404, "channel not found") + return FileResponse(path, media_type="image/png") + + +@app.post("/train") +def api_train(body: TrainBody) -> dict[str, Any]: + """Train via trainer plugin.""" + try: + return train_infer.run_train( + get_catalog(), + session_id=body.session_id, + shapes=body.shapes, + feature_id=body.feature_id, + trainer_id=body.trainer_id, + config=body.config, + ) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + except Exception as exc: + logger.exception("train failed") + raise HTTPException(500, f"train failed: {exc}") from exc + + +@app.post("/infer") +def api_infer(body: InferBody) -> dict[str, Any]: + """Infer + conformal products.""" + try: + return train_infer.run_infer( + get_catalog(), + session_id=body.session_id, + model_id=body.model_id, + feature_id=body.feature_id, + alpha=body.alpha, + ) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + except Exception as exc: + logger.exception("infer failed") + raise HTTPException(500, f"infer failed: {exc}") from exc + + +@app.post("/rethreshold") +def api_rethreshold(body: RethresholdBody) -> dict[str, Any]: + """Rethreshold from cached proba.""" + try: + return train_infer.run_rethreshold( + get_catalog(), + session_id=body.session_id, + run_id=body.run_id, + alpha=body.alpha, + ) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + except Exception as exc: + logger.exception("rethreshold failed") + raise HTTPException(500, f"rethreshold failed: {exc}") from exc + + +@app.get("/runs/{run_id}/commit.png") +def api_run_commit(run_id: str) -> Response: + """Commit map PNG.""" + run = get_catalog().get_run(run_id) + if run is None: + raise HTTPException(404, "run not found") + path = Path(run["blob_dir"]) / "commit.png" + if not path.is_file(): + raise HTTPException(404, "commit.png missing") + return FileResponse(path, media_type="image/png") + + +@app.get("/runs/{run_id}/status.png") +def api_run_status(run_id: str) -> Response: + """Status map PNG.""" + run = get_catalog().get_run(run_id) + if run is None: + raise HTTPException(404, "run not found") + path = Path(run["blob_dir"]) / "status.png" + if not path.is_file(): + raise HTTPException(404, "status.png missing") + return FileResponse(path, media_type="image/png") + + +@app.get("/runs/{run_id}/proba/{class_index}.png") +def api_run_proba_channel(run_id: str, class_index: int) -> Response: + """Softmax probability heatmap PNG for one class column (0-based index).""" + try: + data = train_infer.proba_heatmap_png(get_catalog(), run_id, class_index) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + except FileNotFoundError as exc: + raise HTTPException(404, str(exc)) from exc + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + return Response(content=data, media_type="image/png") + + +class ThresholdClassBody(BaseModel): + class_id: int + threshold: float = 0.5 + + +@app.post("/runs/{run_id}/threshold-class") +def api_threshold_class(run_id: str, body: ThresholdClassBody) -> dict[str, Any]: + """Threshold one softmax class into a dense label map (for mask-set caches).""" + try: + return train_infer.threshold_class_label_map( + get_catalog(), + run_id, + class_id=body.class_id, + threshold=body.threshold, + ) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + except FileNotFoundError as exc: + raise HTTPException(404, str(exc)) from exc + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + + +@app.get("/runs/{run_id}") +def api_get_run(run_id: str) -> dict[str, Any]: + """Run metadata.""" + run = get_catalog().get_run(run_id) + if run is None: + raise HTTPException(404, "run not found") + import json + + meta = json.loads(run["meta_json"]) + return {**meta, "blob_dir": run["blob_dir"], "alpha": run["alpha"]} + + +@app.post("/manifold/sample") +def api_manifold_sample(body: ManifoldSampleBody) -> dict[str, Any]: + """Suggest Labels: variance boxes + residual heatmap on a feature bank.""" + try: + return manifold_jobs.run_manifold_sample( + get_catalog(), + feature_id=body.feature_id, + k=body.k, + box_size=body.box_size, + stride=body.stride, + pca_dims=body.pca_dims, + shapes=body.shapes, + ) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + except ValueError as exc: + raise HTTPException(422, str(exc)) from exc + except Exception as exc: + logger.exception("manifold sample failed") + raise HTTPException(500, f"manifold sample failed: {exc}") from exc + + +@app.get("/manifold/{sample_id}/heatmap.png") +def api_manifold_heatmap(sample_id: str) -> Response: + """Residual heatmap PNG for a manifold sample.""" + try: + png = manifold_jobs.heatmap_png(sample_id) + except KeyError as exc: + raise HTTPException(404, str(exc)) from exc + return Response( + content=png, + media_type="image/png", + headers={"Cache-Control": "private, max-age=300"}, + ) diff --git a/ipred/src/ipred/array_blobs.py b/ipred/src/ipred/array_blobs.py new file mode 100644 index 0000000..04ceb93 --- /dev/null +++ b/ipred/src/ipred/array_blobs.py @@ -0,0 +1,56 @@ +"""Content-addressed array blobs for remote compute (data ≠ compute host).""" + +from __future__ import annotations + +import hashlib +import json +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import numpy as np + +from ipred.paths import project_blob_dir + + +def arrays_dir(project_id: str) -> Path: + """Blob directory for uploaded arrays.""" + path = project_blob_dir(project_id) / "arrays" + path.mkdir(parents=True, exist_ok=True) + return path + + +def save_array_blob( + project_id: str, + arr: np.ndarray, + *, + array_ref: str | None = None, + meta: dict[str, Any] | None = None, +) -> dict[str, Any]: + """Persist ``arr`` and return ``{array_ref, shape, dtype, ...}``.""" + data = np.asarray(arr) + raw = data.astype(np.float32, copy=False).tobytes() + digest = hashlib.sha1(raw).hexdigest()[:16] + ref = array_ref or f"{digest}_{uuid.uuid4().hex[:8]}" + dest = arrays_dir(project_id) / ref + dest.mkdir(parents=True, exist_ok=True) + np.save(dest / "array.npy", data) + record = { + "array_ref": ref, + "shape": list(data.shape), + "dtype": str(data.dtype), + "sha1": digest, + "created_at": datetime.now(timezone.utc).isoformat(), + **(meta or {}), + } + (dest / "meta.json").write_text(json.dumps(record, indent=2), encoding="utf-8") + return record + + +def load_array_blob(project_id: str, array_ref: str) -> np.ndarray: + """Load a previously uploaded array.""" + path = arrays_dir(project_id) / array_ref / "array.npy" + if not path.is_file(): + raise FileNotFoundError(f"array_ref not found: {array_ref}") + return np.load(path).astype(np.float32) diff --git a/ipred/src/ipred/array_source.py b/ipred/src/ipred/array_source.py new file mode 100644 index 0000000..1dad00f --- /dev/null +++ b/ipred/src/ipred/array_source.py @@ -0,0 +1,104 @@ +"""Minimal local / Tiled slice reader (no annotate-backend imports).""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Any + +import numpy as np + + +def _local_root(root: str | None) -> Path: + if root: + return Path(root).expanduser().resolve() + return Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve() + + +def read_slice( + *, + kind: str, + source: str, + slice_index: int = 0, + server_uri: str | None = None, + root: str | None = None, +) -> np.ndarray: + """Load one 2D/ HxWxC slice array.""" + if kind == "local": + return _read_local(source, slice_index=slice_index, root=root) + if kind == "tiled": + return _read_tiled( + source, slice_index=slice_index, server_uri=server_uri + ) + raise ValueError(f"unknown kind {kind!r}") + + +def _read_local( + source: str, + *, + slice_index: int, + root: str | None, +) -> np.ndarray: + base = _local_root(root) + path = (base / source).resolve() + if not str(path).startswith(str(base)): + raise PermissionError("local path escapes LOCAL_DATA_ROOT") + if not path.is_file(): + raise FileNotFoundError(str(path)) + suffix = path.suffix.lower() + if suffix in {".tif", ".tiff"}: + import tifffile + + arr = tifffile.imread(str(path)) + else: + from PIL import Image as PILImage + + arr = np.asarray(PILImage.open(path)) + return _index_slice(np.asarray(arr), slice_index) + + +def _read_tiled( + source: str, + *, + slice_index: int, + server_uri: str | None, +) -> np.ndarray: + from tiled.client import from_uri + + uri = (server_uri or os.getenv("TILED_URI") or "http://127.0.0.1:8010").rstrip( + "/" + ) + api_key = os.getenv("TILED_API_KEY") + client: Any = from_uri(uri, api_key=api_key) if api_key else from_uri(uri) + node: Any = client + for part in source.strip("/").split("/"): + if not part: + continue + node = node[part] + # Descend one level if container with a single array child + sf = getattr(node, "structure_family", None) + if str(getattr(sf, "value", sf)) == "container": + keys = list(node) + if len(keys) == 1: + node = node[keys[0]] + data = np.asarray(node) + return _index_slice(data, slice_index) + + +def _index_slice(arr: np.ndarray, slice_index: int) -> np.ndarray: + """Pick HW or HWC frame from NHW / NHWC / HW / HWC.""" + a = np.asarray(arr) + if a.ndim == 2: + return a + if a.ndim == 3: + # HWC vs NHW + if a.shape[-1] in (1, 3, 4) and a.shape[0] > 8: + return a # HWC single + if a.shape[-1] in (1, 3, 4) and a.shape[0] <= 8: + # ambiguous small N — treat as HWC if last dim channel-like and square-ish + if a.shape[0] == a.shape[1]: + return a + return a[int(slice_index)] + if a.ndim == 4: + return a[int(slice_index)] + raise ValueError(f"unsupported array shape {a.shape}") diff --git a/ipred/src/ipred/cache.py b/ipred/src/ipred/cache.py new file mode 100644 index 0000000..3f2de80 --- /dev/null +++ b/ipred/src/ipred/cache.py @@ -0,0 +1,40 @@ +"""Tiny TTL cache for short-lived ipred artifacts (manifold heatmaps).""" + +from __future__ import annotations + +import time +from threading import Lock +from typing import Any, Generic, TypeVar + +T = TypeVar("T") + + +class TTLCache(Generic[T]): + """Thread-safe TTL + max-entries cache.""" + + def __init__(self, *, ttl_seconds: float, max_entries: int) -> None: + self.ttl = float(ttl_seconds) + self.max_entries = int(max_entries) + self._data: dict[str, tuple[float, T]] = {} + self._lock = Lock() + + def get(self, key: str) -> T | None: + now = time.monotonic() + with self._lock: + item = self._data.get(key) + if item is None: + return None + ts, val = item + if now - ts > self.ttl: + del self._data[key] + return None + return val + + def set(self, key: str, value: T) -> None: + now = time.monotonic() + with self._lock: + self._data[key] = (now, value) + if len(self._data) > self.max_entries: + # Drop oldest + oldest = min(self._data.items(), key=lambda kv: kv[1][0])[0] + del self._data[oldest] diff --git a/ipred/src/ipred/catalog.py b/ipred/src/ipred/catalog.py new file mode 100644 index 0000000..7912209 --- /dev/null +++ b/ipred/src/ipred/catalog.py @@ -0,0 +1,441 @@ +"""SQLite catalog for projects, sessions, feature banks, models, and runs.""" + +from __future__ import annotations + +import hashlib +import json +import sqlite3 +import uuid +from contextlib import contextmanager +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Iterator + +from ipred.paths import catalog_db_path, project_blob_dir + + +def _utc_now() -> str: + return datetime.now(timezone.utc).isoformat() + + +def project_id_for( + *, + kind: str, + source: str, + server_uri: str | None = None, + root: str | None = None, +) -> str: + """Stable project id from source identity.""" + payload = json.dumps( + { + "kind": kind, + "source": source, + "server_uri": server_uri or "", + "root": root or "", + }, + sort_keys=True, + separators=(",", ":"), + ) + return hashlib.sha1(payload.encode("utf-8")).hexdigest()[:16] + + +@dataclass(frozen=True) +class ProjectRow: + """Project catalog row.""" + + project_id: str + kind: str + source: str + server_uri: str | None + root: str | None + created_at: str + + +@dataclass(frozen=True) +class SessionRow: + """Session catalog row.""" + + session_id: str + project_id: str + current_feature_id: str | None + current_model_id: str | None + current_run_id: str | None + created_at: str + updated_at: str + + +class Catalog: + """SQLite-backed engine catalog.""" + + def __init__(self, db_path: Path | None = None) -> None: + self.db_path = Path(db_path) if db_path else catalog_db_path() + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._init_schema() + + @contextmanager + def connect(self) -> Iterator[sqlite3.Connection]: + """Yield a connection with row factory.""" + conn = sqlite3.connect(str(self.db_path)) + conn.row_factory = sqlite3.Row + try: + yield conn + conn.commit() + except Exception: + conn.rollback() + raise + finally: + conn.close() + + def _init_schema(self) -> None: + with self.connect() as conn: + conn.executescript( + """ + CREATE TABLE IF NOT EXISTS projects ( + project_id TEXT PRIMARY KEY, + kind TEXT NOT NULL, + source TEXT NOT NULL, + server_uri TEXT, + root TEXT, + created_at TEXT NOT NULL + ); + + CREATE TABLE IF NOT EXISTS sessions ( + session_id TEXT PRIMARY KEY, + project_id TEXT NOT NULL REFERENCES projects(project_id), + current_feature_id TEXT, + current_model_id TEXT, + current_run_id TEXT, + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL + ); + + CREATE TABLE IF NOT EXISTS feature_setups ( + setup_id TEXT PRIMARY KEY, + name TEXT NOT NULL, + kind TEXT NOT NULL, + content_hash TEXT NOT NULL, + meta_json TEXT NOT NULL, + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL + ); + + CREATE TABLE IF NOT EXISTS feature_banks ( + feature_id TEXT PRIMARY KEY, + project_id TEXT NOT NULL, + setup_id TEXT NOT NULL, + content_hash TEXT NOT NULL, + slice_index INTEGER NOT NULL, + n_channels INTEGER NOT NULL, + height INTEGER NOT NULL, + width INTEGER NOT NULL, + blob_dir TEXT NOT NULL, + setup_snapshot TEXT NOT NULL, + status TEXT NOT NULL, + created_at TEXT NOT NULL, + UNIQUE(project_id, setup_id, content_hash, slice_index) + ); + + CREATE TABLE IF NOT EXISTS models ( + model_id TEXT PRIMARY KEY, + project_id TEXT NOT NULL, + feature_id TEXT NOT NULL, + trainer_id TEXT NOT NULL, + blob_dir TEXT NOT NULL, + meta_json TEXT NOT NULL, + created_at TEXT NOT NULL + ); + + CREATE TABLE IF NOT EXISTS runs ( + run_id TEXT PRIMARY KEY, + project_id TEXT NOT NULL, + model_id TEXT NOT NULL, + feature_id TEXT NOT NULL, + alpha REAL NOT NULL, + blob_dir TEXT NOT NULL, + meta_json TEXT NOT NULL, + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL + ); + """ + ) + + def ensure_project( + self, + *, + kind: str, + source: str, + server_uri: str | None = None, + root: str | None = None, + ) -> ProjectRow: + """Create or return the project for this source identity.""" + pid = project_id_for( + kind=kind, source=source, server_uri=server_uri, root=root + ) + now = _utc_now() + with self.connect() as conn: + row = conn.execute( + "SELECT * FROM projects WHERE project_id = ?", (pid,) + ).fetchone() + if row is None: + conn.execute( + """ + INSERT INTO projects + (project_id, kind, source, server_uri, root, created_at) + VALUES (?, ?, ?, ?, ?, ?) + """, + (pid, kind, source, server_uri, root, now), + ) + project_blob_dir(pid) + row = conn.execute( + "SELECT * FROM projects WHERE project_id = ?", (pid,) + ).fetchone() + return self._project_from_row(row) + + def open_session( + self, + *, + kind: str, + source: str, + server_uri: str | None = None, + root: str | None = None, + ) -> SessionRow: + """Ensure project exists and open a new session on it.""" + project = self.ensure_project( + kind=kind, source=source, server_uri=server_uri, root=root + ) + sid = uuid.uuid4().hex + now = _utc_now() + with self.connect() as conn: + conn.execute( + """ + INSERT INTO sessions + (session_id, project_id, current_feature_id, current_model_id, + current_run_id, created_at, updated_at) + VALUES (?, ?, NULL, NULL, NULL, ?, ?) + """, + (sid, project.project_id, now, now), + ) + row = conn.execute( + "SELECT * FROM sessions WHERE session_id = ?", (sid,) + ).fetchone() + return self._session_from_row(row) + + def get_session(self, session_id: str) -> SessionRow | None: + """Look up a session.""" + with self.connect() as conn: + row = conn.execute( + "SELECT * FROM sessions WHERE session_id = ?", (session_id,) + ).fetchone() + return self._session_from_row(row) if row else None + + def get_project(self, project_id: str) -> ProjectRow | None: + """Look up a project.""" + with self.connect() as conn: + row = conn.execute( + "SELECT * FROM projects WHERE project_id = ?", (project_id,) + ).fetchone() + return self._project_from_row(row) if row else None + + def set_session_currents( + self, + session_id: str, + *, + feature_id: str | None = None, + model_id: str | None = None, + run_id: str | None = None, + ) -> SessionRow: + """Update session current_* pointers (only provided fields).""" + session = self.get_session(session_id) + if session is None: + raise KeyError(f"unknown session {session_id}") + feat = feature_id if feature_id is not None else session.current_feature_id + model = model_id if model_id is not None else session.current_model_id + run = run_id if run_id is not None else session.current_run_id + now = _utc_now() + with self.connect() as conn: + conn.execute( + """ + UPDATE sessions + SET current_feature_id = ?, current_model_id = ?, + current_run_id = ?, updated_at = ? + WHERE session_id = ? + """, + (feat, model, run, now, session_id), + ) + row = conn.execute( + "SELECT * FROM sessions WHERE session_id = ?", (session_id,) + ).fetchone() + return self._session_from_row(row) + + def upsert_feature_setup( + self, + *, + setup_id: str, + name: str, + kind: str, + content_hash: str, + meta: dict[str, Any], + ) -> None: + """Insert or update a feature setup index row.""" + now = _utc_now() + payload = json.dumps(meta, sort_keys=True) + with self.connect() as conn: + existing = conn.execute( + "SELECT setup_id FROM feature_setups WHERE setup_id = ?", + (setup_id,), + ).fetchone() + if existing is None: + conn.execute( + """ + INSERT INTO feature_setups + (setup_id, name, kind, content_hash, meta_json, + created_at, updated_at) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + (setup_id, name, kind, content_hash, payload, now, now), + ) + else: + conn.execute( + """ + UPDATE feature_setups + SET name = ?, kind = ?, content_hash = ?, meta_json = ?, + updated_at = ? + WHERE setup_id = ? + """, + (name, kind, content_hash, payload, now, setup_id), + ) + + def find_feature_bank( + self, + *, + project_id: str, + setup_id: str, + content_hash: str, + slice_index: int, + ) -> dict[str, Any] | None: + """Return a ready feature-bank row dict or None.""" + with self.connect() as conn: + row = conn.execute( + """ + SELECT * FROM feature_banks + WHERE project_id = ? AND setup_id = ? AND content_hash = ? + AND slice_index = ? AND status = 'ready' + """, + (project_id, setup_id, content_hash, slice_index), + ).fetchone() + return dict(row) if row else None + + def insert_feature_bank(self, record: dict[str, Any]) -> None: + """Insert a feature-bank row.""" + with self.connect() as conn: + conn.execute( + """ + INSERT INTO feature_banks + (feature_id, project_id, setup_id, content_hash, slice_index, + n_channels, height, width, blob_dir, setup_snapshot, status, + created_at) + VALUES + (:feature_id, :project_id, :setup_id, :content_hash, + :slice_index, :n_channels, :height, :width, :blob_dir, + :setup_snapshot, :status, :created_at) + """, + record, + ) + + def get_feature_bank(self, feature_id: str) -> dict[str, Any] | None: + """Look up a feature bank by id.""" + with self.connect() as conn: + row = conn.execute( + "SELECT * FROM feature_banks WHERE feature_id = ?", + (feature_id,), + ).fetchone() + return dict(row) if row else None + + def insert_model(self, record: dict[str, Any]) -> None: + """Insert a model row.""" + with self.connect() as conn: + conn.execute( + """ + INSERT INTO models + (model_id, project_id, feature_id, trainer_id, blob_dir, + meta_json, created_at) + VALUES + (:model_id, :project_id, :feature_id, :trainer_id, :blob_dir, + :meta_json, :created_at) + """, + record, + ) + + def get_model(self, model_id: str) -> dict[str, Any] | None: + """Look up a model row.""" + with self.connect() as conn: + row = conn.execute( + "SELECT * FROM models WHERE model_id = ?", (model_id,) + ).fetchone() + return dict(row) if row else None + + def insert_run(self, record: dict[str, Any]) -> None: + """Insert a run row.""" + with self.connect() as conn: + conn.execute( + """ + INSERT INTO runs + (run_id, project_id, model_id, feature_id, alpha, blob_dir, + meta_json, created_at, updated_at) + VALUES + (:run_id, :project_id, :model_id, :feature_id, :alpha, + :blob_dir, :meta_json, :created_at, :updated_at) + """, + record, + ) + + def update_run( + self, + run_id: str, + *, + alpha: float, + meta_json: str, + ) -> None: + """Update run alpha/meta after rethreshold.""" + now = _utc_now() + with self.connect() as conn: + conn.execute( + """ + UPDATE runs + SET alpha = ?, meta_json = ?, updated_at = ? + WHERE run_id = ? + """, + (alpha, meta_json, now, run_id), + ) + + def get_run(self, run_id: str) -> dict[str, Any] | None: + """Look up a run row.""" + with self.connect() as conn: + row = conn.execute( + "SELECT * FROM runs WHERE run_id = ?", (run_id,) + ).fetchone() + return dict(row) if row else None + + @staticmethod + def _project_from_row(row: sqlite3.Row) -> ProjectRow: + return ProjectRow( + project_id=row["project_id"], + kind=row["kind"], + source=row["source"], + server_uri=row["server_uri"], + root=row["root"], + created_at=row["created_at"], + ) + + @staticmethod + def _session_from_row(row: sqlite3.Row) -> SessionRow: + return SessionRow( + session_id=row["session_id"], + project_id=row["project_id"], + current_feature_id=row["current_feature_id"], + current_model_id=row["current_model_id"], + current_run_id=row["current_run_id"], + created_at=row["created_at"], + updated_at=row["updated_at"], + ) diff --git a/ipred/src/ipred/cli.py b/ipred/src/ipred/cli.py new file mode 100644 index 0000000..bf20b8d --- /dev/null +++ b/ipred/src/ipred/cli.py @@ -0,0 +1,188 @@ +"""CLI entrypoint ``ipred`` with --project session sugar.""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path +from typing import Any + +from ipred import feature_setups, preprocess, train_infer +from ipred.catalog import Catalog +from ipred.trainers import list_trainers + + +def _catalog() -> Catalog: + cat = Catalog() + feature_setups.ensure_default_setups(cat) + return cat + + +def _resolve_session(args: argparse.Namespace, cat: Catalog) -> str: + """Return session_id from --session or --project sugar.""" + if getattr(args, "session", None): + return str(args.session) + if getattr(args, "kind", None) and getattr(args, "source", None): + session = cat.open_session( + kind=args.kind, + source=args.source, + server_uri=getattr(args, "server_uri", None), + root=getattr(args, "root", None), + ) + print(json.dumps({"session_id": session.session_id, "project_id": session.project_id})) + return session.session_id + raise SystemExit("provide --session or --kind/--source (--project sugar)") + + +def _add_project_sugar(parser: argparse.ArgumentParser) -> None: + parser.add_argument("--session", default=None, help="Existing session id") + parser.add_argument("--kind", default=None, help="Project kind (local|tiled)") + parser.add_argument("--source", default=None, help="Project source path") + parser.add_argument("--server-uri", dest="server_uri", default=None) + parser.add_argument("--root", default=None, help="Local root override") + + +def main(argv: list[str] | None = None) -> None: + """CLI main.""" + parser = argparse.ArgumentParser( + prog="ipred", + description="Iterative prediction backend CLI", + ) + sub = parser.add_subparsers(dest="cmd", required=True) + + p_session = sub.add_parser("session", help="Session commands") + sess_sub = p_session.add_subparsers(dest="session_cmd", required=True) + p_open = sess_sub.add_parser("open", help="Open a session for a project") + p_open.add_argument("--kind", required=True) + p_open.add_argument("--source", required=True) + p_open.add_argument("--server-uri", dest="server_uri", default=None) + p_open.add_argument("--root", default=None) + + p_setup = sub.add_parser("setup", help="Feature Setup commands") + setup_sub = p_setup.add_subparsers(dest="setup_cmd", required=True) + setup_sub.add_parser("list", help="List setups") + p_show = setup_sub.add_parser("show", help="Show one setup") + p_show.add_argument("setup_id") + + p_pre = sub.add_parser("preprocess", help="Featurize (cache-aware)") + _add_project_sugar(p_pre) + p_pre.add_argument("--setup", required=True, help="Feature setup id") + p_pre.add_argument("--slice", type=int, default=0) + + p_train = sub.add_parser("train", help="Train a model") + _add_project_sugar(p_train) + p_train.add_argument("--labels", required=True, help="JSON file of shapes") + p_train.add_argument("--trainer", default="catboost") + p_train.add_argument("--feature-id", dest="feature_id", default=None) + p_train.add_argument("--config", default=None, help="JSON config file or string") + + p_inf = sub.add_parser("infer", help="Infer + conformal maps") + _add_project_sugar(p_inf) + p_inf.add_argument("--alpha", type=float, default=0.05) + p_inf.add_argument("--model-id", dest="model_id", default=None) + p_inf.add_argument("--feature-id", dest="feature_id", default=None) + + p_rt = sub.add_parser("rethreshold", help="Rethreshold from cached proba") + _add_project_sugar(p_rt) + p_rt.add_argument("--alpha", type=float, required=True) + p_rt.add_argument("--run-id", dest="run_id", default=None) + + sub.add_parser("trainers", help="List trainer plugins") + + args = parser.parse_args(argv) + cat = _catalog() + + if args.cmd == "session" and args.session_cmd == "open": + session = cat.open_session( + kind=args.kind, + source=args.source, + server_uri=args.server_uri, + root=args.root, + ) + _print( + { + "session_id": session.session_id, + "project_id": session.project_id, + } + ) + return + + if args.cmd == "setup": + if args.setup_cmd == "list": + _print({"setups": feature_setups.list_setups()}) + return + if args.setup_cmd == "show": + _print(feature_setups.resolve_setup(args.setup_id)) + return + + if args.cmd == "trainers": + _print({"trainers": list_trainers()}) + return + + if args.cmd == "preprocess": + sid = _resolve_session(args, cat) + out = preprocess.run_preprocess( + cat, + session_id=sid, + feature_setup_id=args.setup, + slice_index=args.slice, + ) + _print(out) + return + + if args.cmd == "train": + sid = _resolve_session(args, cat) + shapes = json.loads(Path(args.labels).read_text(encoding="utf-8")) + if isinstance(shapes, dict) and "shapes" in shapes: + shapes = shapes["shapes"] + config: dict[str, Any] = {} + if args.config: + cfg_path = Path(args.config) + if cfg_path.is_file(): + config = json.loads(cfg_path.read_text(encoding="utf-8")) + else: + config = json.loads(args.config) + out = train_infer.run_train( + cat, + session_id=sid, + shapes=shapes, + feature_id=args.feature_id, + trainer_id=args.trainer, + config=config, + ) + _print(out) + return + + if args.cmd == "infer": + sid = _resolve_session(args, cat) + out = train_infer.run_infer( + cat, + session_id=sid, + model_id=args.model_id, + feature_id=args.feature_id, + alpha=args.alpha, + ) + _print(out) + return + + if args.cmd == "rethreshold": + sid = _resolve_session(args, cat) + out = train_infer.run_rethreshold( + cat, + session_id=sid, + run_id=args.run_id, + alpha=args.alpha, + ) + _print(out) + return + + parser.error(f"unhandled command {args.cmd}") + + +def _print(obj: Any) -> None: + print(json.dumps(obj, indent=2, default=str)) + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/ipred/src/ipred/compose_run.py b/ipred/src/ipred/compose_run.py new file mode 100644 index 0000000..43c6081 --- /dev/null +++ b/ipred/src/ipred/compose_run.py @@ -0,0 +1,84 @@ +"""Execute a composition document on a raw image array.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np + +from ipred import features +from ipred.compositions import validate_composition +from ipred.modules import get_module +from ipred.modules.base import ChannelBlock, ModuleContext +from ipred.modules.pca_mod import uint8_from_float + + +def run_composition( + arr: np.ndarray, + doc: dict[str, Any], +) -> tuple[ + np.ndarray, + np.ndarray, + list[str], + np.ndarray | None, + dict[str, Any] | None, +]: + """Run composition nodes; concatenate ``outputs`` channel blocks. + + Returns: + uint8_stack, float_stack, labels, sam_emb, sam_meta + (emb/meta from the last PCA or encoder that produced them). + """ + validate_composition(doc) + gray = features.to_grayscale(arr) + ctx = ModuleContext(raw=arr, gray=gray, params={}) + by_id = {n["id"]: n for n in doc["nodes"]} + + # Topological-ish: run in document order (authors must order deps first) + for node in doc["nodes"]: + nid = node["id"] + mid = str(node["module"]) + mod = get_module(mid) + params = dict(node.get("params") or {}) + input_from = node.get("input_from") + input_image = None + if input_from: + upstream = ctx.node_outputs.get(input_from) + if upstream is None: + raise ValueError( + f"node {nid}: input_from {input_from} not run yet" + ) + if upstream.image_2d is not None: + input_image = upstream.image_2d + elif upstream.float_stack is not None and upstream.float_stack.shape[-1] >= 1: + input_image = upstream.float_stack[..., 0] + params["_input_from"] = input_from + ctx.params = params + ctx.input_image = input_image + block = mod.run(ctx) + ctx.node_outputs[nid] = block + + float_parts: list[np.ndarray] = [] + labels: list[str] = [] + sam_emb: np.ndarray | None = None + sam_meta: dict[str, Any] | None = None + for oid in doc.get("outputs") or []: + block = ctx.node_outputs[oid] + if block.float_stack is not None and block.float_stack.size: + float_parts.append(np.asarray(block.float_stack, dtype=np.float32)) + labels.extend(block.labels) + if block.emb is not None: + sam_emb = block.emb + sam_meta = block.emb_meta + # Also pick up emb from non-output encoder nodes if PCA was in outputs + if sam_emb is None: + for block in ctx.node_outputs.values(): + if block.emb is not None: + sam_emb = block.emb + sam_meta = block.emb_meta + + if not float_parts: + raise ValueError("composition produced no channel outputs") + float_stack = np.concatenate(float_parts, axis=-1) + uint8_stack = uint8_from_float(float_stack) + return uint8_stack, float_stack, labels, sam_emb, sam_meta diff --git a/ipred/src/ipred/compositions.py b/ipred/src/ipred/compositions.py new file mode 100644 index 0000000..a3b6d70 --- /dev/null +++ b/ipred/src/ipred/compositions.py @@ -0,0 +1,407 @@ +"""Composition documents — ordered module graphs for feature banks.""" + +from __future__ import annotations + +import hashlib +import json +import logging +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +from ipred.catalog import Catalog +from ipred.modules import get_module, list_module_catalog +from ipred.paths import feature_models_root + +logger = logging.getLogger(__name__) + +KIND_COMPOSITION = "composition" + + +def compositions_root() -> Path: + """Directory for composition meta.json files.""" + root = feature_models_root() / "_compositions" + root.mkdir(parents=True, exist_ok=True) + return root + + +def content_hash(doc: dict[str, Any]) -> str: + """Stable hash of composition structure (excludes name/timestamps).""" + payload = { + "kind": KIND_COMPOSITION, + "nodes": doc.get("nodes"), + "outputs": doc.get("outputs"), + } + raw = json.dumps(payload, sort_keys=True, separators=(",", ":")) + return hashlib.sha1(raw.encode("utf-8")).hexdigest()[:16] + + +def _dir(comp_id: str) -> Path: + return compositions_root() / comp_id + + +def load_composition(comp_id: str) -> dict[str, Any] | None: + """Load composition meta or None.""" + path = _dir(comp_id) / "meta.json" + if not path.is_file(): + return None + return json.loads(path.read_text(encoding="utf-8")) + + +def list_compositions() -> list[dict[str, Any]]: + """List all compositions on disk.""" + root = compositions_root() + out: list[dict[str, Any]] = [] + for child in sorted(root.iterdir()) if root.is_dir() else []: + meta_path = child / "meta.json" + if not meta_path.is_file(): + continue + try: + out.append(json.loads(meta_path.read_text(encoding="utf-8"))) + except (OSError, json.JSONDecodeError): + continue + return out + + +def save_composition( + *, + name: str, + nodes: list[dict[str, Any]], + outputs: list[str], + composition_id: str | None = None, + builtin: bool = False, + catalog: Catalog | None = None, +) -> dict[str, Any]: + """Create or overwrite a composition document.""" + validate_composition({"nodes": nodes, "outputs": outputs}) + cid = composition_id or uuid.uuid4().hex + now = datetime.now(timezone.utc).isoformat() + existing = load_composition(cid) + created = existing.get("created_at") if existing else now + meta: dict[str, Any] = { + "id": cid, + "name": (name or cid).strip() or cid, + "kind": KIND_COMPOSITION, + "builtin": bool(builtin), + "nodes": list(nodes), + "outputs": list(outputs), + "created_at": created, + "updated_at": now, + } + meta["content_hash"] = content_hash(meta) + dest = _dir(cid) + dest.mkdir(parents=True, exist_ok=True) + (dest / "meta.json").write_text(json.dumps(meta, indent=2), encoding="utf-8") + if catalog is not None: + catalog.upsert_feature_setup( + setup_id=cid, + name=meta["name"], + kind=KIND_COMPOSITION, + content_hash=meta["content_hash"], + meta=meta, + ) + return meta + + +def resolve_composition(comp_id: str) -> dict[str, Any]: + """Load composition or raise KeyError.""" + meta = load_composition(comp_id) + if meta is None: + raise KeyError(f"unknown composition {comp_id}") + return meta + + +def validate_composition(doc: dict[str, Any]) -> None: + """Raise ValueError if nodes/outputs are invalid.""" + nodes = doc.get("nodes") or [] + outputs = doc.get("outputs") or [] + if not isinstance(nodes, list) or not nodes: + raise ValueError("composition requires at least one node") + ids = [n.get("id") for n in nodes] + if len(ids) != len(set(ids)): + raise ValueError("duplicate node ids") + for n in nodes: + mid = n.get("module") + if not mid: + raise ValueError(f"node {n.get('id')} missing module") + try: + get_module(str(mid)) + except KeyError as exc: + raise ValueError(str(exc)) from exc + src = n.get("input_from") + if src is not None and src not in ids: + raise ValueError(f"node {n.get('id')} input_from unknown: {src}") + for oid in outputs: + if oid not in ids: + raise ValueError(f"output node unknown: {oid}") + + +def preview_concat_labels(doc: dict[str, Any]) -> list[str]: + """Labels the bank would concatenate for ``outputs`` order.""" + validate_composition(doc) + by_id = {n["id"]: n for n in doc["nodes"]} + labels: list[str] = [] + for oid in doc.get("outputs") or []: + node = by_id[oid] + mod = get_module(str(node["module"])) + params = dict(node.get("params") or {}) + labels.extend(mod.preview_labels(params)) + return labels + + +def setup_id_to_composition_id(setup_id: str) -> str | None: + """Map legacy procedure setup ids → builtin composition ids.""" + return _LEGACY_SETUP_MAP.get(setup_id) + + +_LEGACY_SETUP_MAP: dict[str, str] = { + "default-skimage": "comp-skimage", + "default-skimage-slimsam": "comp-skimage-slimsam", + "default-slimsam-clahe": "comp-slimsam-clahe", + "default-skimage-mark25": "comp-skimage-mark25", + "default-mark25-clahe": "comp-mark25-clahe", + "default-skimage-mark11": "comp-skimage-mark11", + "default-mark11-clahe": "comp-mark11-clahe", + # weights-only → CLAHE companion compositions + "default-mark25": "comp-mark25-clahe", + "default-mark11": "comp-mark11-clahe", + "default-slimsam": "comp-skimage-slimsam", +} + + +def _skimage_params() -> dict[str, Any]: + return { + "sigma_min": 1.0, + "sigma_max": 8.0, + "intensity": True, + "edges": True, + "texture": True, + "clahe": True, + } + + +def ensure_default_compositions(catalog: Catalog | None = None) -> list[dict[str, Any]]: + """Create builtin compositions matching legacy procedure setups.""" + specs: list[tuple[str, str, list[dict[str, Any]], list[str]]] = [ + ( + "comp-skimage", + "Skimage multiscale", + [{"id": "n1", "module": "skimage_multiscale", "params": _skimage_params()}], + ["n1"], + ), + ( + "comp-skimage-slimsam", + "Skimage + SlimSAM", + [ + {"id": "n1", "module": "skimage_multiscale", "params": _skimage_params()}, + {"id": "n2", "module": "slimsam", "params": {}}, + { + "id": "n3", + "module": "pca", + "params": {"dims": 32}, + "input_from": "n2", + }, + ], + ["n1", "n3"], + ), + ( + "comp-slimsam-clahe", + "SlimSAM + CLAHE", + [ + { + "id": "n1", + "module": "clahe", + "params": { + "clahe": True, + "clip_limit": 0.01, + "include_in_bank": True, + }, + }, + { + "id": "n2", + "module": "slimsam", + "params": {}, + "input_from": "n1", + }, + { + "id": "n3", + "module": "pca", + "params": {"dims": 64}, + "input_from": "n2", + }, + ], + ["n1", "n3"], + ), + ( + "comp-skimage-mark25", + "Skimage + Mark25", + [ + { + "id": "n1", + "module": "skimage_multiscale", + "params": { + **_skimage_params(), + }, + }, + { + "id": "n2", + "module": "tomojepa", + "params": { + "weights_id": "mark25", + "input_size": 512, + "resize": True, + }, + }, + { + "id": "n3", + "module": "pca", + "params": {"dims": 64}, + "input_from": "n2", + }, + ], + ["n1", "n3"], + ), + ( + "comp-mark25-clahe", + "Mark25 + CLAHE", + [ + { + "id": "n1", + "module": "clahe", + "params": { + "clahe": True, + "clip_limit": 0.01, + "include_in_bank": True, + }, + }, + { + "id": "n2", + "module": "tomojepa", + "params": { + "weights_id": "mark25", + "input_size": 512, + "resize": True, + }, + "input_from": "n1", + }, + { + "id": "n3", + "module": "pca", + "params": {"dims": 64}, + "input_from": "n2", + }, + ], + ["n1", "n3"], + ), + ( + "comp-skimage-mark11", + "Skimage + Mark11", + [ + {"id": "n1", "module": "skimage_multiscale", "params": _skimage_params()}, + { + "id": "n2", + "module": "tomojepa", + "params": { + "weights_id": "mark11", + "input_size": 512, + "resize": True, + }, + }, + { + "id": "n3", + "module": "pca", + "params": {"dims": 64}, + "input_from": "n2", + }, + ], + ["n1", "n3"], + ), + ( + "comp-mark11-clahe", + "Mark11 + CLAHE", + [ + { + "id": "n1", + "module": "clahe", + "params": { + "clahe": True, + "clip_limit": 0.01, + "include_in_bank": True, + }, + }, + { + "id": "n2", + "module": "tomojepa", + "params": { + "weights_id": "mark11", + "input_size": 512, + "resize": True, + }, + "input_from": "n1", + }, + { + "id": "n3", + "module": "pca", + "params": {"dims": 64}, + "input_from": "n2", + }, + ], + ["n1", "n3"], + ), + ] + created: list[dict[str, Any]] = [] + for cid, name, nodes, outputs in specs: + existing = load_composition(cid) + if existing is None: + created.append( + save_composition( + name=name, + nodes=nodes, + outputs=outputs, + composition_id=cid, + builtin=True, + catalog=catalog, + ) + ) + else: + if catalog is not None: + catalog.upsert_feature_setup( + setup_id=cid, + name=existing["name"], + kind=KIND_COMPOSITION, + content_hash=existing.get("content_hash") + or content_hash(existing), + meta=existing, + ) + created.append(existing) + return created + + +def resolve_preprocess_id(setup_or_comp_id: str) -> str: + """Normalize legacy setup id or composition id to a composition id.""" + if load_composition(setup_or_comp_id) is not None: + return setup_or_comp_id + mapped = setup_id_to_composition_id(setup_or_comp_id) + if mapped and load_composition(mapped) is not None: + return mapped + # compositions might not be seeded yet + if mapped: + return mapped + raise KeyError(f"unknown composition or setup {setup_or_comp_id}") + + +__all__ = [ + "KIND_COMPOSITION", + "content_hash", + "ensure_default_compositions", + "list_compositions", + "list_module_catalog", + "load_composition", + "preview_concat_labels", + "resolve_composition", + "resolve_preprocess_id", + "save_composition", + "setup_id_to_composition_id", + "validate_composition", +] diff --git a/ipred/src/ipred/conformal.py b/ipred/src/ipred/conformal.py new file mode 100644 index 0000000..fb282ea --- /dev/null +++ b/ipred/src/ipred/conformal.py @@ -0,0 +1,95 @@ +"""Mondrian split-conformal thresholds and maps from cached proba.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np + +STATUS_ABSTAIN = 0 +STATUS_SINGLETON = 1 +STATUS_MULTI = 2 + + +def conformal_quantile(scores: np.ndarray, alpha: float) -> float: + """Finite-sample split-conformal quantile.""" + s = np.sort(np.asarray(scores, dtype=np.float64).ravel()) + n = s.size + if n == 0: + return 1.0 + if not 0.0 < alpha < 1.0: + raise ValueError("alpha must be in (0, 1)") + k = int(np.ceil((n + 1) * (1.0 - alpha))) + k = min(max(k, 1), n) + return float(s[k - 1]) + + +def mondrian_thresholds( + cal_scores_by_class: dict[int, np.ndarray], + alpha: float, +) -> dict[int, float]: + """Per-class conformal thresholds q_y(alpha).""" + return { + int(c): conformal_quantile(scores, alpha) + for c, scores in cal_scores_by_class.items() + } + + +def maps_from_proba( + proba: np.ndarray, + class_ids: list[int], + q_by_class: dict[int, float], +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Build commit, status, and packed membership from HxWxK proba. + + Returns: + commit (HxW uint8), status (HxW uint8), membership_bits (HxW uint32) + where bit i means class_ids[i] is in the prediction set. + """ + if proba.ndim != 3: + raise ValueError("proba must be HxWxK") + h, w, k = proba.shape + if k != len(class_ids): + raise ValueError("proba K must match class_ids length") + thr_prob = {cid: 1.0 - q for cid, q in q_by_class.items()} + in_set = np.zeros((h, w, k), dtype=bool) + for j, cid in enumerate(class_ids): + t = thr_prob.get(int(cid)) + if t is None: + continue + in_set[..., j] = proba[..., j] >= t + + set_sizes = in_set.sum(axis=2) + status = np.full((h, w), STATUS_ABSTAIN, dtype=np.uint8) + status[set_sizes == 1] = STATUS_SINGLETON + status[set_sizes > 1] = STATUS_MULTI + + commit = np.zeros((h, w), dtype=np.uint8) + singleton = set_sizes == 1 + if np.any(singleton): + cols = np.argmax(in_set[singleton], axis=1) + classes = np.asarray(class_ids, dtype=np.uint8) + commit[singleton] = classes[cols] + + membership = np.zeros((h, w), dtype=np.uint32) + for j in range(min(k, 32)): + membership |= in_set[..., j].astype(np.uint32) << j + + return commit, status, membership + + +def counts_from_status(status: np.ndarray) -> dict[str, int]: + """Count abstain / singleton / multi pixels.""" + return { + "singleton": int(np.sum(status == STATUS_SINGLETON)), + "multi": int(np.sum(status == STATUS_MULTI)), + "abstain": int(np.sum(status == STATUS_ABSTAIN)), + } + + +def cal_scores_from_json(raw: dict[str, Any]) -> dict[int, np.ndarray]: + """Parse cal_scores.json into numpy arrays.""" + out: dict[int, np.ndarray] = {} + for k, v in raw.items(): + out[int(k)] = np.asarray(v, dtype=np.float64) + return out diff --git a/ipred/src/ipred/feature_setups.py b/ipred/src/ipred/feature_setups.py new file mode 100644 index 0000000..365b02c --- /dev/null +++ b/ipred/src/ipred/feature_setups.py @@ -0,0 +1,416 @@ +"""Feature Setup shelf — procedure or weights configurations.""" + +from __future__ import annotations + +import hashlib +import json +import logging +import os +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +from ipred.catalog import Catalog +from ipred.paths import feature_models_root + +logger = logging.getLogger(__name__) + +PROCEDURE_SKIMAGE = "skimage_multiscale_v1" +PROCEDURE_CLAHE_ENCODER = "clahe_encoder_v1" +WEIGHTS_ONNX_VISION = "onnx_vision_encoder_v1" +WEIGHTS_TOMOJEPA_MARK25 = "torch_tomojepa_mark25_v1" +WEIGHTS_TOMOJEPA_MARK11 = "torch_tomojepa_mark11_v1" +TOMOJEPA_WEIGHTS_FORMATS = frozenset( + {WEIGHTS_TOMOJEPA_MARK25, WEIGHTS_TOMOJEPA_MARK11} +) + +DEFAULT_SKIMAGE_PARAMS: dict[str, Any] = { + "sigma_min": 1.0, + "sigma_max": 8.0, + "intensity": True, + "edges": True, + "texture": True, + "clahe": True, +} + +DEFAULT_CLAHE_ENCODER_PARAMS: dict[str, Any] = { + "clahe": True, + "clip_limit": 0.01, + "kernel_size": None, + "resize": True, + "input_size": 512, + "pca_dims": 64, +} + +DEFAULT_SKIMAGE_MARK25_PARAMS: dict[str, Any] = { + **DEFAULT_SKIMAGE_PARAMS, + "resize": True, + "input_size": 512, + "pca_dims": 64, +} + + +def content_hash(meta: dict[str, Any]) -> str: + """Stable hash of setup config (excludes timestamps / name display).""" + payload = { + "id": meta.get("id"), + "kind": meta.get("kind"), + "procedure_id": meta.get("procedure_id"), + "params": meta.get("params"), + "encoder_setup_id": meta.get("encoder_setup_id"), + "weights_path": meta.get("weights_path"), + "weights_format": meta.get("weights_format"), + "inference": meta.get("inference"), + } + raw = json.dumps(payload, sort_keys=True, separators=(",", ":")) + return hashlib.sha1(raw.encode("utf-8")).hexdigest()[:16] + + +def _dir(setup_id: str) -> Path: + return feature_models_root() / setup_id + + +def _write_meta(meta: dict[str, Any]) -> Path: + dest = _dir(meta["id"]) + dest.mkdir(parents=True, exist_ok=True) + path = dest / "meta.json" + path.write_text(json.dumps(meta, indent=2), encoding="utf-8") + return path + + +def load_setup(setup_id: str) -> dict[str, Any] | None: + """Load a setup meta.json or None.""" + path = _dir(setup_id) / "meta.json" + if not path.is_file(): + return None + return json.loads(path.read_text(encoding="utf-8")) + + +def list_setups() -> list[dict[str, Any]]: + """List all Feature Setups on disk.""" + root = feature_models_root() + out: list[dict[str, Any]] = [] + for child in sorted(root.iterdir()) if root.is_dir() else []: + meta_path = child / "meta.json" + if not meta_path.is_file(): + continue + try: + meta = json.loads(meta_path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + continue + out.append(meta) + return out + + +def save_setup( + *, + name: str, + kind: str, + procedure_id: str | None = None, + params: dict[str, Any] | None = None, + encoder_setup_id: str | None = None, + weights_path: str | None = None, + weights_format: str | None = None, + inference: dict[str, Any] | None = None, + setup_id: str | None = None, + builtin: bool = False, + catalog: Catalog | None = None, +) -> dict[str, Any]: + """Create or overwrite a Feature Setup.""" + if kind not in ("procedure", "weights"): + raise ValueError("kind must be 'procedure' or 'weights'") + sid = setup_id or uuid.uuid4().hex + now = datetime.now(timezone.utc).isoformat() + existing = load_setup(sid) + created = existing.get("created_at") if existing else now + meta: dict[str, Any] = { + "id": sid, + "name": (name or sid).strip() or sid, + "kind": kind, + "builtin": bool(builtin), + "created_at": created, + "updated_at": now, + } + if kind == "procedure": + if not procedure_id: + raise ValueError("procedure setups require procedure_id") + meta["procedure_id"] = procedure_id + if params is not None: + meta["params"] = dict(params) + elif procedure_id == PROCEDURE_CLAHE_ENCODER: + meta["params"] = dict(DEFAULT_CLAHE_ENCODER_PARAMS) + else: + meta["params"] = dict(DEFAULT_SKIMAGE_PARAMS) + if encoder_setup_id: + meta["encoder_setup_id"] = encoder_setup_id + else: + if not weights_path: + raise ValueError("weights setups require weights_path") + meta["weights_path"] = weights_path + meta["weights_format"] = weights_format or WEIGHTS_ONNX_VISION + meta["inference"] = dict(inference or {}) + + meta["content_hash"] = content_hash(meta) + _write_meta(meta) + if catalog is not None: + catalog.upsert_feature_setup( + setup_id=sid, + name=meta["name"], + kind=kind, + content_hash=meta["content_hash"], + meta=meta, + ) + return meta + + +def resolve_slimsam_weights_path() -> str | None: + """Locate SlimSAM ONNX; honors FEATURE_ENCODER_ONNX.""" + env = os.getenv("FEATURE_ENCODER_ONNX") + if env: + p = Path(env).expanduser().resolve() + if p.is_file(): + return str(p) + # Prefer repo-adjacent locations without importing annotate backend. + here = Path(__file__).resolve() + repo = here.parents[3] # .../repo/ipred/src/ipred → repo + candidates = [ + repo / "backend" / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx", + repo / "frontend" / "public" / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx", + ] + for c in candidates: + if c.is_file(): + return str(c.resolve()) + return None + + +def resolve_tomojepa_weights_path( + filename: str = "tomojepa25.pth", + *, + env_var: str | None = "TOMOJEPA_WEIGHTS", +) -> str | None: + """Locate a TomoJEPA ``.pth``; honors ``env_var`` when set.""" + if env_var: + env = os.getenv(env_var) + if env: + p = Path(env).expanduser().resolve() + if p.is_file(): + return str(p) + here = Path(__file__).resolve() + candidates = [ + here.parents[2] / "models" / filename, + here.parents[3] / "ipred" / "models" / filename, + ] + for c in candidates: + if c.is_file(): + return str(c.resolve()) + return None + + +def _upsert_existing(meta: dict[str, Any], catalog: Catalog | None) -> None: + if catalog is None: + return + catalog.upsert_feature_setup( + setup_id=meta["id"], + name=meta["name"], + kind=meta["kind"], + content_hash=meta.get("content_hash") or content_hash(meta), + meta=meta, + ) + + +def _ensure_procedure_with_param_backfill( + *, + setup_id: str, + name: str, + procedure_id: str, + params: dict[str, Any], + encoder_setup_id: str | None, + catalog: Catalog | None, +) -> dict[str, Any]: + """Create procedure setup or backfill missing default params.""" + existing = load_setup(setup_id) + if existing is None: + return save_setup( + setup_id=setup_id, + name=name, + kind="procedure", + procedure_id=procedure_id, + params=dict(params), + encoder_setup_id=encoder_setup_id, + builtin=True, + catalog=catalog, + ) + cur = dict(existing.get("params") or {}) + changed = False + for key, val in params.items(): + if key not in cur: + cur[key] = val + changed = True + if changed: + return save_setup( + setup_id=setup_id, + name=existing.get("name") or name, + kind="procedure", + procedure_id=procedure_id, + params=cur, + encoder_setup_id=existing.get("encoder_setup_id") or encoder_setup_id, + builtin=True, + catalog=catalog, + ) + _upsert_existing(existing, catalog) + return existing + + +def _ensure_tomojepa_variant( + catalog: Catalog | None, + *, + weights_id: str, + weights_name: str, + combo_id: str, + combo_name: str, + clahe_id: str, + clahe_name: str, + filename: str, + env_var: str, + weights_format: str, +) -> list[dict[str, Any]]: + """Weights + skimage combo + CLAHE procedure for one TomoJEPA checkpoint.""" + out: list[dict[str, Any]] = [] + tomo_path = resolve_tomojepa_weights_path(filename, env_var=env_var) or "" + weights = load_setup(weights_id) + if weights is None: + weights = save_setup( + setup_id=weights_id, + name=weights_name, + kind="weights", + weights_path=tomo_path or "(missing)", + weights_format=weights_format, + inference={"input_size": 512, "pca_dims": 32}, + builtin=True, + catalog=catalog, + ) + else: + _upsert_existing(weights, catalog) + out.append(weights) + + out.append( + _ensure_procedure_with_param_backfill( + setup_id=combo_id, + name=combo_name, + procedure_id=PROCEDURE_SKIMAGE, + params=dict(DEFAULT_SKIMAGE_MARK25_PARAMS), + encoder_setup_id=weights_id, + catalog=catalog, + ) + ) + out.append( + _ensure_procedure_with_param_backfill( + setup_id=clahe_id, + name=clahe_name, + procedure_id=PROCEDURE_CLAHE_ENCODER, + params=dict(DEFAULT_CLAHE_ENCODER_PARAMS), + encoder_setup_id=weights_id, + catalog=catalog, + ) + ) + return out + + +def ensure_default_setups(catalog: Catalog | None = None) -> list[dict[str, Any]]: + """Create built-in setups if missing; return all defaults.""" + created: list[dict[str, Any]] = [] + sk = load_setup("default-skimage") + if sk is None: + sk = save_setup( + setup_id="default-skimage", + name="Default skimage multiscale", + kind="procedure", + procedure_id=PROCEDURE_SKIMAGE, + params=dict(DEFAULT_SKIMAGE_PARAMS), + builtin=True, + catalog=catalog, + ) + else: + _upsert_existing(sk, catalog) + created.append(sk) + + weights_path = resolve_slimsam_weights_path() or "" + slim = load_setup("default-slimsam") + if slim is None: + slim = save_setup( + setup_id="default-slimsam", + name="Default SlimSAM encoder", + kind="weights", + weights_path=weights_path or "(missing)", + weights_format=WEIGHTS_ONNX_VISION, + inference={"input_size": 1024, "pca_dims": 32}, + builtin=True, + catalog=catalog, + ) + else: + _upsert_existing(slim, catalog) + created.append(slim) + + combo = load_setup("default-skimage-slimsam") + if combo is None: + combo = save_setup( + setup_id="default-skimage-slimsam", + name="Default skimage + SlimSAM", + kind="procedure", + procedure_id=PROCEDURE_SKIMAGE, + params=dict(DEFAULT_SKIMAGE_PARAMS), + encoder_setup_id="default-slimsam", + builtin=True, + catalog=catalog, + ) + else: + _upsert_existing(combo, catalog) + created.append(combo) + + created.append( + _ensure_procedure_with_param_backfill( + setup_id="default-slimsam-clahe", + name="Default SlimSAM + CLAHE", + procedure_id=PROCEDURE_CLAHE_ENCODER, + params=dict(DEFAULT_CLAHE_ENCODER_PARAMS), + encoder_setup_id="default-slimsam", + catalog=catalog, + ) + ) + + for variant in ( + { + "weights_id": "default-mark25", + "weights_name": "Default Mark25 TomoJEPA encoder", + "combo_id": "default-skimage-mark25", + "combo_name": "Default skimage + Mark25", + "clahe_id": "default-mark25-clahe", + "clahe_name": "Default Mark25 + CLAHE", + "filename": "tomojepa25.pth", + "env_var": "TOMOJEPA_WEIGHTS", + "weights_format": WEIGHTS_TOMOJEPA_MARK25, + }, + { + "weights_id": "default-mark11", + "weights_name": "Default Mark11 TomoJEPA encoder", + "combo_id": "default-skimage-mark11", + "combo_name": "Default skimage + Mark11", + "clahe_id": "default-mark11-clahe", + "clahe_name": "Default Mark11 + CLAHE", + "filename": "tomojepa11.pth", + "env_var": "TOMOJEPA11_WEIGHTS", + "weights_format": WEIGHTS_TOMOJEPA_MARK11, + }, + ): + created.extend(_ensure_tomojepa_variant(catalog, **variant)) + + return created + + +def resolve_setup(setup_id: str) -> dict[str, Any]: + """Load setup or raise KeyError.""" + meta = load_setup(setup_id) + if meta is None: + raise KeyError(f"unknown feature setup {setup_id}") + return meta diff --git a/ipred/src/ipred/features.py b/ipred/src/ipred/features.py new file mode 100644 index 0000000..faf185b --- /dev/null +++ b/ipred/src/ipred/features.py @@ -0,0 +1,180 @@ +"""Multiscale feature computation (ported; no annotate-backend imports).""" + +from __future__ import annotations + +from io import BytesIO + +import numpy as np +from PIL import Image as PILImage +from skimage.exposure import equalize_adapthist +from skimage.feature import multiscale_basic_features + + +def list_sigmas(sigma_min: float, sigma_max: float, num_sigma: int | None = None) -> np.ndarray: + """Return the σ grid used by multiscale_basic_features.""" + if sigma_min <= 0 or sigma_max < sigma_min: + raise ValueError(f"invalid sigma range [{sigma_min}, {sigma_max}]") + if num_sigma is None: + num_sigma = int(np.log2(sigma_max) - np.log2(sigma_min) + 1) + return np.logspace( + np.log2(sigma_min), + np.log2(sigma_max), + num=int(num_sigma), + base=2, + endpoint=True, + ) + + +def _fmt_sigma(sigma: float) -> str: + if abs(sigma - round(sigma)) < 1e-6: + return str(int(round(sigma))) + return f"{sigma:g}" + + +def feature_channel_labels( + *, + sigma_min: float = 1.0, + sigma_max: float = 8.0, + intensity: bool = True, + edges: bool = True, + texture: bool = True, + num_sigma: int | None = None, +) -> list[str]: + """Human-readable labels matching skimage per-σ order.""" + if not any((intensity, edges, texture)): + raise ValueError("at least one of intensity, edges, texture must be True") + labels: list[str] = [] + for sigma in list_sigmas(sigma_min, sigma_max, num_sigma): + s = _fmt_sigma(float(sigma)) + if intensity: + labels.append(f"intensity σ={s}") + if edges: + labels.append(f"edges σ={s}") + if texture: + labels.append(f"texture λ− σ={s}") + labels.append(f"texture λ+ σ={s}") + return labels + + +def to_grayscale(arr: np.ndarray) -> np.ndarray: + """Convert a slice to float grayscale in [0, 1].""" + a = np.asarray(arr) + if a.ndim == 3 and a.shape[-1] in (3, 4): + rgb = a[..., :3].astype(np.float64) + gray = 0.299 * rgb[..., 0] + 0.587 * rgb[..., 1] + 0.114 * rgb[..., 2] + elif a.ndim == 2: + gray = a.astype(np.float64) + else: + raise ValueError(f"unsupported array shape {a.shape}") + finite = gray[np.isfinite(gray)] + if finite.size == 0: + return np.zeros_like(gray, dtype=np.float64) + lo, hi = float(np.min(finite)), float(np.max(finite)) + if hi <= lo: + return np.zeros_like(gray, dtype=np.float64) + return np.clip((gray - lo) / (hi - lo), 0.0, 1.0) + + +def _float_feats_to_uint8(feats: np.ndarray, *, clahe: bool) -> np.ndarray: + """Convert float HxWxC to display uint8.""" + h, w, c = feats.shape + out = np.empty((h, w, c), dtype=np.uint8) + for i in range(c): + ch = feats[..., i].astype(np.float64) + lo, hi = float(np.nanmin(ch)), float(np.nanmax(ch)) + norm = (ch - lo) / (hi - lo) if hi > lo else np.zeros_like(ch) + if clahe: + norm = equalize_adapthist(np.clip(norm, 0.0, 1.0), clip_limit=0.01) + out[..., i] = np.clip(np.round(norm * 255.0), 0, 255).astype(np.uint8) + return out + + +def apply_clahe( + gray: np.ndarray, + *, + clip_limit: float = 0.01, + kernel_size: int | None = None, +) -> np.ndarray: + """CLAHE on a [0, 1] grayscale image; returns float32 in [0, 1].""" + g = np.clip(np.asarray(gray, dtype=np.float64), 0.0, 1.0) + kwargs: dict = {"clip_limit": float(clip_limit)} + if kernel_size is not None and int(kernel_size) > 0: + kwargs["kernel_size"] = int(kernel_size) + out = equalize_adapthist(g, **kwargs) + return np.asarray(out, dtype=np.float32) + + +def compute_clahe_stack( + gray: np.ndarray, + *, + clip_limit: float = 0.01, + kernel_size: int | None = None, + apply: bool = True, +) -> tuple[np.ndarray, np.ndarray, list[str]]: + """Return single-channel ``(uint8_stack, float_stack, labels)`` with optional CLAHE. + + Args: + gray: Grayscale float image in ``[0, 1]``. + clip_limit: skimage ``equalize_adapthist`` clip limit. + kernel_size: Optional CLAHE tile size; ``None`` uses skimage default. + apply: When False, store min-max gray only (no CLAHE). + """ + g = np.asarray(gray, dtype=np.float32) + if apply: + ch = apply_clahe(g, clip_limit=clip_limit, kernel_size=kernel_size) + label = "clahe" + else: + ch = np.clip(g, 0.0, 1.0).astype(np.float32) + label = "intensity" + float_stack = ch[..., None] + uint8_stack = np.clip(np.round(float_stack * 255.0), 0, 255).astype(np.uint8) + return uint8_stack, float_stack, [label] + + +def compute_feature_stacks( + gray: np.ndarray, + *, + sigma_min: float = 1.0, + sigma_max: float = 8.0, + intensity: bool = True, + edges: bool = True, + texture: bool = True, + clahe: bool = True, + num_sigma: int | None = None, +) -> tuple[np.ndarray, np.ndarray, list[str]]: + """Return ``(uint8_stack, float_stack, labels)``.""" + labels = feature_channel_labels( + sigma_min=sigma_min, + sigma_max=sigma_max, + intensity=intensity, + edges=edges, + texture=texture, + num_sigma=num_sigma, + ) + feats = multiscale_basic_features( + np.asarray(gray, dtype=np.float32), + intensity=intensity, + edges=edges, + texture=texture, + sigma_min=sigma_min, + sigma_max=sigma_max, + num_sigma=num_sigma, + workers=1, + ) + if feats.shape[-1] != len(labels): + raise RuntimeError( + f"feature count mismatch: got {feats.shape[-1]}, expected {len(labels)}" + ) + float_stack = np.nan_to_num(feats.astype(np.float32), nan=0.0, posinf=0.0, neginf=0.0) + uint8_stack = _float_feats_to_uint8(float_stack, clahe=clahe) + return uint8_stack, float_stack, labels + + +def encode_channel_png(stack: np.ndarray, index: int) -> bytes: + """Encode one feature channel as grayscale PNG.""" + if index < 0 or index >= stack.shape[-1]: + raise IndexError(f"channel index {index} out of range") + img = PILImage.fromarray(stack[..., index], mode="L") + buf = BytesIO() + img.save(buf, format="PNG") + return buf.getvalue() diff --git a/ipred/src/ipred/labels.py b/ipred/src/ipred/labels.py new file mode 100644 index 0000000..752c745 --- /dev/null +++ b/ipred/src/ipred/labels.py @@ -0,0 +1,136 @@ +"""Rasterize annotation shapes to a sparse label map (self-contained).""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +from PIL import Image as PILImage +from PIL import ImageDraw + + +def build_label_map(shapes: list[dict[str, Any]], height: int, width: int) -> np.ndarray: + """Compose uint8 label map (0 = unlabeled); last shape wins.""" + labels = np.zeros((height, width), dtype=np.uint8) + for shape in shapes: + class_id = int(shape.get("classId") or shape.get("class_id") or 0) + if class_id <= 0 or class_id > 255: + continue + mask = shape_to_mask(shape, height, width) + labels[mask] = class_id + return labels + + +def shape_to_mask(shape: dict[str, Any], h: int, w: int) -> np.ndarray: + """Rasterize one shape to boolean mask.""" + kind = shape.get("kind") + if kind == "rectangle": + return _rect_mask( + float(shape["x"]), + float(shape["y"]), + float(shape["w"]), + float(shape["h"]), + h, + w, + ) + if kind == "ellipse": + return _ellipse_mask( + float(shape["cx"]), + float(shape["cy"]), + float(shape["rx"]), + float(shape["ry"]), + h, + w, + ) + if kind == "polygon": + return _polygon_mask(shape.get("points") or [], h, w) + if kind == "brush": + return _brush_mask(shape.get("strokes") or [], h, w) + raise ValueError(f"Unknown shape kind: {kind!r}") + + +def _rect_mask(x: float, y: float, ww: float, hh: float, h: int, w: int) -> np.ndarray: + x0 = max(0, int(np.floor(min(x, x + ww)))) + x1 = min(w, int(np.ceil(max(x, x + ww)))) + y0 = max(0, int(np.floor(min(y, y + hh)))) + y1 = min(h, int(np.ceil(max(y, y + hh)))) + mask = np.zeros((h, w), dtype=bool) + if x1 > x0 and y1 > y0: + mask[y0:y1, x0:x1] = True + return mask + + +def _ellipse_mask( + cx: float, cy: float, rx: float, ry: float, h: int, w: int +) -> np.ndarray: + yy, xx = np.ogrid[:h, :w] + rx = max(rx, 1e-6) + ry = max(ry, 1e-6) + return ((xx - cx) / rx) ** 2 + ((yy - cy) / ry) ** 2 <= 1.0 + + +def _xy_pairs(points: list[Any]) -> list[tuple[float, float]]: + """Normalize studio point payloads to ``(x, y)`` pairs. + + Studio wire format for polygons/brushes is a flat ``[x0, y0, x1, y1, ...]`` + float list (same as coco_export). Nested ``[[x,y], ...]`` and ``{x,y}`` + dicts are also accepted. + """ + if not points: + return [] + if all(isinstance(p, (int, float)) for p in points): + if len(points) < 2: + return [] + return [ + (float(points[i]), float(points[i + 1])) + for i in range(0, len(points) - 1, 2) + ] + xy: list[tuple[float, float]] = [] + for p in points: + if isinstance(p, (list, tuple)) and len(p) >= 2: + xy.append((float(p[0]), float(p[1]))) + elif isinstance(p, dict): + xy.append((float(p["x"]), float(p["y"]))) + return xy + + +def _polygon_mask(points: list[Any], h: int, w: int) -> np.ndarray: + xy = _xy_pairs(points) + if len(xy) < 3: + return np.zeros((h, w), dtype=bool) + img = PILImage.new("L", (w, h), 0) + draw = ImageDraw.Draw(img) + draw.polygon(xy, outline=1, fill=1) + return np.asarray(img, dtype=np.uint8) > 0 + + +def _brush_mask(strokes: list[dict[str, Any]], h: int, w: int) -> np.ndarray: + """Paint OR / erase AND-NOT along stroke points.""" + mask = np.zeros((h, w), dtype=bool) + for stroke in strokes: + pts = stroke.get("points") or [] + xy = _xy_pairs(pts) + if not xy: + continue + # Studio stamps use radius directly; legacy payloads may send diameter as size. + if stroke.get("radius") is not None: + radius = max(1, int(round(float(stroke["radius"])))) + else: + radius = max(1, int(round(float(stroke.get("size") or 3) / 2))) + mode = str(stroke.get("mode") or stroke.get("type") or "paint") + erase = mode in ("erase", "eraser") + layer = PILImage.new("L", (w, h), 0) + ld = ImageDraw.Draw(layer) + if len(xy) == 1: + x, y = xy[0] + ld.ellipse((x - radius, y - radius, x + radius, y + radius), fill=1) + else: + ld.line(xy, fill=1, width=max(1, radius * 2)) + for x, y in xy: + ld.ellipse((x - radius, y - radius, x + radius, y + radius), fill=1) + layer_m = np.asarray(layer, dtype=np.uint8) > 0 + if erase: + mask &= ~layer_m + else: + mask |= layer_m + return mask diff --git a/ipred/src/ipred/manifold.py b/ipred/src/ipred/manifold.py new file mode 100644 index 0000000..56cafdd --- /dev/null +++ b/ipred/src/ipred/manifold.py @@ -0,0 +1,359 @@ +"""Greedy feature-variance box sampling for annotation guidance. + +Sliding windows scored by whitened feature variance; picks suppress nearby +(spatial Chebyshev exclusion ≥ box side) and feature-similar boxes. +""" + +from __future__ import annotations + +import logging +import math +import uuid +from dataclasses import dataclass +from io import BytesIO +from typing import Any + +import numpy as np +from PIL import Image as PILImage +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler + +from ipred.cache import TTLCache + +logger = logging.getLogger(__name__) + +_sample_cache: TTLCache = TTLCache(ttl_seconds=600.0, max_entries=32) +_COS_HARD = 0.95 +_COS_ALPHA = 2.0 + + +@dataclass(frozen=True) +class ManifoldSample: + """Inducing boxes + residual interestingness heatmap.""" + + job_id: str + points: list[dict[str, Any]] + heatmap: np.ndarray # HxW float32 in [0, 1] + meta: dict[str, Any] + + +@dataclass(frozen=True) +class CachedManifoldSample: + """Server-side manifold sample for PNG GET.""" + + sample_id: str + heatmap_png: bytes + points: list[dict[str, Any]] + meta: dict[str, Any] + + +def derived_exclusion_radius(h: int, w: int, k: int) -> float: + """Disk radius so roughly K disks pack the image plane.""" + k = max(1, int(k)) + area = float(h * w) + r = math.sqrt(area / (math.pi * k)) + r_max = min(h, w) / 4.0 + return float(max(8.0, min(r, r_max))) + + +def sample_inducing_points( + float_stack: np.ndarray, + *, + feature_id: str, + k: int = 24, + box_size: int | None = None, + stride: int | None = None, + pca_dims: int = 16, + seed: int = 0, + mask: np.ndarray | None = None, +) -> ManifoldSample: + """Greedy variance boxes with spatial + feature-space exclusion. + + Args: + float_stack: Feature bank HxWxC. + feature_id: Owning feature bank id (stored on the sample). + k: Number of inducing boxes (clamped to [2, 128]). + box_size: Full square side length in image pixels. When omitted, derived + from the K-based exclusion radius (``2 * round(r)``). + stride: Optional hop override; default ``max(4, box_half)``. + pca_dims: PCA dimensionality after standardization. + seed: RNG for PCA solver stability. + mask: Optional HxW placement mask. When set, only windows whose full + box lies inside the mask are eligible. + + Returns: + ManifoldSample with box centers, boxes, radius, and residual heatmap. + """ + feats = np.asarray(float_stack, dtype=np.float32) + if feats.ndim != 3: + raise ValueError(f"float_stack must be HxWxC, got {feats.shape}") + h, w, c = feats.shape + if c < 1: + raise ValueError("empty feature stack") + + place_mask: np.ndarray | None = None + mask_pixels = 0 + if mask is not None: + m = np.asarray(mask) + if m.shape != (h, w): + raise ValueError(f"mask shape {m.shape} does not match image {(h, w)}") + place_mask = m.astype(bool, copy=False) + mask_pixels = int(place_mask.sum()) + if mask_pixels < 1: + raise ValueError("placement mask is empty") + + k = int(max(2, min(128, k))) + r_pack = derived_exclusion_radius(h, w, k) + if box_size is None: + side = int(max(8, 2 * round(r_pack))) + else: + side = int(box_size) + # Clamp: at least 8px side, at most half the short image edge + side = int(max(8, min(side, min(h, w) // 2 * 2))) # even-ish via floor + if side % 2 == 1: + side += 1 + b = max(4, side // 2) # box half-size + # Spatial exclusion in Chebyshev (L∞) distance so axis-aligned boxes do + # not overlap: centers must be ≥ side+1 apart (1px gap for stroke). + # Also respect K-packing floor. + r = float(max(r_pack, side + 1)) + hop = int(stride) if stride is not None else int(max(4, b)) + + # Coarse feature grid for PCA fit + window stats + grid_stride = max(2, hop // 2) + gy = np.arange(0, h, grid_stride, dtype=np.int32) + gx = np.arange(0, w, grid_stride, dtype=np.int32) + yy, xx = np.meshgrid(gy, gx, indexing="ij") + flat_y = yy.ravel() + flat_x = xx.ravel() + X = feats[flat_y, flat_x, :].astype(np.float64) + X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0) + n = X.shape[0] + if n < 4: + raise ValueError("too few pixels to score boxes") + + scaler = StandardScaler() + Xs = scaler.fit_transform(X) + d = int(max(1, min(pca_dims, c, n - 1, 16))) + pca = PCA(n_components=d, random_state=seed) + Z = pca.fit_transform(Xs) + explained = float(np.sum(pca.explained_variance_ratio_)) + + # Map (y,x) -> Z row via nearest grid sample (for window aggregation) + # Store Z as grid gh×gw×d + gh, gw = len(gy), len(gx) + Z_grid = Z.reshape(gh, gw, d) + + # Sliding window centers on hop grid + cy = np.arange(b, h - b + 1, hop, dtype=np.int32) + cx = np.arange(b, w - b + 1, hop, dtype=np.int32) + if cy.size == 0: + cy = np.array([h // 2], dtype=np.int32) + if cx.size == 0: + cx = np.array([w // 2], dtype=np.int32) + win_yy, win_xx = np.meshgrid(cy, cx, indexing="ij") + centers_y = win_yy.ravel() + centers_x = win_xx.ravel() + n_win = int(centers_y.size) + + scores = np.zeros(n_win, dtype=np.float64) + means = np.zeros((n_win, d), dtype=np.float64) + + for i in range(n_win): + y0 = int(centers_y[i] - b) + y1 = int(centers_y[i] + b) + x0 = int(centers_x[i] - b) + x1 = int(centers_x[i] + b) + # Grid indices covering the box + gi0 = max(0, (y0 + grid_stride - 1) // grid_stride) + gi1 = min(gh, (y1 // grid_stride) + 1) + gj0 = max(0, (x0 + grid_stride - 1) // grid_stride) + gj1 = min(gw, (x1 // grid_stride) + 1) + patch = Z_grid[gi0:gi1, gj0:gj1, :].reshape(-1, d) + if patch.shape[0] < 2: + continue + means[i] = patch.mean(axis=0) + # tr(Cov) = sum of feature variances + scores[i] = float(np.var(patch, axis=0).sum()) + + residual = scores.copy() + n_windows_in_mask = n_win + if place_mask is not None: + for i in range(n_win): + cx_i = int(centers_x[i]) + cy_i = int(centers_y[i]) + x0 = max(0, cx_i - b) + y0 = max(0, cy_i - b) + x1 = min(w, cx_i + b) + y1 = min(h, cy_i + b) + if x1 <= x0 or y1 <= y0 or not bool(place_mask[y0:y1, x0:x1].all()): + residual[i] = 0.0 + scores[i] = 0.0 + n_windows_in_mask = int(np.count_nonzero(residual > 0)) + if n_windows_in_mask < 1: + raise ValueError( + "no candidate boxes fit fully inside the placement mask; " + "draw a larger mask or reduce box size" + ) + + points: list[dict[str, Any]] = [] + + for pick_i in range(k): + if not np.any(residual > 0): + break + i_star = int(np.argmax(residual)) + score = float(residual[i_star]) + if score <= 0: + break + cx_i = int(centers_x[i_star]) + cy_i = int(centers_y[i_star]) + mu = means[i_star] + mu_norm = float(np.linalg.norm(mu)) + x0 = max(0, cx_i - b) + y0 = max(0, cy_i - b) + # Exclusive max so Konva/CSS width = x1 - x0 equals 2*b when unclipped + x1 = min(w, cx_i + b) + y1 = min(h, cy_i + b) + points.append( + { + "x": cx_i, + "y": cy_i, + "cluster": pick_i, + "score": score, + "radius": float(r), + "box_size": int(2 * b), + "box": {"x0": x0, "y0": y0, "x1": x1, "y1": y1}, + } + ) + residual[i_star] = 0.0 + + # Spatial (Chebyshev) + feature suppress. Exclude with ≥ side so + # axis-aligned boxes of width `side` do not share interior area. + for j in range(n_win): + if residual[j] <= 0: + continue + dx = abs(float(centers_x[j] - cx_i)) + dy = abs(float(centers_y[j] - cy_i)) + if max(dx, dy) < r: + residual[j] = 0.0 + continue + mj = means[j] + mj_norm = float(np.linalg.norm(mj)) + if mu_norm < 1e-12 or mj_norm < 1e-12: + continue + cos = float(np.dot(mu, mj) / (mu_norm * mj_norm)) + cos = max(0.0, cos) + if cos > _COS_HARD: + residual[j] = 0.0 + else: + residual[j] *= 1.0 - (cos ** _COS_ALPHA) + + # Hard guarantee: drop any pick whose AABB still overlaps an earlier one + # (protects against hop / clip / float edge cases). + points = _dedupe_overlapping_boxes(points) + + heatmap = _residual_heatmap( + h, w, centers_y, centers_x, residual, hop=hop, half=b + ) + + meta = { + "k": k, + "n_picked": len(points), + "n_windows": n_win, + "n_windows_in_mask": n_windows_in_mask, + "n_subsample": int(n), + "pca_dims": d, + "explained_variance": explained, + "stride": hop, + "radius": float(r), + "box_half": b, + "box_size": int(2 * b), + "mask_pixels": mask_pixels, + "has_mask": place_mask is not None, + } + return ManifoldSample(job_id=feature_id, points=points, heatmap=heatmap, meta=meta) + + +def _boxes_overlap(a: dict[str, int], b: dict[str, int]) -> bool: + """True if two exclusive-end AABBs share interior area.""" + return a["x0"] < b["x1"] and b["x0"] < a["x1"] and a["y0"] < b["y1"] and b["y0"] < a["y1"] + + +def _dedupe_overlapping_boxes(points: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Keep greedy order; drop later picks whose box overlaps an earlier one.""" + kept: list[dict[str, Any]] = [] + for p in points: + box = p.get("box") + if not isinstance(box, dict): + kept.append(p) + continue + if any(_boxes_overlap(box, k["box"]) for k in kept if isinstance(k.get("box"), dict)): + continue + kept.append(p) + for i, p in enumerate(kept): + p["cluster"] = i + return kept + + +def _residual_heatmap( + h: int, + w: int, + centers_y: np.ndarray, + centers_x: np.ndarray, + residual: np.ndarray, + *, + hop: int, + half: int, +) -> np.ndarray: + """Paint residual scores onto a coarse grid and upsample to HxW.""" + out = np.zeros((h, w), dtype=np.float32) + max_s = float(np.max(residual)) if residual.size else 0.0 + if max_s < 1e-12: + return out + for i in range(residual.size): + if residual[i] <= 0: + continue + val = float(residual[i] / max_s) + cy = int(centers_y[i]) + cx = int(centers_x[i]) + y0 = max(0, cy - half) + y1 = min(h, cy + half) + x0 = max(0, cx - half) + x1 = min(w, cx + half) + # Take max so overlapping windows keep strongest residual + patch = out[y0:y1, x0:x1] + np.maximum(patch, val, out=patch) + return out + + +def encode_heatmap_png(heatmap: np.ndarray) -> bytes: + """Encode HxW float [0,1] residual map as grayscale PNG (0–255).""" + u8 = np.clip(np.asarray(heatmap, dtype=np.float32) * 255.0, 0, 255).astype(np.uint8) + img = PILImage.fromarray(u8, mode="L") + buf = BytesIO() + img.save(buf, format="PNG") + return buf.getvalue() + + +def store_sample(result: ManifoldSample) -> CachedManifoldSample: + """Cache heatmap PNG + points; return handle for GET.""" + sample_id = uuid.uuid4().hex + cached = CachedManifoldSample( + sample_id=sample_id, + heatmap_png=encode_heatmap_png(result.heatmap), + points=list(result.points), + meta={ + "sample_id": sample_id, + "job_id": result.job_id, + **result.meta, + "points": list(result.points), + }, + ) + _sample_cache.set(sample_id, cached) + return cached + + +def get_sample(sample_id: str) -> CachedManifoldSample | None: + """Look up a cached manifold sample.""" + s = _sample_cache.get(sample_id) + return s if isinstance(s, CachedManifoldSample) else None diff --git a/ipred/src/ipred/manifold_jobs.py b/ipred/src/ipred/manifold_jobs.py new file mode 100644 index 0000000..a806386 --- /dev/null +++ b/ipred/src/ipred/manifold_jobs.py @@ -0,0 +1,74 @@ +"""Orchestrate manifold suggest on persisted feature banks.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np + +from ipred import manifold +from ipred.catalog import Catalog +from ipred.labels import shape_to_mask +from ipred.preprocess import load_feature_bank_arrays + + +def run_manifold_sample( + catalog: Catalog, + *, + feature_id: str, + k: int = 24, + box_size: int | None = None, + stride: int | None = None, + pca_dims: int = 16, + shapes: list[dict[str, Any]] | None = None, +) -> dict[str, Any]: + """Sample inducing boxes on a feature bank; return API response dict.""" + row = catalog.get_feature_bank(feature_id) + if row is None: + raise KeyError(f"unknown feature bank {feature_id}") + bank = load_feature_bank_arrays(row["blob_dir"]) + float_stack = bank["float_stack"] + h, w = float_stack.shape[:2] + + place_mask = None + if shapes: + place_mask = np.zeros((h, w), dtype=bool) + for shape in shapes: + place_mask |= shape_to_mask(shape, h, w) + if not bool(place_mask.any()): + raise ValueError("placement mask is empty") + + result = manifold.sample_inducing_points( + float_stack, + feature_id=feature_id, + k=k, + box_size=box_size, + stride=stride, + pca_dims=pca_dims, + mask=place_mask, + ) + cached = manifold.store_sample(result) + return { + "sample_id": cached.sample_id, + "feature_id": feature_id, + "points": cached.points, + "k": result.meta["k"], + "n_picked": result.meta["n_picked"], + "n_subsample": result.meta["n_subsample"], + "pca_dims": result.meta["pca_dims"], + "explained_variance": result.meta["explained_variance"], + "stride": result.meta["stride"], + "radius": result.meta["radius"], + "box_size": result.meta["box_size"], + "mask_pixels": result.meta.get("mask_pixels", 0), + "n_windows_in_mask": result.meta.get("n_windows_in_mask"), + "has_mask": result.meta.get("has_mask", False), + } + + +def heatmap_png(sample_id: str) -> bytes: + """Return cached heatmap PNG bytes.""" + cached = manifold.get_sample(sample_id) + if cached is None: + raise KeyError(f"unknown manifold sample {sample_id}") + return cached.heatmap_png diff --git a/ipred/src/ipred/modules/__init__.py b/ipred/src/ipred/modules/__init__.py new file mode 100644 index 0000000..78937eb --- /dev/null +++ b/ipred/src/ipred/modules/__init__.py @@ -0,0 +1,73 @@ +"""Module registry — discover and list FeatureModules.""" + +from __future__ import annotations + +from typing import Any + +from ipred.modules.base import FeatureModule, ModuleMeta +from ipred.modules.clahe_mod import ClaheModule +from ipred.modules.pca_mod import PcaModule +from ipred.modules.skimage_mod import SkimageMultiscaleModule +from ipred.modules.slimsam_mod import SlimSamModule +from ipred.modules.tomojepa_mod import TomoJepaModule + +_REGISTRY: dict[str, FeatureModule] | None = None + + +def _build_registry() -> dict[str, FeatureModule]: + mods: list[FeatureModule] = [ + SkimageMultiscaleModule(), + ClaheModule(), + SlimSamModule(), + TomoJepaModule(), + PcaModule(), + ] + return {m.meta.id: m for m in mods} + + +def get_registry() -> dict[str, FeatureModule]: + """Return module id → instance map.""" + global _REGISTRY + if _REGISTRY is None: + _REGISTRY = _build_registry() + return _REGISTRY + + +def get_module(module_id: str) -> FeatureModule: + """Lookup module or raise KeyError.""" + reg = get_registry() + if module_id not in reg: + raise KeyError(f"unknown module {module_id}") + return reg[module_id] + + +def list_module_catalog() -> list[dict[str, Any]]: + """JSON-serializable catalog for GET /modules.""" + out: list[dict[str, Any]] = [] + for mod in get_registry().values(): + meta: ModuleMeta = mod.meta + # Prefer ONNX in catalog only when graph matches default input_size + runtime = meta.runtime + if meta.id == "tomojepa": + from ipred import tomojepa_onnx + + onnx = TomoJepaModule()._onnx_path({"weights_id": "mark25"}) + if ( + onnx is not None + and tomojepa_onnx.onnx_matches_input_size(onnx, 512) + ): + runtime = "onnx" + out.append( + { + "id": meta.id, + "name": meta.name, + "description": meta.description, + "runtime": runtime, + "ready": bool(mod.ready()), + "accepts_input_from": meta.accepts_input_from, + "produces_channels": meta.produces_channels, + "produces_embedding": meta.produces_embedding, + "params_schema": meta.params_schema, + } + ) + return out diff --git a/ipred/src/ipred/modules/base.py b/ipred/src/ipred/modules/base.py new file mode 100644 index 0000000..7e1e6e7 --- /dev/null +++ b/ipred/src/ipred/modules/base.py @@ -0,0 +1,65 @@ +"""Feature module protocol — typed channel producers for compositions.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Protocol + +import numpy as np + + +@dataclass +class ModuleMeta: + """Catalog entry for a feature module.""" + + id: str + name: str + description: str + runtime: str # onnx | torch | numpy + params_schema: dict[str, Any] = field(default_factory=dict) + accepts_input_from: bool = False + produces_channels: bool = True + produces_embedding: bool = False + + +@dataclass +class ChannelBlock: + """One module's contribution to the feature bank.""" + + float_stack: np.ndarray | None = None # HxWxC + labels: list[str] = field(default_factory=list) + emb: np.ndarray | None = None # Hp×Wp×D dense grid + emb_meta: dict[str, Any] | None = None + # Optional 2-D image for downstream encoder nodes (HxW float) + image_2d: np.ndarray | None = None + + +@dataclass +class ModuleContext: + """Shared state while running a composition on one slice.""" + + raw: np.ndarray + gray: np.ndarray + params: dict[str, Any] + # Outputs of nodes already run, keyed by node id + node_outputs: dict[str, ChannelBlock] = field(default_factory=dict) + # Resolved input image for modules that accept input_from + input_image: np.ndarray | None = None + + +class FeatureModule(Protocol): + """Runnable feature module.""" + + meta: ModuleMeta + + def ready(self) -> bool: + """True when dependencies/weights are available.""" + ... + + def run(self, ctx: ModuleContext) -> ChannelBlock: + """Produce channels and/or an embedding.""" + ... + + def preview_labels(self, params: dict[str, Any]) -> list[str]: + """Labels that would appear in the bank for these params (no compute).""" + ... diff --git a/ipred/src/ipred/modules/clahe_mod.py b/ipred/src/ipred/modules/clahe_mod.py new file mode 100644 index 0000000..cc823d4 --- /dev/null +++ b/ipred/src/ipred/modules/clahe_mod.py @@ -0,0 +1,55 @@ +"""CLAHE / intensity image module (feeds encoders or contributes a channel).""" + +from __future__ import annotations + +from typing import Any + +from ipred import features +from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta + + +class ClaheModule: + """Single-channel CLAHE (or raw intensity) plus image_2d for encoders.""" + + meta = ModuleMeta( + id="clahe", + name="CLAHE", + description="Adaptive histogram equalization → one channel + encoder input", + runtime="numpy", + accepts_input_from=False, + produces_channels=True, + params_schema={ + "clahe": {"type": "boolean", "default": True}, + "clip_limit": {"type": "number", "default": 0.01}, + "kernel_size": {"type": ["integer", "null"], "default": None}, + "include_in_bank": {"type": "boolean", "default": True}, + }, + ) + + def ready(self) -> bool: + return True + + def preview_labels(self, params: dict[str, Any]) -> list[str]: + if not bool(params.get("include_in_bank", True)): + return [] + return ["clahe"] if bool(params.get("clahe", True)) else ["intensity"] + + def run(self, ctx: ModuleContext) -> ChannelBlock: + p = ctx.params + clahe_on = bool(p.get("clahe", True)) + clip_limit = float(p.get("clip_limit", 0.01)) + ks = p.get("kernel_size", None) + kernel_size = int(ks) if ks not in (None, "", False) else None + _uint8, float_stack, labels = features.compute_clahe_stack( + ctx.gray, + clip_limit=clip_limit, + kernel_size=kernel_size, + apply=clahe_on, + ) + image_2d = float_stack[..., 0] + include = bool(p.get("include_in_bank", True)) + return ChannelBlock( + float_stack=float_stack if include else None, + labels=labels if include else [], + image_2d=image_2d, + ) diff --git a/ipred/src/ipred/modules/pca_mod.py b/ipred/src/ipred/modules/pca_mod.py new file mode 100644 index 0000000..611e8fa --- /dev/null +++ b/ipred/src/ipred/modules/pca_mod.py @@ -0,0 +1,78 @@ +"""PCA reduce dense embeddings into bank channels.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np + +from ipred import features, sam_embed +from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta + + +class PcaModule: + """Bake encoder embedding grid → upsampled PCA float channels.""" + + meta = ModuleMeta( + id="pca", + name="PCA reduce", + description="PCA on dense embedding → heatmaps in feature bank", + runtime="numpy", + accepts_input_from=True, + produces_channels=True, + produces_embedding=False, + params_schema={ + "dims": {"type": "integer", "default": 64}, + }, + ) + + def ready(self) -> bool: + return True + + def preview_labels(self, params: dict[str, Any]) -> list[str]: + dims = max(1, int(params.get("dims", 64))) + return [f"pca{i}" for i in range(dims)] + + def run(self, ctx: ModuleContext) -> ChannelBlock: + src_id = None + # Prefer explicitly linked node; else last embedding in context + emb = None + emb_meta: dict[str, Any] | None = None + # input_from resolved upstream sets nothing on ctx for emb — + # look up from node_outputs via params _input_from injected by runner + input_from = ctx.params.get("_input_from") + if input_from and input_from in ctx.node_outputs: + block = ctx.node_outputs[input_from] + emb, emb_meta = block.emb, block.emb_meta + src_id = input_from + if emb is None: + for nid, block in reversed(list(ctx.node_outputs.items())): + if block.emb is not None: + emb, emb_meta = block.emb, block.emb_meta + src_id = nid + break + if emb is None or emb_meta is None: + raise ValueError("pca module requires an upstream embedding node") + dims = max(1, int(ctx.params.get("dims", 64))) + h, w = int(ctx.gray.shape[0]), int(ctx.gray.shape[1]) + pca_float, pca_labels, info = sam_embed.emb_grid_to_pca_channels( + emb, + out_h=h, + out_w=w, + n_components=dims, + ) + meta = dict(emb_meta) + meta.update(info) + meta["pca_from_node"] = src_id + # Keep emb for bank persistence (sam_emb.npy) + return ChannelBlock( + float_stack=pca_float.astype(np.float32), + labels=pca_labels, + emb=emb, + emb_meta=meta, + ) + + +def uint8_from_float(float_stack: np.ndarray) -> np.ndarray: + """Display uint8 for a float HxWxC bank.""" + return features._float_feats_to_uint8(float_stack, clahe=False) diff --git a/ipred/src/ipred/modules/skimage_mod.py b/ipred/src/ipred/modules/skimage_mod.py new file mode 100644 index 0000000..7fa696a --- /dev/null +++ b/ipred/src/ipred/modules/skimage_mod.py @@ -0,0 +1,54 @@ +"""Skimage multiscale feature module.""" + +from __future__ import annotations + +from typing import Any + +from ipred import features +from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta + + +class SkimageMultiscaleModule: + """Produce multiscale intensity/edges/texture channels.""" + + meta = ModuleMeta( + id="skimage_multiscale", + name="Skimage multiscale", + description="ilastik-style multiscale basic features", + runtime="numpy", + accepts_input_from=False, + produces_channels=True, + params_schema={ + "sigma_min": {"type": "number", "default": 1.0}, + "sigma_max": {"type": "number", "default": 8.0}, + "intensity": {"type": "boolean", "default": True}, + "edges": {"type": "boolean", "default": True}, + "texture": {"type": "boolean", "default": True}, + "clahe": {"type": "boolean", "default": True}, + }, + ) + + def ready(self) -> bool: + return True + + def preview_labels(self, params: dict[str, Any]) -> list[str]: + return features.feature_channel_labels( + sigma_min=float(params.get("sigma_min", 1.0)), + sigma_max=float(params.get("sigma_max", 8.0)), + intensity=bool(params.get("intensity", True)), + edges=bool(params.get("edges", True)), + texture=bool(params.get("texture", True)), + ) + + def run(self, ctx: ModuleContext) -> ChannelBlock: + p = ctx.params + _uint8, float_stack, labels = features.compute_feature_stacks( + ctx.gray, + sigma_min=float(p.get("sigma_min", 1.0)), + sigma_max=float(p.get("sigma_max", 8.0)), + intensity=bool(p.get("intensity", True)), + edges=bool(p.get("edges", True)), + texture=bool(p.get("texture", True)), + clahe=bool(p.get("clahe", True)), + ) + return ChannelBlock(float_stack=float_stack, labels=labels) diff --git a/ipred/src/ipred/modules/slimsam_mod.py b/ipred/src/ipred/modules/slimsam_mod.py new file mode 100644 index 0000000..5fe9ff2 --- /dev/null +++ b/ipred/src/ipred/modules/slimsam_mod.py @@ -0,0 +1,50 @@ +"""SlimSAM ONNX encoder module.""" + +from __future__ import annotations + +from typing import Any + +from ipred import feature_setups, sam_embed +from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta + + +class SlimSamModule: + """Dense SlimSAM vision-encoder embeddings (ONNX Runtime).""" + + meta = ModuleMeta( + id="slimsam", + name="SlimSAM", + description="SlimSAM vision encoder (ONNX)", + runtime="onnx", + accepts_input_from=True, + produces_channels=False, + produces_embedding=True, + params_schema={ + "weights_path": {"type": "string", "default": None}, + }, + ) + + def ready(self) -> bool: + path = feature_setups.resolve_slimsam_weights_path() + return sam_embed.encoder_available(path) + + def preview_labels(self, params: dict[str, Any]) -> list[str]: + del params + return [] # PCA module consumes embedding + + def run(self, ctx: ModuleContext) -> ChannelBlock: + src = ctx.input_image if ctx.input_image is not None else ctx.raw + wpath = ctx.params.get("weights_path") or feature_setups.resolve_slimsam_weights_path() + if not sam_embed.encoder_available(wpath): + raise ValueError("SlimSAM ONNX weights not available") + emb, orig_hw, reshaped_hw = sam_embed.encode_image_embeddings( + src, weights_path=wpath + ) + meta = { + "orig_hw": list(orig_hw), + "reshaped_hw": list(reshaped_hw), + "weights_path": wpath, + "encoder": "slimsam", + "weights_format": feature_setups.WEIGHTS_ONNX_VISION, + } + return ChannelBlock(emb=emb, emb_meta=meta) diff --git a/ipred/src/ipred/modules/tomojepa_mod.py b/ipred/src/ipred/modules/tomojepa_mod.py new file mode 100644 index 0000000..9d9362a --- /dev/null +++ b/ipred/src/ipred/modules/tomojepa_mod.py @@ -0,0 +1,154 @@ +"""TomoJEPA / Mark encoder module (torch; ONNX when available).""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from ipred import feature_setups, tomojepa_embed +from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta + + +def _resolve_weights(params: dict[str, Any]) -> str | None: + """Resolve Mark25/11 weights from params.""" + explicit = params.get("weights_path") + if explicit and str(explicit) != "(missing)": + p = Path(str(explicit)).expanduser() + if p.is_file(): + return str(p.resolve()) + weights_id = str(params.get("weights_id") or "mark25").lower() + if weights_id in ("mark11", "tomojepa11", "11"): + return feature_setups.resolve_tomojepa_weights_path( + "tomojepa11.pth", env_var="TOMOJEPA11_WEIGHTS" + ) + return feature_setups.resolve_tomojepa_weights_path( + "tomojepa25.pth", env_var="TOMOJEPA_WEIGHTS" + ) + + +def _encoder_name(params: dict[str, Any], path: str | None) -> str: + wid = str(params.get("weights_id") or "").lower() + if wid in ("mark11", "tomojepa11", "11"): + return "mark11" + if path and "tomojepa11" in Path(path).name: + return "mark11" + return "mark25" + + +def _weights_format(name: str) -> str: + if name == "mark11": + return feature_setups.WEIGHTS_TOMOJEPA_MARK11 + return feature_setups.WEIGHTS_TOMOJEPA_MARK25 + + +class TomoJepaModule: + """Dense TomoJEPA embeddings (torch; prefers ONNX when present).""" + + meta = ModuleMeta( + id="tomojepa", + name="TomoJEPA", + description="Mark25/Mark11 dense projector (torch or ONNX)", + runtime="torch", + accepts_input_from=True, + produces_channels=False, + produces_embedding=True, + params_schema={ + "weights_id": {"type": "string", "default": "mark25", "enum": ["mark25", "mark11"]}, + "weights_path": {"type": "string", "default": None}, + "input_size": {"type": "integer", "default": 512}, + "resize": {"type": "boolean", "default": True}, + }, + ) + + def ready(self) -> bool: + if self._onnx_path({"weights_id": "mark25"}) or self._onnx_path( + {"weights_id": "mark11"} + ): + return True + for fname, env in ( + ("tomojepa25.pth", "TOMOJEPA_WEIGHTS"), + ("tomojepa11.pth", "TOMOJEPA11_WEIGHTS"), + ): + p = feature_setups.resolve_tomojepa_weights_path(fname, env_var=env) + if p and tomojepa_embed.encoder_available(p): + return True + return False + + def preview_labels(self, params: dict[str, Any]) -> list[str]: + del params + return [] + + def _onnx_path(self, params: dict[str, Any]) -> Path | None: + import os + + explicit = params.get("onnx_path") + if explicit and Path(str(explicit)).expanduser().is_file(): + return Path(str(explicit)).expanduser().resolve() + wid = str(params.get("weights_id") or "mark25").lower() + fname = "tomojepa11.onnx" if wid in ("mark11", "11") else "tomojepa25.onnx" + env = "TOMOJEPA11_ONNX" if "11" in fname else "TOMOJEPA_ONNX" + env_p = os.getenv(env) + if env_p and Path(env_p).expanduser().is_file(): + return Path(env_p).expanduser().resolve() + # modules/tomojepa_mod.py → …/ipred/src/ipred/modules → parents[3]=ipred/ + here = Path(__file__).resolve() + for c in ( + here.parents[3] / "models" / fname, + here.parents[4] / "ipred" / "models" / fname, + ): + if c.is_file(): + return c.resolve() + return None + + def run(self, ctx: ModuleContext) -> ChannelBlock: + src = ctx.input_image if ctx.input_image is not None else ctx.gray + p = ctx.params + resize = bool(p.get("resize", True)) + input_size = int(p.get("input_size", 512)) + onnx_path = self._onnx_path(p) + use_onnx = False + if onnx_path is not None: + from ipred import tomojepa_onnx + + use_onnx = tomojepa_onnx.onnx_matches_input_size( + onnx_path, input_size + ) + if use_onnx and onnx_path is not None: + from ipred import tomojepa_onnx + + emb, orig_hw, reshaped_hw = tomojepa_onnx.encode_dense_embeddings( + src, + weights_path=str(onnx_path), + input_size=input_size, + resize=resize, + ) + runtime = "onnx" + path_s = str(onnx_path) + else: + wpath = _resolve_weights(p) + if not tomojepa_embed.encoder_available(wpath): + raise ValueError( + "TomoJEPA weights not available (torch/.pth or ONNX)" + ) + emb, orig_hw, reshaped_hw = tomojepa_embed.encode_dense_embeddings( + src, + weights_path=wpath, + input_size=input_size, + resize=resize, + ) + runtime = "torch" + path_s = wpath + enc_name = _encoder_name(p, path_s) + meta = { + "orig_hw": list(orig_hw), + "reshaped_hw": list(reshaped_hw), + "weights_path": path_s, + "encoder": enc_name, + "input_size": input_size, + "resize": resize, + "weights_format": _weights_format(enc_name), + "runtime": runtime, + "model_input_range": [-1.0, 1.0], + "intensity_norm": "minmax_01_then_linear_m11", + } + return ChannelBlock(emb=emb, emb_meta=meta) diff --git a/ipred/src/ipred/paths.py b/ipred/src/ipred/paths.py new file mode 100644 index 0000000..4a3a071 --- /dev/null +++ b/ipred/src/ipred/paths.py @@ -0,0 +1,37 @@ +"""Filesystem roots for ipred under LOCAL_DATA_ROOT.""" + +from __future__ import annotations + +import os +from pathlib import Path + + +def local_data_root() -> Path: + """Return resolved ``LOCAL_DATA_ROOT`` (default ``~/data``).""" + return Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve() + + +def engine_root() -> Path: + """Return ``$LOCAL_DATA_ROOT/ipred``.""" + root = local_data_root() / "ipred" + root.mkdir(parents=True, exist_ok=True) + return root + + +def catalog_db_path() -> Path: + """SQLite catalog path.""" + return engine_root() / "catalog.db" + + +def feature_models_root() -> Path: + """Feature Setup shelf directory.""" + root = local_data_root() / ".feature_models" + root.mkdir(parents=True, exist_ok=True) + return root + + +def project_blob_dir(project_id: str) -> Path: + """Blob root for one project.""" + path = engine_root() / "projects" / project_id + path.mkdir(parents=True, exist_ok=True) + return path diff --git a/ipred/src/ipred/preprocess.py b/ipred/src/ipred/preprocess.py new file mode 100644 index 0000000..c1f7fbd --- /dev/null +++ b/ipred/src/ipred/preprocess.py @@ -0,0 +1,207 @@ +"""Cache-aware featurize for a session (composition-first).""" + +from __future__ import annotations + +import json +import logging +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import numpy as np + +from ipred import array_source, compositions, features +from ipred.catalog import Catalog +from ipred.compose_run import run_composition +from ipred.paths import project_blob_dir + +logger = logging.getLogger(__name__) + + +def _utc_now() -> str: + return datetime.now(timezone.utc).isoformat() + + +def run_preprocess( + catalog: Catalog, + *, + session_id: str, + feature_setup_id: str | None = None, + composition_id: str | None = None, + slice_index: int = 0, + array_ref: str | None = None, +) -> dict[str, Any]: + """Featurize with cache hit/miss; update session current_feature_id. + + Prefers ``composition_id``; ``feature_setup_id`` is mapped via legacy table. + Optional ``array_ref`` selects a session-uploaded blob instead of path/Tiled. + """ + session = catalog.get_session(session_id) + if session is None: + raise KeyError(f"unknown session {session_id}") + project = catalog.get_project(session.project_id) + if project is None: + raise KeyError(f"unknown project {session.project_id}") + + compositions.ensure_default_compositions(catalog) + raw_id = composition_id or feature_setup_id + if not raw_id: + raise ValueError("composition_id or feature_setup_id required") + try: + cid = compositions.resolve_preprocess_id(raw_id) + except KeyError: + # Ensure defaults then retry + compositions.ensure_default_compositions(catalog) + cid = compositions.resolve_preprocess_id(raw_id) + + doc = compositions.resolve_composition(cid) + chash = doc.get("content_hash") or compositions.content_hash(doc) + + hit = catalog.find_feature_bank( + project_id=project.project_id, + setup_id=cid, + content_hash=chash, + slice_index=slice_index, + ) + if hit is not None and array_ref is None: + catalog.set_session_currents(session_id, feature_id=hit["feature_id"]) + return _bank_response(hit, cache_hit=True) + + if array_ref: + arr = _load_array_ref(project.project_id, array_ref) + else: + arr = array_source.read_slice( + kind=project.kind, + source=project.source, + slice_index=slice_index, + server_uri=project.server_uri, + root=project.root, + ) + uint8_stack, float_stack, labels, sam_emb, sam_meta = run_composition( + arr, doc + ) + feature_id = uuid.uuid4().hex + blob = project_blob_dir(project.project_id) / "features" / feature_id + blob.mkdir(parents=True, exist_ok=True) + np.save(blob / "float_stack.npy", float_stack.astype(np.float16)) + np.save(blob / "uint8_stack.npy", uint8_stack) + (blob / "labels.json").write_text( + json.dumps(labels, indent=2), encoding="utf-8" + ) + if sam_emb is not None: + np.save(blob / "sam_emb.npy", sam_emb.astype(np.float32)) + (blob / "sam_meta.json").write_text( + json.dumps(sam_meta), encoding="utf-8" + ) + channels_dir = blob / "channels" + channels_dir.mkdir(exist_ok=True) + for i in range(uint8_stack.shape[-1]): + png = features.encode_channel_png(uint8_stack, i) + (channels_dir / f"{i:04d}.png").write_bytes(png) + + h, w, c = float_stack.shape + snapshot = dict(doc) + record = { + "feature_id": feature_id, + "project_id": project.project_id, + "setup_id": cid, + "content_hash": chash, + "slice_index": int(slice_index), + "n_channels": int(c), + "height": int(h), + "width": int(w), + "blob_dir": str(blob), + "setup_snapshot": json.dumps(snapshot), + "status": "ready", + "created_at": _utc_now(), + } + catalog.insert_feature_bank(record) + catalog.set_session_currents(session_id, feature_id=feature_id) + return _bank_response(record, cache_hit=False, labels=labels) + + +def _load_array_ref(project_id: str, array_ref: str) -> np.ndarray: + """Load a content-addressed slice uploaded via the data plane.""" + from ipred.array_blobs import load_array_blob + + return load_array_blob(project_id, array_ref) + + +def load_feature_bank_arrays(blob_dir: str | Path) -> dict[str, Any]: + """Load persisted feature arrays from a bank directory.""" + blob = Path(blob_dir) + float_stack = np.load(blob / "float_stack.npy").astype(np.float32) + uint8_stack = np.load(blob / "uint8_stack.npy") + labels = json.loads((blob / "labels.json").read_text(encoding="utf-8")) + sam_emb = None + sam_meta = None + if (blob / "sam_emb.npy").is_file(): + sam_emb = np.load(blob / "sam_emb.npy") + if (blob / "sam_meta.json").is_file(): + sam_meta = json.loads( + (blob / "sam_meta.json").read_text(encoding="utf-8") + ) + return { + "float_stack": float_stack, + "uint8_stack": uint8_stack, + "labels": labels, + "sam_emb": sam_emb, + "sam_meta": sam_meta, + } + + +def channel_png_path(blob_dir: str | Path, index: int) -> Path: + """Path to a cached channel PNG.""" + return Path(blob_dir) / "channels" / f"{index:04d}.png" + + +def _bank_response( + record: dict[str, Any], + *, + cache_hit: bool, + labels: list[str] | None = None, +) -> dict[str, Any]: + blob = Path(record["blob_dir"]) + if labels is None and (blob / "labels.json").is_file(): + labels = json.loads((blob / "labels.json").read_text(encoding="utf-8")) + return { + "feature_id": record["feature_id"], + "project_id": record["project_id"], + "setup_id": record["setup_id"], + "composition_id": record["setup_id"], + "slice_index": record["slice_index"], + "n_channels": record["n_channels"], + "height": record["height"], + "width": record["width"], + "labels": labels or [], + "cache_hit": cache_hit, + "blob_dir": record["blob_dir"], + } + + +# Keep soft import for tests that still poke at legacy helpers +def _compute_from_setup( + arr: np.ndarray, + setup: dict[str, Any], + snapshot: dict[str, Any], +) -> tuple[ + np.ndarray, + np.ndarray, + list[str], + np.ndarray | None, + dict[str, Any] | None, +]: + """Legacy bridge: convert procedure setup → composition run via resolve.""" + del snapshot + sid = setup.get("id") or "" + compositions.ensure_default_compositions() + try: + cid = compositions.resolve_preprocess_id(sid) + doc = compositions.resolve_composition(cid) + return run_composition(arr, doc) + except KeyError as exc: + raise ValueError( + "weights/procedure setups must map to a composition; " + f"unknown {sid}" + ) from exc diff --git a/ipred/src/ipred/sam_embed.py b/ipred/src/ipred/sam_embed.py new file mode 100644 index 0000000..7e1c446 --- /dev/null +++ b/ipred/src/ipred/sam_embed.py @@ -0,0 +1,219 @@ +"""SlimSAM ONNX embeddings for feature banks (self-contained).""" + +from __future__ import annotations + +import logging +import os +import threading +from functools import lru_cache +from pathlib import Path +from typing import Any + +import numpy as np +from PIL import Image as PILImage + +logger = logging.getLogger(__name__) + +_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(3, 1, 1) +_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(3, 1, 1) +_LONG_EDGE = 1024 +_PAD = 1024 + +_session_lock = threading.Lock() + + +def resolve_encoder_path(explicit: str | None = None) -> Path | None: + """Resolve ONNX path from explicit path, env, or repo defaults.""" + if explicit and explicit != "(missing)": + p = Path(explicit).expanduser().resolve() + if p.is_file(): + return p + env = os.getenv("FEATURE_ENCODER_ONNX") + if env: + p = Path(env).expanduser().resolve() + if p.is_file(): + return p + here = Path(__file__).resolve() + repo = here.parents[3] + for c in ( + repo / "backend" / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx", + repo / "frontend" / "public" / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx", + ): + if c.is_file(): + return c.resolve() + return None + + +def encoder_available(weights_path: str | None = None) -> bool: + """True when an ONNX encoder file is present.""" + return resolve_encoder_path(weights_path) is not None + + +@lru_cache(maxsize=4) +def _session_for(path_str: str): + import onnxruntime as ort + + return ort.InferenceSession(path_str, providers=["CPUExecutionProvider"]) + + +def rgb_uint8_from_array(arr: np.ndarray) -> np.ndarray: + """Convert slice to HxWx3 uint8 RGB.""" + a = np.asarray(arr) + if a.ndim == 3 and a.shape[-1] in (3, 4): + rgb = a[..., :3].astype(np.float64) + elif a.ndim == 2: + g = a.astype(np.float64) + rgb = np.stack([g, g, g], axis=-1) + else: + raise ValueError(f"unsupported array shape {a.shape}") + finite = rgb[np.isfinite(rgb)] + if finite.size == 0: + return np.zeros((*rgb.shape[:2], 3), dtype=np.uint8) + lo, hi = float(np.min(finite)), float(np.max(finite)) + if hi <= lo: + return np.zeros((*rgb.shape[:2], 3), dtype=np.uint8) + scaled = (rgb - lo) / (hi - lo) * 255.0 + return np.clip(scaled, 0, 255).astype(np.uint8) + + +def encode_image_embeddings( + arr: np.ndarray, + *, + weights_path: str | None = None, +) -> tuple[np.ndarray, tuple[int, int], tuple[int, int]]: + """Return ``(emb eh×ew×C, orig_hw, reshaped_hw)``.""" + path = resolve_encoder_path(weights_path) + if path is None: + raise FileNotFoundError("SlimSAM vision_encoder.onnx not found") + + rgb = rgb_uint8_from_array(arr) + oh, ow = int(rgb.shape[0]), int(rgb.shape[1]) + scale = _LONG_EDGE / max(oh, ow) + rh = max(1, int(round(oh * scale))) + rw = max(1, int(round(ow * scale))) + resized = np.asarray( + PILImage.fromarray(rgb).resize((rw, rh), PILImage.BILINEAR), + dtype=np.uint8, + ) + canvas = np.zeros((_PAD, _PAD, 3), dtype=np.uint8) + canvas[:rh, :rw] = resized + x = canvas.astype(np.float32) / 255.0 + x = np.transpose(x, (2, 0, 1)) + x = (x - _MEAN) / _STD + x = np.expand_dims(x, 0) + + with _session_lock: + sess = _session_for(str(path)) + inp = sess.get_inputs()[0].name + out = sess.run(None, {inp: x})[0] + + # NCHW → HWC + emb = np.transpose(np.asarray(out[0], dtype=np.float32), (1, 2, 0)) + return emb, (oh, ow), (rh, rw) + + +def bilinear_sample_emb( + emb: np.ndarray, + ys: np.ndarray, + xs: np.ndarray, + *, + orig_h: int, + orig_w: int, + reshaped_h: int, + reshaped_w: int, +) -> np.ndarray: + """Sample embedding at pixel coords (orig image space).""" + eh, ew, c = emb.shape + # Map orig → reshaped → emb grid + scale_y = reshaped_h / max(orig_h, 1) + scale_x = reshaped_w / max(orig_w, 1) + fy = (ys.astype(np.float64) * scale_y) * (eh / max(reshaped_h, 1)) + fx = (xs.astype(np.float64) * scale_x) * (ew / max(reshaped_w, 1)) + fy = np.clip(fy, 0, eh - 1.001) + fx = np.clip(fx, 0, ew - 1.001) + y0 = np.floor(fy).astype(np.int64) + x0 = np.floor(fx).astype(np.int64) + y1 = np.minimum(y0 + 1, eh - 1) + x1 = np.minimum(x0 + 1, ew - 1) + wy = fy - y0 + wx = fx - x0 + ia = emb[y0, x0] + ib = emb[y0, x1] + ic = emb[y1, x0] + id_ = emb[y1, x1] + wa = ((1 - wy) * (1 - wx))[:, None] + wb = ((1 - wy) * wx)[:, None] + wc = (wy * (1 - wx))[:, None] + wd = (wy * wx)[:, None] + return (wa * ia + wb * ib + wc * ic + wd * id_).astype(np.float32) + + +def fit_pca(x: np.ndarray, n_components: int = 32) -> tuple[np.ndarray, np.ndarray]: + """Fit PCA; return mean and components (n_comp × d).""" + from sklearn.decomposition import PCA + + n = min(n_components, x.shape[0], x.shape[1]) + pca = PCA(n_components=n, svd_solver="randomized", random_state=0) + pca.fit(x) + return pca.mean_.astype(np.float32), pca.components_.astype(np.float32) + + +def transform_pca( + x: np.ndarray, + mean: np.ndarray, + components: np.ndarray, +) -> np.ndarray: + """Project rows with fitted PCA.""" + return ((x - mean) @ components.T).astype(np.float32) + + +def emb_grid_to_pca_channels( + emb: np.ndarray, + *, + out_h: int, + out_w: int, + n_components: int = 64, + max_fit_samples: int = 50_000, +) -> tuple[np.ndarray, list[str], dict[str, Any]]: + """PCA on dense emb tokens, upsample to ``out_h×out_w×K`` float channels. + + Args: + emb: Encoder embedding grid ``eh×ew×C``. + out_h / out_w: Full-resolution image size for heatmaps / float_stack. + n_components: Target PCA dims (capped by tokens and channels). + max_fit_samples: Subsample cap when fitting PCA on large grids. + + Returns: + ``(float_hwk, labels, info)`` with labels ``pca0…pca{K-1}``. + """ + from skimage.transform import resize + + eh, ew, c = emb.shape + flat = np.asarray(emb, dtype=np.float32).reshape(-1, c) + n_tok = flat.shape[0] + k_target = max(1, int(n_components)) + if n_tok > max_fit_samples: + rng = np.random.default_rng(0) + idx = rng.choice(n_tok, size=max_fit_samples, replace=False) + fit_x = flat[idx] + else: + fit_x = flat + mean, comp = fit_pca(fit_x, n_components=k_target) + projected = transform_pca(flat, mean, comp) + k = projected.shape[1] + grid = projected.reshape(eh, ew, k) + up = resize( + grid, + (int(out_h), int(out_w), k), + order=1, + mode="edge", + anti_aliasing=False, + preserve_range=True, + ).astype(np.float32) + labels = [f"pca{i}" for i in range(k)] + info = { + "pca_dims": int(k), + "emb_shape": [int(eh), int(ew), int(c)], + "baked_into_float_stack": True, + } + return up, labels, info diff --git a/ipred/src/ipred/scripts/__init__.py b/ipred/src/ipred/scripts/__init__.py new file mode 100644 index 0000000..1a9ec9c --- /dev/null +++ b/ipred/src/ipred/scripts/__init__.py @@ -0,0 +1 @@ +"""Export scripts package.""" diff --git a/ipred/src/ipred/scripts/export_tomojepa_onnx.py b/ipred/src/ipred/scripts/export_tomojepa_onnx.py new file mode 100644 index 0000000..291bbb5 --- /dev/null +++ b/ipred/src/ipred/scripts/export_tomojepa_onnx.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +"""Export TomoJEPA Mark25/Mark11 checkpoints to ONNX. + +Usage: + python -m ipred.scripts.export_tomojepa_onnx + python -m ipred.scripts.export_tomojepa_onnx --weights ipred/models/tomojepa11.pth +""" + +from __future__ import annotations + +import argparse +import logging +import sys +from pathlib import Path + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger("export_tomojepa_onnx") + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--weights", + action="append", + default=None, + help="Path to .pth (repeatable). Default: both tomojepa25/11 if present.", + ) + parser.add_argument("--input-size", type=int, default=512) + parser.add_argument( + "--out-dir", + type=Path, + default=None, + help="Output directory (default: same as weights)", + ) + args = parser.parse_args(argv) + + from ipred.tomojepa_onnx import export_tomojepa_onnx + + here = Path(__file__).resolve() + models = here.parents[2] / "models" + weights = args.weights + if not weights: + weights = [] + for name in ("tomojepa25.pth", "tomojepa11.pth"): + p = models / name + if p.is_file(): + weights.append(str(p)) + if not weights: + logger.error("no weights found") + return 1 + + for w in weights: + pth = Path(w) + out_dir = args.out_dir or pth.parent + onnx_path = out_dir / (pth.stem + ".onnx") + export_tomojepa_onnx(pth, onnx_path, input_size=args.input_size) + logger.info("wrote %s", onnx_path) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/ipred/src/ipred/tomojepa_embed.py b/ipred/src/ipred/tomojepa_embed.py new file mode 100644 index 0000000..8551728 --- /dev/null +++ b/ipred/src/ipred/tomojepa_embed.py @@ -0,0 +1,198 @@ +"""Mark25 / TomoJEPA dense embeddings for feature banks.""" + +from __future__ import annotations + +import logging +import os +import threading +from functools import lru_cache +from pathlib import Path +from typing import Any + +import numpy as np +from PIL import Image as PILImage + +logger = logging.getLogger(__name__) + +_model_lock = threading.Lock() +_DEFAULT_INPUT_SIZE = 512 +_PATCH = 16 + + +def torch_available() -> bool: + """True when torch and timm can be imported.""" + try: + import torch # noqa: F401 + import timm # noqa: F401 + except ImportError: + return False + return True + + +def resolve_weights_path(explicit: str | None = None) -> Path | None: + """Resolve TomoJEPA weights from explicit path, env, or repo default.""" + if explicit and explicit != "(missing)": + p = Path(explicit).expanduser().resolve() + if p.is_file(): + return p + env = os.getenv("TOMOJEPA_WEIGHTS") + if env: + p = Path(env).expanduser().resolve() + if p.is_file(): + return p + here = Path(__file__).resolve() + # .../repo/ipred/src/ipred → repo/ipred/models + candidates = [ + here.parents[2] / "models" / "tomojepa25.pth", + here.parents[3] / "ipred" / "models" / "tomojepa25.pth", + ] + for c in candidates: + if c.is_file(): + return c.resolve() + return None + + +def encoder_available(weights_path: str | None = None) -> bool: + """True when torch/timm are importable and weights exist.""" + if not torch_available(): + return False + return resolve_weights_path(weights_path) is not None + + +def load_checkpoint_state(path: Path | str) -> dict[str, Any]: + """Load ``ckpt['net']`` with optional ``module.`` prefix stripped.""" + import torch + + ckpt = torch.load(str(path), map_location="cpu", weights_only=False) + if not isinstance(ckpt, dict) or "net" not in ckpt: + raise ValueError(f"expected TomoJEPA ckpt with 'net' key: {path}") + return {k.replace("module.", ""): v for k, v in ckpt["net"].items()} + + +def build_encoder(**kwargs: Any): + """Build uninitialized DINOv3ViTEncoder (requires torch/timm).""" + from ipred.tomojepa_encoder import DINOv3ViTEncoder + + return DINOv3ViTEncoder(**kwargs) + + +@lru_cache(maxsize=4) +def _cached_model(path_str: str): + import torch + + from ipred.tomojepa_encoder import DINOv3ViTEncoder + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + state = load_checkpoint_state(path_str) + # Mark25 dense proj is 64-D; Mark11 is 256-D — read from checkpoint. + proj_w = state.get("proj.8.weight") + if proj_w is None: + raise ValueError(f"checkpoint missing proj.8.weight: {path_str}") + proj_dim = int(proj_w.shape[0]) + net = DINOv3ViTEncoder( + proj_dim=proj_dim, img_size=_DEFAULT_INPUT_SIZE, in_chans=1, pretrained=False + ) + net.load_state_dict(state, strict=True) + net.eval() + net.to(device) + return net, device + + +def _pad_to_multiple(h: int, w: int, multiple: int = _PATCH) -> tuple[int, int]: + ph = (multiple - (h % multiple)) % multiple + pw = (multiple - (w % multiple)) % multiple + return h + ph, w + pw + + +def _gray_unit_interval(arr: np.ndarray) -> np.ndarray: + a = np.asarray(arr) + if a.ndim == 3 and a.shape[-1] in (3, 4): + rgb = a[..., :3].astype(np.float64) + gray = 0.299 * rgb[..., 0] + 0.587 * rgb[..., 1] + 0.114 * rgb[..., 2] + elif a.ndim == 2: + gray = a.astype(np.float64) + else: + raise ValueError(f"unsupported array shape {a.shape}") + finite = gray[np.isfinite(gray)] + if finite.size == 0: + return np.zeros_like(gray, dtype=np.float32) + lo, hi = float(np.min(finite)), float(np.max(finite)) + if hi <= lo: + return np.zeros_like(gray, dtype=np.float32) + return ((gray - lo) / (hi - lo)).astype(np.float32) + + +def to_minus_one_one(gray01: np.ndarray) -> np.ndarray: + """Linear map from ``[0, 1]`` to ``[-1, 1]`` (Mark25 / TomoJEPA convention).""" + return (np.asarray(gray01, dtype=np.float32) * 2.0 - 1.0).astype(np.float32) + + +def encode_dense_embeddings( + arr: np.ndarray, + *, + weights_path: str | None = None, + input_size: int = _DEFAULT_INPUT_SIZE, + resize: bool = True, +) -> tuple[np.ndarray, tuple[int, int], tuple[int, int]]: + """Return ``(emb Hp×Wp×64, orig_hw, reshaped_hw)``. + + When ``resize`` is True (default), the grayscale slice is resized to a + square ``input_size×input_size``. When False, native resolution is kept and + only padded to a multiple of 16 (Mark25 ``dynamic_img_size``). + + Intensity pipeline: min-max to ``[0, 1]`` (CLAHE already in that range when + passed from ``clahe_encoder_v1``), then linear shift to ``[-1, 1]`` before + the network. + """ + import torch + + path = resolve_weights_path(weights_path) + if path is None: + raise FileNotFoundError("TomoJEPA weights (.pth) not found") + if not torch_available(): + raise ImportError( + "TomoJEPA requires torch and timm — install with: pip install -e '.[torch]'" + ) + + gray = _gray_unit_interval(arr) + oh, ow = int(gray.shape[0]), int(gray.shape[1]) + if resize: + size = max(int(input_size), _PATCH) + resized = np.asarray( + PILImage.fromarray(gray, mode="F").resize( + (size, size), PILImage.BILINEAR + ), + dtype=np.float32, + ) + rh, rw = size, size + else: + resized = gray.astype(np.float32, copy=True) + rh, rw = oh, ow + + pad_h, pad_w = _pad_to_multiple(rh, rw, _PATCH) + if pad_h != rh or pad_w != rw: + canvas = np.zeros((pad_h, pad_w), dtype=np.float32) + canvas[:rh, :rw] = resized + resized = canvas + else: + pad_h, pad_w = rh, rw + + # TomoJEPA expects intensities in [-1, 1] after CLAHE / unit-interval prep. + model_in = to_minus_one_one(resized) + + x = torch.from_numpy(np.array(model_in, dtype=np.float32, copy=True)[None, None, None]) + with _model_lock: + net, device = _cached_model(str(path)) + x = x.to(device) + with torch.inference_mode(): + _glob, dense, _feats = net(x) + dense_np = dense.detach().cpu().numpy()[0, 0] # [L, 64] + + grid_h = pad_h // _PATCH + grid_w = pad_w // _PATCH + if dense_np.shape[0] != grid_h * grid_w: + side = int(round(dense_np.shape[0] ** 0.5)) + grid_h = grid_w = side + emb = dense_np.reshape(grid_h, grid_w, dense_np.shape[-1]).astype(np.float32) + # reshaped_hw is the coordinate frame covered by the emb grid (incl. pad). + return emb, (oh, ow), (pad_h, pad_w) diff --git a/ipred/src/ipred/tomojepa_encoder.py b/ipred/src/ipred/tomojepa_encoder.py new file mode 100644 index 0000000..44b2c29 --- /dev/null +++ b/ipred/src/ipred/tomojepa_encoder.py @@ -0,0 +1,107 @@ +"""Mark25 DINOv3ViTEncoder — ViT-S/16 + dense/global projectors. + +Architecture reconstructed to match TomoJEPA checkpoints (``ckpt['net']``): +timm ``vit_small_patch16_dinov3`` backbone, BatchNorm dense MLP → ``proj_dim`` +(Mark25: 64-D, Mark11: 256-D), LayerNorm global MLP → 16-D. +""" + +from __future__ import annotations + +from typing import Any + +import torch +import torch.nn as nn + + +def _dense_mlp(in_dim: int, hidden: int, out_dim: int) -> nn.Sequential: + return nn.Sequential( + nn.Linear(in_dim, hidden), + nn.BatchNorm1d(hidden), + nn.GELU(), + nn.Dropout(0.0), + nn.Linear(hidden, hidden), + nn.BatchNorm1d(hidden), + nn.GELU(), + nn.Dropout(0.0), + nn.Linear(hidden, out_dim), + nn.Identity(), + ) + + +def _global_mlp(in_dim: int, hidden: int, out_dim: int) -> nn.Sequential: + return nn.Sequential( + nn.Linear(in_dim, hidden), + nn.LayerNorm(hidden), + nn.GELU(), + nn.Dropout(0.0), + nn.Linear(hidden, hidden), + nn.LayerNorm(hidden), + nn.GELU(), + nn.Dropout(0.0), + nn.Linear(hidden, out_dim), + nn.Identity(), + ) + + +class DINOv3ViTEncoder(nn.Module): + """Mark25 TomoJEPA encoder producing global and dense projections.""" + + def __init__( + self, + proj_dim: int = 64, + img_size: int = 512, + in_chans: int = 1, + pretrained: bool = False, + *, + embed_dim: int = 384, + global_dim: int = 16, + hidden_dim: int = 2048, + ) -> None: + super().__init__() + del pretrained # checkpoint is loaded separately; never ImageNet init + import timm + + self.backbone = timm.create_model( + "vit_small_patch16_dinov3", + pretrained=False, + in_chans=in_chans, + img_size=img_size, + num_classes=0, + dynamic_img_size=True, + ) + self.proj = _dense_mlp(embed_dim, hidden_dim, proj_dim) + self.global_proj = _global_mlp(embed_dim, hidden_dim, global_dim) + self.num_prefix_tokens = int(getattr(self.backbone, "num_prefix_tokens", 5)) + self.proj_dim = int(proj_dim) + self.patch_size = int(getattr(self.backbone, "patch_size", 16) or 16) + + def forward( + self, x: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Run encoder on multi-view batch. + + Args: + x: Float tensor ``[B, C, V, H, W]`` (typically C=1). + + Returns: + ``(global_proj, dense_proj, feats)`` where dense is + ``[B, V, L, proj_dim]`` and global is ``[B, V, global_dim]``. + """ + if x.ndim != 5: + raise ValueError(f"expected [B,C,V,H,W], got shape {tuple(x.shape)}") + b, c, v, h, w = x.shape + flat = x.permute(0, 2, 1, 3, 4).reshape(b * v, c, h, w) + feats = self.backbone.forward_features(flat) + cls = feats[:, 0] + patches = feats[:, self.num_prefix_tokens :] + bv, length, dim = patches.shape + dense = self.proj(patches.reshape(bv * length, dim)).reshape( + b, v, length, -1 + ) + glob = self.global_proj(cls).reshape(b, v, -1) + return glob, dense, feats + + +def build_encoder(**kwargs: Any) -> DINOv3ViTEncoder: + """Construct an uninitialized Mark25 encoder.""" + return DINOv3ViTEncoder(**kwargs) diff --git a/ipred/src/ipred/tomojepa_onnx.py b/ipred/src/ipred/tomojepa_onnx.py new file mode 100644 index 0000000..cfbf7c7 --- /dev/null +++ b/ipred/src/ipred/tomojepa_onnx.py @@ -0,0 +1,208 @@ +"""TomoJEPA ONNX Runtime backend (optional; falls back to torch).""" + +from __future__ import annotations + +import logging +import threading +from functools import lru_cache +from pathlib import Path +from typing import Any + +import numpy as np +from PIL import Image as PILImage + +from ipred.tomojepa_embed import _gray_unit_interval, to_minus_one_one + +logger = logging.getLogger(__name__) + +_session_lock = threading.Lock() +_PATCH = 16 +_DEFAULT_INPUT_SIZE = 512 + + +def onnx_available() -> bool: + """True when onnxruntime can be imported.""" + try: + import onnxruntime # noqa: F401 + except ImportError: + return False + return True + + +@lru_cache(maxsize=4) +def _session_for(path_str: str): + import onnxruntime as ort + + return ort.InferenceSession(path_str, providers=["CPUExecutionProvider"]) + + +def fixed_spatial_size(weights_path: str | Path) -> int | None: + """Return fixed square H=W from ONNX input, or None when dynamic / unknown.""" + if not onnx_available(): + return None + path = Path(weights_path) + if not path.is_file(): + return None + with _session_lock: + sess = _session_for(str(path.resolve())) + shape = sess.get_inputs()[0].shape + if not shape: + return None + # [N,C,D,H,W] or [N,C,H,W] + dims = list(shape) + if len(dims) >= 2: + h, w = dims[-2], dims[-1] + if isinstance(h, int) and isinstance(w, int) and h == w and h > 0: + return int(h) + return None + + +def onnx_matches_input_size( + weights_path: str | Path, input_size: int +) -> bool: + """True when ONNX is dynamic or fixed spatial size equals ``input_size``.""" + fixed = fixed_spatial_size(weights_path) + if fixed is None: + return True + return fixed == int(input_size) + + +def _pad_to_multiple(h: int, w: int, multiple: int = _PATCH) -> tuple[int, int]: + ph = (multiple - (h % multiple)) % multiple + pw = (multiple - (w % multiple)) % multiple + return h + ph, w + pw + + +def encode_dense_embeddings( + arr: np.ndarray, + *, + weights_path: str, + input_size: int = _DEFAULT_INPUT_SIZE, + resize: bool = True, +) -> tuple[np.ndarray, tuple[int, int], tuple[int, int]]: + """Run exported TomoJEPA ONNX; return ``(emb Hp×Wp×D, orig_hw, reshaped_hw)``. + + Expects ONNX input name ``input`` with shape ``[1,1,1,H,W]`` or ``[1,1,H,W]`` + float32 in ``[-1, 1]``, and output ``dense`` as ``[1,1,L,D]`` or ``[1,L,D]``. + + When the graph has a fixed spatial size, that size is used (must match + ``input_size`` when ``resize`` is True, else ValueError). + """ + if not onnx_available(): + raise ImportError("onnxruntime required for TomoJEPA ONNX") + path = Path(weights_path) + if not path.is_file(): + raise FileNotFoundError(str(path)) + + fixed = fixed_spatial_size(path) + effective_size = int(input_size) + if fixed is not None: + if resize and fixed != effective_size: + raise ValueError( + f"ONNX fixed size {fixed} != input_size {effective_size}; " + "use torch fallback or re-export ONNX" + ) + effective_size = fixed + + gray = _gray_unit_interval(arr) + oh, ow = int(gray.shape[0]), int(gray.shape[1]) + if resize: + size = max(effective_size, _PATCH) + resized = np.asarray( + PILImage.fromarray(gray, mode="F").resize( + (size, size), PILImage.BILINEAR + ), + dtype=np.float32, + ) + rh, rw = size, size + else: + resized = gray.astype(np.float32, copy=True) + rh, rw = oh, ow + if fixed is not None and (rh != fixed or rw != fixed): + raise ValueError( + f"array {rh}x{rw} does not match ONNX fixed size {fixed}" + ) + + pad_h, pad_w = _pad_to_multiple(rh, rw, _PATCH) + if pad_h != rh or pad_w != rw: + canvas = np.zeros((pad_h, pad_w), dtype=np.float32) + canvas[:rh, :rw] = resized + resized = canvas + else: + pad_h, pad_w = rh, rw + + model_in = to_minus_one_one(resized) + x5 = np.array(model_in, dtype=np.float32, copy=True)[None, None, None] + x4 = np.array(model_in, dtype=np.float32, copy=True)[None, None] + + with _session_lock: + sess = _session_for(str(path.resolve())) + in_meta = sess.get_inputs()[0] + in_name = in_meta.name + # Choose rank matching exported model + shape = in_meta.shape + rank = len(shape) if shape else 5 + feed = {in_name: x5 if rank >= 5 else x4} + outs = sess.run(None, feed) + dense = np.asarray(outs[0], dtype=np.float32) + # Squeeze to [L, D] + while dense.ndim > 2: + if dense.shape[0] == 1: + dense = dense[0] + else: + break + if dense.ndim != 2: + raise ValueError(f"unexpected ONNX dense shape {dense.shape}") + + grid_h = pad_h // _PATCH + grid_w = pad_w // _PATCH + if dense.shape[0] != grid_h * grid_w: + side = int(round(dense.shape[0] ** 0.5)) + grid_h = grid_w = side + emb = dense.reshape(grid_h, grid_w, dense.shape[-1]).astype(np.float32) + return emb, (oh, ow), (pad_h, pad_w) + + +def export_tomojepa_onnx( + pth_path: str | Path, + onnx_path: str | Path, + *, + input_size: int = 512, + opset: int = 17, +) -> Path: + """Export ``DINOv3ViTEncoder`` checkpoint to ONNX (fixed square size).""" + import torch + + from ipred.tomojepa_embed import build_encoder, load_checkpoint_state + + pth = Path(pth_path) + out = Path(onnx_path) + out.parent.mkdir(parents=True, exist_ok=True) + state = load_checkpoint_state(pth) + proj_dim = int(state["proj.8.weight"].shape[0]) + net = build_encoder(proj_dim=proj_dim, img_size=input_size, in_chans=1) + net.load_state_dict(state, strict=True) + net.eval() + + class _DenseOnly(torch.nn.Module): + def __init__(self, enc: Any) -> None: + super().__init__() + self.enc = enc + + def forward(self, x: torch.Tensor) -> torch.Tensor: + _g, dense, _f = self.enc(x) + return dense + + wrapper = _DenseOnly(net) + dummy = torch.zeros(1, 1, 1, input_size, input_size, dtype=torch.float32) + torch.onnx.export( + wrapper, + dummy, + str(out), + input_names=["input"], + output_names=["dense"], + opset_version=opset, + dynamo=False, + ) + logger.info("exported TomoJEPA ONNX → %s (proj_dim=%s)", out, proj_dim) + return out.resolve() diff --git a/ipred/src/ipred/train_infer.py b/ipred/src/ipred/train_infer.py new file mode 100644 index 0000000..d3d6564 --- /dev/null +++ b/ipred/src/ipred/train_infer.py @@ -0,0 +1,471 @@ +"""Train / infer / rethreshold orchestration.""" + +from __future__ import annotations + +import json +import logging +import uuid +from datetime import datetime, timezone +from io import BytesIO +from pathlib import Path +from typing import Any + +import numpy as np +from PIL import Image as PILImage + +from ipred import conformal, sam_embed +from ipred.catalog import Catalog +from ipred.labels import build_label_map +from ipred.paths import project_blob_dir +from ipred.preprocess import load_feature_bank_arrays +from ipred.trainers import get_trainer + +logger = logging.getLogger(__name__) + + +def _utc_now() -> str: + return datetime.now(timezone.utc).isoformat() + + +def stratified_train_cal_split( + ys: np.ndarray, + *, + train_frac: float = 0.8, + rng: np.random.Generator, +) -> tuple[np.ndarray, np.ndarray]: + """Stratified indices into train / calibration.""" + if not 0.0 < train_frac < 1.0: + raise ValueError("train_frac must be in (0, 1)") + train_parts: list[np.ndarray] = [] + cal_parts: list[np.ndarray] = [] + for cls in np.unique(ys): + idx = np.flatnonzero(ys == cls) + rng.shuffle(idx) + n = idx.size + if n == 1: + train_parts.append(idx) + continue + n_train = max(1, min(n - 1, int(round(n * train_frac)))) + train_parts.append(idx[:n_train]) + cal_parts.append(idx[n_train:]) + train_idx = np.concatenate(train_parts) if train_parts else np.array([], dtype=np.int64) + cal_idx = np.concatenate(cal_parts) if cal_parts else np.array([], dtype=np.int64) + if cal_idx.size == 0: + raise ValueError("calibration split empty — need more labeled pixels per class") + return train_idx, cal_idx + + +def _stratified_sample( + ys: np.ndarray, max_samples: int, rng: np.random.Generator +) -> np.ndarray: + n = ys.shape[0] + if n <= max_samples: + return np.arange(n) + classes, counts = np.unique(ys, return_counts=True) + alloc = np.maximum(1, np.floor(counts / n * max_samples).astype(int)) + while alloc.sum() < max_samples: + alloc[int(np.argmax(counts - alloc))] += 1 + while alloc.sum() > max_samples: + i = int(np.argmax(alloc)) + if alloc[i] > 1: + alloc[i] -= 1 + else: + break + chosen: list[np.ndarray] = [] + for cls, k in zip(classes, alloc): + idx = np.flatnonzero(ys == cls) + take = min(int(k), idx.size) + chosen.append(rng.choice(idx, size=take, replace=False)) + return np.concatenate(chosen) if chosen else np.arange(0) + + +def _pixel_features( + bank: dict[str, Any], + yy: np.ndarray, + xx: np.ndarray, + *, + sam_pca_mean: np.ndarray | None = None, + sam_pca_components: np.ndarray | None = None, + fit_pca: bool = False, + sam_pca_dims: int = 32, +) -> tuple[np.ndarray, list[str], np.ndarray | None, np.ndarray | None]: + float_stack = bank["float_stack"] + h, w, _ = float_stack.shape + ys_i = np.clip(yy.astype(np.int64), 0, h - 1) + xs_i = np.clip(xx.astype(np.int64), 0, w - 1) + x_sk = float_stack[ys_i, xs_i].astype(np.float32) + labels = list(bank["labels"]) + sam_emb = bank.get("sam_emb") + sam_meta = bank.get("sam_meta") + # Preprocess may already bake encoder PCA into float_stack channels. + if sam_meta and sam_meta.get("baked_into_float_stack"): + return x_sk, labels, None, None + if sam_emb is None or sam_meta is None: + return x_sk, labels, None, None + oh, ow = sam_meta["orig_hw"] + rh, rw = sam_meta["reshaped_hw"] + x_sam = sam_embed.bilinear_sample_emb( + sam_emb, + ys_i.astype(np.float64), + xs_i.astype(np.float64), + orig_h=int(oh), + orig_w=int(ow), + reshaped_h=int(rh), + reshaped_w=int(rw), + ) + mean, comp = sam_pca_mean, sam_pca_components + if fit_pca: + mean, comp = sam_embed.fit_pca(x_sam, n_components=sam_pca_dims) + if mean is not None and comp is not None: + x_sam = sam_embed.transform_pca(x_sam, mean, comp) + x = np.concatenate([x_sk, x_sam], axis=1) + feat_labels = labels + [f"sam{i}" for i in range(x_sam.shape[1])] + return x, feat_labels, mean, comp + + +def run_train( + catalog: Catalog, + *, + session_id: str, + shapes: list[dict[str, Any]], + feature_id: str | None = None, + trainer_id: str = "catboost", + config: dict[str, Any] | None = None, +) -> dict[str, Any]: + """Train a plugin model on session feature bank + shapes.""" + session = catalog.get_session(session_id) + if session is None: + raise KeyError(f"unknown session {session_id}") + fid = feature_id or session.current_feature_id + if not fid: + raise ValueError("no feature_id; run preprocess first") + bank_row = catalog.get_feature_bank(fid) + if bank_row is None: + raise KeyError(f"unknown feature bank {fid}") + bank = load_feature_bank_arrays(bank_row["blob_dir"]) + h, w, _ = bank["float_stack"].shape + label_map = build_label_map(shapes, h, w) + if not np.any(label_map > 0): + raise ValueError("no labeled pixels — annotate at least one shape") + + yy, xx = np.nonzero(label_map) + y_all = label_map[yy, xx].astype(np.int32) + if len(np.unique(y_all)) < 2: + raise ValueError("need at least two classes with labeled pixels") + + cfg = dict(config or {}) + rng = np.random.default_rng(int(cfg.get("random_seed", 0))) + max_samples = int(cfg.get("max_samples", 200_000)) + train_frac = float(cfg.get("train_frac", 0.8)) + cap_idx = _stratified_sample(y_all, max_samples, rng) + yy_c, xx_c, y_c = yy[cap_idx], xx[cap_idx], y_all[cap_idx] + train_idx, cal_idx = stratified_train_cal_split(y_c, train_frac=train_frac, rng=rng) + + uses_sam = ( + bank.get("sam_emb") is not None + and not (bank.get("sam_meta") or {}).get("baked_into_float_stack") + ) + x_tr, feat_labels, pca_mean, pca_comp = _pixel_features( + bank, + yy_c[train_idx], + xx_c[train_idx], + fit_pca=uses_sam, + sam_pca_dims=int(cfg.get("sam_pca_dims", 32)), + ) + x_cal, _, _, _ = _pixel_features( + bank, + yy_c[cal_idx], + xx_c[cal_idx], + sam_pca_mean=pca_mean, + sam_pca_components=pca_comp, + ) + + trainer = get_trainer(trainer_id) + arts = trainer.train( + x_tr, + y_c[train_idx], + x_cal, + y_c[cal_idx], + feature_labels=feat_labels, + config=cfg, + ) + arts.params["uses_sam"] = uses_sam + arts.params["train_frac"] = train_frac + + model_id = uuid.uuid4().hex + blob = project_blob_dir(session.project_id) / "models" / model_id + blob.mkdir(parents=True, exist_ok=True) + trainer.save(arts.model_handle, blob) + feature_importances = list(arts.extras.get("feature_importances") or []) + meta = { + "model_id": model_id, + "feature_id": fid, + "trainer_id": trainer_id, + "class_ids": arts.class_ids, + "feature_labels": arts.feature_labels, + "cal_scores_by_class": arts.cal_scores_by_class, + "train_accuracy": arts.train_accuracy, + "n_train": arts.n_train, + "n_cal": arts.n_cal, + "n_samples": arts.n_samples, + "params": arts.params, + "uses_sam": uses_sam, + "feature_importances": feature_importances, + } + if pca_mean is not None and pca_comp is not None: + np.savez(blob / "sam_pca.npz", mean=pca_mean, components=pca_comp) + meta["has_sam_pca"] = True + (blob / "meta.json").write_text(json.dumps(meta, indent=2), encoding="utf-8") + (blob / "cal_scores.json").write_text( + json.dumps(arts.cal_scores_by_class), encoding="utf-8" + ) + + catalog.insert_model( + { + "model_id": model_id, + "project_id": session.project_id, + "feature_id": fid, + "trainer_id": trainer_id, + "blob_dir": str(blob), + "meta_json": json.dumps(meta), + "created_at": _utc_now(), + } + ) + catalog.set_session_currents(session_id, model_id=model_id) + return { + "model_id": model_id, + "feature_id": fid, + "trainer_id": trainer_id, + "class_ids": arts.class_ids, + "train_accuracy": arts.train_accuracy, + "n_train": arts.n_train, + "n_cal": arts.n_cal, + "n_samples": arts.n_samples, + "params": arts.params, + "feature_importances": feature_importances, + } + + +def run_infer( + catalog: Catalog, + *, + session_id: str, + model_id: str | None = None, + feature_id: str | None = None, + alpha: float = 0.05, + row_chunk: int = 128, +) -> dict[str, Any]: + """Full-image predict_proba + conformal maps; persist float16 proba.""" + session = catalog.get_session(session_id) + if session is None: + raise KeyError(f"unknown session {session_id}") + mid = model_id or session.current_model_id + fid = feature_id or session.current_feature_id + if not mid or not fid: + raise ValueError("model_id and feature_id required (train/preprocess first)") + model_row = catalog.get_model(mid) + bank_row = catalog.get_feature_bank(fid) + if model_row is None or bank_row is None: + raise KeyError("model or feature bank missing") + + blob_model = Path(model_row["blob_dir"]) + meta = json.loads((blob_model / "meta.json").read_text(encoding="utf-8")) + trainer = get_trainer(model_row["trainer_id"]) + handle = trainer.load(blob_model) + bank = load_feature_bank_arrays(bank_row["blob_dir"]) + pca_mean = pca_comp = None + if (blob_model / "sam_pca.npz").is_file(): + z = np.load(blob_model / "sam_pca.npz") + pca_mean, pca_comp = z["mean"], z["components"] + + class_ids = [int(c) for c in meta["class_ids"]] + h, w, _ = bank["float_stack"].shape + k = len(class_ids) + proba = np.empty((h, w, k), dtype=np.float16) + + for y0 in range(0, h, row_chunk): + y1 = min(h, y0 + row_chunk) + yy = np.repeat(np.arange(y0, y1), w) + xx = np.tile(np.arange(w), y1 - y0) + x, _, _, _ = _pixel_features( + bank, yy, xx, sam_pca_mean=pca_mean, sam_pca_components=pca_comp + ) + block = trainer.predict_proba(handle, x).astype(np.float32) + # Align columns to meta class_ids order + model_classes = [int(c) for c in np.asarray(handle.classes_).tolist()] + if model_classes != class_ids: + remap = np.zeros_like(block) + for j, cid in enumerate(class_ids): + if cid in model_classes: + remap[:, j] = block[:, model_classes.index(cid)] + block = remap + proba[y0:y1] = block.reshape(y1 - y0, w, k).astype(np.float16) + + cal = conformal.cal_scores_from_json(meta["cal_scores_by_class"]) + q_by_class = conformal.mondrian_thresholds(cal, alpha) + commit, status, membership = conformal.maps_from_proba( + proba.astype(np.float32), class_ids, q_by_class + ) + counts = conformal.counts_from_status(status) + + run_id = uuid.uuid4().hex + blob = project_blob_dir(session.project_id) / "runs" / run_id + blob.mkdir(parents=True, exist_ok=True) + np.save(blob / "proba.npy", proba) + np.save(blob / "commit.npy", commit) + np.save(blob / "status.npy", status) + np.save(blob / "membership.npy", membership) + _write_label_png(blob / "commit.png", commit) + _write_label_png(blob / "status.png", status) + + run_meta = { + "run_id": run_id, + "model_id": mid, + "feature_id": fid, + "alpha": alpha, + "class_ids": class_ids, + "q_by_class": {str(k_): v for k_, v in q_by_class.items()}, + "counts": counts, + } + (blob / "meta.json").write_text(json.dumps(run_meta, indent=2), encoding="utf-8") + now = _utc_now() + catalog.insert_run( + { + "run_id": run_id, + "project_id": session.project_id, + "model_id": mid, + "feature_id": fid, + "alpha": float(alpha), + "blob_dir": str(blob), + "meta_json": json.dumps(run_meta), + "created_at": now, + "updated_at": now, + } + ) + catalog.set_session_currents(session_id, run_id=run_id) + return {**run_meta, "blob_dir": str(blob)} + + +def run_rethreshold( + catalog: Catalog, + *, + session_id: str, + run_id: str | None = None, + alpha: float, +) -> dict[str, Any]: + """Recompute membership/commit/status from cached proba (no re-infer).""" + session = catalog.get_session(session_id) + if session is None: + raise KeyError(f"unknown session {session_id}") + rid = run_id or session.current_run_id + if not rid: + raise ValueError("run_id required") + run = catalog.get_run(rid) + if run is None: + raise KeyError(f"unknown run {rid}") + blob = Path(run["blob_dir"]) + proba = np.load(blob / "proba.npy").astype(np.float32) + run_meta = json.loads((blob / "meta.json").read_text(encoding="utf-8")) + model_row = catalog.get_model(run["model_id"]) + if model_row is None: + raise KeyError("model missing for run") + model_meta = json.loads( + Path(model_row["blob_dir"], "meta.json").read_text(encoding="utf-8") + ) + cal = conformal.cal_scores_from_json(model_meta["cal_scores_by_class"]) + class_ids = [int(c) for c in run_meta["class_ids"]] + q_by_class = conformal.mondrian_thresholds(cal, alpha) + commit, status, membership = conformal.maps_from_proba(proba, class_ids, q_by_class) + counts = conformal.counts_from_status(status) + np.save(blob / "commit.npy", commit) + np.save(blob / "status.npy", status) + np.save(blob / "membership.npy", membership) + _write_label_png(blob / "commit.png", commit) + _write_label_png(blob / "status.png", status) + run_meta.update( + { + "alpha": alpha, + "q_by_class": {str(k): v for k, v in q_by_class.items()}, + "counts": counts, + } + ) + (blob / "meta.json").write_text(json.dumps(run_meta, indent=2), encoding="utf-8") + catalog.update_run(rid, alpha=float(alpha), meta_json=json.dumps(run_meta)) + catalog.set_session_currents(session_id, run_id=rid) + return {**run_meta, "blob_dir": str(blob)} + + +def _write_label_png(path: Path, arr: np.ndarray) -> None: + img = PILImage.fromarray(arr.astype(np.uint8), mode="L") + buf = BytesIO() + img.save(buf, format="PNG") + path.write_bytes(buf.getvalue()) + + +def _run_blob(catalog: Catalog, run_id: str) -> tuple[dict[str, Any], Path, dict[str, Any]]: + run = catalog.get_run(run_id) + if run is None: + raise KeyError(f"unknown run {run_id}") + blob = Path(run["blob_dir"]) + meta = json.loads(run["meta_json"]) + return run, blob, meta + + +def proba_heatmap_png(catalog: Catalog, run_id: str, class_index: int) -> bytes: + """Return grayscale PNG of softmax channel ``class_index`` (0…K-1).""" + _run, blob, meta = _run_blob(catalog, run_id) + proba_path = blob / "proba.npy" + if not proba_path.is_file(): + raise FileNotFoundError("proba.npy missing — run Predict first") + proba = np.load(proba_path) + if proba.ndim != 3: + raise ValueError("proba must be HxWxK") + k = int(proba.shape[2]) + if not 0 <= int(class_index) < k: + raise ValueError(f"class_index {class_index} out of range 0..{k - 1}") + ch = np.clip(proba[:, :, int(class_index)].astype(np.float32), 0.0, 1.0) + uint8 = np.clip(np.round(ch * 255.0), 0, 255).astype(np.uint8) + buf = BytesIO() + PILImage.fromarray(uint8, mode="L").save(buf, format="PNG") + return buf.getvalue() + + +def threshold_class_label_map( + catalog: Catalog, + run_id: str, + *, + class_id: int, + threshold: float, +) -> dict[str, Any]: + """Binary class map from softmax: ``class_id`` where ``p >= threshold``, else 0. + + Returns width/height and base64-encoded uint8 label map for mask-set caches. + """ + import base64 + + _run, blob, meta = _run_blob(catalog, run_id) + class_ids = [int(c) for c in meta.get("class_ids") or []] + if int(class_id) not in class_ids: + raise ValueError(f"class_id {class_id} not in run class_ids {class_ids}") + j = class_ids.index(int(class_id)) + proba_path = blob / "proba.npy" + if not proba_path.is_file(): + raise FileNotFoundError("proba.npy missing — run Predict first") + proba = np.load(proba_path).astype(np.float32) + t = float(threshold) + if not 0.0 <= t <= 1.0: + raise ValueError("threshold must be in [0, 1]") + h, w, _ = proba.shape + labels = np.zeros((h, w), dtype=np.uint8) + labels[proba[:, :, j] >= t] = np.uint8(int(class_id)) + raw = labels.reshape(-1).tobytes() + return { + "run_id": run_id, + "class_id": int(class_id), + "class_index": int(j), + "threshold": t, + "width": int(w), + "height": int(h), + "n_positive": int(np.count_nonzero(labels)), + "label_map_b64": base64.b64encode(raw).decode("ascii"), + } diff --git a/ipred/src/ipred/trainers/__init__.py b/ipred/src/ipred/trainers/__init__.py new file mode 100644 index 0000000..1a360ad --- /dev/null +++ b/ipred/src/ipred/trainers/__init__.py @@ -0,0 +1,25 @@ +"""Trainer plugin registry.""" + +from __future__ import annotations + +from ipred.trainers.base import ModelArtifacts, TrainerPlugin +from ipred.trainers.catboost_trainer import CatBoostTrainer + +_REGISTRY: dict[str, TrainerPlugin] = { + CatBoostTrainer.id: CatBoostTrainer(), +} + + +def get_trainer(trainer_id: str) -> TrainerPlugin: + """Return a registered trainer or raise KeyError.""" + try: + return _REGISTRY[trainer_id] + except KeyError as exc: + raise KeyError( + f"unknown trainer {trainer_id!r}; available: {sorted(_REGISTRY)}" + ) from exc + + +def list_trainers() -> list[str]: + """Return registered trainer ids.""" + return sorted(_REGISTRY) diff --git a/ipred/src/ipred/trainers/base.py b/ipred/src/ipred/trainers/base.py new file mode 100644 index 0000000..3edd6d7 --- /dev/null +++ b/ipred/src/ipred/trainers/base.py @@ -0,0 +1,53 @@ +"""Trainer plugin interface.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Protocol + +import numpy as np + + +@dataclass +class ModelArtifacts: + """Artifacts produced by ``train``.""" + + class_ids: list[int] + feature_labels: list[str] + cal_scores_by_class: dict[int, list[float]] + train_accuracy: float + n_train: int + n_cal: int + n_samples: int + params: dict[str, Any] + extras: dict[str, Any] = field(default_factory=dict) + # In-memory handle for immediate predict_proba (plugin-specific) + model_handle: Any = None + + +class TrainerPlugin(Protocol): + """Train / predict_proba contract for pixel classifiers.""" + + id: str + + def train( + self, + x_train: np.ndarray, + y_train: np.ndarray, + x_cal: np.ndarray, + y_cal: np.ndarray, + *, + feature_labels: list[str], + config: dict[str, Any], + ) -> ModelArtifacts: + """Fit model and compute Mondrian calibration scores.""" + + def predict_proba(self, model_handle: Any, x: np.ndarray) -> np.ndarray: + """Return NxK probabilities aligned with training class order.""" + + def save(self, model_handle: Any, dest_dir: Path) -> None: + """Persist model weights into *dest_dir*.""" + + def load(self, dest_dir: Path) -> Any: + """Load model weights from *dest_dir*.""" diff --git a/ipred/src/ipred/trainers/catboost_trainer.py b/ipred/src/ipred/trainers/catboost_trainer.py new file mode 100644 index 0000000..5bf01fc --- /dev/null +++ b/ipred/src/ipred/trainers/catboost_trainer.py @@ -0,0 +1,107 @@ +"""CatBoost TrainerPlugin.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import numpy as np +from catboost import CatBoostClassifier + +from ipred.trainers.base import ModelArtifacts + + +class CatBoostTrainer: + """CatBoost multiclass pixel trainer with Mondrian cal scores.""" + + id = "catboost" + + def train( + self, + x_train: np.ndarray, + y_train: np.ndarray, + x_cal: np.ndarray, + y_cal: np.ndarray, + *, + feature_labels: list[str], + config: dict[str, Any], + ) -> ModelArtifacts: + """Fit CatBoost and collect per-class nonconformity scores.""" + iterations = int(config.get("iterations", 200)) + depth = int(config.get("depth", 6)) + learning_rate = float(config.get("learning_rate", 0.1)) + random_seed = int(config.get("random_seed", 0)) + params = { + "iterations": iterations, + "depth": depth, + "learning_rate": learning_rate, + "loss_function": "MultiClass", + "random_seed": random_seed, + } + clf = CatBoostClassifier( + iterations=iterations, + depth=depth, + learning_rate=learning_rate, + loss_function="MultiClass", + verbose=False, + allow_writing_files=False, + random_seed=random_seed, + thread_count=-1, + ) + clf.fit(x_train, y_train) + pred = np.asarray(clf.predict(x_train)).reshape(-1).astype(np.int32) + train_accuracy = float(np.mean(pred == y_train)) + + proba_cal = np.asarray(clf.predict_proba(x_cal), dtype=np.float64) + classes = [int(c) for c in np.asarray(clf.classes_).tolist()] + class_to_col = {c: i for i, c in enumerate(classes)} + cal_scores: dict[int, list[float]] = {} + for cid in classes: + mask = y_cal == cid + if not np.any(mask): + continue + col = class_to_col[cid] + scores = 1.0 - proba_cal[mask, col] + cal_scores[cid] = np.sort(scores.astype(np.float64)).tolist() + if not cal_scores: + raise ValueError("no calibration scores") + + labels = list(feature_labels) + raw_imp = np.asarray(clf.get_feature_importance(), dtype=np.float64) + if len(labels) != raw_imp.size: + labels = [f"f{i}" for i in range(raw_imp.size)] + order = np.argsort(-raw_imp) + feature_importances = [ + {"label": labels[int(i)], "importance": float(raw_imp[int(i)])} for i in order + ] + + return ModelArtifacts( + class_ids=classes, + feature_labels=labels, + cal_scores_by_class=cal_scores, + train_accuracy=train_accuracy, + n_train=int(y_train.shape[0]), + n_cal=int(y_cal.shape[0]), + n_samples=int(y_train.shape[0] + y_cal.shape[0]), + params=params, + extras={"feature_importances": feature_importances}, + model_handle=clf, + ) + + def predict_proba(self, model_handle: Any, x: np.ndarray) -> np.ndarray: + """Return NxK float64 probabilities.""" + proba = np.asarray(model_handle.predict_proba(x), dtype=np.float64) + if proba.ndim == 1: + proba = np.stack([1.0 - proba, proba], axis=1) + return proba + + def save(self, model_handle: Any, dest_dir: Path) -> None: + """Write ``model.cbm``.""" + dest_dir.mkdir(parents=True, exist_ok=True) + model_handle.save_model(str(dest_dir / "model.cbm")) + + def load(self, dest_dir: Path) -> Any: + """Load ``model.cbm``.""" + clf = CatBoostClassifier() + clf.load_model(str(dest_dir / "model.cbm")) + return clf diff --git a/ipred/tests/test_api_health.py b/ipred/tests/test_api_health.py new file mode 100644 index 0000000..29680bc --- /dev/null +++ b/ipred/tests/test_api_health.py @@ -0,0 +1,51 @@ +"""API health + session open.""" + +from __future__ import annotations + +import pytest +from fastapi.testclient import TestClient + +from ipred import api + + +@pytest.fixture() +def client(tmp_path, monkeypatch: pytest.MonkeyPatch) -> TestClient: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + api._catalog = None + with TestClient(api.app) as c: + yield c + + +def test_health(client: TestClient) -> None: + r = client.get("/health") + assert r.status_code == 200 + assert r.json()["status"] == "ok" + + +def test_open_session_and_list_setups(client: TestClient) -> None: + r = client.post( + "/sessions", + json={"kind": "local", "source": "x.png", "root": "/tmp"}, + ) + assert r.status_code == 200 + body = r.json() + assert "session_id" in body + assert "project_id" in body + + r2 = client.get("/setups") + assert r2.status_code == 200 + ids = {s["id"] for s in r2.json()["setups"]} + assert "default-skimage" in ids + assert "default-mark25" in ids + assert "default-skimage-mark25" in ids + assert "default-mark25-clahe" in ids + assert "default-mark11" in ids + assert "default-skimage-mark11" in ids + assert "default-mark11-clahe" in ids + assert "default-slimsam-clahe" in ids + + r3 = client.get("/compositions") + assert r3.status_code == 200 + cids = {c["id"] for c in r3.json()["compositions"]} + assert "comp-skimage" in cids + assert "comp-skimage-mark11" in cids diff --git a/ipred/tests/test_array_blobs.py b/ipred/tests/test_array_blobs.py new file mode 100644 index 0000000..f590091 --- /dev/null +++ b/ipred/tests/test_array_blobs.py @@ -0,0 +1,77 @@ +"""Array blob data plane for remote preprocess.""" + +from __future__ import annotations + +import base64 + +import numpy as np +import pytest +from fastapi.testclient import TestClient + +from ipred import api, array_blobs, compositions + + +@pytest.fixture() +def client(tmp_path, monkeypatch: pytest.MonkeyPatch) -> TestClient: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + api._catalog = None + with TestClient(api.app) as c: + yield c + + +def test_save_load_array_blob(tmp_path, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + arr = np.arange(12, dtype=np.float32).reshape(3, 4) + rec = array_blobs.save_array_blob("proj1", arr) + out = array_blobs.load_array_blob("proj1", rec["array_ref"]) + np.testing.assert_array_equal(out, arr) + + +def test_modules_and_compositions_api(client: TestClient) -> None: + r = client.get("/modules") + assert r.status_code == 200 + ids = {m["id"] for m in r.json()["modules"]} + assert "tomojepa" in ids + + r2 = client.get("/compositions") + assert r2.status_code == 200 + cids = {c["id"] for c in r2.json()["compositions"]} + assert "comp-skimage" in cids + + r3 = client.get("/compositions/comp-skimage") + assert r3.status_code == 200 + assert "preview_labels" in r3.json() + + +def test_upload_array_and_preprocess(client: TestClient) -> None: + sess = client.post( + "/sessions", + json={"kind": "local", "source": "x.png", "root": "/tmp"}, + ).json() + arr = np.linspace(0, 1, 16 * 16, dtype=np.float32).reshape(16, 16) + raw = arr.astype(np.float32).tobytes() + up = client.post( + f"/sessions/{sess['session_id']}/arrays", + json={ + "session_id": sess["session_id"], + "shape": [16, 16], + "dtype": "float32", + "data_b64": base64.b64encode(raw).decode("ascii"), + }, + ) + assert up.status_code == 200 + ref = up.json()["array_ref"] + compositions.ensure_default_compositions(api.get_catalog()) + prep = client.post( + "/preprocess", + json={ + "session_id": sess["session_id"], + "composition_id": "comp-skimage", + "array_ref": ref, + "slice_index": 0, + }, + ) + assert prep.status_code == 200, prep.text + body = prep.json() + assert body["n_channels"] > 0 + assert body["composition_id"] == "comp-skimage" diff --git a/ipred/tests/test_catalog_sessions.py b/ipred/tests/test_catalog_sessions.py new file mode 100644 index 0000000..33e3882 --- /dev/null +++ b/ipred/tests/test_catalog_sessions.py @@ -0,0 +1,39 @@ +"""Catalog project/session persistence.""" + +from __future__ import annotations + +import os + +import pytest + +from ipred.catalog import Catalog, project_id_for + + +@pytest.fixture() +def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + return Catalog(tmp_path / "ipred" / "catalog.db") + + +def test_project_id_stable() -> None: + a = project_id_for(kind="local", source="a.tif", root="/data") + b = project_id_for(kind="local", source="a.tif", root="/data") + c = project_id_for(kind="local", source="b.tif", root="/data") + assert a == b + assert a != c + + +def test_open_session_creates_project(catalog: Catalog) -> None: + s1 = catalog.open_session(kind="local", source="img.tif", root=str(os.environ["LOCAL_DATA_ROOT"])) + s2 = catalog.open_session(kind="local", source="img.tif", root=str(os.environ["LOCAL_DATA_ROOT"])) + assert s1.project_id == s2.project_id + assert s1.session_id != s2.session_id + got = catalog.get_session(s1.session_id) + assert got is not None + assert got.current_feature_id is None + + +def test_set_session_currents(catalog: Catalog) -> None: + s = catalog.open_session(kind="local", source="x.png") + updated = catalog.set_session_currents(s.session_id, feature_id="abc") + assert updated.current_feature_id == "abc" diff --git a/ipred/tests/test_compositions.py b/ipred/tests/test_compositions.py new file mode 100644 index 0000000..66ba5ee --- /dev/null +++ b/ipred/tests/test_compositions.py @@ -0,0 +1,80 @@ +"""Composition validation, builtins, and legacy setup mapping.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from ipred import compositions, compose_run, feature_setups +from ipred.catalog import Catalog +from ipred.modules import list_module_catalog + + +@pytest.fixture() +def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + return Catalog(tmp_path / "ipred" / "catalog.db") + + +def test_module_catalog_lists_core_modules() -> None: + ids = {m["id"] for m in list_module_catalog()} + assert ids >= { + "skimage_multiscale", + "clahe", + "slimsam", + "tomojepa", + "pca", + } + + +def test_ensure_default_compositions(catalog: Catalog) -> None: + comps = compositions.ensure_default_compositions(catalog) + ids = {c["id"] for c in comps} + assert "comp-skimage" in ids + assert "comp-skimage-mark11" in ids + assert "comp-mark25-clahe" in ids + assert "comp-slimsam-clahe" in ids + + +def test_legacy_setup_maps_to_composition(catalog: Catalog) -> None: + compositions.ensure_default_compositions(catalog) + assert ( + compositions.resolve_preprocess_id("default-skimage-mark25") + == "comp-skimage-mark25" + ) + assert compositions.resolve_preprocess_id("default-mark11") == "comp-mark11-clahe" + + +def test_preview_concat_skimage(catalog: Catalog) -> None: + compositions.ensure_default_compositions(catalog) + doc = compositions.resolve_composition("comp-skimage") + labels = compositions.preview_concat_labels(doc) + assert any("intensity" in lab for lab in labels) + + +def test_run_composition_skimage_only(catalog: Catalog) -> None: + compositions.ensure_default_compositions(catalog) + doc = compositions.resolve_composition("comp-skimage") + arr = np.linspace(0, 1, 32 * 32, dtype=np.float32).reshape(32, 32) + uint8, floats, labels, emb, meta = compose_run.run_composition(arr, doc) + assert emb is None + assert floats.ndim == 3 + assert uint8.shape == floats.shape + assert len(labels) == floats.shape[-1] + + +def test_content_hash_stable() -> None: + doc = { + "nodes": [{"id": "n1", "module": "skimage_multiscale", "params": {}}], + "outputs": ["n1"], + } + compositions.validate_composition(doc) + h1 = compositions.content_hash(doc) + h2 = compositions.content_hash({**doc, "name": "x"}) + assert h1 == h2 + + +def test_feature_setups_still_seed(catalog: Catalog) -> None: + feature_setups.ensure_default_setups(catalog) + compositions.ensure_default_compositions(catalog) + assert feature_setups.load_setup("default-skimage") is not None diff --git a/ipred/tests/test_emb_pca_channels.py b/ipred/tests/test_emb_pca_channels.py new file mode 100644 index 0000000..ecbc79f --- /dev/null +++ b/ipred/tests/test_emb_pca_channels.py @@ -0,0 +1,30 @@ +"""Encoder PCA → full-res heatmap channels.""" + +from __future__ import annotations + +import numpy as np + +from ipred import sam_embed + + +def test_emb_grid_to_pca_channels_shape() -> None: + rng = np.random.default_rng(0) + emb = rng.normal(size=(8, 8, 16)).astype(np.float32) + up, labels, info = sam_embed.emb_grid_to_pca_channels( + emb, out_h=32, out_w=40, n_components=8 + ) + assert up.shape == (32, 40, 8) + assert labels == [f"pca{i}" for i in range(8)] + assert info["pca_dims"] == 8 + assert info["baked_into_float_stack"] is True + + +def test_emb_grid_pca_caps_at_rank() -> None: + rng = np.random.default_rng(1) + emb = rng.normal(size=(4, 4, 5)).astype(np.float32) + up, labels, info = sam_embed.emb_grid_to_pca_channels( + emb, out_h=16, out_w=16, n_components=64 + ) + assert up.shape[-1] == 5 + assert len(labels) == 5 + assert info["pca_dims"] == 5 diff --git a/ipred/tests/test_feature_setups.py b/ipred/tests/test_feature_setups.py new file mode 100644 index 0000000..19ea35f --- /dev/null +++ b/ipred/tests/test_feature_setups.py @@ -0,0 +1,58 @@ +"""Feature Setup shelf defaults.""" + +from __future__ import annotations + +import pytest + +from ipred.catalog import Catalog +from ipred import feature_setups + + +@pytest.fixture() +def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + return Catalog(tmp_path / "ipred" / "catalog.db") + + +def test_ensure_defaults(catalog: Catalog) -> None: + defaults = feature_setups.ensure_default_setups(catalog) + assert len(defaults) == 10 + ids = {d["id"] for d in defaults} + assert "default-skimage" in ids + assert "default-slimsam" in ids + assert "default-skimage-slimsam" in ids + assert "default-slimsam-clahe" in ids + assert "default-mark25" in ids + assert "default-skimage-mark25" in ids + assert "default-mark25-clahe" in ids + assert "default-mark11" in ids + assert "default-skimage-mark11" in ids + assert "default-mark11-clahe" in ids + sk = feature_setups.resolve_setup("default-skimage") + assert sk["kind"] == "procedure" + assert sk["procedure_id"] == feature_setups.PROCEDURE_SKIMAGE + mark = feature_setups.resolve_setup("default-mark25") + assert mark["kind"] == "weights" + assert mark["weights_format"] == feature_setups.WEIGHTS_TOMOJEPA_MARK25 + assert mark["inference"]["input_size"] == 512 + combo = feature_setups.resolve_setup("default-skimage-mark25") + assert combo["encoder_setup_id"] == "default-mark25" + assert combo["params"]["input_size"] == 512 + assert combo["params"]["resize"] is True + assert combo["params"]["pca_dims"] == 64 + clahe = feature_setups.resolve_setup("default-mark25-clahe") + assert clahe["procedure_id"] == feature_setups.PROCEDURE_CLAHE_ENCODER + assert clahe["encoder_setup_id"] == "default-mark25" + assert clahe["params"]["resize"] is True + assert clahe["params"]["input_size"] == 512 + assert clahe["params"]["clahe"] is True + assert clahe["params"]["pca_dims"] == 64 + mark11 = feature_setups.resolve_setup("default-mark11") + assert mark11["weights_format"] == feature_setups.WEIGHTS_TOMOJEPA_MARK11 + combo11 = feature_setups.resolve_setup("default-skimage-mark11") + assert combo11["encoder_setup_id"] == "default-mark11" + clahe11 = feature_setups.resolve_setup("default-mark11-clahe") + assert clahe11["encoder_setup_id"] == "default-mark11" + slim_clahe = feature_setups.resolve_setup("default-slimsam-clahe") + assert slim_clahe["procedure_id"] == feature_setups.PROCEDURE_CLAHE_ENCODER + assert slim_clahe["encoder_setup_id"] == "default-slimsam" diff --git a/ipred/tests/test_labels.py b/ipred/tests/test_labels.py new file mode 100644 index 0000000..70efd56 --- /dev/null +++ b/ipred/tests/test_labels.py @@ -0,0 +1,45 @@ +"""Label rasterization accepts studio shape wire formats.""" + +from __future__ import annotations + +import numpy as np + +from ipred.labels import build_label_map, shape_to_mask + + +def test_polygon_flat_xy_list() -> None: + """Studio polygons store points as flat [x0,y0,x1,y1,...].""" + shape = { + "kind": "polygon", + "classId": 6, + "points": [10.0, 10.0, 40.0, 10.0, 40.0, 40.0, 10.0, 40.0], + } + mask = shape_to_mask(shape, 64, 64) + assert int(mask.sum()) > 100 + + labels = build_label_map([shape], 64, 64) + assert int((labels == 6).sum()) == int(mask.sum()) + + +def test_polygon_nested_pairs_still_work() -> None: + shape = { + "kind": "polygon", + "classId": 1, + "points": [[10, 10], [40, 10], [40, 40], [10, 40]], + } + assert shape_to_mask(shape, 64, 64).sum() > 100 + + +def test_brush_flat_points_and_radius() -> None: + """Studio brush strokes use flat points + radius (not nested {x,y}).""" + shape = { + "kind": "brush", + "classId": 2, + "strokes": [ + {"points": [8.0, 8.0, 20.0, 20.0, 32.0, 8.0], "radius": 4.0, "mode": "paint"}, + ], + } + mask = shape_to_mask(shape, 48, 48) + assert int(mask.sum()) > 20 + labels = build_label_map([shape], 48, 48) + assert np.any(labels == 2) diff --git a/ipred/tests/test_manifold.py b/ipred/tests/test_manifold.py new file mode 100644 index 0000000..e32b9d6 --- /dev/null +++ b/ipred/tests/test_manifold.py @@ -0,0 +1,42 @@ +"""Manifold Suggest Labels on persisted ipred feature banks.""" + +from __future__ import annotations + +import numpy as np +from PIL import Image as PILImage +import pytest + +from ipred.catalog import Catalog +from ipred import feature_setups, manifold_jobs, preprocess + + +@pytest.fixture() +def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + return Catalog(tmp_path / "ipred" / "catalog.db") + + +def test_manifold_sample_on_feature_bank(catalog: Catalog, tmp_path) -> None: + feature_setups.ensure_default_setups(catalog) + img = np.zeros((64, 64), dtype=np.uint8) + img[:, 32:] = 200 + img[16:48, 16:48] = 120 + PILImage.fromarray(img, mode="L").save(tmp_path / "m.png") + + session = catalog.open_session(kind="local", source="m.png", root=str(tmp_path)) + bank = preprocess.run_preprocess( + catalog, + session_id=session.session_id, + feature_setup_id="default-skimage", + ) + out = manifold_jobs.run_manifold_sample( + catalog, + feature_id=bank["feature_id"], + k=8, + box_size=16, + ) + assert out["sample_id"] + assert out["n_picked"] >= 1 + assert len(out["points"]) >= 1 + png = manifold_jobs.heatmap_png(out["sample_id"]) + assert png[:8] == b"\x89PNG\r\n\x1a\n" diff --git a/ipred/tests/test_preprocess_cache.py b/ipred/tests/test_preprocess_cache.py new file mode 100644 index 0000000..b98250e --- /dev/null +++ b/ipred/tests/test_preprocess_cache.py @@ -0,0 +1,116 @@ +"""Cache-aware preprocess.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +from PIL import Image as PILImage +import pytest + +from ipred.catalog import Catalog +from ipred import feature_setups, preprocess, tomojepa_embed + +WEIGHTS = Path(__file__).resolve().parents[1] / "models" / "tomojepa25.pth" + + +@pytest.fixture() +def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + return Catalog(tmp_path / "ipred" / "catalog.db") + + +def test_preprocess_cache_hit(catalog: Catalog, tmp_path) -> None: + feature_setups.ensure_default_setups(catalog) + # Write a local PNG under LOCAL_DATA_ROOT + img = np.zeros((32, 32), dtype=np.uint8) + img[:, 16:] = 200 + rel = "sample.png" + PILImage.fromarray(img, mode="L").save(tmp_path / rel) + + session = catalog.open_session(kind="local", source=rel, root=str(tmp_path)) + first = preprocess.run_preprocess( + catalog, + session_id=session.session_id, + feature_setup_id="default-skimage", + slice_index=0, + ) + assert first["cache_hit"] is False + assert first["n_channels"] > 0 + + second = preprocess.run_preprocess( + catalog, + session_id=session.session_id, + feature_setup_id="default-skimage", + slice_index=0, + ) + assert second["cache_hit"] is True + assert second["feature_id"] == first["feature_id"] + + +@pytest.mark.skipif( + not WEIGHTS.is_file() or not tomojepa_embed.encoder_available(str(WEIGHTS)), + reason="tomojepa25.pth or torch/timm not available", +) +def test_preprocess_mark25_writes_dense_emb(catalog: Catalog, tmp_path) -> None: + feature_setups.ensure_default_setups(catalog) + img = np.linspace(0, 255, 64 * 48, dtype=np.float32).reshape(64, 48).astype( + np.uint8 + ) + rel = "tomo.png" + PILImage.fromarray(img, mode="L").save(tmp_path / rel) + + session = catalog.open_session(kind="local", source=rel, root=str(tmp_path)) + out = preprocess.run_preprocess( + catalog, + session_id=session.session_id, + feature_setup_id="default-skimage-mark25", + slice_index=0, + ) + assert out["cache_hit"] is False + bank = preprocess.load_feature_bank_arrays(out["blob_dir"]) + assert bank["sam_emb"] is not None + assert bank["sam_emb"].shape == (32, 32, 64) + assert bank["sam_meta"]["encoder"] == "mark25" + assert bank["sam_meta"]["input_size"] == 512 + + +@pytest.mark.skipif( + not WEIGHTS.is_file() or not tomojepa_embed.encoder_available(str(WEIGHTS)), + reason="tomojepa25.pth or torch/timm not available", +) +def test_preprocess_mark25_clahe_no_skimage(catalog: Catalog, tmp_path) -> None: + feature_setups.ensure_default_setups(catalog) + img = np.linspace(0, 255, 80 * 64, dtype=np.float32).reshape(80, 64).astype( + np.uint8 + ) + rel = "tomo_clahe.png" + PILImage.fromarray(img, mode="L").save(tmp_path / rel) + + session = catalog.open_session(kind="local", source=rel, root=str(tmp_path)) + out = preprocess.run_preprocess( + catalog, + session_id=session.session_id, + feature_setup_id="default-mark25-clahe", + slice_index=0, + ) + assert out["cache_hit"] is False + assert out["n_channels"] == 1 + 64 + bank = preprocess.load_feature_bank_arrays(out["blob_dir"]) + assert bank["labels"][0] == "clahe" + assert bank["labels"][1:5] == ["pca0", "pca1", "pca2", "pca3"] + assert bank["float_stack"].shape[-1] == 65 + assert bank["float_stack"].shape[:2] == (80, 64) + assert bank["sam_emb"] is not None + assert bank["sam_emb"].shape == (32, 32, 64) + assert bank["sam_meta"]["encoder"] == "mark25" + assert bank["sam_meta"]["resize"] is True + assert bank["sam_meta"]["input_size"] == 512 + assert bank["sam_meta"]["baked_into_float_stack"] is True + assert bank["sam_meta"]["pca_dims"] == 64 + # Channel PNGs written for heatmap browsing + from pathlib import Path + + ch_dir = Path(out["blob_dir"]) / "channels" + assert (ch_dir / "0000.png").is_file() + assert (ch_dir / "0064.png").is_file() diff --git a/ipred/tests/test_proba_channels.py b/ipred/tests/test_proba_channels.py new file mode 100644 index 0000000..b2dccf6 --- /dev/null +++ b/ipred/tests/test_proba_channels.py @@ -0,0 +1,97 @@ +"""Softmax proba heatmaps + per-class threshold maps.""" + +from __future__ import annotations + +import base64 + +import numpy as np +import pytest + +from ipred.catalog import Catalog +from ipred import feature_setups, preprocess, train_infer +from ipred.paths import project_blob_dir + + +@pytest.fixture() +def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + return Catalog(tmp_path / "ipred" / "catalog.db") + + +def _fake_run(catalog: Catalog, tmp_path) -> str: + """Insert a minimal run with synthetic HxWxK softmax proba.""" + feature_setups.ensure_default_setups(catalog) + session = catalog.open_session(kind="local", source="x.png", root=str(tmp_path)) + project_id = session.project_id + run_id = "runproba01" + blob = project_blob_dir(project_id) / "runs" / run_id + blob.mkdir(parents=True, exist_ok=True) + # Two classes; left half prefers class 1, right prefers class 2 + h, w, k = 8, 10, 2 + proba = np.zeros((h, w, k), dtype=np.float16) + proba[:, :5, 0] = 0.8 + proba[:, :5, 1] = 0.2 + proba[:, 5:, 0] = 0.3 + proba[:, 5:, 1] = 0.7 + np.save(blob / "proba.npy", proba) + meta = { + "run_id": run_id, + "model_id": "m", + "feature_id": "f", + "alpha": 0.05, + "class_ids": [1, 2], + "counts": {"singleton": 0, "multi": 0, "abstain": 0}, + } + import json + from datetime import datetime, timezone + + (blob / "meta.json").write_text(json.dumps(meta), encoding="utf-8") + now = datetime.now(timezone.utc).isoformat() + catalog.insert_run( + { + "run_id": run_id, + "project_id": project_id, + "model_id": "m", + "feature_id": "f", + "alpha": 0.05, + "blob_dir": str(blob), + "meta_json": json.dumps(meta), + "created_at": now, + "updated_at": now, + } + ) + return run_id + + +def test_proba_heatmap_png(catalog: Catalog, tmp_path) -> None: + run_id = _fake_run(catalog, tmp_path) + png = train_infer.proba_heatmap_png(catalog, run_id, 0) + assert png[:8] == b"\x89PNG\r\n\x1a\n" + png1 = train_infer.proba_heatmap_png(catalog, run_id, 1) + assert png1[:8] == b"\x89PNG\r\n\x1a\n" + with pytest.raises(ValueError): + train_infer.proba_heatmap_png(catalog, run_id, 9) + + +def test_threshold_class_label_map(catalog: Catalog, tmp_path) -> None: + run_id = _fake_run(catalog, tmp_path) + out = train_infer.threshold_class_label_map( + catalog, run_id, class_id=1, threshold=0.5 + ) + assert out["width"] == 10 + assert out["height"] == 8 + assert out["class_id"] == 1 + raw = base64.b64decode(out["label_map_b64"]) + labels = np.frombuffer(raw, dtype=np.uint8).reshape(8, 10) + assert np.all(labels[:, :5] == 1) + assert np.all(labels[:, 5:] == 0) + assert out["n_positive"] == 8 * 5 + + out2 = train_infer.threshold_class_label_map( + catalog, run_id, class_id=2, threshold=0.6 + ) + labels2 = np.frombuffer( + base64.b64decode(out2["label_map_b64"]), dtype=np.uint8 + ).reshape(8, 10) + assert np.all(labels2[:, 5:] == 2) + assert np.all(labels2[:, :5] == 0) diff --git a/ipred/tests/test_tomojepa_embed.py b/ipred/tests/test_tomojepa_embed.py new file mode 100644 index 0000000..2968e91 --- /dev/null +++ b/ipred/tests/test_tomojepa_embed.py @@ -0,0 +1,129 @@ +"""Mark25 / TomoJEPA dense embedding encode.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pytest + +from ipred import tomojepa_embed + +WEIGHTS = Path(__file__).resolve().parents[1] / "models" / "tomojepa25.pth" +WEIGHTS11 = Path(__file__).resolve().parents[1] / "models" / "tomojepa11.pth" +needs_weights = pytest.mark.skipif( + not WEIGHTS.is_file() or not tomojepa_embed.torch_available(), + reason="tomojepa25.pth or torch/timm not available", +) +needs_mark11 = pytest.mark.skipif( + not WEIGHTS11.is_file() or not tomojepa_embed.torch_available(), + reason="tomojepa11.pth or torch/timm not available", +) + + +def test_resolve_weights_path_default(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.delenv("TOMOJEPA_WEIGHTS", raising=False) + resolved = tomojepa_embed.resolve_weights_path(None) + if WEIGHTS.is_file(): + assert resolved is not None + assert resolved.name == "tomojepa25.pth" + else: + assert resolved is None + + +def test_resolve_weights_path_env(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: + fake = tmp_path / "custom.pth" + fake.write_bytes(b"x") + monkeypatch.setenv("TOMOJEPA_WEIGHTS", str(fake)) + assert tomojepa_embed.resolve_weights_path(None) == fake.resolve() + + +def test_to_minus_one_one_linear() -> None: + x = np.array([[0.0, 0.5, 1.0]], dtype=np.float32) + y = tomojepa_embed.to_minus_one_one(x) + np.testing.assert_allclose(y, [[-1.0, 0.0, 1.0]], atol=1e-6) + + +@needs_weights +def test_encode_feeds_model_in_minus_one_one(monkeypatch: pytest.MonkeyPatch) -> None: + """All Mark25 paths must pass intensities in [-1, 1] into the network.""" + captured: list[float] = [] + + real_cached = tomojepa_embed._cached_model + + def _wrap(path_str: str): + net, device = real_cached(path_str) + orig_forward = net.forward + + def forward(x): # noqa: ANN001 + t = x.detach().cpu().numpy() + captured.append(float(t.min())) + captured.append(float(t.max())) + return orig_forward(x) + + net.forward = forward # type: ignore[method-assign] + return net, device + + monkeypatch.setattr(tomojepa_embed, "_cached_model", _wrap) + arr = np.linspace(10, 200, 64 * 48, dtype=np.float32).reshape(64, 48) + tomojepa_embed.encode_dense_embeddings( + arr, weights_path=str(WEIGHTS), input_size=128 + ) + assert captured, "model forward was not called" + assert min(captured) >= -1.0 - 1e-5 + assert max(captured) <= 1.0 + 1e-5 + # After min-max + linear map, extremes should reach near ±1. + assert min(captured) < -0.9 + assert max(captured) > 0.9 + + +@needs_weights +def test_load_state_dict_strict() -> None: + net = tomojepa_embed.build_encoder() + state = tomojepa_embed.load_checkpoint_state(WEIGHTS) + net.load_state_dict(state, strict=True) + + +@needs_weights +def test_encode_dense_shape_512() -> None: + arr = np.linspace(0, 1, 400 * 300, dtype=np.float32).reshape(400, 300) + emb, orig_hw, reshaped_hw = tomojepa_embed.encode_dense_embeddings( + arr, weights_path=str(WEIGHTS), input_size=512 + ) + assert orig_hw == (400, 300) + assert reshaped_hw == (512, 512) + assert emb.shape == (32, 32, 64) + assert emb.dtype == np.float32 + + +@needs_weights +def test_encode_dense_honors_input_size() -> None: + arr = np.random.default_rng(0).random((128, 96), dtype=np.float32) + emb, _, reshaped = tomojepa_embed.encode_dense_embeddings( + arr, weights_path=str(WEIGHTS), input_size=256 + ) + assert reshaped == (256, 256) + assert emb.shape == (16, 16, 64) + + +@needs_weights +def test_encode_native_no_resize() -> None: + arr = np.random.default_rng(1).random((100, 80), dtype=np.float32) + emb, orig_hw, reshaped = tomojepa_embed.encode_dense_embeddings( + arr, weights_path=str(WEIGHTS), resize=False + ) + assert orig_hw == (100, 80) + # padded to multiple of 16 + assert reshaped == (112, 80) + assert emb.shape == (7, 5, 64) + + +@needs_mark11 +def test_encode_mark11_dense_is_256d() -> None: + """Mark11 dense projector is 256-D (Mark25 is 64-D).""" + tomojepa_embed._cached_model.cache_clear() + arr = np.linspace(0, 1, 64 * 48, dtype=np.float32).reshape(64, 48) + emb, _, _ = tomojepa_embed.encode_dense_embeddings( + arr, weights_path=str(WEIGHTS11), input_size=128 + ) + assert emb.shape == (8, 8, 256) diff --git a/ipred/tests/test_tomojepa_onnx.py b/ipred/tests/test_tomojepa_onnx.py new file mode 100644 index 0000000..5e69528 --- /dev/null +++ b/ipred/tests/test_tomojepa_onnx.py @@ -0,0 +1,27 @@ +"""TomoJEPA ONNX encode smoke (skips if onnx file missing).""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pytest + +from ipred import tomojepa_onnx + +ONNX25 = Path(__file__).resolve().parents[1] / "models" / "tomojepa25.onnx" +needs_onnx = pytest.mark.skipif( + not ONNX25.is_file() or not tomojepa_onnx.onnx_available(), + reason="tomojepa25.onnx not available", +) + + +@needs_onnx +def test_onnx_encode_dense_shape() -> None: + arr = np.linspace(0, 1, 64 * 48, dtype=np.float32).reshape(64, 48) + emb, orig, reshaped = tomojepa_onnx.encode_dense_embeddings( + arr, weights_path=str(ONNX25), input_size=128 + ) + assert orig == (64, 48) + assert reshaped == (128, 128) + assert emb.shape == (8, 8, 64) diff --git a/ipred/tests/test_train_rethreshold.py b/ipred/tests/test_train_rethreshold.py new file mode 100644 index 0000000..2d896b9 --- /dev/null +++ b/ipred/tests/test_train_rethreshold.py @@ -0,0 +1,71 @@ +"""Train → infer → rethreshold without recompute.""" + +from __future__ import annotations + +import numpy as np +from PIL import Image as PILImage +import pytest + +from ipred.catalog import Catalog +from ipred import feature_setups, preprocess, train_infer + + +@pytest.fixture() +def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog: + monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path)) + return Catalog(tmp_path / "ipred" / "catalog.db") + + +def test_train_infer_rethreshold(catalog: Catalog, tmp_path) -> None: + feature_setups.ensure_default_setups(catalog) + img = np.zeros((48, 48), dtype=np.uint8) + img[:, 24:] = 220 + PILImage.fromarray(img, mode="L").save(tmp_path / "blob.png") + + session = catalog.open_session( + kind="local", source="blob.png", root=str(tmp_path) + ) + preprocess.run_preprocess( + catalog, + session_id=session.session_id, + feature_setup_id="default-skimage", + ) + shapes = [ + {"kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 18, "h": 40}, + {"kind": "rectangle", "classId": 2, "x": 28, "y": 2, "w": 18, "h": 40}, + ] + trained = train_infer.run_train( + catalog, + session_id=session.session_id, + shapes=shapes, + trainer_id="catboost", + config={"iterations": 20, "depth": 4, "random_seed": 0}, + ) + assert trained["model_id"] + imps = trained.get("feature_importances") or [] + assert imps, "CatBoost train must return ranked feature_importances" + assert "label" in imps[0] and "importance" in imps[0] + assert imps[0]["importance"] >= imps[-1]["importance"] + + run = train_infer.run_infer( + catalog, + session_id=session.session_id, + alpha=0.2, + ) + assert run["run_id"] + blob = run["blob_dir"] + assert (tmp_path / "ipred").exists() or True + from pathlib import Path + + proba_path = Path(blob) / "proba.npy" + assert proba_path.is_file() + proba_before = np.load(proba_path).copy() + + rerun = train_infer.run_rethreshold( + catalog, + session_id=session.session_id, + alpha=0.05, + ) + assert rerun["alpha"] == 0.05 + proba_after = np.load(proba_path) + np.testing.assert_array_equal(proba_before, proba_after) diff --git a/start_all.sh b/start_all.sh index d0ca559..ac2c3a0 100755 --- a/start_all.sh +++ b/start_all.sh @@ -1,20 +1,39 @@ #!/usr/bin/env bash -# start_all.sh — Start Tiled, Browse backend, and frontend together. +# start_all.sh — Start Tiled, ipred, Annotate backend, and frontend. # Usage: ./start_all.sh # Stop everything: Ctrl+C +# +# Processes (all bind 127.0.0.1): +# Tiled : TILED_PORT (default 8010) +# ipred : IPRED_PORT (default 8003) — preprocess / train / conformal +# Backend : BACKEND_PORT (default 8002) — Annotate API (proxies to ipred) +# Frontend : FRONTEND_PORT (default 5173) +# +# NOTE: Local Tiled allows anonymous *read*. Writes require the +# authentication.single_user_api_key in tiled/config.yml — the backend picks +# that up via tiled_config (and this script exports it as TILED_API_KEY). +# Auth posture is declared in tiled/config.yml via: +# authentication: +# allow_anonymous_access: true # read-only for anon +# single_user_api_key: "..." # write scopes for backend/ingest +# Server binds to 127.0.0.1 (local-only). Do NOT change --host to 0.0.0.0 +# without reconsidering anonymous access. set -e SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" BACKEND_DIR="$SCRIPT_DIR/backend" +IPRED_DIR="$SCRIPT_DIR/ipred" FRONTEND_DIR="$SCRIPT_DIR/frontend" TILED_CONFIG="$SCRIPT_DIR/tiled/config.yml" TILED_PORT="${TILED_PORT:-8010}" BACKEND_PORT="${BACKEND_PORT:-8002}" +IPRED_PORT="${IPRED_PORT:-8003}" FRONTEND_PORT="${FRONTEND_PORT:-5173}" RUN_DIR="$SCRIPT_DIR/.run" TILED_PID_FILE="$RUN_DIR/tiled.pid" BACKEND_PID_FILE="$RUN_DIR/backend.pid" +IPRED_PID_FILE="$RUN_DIR/ipred.pid" FRONTEND_PID_FILE="$RUN_DIR/frontend.pid" ENV_DIR="" ENV_KIND="" @@ -188,6 +207,7 @@ reclaim_orphaned_repo_ports() { fi stop_repo_listener_on_port "$FRONTEND_PORT" "frontend" "$FRONTEND_DIR" "vite" "" stop_repo_listener_on_port "$BACKEND_PORT" "backend" "$BACKEND_DIR" "annotation_server:app" "uvicorn" + stop_repo_listener_on_port "$IPRED_PORT" "ipred" "$IPRED_DIR" "ipred.api:app" "uvicorn" stop_repo_listener_on_port "$TILED_PORT" "Tiled" "$SCRIPT_DIR" "$TILED_CONFIG" "tiled" } @@ -219,12 +239,21 @@ ensure_backend_env() { PYTHON="$ENV_DIR/bin/python" export PATH="$ENV_DIR/bin:$PATH" - if ! "$PYTHON" -c "import tiled, uvicorn" >/dev/null 2>&1; then + # Include clf/manifold deps — import check keeps a fresh/partial venv filled in. + if ! "$PYTHON" -c "import tiled, uvicorn, sklearn, catboost, onnxruntime" >/dev/null 2>&1; then echo -e "${YELLOW} Installing backend dependencies via uv...${NC}" uv pip install --python "$PYTHON" \ "fastapi>=0.115" "uvicorn[standard]>=0.30" "tiled[all]>=0.1" \ "numpy>=1.26" "pillow>=10.3" "python-dotenv>=1.0" "matplotlib>=3.8" \ - "pycocotools>=2.0.7" "scikit-image>=0.22" "tifffile>=2024.0" "imagecodecs" + "pycocotools>=2.0.7" "scikit-image>=0.22" "scikit-learn>=1.4" \ + "catboost>=1.2" "onnxruntime>=1.17" \ + "tifffile>=2024.0" "imagecodecs" "httpx>=0.27" + fi + + # Disjoint iterative prediction backend (features / train plugins / conformal). + if ! "$PYTHON" -c "import ipred" >/dev/null 2>&1; then + echo -e "${YELLOW} Installing ipred editable...${NC}" + uv pip install --python "$PYTHON" -e "$IPRED_DIR" fi } @@ -254,9 +283,10 @@ tiled_cmd() { cleanup() { echo "" echo -e "${YELLOW}Shutting down...${NC}" - kill "$TILED_PID" "$BACKEND_PID" "$FRONTEND_PID" 2>/dev/null || true - wait "$TILED_PID" "$BACKEND_PID" "$FRONTEND_PID" 2>/dev/null || true + kill "$TILED_PID" "$IPRED_PID" "$BACKEND_PID" "$FRONTEND_PID" 2>/dev/null || true + wait "$TILED_PID" "$IPRED_PID" "$BACKEND_PID" "$FRONTEND_PID" 2>/dev/null || true cleanup_pid_file "$TILED_PID_FILE" + cleanup_pid_file "$IPRED_PID_FILE" cleanup_pid_file "$BACKEND_PID_FILE" cleanup_pid_file "$FRONTEND_PID_FILE" echo -e "${GREEN}Done.${NC}" @@ -270,7 +300,9 @@ cleanup_managed_processes reclaim_orphaned_repo_ports require_free_port "$TILED_PORT" "Tiled" require_free_port "$BACKEND_PORT" "Backend" +require_free_port "$IPRED_PORT" "ipred" require_free_port "$FRONTEND_PORT" "Frontend" +IPRED_PID="" # --------------------------------------------------------------------------- # Load .env — create it from .env.example if missing @@ -285,23 +317,38 @@ set -a source "$BACKEND_DIR/.env" set +a -# Generate a stable API key if TILED_API_KEY is empty or missing +# Empty TILED_API_KEY breaks the tiled Python client: it builds the illegal +# header "Apikey " (trailing space, no key). Prefer the single-user key from +# tiled/config.yml so ingest/writes get create:node / write:* scopes. if [ -z "${TILED_API_KEY:-}" ]; then - TILED_API_KEY=$("$PYTHON" -c "import secrets; print(secrets.token_hex(32))") - # Persist it back into .env so it survives restarts - if grep -q "^TILED_API_KEY=" "$BACKEND_DIR/.env"; then - sed -i.bak "s|^TILED_API_KEY=.*|TILED_API_KEY=${TILED_API_KEY}|" "$BACKEND_DIR/.env" && rm -f "$BACKEND_DIR/.env.bak" - else - echo "TILED_API_KEY=${TILED_API_KEY}" >> "$BACKEND_DIR/.env" + unset TILED_API_KEY + TILED_API_KEY="$("$PYTHON" - "$TILED_CONFIG" <<'PY' +from pathlib import Path +import sys +try: + import yaml +except ImportError: + sys.exit(0) +path = Path(sys.argv[1]) +raw = yaml.safe_load(path.read_text(encoding="utf-8")) or {} +key = (raw.get("authentication") or {}).get("single_user_api_key") or "" +print(key.strip(), end="") +PY +)" + if [ -n "${TILED_API_KEY:-}" ]; then + export TILED_API_KEY + echo -e "${GREEN} Using single_user_api_key from tiled/config.yml for write access${NC}" fi - echo -e "${YELLOW} Generated new TILED_API_KEY and saved to backend/.env${NC}" fi -export TILED_API_KEY +if [ -z "${TILED_LOCAL_API_KEY:-}" ]; then + unset TILED_LOCAL_API_KEY +fi # --------------------------------------------------------------------------- -# Tiled (must match backend/tiled_config.py default: port 8010) +# Tiled — anonymous read; write via single_user_api_key (see tiled/config.yml). +# (must match backend/tiled_config.py default: port 8010) # --------------------------------------------------------------------------- -echo -e "${CYAN}==> Starting Tiled (port ${TILED_PORT})...${NC}" +echo -e "${CYAN}==> Starting Tiled (port ${TILED_PORT}, anon read / key for write)...${NC}" # Repair catalog asset paths in case the repo was moved or cloned to a new location. "$PYTHON" "$SCRIPT_DIR/backend/scripts/repair_catalog_paths.py" @@ -314,14 +361,22 @@ fi mkdir -p "$SCRIPT_DIR/.tiled" if [ ! -f "$SCRIPT_DIR/.tiled/catalog.db" ]; then echo -e "${YELLOW} Initializing Tiled catalog (first run)...${NC}" - (cd "$SCRIPT_DIR" && TILED_SINGLE_USER_API_KEY="$TILED_API_KEY" tiled_cmd catalog init --if-not-exists \ + (cd "$SCRIPT_DIR" && tiled_cmd catalog init --if-not-exists \ "sqlite+aiosqlite:///./.tiled/catalog.db") || { echo -e "${RED} Tiled catalog init failed. Install: pip install 'tiled[server]'${NC}" exit 1 } fi -(cd "$SCRIPT_DIR" && TILED_SINGLE_USER_API_KEY="$TILED_API_KEY" tiled_cmd serve config "$TILED_CONFIG" --host 127.0.0.1 --port "$TILED_PORT") & +# ``exec`` so $! is the tiled/uvicorn process itself, not a short-lived wrapper +# subshell — otherwise the ready-check can see a dead PID while the server is up. +( + cd "$SCRIPT_DIR" || exit 1 + if [ -x "$ENV_DIR/bin/tiled" ]; then + exec "$ENV_DIR/bin/tiled" serve config "$TILED_CONFIG" --host 127.0.0.1 --port "$TILED_PORT" + fi + exec tiled serve config "$TILED_CONFIG" --host 127.0.0.1 --port "$TILED_PORT" +) & TILED_PID=$! echo "$TILED_PID" > "$TILED_PID_FILE" echo -e "${GREEN} Tiled PID: $TILED_PID${NC}" @@ -335,7 +390,8 @@ for i in $(seq 1 40); do TILED_READY=1 break fi - if ! kill -0 "$TILED_PID" 2>/dev/null; then + # Prefer port/HTTP over PID: uvicorn may replace/fork the launcher process. + if ! port_is_listening "$TILED_PORT" && ! kill -0 "$TILED_PID" 2>/dev/null; then echo -e "${RED} Tiled failed to start. Install: pip install 'tiled[server]' (see backend/requirements.txt).${NC}" exit 1 fi @@ -347,9 +403,9 @@ if [ "$TILED_READY" != 1 ]; then fi # --------------------------------------------------------------------------- -# Backend +# ipred (disjoint preprocess / train / conformal) # --------------------------------------------------------------------------- -echo -e "${CYAN}==> Starting backend (port ${BACKEND_PORT})...${NC}" +echo -e "${CYAN}==> Starting ipred (port ${IPRED_PORT})...${NC}" UVICORN_CMD=("$ENV_DIR/bin/uvicorn") if [ ! -x "${UVICORN_CMD[0]}" ]; then @@ -358,6 +414,35 @@ if [ ! -x "${UVICORN_CMD[0]}" ]; then exit 1 fi +export IPRED_URL="http://127.0.0.1:${IPRED_PORT}" +( + cd "$IPRED_DIR" || exit 1 + # Ensure ipred is importable from src even if editable install drifted. + export PYTHONPATH="$IPRED_DIR/src${PYTHONPATH:+:$PYTHONPATH}" + exec "${UVICORN_CMD[@]}" ipred.api:app --host 127.0.0.1 --port "$IPRED_PORT" +) & +IPRED_PID=$! +echo "$IPRED_PID" > "$IPRED_PID_FILE" +echo -e "${GREEN} ipred PID: $IPRED_PID${NC}" + +echo -e "${CYAN} Waiting for ipred...${NC}" +for i in $(seq 1 20); do + if curl -sf "http://127.0.0.1:${IPRED_PORT}/health" >/dev/null 2>&1; then + echo -e "${GREEN} ipred ready at http://127.0.0.1:${IPRED_PORT}${NC}" + break + fi + if ! kill -0 "$IPRED_PID" 2>/dev/null; then + echo -e "${RED} ipred failed to start. Check logs above.${NC}" + exit 1 + fi + sleep 0.5 +done + +# --------------------------------------------------------------------------- +# Backend (Annotate GUI API — proxies to ipred) +# --------------------------------------------------------------------------- +echo -e "${CYAN}==> Starting backend (port ${BACKEND_PORT})...${NC}" + cd "$BACKEND_DIR" "${UVICORN_CMD[@]}" annotation_server:app --host 127.0.0.1 --port "$BACKEND_PORT" & BACKEND_PID=$! @@ -400,9 +485,10 @@ echo -e "${GREEN} Frontend PID: $FRONTEND_PID${NC}" echo "" echo -e "${GREEN}==========================================${NC}" echo -e "${GREEN} SAM3 Annotation Studio is running!${NC}" -echo -e "${GREEN} Tiled : http://127.0.0.1:${TILED_PORT}${NC}" -echo -e "${GREEN} Frontend : http://127.0.0.1:${FRONTEND_PORT}${NC}" -echo -e "${GREEN} Backend : http://127.0.0.1:${BACKEND_PORT}${NC}" + echo -e "${GREEN} Tiled : http://127.0.0.1:${TILED_PORT} (anon read; API key for write)${NC}" +echo -e "${GREEN} Frontend : http://127.0.0.1:${FRONTEND_PORT}${NC}" +echo -e "${GREEN} Backend : http://127.0.0.1:${BACKEND_PORT}${NC}" +echo -e "${GREEN} ipred: http://127.0.0.1:${IPRED_PORT}${NC}" echo -e "${GREEN} Press Ctrl+C to stop all servers.${NC}" echo -e "${GREEN}==========================================${NC}" echo "" diff --git a/tiled/config.yml b/tiled/config.yml index 8b4cd9b..4dffdad 100644 --- a/tiled/config.yml +++ b/tiled/config.yml @@ -1,3 +1,7 @@ +authentication: + allow_anonymous_access: true + single_user_api_key: "3b1d23cdd45e7ada521c729cbd71763dd51b058e0a3e0c1cdeddbcbe13168c88" + allow_origins: - "http://127.0.0.1:5173" - "http://localhost:5173" @@ -8,4 +12,7 @@ trees: args: uri: "sqlite+aiosqlite:///./.tiled/catalog.db" writable_storage: ".tiled/data" + readable_storage: + - "/Users/david/Documents/data" init_if_not_exists: true +