Library of an opencv wrapper around the efficient graph based superpixel segmentation algorithm described by Pedro F. Felzenszwalb and Daniel P. Huttenlocher.
Basically this library consists of the C++ implementation by Pedro F. Felzenszwalb (http://cs.brown.edu/~pff/segment/) and the opencv wrapper by Christoffer Holmstedt (https://github.com/christofferholmstedt/opencv-wrapper-egbis).
For instructions view the INSTALL file.
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <segment.hpp>
using namespace cv;
using namespace std;
int main(int argc, char **argv)
{
Mat img = imread("image.jpg", CV_LOAD_IMAGE_COLOR);
if (!img.data)
{
cout << "Could not open or find the image." << endl;
return -1;
}
int num_ccs;
Mat egbisImage = segment(&img, 0.5, 500, 200, &num_ccs);
namedWindow(imageName, CV_WINDOW_AUTOSIZE);
imshow(imageName, img);
namedWindow("EGBIS", CV_WINDOW_AUTOSIZE);
imshow("EGBIS", egbisImage);
waitKey(0);
return 0;
}Implementation of the segmentation algorithm described in:
Efficient Graph-Based Image Segmentation Pedro F. Felzenszwalb and Daniel P. Huttenlocher International Journal of Computer Vision, 59(2) September 2004.
The program takes a color image (PPM format) and produces a segmentation with a random color assigned to each region.
-
Type "make" to compile "segment".
-
Run "segment sigma k min input output".
The parameters are: (see the paper for details)
sigma: Used to smooth the input image before segmenting it. k: Value for the threshold function. min: Minimum component size enforced by post-processing. input: Input image. output: Output image.
Typical parameters are sigma = 0.5, k = 500, min = 20. Larger values for k result in larger components in the result.