GrabCut. Interactive Foreground Extraction using Iterated Graph Cuts. Carsten Rother. Vladimir Kolmogorov. Andrew Blake. Microsoft Research Cambridge-UK . GrabCut algorithm was designed by Carsten Rother, Vladimir Kolmogorov their paper, “GrabCut”: interactive foreground extraction using iterated graph cuts. GrabCut: interactive foreground extraction using iterated graph cuts – nadr0/ GrabCut.
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Interactive Foreground Extraction using GrabCut Algorithm — OpenCV-Python Tutorials 1 documentation
See the image below. Then filled remaining background with gray. The cost function is the sum usihg all weights of the edges that are cut.
What I actually did is that, I opened input image in paint application and added another layer to the image. Also watch this youtube video on how to use it.
Initially user draws a rectangle around the foreground region foreground region shoule be completely inside the rectangle.
GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts
The process is continued until the classification converges. So we modify our resulting mask in previous case as we told now. Then in the next iteration, you get better results. Now our final mask is ready. We load the image, create a similar mask image. Check the code below: It modifies the mask image.
If there is a large difference in pixel color, the edge between them will get a low weight. Additional two nodes are added, Source node and Sink node.
It is done by the following flags, cv2. Here, you can make this into a interactive sample with drawing rectangle and strokes with mouse, create trackbar to adjust stroke width etc. The weights between the pixels are defined by the edge information or pixel similarity.
OpenCV has the function, cv2. User inputs the rectangle. Nodes in the graphs are pixels. It labels the foreground and background pixels or it hard-labels Now a Gaussian Mixture Model GMM is used to model the foreground and background. An algorithm was needed for foreground extraction with minimal user interaction, and the result was GrabCut. That is, the unknown pixels are labelled either probable foreground or probable background depending on its relation with the other hard-labelled pixels in terms of color statistics It is just like clustering.
A graph is built from this pixel distribution. Mode should be cv2. Just give some strokes on the images where some faulty results are there. We will see its arguments first: We need to bring it back.
GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts – Microsoft Research
Then algorithm segments it iteratively to get the best result. Everything inside rectangle is unknown. Just multiply it intractive input image to get the segmented image. We give the rectangle parameters. Everything outside this rectangle will be taken as sure background That is the reason it is mentioned before that uaing rectangle should include all the objects.
So we modify the mask such that all 0-pixels and 2-pixels are put to 0 ie background and all 1-pixels and 3-pixels are put to 1 ie foreground pixels.
It cuts the graph into two separating source node and sink node with minimum cost function. There we give some 0-pixel touchup sure background. We will see unteractive arguments first:. And we get a nice result. In that case, user need to do fine touch-ups.