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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GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts

You just create two np. A graph is built from this pixel distribution.

It modifies the mask image. If there is a large difference in pixel color, the edge between them will get a low weight.

Initially user draws a rectangle around the foreground region foreground region shoule be completely inside the rectangle. The process is continued until the classification converges. So we grap the mask such that all 0-pixels and 2-pixels are put extractio 0 ie background and all 1-pixels and 3-pixels are put to 1 ie foreground pixels.

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. How it works from user point of view?


Let the algorithm run for 5 iterations. So we will give there a fine touchup with 1-pixel sure foreground.

Interactive Foreground Extraction using GrabCut Algorithm — OpenCV-Python Tutorials 1 documentation

Everything inside rectangle is unknown. Now our final mask is ready. It is done by the following flags, cv2. Additional two nodes are added, Source node and Sink node. Just multiply it with input image to get the segmented image.

There we give some 0-pixel touchup sure background. Everything outside this rectangle will be taken as sure background That is the reason it is mentioned before that your rectangle should include all the objects. Using brush tool in the paint, I marked missed foreground hair, shoes, ball usjng with white and unwanted background like logo, ground etc with black on this new layer. See the image below. So we modify our resulting mask in previous case as we told now. We need to remove them.

Then in the next iteration, you get geaph results. Then a mincut algorithm is used to segment the graph. It is illustrated in below image Image Courtesy: And we get a nice result.

Depending on the data we gave, GMM learns and create new pixel distribution. Check the code below: Just give some strokes on the images where some faulty results are there. An algorithm was needed for foreground extraction with minimal user interaction, and the result was GrabCut. After the cut, all the pixels connected to Source node become foreground and those garph to Sink node become background.


Now we go for grabcut algorithm with OpenCV. Here instead of initializing in rect mode, you can directly go into mask mode. Computer does an initial labelling depeding on the data we gave.

We give the rectangle parameters. In that case, user need to do fine touch-ups. Here, you can make this into a interactive sample with drawing rectangle and strokes with mouse, create trackbar to adjust stroke width etc.

It cuts the graph into two separating source node and sink node with minimum cost function. First player and football is enclosed in a blue rectangle. OpenCV has the function, cv2. Read the Docs v: Then algorithm segments it iteratively to get the best result. Then filled remaining background with gray.

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