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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We give the rectangle parameters. An algorithm was needed for foreground extraction interaactive minimal user interaction, and the result was GrabCut. Now our final mask is ready.

Now we go for grabcut algorithm with OpenCV. First player and football is enclosed in a blue rectangle.

Then in the next iteration, you get better results. Then a mincut algorithm is used to segment the graph. How it works from user point of view? We will see its arguments first:. It modifies the mask image. If there is a large difference in pixel color, the edge between them will get a low weight. The process is continued until the classification converges.

It cuts the graph into two separating source node and sink node with minimum cost function. 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. We need to bring it back.


Then filled remaining background with gray. Initially user draws a rectangle around the foreground region foreground region shoule be completely inside the rectangle. Depending on the data we gave, GMM learns and create new pixel distribution.

GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts

Here instead of initializing in rect mode, you can directly go into mask mode. The weights between the pixels are defined by the edge information or pixel similarity. Who likes Messi without his hair?

Just give some strokes on the images where some faulty results are there. 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.

Nodes in the graphs are pixels. We load the image, create a similar mask image.

After the cut, all the pixels connected to Source node become foreground and those connected to Sink node become background. Mode should be cv2. Here, you can make this into a interactive sample with drawing rectangle and strokes with mouse, create trackbar to adjust stroke width etc.

GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts – Microsoft Research

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. Computer does an initial labelling depeding on the data intractive gave.


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. Every foreground pixel is connected to Source node and extracyion background pixel is connected to Sink node.

Everything inside rectangle is unknown. Additional two nodes are added, Source node and Sink node. It is illustrated in below image Image Courtesy: Then loaded that mask image in OpenCV, edited original mask image we got with corresponding values in newly added mask image. Just multiply it with input image to get the segmented image. Igerated brush tool in the paint, I marked missed foreground hair, shoes, ball etc with white and unwanted background like logo, ground etc with black on this new layer.