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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It is done by the following flags, cv2. 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.

It cuts the graph into two separating source node and sink node with minimum cost function. Then a mincut algorithm is used to segment the graph.

Here instead of initializing in rect mode, you can directly go into mask mode. So we will give there a fine touchup with 1-pixel sure foreground. Everything inside rectangle is unknown. A graph is built from this pixel distribution. Nodes in the graphs are pixels. Here, you can make this into a interactive sample with drawing rectangle and strokes with mouse, create trackbar to adjust stroke width etc.

It modifies the mask image. We will see its arguments first:. Now our final mask is ready. In that case, user need to do fine touch-ups. Using 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. Just multiply it with input image to get the segmented image.


You just create two np. Then directly apply the grabCut function with mask mode. We give the rectangle parameters. Then some final touchups with white strokes denoting foreground and black strokes denoting background is made. An algorithm was needed for foreground extraction with minimal user interaction, and the result was GrabCut.

Then in the next iteration, you get better results. Additional two nodes are added, Source node and Sink node. Also watch this youtube video on how to use it. Every foreground pixel is connected to Source node and every background pixel is connected to Sink node.

So we modify our resulting mask in previous case as we told now. What I actually did is that, I opened input image in paint application and added another layer to the image.

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

We need to bring it grabcuut. Then loaded that mask image in OpenCV, edited original mask image we got with corresponding values in newly added mask image. We create fgdModel and bgdModel.

The weights between the pixels are defined by the edge information or pixel similarity. Check the code below: See the image below. It is illustrated in below image Image Courtesy: Computer does an initial labelling depeding on the data we gave. Then filled remaining background with gray. Who likes Messi without his hair? First player and football is enclosed in a blue rectangle.


Mode should be cv2. We need to remove them.

GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts

We will see its arguments first: After the cut, all the pixels connected to Source node become foreground and those connected to Sink node interactice background. Then algorithm segments extracton iteratively to get the best result.

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. Then run the grabcut. There we give some 0-pixel touchup sure background. 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. Read the Docs v: Now we go for grabcut algorithm with OpenCV. The cute function is the sum of all weights of the edges that are cut.

Let the algorithm run for 5 iterations. Depending on the data we gave, GMM learns and create new pixel distribution.

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

We load the image, create a similar mask image. The process is continued until the classification converges. Just give some strokes on the images where some faulty results are there.

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