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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So we modify our resulting mask in previous case as we told now. So we will iterqted there a fine touchup with 1-pixel sure foreground. See the image below.
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.
Who likes Messi without his hair?
GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts – Microsoft Research
You just create two np. We give the rectangle parameters. If there is a large difference in pixel color, the edge between them will get a low weight. We need to bring it back.
It modifies the mask image. Here, you can make this into a interactive sample with drawing rectangle and strokes with mouse, create trackbar to adjust stroke width etc. Then filled remaining background with gray. A graph is built from this pixel distribution. It is done by the following flags, cv2. The cost function is the sum of all weights of the edges that are cut. Nodes in the graphs are pixels. In that forwground, user need to do fine touch-ups.
hraph We will see its arguments first: 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. Then directly apply the grabCut function with mask mode.
Initially user draws a rectangle around the foreground region foreground region shoule be completely inside the rectangle. Then run the grabcut.
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.
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 inside rectangle is unknown. There we give ineractive 0-pixel touchup sure background. Now we go for grabcut algorithm with OpenCV. We create fgdModel and bgdModel. OpenCV has the function, cv2. Depending on the data we gave, GMM learns and create new pixel distribution. An algorithm was needed for foreground extraction with minimal user interaction, and the result was GrabCut.
Interactive Foreground Extraction using GrabCut Algorithm — OpenCV-Python Tutorials 1 documentation
Now our final mask is ready. Then a mincut algorithm is used to segment the graph. 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. Then forground segments it iteratively to get the best result.
GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts
Read the Docs v: 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.
Also watch this youtube video on how to interctive it. Mode should be cv2. How it works from user point of view? Then some final touchups with white strokes denoting foreground and black strokes denoting background is made. Then loaded that mask image in OpenCV, edited original mask image we toreground with corresponding values in newly added mask image. User inputs the rectangle. It is illustrated in below image Image Courtesy: We load the image, create a similar mask image.
And we get a nice result. Here instead of initializing in rect mode, yrabcut can directly go into mask mode. Check the code below: We will see its arguments first:. Additional two nodes are added, Source node and Sink node.
What I actually did is grabccut, I opened input image in paint application and added another layer to the image. Computer does an initial labelling depeding on the data we gave. Just give some strokes on the images where some faulty results are there.
First player and football is enclosed in a blue rectangle. Let the algorithm run for 5 iterations. We need to remove them.