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 create fgdModel and bgdModel. 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.
A graph is built from this pixel distribution.
We will see its arguments first:. Then loaded that mask iteratef in OpenCV, edited original mask image we got with corresponding values in newly added mask image. An algorithm was needed for foreground extraction with minimal user interaction, and the result was GrabCut. The cost function is the sum of all weights of the edges that are cut.
Interactive Foreground Extraction using GrabCut Algorithm — OpenCV-Python Tutorials 1 documentation
So we will give there a fine touchup with 1-pixel sure foreground. Everything inside rectangle is unknown. Let the algorithm run hraph 5 iterations. So we modify the mask such that all 0-pixels and 2-pixels are put to 0 ie background and all 1-pixels and cutts are put to 1 ie foreground pixels.
Initially user draws a rectangle around the foreground region foreground region shoule be completely inside the rectangle. 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.
GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts
Here instead of initializing in rect mode, you can directly go into mask mode. 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. Then in the next iteration, you get better results.
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 extradtion statistics It is just like clustering.
Here, you can make this into a interactive sample with drawing rectangle and strokes with mouse, create trackbar edtraction adjust stroke width etc. We need to remove them. Depending on the data we gave, GMM learns and create new pixel distribution.
You just create two np. Now we go for grabcut inetractive with OpenCV. Nodes in the graphs are pixels. Just give some strokes on the images where some faulty results are there.
Now our final mask is ready. What I actually did is that, I opened input image in paint application and added another layer to the image. User inputs the rectangle. Then run the grabcut.
After the cut, all the pixels connected to Source node become foreground and those connected to Sink node become background. Read the Iteratee v: It is illustrated in below image Image Courtesy: It is done by the following flags, cv2.
It labels the suing and background pixels or it hard-labels Now a Gaussian Mixture Model GMM is used to model the foreground and background. Then algorithm segments it iteratively to get the best result.
There we give some 0-pixel touchup sure background. It cuts the graph into two separating source node and sink node with minimum cost function. See the image below. Every foreground pixel is connected to Source node and every background pixel is connected to Sink node. Check the code below: