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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First player and football is enclosed in a blue rectangle.
Just multiply it with input image to get the segmented image. Let the algorithm run for 5 iterations.
Additional two nodes are added, Source node and Sink node. 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. It modifies the mask image. Then in the next iteration, you get better results.
GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts
User inputs the rectangle. Then algorithm segments it iteratively to get the best result. Computer does an initial labelling depeding on the data we gave.
What I actually did is that, I opened input image in paint application and added another layer to the image. You just create jterated np. Depending on the data we gave, GMM learns and create new pixel distribution.
It cuts the graph into two separating source node and sink node with minimum cost function.
Nodes in the graphs are pixels. Every foreground pixel is connected to Source node and every background pixel is connected to Sink node. 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.
So we will give there a fine touchup with 1-pixel sure foreground. A graph is built from this pixel distribution. The cost function is the sum of all weights of the edges that are cut. And we get a nice result. 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
See the image below. We will see its arguments first: Just give some strokes on the images where some faulty results are there. Check the code below: Mode should be cv2.
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. Read the Docs v: Interative give the rectangle parameters.
OpenCV has the function, cv2. Now our final mask is ready. Also watch this youtube usig on how to use it. Here instead of initializing in rect mode, you can directly go into mask mode.