init - 初始化项目

doc/js_tutorials/js_imgproc/js_grabcut/js_grabcut.markdown

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+Foreground Extraction using GrabCut Algorithm {#tutorial_js_grabcut}
+=========================================================
+
+Goal
+----
+
+-   We will learn GrabCut algorithm to extract foreground in images
+
+Theory
+------
+
+GrabCut algorithm was designed by Carsten Rother, Vladimir Kolmogorov & Andrew Blake from Microsoft
+Research Cambridge, UK. in their paper, ["GrabCut": interactive foreground extraction using iterated
+graph cuts](http://dl.acm.org/citation.cfm?id=1015720) . An algorithm was needed for foreground
+extraction with minimal user interaction, and the result was GrabCut.
+
+How it works from user point of view ? Initially user draws a rectangle around the foreground region
+(foreground region should be completely inside the rectangle). Then algorithm segments it
+iteratively to get the best result. Done. But in some cases, the segmentation won't be fine, like,
+it may have marked some foreground region as background and vice versa. In that case, user need to
+do fine touch-ups. Just give some strokes on the images where some faulty results are there. Strokes
+basically says *"Hey, this region should be foreground, you marked it background, correct it in next
+iteration"* or its opposite for background. Then in the next iteration, you get better results.
+
+What happens in background ?
+
+-   User inputs the rectangle. 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). Everything inside rectangle is unknown. Similarly any user input specifying
+    foreground and background are considered as hard-labelling which means they won't change in
+    the process.
+-   Computer does an initial labelling depending on the data we gave. 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.
+-   Depending on the data we gave, GMM learns and create new pixel distribution. 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. Additional two
+    nodes are added, **Source node** and **Sink node**. Every foreground pixel is connected to
+    Source node and every background pixel is connected to Sink node.
+-   The weights of edges connecting pixels to source node/end node are defined by the probability
+    of a pixel being foreground/background. The weights between the pixels are defined by the edge
+    information or pixel similarity. If there is a large difference in pixel color, the edge
+    between them will get a low weight.
+-   Then a mincut algorithm is used to segment the graph. It cuts the graph into two separating
+    source node and sink node with minimum cost function. The cost function is the sum of all
+    weights of the edges that are cut. After the cut, all the pixels connected to Source node
+    become foreground and those connected to Sink node become background.
+-   The process is continued until the classification converges.
+
+It is illustrated in below image (Image Courtesy: <http://www.cs.ru.ac.za/research/g02m1682/>)
+
+![image](images/grabcut_scheme.jpg)
+
+Demo
+----
+
+We use the function: **cv.grabCut (image, mask, rect, bgdModel, fgdModel, iterCount, mode = cv.GC_EVAL)**
+
+@param image      input 8-bit 3-channel image.
+@param mask       input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the cv.grabCutClasses.
+@param rect       ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT.
+@param bgdModel   temporary array for the background model. Do not modify it while you are processing the same image.
+@param fgdModel   temporary arrays for the foreground model. Do not modify it while you are processing the same image.
+@param iterCount  number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .
+@param mode       operation mode that could be one of the cv::GrabCutModes
+
+Try it
+------
+
+\htmlonly
+<iframe src="../../js_grabcut_grabCut.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly