init - 初始化项目

doc/js_tutorials/js_video/js_bg_subtraction/js_bg_subtraction.markdown

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+Background Subtraction {#tutorial_js_bg_subtraction}
+======================
+
+Goal
+----
+
+-   We will familiarize with the background subtraction methods available in OpenCV.js.
+
+Basics
+------
+
+Background subtraction is a major preprocessing steps in many vision based applications. For
+example, consider the cases like visitor counter where a static camera takes the number of visitors
+entering or leaving the room, or a traffic camera extracting information about the vehicles etc. In
+all these cases, first you need to extract the person or vehicles alone. Technically, you need to
+extract the moving foreground from static background.
+
+If you have an image of background alone, like image of the room without visitors, image of the road
+without vehicles etc, it is an easy job. Just subtract the new image from the background. You get
+the foreground objects alone. But in most of the cases, you may not have such an image, so we need
+to extract the background from whatever images we have. It become more complicated when there is
+shadow of the vehicles. Since shadow is also moving, simple subtraction will mark that also as
+foreground. It complicates things.
+
+OpenCV.js has implemented one algorithm for this purpose, which is very easy to use.
+
+BackgroundSubtractorMOG2
+------------------------
+
+It is a Gaussian Mixture-based Background/Foreground Segmentation Algorithm. It is based on two
+papers by Z.Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction" in 2004
+and "Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction"
+in 2006. One important feature of this algorithm is that it selects the appropriate number of
+gaussian distribution for each pixel. It provides better adaptibility to varying scenes due illumination
+changes etc.
+
+While coding, we use the constructor: **cv.BackgroundSubtractorMOG2 (history = 500, varThreshold = 16,
+detectShadows = true)**
+@param history         Length of the history.
+@param varThreshold    Threshold on the squared distance between the pixel and the sample to decide
+whether a pixel is close to that sample. This parameter does not affect the background update.
+@param detectShadows   If true, the algorithm will detect shadows and mark them. It decreases the
+speed a bit, so if you do not need this feature, set the parameter to false.
+@return                instance of cv.BackgroundSubtractorMOG2
+
+Use **apply (image, fgmask, learningRate = -1)** method to get the foreground mask
+@param image         Next video frame. Floating point frame will be used without scaling and should
+be in range [0,255].
+@param fgmask        The output foreground mask as an 8-bit binary image.
+@param learningRate  The value between 0 and 1 that indicates how fast the background model is learnt.
+Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means
+that the background model is not updated at all, 1 means that the background model is completely
+reinitialized from the last frame.
+
+@note The instance of cv.BackgroundSubtractorMOG2 should be deleted manually.
+
+Try it
+------
+
+\htmlonly
+<iframe src="../../js_bg_subtraction.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly