init - 初始化项目
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Lee Nony
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doc/js_tutorials/js_imgproc/js_watershed/js_watershed.markdown
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doc/js_tutorials/js_imgproc/js_watershed/js_watershed.markdown
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Image Segmentation with Watershed Algorithm {#tutorial_js_watershed}
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===========================================
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Goal
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----
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- We will learn how to use marker-based image segmentation using watershed algorithm
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- We will learn: **cv.watershed()**
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Theory
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------
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Any grayscale image can be viewed as a topographic surface where high intensity denotes peaks and
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hills while low intensity denotes valleys. You start filling every isolated valleys (local minima)
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with different colored water (labels). As the water rises, depending on the peaks (gradients)
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nearby, water from different valleys, obviously with different colors will start to merge. To avoid
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that, you build barriers in the locations where water merges. You continue the work of filling water
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and building barriers until all the peaks are under water. Then the barriers you created gives you
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the segmentation result. This is the "philosophy" behind the watershed. You can visit the [CMM
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webpage on watershed](http://cmm.ensmp.fr/~beucher/wtshed.html) to understand it with the help of
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some animations.
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But this approach gives you oversegmented result due to noise or any other irregularities in the
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image. So OpenCV implemented a marker-based watershed algorithm where you specify which are all
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valley points are to be merged and which are not. It is an interactive image segmentation. What we
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do is to give different labels for our object we know. Label the region which we are sure of being
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the foreground or object with one color (or intensity), label the region which we are sure of being
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background or non-object with another color and finally the region which we are not sure of
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anything, label it with 0. That is our marker. Then apply watershed algorithm. Then our marker will
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be updated with the labels we gave, and the boundaries of objects will have a value of -1.
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Code
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----
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Below we will see an example on how to use the Distance Transform along with watershed to segment
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mutually touching objects.
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Consider the coins image below, the coins are touching each other. Even if you threshold it, it will
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be touching each other.
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We start with finding an approximate estimate of the coins. For that, we can use the Otsu's
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binarization.
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Try it
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------
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\htmlonly
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<iframe src="../../js_watershed_threshold.html" width="100%"
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onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
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</iframe>
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\endhtmlonly
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Now we need to remove any small white noises in the image. For that we can use morphological
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opening. To remove any small holes in the object, we can use morphological closing. So, now we know
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for sure that region near to center of objects are foreground and region much away from the object
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are background. Only region we are not sure is the boundary region of coins.
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So we need to extract the area which we are sure they are coins. Erosion removes the boundary
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pixels. So whatever remaining, we can be sure it is coin. That would work if objects were not
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touching each other. But since they are touching each other, another good option would be to find
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the distance transform and apply a proper threshold. Next we need to find the area which we are sure
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they are not coins. For that, we dilate the result. Dilation increases object boundary to
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background. This way, we can make sure whatever region in background in result is really a
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background, since boundary region is removed. See the image below.
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Try it
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------
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\htmlonly
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<iframe src="../../js_watershed_background.html" width="100%"
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onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
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</iframe>
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\endhtmlonly
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The remaining regions are those which we don't have any idea, whether it is coins or background.
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Watershed algorithm should find it. These areas are normally around the boundaries of coins where
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foreground and background meet (Or even two different coins meet). We call it border. It can be
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obtained from subtracting sure_fg area from sure_bg area.
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We use the function: **cv.distanceTransform (src, dst, distanceType, maskSize, labelType = cv.CV_32F)**
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@param src 8-bit, single-channel (binary) source image.
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@param dst output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src.
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@param distanceType type of distance(see cv.DistanceTypes).
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@param maskSize size of the distance transform mask, see (cv.DistanceTransformMasks).
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@param labelType type of output image. It can be cv.CV_8U or cv.CV_32F. Type cv.CV_8U can be used only for the first variant of the function and distanceType == DIST_L1.
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Try it
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------
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\htmlonly
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<iframe src="../../js_watershed_distanceTransform.html" width="100%"
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onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
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</iframe>
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\endhtmlonly
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In the thresholded image, we get some regions of coins which we are sure of coins
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and they are detached now. (In some cases, you may be interested in only foreground segmentation,
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not in separating the mutually touching objects. In that case, you need not use distance transform,
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just erosion is sufficient. Erosion is just another method to extract sure foreground area, that's
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all.)
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Try it
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------
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\htmlonly
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<iframe src="../../js_watershed_foreground.html" width="100%"
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onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
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</iframe>
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\endhtmlonly
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Now we know for sure which are region of coins, which are background and all. So we create marker
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(it is an array of same size as that of original image, but with int32 datatype) and label the
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regions inside it. The regions we know for sure (whether foreground or background) are labelled with
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any positive integers, but different integers, and the area we don't know for sure are just left as
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zero. For this we use **cv.connectedComponents()**. It labels background of the image with 0, then
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other objects are labelled with integers starting from 1.
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But we know that if background is marked with 0, watershed will consider it as unknown area. So we
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want to mark it with different integer. Instead, we will mark unknown region, defined by unknown,
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with 0.
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Now our marker is ready. It is time for final step, apply watershed. Then marker image will be
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modified. The boundary region will be marked with -1.
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We use the function: **cv.connectedComponents (image, labels, connectivity = 8, ltype = cv.CV_32S)**
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@param image the 8-bit single-channel image to be labeled.
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@param labels destination labeled image(cv.CV_32SC1 type).
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@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively.
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@param ltype output image label type. Currently cv.CV_32S and cv.CV_16U are supported.
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We use the function: **cv.watershed (image, markers)**
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@param image input 8-bit 3-channel image.
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@param markers input/output 32-bit single-channel image (map) of markers. It should have the same size as image .
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Try it
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------
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\htmlonly
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<iframe src="../../js_watershed_watershed.html" width="100%"
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onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
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</iframe>
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\endhtmlonly
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