OpenCV findContours

findContours() [1/2]

void cv::findContours	(	
InputArray 	        image,
OutputArrayOfArrays contours, //轮廓
OutputArray 	    hierarchy,//轮廓的拓扑结构
int 	            mode,     //
int 	            method,   //
Point 	            offset = Point() 
)	

Python:
contours, hierarchy	=cv.findContours(image, mode, method[, contours[, hierarchy[, offset]]])

#include

Finds contours in a binary image.

The function retrieves contours from the binary image using the algorithm [216] . The contours are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the OpenCV sample directory.

Note
Since opencv 3.2 source image is not modified by this function.
Parameters
image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1’s. Zero pixels remain 0’s, so the image is treated as binary . You can use compare, inRange, threshold , adaptiveThreshold, Canny, and others to create a binary image out of a grayscale or color one. If mode equals to RETR_CCOMP or RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
contours Detected contours. Each contour is stored as a vector of points (e.g. std::vector).
hierarchy Optional output vector (e.g. std::vectorcv::Vec4i), containing information about the image topology. It has as many elements as the number of contours. For each i-th contour contours[i], the elements hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices in
contours of the next and previous contours at the same hierarchical level, the first child contour and the parent contour（在同一层级的下一个轮廓/上一个轮廓，第一个子轮廓，父轮廓）, respectively. If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.

mode Contour retrieval mode, see RetrievalModes

method Contour approximation method, see ContourApproximationModes

offset Optional offset by which every contour point is shifted. This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context.

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