OpenCV 官方文档

findChessboardCorners()：Finds the positions of internal corners of the chessboard.

bool cv::findChessboardCorners( InputArray     image,
　　　　　　　　　　　　　　　　　　　　　Size        patternSize,
　　　　　　　　　　　　　　　　　　　OutputArray    corners,
　　　　　　　　　　　　　　　　　　　　　int         flags = CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE 
　　　　　　　　　　　　　　　　　　 )        
Python:
retval, corners  =  cv.findChessboardCorners(image, patternSize[, corners[, flags]])

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows)).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before applying fixed or adaptive thresholding. CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage. CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed.

 

The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with different parameters if returned coordinates are not accurate enough.

 

cornerSubPix():Refines the corner locations.

Python: cv2.cornerSubPix(image, corners, winSize, zeroZone, criteria)

Parameter:

image	Input image
corners	Initial coordinates of the input corners and refined coordinates provided for output
winSize	Half of the side length of the search window. For example, if winSize=Size(5,5) , then a 5*2+1 x 5*2+1 = 11 x 11 search window is used.
zeroZone	Half of the size of the dead region in the middle of the search zone over which the summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such a size.
criteria	Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after criteria.maxCount iterations or when the corner position moves by less than criteria.epsilon on some iteration.