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]])
|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:
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)
|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
|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