38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
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import numpy as np
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import cv2
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def _findChessboardCorners(img, pattern):
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"basic function"
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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retval, corners = cv2.findChessboardCorners(img, pattern,
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flags=cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_FILTER_QUADS)
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if not retval:
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return False, None
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corners = cv2.cornerSubPix(img, corners, (11, 11), (-1, -1), criteria)
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corners = corners.squeeze()
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return True, corners
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def _findChessboardCornersAdapt(img, pattern):
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"Adapt mode"
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img = cv2.adaptiveThreshold(img, 255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
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cv2.THRESH_BINARY,21, 2)
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return _findChessboardCorners(img, pattern)
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def findChessboardCorners(img, annots, pattern):
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if annots['visited']:
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return None
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annots['visited'] = True
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gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
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# Find the chess board corners
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for func in [_findChessboardCornersAdapt, _findChessboardCorners]:
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ret, corners = func(gray, pattern)
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if ret:break
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else:
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return None
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# found the corners
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show = img.copy()
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show = cv2.drawChessboardCorners(show, pattern, corners, ret)
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assert corners.shape[0] == len(annots['keypoints2d'])
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corners = np.hstack((corners, np.ones((corners.shape[0], 1))))
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annots['keypoints2d'] = corners.tolist()
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return show
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