flags:int,不同的操作标记,能够为0,None或者下述值的组合:CALIB_CB_ADAPTIVE_THRESH: 使用自适应阈值法把图像转换为黑白图,而不是使用一个固定的阈值。CALIB_CB_NORMALIZE_IMAGE: 在利用固定阈值或自适应阈值法二值化图像之前,利用直方图均衡化图像。CALIB_CB_FILTER_QUADS: 使用额外的标准(如轮廓面积,周长,正方形...
'BORDER_REFLECT_101', 'BORDER_REPLICATE', 'BORDER_TRANSPARENT', 'BORDER_WRAP', 'BOWImgDescriptorExtractor', 'BOWKMeansTrainer', 'BOWTrainer', 'BRISK', 'BRISK_create', 'BackgroundSubtractor', 'BackgroundSubtractorKNN', 'BackgroundSubtractorMOG2', 'BaseCascadeClassifier', 'CALIB_CB_ACCURACY', '...
Static CALIB_CB_ADAPTIVE_THRESH := 1 Static CALIB_CB_NORMALIZE_IMAGE := 2 Static CALIB_CB_FILTER_QUADS := 4 Static CALIB_CB_FAST_CHECK := 8 Static CALIB_CB_EXHAUSTIVE := 16 Static CALIB_CB_ACCURACY := 32 Static CALIB_CB_LARGER := 64 ...
CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE small_frame = cv2.resize(frame, (0, 0), fx=0.3, fy=0.3) return cv2.findChessboardCorners(small_frame, (9, 6), chessboard_flags)[0] and \ cv2.findChessboardCorners(frame, (9, 6), chessboard_flags)[0] ...
# 需要导入模块: import cv2 [as 别名]# 或者: from cv2 importCALIB_CB_FAST_CHECK[as 别名]deffind_chessboard(frame):chessboard_flags = cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK+ cv2.CALIB_CB_NORMALIZE_IMAGE small_frame = cv2.resize(frame, (0,0), fx=0.3, fy=0.3)returncv2...
常用的有cv2.CALIB_CB_ADAPTIVE_THRESH和cv2.CALIB_CB_NORMALIZE_IMAGE,它们分别用于自适应阈值化和图像归一化以提高角点检测的准确性。 criteria:迭代算法的终止条件,通常是一个包含迭代次数最大值、角点检测精度和使用的算法(如cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER)的元组。
import cv2 test = cv2.imread("/path/to/the/image/27-04-2023_14-13-21_494.png") found, corners = cv2.findChessboardCorners(image=test, patternSize=(26, 16), flags=(cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_NORMALIZE_IMAGE)) print(found) ...
我的方法是先进行颜色分割得到二值化模板，然后利用二值化模板去除背景，使棋盘可见，去除伪影，最后精确...
我的方法是先进行颜色分割得到二值化模板，然后利用二值化模板去除背景，使棋盘可见，去除伪影，最后精确...
(detection, corners) = cv2.findCirclesGrid(image_cv, self.circlepattern_dims, flags=cv2.CALIB_CB_SYMMETRIC_GRID, blobDetector=simple_blob_detector) elapsed_time = (rospy.Time.now() - start).to_sec() self.pub_time_elapsed.publish(elapsed_time) ...