surf = cv2.xfeatures2d.SURF_create() cv2.error: OpenCV(4.5.3) C:\Users\runneradmin\AppData\Local\Temp\pip-req-build-1i5nllza\opencv_contrib\modules\xfeatures2d\src\surf.cpp:1029: error: (-213:The function/feature is not implemented) This algorithm is patented and is excluded in this ...
在OpenCV 4.x中,你应该直接从cv2中导入SURF,而不是从cv2.xfeatures2d。正确的导入方式如下: python import cv2 surf = cv2.xfeatures2d.SURF_create() 如果你的OpenCV版本是3.x且已安装opencv-contrib,上述导入方式仍然有效。 尝试清理环境并重新安装OpenCV库: 有时候,环境中的包可能由于各种原因(如依赖冲突...
没有,直接调用cv2.SURF_create()依然报错 回复 2021-08-28 08:24:52 相似问题cv2.xfeatures2d.SURF_create 报错 2408 1 3 module 'cv2.cv2' has no attribute 'waitkey' 6385 1 1 图片无法关闭了 1046 0 3 cv2.waitKey(0)任意键,卡死 4875 0 3 windows下录制多媒体文件无法写入数据,文...
方法1: 在opencv ==4.4.0.46版本下,将 sift = cv2.xfeatures2d.SIFT_create() 改为 sift = cv2.SIFT_create() 方法2: 将opencv 版本从opencv ==4.4.0.46降低到 opencv == 3.4.2.17即可 关于surf算法 错误: surf = cv2.xfeatures2d.SURF_create() AttributeError: module 'cv2.cv2' has no attribute ...
surf = cv2.xfeatures2d.SURF_create(800) train_keypoints, train_descriptor = surf.detectAndCompute(training_gray, None) test_keypoints, test_descriptor = surf.detectAndCompute(test_gray, None) keypoints_without_size = np.copy(training_image) ...
surf = cv2.xfeatures2d.SURF_create(30000) kp = surf.detect(img, None) img2 = cv2.drawKeypoints(img, kp, None, (255, 0, 0), 4) surf.setUpright(True) surf.setHessianThreshold(40000) kp = surf.detect(img, None) img3 = cv2.drawKeypoints(img, kp, None, (255, 0, 0), 4) ...
surf=cv2.xfeatures2d.SURF_create(30000) kp=surf.detect(img,None) img2=cv2.drawKeypoints(img, kp,None, (255,0,0),4) surf.setUpright(True) surf.setHessianThreshold(40000) kp=surf.detect(img,None) img3=cv2.drawKeypoints(img, kp,None, (255,0,0),4) ...
surf = cv2.xfeatures2d.SURF_create() # 检测关键点和计算特征描述子 keypoints, descriptors = surf.detectAndCompute(image, None) # 绘制关键点 image_with_keypoints = cv2.drawKeypoints(image, keypoints, None, (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) ...
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(1)cv2.xfeatures2d.SURF_create(hessianThreshold, nOctaves, nOctaveLayers, extended, upright) 生成关键点的函数 hessianThreshold:默认100,关键点检测的阈值,越高监测的点越少 nOctaves:默认4,金字塔组数 nOctaveLayers:默认3,每组金子塔的层数 extended:默认False,扩展描述符标志,True表示使用扩展的128个元素描述...