OpenCV哈里斯(Harris)角点检测的实现

技术  /  管理员 发布于 7年前   337

环境

pip install opencv-python==3.4.2.16 pip install opencv-contrib-python==3.4.2.16

理论

克里斯・哈里斯（Chris Harris）和迈克・史蒂芬斯（Mike Stephens）在1988年的论文《组合式拐角和边缘检测器》中做了一次尝试找到这些拐角的尝试，所以现在将其称为哈里斯拐角检测器。

函数：cv2.cornerHarris（），cv2.cornerSubPix（）

示例代码

import cv2import numpy as np filename = 'molecule.png'img = cv2.imread(filename)gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) gray = np.float32(gray)dst = cv2.cornerHarris(gray,2,3,0.04) #result is dilated for marking the corners, not importantdst = cv2.dilate(dst,None) # Threshold for an optimal value, it may vary depending on the image.img[dst>0.01*dst.max()]=[0,0,255] cv2.imshow('dst',img)if cv2.waitKey(0) & 0xff == 27:  cv2.destroyAllWindows()

原图

输出图

SubPixel精度的角落

import cv2import numpy as np filename = 'molecule.png'img = cv2.imread(filename)gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # find Harris cornersgray = np.float32(gray)dst = cv2.cornerHarris(gray,2,3,0.04)dst = cv2.dilate(dst,None)ret, dst = cv2.threshold(dst,0.01*dst.max(),255,0)dst = np.uint8(dst) # find centroidsret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst) # define the criteria to stop and refine the cornerscriteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)corners = cv2.cornerSubPix(gray,np.float32(centroids),(5,5),(-1,-1),criteria) # Now draw themres = np.hstack((centroids,corners))res = np.int0(res)img[res[:,1],res[:,0]]=[0,0,255]img[res[:,3],res[:,2]] = [0,255,0] cv2.imwrite('subpixel5.png',img)

输出图

参考

https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_features_harris/py_features_harris.html#harris-corners

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