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 cv2 import 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 important dst = 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 cv2 import numpy as np filename = 'molecule.png' img = cv2.imread(filename) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # find Harris corners gray = 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 centroids ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst) # define the criteria to stop and refine the corners criteria = (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 them res = 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
