OpenCV哈里斯(Harris)角點檢測的實現
阿新 • • 發佈:2020-01-16
環境
pip install opencv-python==3.4.2.16 pip install opencv-contrib-python==3.4.2.16
理論
克里斯·哈里斯(Chris Harris)和邁克·史蒂芬斯(Mike Stephens)在1988年的論文《組合式拐角和邊緣檢測器》中做了一次嘗試找到這些拐角的嘗試,所以現在將其稱為哈里斯拐角檢測器。
函式:cv2.cornerHarris(),cv2.cornerSubPix()
示例程式碼
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()]=[0,255] cv2.imshow('dst',img) if cv2.waitKey(0) & 0xff == 27: cv2.destroyAllWindows()
原圖
輸出圖
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,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]]=[0,255] img[res[:,3],2]] = [0,0] cv2.imwrite('subpixel5.png',img)
輸出圖
參考
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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