OpenCV哈里斯角检测|Harris Corner理论实践

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('chessboard.png')
img_copy = img.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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()]=[255,0,0]
# plot
plt.subplot(121)
plt.imshow(img_copy, cmap='gray')
plt.xticks([])
plt.yticks([])
plt.subplot(122)
plt.imshow(img, cmap='gray')
plt.xticks([])
plt.yticks([])
plt.show()

# sub pixel更精度角点
import cv2
import numpy as np
img = cv2.imread('chessboard2.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find Harris corners
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.imshow('subpixel', img)
cv2.waitKey(0)
cv2.destroyAllWindows()