OpenCV 中的 Harris 角点检测
函数
cv2.cornerHarris(src, blockSize, ksize, k, dst=None, borderType=None)
参数 | |
---|
src | 数据类型为 float32 的输入图像 |
blockSize | 角点检测中考虑的区域大小 |
ksize | Sobel求导中使用的窗口大小 |
k | Harris 角点检测方程中的自由参数,取值参数为 [0.04 0.06] |
dst | 输出图像 |
borderType | 边界的类型 |
代码
import cv2
import numpy as np
#图像读取 格式转换
filename = 'image.jpg'
img = cv2.imread(filename)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
#角点检测
dst = cv2.cornerHarris(gray,2,3,0.04)
dst = cv2.dilate(dst,None)
img[dst>0.01*dst.max()]=[0,0,255]
cv2.imshow('dst',img)
if cv2.waitKey(0) & 0xff == 27:
cv2.destroyAllWindows()

亚像素级精确度的角点
import cv2
import numpy as np
filename = 'image.jpg'
img = cv2.imread(filename)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
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)
ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
# 返回值由角点坐标组成的一个数组(而非图像)
corners = cv2.cornerSubPix(gray,np.float32(centroids),(5,5),(-1,-1),criteria)
# Now draw them
res = np.hstack((centroids,corners))
#np.int0 可以用来省略小数点后面的数字(非四㮼五入)。
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)
