opencv python Harris角点检测

Harris Corner Detection

角点

角点所具有的特征：

• 轮廓之间的交点
• 对于同一场景，即使视角发生变化，通常具备稳定性质的特征
• 该点附近区域的像素点无论在梯度方向上还是其梯度幅值上有着较大变化

角点检测基本原理

使用一个固定窗口在图像上进行任意方向上的滑动，比较滑动前与滑动后两种情况，窗口中的像素灰度变化程度，如果存在任意方向上的滑动，都有着较大灰度变化，那么我们可以认为该窗口中存在角点.

OpenCV中的Harris角点检测器

cv2.cornerHarris(src, blockSize, ksize, k[, dst[, borderType]])

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('img5.png')
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()

亚像素级的角点

有时需要以最高精度找到角落，
cv2.cornerSubPix(image, corners, winSize, zeroZone, criteria)

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('img5.png')
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.imshow('dst',img)
if cv2.waitKey(0) & 0xff == 27:
    cv2.destroyAllWindows()