算法流程：

将图像转换为灰度图像

利用Sobel滤波器求出 海森矩阵 (Hessian matrix) :

将高斯滤波器分别作用于Ix²、Iy²、IxIy

计算每个像素的 R= det(H) - k(trace(H))²。det(H)表示矩阵H的行列式，trace表示矩阵H的迹。通常k的取值范围为[0.04,0.16]。

满足 R>=max(R) * th 的像素点即为角点。th常取0.1。

Harris算法实现：

import cv2 as cv

import numpy as np

import matplotlib.pyplot as plt

# Harris corner detection

def Harris_corner(img):

## Grayscale

def BGR2GRAY(img):

gray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]

gray = gray.astype(np.uint8)

return gray

## Sobel

def Sobel_filtering(gray):

# get shape

H, W = gray.shape

# sobel kernel

sobely = np.array(((1, 2, 1),

(0, 0, 0),

(-1, -2, -1)), dtype=np.float32)

sobelx = np.array(((1, 0, -1),

(2, 0, -2),

(1, 0, -1)), dtype=np.float32)

# padding

tmp = np.pad(gray, (1, 1), "edge")

# prepare

Ix = np.zeros_like(gray, dtype=np.float32)

Iy = np.zeros_like(gray, dtype=np.float32)

# get differential

for y in range(H):

for x in range(W):

Ix[y, x] = np.mean(tmp[y : y + 3, x : x + 3] * sobelx)

Iy[y, x] = np.mean(tmp[y : y + 3, x : x + 3] * sobely)

Ix2 = Ix ** 2

Iy2 = Iy ** 2

Ixy = Ix * Iy

return Ix2, Iy2, Ixy

# gaussian filtering

def gaussian_filtering(I, K_size=3, sigma=3):

# get shape

H, W = I.shape

## gaussian

I_t = np.pad(I, (K_size // 2, K_size // 2), "edge")

# gaussian kernel

K = np.zeros((K_size, K_size), dtype=np.float)

for x in range(K_size):

for y in range(K_size):

_x = x - K_size // 2

_y = y - K_size // 2

K[y, x] = np.exp( -(_x ** 2 + _y ** 2) / (2 * (sigma ** 2)))

K /= (sigma * np.sqrt(2 * np.pi))

K /= K.sum()

# filtering

for y in range(H):

for x in range(W):

I[y,x] = np.sum(I_t[y : y + K_size, x : x + K_size] * K)

return I

# corner detect

def corner_detect(gray, Ix2, Iy2, Ixy, k=0.04, th=0.1):

# prepare output image

out = np.array((gray, gray, gray))

out = np.transpose(out, (1,2,0))

# get R

R = (Ix2 * Iy2 - Ixy ** 2) - k * ((Ix2 + Iy2) ** 2)

# detect corner

out[R >= np.max(R) * th] = [255, 0, 0]

out = out.astype(np.uint8)

return out

# 1. grayscale

gray = BGR2GRAY(img)

# 2. get difference image

Ix2, Iy2, Ixy = Sobel_filtering(gray)

# 3. gaussian filtering

Ix2 = gaussian_filtering(Ix2, K_size=3, sigma=3)

Iy2 = gaussian_filtering(Iy2, K_size=3, sigma=3)

Ixy = gaussian_filtering(Ixy, K_size=3, sigma=3)

# 4. corner detect

out = corner_detect(gray, Ix2, Iy2, Ixy)

return out

# Read image

img = cv.imread("../qiqiao.jpg").astype(np.float32)

# Harris corner detection

out = Harris_corner(img)

cv.imwrite("out.jpg", out)

cv.imshow("result", out)

cv.waitKey(0)

cv.destroyAllWindows()

实验结果：

原图：

Harris角点检测算法检测结果：

以上就是python 实现Harris角点检测算法的详细内容，更多关于python Harris角点检测算法的资料请关注云海天教程其它相关文章！

原文链接：https://www.cnblogs.com/wojianxin/p/12574909.html