opencv 边缘检测原理

只是实现一下，暂不考虑效率

import cv2 as cv
import numpy as np
import math

# 从源码层面实现边缘检测
img = cv.imread('../images/face.jpg', flags=1)  # flags=1读取为彩色，flags=0读取为灰度
h, w = img.shape[:2]
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
dst = np.zeros((h, w, 1), np.uint8)
# 1.sobel 算子模板
# [1  2  1         [1  0  -1
#  0  0  0          2  0  -2
#  -1 -2 -1]        1  0  -1]
# 2.图片卷积
# [1 2 3 4] [a b c d] ->  a*1+b*2+c*3+d*4=dst
# 3.阈值判决
# sqrt(a*a+b*b) = f > th

for i in range(h-2):
    for j in range(w-2):
        gy = gray[i, j] + gray[i, j+1]*2 + gray[i, j+2] - gray[i+2, j] - gray[i+2, j+1]*2 - gray[i+2, j+2]
        gx = gray[i, j] + gray[i+1, j]*2 + gray[i+2, j] - gray[i, j+2] - gray[i+1, j+2]*2 - gray[i+2, j+2]
        grad = math.sqrt(gx*gx+gy*gy)
        if grad > 100:  # 阈值假设为100
            dst[i, j] = 255
        else:
            dst[i, j] = 0

cv.imshow('img1', img)
cv.imshow('img2', dst)
cv.waitKey(0)

效果