opencv 边缘检测原理

只是实现一下，暂不考虑效率

 1 import cv2 as cv
 2 import numpy as np
 3 import math
 4 
 5 # 从源码层面实现边缘检测
 6 img = cv.imread('../images/face.jpg', flags=1)  # flags=1读取为彩色，flags=0读取为灰度
 7 h, w = img.shape[:2]
 8 gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
 9 dst = np.zeros((h, w, 1), np.uint8)
10 # 1.sobel 算子模板
11 # [1  2  1         [1  0  -1
12 #  0  0  0          2  0  -2
13 #  -1 -2 -1]        1  0  -1]
14 # 2.图片卷积
15 # [1 2 3 4] [a b c d] ->  a*1+b*2+c*3+d*4=dst
16 # 3.阈值判决
17 # sqrt(a*a+b*b) = f > th
18 
19 for i in range(h-2):
20     for j in range(w-2):
21         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]
22         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]
23         grad = math.sqrt(gx*gx+gy*gy)
24         if grad > 100:  # 阈值假设为100
25             dst[i, j] = 255
26         else:
27             dst[i, j] = 0
28 
29 cv.imshow('img1', img)
30 cv.imshow('img2', dst)
31 cv.waitKey(0)

效果

 

转载于:https://www.cnblogs.com/MC-Curry/p/10414617.html