边缘检测（Canny算子、Sobel算子）

原始图像

####（1）canny算子

# coding=utf-8
import cv2
import numpy as np

img = cv2.imread("1.jpg", 0)

img = cv2.GaussianBlur(img, (3, 3), 0)
canny = cv2.Canny(img, 50, 150)

cv2.imshow('Canny', canny)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('picture\\candy.jpg', canny)

效果：

（2）sobel算子

# coding=utf-8
import cv2
import numpy as np

img = cv2.imread("1.jpg", 0)

img = cv2.GaussianBlur(img, (3, 3), 0)

sobel_x = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=3)
sobel_y = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=3)

sobel_x = cv2.convertScaleAbs(sobel_x)
sobel_y = cv2.convertScaleAbs(sobel_y)

sobel = cv2.addWeighted(sobel_x, 0.5, sobel_y, 0.5, 0)

cv2.imshow('Sobel', sobel)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('picture/sobel.jpg', sobel)

效果：