python通过robert、sobel、Laplace算子实现图像边缘提取详解
实现思路：
1，将传进来的图片矩阵用算子进行卷积求和（卷积和取绝对值）
2，用新的矩阵（与原图一样大小）去接收每次的卷积和的值
3，卷积图片所有的像素点后，把新的矩阵数据类型转化为uint8
注意：
必须对求得的卷积和的值求绝对值；矩阵数据类型进行转化。
完整代码：
import cv2
import numpy as np
# robert 算子[[-1,-1],[1,1]]
def robert_suanzi(img):
r, c = img.shape
r_sunnzi = [[-1,-1],[1,1]]
for x in range(r):
for y in range(c):
if (y + 2 <= c) and (x + 2 <= r):
imgChild = img[x:x+2, y:y+2]
list_robert = r_sunnzi*imgChild
img[x, y] = abs(list_robert.sum()) # 求和加绝对值
return img
# # sobel算子的实现
def sobel_suanzi(img):
r, c = img.shape
new_image = np.zeros((r, c))
new_imageX = np.zeros(img.shape)
new_imageY = np.zeros(img.shape)
s_suanziX = np.array([[-1,0,1],[-2,0,2],[-1,0,1]]) # X方向
s_suanziY = np.array([[-1,-2,-1],[0,0,0],[1,2,1]])
for i in range(r-2):
for j in range(c-2):
new_imageX[i+1, j+1] = abs(np.sum(img[i:i+3, j:j+3] * s_suanziX))
new_imageY[i+1, j+1] = abs(np.sum(img[i:i+3, j:j+3] * s_suanziY))
new_image[i+1, j+1] = (new_imageX[i+1, j+1]*new_imageX[i+1,j+1] + new_imageY[i+1, j+1]*new_imageY[i+1,j+1])**0.5
# return np.uint8(new_imageX)
# return np.uint8(new_imageY)
return np.uint8(new_image) # 无方向算子处理的图像
# Laplace算子
# 常用的Laplace算子模板 [[0,1,0],[1,-4,1],[0,1,0]] [[1,1,1],[1,-8,1],[1,1,1]]
def Laplace_suanzi(img):
r, c = img.shape
new_image = np.zeros((r, c))
L_sunnzi = np.array([[0,-1,0],[-1,4,-1],[0,-1,0]])
# L_sunnzi = np.array([[1,1,1],[1,-8,1],[1,1,1]])
for i in range(r-2):
for j in range(c-2):
new_image[i+1, j+1] = abs(np.sum(img[i:i+3, j:j+3] * L_sunnzi))
return np.uint8(new_image)
img = cv2.imread('1.jpg', cv2.IMREAD_GRAYSCALE)
cv2.imshow('image', img)
# # robers算子
out_robert = robert_suanzi(img)
cv2.imshow('out_robert_image', out_robert)
# sobel 算子
out_sobel = sobel_suanzi(img)
cv2.imshow('out_sobel_image', out_sobel)
# Laplace算子
out_laplace = Laplace_suanzi(img)
cv2.imshow('out_laplace_image', out_laplace)
cv2.waitKey(0)
cv2.destroyAllWindows()
结果：
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