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：

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持龙方网络。