python通过robert、sobel、Laplace算子实现图像边缘提取详解

2019-11-25 11:54:12

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来源：转载

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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 cv2import 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：