Python3 图像边界识别

 # -*- coding: utf-8 -*-

 """

 Created on Wed Mar  7 11:04:15 2018

 @author: markli

 """

 import numpy as np;

 from PIL import Image;

 import matplotlib.pyplot as pyplot;

 class ImageFilter:

     def __init__(self,filepath):

         self.path = filepath;

     def Filter(self,filtermatrix):

         """步长设定为1"""

         img = Image.open(self.path);

         #img = img.resize((32,32));

         r,g,b = img.split(); #rgb 通道分离

         #转为数字矩阵

         r_arr = np.array(r);

         g_arr = np.array(g);

         b_arr = np.array(b);

         matrix = [r_arr,g_arr,b_arr];

         #过滤后的结果矩阵

         fm = np.ones((r_arr.shape[0] - filtermatrix.shape[0] + 1,r_arr.shape[1] - filtermatrix.shape[1]+1));

         fm_rgb = [];

         #卷积运算 实现过滤

         for m in matrix:

             row = 0;

             for i in range(fm.shape[0]):

                 col = 0;

                 for j in range(fm.shape[1]):

                     temp = m[row:row + filtermatrix.shape[0],col:col + filtermatrix.shape[1]];

                     fm[i][j] = np.sum(np.multiply(temp,filtermatrix));

                     col = col + 1;

                 row = row + 1;

             fm_rgb.append(fm);

         return fm_rgb;

 #        #数字矩阵转为RGB通道像素

 #        r = Image.fromarray(fm_rgb[0]).convert('L');

 #        g = Image.fromarray(fm_rgb[1]).convert('L');

 #        b = Image.fromarray(fm_rgb[2]).convert('L');

 #        image = Image.merge("RGB", (r, g, b));

 #        #图片显示

 #        pyplot.imshow(image);

 #        pyplot.show();

     def MergeEdage(self,savepath):

         leftmatrix = np.array([[1,0,-1],[1,0,-1],[1,0,-1]]);#左边界

         left = self.Filter(leftmatrix);

         rightmatrix = np.array([[-1,0,1],[-1,0,1],[-1,0,1]]);#右边界

         right = self.Filter(rightmatrix);

         w_1,h_1 = left[0].shape; #left 和 right 维数相同，使用哪个都可以

         full_edage = [];

         for i in range(3):

             m = np.hstack((left[i][:,:int(w_1/2)],right[i][:,w_1-int(w_1/2):]));

             full_edage.append(m);

         #数字矩阵转为RGB通道像素

         r = Image.fromarray(full_edage[0]).convert('L');

         g = Image.fromarray(full_edage[1]).convert('L');

         b = Image.fromarray(full_edage[2]).convert('L');

         image = Image.merge("RGB", (r, g, b));

         #图片显示

         pyplot.imshow(image);

         pyplot.show();

         image.save(savepath);

 img = ImageFilter("C:\\Users\\yangp\\Desktop\\612474963277897033.jpg");

 img.MergeEdage("C:\\Users\\yangp\\Desktop\\fulledage.jpg");

下面给出原图、左边界和右边界识别情况：

Python3 图像边界识别

最后是将左右边界合并，形成整体：

Python3 图像边界识别

最后说明一下，其中的过滤矩阵可以扩大，将过滤矩阵值的变化放慢，可以使图像的识别更加细致，在这里本人的电脑配置太低，就不演示了。当然图片的轮廓可以使用Image库中的filter方法显现出来，语法是img = Image.open("C:\\Users\\yangp\\Desktop\\612474963277897033.jpg");img.filter(ImageFilter.FIND_EDGES);img.show();

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Python3 图像边界识别

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