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：

最后是将左右边界合并，形成整体：

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();