python实现求纯色彩图像的边框

 # # demo.py # 识别单张图片 # import argparse import os import numpy as np import time from modeling.deeplab import * from dataloaders import custom_transforms as tr from PIL import Image from torchvision import transforms from dataloaders.utils import  * from torchvision.utils import make_grid, save_image,to_image import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt def main(): im = Image.open("test_border.png-600") npimg = np.array(im) # 这个图片是1维的索引图. # chwimg = npimg.transpose(2,0,1) # HWC 变成 CHW 格式的矩阵 print(npimg.shape) h,w,c = npimg.shape src = np.sum(npimg,axis=2) # 这里测试用, 先把3通道的合成了一个通道的, 实际使用的时候也是1通道的. print(src.shape) borderimg = np.zeros(src.shape) #默认都输出了0 后面就不用输出0了. # 修补bug, 解决边框线会丢失的问题. borderimg[0,:]=src[0,:] borderimg[:,0]=src[:,0] borderimg[-1,:]=src[-1,:] borderimg[:,-1]=src[:,-1] t1= time.time() for x in range(0,h-1,1): for y in range(0,w-1,1): # point = src[x,y] # if(point>0): # print(point) if not (src[x,y] == src[x+1,y] == src[x,y+1] == src[x+1,y+1]): # 发现4个像素不一致的就输出到结果图上. borderimg[x,y] = src[x,y] borderimg[x+1,y] = src[x+1,y] borderimg[x,y+1] = src[x,y+1] borderimg[x+1,y+1] = src[x+1,y+1] t2= time.time() print("耗时",t2-t1) plt.figure() plt.title('display') plt.imshow(src) plt.show( ) plt.imshow(borderimg) plt.show( ) print("start test get image border ...") if __name__ == "__main__": main() else: main()