#
# 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")
    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()