U-NET 图像预处理

from PIL import Image
import glob
import os

out_dir = 'D:/图像数据/橙子/TestIMG/'
cnt = 501
for img in glob.glob('D:/图像数据/橙子/测试图像/*.bmp'):
    Image.open(img).resize((500,500)).save(os.path.join(out_dir, format(str(cnt), '0>3s') + '.png'))
    cnt += 1

#!/usr/bin/python
# -*- coding: UTF-8 -*-
# !H:\Anaconda3\envs\new_labelme\python.exe
import argparse
import json
import os
import os.path as osp
import base64
import warnings

import PIL.Image
import yaml

from labelme import utils

import cv2
import numpy as np
from skimage import img_as_ubyte


# from sys import argv

def main():
    warnings.warn("This script is aimed to demonstrate how to convert the\n"
                  "JSON file to a single image dataset, and not to handle\n"
                  "multiple JSON files to generate a real-use dataset.")

    json_file = "D:/GPU/labelme/data3/json20201030"

    # freedom
    list_path = os.listdir(json_file)
    print('freedom =', json_file)
    for i in range(0, len(list_path)):
        path = os.path.join(json_file, list_path[i])
        if os.path.isfile(path):

            data = json.load(open(path))
            img = utils.img_b64_to_arr(data['imageData'])
            lbl, lbl_names = utils.labelme_shapes_to_label(img.shape, data['shapes'])

            captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)]

            lbl_viz = utils.draw_label(lbl, img, captions)
            # out_dir = osp.basename(path).replace('.', '_')
            out_dir = osp.basename(path).split('.json')[0]
            save_file_name = out_dir
            # out_dir = osp.join(osp.dirname(path), out_dir)

            if not osp.exists(json_file + 'mask'):
                os.mkdir(json_file + 'mask')
            maskdir = json_file + 'mask'

            if not osp.exists(json_file + 'mask_viz'):
                os.mkdir(json_file + 'mask_viz')
            maskvizdir = json_file + 'mask_viz'

            out_dir1 = maskdir
           
            PIL.Image.fromarray(lbl).save(out_dir1 + '/' + save_file_name + '.png')

            PIL.Image.fromarray(lbl_viz).save(maskvizdir + '/' + save_file_name +
                                              '_label_viz.png')
          
            with open(osp.join(out_dir1, 'label_names.txt'), 'w') as f:
                for lbl_name in lbl_names:
                    f.write(lbl_name + '\n')

            warnings.warn('info.yaml is being replaced by label_names.txt')
            info = dict(label_names=lbl_names)
            with open(osp.join(out_dir1, 'info.yaml'), 'w') as f:
                yaml.safe_dump(info, f, default_flow_style=False)

            print('Saved to: %s' % out_dir1)


if __name__ == '__main__':
    # base64path = argv[1]
    main()

from PIL import Image
import glob
import os

out_dir = 'D:/图像数据/橙子/GPU/trainIMG/'
in_dir='D:/图像数据/橙子/GPU/SourceIMG/'
i=0
for img in glob.glob('D:/图像数据/橙子/GPU/labelmemask/*.png'):
    img=os.path.join(in_dir, os.path.basename(img))
    Image.open(img).save(os.path.join(out_dir, os.path.basename(img)))
    i=i+1
    print(i)