DocumentAIKit/Data_Copy/train_and_val_divide_copy.py

import os
import json
import argparse
import numpy as np
import glob
import cv2
from sklearn.model_selection import train_test_split
from labelme import utils
from tqdm import tqdm
import shutil

np.random.seed(5)

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--anno_dirs', type=str, nargs="+", default=[])
    parser.add_argument('--train_ratio', type=float, default=0.9)
    parser.add_argument('--train_dir', type=str)
    parser.add_argument('--val_dir', type=str)
    args = parser.parse_args()
    labelme_folds = args.anno_dirs
    json_list_path = []
    train_path = []
    val_path = []
    train_val_path_list = []
    train_val_folds = [args.train_dir, args.val_dir]

    # 每个文件夹按照比例划分，遍历完，最后合并
    for labelme_path in labelme_folds:
        list_path = glob.glob(labelme_path + "/*.json")
        json_list_path.extend(list_path)
        train_path1, val_path1 = train_test_split(list_path, test_size=1-args.train_ratio, train_size=args.train_ratio)
        train_path.extend(train_path1)
        val_path.extend(val_path1)

    train_val_path_list.append(train_path)
    train_val_path_list.append(val_path)

    print("train_n:", len(train_path), 'val_n:', len(val_path))

    # print("train images: %d" % len(train_path))
    for index, data_path in enumerate(train_val_path_list):
        for file in tqdm(data_path):
            json_path = file
            temp_json = {}
            img_name = ''
            with open(json_path, 'r', encoding='utf-8')as jp:
                info = json.load(jp)
                info['imagePath'] = os.path.basename(info['imagePath'])
                img_name = info['imagePath']
                info['imageData'] = None
                temp_json = info
            jp.close()
            img_path = file.replace('json', str(img_name).split('.')[1])
            try:
                shutil.copy(img_path, train_val_folds[index])
                json.dump(temp_json,
                          open(os.path.join(train_val_folds[index], os.path.basename(json_path)), 'w', encoding='utf-8'),
                          ensure_ascii=False, indent=4)
            except Exception as e:
                print(e)
                print('Wrong Image:', img_name)
                continue
        # print(img_name + '-->', img_name.replace('jpg', 'jpg'))
    # print("eval images: %d" % len(val_path))
    # for file in tqdm(val_path):
    #     json_path = file
    #     img_name = ''
    #     with open(json_path, 'r', encoding='utf-8') as jp:
    #         info = json.load(jp)
    #         img_name = os.path.basename(info['imagePath'])
    #     jp.close()
    #     img_path = file.replace('json', str(img_name).split('.')[1])
    #     try:
    #         shutil.copy(img_path, args.val_path)
    #         shutil.copy(json_path, args.val_path)
    #     except Exception as e:
    #         print(e)
    #         print('Wrong Image:', img_name)
    #         continue