| 11 | |
| 12 | |
| 13 | def main(): |
| 14 | # 保证随机可复现 |
| 15 | random.seed(0) |
| 16 | |
| 17 | #将数据集中的10%数据划分到验证集中 |
| 18 | split_rate = 0.1 |
| 19 | |
| 20 | #指向解压后的flower_photos文件夹 |
| 21 | cwd = os.getcwd() |
| 22 | data_root = os.path.join(cwd, "flower_data") |
| 23 | origin_flower_path = os.path.join(data_root, "flower_photos") |
| 24 | assert os.path.exists(origin_flower_path), "path '{}' does not exist.".format(origin_flower_path) |
| 25 | |
| 26 | flower_class = [cla for cla in os.listdir(origin_flower_path) |
| 27 | if os.path.isdir(os.path.join(origin_flower_path, cla))] |
| 28 | |
| 29 | # 建立保存训练集的文件夹 |
| 30 | train_root = os.path.join(data_root, "train") |
| 31 | mk_file(train_root) |
| 32 | for cla in flower_class: |
| 33 | # 建立每个类别对应的文件夹 |
| 34 | mk_file(os.path.join(train_root, cla)) |
| 35 | |
| 36 | # 建立保存验证集的文件夹 |
| 37 | val_root = os.path.join(data_root, "val") |
| 38 | mk_file(val_root) |
| 39 | for cla in flower_class: |
| 40 | |
| 41 | # 2022/6/29补充，author:WH |
| 42 | # 建立保存验证集中对应每个类别的文件夹 |
| 43 | mk_file(os.path.join(val_root, cla)) |
| 44 | |
| 45 | |
| 46 | cla_path = os.path.join(origin_flower_path, cla) |
| 47 | images = os.listdir(cla_path) |
| 48 | num = len(images) |
| 49 | # 随机采样验证机的索引 |
| 50 | eval_index =random.sample(images, k = int(num*split_rate)) |
| 51 | for index, image in enumerate(images): |
| 52 | if image in eval_index: |
| 53 | # 将分配至验证集中文件复制到相应的目录 |
| 54 | image_path = os.path.join(cla_path, image) |
| 55 | new_path = os.path.join(val_root, cla) |
| 56 | copy(image_path, new_path) |
| 57 | else: |
| 58 | # 将分配至训练集中的文件复制到相应的目录 |
| 59 | image_path = os.path.join(cla_path, image) |
| 60 | new_path = os.path.join(train_root, cla) |
| 61 | copy(image_path, new_path) |
| 62 | print("\r[{}] processing [{}/{}]".format(cla, index+1, num), end = "") |
| 63 | print() |
| 64 | |
| 65 | print("processing done!") |
| 66 | |
| 67 | if __name__ == '__main__': |
| 68 | main() |