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b58079c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
根据 FairTalking-Bench 的 CSV 文件划分数据集
将预处理后的数据（datasets/AVLips/）按照 CSV 中的 basename 划分为 train/val/test
匹配规则：文件名包含 basename 即可（模糊匹配）
"""
import os
import shutil
import pandas as pd
from pathlib import Path
from tqdm import tqdm
# 路径配置
DATASET_DIR = "/apdcephfs_gy4/share_303628665/joywu/research/LipFD/datasets"
AVLIPS_DIR = os.path.join(DATASET_DIR, "AVLips")
CSV_DIR = "/apdcephfs_gy4/share_303628665/joywu/dataset/FairTalking-Bench"
# CSV 文件
TRAIN_CSV = os.path.join(CSV_DIR, "train.csv")
VAL_CSV = os.path.join(CSV_DIR, "val.csv")
TEST_CSV = os.path.join(CSV_DIR, "test.csv")
# 输出目录
OUTPUT_DIR = os.path.join(DATASET_DIR, "FairTalking-Bench")
def load_basenames(csv_path):
"""从 CSV 文件加载 basename 列表和对应的 label"""
df = pd.read_csv(csv_path)
# 返回 basename -> label 的字典
basename_dict = {}
for _, row in df.iterrows():
basename = row['basename']
label = row['Label'] # 0=Real, 1=Fake
basename_dict[basename] = label
return basename_dict
def find_and_copy_files(avlips_dir, basename_dict, output_split_dir):
"""
在 AVLips 目录中查找匹配的文件，并复制到输出目录
匹配规则：文件名包含 basename 即可
优化：使用反向索引，先构建文件列表，然后直接匹配 basename
"""
# 创建输出目录
real_output_dir = os.path.join(output_split_dir, "0_real")
fake_output_dir = os.path.join(output_split_dir, "1_fake")
os.makedirs(real_output_dir, exist_ok=True)
os.makedirs(fake_output_dir, exist_ok=True)
# 统计
real_count = 0
fake_count = 0
matched_files = set() # 记录已匹配的文件，避免重复
# 获取所有 basename
basenames = list(basename_dict.keys())
print(f" 开始匹配 {len(basenames)} 个 basename...")
# 遍历每个 basename，在 AVLips 目录中查找匹配的文件
for basename in tqdm(basenames, desc=" 匹配进度"):
label = basename_dict[basename] # 0=Real, 1=Fake
# 根据 label 确定要搜索的目录
if label == 0:
search_dirs = [os.path.join(avlips_dir, '0_real')]
dst_dir = real_output_dir
else:
search_dirs = [os.path.join(avlips_dir, '1_fake')]
dst_dir = fake_output_dir
# 在每个目录中查找匹配的文件
for search_dir in search_dirs:
if not os.path.exists(search_dir):
continue
# 查找包含 basename 的文件
files = os.listdir(search_dir)
for filename in files:
if basename in filename and filename.endswith('.png'):
# 找到匹配！
src_path = os.path.join(search_dir, filename)
dst_path = os.path.join(dst_dir, filename)
if not os.path.exists(dst_path):
shutil.copy2(src_path, dst_path)
# 统计
if label == 0:
real_count += 1
else:
fake_count += 1
matched_files.add(filename)
print(f" 匹配完成: {len(matched_files)} 个文件")
return real_count, fake_count
def main():
print("=" * 60)
print("开始划分 FairTalking-Bench 数据集")
print("=" * 60)
# 1. 加载 CSV 文件中的 basename
print("\n1. 加载 CSV 文件...")
train_dict = load_basenames(TRAIN_CSV)
val_dict = load_basenames(VAL_CSV)
test_dict = load_basenames(TEST_CSV)
print(f" Train: {len(train_dict)} 个样本")
print(f" Val: {len(val_dict)} 个样本")
print(f" Test: {len(test_dict)} 个样本")
# 2. 查找并复制文件
print("\n2. 查找并复制文件...")
print(f" 输出目录: {OUTPUT_DIR}")
# 训练集
print("\n 处理训练集...")
train_real, train_fake = find_and_copy_files(
AVLIPS_DIR, train_dict, os.path.join(OUTPUT_DIR, "train")
)
# 验证集
print("\n 处理验证集...")
val_real, val_fake = find_and_copy_files(
AVLIPS_DIR, val_dict, os.path.join(OUTPUT_DIR, "val")
)
# 测试集
print("\n 处理测试集...")
test_real, test_fake = find_and_copy_files(
AVLIPS_DIR, test_dict, os.path.join(OUTPUT_DIR, "test")
)
# 3. 打印统计信息
print("\n" + "=" * 60)
print("数据集划分完成！")
print("=" * 60)
print(f"\n训练集: Real={train_real}, Fake={train_fake}, Total={train_real + train_fake}")
print(f"验证集: Real={val_real}, Fake={val_fake}, Total={val_real + val_fake}")
print(f"测试集: Real={test_real}, Fake={test_fake}, Total={test_real + test_fake}")
# 4. 创建数据集信息文件
print("\n3. 创建数据集信息文件...")
create_dataset_info(OUTPUT_DIR, train_real, train_fake, val_real, val_fake, test_real, test_fake)
print("\n✅ 全部完成！")
print(f"\n数据集已保存到: {OUTPUT_DIR}")
def create_dataset_info(output_dir, train_real, train_fake, val_real, val_fake, test_real, test_fake):
"""创建数据集信息文件"""
info_path = os.path.join(output_dir, "dataset_info.txt")
with open(info_path, 'w') as f:
f.write("FairTalking-Bench 数据集划分信息\n")
f.write("=" * 60 + "\n\n")
f.write("数据来源:\n")
f.write(f" CSV 目录: {CSV_DIR}\n")
f.write(f" 预处理数据: {AVLIPS_DIR}\n\n")
f.write("匹配规则:\n")
f.write(" 文件名包含 basename 即可（模糊匹配）\n\n")
f.write("数据集划分:\n")
f.write(f" 训练集: Real={train_real}, Fake={train_fake}, Total={train_real + train_fake}\n")
f.write(f" 验证集: Real={val_real}, Fake={val_fake}, Total={val_real + val_fake}\n")
f.write(f" 测试集: Real={test_real}, Fake={test_fake}, Total={test_real + test_fake}\n\n")
f.write("目录结构:\n")
f.write(f" {output_dir}/\n")
f.write(f" ├── train/\n")
f.write(f" │ ├── 0_real/ ({train_real} 张图片)\n")
f.write(f" │ └── 1_fake/ ({train_fake} 张图片)\n")
f.write(f" ├── val/\n")
f.write(f" │ ├── 0_real/ ({val_real} 张图片)\n")
f.write(f" │ └── 1_fake/ ({val_fake} 张图片)\n")
f.write(f" └── test/\n")
f.write(f" ├── 0_real/ ({test_real} 张图片)\n")
f.write(f" └── 1_fake/ ({test_fake} 张图片)\n")
print(f" 信息文件已保存: {info_path}")
if __name__ == "__main__":
main()
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