split_dataset.py

#!/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()