dataset_code/sekai/preprocess/get_temp_input_csv.py

import os
import argparse
import pandas as pd
from tqdm import tqdm

from concurrent.futures import ThreadPoolExecutor, as_completed
import threading

# 添加线程锁保护共享资源
video_data_lock = threading.Lock()
matched_count_lock = threading.Lock()

def process_video_file(video_file, args, csv_video_mapping):
    """处理单个视频文件的函数"""
    video_path = os.path.join(args.input_video_root, video_file)
    video_filename = os.path.splitext(video_file)[0]
    
    matched_row = None
    for csv_prefix, row in csv_video_mapping.items():
        if video_filename.startswith(csv_prefix):
            matched_row = row
            break

    result = None
    if matched_row is not None:
        final_csv_path = os.path.join(args.output_csv_path, (video_filename + ".csv"))
        
        if os.path.exists(final_csv_path):
            # 检查CSV文件是否损坏
            try:
                import pandas as pd
                # 尝试读取CSV文件来验证其完整性
                pd.read_csv(final_csv_path)
                return None  # 文件存在且有效，不需要重新处理
            except (pd.errors.EmptyDataError, pd.errors.ParserError, UnicodeDecodeError, FileNotFoundError) as e:
                # CSV文件损坏，删除它
                print(f"Warning: CSV file {final_csv_path} is corrupted ({e}). Deleting and will recreate.")
                os.remove(final_csv_path)

        result = {
            'videoFile': video_filename + ".mp4",
            'cameraFile': matched_row['cameraFile'],
            'location': matched_row['location'],  
            'scene': matched_row['scene'], 
            'crowdDensity': matched_row['crowdDensity'], 
            'weather': matched_row['weather'], 
            'timeOfDay': matched_row['timeOfDay'],
        }
    else:
        print(f"Warning: No CSV record found for video file: {video_file}")
    
    return result

# 多线程处理主代码
def process_videos_multithreaded(video_files, args, csv_video_mapping, max_workers=4):
    video_data = []
    matched_count = 0
    
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        # 提交所有任务
        future_to_video = {
            executor.submit(process_video_file, video_file, args, csv_video_mapping): video_file 
            for video_file in video_files
        }
        
        # 处理完成的任务
        for future in tqdm(as_completed(future_to_video), total=len(video_files), desc="Processing videos"):
            video_file = future_to_video[future]
            try:
                result = future.result()
                if result is not None:
                    with video_data_lock:
                        video_data.append(result)
                    with matched_count_lock:
                        matched_count += 1
            except Exception as exc:
                print(f'Video {video_file} generated an exception: {exc}')
    
    return video_data, matched_count

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--input_csv",
        type=str,
        default="/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/0_final_sekai_dataset/yamls/sekai-game-walking_updated.csv",
    )
    parser.add_argument(
        "--input_video_root", type=str, default="/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/0_final_sekai_dataset/sekai-game-walking-386"
    )
    parser.add_argument(
        "--output_csv_path",
        type=str,
        default="/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/0_final_sekai_dataset/yamls/sekai-game-walking-386",
    )
    parser.add_argument(
        "--output_csv_file",
        type=str,
        default="/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/0_final_sekai_dataset/yamls/temp_input_csv/sekai-game-walking-386.csv",
    )
    parser.add_argument("--num_workers", type=int, default=16)
    args = parser.parse_args()
    return args

if __name__ == "__main__":
    args = parse_args()

    # 读取CSV文件
    df = pd.read_csv(args.input_csv)

    # 保留需要的字段，过滤掉不需要的字段
    keep_columns = ['videoFile', 'cameraFile', 'caption', 'location', 'scene', 'crowdDensity', 'weather', 'timeOfDay']
    df = df[keep_columns].copy()

    # 创建CSV中视频文件名前缀到记录的映射
    csv_video_mapping = {}
    for idx, row in df.iterrows():
        video_prefix = os.path.splitext(os.path.basename(row['videoFile']))[0]
        csv_video_mapping[video_prefix] = row

    # 获取视频文件夹中的所有视频文件
    video_files = []
    for file in os.listdir(args.input_video_root):
        if file.lower().endswith(('.mp4', '.avi', '.mov', '.mkv', '.wmv', '.flv')):  # 添加更多视频格式
            video_files.append(file)

    # # 准备视频数据
    # video_data = []
    # matched_count = 0
    
    # for video_file in tqdm(video_files):
    #     video_path = os.path.join(args.input_video_root, video_file)
    #     video_filename = os.path.splitext(video_file)[0]
        
    #     matched_row = None
    #     for csv_prefix, row in csv_video_mapping.items():
    #         if video_filename.startswith(csv_prefix):
    #             matched_row = row
    #             break

    #     if matched_row is not None:
    #         final_csv_path = os.path.join(args.output_csv_path, (video_filename + ".csv"))
    #         if not os.path.exists(final_csv_path):
    #             video_data.append({
    #                 "video_key": video_filename,
    #                 'videoFile': video_filename + ".mp4",
    #                 'cameraFile': matched_row['cameraFile'],
    #                 'location': matched_row['location'],  
    #                 'scene': matched_row['scene'], 
    #                 'crowdDensity': matched_row['crowdDensity'], 
    #                 'weather': matched_row['weather'], 
    #                 'timeOfDay': matched_row['timeOfDay'],
    #             })
    #             matched_count += 1
    #     else:
    #         print(f"Warning: No CSV record found for video file: {video_file}")
    video_data, matched_count = process_videos_multithreaded(video_files, args, csv_video_mapping, max_workers=args.num_workers)
    
    print(f"Successfully matched {matched_count} videos with CSV records")
    print(f"Total video data to process: {len(video_data)}")

    if video_data:
        output_df = pd.DataFrame(video_data)
        output_csv_file = args.output_csv_file
        output_df.to_csv(output_csv_file, index=False)
        print(f"Video data saved to: {output_csv_file}")
        print(f"Saved {len(video_data)} video records")
    else:
        output_df = pd.DataFrame()
        output_csv_file = args.output_csv_file
        output_df.to_csv(output_csv_file, index=False)
        print(f"Empty video data saved to: {output_csv_file}")
        print("No video data to save - created empty CSV file")