import pandas as pd import cv2 import os from pathlib import Path from concurrent.futures import ThreadPoolExecutor, as_completed from threading import Lock import time import ffmpeg class VideoProcessor: def __init__(self, max_workers=4): self.max_workers = max_workers self.progress_lock = Lock() self.processed_count = 0 self.total_count = 0 # def get_video_properties(self, video_path): # """ # 获取视频的基本属性：高度、宽度、帧率 # Args: # video_path (str): 视频文件路径 # Returns: # tuple: (height, width, fps) 或 (None, None, None) 如果读取失败 # """ # try: # # 打开视频文件 # cap = cv2.VideoCapture(video_path) # if not cap.isOpened(): # return None, None, None # # 获取视频属性 # num_frame = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # fps = cap.get(cv2.CAP_PROP_FPS) # # 释放视频捕获对象 # cap.release() # return num_frame, height, width, fps # except Exception as e: # print(f"读取视频 {video_path} 时出错: {str(e)}") # return None, None, None def get_video_properties(self, video_path): try: probe = ffmpeg.probe(video_path) video_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'video'), None) if not video_stream: return None, None, None, None width = int(video_stream['width']) height = int(video_stream['height']) fps = eval(video_stream['r_frame_rate']) if 'nb_frames' in video_stream: num_frames = int(video_stream['nb_frames']) else: duration = float(probe['format']['duration']) num_frames = int(duration * fps) return num_frames, height, width, fps except Exception as e: print(f"读取视频 {video_path} 时出错: {str(e)}") return None, None, None, None def process_single_video(self, args): """ 处理单个视频文件 Args: args: (idx, video_file, video_dir) Returns: tuple: (idx, num_frame, height, width, fps, success, message) """ idx, video_file, video_dir = args video_path = os.path.join(video_dir, video_file) # 检查视频文件是否存在 if not os.path.exists(video_path): message = f"视频文件不存在: {video_path}" return idx, None, None, None, None, False, message # 获取视频属性 num_frame, height, width, fps = self.get_video_properties(video_path) # 更新进度 with self.progress_lock: self.processed_count += 1 progress = (self.processed_count / self.total_count) * 100 if height is not None: message = f"[{self.processed_count}/{self.total_count}] ({progress:.1f}%) {video_file} → {num_frame}, {width}x{height}, {fps:.2f}fps" success = True fps = round(fps, 2) else: message = f"[{self.processed_count}/{self.total_count}] ({progress:.1f}%) {video_file} → 获取信息失败" success = False print(message) return idx, num_frame, height, width, fps, success, message def process_video_csv(self, csv_path, video_dir="./", output_csv_path=None, max_workers=None): # """ # 多线程处理CSV文件，添加视频的height、width、fps信息 # Args: # csv_path (str): 输入CSV文件路径 # video_dir (str): 视频文件所在目录 # output_csv_path (str): 输出CSV文件路径，如果为None则覆盖原文件 # max_workers (int): 最大线程数，如果为None则使用初始化时的值 # """ if max_workers is None: max_workers = self.max_workers # try: # 读取CSV文件 df = pd.read_csv(csv_path) self.total_count = len(df) self.processed_count = 0 print(f"成功读取CSV文件，共 {len(df)} 行数据") print(f"使用 {max_workers} 个线程进行处理...") # 初始化新列 df['new_num_frame'] = None df['new_height'] = None df['new_width'] = None df['new_fps'] = None # 准备任务列表 tasks = [(idx, row['video path'], video_dir) for idx, row in df.iterrows()] # 记录开始时间 start_time = time.time() # 使用线程池执行任务 with ThreadPoolExecutor(max_workers=max_workers) as executor: # 提交所有任务 future_to_task = {executor.submit(self.process_single_video, task): task for task in tasks} # 处理完成的任务 for future in as_completed(future_to_task): idx, num_frame, height, width, fps, success, message = future.result() # 更新DataFrame if success and height is not None: df.at[idx, 'new_num_frame'] = num_frame df.at[idx, 'new_height'] = height df.at[idx, 'new_width'] = width df.at[idx, 'new_fps'] = fps # 计算处理时间 end_time = time.time() processing_time = end_time - start_time # 保存结果 if output_csv_path is None: output_csv_path = csv_path df.to_csv(output_csv_path, index=False) # 显示统计信息 valid_videos = df['new_height'].notna().sum() print(f"\n{'='*60}") print(f"处理完成！") print(f"总处理时间: {processing_time:.2f}秒") print(f"平均每个视频: {processing_time/len(df):.2f}秒") print(f"成功处理视频数量: {valid_videos}/{len(df)}") print(f"结果已保存到: {output_csv_path}") print(f"{'='*60}") return df # except Exception as e: # print(f"处理过程中出错: {str(e)}") # return None # 便捷函数 def process_video_csv_multithread(csv_path, video_dir="./", output_csv_path=None, max_workers=4): """ 便捷的多线程视频处理函数 Args: csv_path (str): 输入CSV文件路径 video_dir (str): 视频文件所在目录 output_csv_path (str): 输出CSV文件路径 max_workers (int): 最大线程数 """ processor = VideoProcessor(max_workers=max_workers) return processor.process_video_csv(csv_path, video_dir, output_csv_path, max_workers) # 使用示例 if __name__ == "__main__": # 配置参数 # base_names = ["sekai-real-walking-hq-193", "sekai-game-walking-193", "sekai-real-walking-hq-386", "sekai-game-walking-386"] # base_names = ["sekai-real-walking-hq-193"] # base_names = ["sekai-game-walking-193"] # base_names = ["sekai-real-walking-hq-386"] base_names = ["sekai-game-walking-386"] for base_name in base_names: csv_file_path = f"/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final/data/train/SpatialVID_HQ_metadata.csv" video_directory = f"/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final" output_file_path = f"/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final/data/SpatialVID_HQ_step0.csv" thread_count = 192 # 方法1: 使用便捷函数 result_df = process_video_csv_multithread( csv_path=csv_file_path, video_dir=video_directory, output_csv_path=output_file_path, max_workers=thread_count ) # 方法2: 使用类的方式（更灵活） """ processor = VideoProcessor(max_workers=thread_count) result_df = processor.process_video_csv( csv_path=csv_file_path, video_dir=video_directory, output_csv_path=output_file_path ) """ # 显示前几行结果 if result_df is not None: print("\n处理后的数据预览:") print(result_df[['videoFile', 'new_num_frame', 'new_height', 'new_width', 'new_fps']].head()) # 显示一些统计信息 print(f"\n视频分辨率统计:") resolution_stats = result_df.groupby(['new_width', 'new_height']).size().reset_index(name='count') print(resolution_stats.head(10))