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useful_code / dataset_code /sekai /preprocess /add_config.py

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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

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
            filename = os.path.splitext(os.path.basename(video_path))[0]
            parts = filename.split('_')
            num_frame = int(parts[-1]) - int(parts[-2])

            # 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 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, 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['num_frame'] = None
            df['height'] = None
            df['width'] = None
            df['fps'] = None
            
            # 准备任务列表
            tasks = [(idx, row['videoFile'], 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, 'num_frame'] = num_frame
                        df.at[idx, 'height'] = height
                        df.at[idx, 'width'] = width
                        df.at[idx, '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['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/Lixsp11/0_final_sekai_dataset/yamls/{base_name}.csv"
        video_directory = f"/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/0_final_sekai_dataset/{base_name}"
        output_file_path = f"/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/0_final_sekai_dataset/yamls/{base_name}_updated.csv"
        thread_count = 32
        
        # 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', 'num_frame', 'height', 'width', '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(['width', 'height']).size().reset_index(name='count')
            print(resolution_stats.head(10))

	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

	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
	filename = os.path.splitext(os.path.basename(video_path))[0]
	parts = filename.split('_')
	num_frame = int(parts[-1]) - int(parts[-2])

	# 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 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, 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['num_frame'] = None
	df['height'] = None
	df['width'] = None
	df['fps'] = None

	# 准备任务列表
	tasks = [(idx, row['videoFile'], 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, 'num_frame'] = num_frame
	df.at[idx, 'height'] = height
	df.at[idx, 'width'] = width
	df.at[idx, '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['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/Lixsp11/0_final_sekai_dataset/yamls/{base_name}.csv"
	video_directory = f"/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/0_final_sekai_dataset/{base_name}"
	output_file_path = f"/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/0_final_sekai_dataset/yamls/{base_name}_updated.csv"
	thread_count = 32

	# 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', 'num_frame', 'height', 'width', '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(['width', 'height']).size().reset_index(name='count')
	print(resolution_stats.head(10))