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useful_code / dataset_code /spatialvid /add_config_step2.py

Add files using upload-large-folder tool

e051419 verified about 1 year ago

5.5 kB

from tqdm import tqdm
import pandas as pd
import json
import os
from concurrent.futures import ThreadPoolExecutor, as_completed
import threading

def process_single_row(args):
    """
    处理单个行的函数
    
    Args:
        args: 元组，包含 (index, row, video_folder)
    
    Returns:
        tuple: (index, prompt)
    """
    index, row, video_folder = args
    
    try:
        # 构建caption.json文件路径
        prompt_path = os.path.join(video_folder, row["annotation path"], "caption.json")
        
        # 读取JSON文件
        with open(prompt_path, 'r', encoding='utf-8') as f:
            data = json.load(f)
        
        # 构建prompt
        prompt = data['SceneDescription'] + " " + data["CameraMotion"]
        return (index, prompt)
        
    except FileNotFoundError:
        print(f"Warning: File not found - {prompt_path}")
        return (index, "")
    except KeyError as e:
        print(f"Warning: Key {e} not found in {prompt_path}")
        return (index, "")
    except Exception as e:
        print(f"Error processing row {index}: {e}")
        return (index, "")

def add_prompt_to_csv(csv_path, video_folder, output_path=None, max_workers=4):
    """
    为CSV文件添加prompt字段（多线程版本）
    
    Args:
        csv_path: 输入CSV文件路径
        video_folder: 视频文件夹路径（self.video_folder的值）
        output_path: 输出CSV文件路径，如果为None则覆盖原文件
        max_workers: 最大线程数，默认为4
    """
    # 读取CSV文件
    df = pd.read_csv(csv_path)
    
    # 准备任务参数
    tasks = [(index, row, video_folder) for index, row in df.iterrows()]
    
    # 初始化结果字典
    results = {}
    
    # 使用ThreadPoolExecutor进行多线程处理
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        # 提交所有任务
        future_to_index = {executor.submit(process_single_row, task): task[0] for task in tasks}
        
        # 收集结果，使用tqdm显示进度
        for future in tqdm(as_completed(future_to_index), 
                          desc="Processing videos", 
                          total=len(tasks)):
            try:
                index, prompt = future.result()
                results[index] = prompt
            except Exception as e:
                index = future_to_index[future]
                print(f"Error in thread processing row {index}: {e}")
                results[index] = ""
    
    # 按索引顺序构建prompt列表
    prompts = [results[i] for i in range(len(df))]
    
    # 添加prompt列到DataFrame
    df['prompt'] = prompts
    
    # 保存结果
    if output_path is None:
        output_path = csv_path
    
    df.to_csv(output_path, index=False)
    print(f"Updated CSV saved to: {output_path}")
    
    return df

# 如果需要更高的性能，也可以考虑使用进程池版本
def add_prompt_to_csv_multiprocess(csv_path, video_folder, output_path=None, max_workers=4):
    """
    为CSV文件添加prompt字段（多进程版本）
    适用于CPU密集型任务
    
    Args:
        csv_path: 输入CSV文件路径
        video_folder: 视频文件夹路径
        output_path: 输出CSV文件路径，如果为None则覆盖原文件
        max_workers: 最大进程数，默认为4
    """
    from concurrent.futures import ProcessPoolExecutor
    
    # 读取CSV文件
    df = pd.read_csv(csv_path)
    
    # 准备任务参数
    tasks = [(index, row, video_folder) for index, row in df.iterrows()]
    
    # 初始化结果字典
    results = {}
    
    # 使用ProcessPoolExecutor进行多进程处理
    with ProcessPoolExecutor(max_workers=max_workers) as executor:
        # 提交所有任务
        future_to_index = {executor.submit(process_single_row, task): task[0] for task in tasks}
        
        # 收集结果，使用tqdm显示进度
        for future in tqdm(as_completed(future_to_index), 
                          desc="Processing videos", 
                          total=len(tasks)):
            try:
                index, prompt = future.result()
                results[index] = prompt
            except Exception as e:
                index = future_to_index[future]
                print(f"Error in process processing row {index}: {e}")
                results[index] = ""
    
    # 按索引顺序构建prompt列表
    prompts = [results[i] for i in range(len(df))]
    
    # 添加prompt列到DataFrame
    df['prompt'] = prompts
    
    # 保存结果
    if output_path is None:
        output_path = csv_path
    
    df.to_csv(output_path, index=False)
    print(f"Updated CSV saved to: {output_path}")
    
    return df

# 使用示例
if __name__ == "__main__":
    # 替换为您的实际路径
    csv_file_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final/data/SpatialVID_HQ_step1.csv"
    output_csv_file_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final/data/SpatialVID_HQ_step2.csv"
    video_folder_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final"
    
    # 使用多线程版本（推荐用于I/O密集型任务）
    updated_df = add_prompt_to_csv(csv_file_path, video_folder_path, output_csv_file_path, max_workers=128)
    
    # 如果是CPU密集型任务，可以使用多进程版本
    # updated_df = add_prompt_to_csv_multiprocess(csv_file_path, video_folder_path, output_csv_file_path, max_workers=4)

	from tqdm import tqdm
	import pandas as pd
	import json
	import os
	from concurrent.futures import ThreadPoolExecutor, as_completed
	import threading

	def process_single_row(args):
	"""
	处理单个行的函数

	Args:
	args: 元组，包含 (index, row, video_folder)

	Returns:
	tuple: (index, prompt)
	"""
	index, row, video_folder = args

	try:
	# 构建caption.json文件路径
	prompt_path = os.path.join(video_folder, row["annotation path"], "caption.json")

	# 读取JSON文件
	with open(prompt_path, 'r', encoding='utf-8') as f:
	data = json.load(f)

	# 构建prompt
	prompt = data['SceneDescription'] + " " + data["CameraMotion"]
	return (index, prompt)

	except FileNotFoundError:
	print(f"Warning: File not found - {prompt_path}")
	return (index, "")
	except KeyError as e:
	print(f"Warning: Key {e} not found in {prompt_path}")
	return (index, "")
	except Exception as e:
	print(f"Error processing row {index}: {e}")
	return (index, "")

	def add_prompt_to_csv(csv_path, video_folder, output_path=None, max_workers=4):
	"""
	为CSV文件添加prompt字段（多线程版本）

	Args:
	csv_path: 输入CSV文件路径
	video_folder: 视频文件夹路径（self.video_folder的值）
	output_path: 输出CSV文件路径，如果为None则覆盖原文件
	max_workers: 最大线程数，默认为4
	"""
	# 读取CSV文件
	df = pd.read_csv(csv_path)

	# 准备任务参数
	tasks = [(index, row, video_folder) for index, row in df.iterrows()]

	# 初始化结果字典
	results = {}

	# 使用ThreadPoolExecutor进行多线程处理
	with ThreadPoolExecutor(max_workers=max_workers) as executor:
	# 提交所有任务
	future_to_index = {executor.submit(process_single_row, task): task[0] for task in tasks}

	# 收集结果，使用tqdm显示进度
	for future in tqdm(as_completed(future_to_index),
	desc="Processing videos",
	total=len(tasks)):
	try:
	index, prompt = future.result()
	results[index] = prompt
	except Exception as e:
	index = future_to_index[future]
	print(f"Error in thread processing row {index}: {e}")
	results[index] = ""

	# 按索引顺序构建prompt列表
	prompts = [results[i] for i in range(len(df))]

	# 添加prompt列到DataFrame
	df['prompt'] = prompts

	# 保存结果
	if output_path is None:
	output_path = csv_path

	df.to_csv(output_path, index=False)
	print(f"Updated CSV saved to: {output_path}")

	return df

	# 如果需要更高的性能，也可以考虑使用进程池版本
	def add_prompt_to_csv_multiprocess(csv_path, video_folder, output_path=None, max_workers=4):
	"""
	为CSV文件添加prompt字段（多进程版本）
	适用于CPU密集型任务

	Args:
	csv_path: 输入CSV文件路径
	video_folder: 视频文件夹路径
	output_path: 输出CSV文件路径，如果为None则覆盖原文件
	max_workers: 最大进程数，默认为4
	"""
	from concurrent.futures import ProcessPoolExecutor

	# 读取CSV文件
	df = pd.read_csv(csv_path)

	# 准备任务参数
	tasks = [(index, row, video_folder) for index, row in df.iterrows()]

	# 初始化结果字典
	results = {}

	# 使用ProcessPoolExecutor进行多进程处理
	with ProcessPoolExecutor(max_workers=max_workers) as executor:
	# 提交所有任务
	future_to_index = {executor.submit(process_single_row, task): task[0] for task in tasks}

	# 收集结果，使用tqdm显示进度
	for future in tqdm(as_completed(future_to_index),
	desc="Processing videos",
	total=len(tasks)):
	try:
	index, prompt = future.result()
	results[index] = prompt
	except Exception as e:
	index = future_to_index[future]
	print(f"Error in process processing row {index}: {e}")
	results[index] = ""

	# 按索引顺序构建prompt列表
	prompts = [results[i] for i in range(len(df))]

	# 添加prompt列到DataFrame
	df['prompt'] = prompts

	# 保存结果
	if output_path is None:
	output_path = csv_path

	df.to_csv(output_path, index=False)
	print(f"Updated CSV saved to: {output_path}")

	return df

	# 使用示例
	if __name__ == "__main__":
	# 替换为您的实际路径
	csv_file_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final/data/SpatialVID_HQ_step1.csv"
	output_csv_file_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final/data/SpatialVID_HQ_step2.csv"
	video_folder_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final"

	# 使用多线程版本（推荐用于I/O密集型任务）
	updated_df = add_prompt_to_csv(csv_file_path, video_folder_path, output_csv_file_path, max_workers=128)

	# 如果是CPU密集型任务，可以使用多进程版本
	# updated_df = add_prompt_to_csv_multiprocess(csv_file_path, video_folder_path, output_csv_file_path, max_workers=4)