Hugging Face's logo

Datasets:
SuperCS
/
useful_code

License:
Dataset card Files
xet
Community
useful_code
File size: 5,502 Bytes 
e051419
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
 
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)