| import pandas as pd |
| from tqdm import tqdm |
| from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor |
| from functools import partial |
| import numpy as np |
|
|
| length_bucket_options = { |
| 1: [321, 301, 281, 261, 241, 221, 193, 181, 161, 141, 121, 101, 81, 61, 41, 21], |
| 2: [193, 177, 161, 156, 145, 133, 129, 121, 113, 109, 97, 85, 81, 73, 65, 61, 49, 37, 25], |
| } |
|
|
| def find_nearest_length_bucket(length, stride=1): |
| buckets = length_bucket_options[stride] |
| min_bucket = min(buckets) |
| if length < min_bucket: |
| return length |
| valid_buckets = [bucket for bucket in buckets if bucket <= length] |
| return max(valid_buckets) |
|
|
| def split_long_videos(df, stride=1, skip_frames=0, skip_end_frames=0, overlap=0): |
| """ |
| 将长视频分割成多个段，充分利用所有帧 |
| |
| Args: |
| df: 输入DataFrame |
| stride: bucket选择的stride参数 |
| skip_frames: 跳过开头的帧数 |
| skip_end_frames: 跳过结尾的帧数 |
| overlap: 段之间的重叠帧数，默认为0 |
| """ |
| result_rows = [] |
| max_bucket = max(length_bucket_options[stride]) |
| |
| for idx, row in tqdm(df.iterrows(), total=len(df), desc="Processing videos"): |
| if pd.isna(row.get('new_num_frame')): |
| print(f"Skipping row {idx}: new_num_frame is NaN or missing") |
| continue |
|
|
| num_frames = row['new_num_frame'] |
| |
| available_frames = num_frames - skip_frames - skip_end_frames |
| |
| |
| if available_frames < min(length_bucket_options[stride]): |
| continue |
| |
| if available_frames <= max_bucket: |
| |
| new_row = row.copy() |
| new_row['start_frame'] = skip_frames |
| bucket_length = find_nearest_length_bucket(available_frames, stride) |
| new_row['end_frame'] = skip_frames + bucket_length |
| new_row['segment_id'] = 0 |
| result_rows.append(new_row) |
| else: |
| |
| step_size = max_bucket - overlap |
| segment_count = 0 |
| |
| start_pos = skip_frames |
| effective_end = num_frames - skip_end_frames |
| |
| while start_pos < effective_end: |
| remaining_frames = effective_end - start_pos |
| |
| |
| if remaining_frames < min(length_bucket_options[stride]): |
| break |
| |
| new_row = row.copy() |
| new_row['start_frame'] = start_pos |
| |
| |
| segment_length = min(remaining_frames, max_bucket) |
| bucket_length = find_nearest_length_bucket(segment_length, stride) |
| new_row['end_frame'] = start_pos + bucket_length |
| new_row['segment_id'] = segment_count |
| |
| result_rows.append(new_row) |
| |
| |
| start_pos += step_size |
| segment_count += 1 |
| |
| |
| if start_pos + min(length_bucket_options[stride]) > effective_end: |
| break |
| |
| return pd.DataFrame(result_rows) |
|
|
| def add_frame_range_with_segments(csv_path, output_path=None, stride=1, skip_frames=0, skip_end_frames=0, overlap=0): |
| """ |
| 为CSV添加start_frame和end_frame列，并将长视频分割成多个段 |
| |
| Args: |
| csv_path: 输入CSV文件路径 |
| output_path: 输出CSV文件路径，如果为None则覆盖原文件 |
| stride: bucket选择的stride参数 |
| skip_frames: 跳过开头的帧数，默认为0 |
| skip_end_frames: 跳过结尾的帧数，默认为0 |
| overlap: 段之间的重叠帧数，默认为0 |
| """ |
| |
| df = pd.read_csv(csv_path) |
| |
| |
| result_df = split_long_videos(df, stride, skip_frames, skip_end_frames, overlap) |
| |
| |
| if output_path is None: |
| output_path = csv_path |
| result_df.to_csv(output_path, index=False) |
| |
| return result_df |
|
|
| |
| if __name__ == "__main__": |
| input_csv = '/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final/data/SpatialVID_HQ_step0.csv' |
| output_csv = '/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final/data/SpatialVID_HQ_step1.csv' |
| df = add_frame_range_with_segments(input_csv, output_csv, stride=1, skip_frames=11, skip_end_frames=19, overlap=0) |
| |
| |
| print(f"处理后行数: {len(df)}") |
| print(f"分段统计:") |
| print(df['segment_id'].value_counts().sort_index()) |
| print("\n前几行示例:") |
| print(df.head(10)) |
|
|
