SignX/doc/analyze_asllrp_data.py

#!/usr/bin/env python3
"""
分析ASLLRP数据集的结构和关系
"""
import os
import csv
from collections import defaultdict
import numpy as np

# 数据路径
BASE_PATH = "/research/cbim/vast/sf895/code/Sign-X/output/huggingface_asllrp_repo"
MAPPING_FILE = os.path.join(BASE_PATH, "ASLLRP_utterances_mapping.txt")
CSV_FILE = os.path.join(BASE_PATH, "asllrp_sentence_signs_2025_06_28.csv")
RESULTS_DIR = os.path.join(BASE_PATH, "ASLLRP_utterances_results")

def analyze_mapping_file():
    """分析mapping文件"""
    print("="*80)
    print("分析 ASLLRP_utterances_mapping.txt")
    print("="*80)

    with open(MAPPING_FILE, 'r') as f:
        lines = f.readlines()

    print(f"总共有 {len(lines)} 个视频utterance")

    # 示例
    print("\n前5个示例:")
    for i, line in enumerate(lines[:5], 1):
        video_id, glosses = line.strip().split(': ', 1)
        gloss_list = glosses.split()
        print(f"{i}. 视频ID: {video_id}")
        print(f"   Gloss数量: {len(gloss_list)}")
        print(f"   Gloss序列: {' '.join(gloss_list[:10])}{'...' if len(gloss_list) > 10 else ''}")
        print()

    return lines

def analyze_csv_file(example_video_id="10006709"):
    """分析CSV文件 - 包含每个sign的精确时间标注"""
    print("="*80)
    print("分析 asllrp_sentence_signs_2025_06_28.csv")
    print("="*80)

    # 读取CSV
    utterance_signs = defaultdict(list)

    with open(CSV_FILE, 'r') as f:
        reader = csv.DictReader(f)
        headers = reader.fieldnames

        print(f"\nCSV文件包含以下列:")
        for i, header in enumerate(headers, 1):
            print(f"  {i}. {header}")

        # 按utterance video filename分组
        for row in reader:
            utterance_video = row['Utterance video filename']
            utterance_signs[utterance_video].append(row)

    print(f"\n总共有 {len(utterance_signs)} 个不同的utterance视频")
    print(f"总共有 {sum(len(signs) for signs in utterance_signs.values())} 个sign标注")

    # 分析示例视频
    example_key = f"{example_video_id}.mp4"
    if example_key in utterance_signs:
        signs = utterance_signs[example_key]
        print(f"\n示例视频 {example_video_id} 的详细信息:")
        print(f"  包含 {len(signs)} 个sign")
        print(f"\n  前5个signs:")
        for i, sign in enumerate(signs[:5], 1):
            print(f"    {i}. {sign['Main entry gloss label']}")
            print(f"       - Sign开始帧: {sign['Start frame of the sign video']}")
            print(f"       - Sign结束帧: {sign['End frame of the sign video']}")
            print(f"       - Utterance开始帧: {sign['Start frame of the containing utterance']}")
            print(f"       - Utterance结束帧: {sign['End frame of the containing utterance']}")
            print(f"       - Sign类型: {sign['Sign type']}")
            print()

    return utterance_signs

def analyze_results_directory(example_video_id="10006709"):
    """分析results目录结构"""
    print("="*80)
    print("分析 ASLLRP_utterances_results 目录")
    print("="*80)

    video_dirs = [d for d in os.listdir(RESULTS_DIR)
                  if os.path.isdir(os.path.join(RESULTS_DIR, d))]

    print(f"\n总共有 {len(video_dirs)} 个视频文件夹")

    # 分析示例文件夹
    example_dir = os.path.join(RESULTS_DIR, example_video_id)
    if os.path.exists(example_dir):
        print(f"\n示例视频 {example_video_id} 的文件结构:")

        # crop_frame
        crop_frame_dir = os.path.join(example_dir, "crop_frame")
        if os.path.exists(crop_frame_dir):
            frames = sorted([f for f in os.listdir(crop_frame_dir) if f.endswith('.jpg')])
            print(f"  - crop_frame/: {len(frames)} 个裁剪帧")
            print(f"    帧范围: {frames[0]} 到 {frames[-1]}")

        # crop_original_video.mp4
        video_path = os.path.join(example_dir, "crop_original_video.mp4")
        if os.path.exists(video_path):
            size_mb = os.path.getsize(video_path) / (1024 * 1024)
            print(f"  - crop_original_video.mp4: {size_mb:.2f} MB")

        # results_dwpose
        dwpose_dir = os.path.join(example_dir, "results_dwpose/npz")
        if os.path.exists(dwpose_dir):
            npz_files = sorted([f for f in os.listdir(dwpose_dir) if f.endswith('.npz')])
            print(f"  - results_dwpose/npz/: {len(npz_files)} 个姿态估计文件")

            # 查看一个npz文件的内容
            if npz_files:
                sample_npz = np.load(os.path.join(dwpose_dir, npz_files[0]))
                print(f"    NPZ文件包含的数据:")
                for key in sample_npz.files:
                    data = sample_npz[key]
                    print(f"      - {key}: shape={data.shape}, dtype={data.dtype}")

def understand_csv_usage():
    """说明如何使用CSV文件"""
    print("\n" + "="*80)
    print("如何使用 asllrp_sentence_signs_2025_06_28.csv")
    print("="*80)

    print("""
这个CSV文件的主要用途：

1. **精确的时间标注**
   - 每一行代表一个sign（手语词）
   - "Start frame of the sign video" 和 "End frame of the sign video"
     表示这个sign在整个视频中的精确帧范围
   - 可以用来从视频中提取单个sign的片段

2. **Utterance级别的上下文**
   - "Start frame of the containing utterance" 和 "End frame of the containing utterance"
     表示包含这个sign的整个句子（utterance）的帧范围
   - 一个utterance可能包含多个signs

3. **手语学语言特征**
   - Dominant/Non-dominant start/end handshape: 起始和结束手型
   - Sign type: 手语类型（Lexical Signs, Fingerspelled Signs, etc.）
   - Class label: 手语词的分类标签

4. **数据关联**
   - "Utterance video filename": 对应 ASLLRP_utterances_results 中的文件夹名
   - "Sign video filename": 单个sign的视频文件名（在原始ASLLRP数据集中）

使用示例代码：
    """)

    print("""
# 提取特定utterance的所有signs及其时间
import csv

utterance_id = "10006709"
with open(CSV_FILE, 'r') as f:
    reader = csv.DictReader(f)
    for row in reader:
        if row['Utterance video filename'] == f"{utterance_id}.mp4":
            gloss = row['Main entry gloss label']
            start_frame = int(row['Start frame of the sign video'])
            end_frame = int(row['End frame of the sign video'])
            print(f"{gloss}: 帧 {start_frame}-{end_frame}")
    """)

def create_example_script():
    """创建一个示例脚本展示如何使用这些数据"""
    print("\n" + "="*80)
    print("示例：从CSV提取sign的时间信息并与视频对应")
    print("="*80)

    script = '''
import csv
import cv2
import os

def extract_signs_from_utterance(utterance_id):
    """提取一个utterance中所有signs的信息"""
    csv_file = "/research/cbim/vast/sf895/code/Sign-X/output/huggingface_asllrp_repo/asllrp_sentence_signs_2025_06_28.csv"

    signs = []
    with open(csv_file, 'r') as f:
        reader = csv.DictReader(f)
        for row in reader:
            if row['Utterance video filename'] == f"{utterance_id}.mp4":
                signs.append({
                    'gloss': row['Main entry gloss label'],
                    'start_frame': int(row['Start frame of the sign video']),
                    'end_frame': int(row['End frame of the sign video']),
                    'sign_type': row['Sign type']
                })

    return signs

# 示例使用
utterance_id = "10006709"
signs = extract_signs_from_utterance(utterance_id)
print(f"Utterance {utterance_id} 包含 {len(signs)} 个signs:")
for sign in signs[:5]:
    print(f"  {sign['gloss']}: 帧 {sign['start_frame']}-{sign['end_frame']} ({sign['sign_type']})")
'''

    print(script)

if __name__ == "__main__":
    print("\nASLLRP数据集结构分析")
    print("作者: Claude Code")
    print("日期: 2025-12-27\n")

    # 分析三个主要组件
    analyze_mapping_file()
    print()

    analyze_csv_file()
    print()

    analyze_results_directory()

    # 使用说明
    understand_csv_usage()

    # 示例脚本
    create_example_script()

    print("\n" + "="*80)
    print("分析完成!")
    print("="*80)