Upload preprocess.py with huggingface_hub

preprocess.py

@@ -0,0 +1,1333 @@
+#!/usr/bin/env python3
+"""
+数据集预处理统一入口
+
+用法:
+  python preprocess.py extract              # 解析 H5 文件
+  python preprocess.py extract --check      # 仅检查 H5 结构
+  python preprocess.py extract --update     # 更新 metadata（添加热力图/视频路径）
+  python preprocess.py heatmap              # 生成热力图
+  python preprocess.py heatmap --test       # 测试热力图生成
+  python preprocess.py marker_flow          # 生成 xela marker flow 可视化
+  python preprocess.py marker_flow --test   # 测试 marker flow 生成
+  python preprocess.py video                # 生成视频
+  python preprocess.py video --test         # 测试视频生成
+  python preprocess.py pack                 # 打包图像为 tar 文件
+  python preprocess.py pack --delete        # 打包后删除原始图像
+  python preprocess.py unpack               # 解压 tar 文件
+  python preprocess.py unpack --delete      # 解压后删除 tar 文件
+  python preprocess.py clean                # 删除所有 PNG，只保留视频
+  python preprocess.py upload               # 上传到 Hugging Face
+  python preprocess.py upload --sync        # 同步上传（删除远端多余文件）
+  python preprocess.py all                  # 完整流程（extract -> heatmap -> video -> update）
+"""
+
+import argparse
+import json
+import subprocess
+import tempfile
+from pathlib import Path
+from collections import defaultdict
+
+import h5py
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+
+# ============================================================
+# 配置
+# ============================================================
+
+BASE_DIR = Path(__file__).parent
+
+# 热力图配置
+TACTILE_VMIN = 15
+TACTILE_VMAX = 750
+TACTILE_CMAP = 'plasma'
+XELA_VMIN = -5
+XELA_VMAX = 5
+XELA_CMAP = 'RdBu_r'
+
+
+# ============================================================
+# 热力图生成函数
+# ============================================================
+
+def save_tactile_heatmap(data, output_path, rows=11, cols=6):
+    """保存 tactile 热力图"""
+    data = np.array(data)
+    if len(data.shape) == 1:
+        if len(data) == rows * cols:
+            data = data.reshape(rows, cols)
+        else:
+            data = data.reshape(1, -1)
+    
+    fig, ax = plt.subplots(figsize=(cols * 0.5, rows * 0.5))
+    ax.imshow(data, cmap=TACTILE_CMAP, aspect='equal', interpolation='nearest',
+              vmin=TACTILE_VMIN, vmax=TACTILE_VMAX)
+    ax.axis('off')
+    plt.savefig(output_path, dpi=80, bbox_inches='tight', pad_inches=0)
+    plt.close(fig)
+
+
+def save_xela_heatmap(data, output_path):
+    """保存 xela 热力图（Z轴热力图 + XY箭头）"""
+    data = np.array(data)
+    
+    if len(data) == 72:
+        data = data.reshape(4, 6, 3)
+        fx, fy, fz = data[:, :, 0], data[:, :, 1], data[:, :, 2]
+        
+        fig, ax = plt.subplots(figsize=(4, 3))
+        ax.imshow(fz, cmap=XELA_CMAP, aspect='equal', interpolation='nearest',
+                  vmin=XELA_VMIN, vmax=XELA_VMAX)
+        
+        rows, cols = 4, 6
+        y_grid, x_grid = np.mgrid[0:rows, 0:cols]
+        magnitude = np.sqrt(fx**2 + fy**2)
+        max_mag = magnitude.max() if magnitude.max() > 0 else 1
+        scale = 0.4 / max_mag
+        
+        ax.quiver(x_grid, y_grid, fx * scale, -fy * scale,
+                  color='black', scale=1, scale_units='xy',
+                  width=0.02, headwidth=3, headlength=2)
+        ax.axis('off')
+        plt.savefig(output_path, dpi=100, bbox_inches='tight', pad_inches=0)
+        plt.close(fig)
+    else:
+        fig, ax = plt.subplots(figsize=(6, 1))
+        ax.imshow(data.reshape(1, -1), cmap=XELA_CMAP, aspect='auto',
+                  vmin=XELA_VMIN, vmax=XELA_VMAX)
+        ax.axis('off')
+        plt.savefig(output_path, dpi=80, bbox_inches='tight', pad_inches=0)
+        plt.close(fig)
+
+
+def save_xela_marker_flow(data, output_path):
+    """
+    保存 xela marker flow 可视化
+    - 网格上的圆点根据 XY 力偏移（与箭头方向一致）
+    - Z 轴力用圆点大小和颜色表示
+    """
+    data = np.array(data)
+    
+    if len(data) != 72:
+        return
+    
+    data = data.reshape(4, 6, 3)
+    fx, fy, fz = data[:, :, 0], data[:, :, 1], data[:, :, 2]
+    
+    # 使用与箭头相同的 scale 计算
+    magnitude = np.sqrt(fx**2 + fy**2)
+    max_mag = magnitude.max() if magnitude.max() > 0 else 1
+    scale = 0.4 / max_mag  # 最大偏移 0.4 格
+    
+    rows, cols = 4, 6
+    fig, ax = plt.subplots(figsize=(6, 4))
+    
+    # 使用 imshow 建立与 heatmap 完全相同的坐标系
+    bg = np.ones((rows, cols)) * 0.95  # 浅灰背景
+    ax.imshow(bg, cmap='gray', vmin=0, vmax=1, aspect='equal')
+    
+    # 绘制原始网格位置（浅灰色小点）
+    for i in range(rows):
+        for j in range(cols):
+            ax.plot(j, i, 'o', color='#cccccc', markersize=8)
+    
+    # 绘制偏���后的 marker（与 quiver 完全相同的方向处理）
+    for i in range(rows):
+        for j in range(cols):
+            # 偏移量与 quiver 箭头方向完全一致
+            dx = fx[i, j] * scale
+            dy = -fy[i, j] * scale  # 与 quiver 中的 -fy 一致
+            
+            # 新位置
+            new_x = j + dx
+            new_y = i + dy
+            
+            # 连线（从原点到新位置）
+            ax.plot([j, new_x], [i, new_y], '-', color='#888888', linewidth=1, alpha=0.5)
+            
+            # 圆点大小根据 Z 轴力（法向力），使用固定范围
+            z_normalized = abs(fz[i, j]) / XELA_VMAX  # 归一化到 [0, 1]
+            size = 8 + z_normalized * 15  # 基础大小 8，最大 23
+            size = min(max(size, 6), 25)  # 限制范围
+            
+            # 颜色根据 Z 轴力（正负）
+            if fz[i, j] > 0:
+                color = '#e74c3c'  # 红色（正向力/压力）
+            else:
+                color = '#3498db'  # 蓝色（负向力/拉力）
+            
+            ax.plot(new_x, new_y, 'o', color=color, markersize=size, 
+                    markeredgecolor='white', markeredgewidth=0.5)
+    
+    ax.axis('off')
+    plt.savefig(output_path, dpi=100, bbox_inches='tight', pad_inches=0.1)
+    plt.close(fig)
+
+
+# ============================================================
+# H5 解析函数
+# ============================================================
+
+def check_h5_structure():
+    """检查 H5 文件结构"""
+    folder_keys = defaultdict(lambda: defaultdict(set))
+    h5_folders = [d for d in BASE_DIR.iterdir() if d.is_dir() and d.name.endswith('_h5')]
+    
+    for h5_folder in sorted(h5_folders):
+        h5_files = list(h5_folder.rglob('*.h5'))
+        print(f"\n{'='*60}\n文件夹: {h5_folder.name} ({len(h5_files)} 个文件)\n{'='*60}")
+        
+        for h5_path in h5_files[:3]:
+            print(f"\n  {h5_path.name}:")
+            with h5py.File(h5_path, 'r') as f:
+                for key in sorted(f.keys()):
+                    arr = f[key]
+                    print(f"    - {key}: shape={arr.shape}, dtype={arr.dtype}")
+                    folder_keys[h5_folder.name][key].add(str(arr.shape))
+        
+        for h5_path in h5_files:
+            with h5py.File(h5_path, 'r') as f:
+                for key in f.keys():
+                    folder_keys[h5_folder.name][key].add(str(f[key].shape))
+        
+        print(f"\n  汇总:")
+        for key, shapes in sorted(folder_keys[h5_folder.name].items()):
+            print(f"    - {key}: {list(shapes)}")
+
+
+def extract_pose_data(h5_path, output_dir, episode_id, subset_path=""):
+    """解析 pose_data H5 文件"""
+    episode_dir = output_dir / episode_id
+    episode_dir.mkdir(parents=True, exist_ok=True)
+    rel_prefix = f"{subset_path}/{episode_id}" if subset_path else episode_id
+    records = []
+    
+    with h5py.File(h5_path, 'r') as f:
+        keys = list(f.keys())
+        num_frames = len(f['timestamps'][:]) if 'timestamps' in keys else 0
+        data_cache = {}
+        image_paths = {}
+        
+        for key in keys:
+            arr = f[key][:]
+            if arr.dtype == np.uint8:
+                if len(arr.shape) == 3:
+                    filename = "bg.png"
+                    Image.fromarray(arr).save(episode_dir / filename)
+                    data_cache[f"{key}_image"] = f"{rel_prefix}/{filename}"
+                elif len(arr.shape) == 4:
+                    paths = []
+                    for i, img in enumerate(arr):
+                        filename = f"{key}_{i:04d}.png"
+                        Image.fromarray(img).save(episode_dir / filename)
+                        paths.append(f"{rel_prefix}/{filename}")
+                    image_paths[key] = paths
+                elif len(arr.shape) == 5:
+                    num_samples = arr.shape[1]
+                    paths = []
+                    for frame_idx in range(arr.shape[0]):
+                        frame_paths = []
+                        for sample_idx in range(num_samples):
+                            filename = f"{key}_f{frame_idx:04d}_s{sample_idx}.png"
+                            Image.fromarray(arr[frame_idx, sample_idx]).save(episode_dir / filename)
+                            frame_paths.append(f"{rel_prefix}/{filename}")
+                        paths.append(frame_paths)
+                    image_paths[key] = paths
+                    data_cache[f"{key}_num_samples"] = num_samples
+            else:
+                data_cache[key] = arr.tolist()
+        
+        for frame_idx in range(num_frames):
+            record = {"episode_id": episode_id, "frame_idx": frame_idx}
+            if subset_path:
+                record["subset"] = subset_path
+            
+            for key, paths in image_paths.items():
+                if isinstance(paths[0], list):
+                    for s_idx, p in enumerate(paths[frame_idx]):
+                        if s_idx == 0:
+                            record["file_name"] = p
+                        record[f"image_s{s_idx}"] = p
+                else:
+                    record["file_name"] = paths[frame_idx]
+            
+            for key, val in data_cache.items():
+                if key.endswith("_image") or key.endswith("_num_samples"):
+                    record[key] = val
+            
+            if 'timestamps' in data_cache:
+                record["timestamp"] = data_cache['timestamps'][frame_idx]
+            if 'rotations' in data_cache:
+                record["rotation"] = data_cache['rotations'][frame_idx]
+            if 'translations' in data_cache:
+                record["translation"] = data_cache['translations'][frame_idx]
+            if 'tactile' in data_cache:
+                record["tactile"] = data_cache['tactile'][frame_idx]
+            if 'xela' in data_cache:
+                record["xela"] = data_cache['xela'][frame_idx]
+            
+            record["num_frames"] = num_frames
+            records.append(record)
+    
+    return records
+
+
+def extract_force_data(h5_path, output_dir, episode_id, subset_path=""):
+    """解析 force_data H5 文件"""
+    episode_dir = output_dir / episode_id
+    episode_dir.mkdir(parents=True, exist_ok=True)
+    rel_prefix = f"{subset_path}/{episode_id}" if subset_path else episode_id
+    records = []
+    
+    with h5py.File(h5_path, 'r') as f:
+        keys = list(f.keys())
+        num_frames = 0
+        data_cache = {}
+        image_paths = {}
+        
+        for key in keys:
+            arr = f[key][:]
+            if arr.dtype == np.uint8:
+                if len(arr.shape) == 3:
+                    filename = f"{key}.png"
+                    Image.fromarray(arr).save(episode_dir / filename)
+                    data_cache[f"{key}_image"] = f"{rel_prefix}/{filename}"
+                elif len(arr.shape) == 4:
+                    num_frames = max(num_frames, len(arr))
+                    paths = []
+                    for i, img in enumerate(arr):
+                        filename = f"{key}_{i:04d}.png"
+                        Image.fromarray(img).save(episode_dir / filename)
+                        paths.append(f"{rel_prefix}/{filename}")
+                    image_paths[key] = paths
+            else:
+                data_cache[key] = arr.tolist()
+                if len(arr.shape) >= 1:
+                    num_frames = max(num_frames, len(arr))
+        
+        for frame_idx in range(num_frames):
+            record = {"episode_id": episode_id, "frame_idx": frame_idx, "num_frames": num_frames}
+            if subset_path:
+                record["subset"] = subset_path
+            
+            for key, paths in image_paths.items():
+                if frame_idx < len(paths):
+                    record["file_name"] = paths[frame_idx]
+            
+            for key, val in data_cache.items():
+                if key.endswith("_image"):
+                    record[key] = val
+                elif isinstance(val, list) and frame_idx < len(val):
+                    record[key] = val[frame_idx]
+            
+            records.append(record)
+    
+    return records
+
+
+def extract_tacniq_gsmini(h5_path, output_dir, episode_id, subset_path=""):
+    """解析 tacniq_gsmini H5 文件"""
+    episode_dir = output_dir / episode_id
+    episode_dir.mkdir(parents=True, exist_ok=True)
+    gsmini_dir = episode_dir / "gsmini"
+    gsmini_dir.mkdir(parents=True, exist_ok=True)
+    rel_prefix = f"{subset_path}/{episode_id}" if subset_path else episode_id
+    records = []
+    
+    with h5py.File(h5_path, 'r') as f:
+        bg = f['bg'][:]
+        gsmini = f['gsmini'][:]
+        tacniq = f['tacniq'][:].tolist()
+    
+    Image.fromarray(bg).save(episode_dir / "bg.png")
+    num_frames = len(gsmini)
+    
+    for frame_idx in range(num_frames):
+        gsmini_filename = f"frame_{frame_idx:04d}.png"
+        Image.fromarray(gsmini[frame_idx]).save(gsmini_dir / gsmini_filename)
+        
+        records.append({
+            "episode_id": episode_id,
+            "frame_idx": frame_idx,
+            "file_name": f"{rel_prefix}/gsmini/{gsmini_filename}",
+            "gsmini_image": f"{rel_prefix}/gsmini/{gsmini_filename}",
+            "bg_image": f"{rel_prefix}/bg.png",
+            "tacniq": tacniq[frame_idx] if frame_idx < len(tacniq) else None,
+            "num_frames": num_frames,
+            "subset": subset_path if subset_path else None,
+        })
+    
+    return records
+
+
+def extract_xela_9dtact(h5_path, output_dir, episode_id, subset_path=""):
+    """解析 xela_9dtact H5 文件"""
+    episode_dir = output_dir / episode_id
+    episode_dir.mkdir(parents=True, exist_ok=True)
+    dtact_dir = episode_dir / "9dtact"
+    dtact_dir.mkdir(parents=True, exist_ok=True)
+    rel_prefix = f"{subset_path}/{episode_id}" if subset_path else episode_id
+    records = []
+    
+    with h5py.File(h5_path, 'r') as f:
+        bg = f['bg'][:]
+        dtact = f['9dtact'][:]
+        xela = f['xela'][:].tolist()
+    
+    Image.fromarray(bg).save(episode_dir / "bg.png")
+    num_frames = len(dtact)
+    
+    for frame_idx in range(num_frames):
+        dtact_filename = f"frame_{frame_idx:04d}.png"
+        Image.fromarray(dtact[frame_idx]).save(dtact_dir / dtact_filename)
+        
+        records.append({
+            "episode_id": episode_id,
+            "frame_idx": frame_idx,
+            "file_name": f"{rel_prefix}/9dtact/{dtact_filename}",
+            "dtact_image": f"{rel_prefix}/9dtact/{dtact_filename}",
+            "bg_image": f"{rel_prefix}/bg.png",
+            "xela": xela[frame_idx] if frame_idx < len(xela) else None,
+            "num_frames": num_frames,
+            "subset": subset_path if subset_path else None,
+        })
+    
+    return records
+
+
+def extract_all():
+    """解析所有 H5 文件"""
+    h5_folders = [d for d in BASE_DIR.iterdir() if d.is_dir() and d.name.endswith('_h5')]
+    
+    for h5_folder in h5_folders:
+        output_folder = BASE_DIR / h5_folder.name.replace('_h5', '')
+        output_folder.mkdir(exist_ok=True)
+        
+        h5_files = list(h5_folder.rglob('*.h5'))
+        print(f"\n解析 {h5_folder.name}: {len(h5_files)} 个文件")
+        
+        all_records = []
+        
+        for h5_path in tqdm(h5_files, desc=h5_folder.name):
+            relative = h5_path.relative_to(h5_folder)
+            sub_output_dir = output_folder / relative.parent
+            sub_output_dir.mkdir(parents=True, exist_ok=True)
+            
+            episode_id = h5_path.stem
+            subset_path = str(relative.parent) if relative.parent != Path('.') else ""
+            
+            try:
+                if 'pose_data' in h5_folder.name:
+                    records = extract_pose_data(h5_path, sub_output_dir, episode_id, subset_path)
+                elif 'tacniq_gsmini' in h5_folder.name:
+                    records = extract_tacniq_gsmini(h5_path, sub_output_dir, episode_id, subset_path)
+                elif 'xela_9dtact' in h5_folder.name:
+                    records = extract_xela_9dtact(h5_path, sub_output_dir, episode_id, subset_path)
+                elif 'force_data' in h5_folder.name:
+                    records = extract_force_data(h5_path, sub_output_dir, episode_id, subset_path)
+                else:
+                    continue
+                
+                all_records.extend(records)
+                
+                episode_dir = sub_output_dir / episode_id
+                with open(episode_dir / "metadata.json", 'w') as f:
+                    json.dump(records, f, indent=2, ensure_ascii=False)
+                    
+            except Exception as e:
+                print(f"\nError: {h5_path}: {e}")
+        
+        with open(output_folder / "metadata.jsonl", 'w') as f:
+            for record in all_records:
+                f.write(json.dumps(record, ensure_ascii=False) + '\n')
+        
+        print(f"  生成 {len(all_records)} 条记录")
+
+
+def update_metadata():
+    """更新 metadata，添加热力图和视频路径"""
+    data_folders = ['pose_data', 'force_data', 'tacniq_gsmini', 'xela_9dtact']
+    updated_count = 0
+    
+    for folder_name in data_folders:
+        folder = BASE_DIR / folder_name
+        if not folder.exists():
+            continue
+        
+        json_files = list(folder.rglob('metadata.json'))
+        print(f"\n更新 {folder_name}: {len(json_files)} 个文件")
+        
+        for json_path in tqdm(json_files, desc=folder_name):
+            episode_dir = json_path.parent
+            rel_prefix = str(episode_dir.relative_to(BASE_DIR))
+            
+            with open(json_path, 'r') as f:
+                records = json.load(f)
+            
+            modified = False
+            
+            for record in records:
+                frame_idx = record.get('frame_idx', 0)
+                
+                # 删除重复的 image 字段
+                if 'image' in record and 'file_name' in record:
+                    if record['image'] == record['file_name']:
+                        del record['image']
+                        modified = True
+                
+                # 添加热力图路径
+                for s_idx in range(100):
+                    for prefix, key_prefix in [('tactile', 'tactile_heatmap'), ('xela', 'xela_heatmap')]:
+                        heatmap_file = episode_dir / f"{prefix}_f{frame_idx:04d}_s{s_idx:02d}.png"
+                        if heatmap_file.exists():
+                            key = f"{key_prefix}_s{s_idx:02d}"
+                            new_path = f"{rel_prefix}/{prefix}_f{frame_idx:04d}_s{s_idx:02d}.png"
+                            if record.get(key) != new_path:
+                                record[key] = new_path
+                                modified = True
+                        else:
+                            break
+                
+                for prefix in ['tac02', 'xela']:
+                    heatmap_file = episode_dir / f"{prefix}_{frame_idx:04d}.png"
+                    if heatmap_file.exists():
+                        key = f"{prefix}_heatmap"
+                        new_path = f"{rel_prefix}/{prefix}_{frame_idx:04d}.png"
+                        if record.get(key) != new_path:
+                            record[key] = new_path
+                            modified = True
+                
+                for subdir, key in [('tacniq', 'tacniq_heatmap'), ('xela', 'xela_heatmap')]:
+                    heatmap_file = episode_dir / subdir / f"heatmap_{frame_idx:04d}.png"
+                    if heatmap_file.exists():
+                        new_path = f"{rel_prefix}/{subdir}/heatmap_{frame_idx:04d}.png"
+                        if record.get(key) != new_path:
+                            record[key] = new_path
+                            modified = True
+            
+            # 添加视频路径
+            for video_file in episode_dir.glob('video*.mp4'):
+                video_key = video_file.stem
+                video_path = f"{rel_prefix}/{video_file.name}"
+                for record in records:
+                    if record.get(video_key) != video_path:
+                        record[video_key] = video_path
+                        modified = True
+            
+            if modified:
+                with open(json_path, 'w') as f:
+                    json.dump(records, f, indent=2, ensure_ascii=False)
+                updated_count += 1
+    
+    print(f"\n更新 {updated_count} 个文件")
+    
+    # 重新生成 JSONL
+    print("\n重新生成 JSONL...")
+    for folder_name in data_folders:
+        folder = BASE_DIR / folder_name
+        if not folder.exists():
+            continue
+        
+        all_records = []
+        for json_path in folder.rglob('metadata.json'):
+            with open(json_path, 'r') as f:
+                all_records.extend(json.load(f))
+        
+        if all_records:
+            with open(folder / "metadata.jsonl", 'w') as f:
+                for record in all_records:
+                    f.write(json.dumps(record, ensure_ascii=False) + '\n')
+            print(f"  {folder_name}: {len(all_records)} 条记录")
+
+
+# ============================================================
+# 热力图生成
+# ============================================================
+
+def generate_heatmaps(data_type='all', test_only=False):
+    """生成热力图"""
+    
+    def process_tac02_pose():
+        data_dir = BASE_DIR / 'pose_data' / 'tac02_pose_h5'
+        if not data_dir.exists():
+            return
+        print(f"\n处理 tac02_pose_h5...")
+        episode_dirs = list(data_dir.iterdir())
+        if test_only:
+            episode_dirs = episode_dirs[:1]
+        
+        for episode_dir in tqdm([d for d in episode_dirs if d.is_dir()], desc="tac02_pose"):
+            json_path = episode_dir / 'metadata.json'
+            if not json_path.exists():
+                continue
+            with open(json_path, 'r') as f:
+                records = json.load(f)
+            
+            for record in (records[:1] if test_only else records):
+                if 'tactile' not in record or record['tactile'] is None:
+                    continue
+                frame_idx = record['frame_idx']
+                tactile = record['tactile']
+                
+                if isinstance(tactile[0], list):
+                    for s_idx, sample in enumerate(tactile):
+                        output_path = episode_dir / f"tactile_f{frame_idx:04d}_s{s_idx:02d}.png"
+                        save_tactile_heatmap(sample, output_path)
+                    if test_only:
+                        print(f"  生成 {len(tactile)} 个热力图")
+                        return
+    
+    def process_xela_pose():
+        data_dir = BASE_DIR / 'pose_data' / 'xela_pose_h5'
+        if not data_dir.exists():
+            return
+        print(f"\n处理 xela_pose_h5...")
+        episode_dirs = list(data_dir.iterdir())
+        if test_only:
+            episode_dirs = episode_dirs[:1]
+        
+        for episode_dir in tqdm([d for d in episode_dirs if d.is_dir()], desc="xela_pose"):
+            json_path = episode_dir / 'metadata.json'
+            if not json_path.exists():
+                continue
+            with open(json_path, 'r') as f:
+                records = json.load(f)
+            
+            for record in (records[:1] if test_only else records):
+                if 'xela' not in record or record['xela'] is None:
+                    continue
+                frame_idx = record['frame_idx']
+                xela = record['xela']
+                
+                if isinstance(xela[0], list):
+                    for s_idx, sample in enumerate(xela):
+                        output_path = episode_dir / f"xela_f{frame_idx:04d}_s{s_idx:02d}.png"
+                        save_xela_heatmap(sample, output_path)
+                    if test_only:
+                        print(f"  生成 {len(xela)} 个热力图")
+                        return
+    
+    def process_force_data(sensor_type=None):
+        force_dir = BASE_DIR / 'force_data'
+        if not force_dir.exists():
+            return
+        
+        for subset_dir in force_dir.iterdir():
+            if not subset_dir.is_dir():
+                continue
+            
+            if 'tac02' in subset_dir.name:
+                if sensor_type and sensor_type != 'tac02':
+                    continue
+                data_key, prefix = 'tac02', 'tac02'
+            elif 'xela' in subset_dir.name:
+                if sensor_type and sensor_type != 'xela':
+                    continue
+                data_key, prefix = 'xela', 'xela'
+            else:
+                continue
+            
+            print(f"\n处理 {subset_dir.name}...")
+            episode_dirs = list(subset_dir.iterdir())
+            if test_only:
+                episode_dirs = episode_dirs[:1]
+            
+            for episode_dir in tqdm([d for d in episode_dirs if d.is_dir()], desc=subset_dir.name):
+                json_path = episode_dir / 'metadata.json'
+                if not json_path.exists():
+                    continue
+                with open(json_path, 'r') as f:
+                    records = json.load(f)
+                
+                for record in (records[:1] if test_only else records):
+                    if data_key not in record or record[data_key] is None:
+                        continue
+                    frame_idx = record['frame_idx']
+                    heatmap_path = episode_dir / f"{prefix}_{frame_idx:04d}.png"
+                    if prefix == 'tac02':
+                        save_tactile_heatmap(record[data_key], heatmap_path)
+                    else:
+                        save_xela_heatmap(record[data_key], heatmap_path)
+                    if test_only:
+                        print(f"  生成: {heatmap_path}")
+                        return
+    
+    def process_tacniq_gsmini():
+        data_dir = BASE_DIR / 'tacniq_gsmini'
+        if not data_dir.exists():
+            return
+        print(f"\n处理 tacniq_gsmini...")
+        episode_dirs = list(data_dir.iterdir())
+        if test_only:
+            episode_dirs = episode_dirs[:1]
+        
+        for episode_dir in tqdm([d for d in episode_dirs if d.is_dir()], desc="tacniq_gsmini"):
+            json_path = episode_dir / 'metadata.json'
+            if not json_path.exists():
+                continue
+            
+            tacniq_dir = episode_dir / 'tacniq'
+            tacniq_dir.mkdir(parents=True, exist_ok=True)
+            
+            with open(json_path, 'r') as f:
+                records = json.load(f)
+            
+            for record in (records[:1] if test_only else records):
+                if 'tacniq' not in record or record['tacniq'] is None:
+                    continue
+                frame_idx = record['frame_idx']
+                heatmap_path = tacniq_dir / f"heatmap_{frame_idx:04d}.png"
+                save_tactile_heatmap(record['tacniq'], heatmap_path)
+                if test_only:
+                    print(f"  生成: {heatmap_path}")
+                    return
+    
+    def process_xela_9dtact():
+        data_dir = BASE_DIR / 'xela_9dtact'
+        if not data_dir.exists():
+            return
+        print(f"\n处理 xela_9dtact...")
+        episode_dirs = list(data_dir.iterdir())
+        if test_only:
+            episode_dirs = episode_dirs[:1]
+        
+        for episode_dir in tqdm([d for d in episode_dirs if d.is_dir()], desc="xela_9dtact"):
+            json_path = episode_dir / 'metadata.json'
+            if not json_path.exists():
+                continue
+            
+            xela_dir = episode_dir / 'xela'
+            xela_dir.mkdir(parents=True, exist_ok=True)
+            
+            with open(json_path, 'r') as f:
+                records = json.load(f)
+            
+            for record in (records[:1] if test_only else records):
+                if 'xela' not in record or record['xela'] is None:
+                    continue
+                frame_idx = record['frame_idx']
+                heatmap_path = xela_dir / f"heatmap_{frame_idx:04d}.png"
+                save_xela_heatmap(record['xela'], heatmap_path)
+                if test_only:
+                    print(f"  生成: {heatmap_path}")
+                    return
+    
+    t = data_type
+    if t in ['tac02_pose', 'pose', 'all']:
+        process_tac02_pose()
+    if t in ['xela_pose', 'pose', 'all']:
+        process_xela_pose()
+    if t in ['tac02_force', 'force', 'all']:
+        process_force_data('tac02')
+    if t in ['xela_force', 'force', 'all']:
+        process_force_data('xela')
+    if t in ['tacniq_gsmini', 'all']:
+        process_tacniq_gsmini()
+    if t in ['xela_9dtact', 'all']:
+        process_xela_9dtact()
+
+
+def generate_marker_flow(data_type='all', test_only=False):
+    """生成 xela marker flow 可视化"""
+    
+    def process_xela_pose():
+        data_dir = BASE_DIR / 'pose_data' / 'xela_pose_h5'
+        if not data_dir.exists():
+            return
+        print(f"\n生成 xela_pose marker flow...")
+        episode_dirs = list(data_dir.iterdir())
+        if test_only:
+            episode_dirs = episode_dirs[:1]
+        
+        for episode_dir in tqdm([d for d in episode_dirs if d.is_dir()], desc="xela_pose"):
+            json_path = episode_dir / 'metadata.json'
+            if not json_path.exists():
+                continue
+            
+            # 创建 marker_flow 子文件夹
+            flow_dir = episode_dir / 'marker_flow'
+            flow_dir.mkdir(parents=True, exist_ok=True)
+            
+            with open(json_path, 'r') as f:
+                records = json.load(f)
+            
+            for record in (records[:1] if test_only else records):
+                if 'xela' not in record or record['xela'] is None:
+                    continue
+                frame_idx = record['frame_idx']
+                xela = record['xela']
+                
+                if isinstance(xela[0], list):
+                    for s_idx, sample in enumerate(xela):
+                        output_path = flow_dir / f"flow_f{frame_idx:04d}_s{s_idx:02d}.png"
+                        save_xela_marker_flow(sample, output_path)
+                    if test_only:
+                        print(f"  生成 {len(xela)} 个 marker flow")
+                        return
+                else:
+                    output_path = flow_dir / f"flow_{frame_idx:04d}.png"
+                    save_xela_marker_flow(xela, output_path)
+                    if test_only:
+                        print(f"  生成: {output_path}")
+                        return
+    
+    def process_xela_force():
+        force_dir = BASE_DIR / 'force_data'
+        if not force_dir.exists():
+            return
+        
+        for subset_dir in force_dir.iterdir():
+            if not subset_dir.is_dir() or 'xela' not in subset_dir.name:
+                continue
+            
+            print(f"\n生成 {subset_dir.name} marker flow...")
+            episode_dirs = list(subset_dir.iterdir())
+            if test_only:
+                episode_dirs = episode_dirs[:1]
+            
+            for episode_dir in tqdm([d for d in episode_dirs if d.is_dir()], desc=subset_dir.name):
+                json_path = episode_dir / 'metadata.json'
+                if not json_path.exists():
+                    continue
+                
+                flow_dir = episode_dir / 'marker_flow'
+                flow_dir.mkdir(parents=True, exist_ok=True)
+                
+                with open(json_path, 'r') as f:
+                    records = json.load(f)
+                
+                for record in (records[:1] if test_only else records):
+                    if 'xela' not in record or record['xela'] is None:
+                        continue
+                    frame_idx = record['frame_idx']
+                    output_path = flow_dir / f"flow_{frame_idx:04d}.png"
+                    save_xela_marker_flow(record['xela'], output_path)
+                    if test_only:
+                        print(f"  生成: {output_path}")
+                        return
+    
+    def process_xela_9dtact():
+        data_dir = BASE_DIR / 'xela_9dtact'
+        if not data_dir.exists():
+            return
+        print(f"\n生成 xela_9dtact marker flow...")
+        episode_dirs = list(data_dir.iterdir())
+        if test_only:
+            episode_dirs = episode_dirs[:1]
+        
+        for episode_dir in tqdm([d for d in episode_dirs if d.is_dir()], desc="xela_9dtact"):
+            json_path = episode_dir / 'metadata.json'
+            if not json_path.exists():
+                continue
+            
+            # marker_flow 放在 xela 子文件夹内
+            flow_dir = episode_dir / 'xela' / 'marker_flow'
+            flow_dir.mkdir(parents=True, exist_ok=True)
+            
+            with open(json_path, 'r') as f:
+                records = json.load(f)
+            
+            for record in (records[:1] if test_only else records):
+                if 'xela' not in record or record['xela'] is None:
+                    continue
+                frame_idx = record['frame_idx']
+                output_path = flow_dir / f"flow_{frame_idx:04d}.png"
+                save_xela_marker_flow(record['xela'], output_path)
+                if test_only:
+                    print(f"  生成: {output_path}")
+                    return
+    
+    t = data_type
+    if t in ['xela_pose', 'pose', 'all']:
+        process_xela_pose()
+    if t in ['xela_force', 'force', 'all']:
+        process_xela_force()
+    if t in ['xela_9dtact', 'all']:
+        process_xela_9dtact()
+
+
+# ============================================================
+# 视频生成
+# ============================================================
+
+def create_video_from_images(episode_dir, output_path, image_patterns=None,
+                             subdir=None, fps_fallback=10, multi_sample=False,
+                             sample_pattern=None):
+    """从图像序列创建视频"""
+    json_path = episode_dir / 'metadata.json'
+    if not json_path.exists():
+        return False
+    
+    with open(json_path, 'r') as f:
+        records = json.load(f)
+    
+    if not records:
+        return False
+    
+    img_dir = episode_dir / subdir if subdir else episode_dir
+    
+    if multi_sample and sample_pattern:
+        all_frames = []
+        timestamps = []
+        for record in records:
+            frame_idx = record.get('frame_idx', len(timestamps))
+            timestamp = (record.get('sensor_timestamps') or
+                        record.get('force_timestamps') or
+                        record.get('timestamp'))
+            timestamps.append({'frame_idx': frame_idx, 'timestamp': timestamp})
+        
+        timestamps.sort(key=lambda x: x['frame_idx'])
+        
+        for i, ts_info in enumerate(timestamps):
+            frame_idx = ts_info['frame_idx']
+            sample_files = []
+            for sample_idx in range(100):
+                try:
+                    filename = sample_pattern.format(idx=frame_idx, sample=sample_idx)
+                    candidate = img_dir / filename
+                    if candidate.exists():
+                        sample_files.append(candidate)
+                    else:
+                        break
+                except (KeyError, ValueError):
+                    break
+            
+            if not sample_files:
+                continue
+            
+            if i < len(timestamps) - 1 and ts_info['timestamp'] and timestamps[i+1]['timestamp']:
+                frame_duration = max(0.01, min(2.0, timestamps[i+1]['timestamp'] - ts_info['timestamp']))
+            else:
+                frame_duration = 1.0 / fps_fallback
+            
+            sample_duration = frame_duration / len(sample_files)
+            for sample_file in sample_files:
+                all_frames.append({'path': sample_file, 'duration': sample_duration})
+        
+        if len(all_frames) < 2:
+            return False
+        
+        # 把 concat 文件放在 episode 目录，使用相对路径
+        concat_file = str(episode_dir / '_concat.txt')
+        with open(concat_file, 'w') as f:
+            for frame in all_frames:
+                # 使用相对于 episode_dir 的路径
+                rel_path = frame['path'].relative_to(episode_dir)
+                f.write(f"file '{rel_path}'\nduration {frame['duration']:.6f}\n")
+            rel_path = all_frames[-1]['path'].relative_to(episode_dir)
+            f.write(f"file '{rel_path}'\n")
+    else:
+        if image_patterns is None:
+            image_patterns = ["gelsight_{idx:04d}.png", "xela_{idx:04d}.png", "tac02_{idx:04d}.png"]
+        
+        frames = []
+        for record in records:
+            frame_idx = record.get('frame_idx', len(frames))
+            image_file = None
+            
+            for field in ['file_name', 'gsmini_image', 'dtact_image']:
+                if field in record and record[field]:
+                    img_path = record[field].split('/')[-1]
+                    candidate = img_dir / img_path
+                    if candidate.exists():
+                        image_file = candidate
+                        break
+            
+            if not image_file:
+                for pattern in image_patterns:
+                    try:
+                        candidate = img_dir / pattern.format(idx=frame_idx)
+                        if candidate.exists():
+                            image_file = candidate
+                            break
+                    except:
+                        continue
+            
+            if not image_file and subdir:
+                for pattern in [f"frame_{frame_idx:04d}.png", f"heatmap_{frame_idx:04d}.png"]:
+                    candidate = img_dir / pattern
+                    if candidate.exists():
+                        image_file = candidate
+                        break
+            
+            if image_file:
+                timestamp = (record.get('sensor_timestamps') or
+                            record.get('force_timestamps') or
+                            record.get('timestamp'))
+                frames.append({'path': image_file, 'timestamp': timestamp, 'frame_idx': frame_idx})
+        
+        if len(frames) < 2:
+            return False
+        
+        frames.sort(key=lambda x: x['frame_idx'])
+        
+        # 把 concat 文件放在 episode 目录，使用相对路径
+        concat_file = str(episode_dir / '_concat.txt')
+        with open(concat_file, 'w') as f:
+            for i, frame in enumerate(frames):
+                if i < len(frames) - 1 and frame['timestamp'] and frames[i+1]['timestamp']:
+                    duration = max(0.01, min(1.0, frames[i+1]['timestamp'] - frame['timestamp']))
+                else:
+                    duration = 1.0 / fps_fallback
+                # 使用相对于 episode_dir 的路径
+                rel_path = frame['path'].relative_to(episode_dir)
+                f.write(f"file '{rel_path}'\nduration {duration:.6f}\n")
+            rel_path = frames[-1]['path'].relative_to(episode_dir)
+            f.write(f"file '{rel_path}'\n")
+    
+    # scale 确保宽高是 2 的倍数（libx264 要���）
+    cmd = ['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', concat_file,
+           '-vf', 'scale=trunc(iw/2)*2:trunc(ih/2)*2',
+           '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-crf', '23', output_path]
+    
+    try:
+        result = subprocess.run(cmd, capture_output=True, text=True)
+        return result.returncode == 0
+    except FileNotFoundError:
+        print("  错误: ffmpeg 未安装")
+        return False
+    finally:
+        Path(concat_file).unlink(missing_ok=True)
+
+
+def generate_videos(data_type='all', test_only=False):
+    """生成视频"""
+    
+    def process(data_path, name, **kwargs):
+        data_dir = BASE_DIR / data_path
+        if not data_dir.exists():
+            print(f"{data_path} 不存在")
+            return
+        
+        print(f"\n处理 {name}...")
+        episode_dirs = sorted([d for d in data_dir.iterdir() if d.is_dir()],
+                             key=lambda x: int(x.name.split('_')[-1]))
+        if test_only:
+            episode_dirs = episode_dirs[:1]
+        
+        video_name = kwargs.pop('video_name', 'video.mp4')
+        success = 0
+        for episode_dir in tqdm(episode_dirs, desc=name):
+            if create_video_from_images(episode_dir, str(episode_dir / video_name), **kwargs):
+                success += 1
+                if test_only:
+                    print(f"  生成: {episode_dir / video_name}")
+        print(f"  成功: {success}/{len(episode_dirs)}")
+    
+    t = data_type
+    
+    # force_data
+    if t in ['9dtact_force', 'all']:
+        process('force_data/9dtact_force_h5', '9dtact_force', image_patterns=["gelsight_{idx:04d}.png"])
+    if t in ['xela_force', 'all']:
+        process('force_data/xela_force_h5', 'xela_force', image_patterns=["xela_{idx:04d}.png"])
+    if t in ['gelsight_force', 'all']:
+        process('force_data/gelsight_force_h5', 'gelsight_force', image_patterns=["gelsight_{idx:04d}.png"])
+    if t in ['tac02_force', 'all']:
+        process('force_data/tac02_force_h5', 'tac02_force', image_patterns=["tac02_{idx:04d}.png"])
+    
+    # pose_data
+    if t in ['gelsight_pose', 'all']:
+        process('pose_data/gelsight_pose_h5', 'gelsight_pose', multi_sample=True, sample_pattern="images_f{idx:04d}_s{sample}.png")
+    if t in ['9dtact_pose', 'all']:
+        process('pose_data/9dtact_pose_h5', '9dtact_pose', multi_sample=True, sample_pattern="images_f{idx:04d}_s{sample}.png")
+    if t in ['tac02_pose', 'all']:
+        process('pose_data/tac02_pose_h5', 'tac02_pose', multi_sample=True, sample_pattern="tactile_f{idx:04d}_s{sample:02d}.png")
+    if t in ['xela_pose', 'all']:
+        process('pose_data/xela_pose_h5', 'xela_pose', multi_sample=True, sample_pattern="xela_f{idx:04d}_s{sample:02d}.png")
+    
+    # marker_flow 视频
+    if t in ['xela_pose_flow', 'all']:
+        process('pose_data/xela_pose_h5', 'xela_pose (marker_flow)', subdir='marker_flow', 
+                multi_sample=True, sample_pattern="flow_f{idx:04d}_s{sample:02d}.png", video_name="video_flow.mp4")
+    if t in ['xela_force_flow', 'all']:
+        process('force_data/xela_force_h5', 'xela_force (marker_flow)', subdir='marker_flow',
+                image_patterns=["flow_{idx:04d}.png"], video_name="video_flow.mp4")
+    if t in ['xela_9dtact_flow', 'all']:
+        process('xela_9dtact', 'xela_9dtact (marker_flow)', subdir='xela/marker_flow',
+                image_patterns=["flow_{idx:04d}.png"], video_name="video_flow.mp4")
+    
+    # 双传感器
+    if t in ['tacniq_gsmini', 'all']:
+        process('tacniq_gsmini', 'tacniq (gsmini)', subdir='gsmini', image_patterns=["frame_{idx:04d}.png"], video_name="video_gsmini.mp4")
+        process('tacniq_gsmini', 'tacniq (tacniq)', subdir='tacniq', image_patterns=["heatmap_{idx:04d}.png"], video_name="video_tacniq.mp4")
+    if t in ['xela_9dtact', 'all']:
+        process('xela_9dtact', 'xela_9dtact (9dtact)', subdir='9dtact', image_patterns=["frame_{idx:04d}.png"], video_name="video_9dtact.mp4")
+        process('xela_9dtact', 'xela_9dtact (xela)', subdir='xela', image_patterns=["heatmap_{idx:04d}.png"], video_name="video_xela.mp4")
+
+
+# ============================================================
+# 打包图像序列
+# ============================================================
+
+def pack_images(delete_originals=False):
+    """
+    把每个 episode 的图像序列打包成 tar 文件（WebDataset 格式）
+    减少文件数量，便于上传 Hugging Face
+    """
+    import tarfile
+    
+    data_folders = ['pose_data', 'force_data', 'tacniq_gsmini', 'xela_9dtact']
+    
+    for folder_name in data_folders:
+        folder = BASE_DIR / folder_name
+        if not folder.exists():
+            continue
+        
+        # 找到所有 episode 目录
+        episode_dirs = []
+        for p in folder.rglob('metadata.json'):
+            episode_dirs.append(p.parent)
+        
+        print(f"\n打包 {folder_name}: {len(episode_dirs)} 个 episode")
+        
+        for episode_dir in tqdm(episode_dirs, desc=folder_name):
+            # ��集所有图像文件
+            image_files = list(episode_dir.glob('*.png'))
+            
+            # 检查子文件夹中的图像
+            for subdir in ['gsmini', '9dtact', 'tacniq', 'xela', 'marker_flow']:
+                subpath = episode_dir / subdir
+                if subpath.exists():
+                    image_files.extend(subpath.glob('*.png'))
+                    # 嵌套子文件夹
+                    for nested in subpath.iterdir():
+                        if nested.is_dir():
+                            image_files.extend(nested.glob('*.png'))
+            
+            if not image_files:
+                continue
+            
+            # 创建 tar 文件
+            tar_path = episode_dir / 'images.tar'
+            with tarfile.open(tar_path, 'w') as tar:
+                for img_path in image_files:
+                    # 使用相对路径作为 tar 内的文件名
+                    arcname = str(img_path.relative_to(episode_dir))
+                    tar.add(img_path, arcname=arcname)
+            
+            # 删除原始图像文件
+            if delete_originals:
+                for img_path in image_files:
+                    img_path.unlink()
+                # 删除空的子文件夹
+                for subdir in ['gsmini', '9dtact', 'tacniq', 'xela', 'marker_flow']:
+                    subpath = episode_dir / subdir
+                    if subpath.exists():
+                        for nested in subpath.iterdir():
+                            if nested.is_dir() and not any(nested.iterdir()):
+                                nested.rmdir()
+                        if not any(subpath.iterdir()):
+                            subpath.rmdir()
+    
+    print("\n打包完成！")
+    if delete_originals:
+        print("原始图像文件已删除")
+
+
+def unpack_images(delete_tar=False):
+    """
+    解压 tar 文件中的图像
+    """
+    import tarfile
+    
+    data_folders = ['pose_data', 'force_data', 'tacniq_gsmini', 'xela_9dtact']
+    
+    for folder_name in data_folders:
+        folder = BASE_DIR / folder_name
+        if not folder.exists():
+            continue
+        
+        # 找到所有 tar 文件
+        tar_files = list(folder.rglob('images.tar'))
+        if not tar_files:
+            continue
+        
+        print(f"\n解压 {folder_name}: {len(tar_files)} 个 tar 文件")
+        
+        for tar_path in tqdm(tar_files, desc=folder_name):
+            episode_dir = tar_path.parent
+            
+            try:
+                with tarfile.open(tar_path, 'r') as tar:
+                    tar.extractall(path=episode_dir)
+                
+                if delete_tar:
+                    tar_path.unlink()
+            except Exception as e:
+                print(f"\n  解压失败 {tar_path}: {e}")
+    
+    print("\n解压完成！")
+    if delete_tar:
+        print("tar 文件已删除")
+
+
+def clean_images():
+    """删除所有 PNG 图像，只保留视频和 metadata"""
+    data_folders = ['pose_data', 'force_data', 'tacniq_gsmini', 'xela_9dtact']
+    
+    total_deleted = 0
+    for folder_name in data_folders:
+        folder = BASE_DIR / folder_name
+        if not folder.exists():
+            continue
+        
+        png_files = list(folder.rglob('*.png'))
+        print(f"{folder_name}: {len(png_files)} 个 PNG 文件")
+        
+        for png_path in tqdm(png_files, desc=f"删除 {folder_name}"):
+            png_path.unlink()
+            total_deleted += 1
+    
+    # 删除空文件夹
+    for folder_name in data_folders:
+        folder = BASE_DIR / folder_name
+        if not folder.exists():
+            continue
+        for subdir in folder.rglob('*'):
+            if subdir.is_dir() and not any(subdir.iterdir()):
+                subdir.rmdir()
+    
+    print(f"\n删除完成！共删除 {total_deleted} 个文件")
+
+
+# ============================================================
+# 上传
+# ============================================================
+
+def upload_to_hf(sync=False):
+    """上传到 Hugging Face
+    
+    Args:
+        sync: 如果为 True，删除远端存在但本地不存在的文件
+    """
+    from huggingface_hub import HfApi
+    
+    api = HfApi()
+    
+    if sync:
+        # 完全同步模式：删除远端多余文件
+        api.upload_large_folder(
+            repo_id="BorisGuo/pair_touch_13m",
+            repo_type="dataset",
+            folder_path=str(BASE_DIR),
+            ignore_patterns=["__pycache__/**", "*.h5"],
+            delete_patterns=["*"],  # 删除远端存在但本地不存在的文件
+        )
+    else:
+        # 普通模式：只上传/更新，不删除
+        api.upload_large_folder(
+            repo_id="BorisGuo/pair_touch_13m",
+            repo_type="dataset",
+            folder_path=str(BASE_DIR),
+            ignore_patterns=["__pycache__/**", "*.h5"],
+        )
+    print("上传完成！")
+
+
+# ============================================================
+# 主函数
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser(description="数据集预处理")
+    subparsers = parser.add_subparsers(dest='command', help='命令')
+    
+    # extract
+    extract_parser = subparsers.add_parser('extract', help='解析 H5 文件')
+    extract_parser.add_argument('--check', action='store_true', help='仅检查结构')
+    extract_parser.add_argument('--update', action='store_true', help='仅更新 metadata')
+    
+    # heatmap
+    heatmap_parser = subparsers.add_parser('heatmap', help='生成热力图')
+    heatmap_parser.add_argument('--test', action='store_true', help='测试模式')
+    heatmap_parser.add_argument('--type', default='all', help='数据类型')
+    
+    # marker_flow
+    flow_parser = subparsers.add_parser('marker_flow', help='生成 xela marker flow 可视化')
+    flow_parser.add_argument('--test', action='store_true', help='测试模式')
+    flow_parser.add_argument('--type', default='all', 
+                             choices=['xela_pose', 'xela_force', 'xela_9dtact', 'pose', 'force', 'all'],
+                             help='数据类型')
+    
+    # video
+    video_parser = subparsers.add_parser('video', help='生成视频')
+    video_parser.add_argument('--test', action='store_true', help='测试模式')
+    video_parser.add_argument('--type', default='all', help='数据类型')
+    
+    # pack
+    pack_parser = subparsers.add_parser('pack', help='打包图像序列为 tar 文件')
+    pack_parser.add_argument('--delete', action='store_true', help='打包后删除原始图像')
+    
+    # unpack
+    unpack_parser = subparsers.add_parser('unpack', help='解压 tar 文件中的图像')
+    unpack_parser.add_argument('--delete', action='store_true', help='解压后删除 tar 文件')
+    
+    # clean
+    subparsers.add_parser('clean', help='删除所有 PNG 图像，只保留视频')
+    
+    # upload
+    upload_parser = subparsers.add_parser('upload', help='上传到 Hugging Face')
+    upload_parser.add_argument('--sync', action='store_true', 
+                               help='同步模式：删除远端存在但本地不存在的文件')
+    
+    # all
+    subparsers.add_parser('all', help='完整流程')
+    
+    args = parser.parse_args()
+    
+    if args.command == 'extract':
+        if args.check:
+            check_h5_structure()
+        elif args.update:
+            update_metadata()
+        else:
+            extract_all()
+    elif args.command == 'heatmap':
+        print("生成热力图...")
+        generate_heatmaps(args.type, args.test)
+        print("\n完成！")
+    elif args.command == 'marker_flow':
+        print("生成 marker flow...")
+        generate_marker_flow(args.type, args.test)
+        print("\n完成！")
+    elif args.command == 'video':
+        print("生成视频...")
+        generate_videos(args.type, args.test)
+        print("\n完成！")
+    elif args.command == 'pack':
+        print("打包图像序列...")
+        pack_images(delete_originals=args.delete)
+    elif args.command == 'unpack':
+        print("解压图像...")
+        unpack_images(delete_tar=args.delete)
+    elif args.command == 'clean':
+        print("清理图像文件...")
+        clean_images()
+    elif args.command == 'upload':
+        upload_to_hf(sync=args.sync)
+    elif args.command == 'all':
+        print("="*60 + "\n完整流程\n" + "="*60)
+        print("\n[1/4] 解析 H5 文件...")
+        extract_all()
+        print("\n[2/4] 生成热力图...")
+        generate_heatmaps('all', False)
+        print("\n[3/4] 生成视频...")
+        generate_videos('all', False)
+        print("\n[4/4] 更新 metadata...")
+        update_metadata()
+        print("\n" + "="*60 + "\n完成！\n" + "="*60)
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()
+