#!/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 import inspect 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) # 绘制偏移后的 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 要求） 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): # 收集所有图像文件 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() has_large_upload = hasattr(api, "upload_large_folder") if has_large_upload: large_params = set(inspect.signature(api.upload_large_folder).parameters) else: large_params = set() supports_delete = "delete_patterns" in large_params if sync and not supports_delete: # 旧版 huggingface_hub 不支持 upload_large_folder 的 delete_patterns api.upload_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 upload_kwargs = { "repo_id": "BorisGuo/pair_touch_13m", "repo_type": "dataset", "folder_path": str(BASE_DIR), "ignore_patterns": ["__pycache__/**", "*.h5"], } if sync and supports_delete: upload_kwargs["delete_patterns"] = ["*"] if has_large_upload: api.upload_large_folder(**upload_kwargs) else: api.upload_folder(**upload_kwargs) 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()