Simplify example script comments

example_usage.py

@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 """
-Fixed_Viewpoint_Tactile_Dataset 使用示例（可直接运行）
+Fixed_Viewpoint_Tactile_Dataset 加载示例。
 
-环境要求: Python >= 3.10, 已安装 lerobot
-    conda activate zyhand        # 你机器上有 lerobot 0.4.3 的环境
-    python example_usage.py                      # 从 HF 自动下载
-    python example_usage.py --root E:/tachintech/dataset/lerobot_tactile_hand_20fps  # 用本地, 不下载
+    python example_usage.py                          # 从 HuggingFace 下载
+    python example_usage.py --root <本地数据集路径>    # 用本地副本，不下载
+
+需要 Python >= 3.10 且已安装 lerobot。
 """
 import argparse
 import numpy as np
@@ -15,96 +15,61 @@ REPO = "Tachintech/Fixed_Viewpoint_Tactile_Dataset"
 
 def main():
     ap = argparse.ArgumentParser()
-    ap.add_argument("--root", default=None, help="本地数据集路径(给了就不从HF下载)")
-    ap.add_argument("--save-img", default="sample_color.png", help="保存一帧RGB的路径")
+    ap.add_argument("--root", default=None, help="本地数据集路径，给了就不从 HF 下载")
+    ap.add_argument("--save-img", default="sample_color.png")
     args = ap.parse_args()
 
-    # ── 1) 加载数据集 ────────────────────────────────────────────────
     from lerobot.datasets.lerobot_dataset import LeRobotDataset
-    print("加载数据集中 ...")
     ds = LeRobotDataset(REPO, root=args.root)
-    print(f"  总帧数   : {ds.num_frames}")
-    print(f"  episodes : {ds.num_episodes}")
-    print(f"  fps      : {ds.fps}")
+    print(f"frames={ds.num_frames}  episodes={ds.num_episodes}  fps={ds.fps}")
 
-    # ── 2) 列出所有键(字段) ──────────────────────────────────────────
-    print("\n===== 数据集所有键 (来自 meta/info.json) =====")
-    feats = ds.meta.info["features"]
-    for name, spec in feats.items():
-        print(f"  {name:35s} dtype={spec['dtype']:8s} shape={spec['shape']}")
+    # 字段列表
+    print("\n[features]")
+    for name, spec in ds.meta.info["features"].items():
+        print(f"  {name:35s} {spec['dtype']:8s} {spec['shape']}")
 
-    # 触觉每路的 2D 形状
     shapes = ds.meta.info.get("tactile_2d_shapes", {})
-    print("\n触觉传感器 2D 形状 (tactile_2d_shapes):")
-    for k, v in shapes.items():
-        print(f"  {k:12s} -> {v[0]} x {v[1]}")
 
-    # ── 3) 取一帧, 看实际内容 ────────────────────────────────────────
-    print("\n===== 取第 0 帧, 各键的形状 =====")
-    s = ds[0]
+    # 取一帧
+    s = ds[100]
+    print("\n[frame 100]")
     for k in sorted(s.keys()):
         v = s[k]
-        shp = tuple(v.shape) if hasattr(v, "shape") else f"{type(v).__name__}={v}"
-        print(f"  {k:35s} {shp}")
-
-    # ── 4) 动捕: 还原成 20 个点 ──────────────────────────────────────
-    pos = s["observation_motion_positions"].numpy()        # [60]
-    quat = s["observation_motion_quaternions"].numpy()     # [80]
-    pos20 = pos.reshape(20, 3)      # 20 个点 x (x,y,z)
-    quat20 = quat.reshape(20, 4)    # 20 个点 x (w,x,y,z)
-    print("\n===== 动捕 =====")
-    print(f"  20 个标记点位置 reshape -> {pos20.shape}, 第0点 xyz = {np.round(pos20[0],4)}")
-    print(f"  20 个标记点姿态 reshape -> {quat20.shape}, 第0点 quat= {np.round(quat20[0],4)}")
-
-    # ── 5) 触觉: 还原成 2D 阵列 ──────────────────────────────────────
-    print("\n===== 触觉 (还原成 2D) =====")
-    for i in [0, 19]:
-        key = f"tactile_{i}"
+        print(f"  {k:35s} {tuple(v.shape) if hasattr(v, 'shape') else v}")
+
+    # 动捕：20 个标记点，各 3 位置 + 4 四元数
+    pos20 = s["observation_motion_positions"].numpy().reshape(20, 3)
+    quat20 = s["observation_motion_quaternions"].numpy().reshape(20, 4)
+    print(f"\nmotion: pos {pos20.shape}, quat {quat20.shape}")
+
+    # 触觉：展平向量按 tactile_2d_shapes 还原成 2D
+    for i in (0, 19):
         flat = s[f"tactile_tactile_{i}"].numpy()
-        grid = flat.reshape(shapes[key])
-        print(f"  tactile_tactile_{i}: {flat.shape} -> 2D {grid.shape}, 和={grid.sum():.3f}")
-
-    # ── 6) action 是什么 ─────────────────────────────────────────────
-    act = s["action"].numpy()                              # [140]
-    obs_concat = np.concatenate([pos, quat])              # 60+80=140
-    print("\n===== action =====")
-    print(f"  action shape = {act.shape}")
-    print(f"  action 是否 == (positions ⊕ quaternions): "
-          f"{'是(完全相等)' if np.allclose(act, obs_concat) else '否'}")
-    print("  即 action = 当前帧 20 个动捕点的 (位置60 + 四元数80)")
-
-    # ── 7) 视频帧 (已自动从 mp4 解码) ───────────────────────────────
-    color = s["observation.images.color"]                  # [3,480,848] float[0,1]
-    depth = s["observation.images.depth"]
-    print("\n===== 视频 =====")
-    print(f"  color: {tuple(color.shape)} dtype={color.dtype} 值域[{color.min():.2f},{color.max():.2f}]")
-    print(f"  depth: {tuple(depth.shape)}")
+        grid = flat.reshape(shapes[f"tactile_{i}"])
+        print(f"tactile_{i}: {flat.shape} -> {grid.shape}")
+
+    # action 等于当前帧动捕的 位置 + 四元数 拼接
+    act = s["action"].numpy()
+    obs = np.concatenate([s["observation_motion_positions"].numpy(),
+                          s["observation_motion_quaternions"].numpy()])
+    print(f"action == motion(pos+quat): {np.allclose(act, obs)}")
+
+    # 视频已从 mp4 解码为 [3,H,W] 张量，值域 [0,1]
+    color = s["observation.images.color"]
+    print(f"color {tuple(color.shape)}, depth {tuple(s['observation.images.depth'].shape)}")
     try:
         from PIL import Image
         img = (color.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
         Image.fromarray(img).save(args.save_img)
-        print(f"  已保存一帧 RGB -> {args.save_img}")
+        print(f"saved {args.save_img}")
     except Exception as e:
-        print(f"  (保存图片跳过: {e})")
-
-    # ── 8) 按 episode 取 + DataLoader 批训练 ────────────────────────
-    print("\n===== 遍历 / 训练 =====")
-    # episode 边界
-    try:
-        froms = ds.meta.episodes["dataset_from_index"]
-        tos = ds.meta.episodes["dataset_to_index"]
-        print(f"  episode 0: 帧 [{froms[0]}, {tos[0]}) 共 {tos[0]-froms[0]} 帧")
-    except Exception:
-        print("  (episode 边界 API 视版本而定, 可从 meta/episodes parquet 读 dataset_from/to_index)")
+        print(f"skip save image: {e}")
 
+    # 批训练
     from torch.utils.data import DataLoader
-    dl = DataLoader(ds, batch_size=8, shuffle=True, num_workers=0)
-    batch = next(iter(dl))
-    print(f"  一个 batch: action={tuple(batch['action'].shape)} "
-          f"color={tuple(batch['observation.images.color'].shape)} "
-          f"tactile_0={tuple(batch['tactile_tactile_0'].shape)}")
-
-    print("\n===== 完成: 数据集可正常加载和使用 =====")
+    batch = next(iter(DataLoader(ds, batch_size=8, shuffle=True, num_workers=0)))
+    print(f"\nbatch: action {tuple(batch['action'].shape)}, "
+          f"color {tuple(batch['observation.images.color'].shape)}")
 
 
 if __name__ == "__main__":