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{"f", "c", "i", "u", "b"}: + data[k] = torch.from_numpy(v) + else: + data[k] = v # keep python objects (e.g. list[str]) as-is + return data # type: ignore[return-value] + # torch files + return torch.load(path, map_location="cpu") # type: ignore[return-value] + + +def _tensor_range(t: torch.Tensor): + return t.min().item(), t.max().item() + + +def _analyze_field(value: Any) -> Dict[str, Any]: + """Analyze a field value and return metadata about it.""" + if isinstance(value, torch.Tensor): + return { + "type": "tensor", + "dtype": str(value.dtype), + "shape": list(value.shape), + "ndim": value.ndim, + "size": value.numel(), + } + elif isinstance(value, np.ndarray): + return { + "type": "numpy_array", + "dtype": str(value.dtype), + "shape": list(value.shape), + "ndim": value.ndim, + "size": value.size, + } + elif isinstance(value, list): + return { + "type": "list", + "length": len(value), + "element_type": type(value[0]).__name__ if value else "unknown", + } + elif isinstance(value, (str, int, float, bool)): + return { + "type": type(value).__name__, + "value": value if isinstance(value, (int, float, bool)) else f"", + } + else: + return { + "type": type(value).__name__, + "repr": str(value)[:100] + ("..." if len(str(value)) > 100 else ""), + } + +def _determine_angular_unit(max_vel: float) -> str: + """Determine if angular velocity is in deg/s or rad/s based on magnitude.""" + return "deg/s" if max_vel > 20.0 else "rad/s" + +def _analyze_angular_velocity(velocities: torch.Tensor, name: str = "") -> Dict[str, Any]: + """Analyze angular velocity data to determine if it's in deg/s or rad/s. + + Uses multiple heuristics: + 1. Physical limits - robots rarely exceed 1000 deg/s or ~17 rad/s + 2. Distribution of values - deg/s values tend to be larger + 3. Common ranges for motion capture data + + Args: + velocities: Tensor of angular velocities + name: Name of the joint/axis for reporting + + Returns: + Dict with analysis results + """ + abs_max = float(torch.abs(velocities).max()) + abs_mean = float(torch.abs(velocities).mean()) + + # Convert to both units for analysis + if abs_max > 20.0: # Assuming it might be deg/s + rad_max = abs_max * np.pi / 180 + rad_mean = abs_mean * np.pi / 180 + deg_max = abs_max + deg_mean = abs_mean + original_unit = "deg/s" + else: # Assuming it might be rad/s + rad_max = abs_max + rad_mean = abs_mean + deg_max = abs_max * 180 / np.pi + deg_mean = abs_mean * 180 / np.pi + original_unit = "rad/s" + + # Scoring system for unit determination + deg_score = 0 + rad_score = 0 + + # Physical limits check + if deg_max > 1000: # Unusually high for deg/s + rad_score += 2 + if rad_max > 17: # Unusually high for rad/s (~1000 deg/s) + deg_score += 2 + + # Common ranges check + if 20 <= deg_max <= 720: # Common range for deg/s in motion capture + deg_score += 1 + if 0.3 <= rad_max <= 12: # Common range for rad/s in motion capture + rad_score += 1 + + # Mean value check + if 5 <= deg_mean <= 180: # Common mean range for deg/s + deg_score += 1 + if 0.1 <= rad_mean <= 3: # Common mean range for rad/s + rad_score += 1 + + # Determine most likely unit + likely_unit = "deg/s" if deg_score > rad_score else "rad/s" + confidence = abs(deg_score - rad_score) / (deg_score + rad_score) if (deg_score + rad_score) > 0 else 0 + + return { + "likely_unit": likely_unit, + "confidence": confidence, + "max_value": abs_max, + "mean_value": abs_mean, + "deg_score": deg_score, + "rad_score": rad_score, + "analysis": { + "deg/s": {"max": deg_max, "mean": deg_mean}, + "rad/s": {"max": rad_max, "mean": rad_mean} + }, + "original_unit": original_unit + } + +# ---------------------------------------------------------------------------------- +# main analysis +# ---------------------------------------------------------------------------------- + +def analyse_dataset(root: Path) -> None: + # discover files + files: List[Path] = [] + for p, _, names in os.walk(root): + for n in names: + if n == "shape_optimized.pkl": + continue + if Path(n).suffix in _ALLOWED_EXT: + files.append(Path(p) / n) + files.sort() + + if not files: + raise RuntimeError(f"No trajectory files found under {root}") + + print(f"Found {len(files)} trajectory files. Analysing…") + + # aggregate accumulators + agg = { + "num_episodes": len(files), + "total_frames": 0, + "lengths": [], + "base_lin_vel_b": {"min": torch.full((3,), torch.inf), "max": torch.full((3,), -torch.inf)}, + "base_ang_vel_b": {"min": torch.full((3,), torch.inf), "max": torch.full((3,), -torch.inf)}, + "base_height": {"min": torch.tensor(torch.inf), "max": torch.tensor(-torch.inf)}, + "base_quat": {"min": torch.full((4,), torch.inf), "max": torch.full((4,), -torch.inf)}, + "joint_pos": {}, # type: ignore[dict[str, Any]] + "joint_vel": {}, # type: ignore[dict[str, Any]] + "base_pos": {"min": torch.full((3,), torch.inf), "max": torch.full((3,), -torch.inf)}, + "start_pos_list": [], + } + + # field analysis accumulator + field_analysis = defaultdict(lambda: { + "count": 0, + "files_with_field": [], + "metadata": None, + "consistent_shape": True, + "shapes_seen": set(), + }) + + episode_stats: List[Dict[str, Any]] = [] + + for f_idx, path in enumerate(files): + data = _load_file(path) + + # Analyze all fields in this file + for field_name, field_value in data.items(): + field_info = field_analysis[field_name] + field_info["count"] += 1 + field_info["files_with_field"].append(str(path.relative_to(root))) + + # Analyze field metadata + metadata = _analyze_field(field_value) + if field_info["metadata"] is None: + field_info["metadata"] = metadata + + # Track shape consistency for tensors/arrays + if metadata["type"] in ["tensor", "numpy_array"]: + shape_tuple = tuple(metadata["shape"]) + field_info["shapes_seen"].add(shape_tuple) + if len(field_info["shapes_seen"]) > 1: + field_info["consistent_shape"] = False + + # Continue with existing analysis for specific fields + if "qpos" not in data or "qvel" not in data: + print(f"[WARN] {path.name}: Missing qpos or qvel, skipping detailed analysis") + continue + + qpos, qvel = data["qpos"].float(), data["qvel"].float() + n = qpos.shape[0] + agg["total_frames"] += n + agg["lengths"].append(n) + + # root quantities + base_pos = qpos[:, :3] + base_quat = math_utils.quat_unique(qpos[:, 3:7]) # shape [n,4] + base_lin_vel = qvel[:, :3] + base_ang_vel = qvel[:, 3:6] + base_lin_vel_b = math_utils.quat_rotate_inverse(base_quat, base_lin_vel) + base_ang_vel_b = math_utils.quat_rotate_inverse(base_quat, base_ang_vel) + + height = base_pos[:, 2] + + # update global min/max + agg["base_height"]["min"] = torch.minimum(agg["base_height"]["min"], height.min()) + agg["base_height"]["max"] = torch.maximum(agg["base_height"]["max"], height.max()) + for k, tensor in zip(["base_lin_vel_b", "base_ang_vel_b"], [base_lin_vel_b, base_ang_vel_b]): + agg[k]["min"] = torch.minimum(agg[k]["min"], tensor.min(dim=0).values) + agg[k]["max"] = torch.maximum(agg[k]["max"], tensor.max(dim=0).values) + agg["base_quat"]["min"] = torch.minimum(agg["base_quat"]["min"], base_quat.min(dim=0).values) + agg["base_quat"]["max"] = torch.maximum(agg["base_quat"]["max"], base_quat.max(dim=0).values) + + # update base_pos ranges + agg["base_pos"]["min"] = torch.minimum(agg["base_pos"]["min"], base_pos.min(dim=0).values) + agg["base_pos"]["max"] = torch.maximum(agg["base_pos"]["max"], base_pos.max(dim=0).values) + + # save starting position + agg["start_pos_list"].append(base_pos[0].tolist()) + + # joint ranges per episode + joint_pos = qpos[:, 7:] + joint_vel = qvel[:, 6:] # skip floating base velocities (first 6) + num_joints = joint_pos.shape[1] + joint_names = data.get("joint_names", None) + if joint_names is not None and len(joint_names) == num_joints + 1: + # Likely the first entry is the floating base/root joint which is not in joint_pos + joint_names = joint_names[1:] + + if joint_names is None or len(joint_names) != num_joints: + # Generate fallback names if missing or mismatched + if joint_names is not None and len(joint_names) != num_joints: + print(f"[WARN] {path.name}: joint_names length {len(joint_names)} != joint dim {num_joints}. Using generic names.") + joint_names = [f"joint_{i}" for i in range(num_joints)] + + ep_joint_range = {} + ep_joint_vel_range = {} + for j in range(num_joints): + name = joint_names[j] + # Position ranges + j_min, j_max = _tensor_range(joint_pos[:, j]) + ep_joint_range[name] = {"min": j_min, "max": j_max} + + # Velocity ranges + v_min, v_max = _tensor_range(joint_vel[:, j]) + ep_joint_vel_range[name] = {"min": v_min, "max": v_max} + + # accumulate global position ranges + if name not in agg["joint_pos"]: + agg["joint_pos"][name] = {"min": j_min, "max": j_max} + else: + agg["joint_pos"][name]["min"] = min(agg["joint_pos"][name]["min"], j_min) + agg["joint_pos"][name]["max"] = max(agg["joint_pos"][name]["max"], j_max) + + # accumulate global velocity ranges + if name not in agg["joint_vel"]: + agg["joint_vel"][name] = {"min": v_min, "max": v_max} + else: + agg["joint_vel"][name]["min"] = min(agg["joint_vel"][name]["min"], v_min) + agg["joint_vel"][name]["max"] = max(agg["joint_vel"][name]["max"], v_max) + + # Analyze base angular velocities + base_ang_vel_analysis = {} + for i, axis in enumerate(['x', 'y', 'z']): + base_ang_vel_axis = base_ang_vel_b[:, i] + base_ang_vel_analysis[axis] = _analyze_angular_velocity(base_ang_vel_axis, f"base_ang_vel_{axis}") + + # Analyze joint velocities + joint_vel_analysis = {} + for j in range(num_joints): + name = joint_names[j] + joint_vel_analysis[name] = _analyze_angular_velocity(joint_vel[:, j], name) + + # store per-episode stats + episode_stats.append({ + "file": str(path.relative_to(root)), + "length": n, + "base_height": _tensor_range(height), + "base_pos_start": base_pos[0].tolist(), + "base_pos_range": { + "min": base_pos.min(dim=0).values.tolist(), + "max": base_pos.max(dim=0).values.tolist(), + }, + "base_lin_vel_b": { + "min": base_lin_vel_b.min(dim=0).values.tolist(), + "max": base_lin_vel_b.max(dim=0).values.tolist(), + }, + "base_ang_vel_b": { + "min": base_ang_vel_b.min(dim=0).values.tolist(), + "max": base_ang_vel_b.max(dim=0).values.tolist(), + }, + "base_quat": {k: v.tolist() for k, v in agg["base_quat"].items()}, + "joint_pos_range": ep_joint_range, + "joint_vel_range": ep_joint_vel_range, + "base_ang_vel_analysis": base_ang_vel_analysis, + "joint_vel_analysis": joint_vel_analysis, + }) + + if (f_idx + 1) % 50 == 0: + print(f"Processed {f_idx+1}/{len(files)} files…") + + # Process field analysis results + # print("\n" + "="*60) + # print("DATASET FIELD ANALYSIS") + # print("="*60) + + # all_fields = sorted(field_analysis.keys()) + # for field_name in all_fields: + # info = field_analysis[field_name] + # print(f"\nField: '{field_name}'") + # print(f" Present in: {info['count']}/{len(files)} files ({info['count']/len(files)*100:.1f}%)") + + # if info['metadata']: + # meta = info['metadata'] + # if meta['type'] in ['tensor', 'numpy_array']: + # if info['consistent_shape']: + # print(f" Type: {meta['type']} ({meta['dtype']})") + # print(f" Shape: {meta['shape']}") + # else: + # print(f" Type: {meta['type']} ({meta['dtype']}) - INCONSISTENT SHAPES") + # print(f" Shapes seen: {sorted(info['shapes_seen'])}") + # elif meta['type'] == 'list': + # print(f" Type: {meta['type']} (length: {meta['length']}, elements: {meta['element_type']})") + # else: + # print(f" Type: {meta['type']}") + # if 'value' in meta: + # print(f" Value: {meta['value']}") + + # Create field summary for JSON output + field_summary = {} + for field_name, info in field_analysis.items(): + field_summary[field_name] = { + "present_in_files": info["count"], + "present_in_percentage": round(info["count"] / len(files) * 100, 1), + "metadata": info["metadata"], + "consistent_shape": info["consistent_shape"], + } + if not info["consistent_shape"]: + field_summary[field_name]["shapes_seen"] = sorted([list(s) for s in info["shapes_seen"]]) + + # final aggregate statistics + agg_stats = { + "num_episodes": agg["num_episodes"], + "average_length": float(np.mean(agg["lengths"])), + "min_length": int(min(agg["lengths"])), + "max_length": int(max(agg["lengths"])), + "base_height": {k: v.item() if torch.is_tensor(v) else v.tolist() for k, v in agg["base_height"].items()}, + "base_lin_vel_b": {k: v.tolist() for k, v in agg["base_lin_vel_b"].items()}, + "base_ang_vel_b": {k: v.tolist() for k, v in agg["base_ang_vel_b"].items()}, + "base_quat": {k: v.tolist() for k, v in agg["base_quat"].items()}, + "joint_pos_global_range": agg["joint_pos"], + "joint_vel_global_range": agg["joint_vel"], # Added joint velocity global ranges + "base_pos": {k: v.tolist() for k, v in agg["base_pos"].items()}, + "avg_start_pos": np.mean(agg["start_pos_list"], axis=0).tolist(), + "field_analysis": field_summary, + "angular_velocity_analysis": { + "base": base_ang_vel_analysis, + "joints": joint_vel_analysis, + } + } + + # Determine angular-velocity unit (deg/s vs rad/s) + max_ang_vel = max(abs(x) for x in agg_stats["base_ang_vel_b"]["max"]) + base_ang_unit = _determine_angular_unit(max_ang_vel) + agg_stats["base_ang_vel_unit"] = base_ang_unit + + # Analyze joint velocity units + joint_vel_units = {} + max_joint_vels = {} + for joint_name, vel_range in agg["joint_vel"].items(): + max_vel = max(abs(vel_range["min"]), abs(vel_range["max"])) + max_joint_vels[joint_name] = max_vel + joint_vel_units[joint_name] = _determine_angular_unit(max_vel) + + # Check if all joints use the same unit + unique_units = set(joint_vel_units.values()) + if len(unique_units) == 1: + joint_vel_unit = next(iter(unique_units)) + print(f"\nAll joint velocities appear to be in {joint_vel_unit}") + else: + print("\nWARNING: Inconsistent joint velocity units detected:") + for unit in unique_units: + joints = [name for name, u in joint_vel_units.items() if u == unit] + print(f" {unit}: {', '.join(joints)}") + + # Add joint velocity analysis to aggregate stats + agg_stats["joint_vel_units"] = joint_vel_units + agg_stats["joint_vel_max_magnitude"] = max_joint_vels + + print(f"\nEstimated base angular velocity unit: {base_ang_unit} (max ω ≈ {max_ang_vel:.2f})") + print("Joint velocity analysis added to aggregate_stats.json") + + # Print detailed analysis + print("\nAngular Velocity Analysis:") + print("\nBase Angular Velocity:") + for axis, analysis in base_ang_vel_analysis.items(): + print(f" {axis}-axis: Likely {analysis['likely_unit']} (confidence: {analysis['confidence']:.2f})") + print(f" Max: {analysis['max_value']:.2f} {analysis['original_unit']}") + print(f" Mean: {analysis['mean_value']:.2f} {analysis['original_unit']}") + + print("\nJoint Velocities:") + for joint, analysis in joint_vel_analysis.items(): + print(f" {joint}: Likely {analysis['likely_unit']} (confidence: {analysis['confidence']:.2f})") + print(f" Max: {analysis['max_value']:.2f} {analysis['original_unit']}") + print(f" Mean: {analysis['mean_value']:.2f} {analysis['original_unit']}") + + # write files + (root / "episode_stats.json").write_text(json.dumps(episode_stats, indent=2)) + (root / "aggregate_stats.json").write_text(json.dumps(agg_stats, indent=2)) + + print(f"\nAnalysis complete. Results saved to episode_stats.json and aggregate_stats.json") + # print(f"Field analysis included for {len(all_fields)} unique fields found across all files.") + +# ---------------------------------------------------------------------------------- + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Analyse Unitree G1 AMASS dataset") + parser.add_argument("--root", type=str, required=True, help="Root folder of trajectories") + args = parser.parse_args() + + analyse_dataset(Path(args.root).expanduser()) diff --git a/augment_dataset.py b/augment_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..f9406a3fbb462b8ee5d6e6532b26676ef3d2abba --- /dev/null +++ b/augment_dataset.py @@ -0,0 +1,173 @@ +#!/usr/bin/env python3 +"""Filter and augment Unitree G1 dataset to contain only walking fragments. + +The script: +1. Reads *episode_stats.json* produced by **analyze_dataset.py**. +2. Determines whether base angular velocity appears to be in rad/s or deg/s. +3. Walks through each trajectory file, extracts contiguous segments where + linear speed <= 2 m/s, and saves them as new .pt files in an output folder. +4. Optionally performs left↔right mirroring augmentation. + +Run: + python augment_dataset.py \ + --root /home/ubuntu/MoCapDataset/AMASSDataset/UnitreeG1 \ + --out /home/ubuntu/MoCapDataset/AMASSDataset/UnitreeG1_WalkOnly \ + --mirror +""" +from __future__ import annotations + +import argparse +import json +import os +from pathlib import Path +from typing import List, Dict, Tuple + +import torch +import numpy as np + +import isaaclab.utils.math as math_utils + +_ALLOWED_EXT = {".pt", ".pth", ".pkl", ".npz"} + +# ---------------------------------------------------------------------------------- +# helpers +# ---------------------------------------------------------------------------------- + +def load_file(path: Path) -> Dict[str, torch.Tensor]: + if path.suffix in {".npz", ".pkl"}: + data = dict(np.load(path, allow_pickle=True)) + for k, v in data.items(): + if isinstance(v, np.ndarray) and v.dtype.kind in {"f", "c", "i", "u", "b"}: + data[k] = torch.from_numpy(v) + else: + data[k] = v + return data # type: ignore[return-value] + return torch.load(path, map_location="cpu") # type: ignore[return-value] + + +def save_pt(data: Dict[str, torch.Tensor], path: Path): + path.parent.mkdir(parents=True, exist_ok=True) + torch.save(data, path) + + +def mirror_left_right(data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + """Simple left↔right mirror assuming naming pattern 'left_' / 'right_'.""" + mirrored = {k: v.clone() if torch.is_tensor(v) else v for k, v in data.items()} + names = data.get("joint_names", None) + if names is None: + return mirrored # cannot mirror + names = list(names) + swap = {} + for i, n in enumerate(names): + if n.startswith("left_"): + mirror_name = "right_" + n[5:] + elif n.startswith("right_"): + mirror_name = "left_" + n[6:] + else: + continue + if mirror_name in names: + swap[i] = names.index(mirror_name) + if not swap: + return mirrored + + qpos = mirrored["qpos"] + qvel = mirrored["qvel"] + qpos_new = qpos.clone() + qvel_new = qvel.clone() + for i, j in swap.items(): + qpos_new[:, 7+i] = qpos[:, 7+j] + qpos_new[:, 7+j] = qpos[:, 7+i] + qvel_new[:, 6+i] = qvel[:, 6+j] + qvel_new[:, 6+j] = qvel[:, 6+i] + # flip Y to mirror along sagittal plane + qpos_new[:, 1] = -qpos_new[:, 1] + qvel_new[:, 1] = -qvel_new[:, 1] + qpos_new[:, 4] = -qpos_new[:, 4] # quaternion y + qpos_new[:, 6] = -qpos_new[:, 6] # quaternion z + mirrored["qpos"] = qpos_new + mirrored["qvel"] = qvel_new + return mirrored + + +def extract_walking_segments(data: Dict[str, torch.Tensor], min_len: int = 50) -> List[Dict[str, torch.Tensor]]: + """Return list of new dicts containing walking-only contiguous clips. + Filtering is done using body-frame velocities: + |vx_body| < 1.5 m/s and |vy_body| < 0.5 m/s + """ + qpos = data["qpos"] + qvel = data["qvel"] + base_lin_vel = qvel[:, :3] # (N, 3) + base_quat = qpos[:, 3:7] # (N, 4), assumed (w, x, y, z) + # Convert world-frame velocity to body-frame + base_lin_vel_body = math_utils.quat_rotate_inverse(base_quat, base_lin_vel) + # Apply thresholds + mask = (base_lin_vel_body[:, 0].abs() < 1.5) & (base_lin_vel_body[:, 1].abs() < 0.5) + + segments: List[Tuple[int, int]] = [] + start = None + for i, m in enumerate(mask): + if m and start is None: + start = i + elif not m and start is not None: + if i - start >= min_len: + segments.append((start, i)) + start = None + # tail segment + if start is not None and len(qpos) - start >= min_len: + segments.append((start, len(qpos))) + + clips = [] + for s, e in segments: + clip = { + k: v[s:e].clone() if torch.is_tensor(v) and v.ndim > 0 else (v.clone() if torch.is_tensor(v) else v) + for k, v in data.items() + } + clips.append(clip) + return clips + + +def main(): + parser = argparse.ArgumentParser(description="Filter and augment walking trajectories") + parser.add_argument("--root", required=True, type=str, help="Original dataset root") + parser.add_argument("--out", required=True, type=str, help="Output folder for walking clips") + parser.add_argument("--mirror", default=False, action="store_true", help="Generate left-right mirrored copies") + parser.add_argument("--speed_thr", type=float, default=1.5, help="Max linear speed (m/s) for walking") + parser.add_argument("--min_len", type=int, default=50, help="Minimum clip length to keep") + args = parser.parse_args() + + root = Path(args.root) + out_root = Path(args.out) + out_root.mkdir(parents=True, exist_ok=True) + + # iterate files + files = [] + for p, _, names in os.walk(root): + for n in names: + if n == "shape_optimized.pkl": + continue + if Path(n).suffix in _ALLOWED_EXT: + files.append(Path(p) / n) + files.sort() + + total_clips = 0 + for f_idx, path in enumerate(files): + data = load_file(path) + clips = extract_walking_segments(data, min_len=args.min_len) + for idx, clip in enumerate(clips): + rel_dir = path.relative_to(root).parent + name = path.stem + f"_walk_{idx}.pt" + save_pt(clip, out_root / rel_dir / name) + total_clips += 1 + if args.mirror: + mirror_clip = mirror_left_right(clip) + name_m = path.stem + f"_walk_{idx}_mir.pt" + save_pt(mirror_clip, out_root / rel_dir / name_m) + total_clips += 1 + if (f_idx + 1) % 20 == 0: + print(f"Processed {f_idx+1}/{len(files)} files…") + + print(f"Done. Saved {total_clips} walking clips to {out_root}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/filter_dataset.py b/filter_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..569f754b2f9965f51f7362112b5251a159e0f301 --- /dev/null +++ b/filter_dataset.py @@ -0,0 +1,76 @@ +import os +import numpy as np + +input_path = ["/home/ubuntu/MoCapDataset/AMASSDataset/UnitreeG1_29dof", "/home/ubuntu/MoCapDataset/Retarget/UnitreeG1_29dof"] + +files = [] +for input_dir in input_path: + for path, _, filenames in os.walk(input_dir): + for file in filenames: + if file.endswith(".npy") or file.endswith(".npz"): + files.append(os.path.join(path, file)) + +print(f"Found {len(files)} files") + +# Velocity threshold for classification (m/s) - horizontal speed only +WALKING_RUNNING_THRESHOLD = 1.5 # Below this is walking, above is running + +walking_count = 0 +running_count = 0 +skipped_count = 0 + +for file_path in files: + try: + # Load data + if file_path.endswith(".npy"): + data = np.load(file_path, allow_pickle=True).item() + elif file_path.endswith(".npz"): + data = dict(np.load(file_path, allow_pickle=True)) + + # Check if qvel exists + if 'qvel' not in data: + print(f"Skipping {file_path}: No qvel data") + skipped_count += 1 + continue + + qvel = data['qvel'] + + horizontal_velocities = qvel[:, :2] + horizontal_speeds = np.linalg.norm(horizontal_velocities, axis=1) + + avg_horizontal_speed = np.mean(horizontal_speeds) + max_horizontal_speed = np.max(horizontal_speeds) + + if avg_horizontal_speed < WALKING_RUNNING_THRESHOLD: + motion_type = "walking" + walking_count += 1 + else: + motion_type = "running" + running_count += 1 + + # Add motion type and speed metrics to data + data['motion_type'] = motion_type + data['avg_horizontal_speed'] = avg_horizontal_speed + data['max_horizontal_speed'] = max_horizontal_speed + + if file_path.endswith(".npy"): + np.save(file_path, data) + elif file_path.endswith(".npz"): + np.savez(file_path, **data) + + print(f"Processed {os.path.basename(file_path)}: {motion_type} (avg: {avg_horizontal_speed:.2f} m/s, max: {max_horizontal_speed:.2f} m/s)") + + except Exception as e: + print(f"Error processing {file_path}: {str(e)}") + skipped_count += 1 + continue + +print(f"\nProcessing complete!") +print(f"Walking files: {walking_count}") +print(f"Running files: {running_count}") +print(f"Skipped files: {skipped_count}") +print(f"Total processed: {walking_count + running_count}") +print(f"\nMotion type markers added to original files.") +print(f"Threshold used: {WALKING_RUNNING_THRESHOLD} m/s (horizontal speed)") + + \ No newline at end of file