#!/usr/bin/env python3 """ EgomotionData Dataset Inspector ================================ This script reads and displays the structure, statistics, and field meanings of the EgomotionData dataset. Usage: python inspect_dataset.py # View dataset overview + random sample details python inspect_dataset.py --sample PATH.npz # View details of a specific npz file python inspect_dataset.py --overview-only # View only dataset overview statistics python inspect_dataset.py --check-consistency N # Randomly check consistency of N files """ import argparse import os import sys import random import numpy as np from pathlib import Path from collections import defaultdict # ============================================================================ # Data Field Descriptions # ============================================================================ FIELD_DESCRIPTIONS = { "input_rgbd": { "en": "Input RGBD image sequence", "detail": ( "Shape (T, 4, H, W): T=frames, 4=RGB+Depth channels, H=height, W=width.\n" " - Channels 0-2: RGB color image, normalized to [0, 1]\n" " - Channel 3: Depth map" ), }, "gt_joints_relativeCam_2Dpos": { "en": "GT joint 2D positions relative to camera (projected)", "detail": ( "Shape (T, J, 2): T=frames, J=joints(22), 2=pixel coords (x, y).\n" " 3D body joints projected onto the camera image plane." ), }, "gt_joints_relativePelvis_3Dpos": { "en": "GT joint 3D positions relative to pelvis", "detail": ( "Shape (T, J, 3): T=frames, J=joints(22), 3=(x, y, z).\n" " 3D joint positions in pelvis-centered local coordinate system.\n" " Pelvis joint is always at origin (0, 0, 0)." ), }, "gt_pelvis_camera_3Dpos": { "en": "GT pelvis 3D position in camera coordinate system", "detail": ( "Shape (T, 3): T=frames, 3=(x, y, z).\n" " Absolute 3D position of pelvis in camera space." ), }, "gt_pelvis_camera_4Drot": { "en": "GT pelvis rotation in camera space (quaternion)", "detail": ( "Shape (T, 4): T=frames, 4=quaternion.\n" " Rotation of pelvis joint in camera coordinate system." ), }, "hmd_position_global_full_gt_list": { "en": "HMD global position/orientation data", "detail": ( "Shape (T, 54): T=frames, 54-dim vector.\n" " Global pose information from HMD tracking (head + hands)." ), }, "head_global_trans_list": { "en": "Head global transformation matrices", "detail": ( "Shape (T, 4, 4): T=frames, 4x4 homogeneous transformation matrix.\n" " Head pose (rotation + translation) in global coordinates per frame." ), }, "body_parms_list": { "en": "SMPL body model parameters", "detail": ( "Dictionary with sub-fields:\n" " - root_orient (T, 3): Root (pelvis) rotation in axis-angle\n" " - pose_body (T, 63): Body joint poses (21 joints × 3 axis-angle)\n" " - trans (T, 3): Global translation (x, y, z)" ), }, "pred_2d": { "en": "Predicted 2D joint positions", "detail": ( "Shape (T, J, 2): T=frames, J=joints(22), 2=pixel coords (x, y).\n" " 2D joint detections from a pretrained pose estimator." ), }, "pred_3d": { "en": "Predicted 3D joint positions", "detail": ( "Shape (T, J, 3): T=frames, J=joints(22), 3=(x, y, z).\n" " 3D joint predictions from a pretrained pose estimator." ), }, } def print_separator(char="=", length=80): print(char * length) def print_header(title): print_separator() print(f" {title}") print_separator() def get_dataset_root(): """Return dataset root directory""" return Path(__file__).parent def load_path_file(filepath): """Load path list file""" if not filepath.exists(): return [] with open(filepath, "r") as f: return [line.strip() for line in f if line.strip()] def dataset_overview(root): """Print dataset overview""" print_header("Dataset Overview") # Scene statistics scenes = sorted([d for d in os.listdir(root) if d.startswith("Scene") and os.path.isdir(root / d)]) print(f"\nNumber of scenes: {len(scenes)}") total_seqs = 0 total_npz = 0 source_counter = defaultdict(int) for scene in scenes: scene_path = root / scene seqs = [d for d in os.listdir(scene_path) if os.path.isdir(scene_path / d)] npz_count = 0 for seq in seqs: seq_path = scene_path / seq npz_files = [f for f in os.listdir(seq_path) if f.endswith(".npz")] npz_count += len(npz_files) # Data source extraction if "CMU" in seq: source_counter["CMU"] += 1 elif "BioMotionLab_NTroje" in seq: source_counter["BioMotionLab_NTroje"] += 1 else: source_counter["Other"] += 1 total_seqs += len(seqs) total_npz += npz_count print(f" {scene}: {len(seqs):>4} sequences, {npz_count:>5} npz files") print(f"\nTotal:") print(f" Sequences: {total_seqs}") print(f" NPZ files: {total_npz}") # Data source statistics print(f"\nData Sources:") for src, cnt in sorted(source_counter.items(), key=lambda x: -x[1]): print(f" {src}: {cnt} sequences") # Train/Val/Test split print(f"\nTrain/Val/Test Split:") for split in ["train", "val", "test", "all"]: path_file = root / f"{split}_npz_paths.txt" paths = load_path_file(path_file) print(f" {split:>5}: {len(paths):>6} samples") print() def inspect_single_npz(npz_path, verbose=True): """Inspect the detailed structure of a single npz file""" data = np.load(npz_path, allow_pickle=True) keys = list(data.keys()) if verbose: print_header(f"NPZ File Details") print(f"Path: {npz_path}") print(f"Size: {os.path.getsize(npz_path) / 1024 / 1024:.2f} MB") print(f"Number of fields: {len(keys)}") print() info = {} for key in keys: arr = data[key] field_info = {"key": key, "dtype": str(arr.dtype), "shape": arr.shape} if arr.dtype == object: # Handle dictionary type (body_parms_list) obj = arr.item() if isinstance(obj, dict): field_info["type"] = "dict" field_info["sub_fields"] = {} for k, v in obj.items(): field_info["sub_fields"][k] = { "shape": v.shape, "dtype": str(v.dtype), "min": float(v.min()), "max": float(v.max()), "mean": float(v.mean()), } else: field_info["type"] = "array" if arr.size > 0 and arr.dtype.kind in ("f", "i", "u"): field_info["min"] = float(arr.min()) field_info["max"] = float(arr.max()) field_info["mean"] = float(arr.mean()) field_info["std"] = float(arr.std()) info[key] = field_info if verbose: desc = FIELD_DESCRIPTIONS.get(key, {}) print_separator("-", 70) print(f"Field: {key}") if desc: print(f" Desc: {desc['en']}") print(f" Dtype: {arr.dtype}") print(f" Shape: {arr.shape}") if field_info["type"] == "dict": print(f" Sub-fields:") for k, v_info in field_info["sub_fields"].items(): print(f" - {k}: shape={v_info['shape']}, dtype={v_info['dtype']}, " f"range=[{v_info['min']:.4f}, {v_info['max']:.4f}], mean={v_info['mean']:.4f}") elif "min" in field_info: print(f" Range: [{field_info['min']:.4f}, {field_info['max']:.4f}]") print(f" Mean: {field_info['mean']:.4f}, Std: {field_info['std']:.4f}") if desc and "detail" in desc: print(f" Details:") for line in desc["detail"].split("\n"): print(f" {line}") print() data.close() return info def print_data_schema(): """Print complete data schema documentation""" print_header("Data Schema Documentation") print(""" Each .npz file represents a motion clip with T consecutive frames (typically T=100). The data is designed for egomotion estimation: recovering 3D human pose from egocentric RGBD views. Directory Structure: EgomotionData/ ├── Scene{0-6}/ # 7 different virtual scenes │ └── AllDataPath_{Source}_{split}_{id}/ # motion sequence directory │ └── {clip_id}.npz # motion clip file ├── train_npz_paths.txt # train set path list ├── val_npz_paths.txt # validation set path list ├── test_npz_paths.txt # test set path list └── all_npz_paths.txt # all paths list Data Sources: - CMU: CMU Graphics Lab Motion Capture Database - BioMotionLab_NTroje: BioMotionLab (NTroje) Dataset """) print("Field Details:") print_separator("-", 70) for key, desc in FIELD_DESCRIPTIONS.items(): print(f"\n[{key}]") print(f" English: {desc['en']}") print(f" Details:") for line in desc["detail"].split("\n"): print(f" {line}") print() print("Joint Definition (22 joints):") print(" SMPL model uses 22 joints, typical order:") print(" 0:Pelvis 1:L_Hip 2:R_Hip 3:Spine1 4:L_Knee 5:R_Knee") print(" 6:Spine2 7:L_Ankle 8:R_Ankle 9:Spine3 10:L_Foot 11:R_Foot") print(" 12:Neck 13:L_Collar 14:R_Collar 15:Head 16:L_Shoulder 17:R_Shoulder") print(" 18:L_Elbow 19:R_Elbow 20:L_Wrist 21:R_Wrist") print() def check_consistency(root, n_samples=10): """Randomly sample and check data consistency""" print_header(f"Consistency Check (N={n_samples})") all_paths_file = root / "all_npz_paths.txt" all_paths = load_path_file(all_paths_file) if not all_paths: print("Error: all_npz_paths.txt is empty or does not exist") return samples = random.sample(all_paths, min(n_samples, len(all_paths))) issues = [] for i, rel_path in enumerate(samples, 1): # Path format: EgomotionData/Scene0/.../1.npz npz_path = root.parent / rel_path if not npz_path.exists(): npz_path = root / "/".join(rel_path.split("/")[1:]) if not npz_path.exists(): issues.append(f"File does not exist: {rel_path}") continue try: data = np.load(npz_path, allow_pickle=True) keys = set(data.keys()) expected_keys = set(FIELD_DESCRIPTIONS.keys()) missing = expected_keys - keys extra = keys - expected_keys # Check frame consistency T = data["input_rgbd"].shape[0] frame_checks = { "gt_joints_relativeCam_2Dpos": data["gt_joints_relativeCam_2Dpos"].shape[0], "gt_joints_relativePelvis_3Dpos": data["gt_joints_relativePelvis_3Dpos"].shape[0], "gt_pelvis_camera_3Dpos": data["gt_pelvis_camera_3Dpos"].shape[0], "pred_2d": data["pred_2d"].shape[0], "pred_3d": data["pred_3d"].shape[0], } frame_mismatch = {k: v for k, v in frame_checks.items() if v != T} status = "OK" if (not missing and not extra and not frame_mismatch) else "WARN" print(f" [{i}/{len(samples)}] {status} - {rel_path} (T={T})") if missing: msg = f" Missing fields: {missing}" print(f" {msg}") issues.append(msg) if extra: print(f" Extra fields: {extra}") if frame_mismatch: msg = f" Frame count mismatch: {frame_mismatch}" print(f" {msg}") issues.append(msg) data.close() except Exception as e: msg = f"Failed to read: {rel_path} - {e}" print(f" [{i}/{len(samples)}] ERROR - {msg}") issues.append(msg) print() if issues: print(f"Found {len(issues)} issues:") for issue in issues: print(f" - {issue}") else: print("All samples passed consistency check.") print() def main(): parser = argparse.ArgumentParser( description="EgomotionData Dataset Inspector", formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Examples: python inspect_dataset.py # Overview + sample check python inspect_dataset.py --overview-only # Show only statistics overview python inspect_dataset.py --schema # Show data schema documentation python inspect_dataset.py --sample Scene0/xxx/1.npz # Check specific file python inspect_dataset.py --check-consistency 20 # Check 20 random files """, ) parser.add_argument("--sample", type=str, default=None, help="Specify npz file path to inspect") parser.add_argument("--overview-only", action="store_true", help="Show only dataset overview statistics") parser.add_argument("--schema", action="store_true", help="Show complete data schema documentation") parser.add_argument("--check-consistency", type=int, default=0, metavar="N", help="Randomly check consistency of N files") parser.add_argument("--seed", type=int, default=42, help="Random seed (default: 42)") args = parser.parse_args() random.seed(args.seed) root = get_dataset_root() if args.schema: print_data_schema() return if args.sample: sample_path = Path(args.sample) if not sample_path.is_absolute(): sample_path = root / args.sample if not sample_path.exists(): print(f"Error: file does not exist - {sample_path}") sys.exit(1) inspect_single_npz(sample_path) return # Default: show overview dataset_overview(root) if args.overview_only: return # Show data schema print_data_schema() # Show details of a random sample all_paths = load_path_file(root / "all_npz_paths.txt") if all_paths: sample_rel = random.choice(all_paths) sample_path = root.parent / sample_rel if not sample_path.exists(): sample_path = root / "/".join(sample_rel.split("/")[1:]) if sample_path.exists(): print_header("Random Sample Details") inspect_single_npz(sample_path) # Consistency check n_check = args.check_consistency if args.check_consistency > 0 else 5 check_consistency(root, n_check) if __name__ == "__main__": main()