""" Preprocess Truebones Zoo: per-species BVH → unified Scheme C + text captions. Each species has its own skeleton topology. We process them all into one unified dataset with per-motion skeleton_id = species name. """ import sys import json import argparse from pathlib import Path import numpy as np from tqdm import tqdm project_root = Path(__file__).parent.parent sys.path.insert(0, str(project_root)) from scripts.preprocess_bvh import process_bvh_file from src.data.skeleton_graph import SkeletonGraph def load_captions(captions_dir: Path) -> dict: """Load all captions into a dict: file_id → list of caption strings.""" captions = {} for species_dir in sorted(captions_dir.iterdir()): if not species_dir.is_dir(): continue for json_file in species_dir.glob('*.json'): try: with open(json_file) as f: data = json.load(f) file_id = data.get('file_id', '') # Extract short captions (most concise) caps = data.get('captions', {}) short_caps = caps.get('short', {}).get('original', []) if short_caps: # Use file_id without extension as key key = file_id.replace('.fbx', '').replace('.bvh', '') captions[key] = short_caps except Exception: continue return captions def main(): parser = argparse.ArgumentParser() parser.add_argument('--zoo_dir', type=str, default='data/raw/Truebones_Zoo/New-FBX-BVH_Z-OO/Truebone_Z-OO') parser.add_argument('--captions_dir', type=str, default='data/raw/Truebones_Zoo_captions/captions') parser.add_argument('--output_dir', type=str, default='data/processed/truebones_zoo') parser.add_argument('--target_fps', type=float, default=20.0) parser.add_argument('--max_frames', type=int, default=196) parser.add_argument('--min_frames', type=int, default=16) # shorter for animals args = parser.parse_args() zoo_dir = Path(args.zoo_dir) output_dir = Path(args.output_dir) (output_dir / 'motions').mkdir(parents=True, exist_ok=True) (output_dir / 'splits').mkdir(parents=True, exist_ok=True) (output_dir / 'skeletons').mkdir(parents=True, exist_ok=True) # Load captions captions_dir = Path(args.captions_dir) if captions_dir.exists(): captions = load_captions(captions_dir) print(f"Loaded captions for {len(captions)} motions") else: captions = {} print("No captions directory found") # Find all species species_dirs = sorted([d for d in zoo_dir.iterdir() if d.is_dir()]) print(f"Found {len(species_dirs)} species") motion_ids = [] skeletons = {} # species_name → skeleton info all_local_pos = [] all_velocities = [] processed = 0 failed = 0 for species_dir in tqdm(species_dirs, desc="Species"): species_name = species_dir.name bvh_files = sorted(species_dir.glob('*.bvh')) if not bvh_files: continue species_skeleton = None for bvh_path in bvh_files: result = process_bvh_file( bvh_path, args.target_fps, args.max_frames, args.min_frames, do_remove_end_sites=True, ) if result is None: failed += 1 continue skeleton = result['skeleton'] data = result['data'] # Store skeleton per species (first one) if species_skeleton is None: species_skeleton = skeleton skeletons[species_name] = { 'num_joints': skeleton.num_joints, 'joint_names': skeleton.joint_names, } np.savez( output_dir / 'skeletons' / f'{species_name}.npz', **skeleton.to_dict(), ) # Match captions # Try: "Species/filename" format bvh_stem = bvh_path.stem.lstrip('_') # remove leading underscores caption_keys = [ f"{species_name}/{bvh_stem}", f"{species_name}/{species_name}-{bvh_stem}", f"{species_name}/{species_name}_{bvh_stem}", ] text_list = [] for ck in caption_keys: if ck in captions: text_list = captions[ck] break # Also try fuzzy match if not text_list: for ck, caps in captions.items(): if species_name.lower() in ck.lower() and bvh_stem.lower().replace('_', '-') in ck.lower().replace('_', '-'): text_list = caps break motion_id = f"{species_name}_{processed:04d}" data['skeleton_id'] = species_name data['texts'] = '|||'.join(text_list[:5]) if text_list else '' data['species'] = species_name data['source_file'] = bvh_path.name np.savez_compressed( output_dir / 'motions' / f'{motion_id}.npz', **data, ) motion_ids.append(motion_id) if processed % 5 == 0: all_local_pos.append(data['local_positions']) all_velocities.append(data['velocities']) processed += 1 print(f"\nProcessed: {processed}, Failed: {failed}") print(f"Species with skeletons: {len(skeletons)}") if not motion_ids: print("No motions processed!") return # Save a "representative" skeleton (the first one, for compatibility) first_species = list(skeletons.keys())[0] first_skel = dict(np.load(output_dir / 'skeletons' / f'{first_species}.npz', allow_pickle=True)) np.savez(output_dir / 'skeleton.npz', **first_skel) # Stats — flatten across variable joint counts all_pos_flat = np.concatenate([p.reshape(-1, 3) for p in all_local_pos], axis=0) all_vel_flat = np.concatenate([v.reshape(-1, 3) for v in all_velocities], axis=0) stats = { 'local_pos_mean': np.zeros((1, 3), dtype=np.float32), 'local_pos_std': all_pos_flat.std(axis=0, keepdims=True).astype(np.float32) + 1e-8, 'velocity_mean': np.zeros((1, 3), dtype=np.float32), 'velocity_std': all_vel_flat.std(axis=0, keepdims=True).astype(np.float32) + 1e-8, 'root_vel_mean': np.zeros(3, dtype=np.float32), 'root_vel_std': np.ones(3, dtype=np.float32), } np.savez(output_dir / 'stats.npz', **stats) # Splits np.random.seed(42) indices = np.random.permutation(len(motion_ids)) n_train = int(0.8 * len(indices)) n_val = int(0.1 * len(indices)) splits = { 'train': [motion_ids[i] for i in indices[:n_train]], 'val': [motion_ids[i] for i in indices[n_train:n_train + n_val]], 'test': [motion_ids[i] for i in indices[n_train + n_val:]], 'all': motion_ids, } for split_name, ids in splits.items(): with open(output_dir / 'splits' / f'{split_name}.txt', 'w') as f: for mid in ids: f.write(f'{mid}\n') print(f" {split_name}: {len(ids)} motions") # Summary print(f"\nSkeleton diversity:") for sp, info in sorted(skeletons.items()): print(f" {sp:15s}: {info['num_joints']:3d} joints") # Count text matches text_count = sum(1 for mid in motion_ids if (output_dir / 'motions' / f'{mid}.npz').exists() and str(np.load(output_dir / 'motions' / f'{mid}.npz', allow_pickle=True).get('texts', '')) != '') print(f"\nMotions with text: {text_count}/{len(motion_ids)}") print(f"\nDone! Output: {output_dir}") if __name__ == '__main__': main()