""" Visualize canonical naming, side tags, and symmetry pairs across all datasets. Produces: 1. Per-dataset skeleton with joints colored by side tag (L=red, R=blue, C=gray) 2. Canonical name labels on joints 3. Symmetry pairs connected by dashed lines 4. Cross-dataset canonical consistency heatmap """ import sys import os from pathlib import Path import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import matplotlib.patches as mpatches project_root = Path(__file__).parent.parent sys.path.insert(0, str(project_root)) from src.data.skeleton_graph import SkeletonGraph from scripts.preprocess_bvh import forward_kinematics RESULT_DIR = project_root / 'results' / 'canonical_check' RESULT_DIR.mkdir(parents=True, exist_ok=True) SIDE_COLORS = {'left': '#e74c3c', 'right': '#3498db', 'center': '#95a5a6'} def plot_skeleton_canonical(ax, positions, parents, side_tags, canonical_names, symmetry_pairs, title, label_fontsize=6): """Plot skeleton with side-colored joints and canonical name labels.""" J = len(parents) pos = positions # Draw bones for j in range(J): p = parents[j] if p >= 0: ax.plot3D([pos[j, 0], pos[p, 0]], [pos[j, 2], pos[p, 2]], [pos[j, 1], pos[p, 1]], color='#bdc3c7', linewidth=1.5, zorder=1) # Draw symmetry pairs as dashed lines for i, j in symmetry_pairs: if i < J and j < J: mid_y = (pos[i, 1] + pos[j, 1]) / 2 ax.plot3D([pos[i, 0], pos[j, 0]], [pos[i, 2], pos[j, 2]], [pos[i, 1], pos[j, 1]], color='#2ecc71', linewidth=0.8, linestyle='--', alpha=0.5, zorder=2) # Draw joints colored by side for j in range(J): color = SIDE_COLORS.get(side_tags[j], '#95a5a6') ax.scatter3D([pos[j, 0]], [pos[j, 2]], [pos[j, 1]], color=color, s=30, zorder=3, edgecolors='black', linewidths=0.3) # Label joints with canonical names (subsample if too many) step = max(1, J // 15) for j in range(0, J, step): ax.text(pos[j, 0], pos[j, 2], pos[j, 1] + 0.02, canonical_names[j], fontsize=label_fontsize, ha='center', va='bottom', color='black', alpha=0.8) ax.set_title(title, fontsize=10, fontweight='bold') ax.set_xlabel('X', fontsize=7) ax.set_ylabel('Z', fontsize=7) ax.set_zlabel('Y', fontsize=7) ax.tick_params(labelsize=5) # Equal axes mid = pos.mean(axis=0) span = max(pos.max(axis=0) - pos.min(axis=0)) / 2 + 0.05 ax.set_xlim(mid[0] - span, mid[0] + span) ax.set_ylim(mid[2] - span, mid[2] + span) ax.set_zlim(mid[1] - span, mid[1] + span) def get_rest_pose(dataset_path, dataset_id): """Get rest pose joint positions from first motion.""" motions_dir = dataset_path / 'motions' files = sorted(os.listdir(motions_dir)) if not files: return None d = dict(np.load(motions_dir / files[0], allow_pickle=True)) return d['joint_positions'][0] # first frame def visualize_human_datasets(): """Plot 1: All 6 human datasets side by side.""" fig = plt.figure(figsize=(24, 8)) datasets = ['humanml3d', 'lafan1', '100style', 'bandai_namco', 'cmu_mocap', 'mixamo'] for idx, ds in enumerate(datasets): ds_path = project_root / 'data' / 'processed' / ds s = dict(np.load(ds_path / 'skeleton.npz', allow_pickle=True)) sg = SkeletonGraph.from_dict(s) canon = [str(n) for n in s['canonical_names']] rest_pos = get_rest_pose(ds_path, ds) if rest_pos is None: continue ax = fig.add_subplot(1, 6, idx + 1, projection='3d') plot_skeleton_canonical( ax, rest_pos, sg.parent_indices, sg.side_tags, canon, sg.symmetry_pairs, f'{ds}\n{sg.num_joints}j, {len(sg.symmetry_pairs)} sym pairs', label_fontsize=5 if sg.num_joints > 30 else 6, ) # Legend patches = [ mpatches.Patch(color=SIDE_COLORS['left'], label='Left'), mpatches.Patch(color=SIDE_COLORS['right'], label='Right'), mpatches.Patch(color=SIDE_COLORS['center'], label='Center'), mpatches.Patch(color='#2ecc71', label='Symmetry pair'), ] fig.legend(handles=patches, loc='lower center', ncol=4, fontsize=9) plt.suptitle('Human Datasets — Canonical Names + Side Tags + Symmetry', fontsize=14, fontweight='bold') plt.tight_layout(rect=[0, 0.05, 1, 0.95]) out = RESULT_DIR / 'human_canonical_overview.png' plt.savefig(out, dpi=150, bbox_inches='tight') plt.close() print(f'Saved: {out}') def visualize_zoo_animals(): """Plot 2: Diverse Zoo animals with canonical names.""" species = ['Dog', 'Cat', 'Horse', 'Eagle', 'Trex', 'Spider', 'Ant', 'Anaconda', 'Dragon', 'Crab', 'Elephant', 'Bat'] fig = plt.figure(figsize=(24, 18)) zoo_path = project_root / 'data' / 'processed' / 'truebones_zoo' motions_dir = zoo_path / 'motions' # Find one motion per species species_motions = {} for f in sorted(os.listdir(motions_dir)): d = dict(np.load(motions_dir / f, allow_pickle=True)) sp = str(d.get('species', '')) if sp in species and sp not in species_motions: species_motions[sp] = d for idx, sp in enumerate(species): if sp not in species_motions: continue d = species_motions[sp] skel_path = zoo_path / 'skeletons' / f'{sp}.npz' if not skel_path.exists(): continue s = dict(np.load(skel_path, allow_pickle=True)) sg = SkeletonGraph.from_dict(s) canon = [str(n) for n in s['canonical_names']] rest_pos = d['joint_positions'][0] ax = fig.add_subplot(3, 4, idx + 1, projection='3d') plot_skeleton_canonical( ax, rest_pos, sg.parent_indices, sg.side_tags, canon, sg.symmetry_pairs, f'{sp} ({sg.num_joints}j, {len(sg.symmetry_pairs)} sym)', label_fontsize=5, ) patches = [ mpatches.Patch(color=SIDE_COLORS['left'], label='Left'), mpatches.Patch(color=SIDE_COLORS['right'], label='Right'), mpatches.Patch(color=SIDE_COLORS['center'], label='Center'), mpatches.Patch(color='#2ecc71', label='Symmetry pair'), ] fig.legend(handles=patches, loc='lower center', ncol=4, fontsize=10) plt.suptitle('Truebones Zoo — Canonical Names + Side Tags + Symmetry', fontsize=14, fontweight='bold') plt.tight_layout(rect=[0, 0.04, 1, 0.96]) out = RESULT_DIR / 'zoo_canonical_overview.png' plt.savefig(out, dpi=150, bbox_inches='tight') plt.close() print(f'Saved: {out}') def visualize_cross_dataset_consistency(): """Plot 3: Heatmap showing canonical name consistency across datasets.""" # Collect canonical names for core body parts across datasets core_parts = [ 'pelvis', 'spine lower', 'spine mid', 'spine upper', 'neck', 'head', 'left collar', 'left upper arm', 'left forearm', 'left hand', 'right collar', 'right upper arm', 'right forearm', 'right hand', 'left upper leg', 'left lower leg', 'left foot', 'left toe', 'right upper leg', 'right lower leg', 'right foot', 'right toe', ] datasets = ['humanml3d', 'lafan1', '100style', 'bandai_namco', 'cmu_mocap', 'mixamo'] matrix = np.zeros((len(core_parts), len(datasets)), dtype=np.float32) for j, ds in enumerate(datasets): s = dict(np.load(f'data/processed/{ds}/skeleton.npz', allow_pickle=True)) canon_set = set(str(n) for n in s['canonical_names']) for i, part in enumerate(core_parts): matrix[i, j] = 1.0 if part in canon_set else 0.0 fig, ax = plt.subplots(figsize=(10, 12)) im = ax.imshow(matrix, cmap='RdYlGn', aspect='auto', vmin=0, vmax=1) ax.set_xticks(range(len(datasets))) ax.set_xticklabels(datasets, rotation=45, ha='right', fontsize=9) ax.set_yticks(range(len(core_parts))) ax.set_yticklabels(core_parts, fontsize=9) # Annotate cells for i in range(len(core_parts)): for j in range(len(datasets)): text = '✓' if matrix[i, j] > 0.5 else '✗' color = 'white' if matrix[i, j] > 0.5 else 'red' ax.text(j, i, text, ha='center', va='center', fontsize=10, color=color) ax.set_title('Cross-Dataset Canonical Name Coverage\n(22 core human body parts)', fontsize=12, fontweight='bold') plt.colorbar(im, ax=ax, label='Present in dataset', shrink=0.6) plt.tight_layout() out = RESULT_DIR / 'canonical_consistency_heatmap.png' plt.savefig(out, dpi=150, bbox_inches='tight') plt.close() print(f'Saved: {out}') def visualize_side_tag_stats(): """Plot 4: Bar chart of side tag distribution per dataset.""" all_data = [] labels = [] datasets = ['humanml3d', 'lafan1', '100style', 'bandai_namco', 'cmu_mocap', 'mixamo'] for ds in datasets: s = dict(np.load(f'data/processed/{ds}/skeleton.npz', allow_pickle=True)) sg = SkeletonGraph.from_dict(s) n_l = sum(1 for t in sg.side_tags if t == 'left') n_r = sum(1 for t in sg.side_tags if t == 'right') n_c = sum(1 for t in sg.side_tags if t == 'center') all_data.append((n_l, n_r, n_c, len(sg.symmetry_pairs))) labels.append(f'{ds}\n({sg.num_joints}j)') # Add Zoo species for sp in ['Dog', 'Cat', 'Horse', 'Eagle', 'Trex', 'Spider', 'Anaconda', 'Dragon']: s = dict(np.load(f'data/processed/truebones_zoo/skeletons/{sp}.npz', allow_pickle=True)) sg = SkeletonGraph.from_dict(s) n_l = sum(1 for t in sg.side_tags if t == 'left') n_r = sum(1 for t in sg.side_tags if t == 'right') n_c = sum(1 for t in sg.side_tags if t == 'center') all_data.append((n_l, n_r, n_c, len(sg.symmetry_pairs))) labels.append(f'{sp}\n({sg.num_joints}j)') fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 10)) x = range(len(labels)) lefts = [d[0] for d in all_data] rights = [d[1] for d in all_data] centers = [d[2] for d in all_data] syms = [d[3] for d in all_data] ax1.bar(x, lefts, color=SIDE_COLORS['left'], label='Left', alpha=0.8) ax1.bar(x, rights, bottom=lefts, color=SIDE_COLORS['right'], label='Right', alpha=0.8) ax1.bar(x, centers, bottom=[l+r for l,r in zip(lefts, rights)], color=SIDE_COLORS['center'], label='Center', alpha=0.8) ax1.set_xticks(x) ax1.set_xticklabels(labels, fontsize=8) ax1.set_ylabel('Joint Count') ax1.set_title('Side Tag Distribution per Dataset/Species', fontweight='bold') ax1.legend() ax1.axvline(x=5.5, color='black', linestyle='--', alpha=0.3) ax1.text(2.5, max(lefts)*2.5, 'Human', ha='center', fontsize=10, style='italic') ax1.text(9.5, max(lefts)*2.5, 'Zoo Animals', ha='center', fontsize=10, style='italic') ax2.bar(x, syms, color='#2ecc71', alpha=0.8) ax2.set_xticks(x) ax2.set_xticklabels(labels, fontsize=8) ax2.set_ylabel('Symmetry Pairs') ax2.set_title('Detected Symmetry Pairs per Dataset/Species', fontweight='bold') ax2.axvline(x=5.5, color='black', linestyle='--', alpha=0.3) plt.tight_layout() out = RESULT_DIR / 'side_tag_stats.png' plt.savefig(out, dpi=150, bbox_inches='tight') plt.close() print(f'Saved: {out}') if __name__ == '__main__': print('Generating canonical name visualizations...\n') visualize_human_datasets() visualize_zoo_animals() visualize_cross_dataset_consistency() visualize_side_tag_stats() print(f'\nAll saved to: {RESULT_DIR}/')