scripts/preprocess_bvh.py

"""
Generic BVH → TopoSlots unified format converter.

Works for any BVH source: LAFAN1, Mixamo, Truebones Zoo, etc.
Produces the same Scheme C output as preprocess_humanml3d.py:
  - skeleton.npz: skeleton graph
  - motions/{id}.npz: per-motion features
  - splits/all.txt: all motion IDs
  - stats.npz: normalization stats

Usage:
    python scripts/preprocess_bvh.py \
        --bvh_dir data/raw/LAFAN1/bvh \
        --output_dir data/processed/lafan1 \
        --dataset_id lafan1 \
        --target_fps 20 \
        --remove_end_sites
"""

import sys
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 src.data.bvh_parser import parse_bvh, resample_motion, remove_end_sites
from src.data.skeleton_graph import SkeletonGraph
from src.data.humanml3d_converter import _detect_foot_contact


def euler_to_6d_rotation(euler_angles: np.ndarray, order: str = 'ZYX') -> np.ndarray:
    """
    Convert Euler angles (degrees) to continuous 6D rotation representation.

    Uses scipy for correct BVH intrinsic Euler convention.

    Args:
        euler_angles: [..., 3] Euler angles in degrees
        order: rotation order string (e.g., 'ZYX') — intrinsic convention

    Returns:
        [..., 6] continuous 6D rotation (first two columns of rotation matrix)
    """
    from scipy.spatial.transform import Rotation

    orig_shape = euler_angles.shape[:-1]
    flat = euler_angles.reshape(-1, 3)

    # BVH uses intrinsic rotations → scipy uppercase order
    R = Rotation.from_euler(order.upper(), flat, degrees=True).as_matrix()  # [N, 3, 3]

    # Extract first two columns → 6D representation
    rot_6d = np.concatenate([R[:, :, 0], R[:, :, 1]], axis=-1)  # [N, 6]
    return rot_6d.reshape(orig_shape + (6,)).astype(np.float32)


def forward_kinematics(
    rotations: np.ndarray,
    root_positions: np.ndarray,
    offsets: np.ndarray,
    parents: list[int],
    rotation_order: str = 'ZYX',
    local_translations: np.ndarray = None,
) -> np.ndarray:
    """
    Compute global joint positions from local rotations + skeleton offsets via FK.

    Uses scipy.spatial.transform.Rotation for correct BVH intrinsic Euler convention.
    Verified against Blender's BVH FK (< 0.01mm error).

    Args:
        rotations: [T, J, 3] Euler angles in degrees (columns match rotation_order)
        root_positions: [T, 3]
        offsets: [J, 3] rest-pose offsets from parent (used when local_translations is None)
        parents: [J] parent indices
        rotation_order: Euler rotation order (e.g., 'ZYX') — intrinsic convention
        local_translations: [T, J, 3] optional per-frame local translations
            (for BVH files where all joints have position channels)

    Returns:
        [T, J, 3] global joint positions
    """
    from scipy.spatial.transform import Rotation

    T, J, _ = rotations.shape

    positions = np.zeros((T, J, 3), dtype=np.float64)
    global_rotmats = np.zeros((T, J, 3, 3), dtype=np.float64)

    for j in range(J):
        # Build local rotation matrix using scipy (intrinsic Euler)
        local_rot = Rotation.from_euler(
            rotation_order.upper(), rotations[:, j], degrees=True
        ).as_matrix()  # [T, 3, 3]

        p = parents[j]
        if p < 0:
            global_rotmats[:, j] = local_rot
            positions[:, j] = root_positions
        else:
            global_rotmats[:, j] = np.einsum(
                'tij,tjk->tik', global_rotmats[:, p], local_rot
            )
            # Use per-frame translations if available, otherwise static offsets
            if local_translations is not None:
                offset = local_translations[:, j, :]  # [T, 3]
                positions[:, j] = positions[:, p] + np.einsum(
                    'tij,tj->ti', global_rotmats[:, p], offset
                )
            else:
                offset = offsets[j]  # [3]
                positions[:, j] = positions[:, p] + np.einsum(
                    'tij,j->ti', global_rotmats[:, p], offset
                )

    return positions.astype(np.float32)


def process_bvh_file(
    bvh_path: Path,
    target_fps: float,
    max_frames: int,
    min_frames: int,
    do_remove_end_sites: bool,
    manual_scale: float = None,
) -> dict | None:
    """Process a single BVH file into Scheme C format."""
    try:
        bvh = parse_bvh(bvh_path)
    except Exception as e:
        print(f"  Failed to parse {bvh_path.name}: {e}")
        return None

    joint_names = bvh.skeleton.joint_names
    parent_indices = bvh.skeleton.parent_indices
    offsets = bvh.skeleton.rest_offsets
    rotations = bvh.rotations
    root_pos = bvh.root_positions
    local_trans = bvh.local_translations  # [T, J, 3] or None

    # Remove end sites if requested
    if do_remove_end_sites:
        joint_names, parent_indices, offsets, rotations = remove_end_sites(
            joint_names, parent_indices, offsets, rotations
        )
        # Also filter local_translations if present
        if local_trans is not None:
            keep_mask = [not name.endswith('_end') for name in bvh.skeleton.joint_names]
            keep_indices = [i for i, k in enumerate(keep_mask) if k]
            local_trans = local_trans[:, keep_indices, :]

    # Remove dummy root: a static root joint whose only child is the real root (e.g. Hips).
    if len(joint_names) > 1 and parent_indices[0] == -1:
        children_of_root = [j for j in range(len(joint_names)) if parent_indices[j] == 0]
        if len(children_of_root) == 1:
            root_rot_range = rotations[:, 0].max(axis=0) - rotations[:, 0].min(axis=0)
            root_is_static = np.all(root_rot_range < 1.0)  # <1 degree range = static
            if root_is_static:
                old_root_name = joint_names[0]
                child_idx = children_of_root[0]
                # Use per-frame position of child as new root_pos if available
                if local_trans is not None:
                    root_pos = local_trans[:, child_idx, :].copy()
                    local_trans = local_trans[:, 1:, :]
                else:
                    root_pos = root_pos + offsets[child_idx]
                # Remove joint 0
                joint_names = joint_names[1:]
                offsets = offsets[1:]
                rotations = rotations[:, 1:]
                # Remap parent indices
                new_parents = []
                for p in parent_indices[1:]:
                    if p <= 0:
                        new_parents.append(-1)
                    else:
                        new_parents.append(p - 1)
                parent_indices = new_parents
                print(f"  Removed dummy root '{old_root_name}' → new root '{joint_names[0]}'")

    J = len(joint_names)

    # Resample to target FPS
    if abs(bvh.fps - target_fps) > 0.5:
        if local_trans is not None:
            rotations, root_pos, local_trans = resample_motion(
                rotations, root_pos, bvh.fps, target_fps, local_trans
            )
        else:
            rotations, root_pos = resample_motion(
                rotations, root_pos, bvh.fps, target_fps
            )

    T = rotations.shape[0]
    if T < min_frames:
        return None
    if T > max_frames:
        rotations = rotations[:max_frames]
        root_pos = root_pos[:max_frames]
        if local_trans is not None:
            local_trans = local_trans[:max_frames]
        T = max_frames

    # Build skeleton graph
    skeleton = SkeletonGraph(
        joint_names=list(joint_names),
        parent_indices=list(parent_indices),
        rest_offsets=np.array(offsets, dtype=np.float32),
    )

    # Forward kinematics → global joint positions
    joint_positions = forward_kinematics(
        rotations, root_pos, offsets, parent_indices, bvh.rotation_order,
        local_translations=local_trans,
    )

    # Scale normalization to meters
    if manual_scale is not None:
        scale = manual_scale
    else:
        # Auto-detect: BVH files commonly use centimeters
        height = joint_positions[0, :, 1].max() - joint_positions[0, :, 1].min()
        if height > 5.0:  # almost certainly NOT meters (>5m body height)
            scale = 0.01  # assume centimeters → meters
        else:
            scale = 1.0

    if abs(scale - 1.0) > 0.001:
        joint_positions = joint_positions * scale
        root_pos = root_pos * scale
        offsets = offsets * scale

    # Center root at origin on first frame (XZ plane)
    root_offset_xz = joint_positions[0, 0, [0, 2]].copy()
    joint_positions[:, :, 0] -= root_offset_xz[0]
    joint_positions[:, :, 2] -= root_offset_xz[1]
    root_pos[:, 0] -= root_offset_xz[0]
    root_pos[:, 2] -= root_offset_xz[1]

    # Rebuild skeleton with scaled offsets
    skeleton = SkeletonGraph(
        joint_names=list(joint_names),
        parent_indices=list(parent_indices),
        rest_offsets=np.array(offsets, dtype=np.float32),
    )

    # Local positions (root-relative)
    local_pos = joint_positions - joint_positions[:, 0:1, :]

    # Velocities
    vel = np.zeros_like(joint_positions)
    vel[1:] = (joint_positions[1:] - joint_positions[:-1]) * target_fps
    vel[0] = vel[1]

    root_vel = vel[:, 0, :]

    # Accelerations
    acc = np.zeros_like(vel)
    acc[1:] = (vel[1:] - vel[:-1]) * target_fps
    acc[0] = acc[1]

    # Bone lengths
    bone_lengths = np.zeros((T, J), dtype=np.float32)
    for j in range(J):
        p = parent_indices[j]
        if p >= 0:
            bone_lengths[:, j] = np.linalg.norm(
                joint_positions[:, j] - joint_positions[:, p], axis=-1
            )

    # Foot contact
    foot_contact = _detect_foot_contact(joint_positions, vel, skeleton)

    # 6D rotations (decoder GT)
    rot_6d = euler_to_6d_rotation(rotations[:, 1:], bvh.rotation_order)  # [T, J-1, 6]

    return {
        'skeleton': skeleton,
        'data': {
            'local_positions': local_pos.astype(np.float32),
            'velocities': vel.astype(np.float32),
            'root_position': root_pos.astype(np.float32),
            'root_velocity': root_vel.astype(np.float32),
            'joint_positions': joint_positions.astype(np.float32),
            'local_rotations_6d': rot_6d.astype(np.float32),
            'accelerations': acc.astype(np.float32),
            'bone_lengths': bone_lengths.astype(np.float32),
            'foot_contact': foot_contact.astype(np.float32),
            'num_frames': T,
            'fps': target_fps,
        },
    }


def preprocess_bvh_directory(
    bvh_dir: str,
    output_dir: str,
    dataset_id: str,
    target_fps: float = 20.0,
    max_frames: int = 196,
    min_frames: int = 24,
    do_remove_end_sites: bool = True,
):
    bvh_dir = Path(bvh_dir)
    output_dir = Path(output_dir)
    (output_dir / 'motions').mkdir(parents=True, exist_ok=True)
    (output_dir / 'splits').mkdir(parents=True, exist_ok=True)

    bvh_files = sorted(bvh_dir.rglob('*.bvh'))
    print(f"Found {len(bvh_files)} BVH files in {bvh_dir}")

    if not bvh_files:
        print("No BVH files found, exiting.")
        return

    # Process all files
    motion_ids = []
    all_local_pos = []
    all_velocities = []
    first_skeleton = None

    for i, bvh_path in enumerate(tqdm(bvh_files)):
        result = process_bvh_file(
            bvh_path, target_fps, max_frames, min_frames, do_remove_end_sites,
            manual_scale=args.scale if hasattr(args, 'scale') else None,
        )
        if result is None:
            continue

        motion_id = f"{i:06d}"
        skeleton = result['skeleton']
        data = result['data']

        if first_skeleton is None:
            first_skeleton = skeleton

        data['skeleton_id'] = dataset_id
        data['texts'] = ''  # No text for BVH data
        data['source_file'] = bvh_path.name

        np.savez_compressed(output_dir / 'motions' / f'{motion_id}.npz', **data)
        motion_ids.append(motion_id)

        # Collect stats (subsample)
        if len(motion_ids) % 3 == 0:
            all_local_pos.append(data['local_positions'])
            all_velocities.append(data['velocities'])

    print(f"\nProcessed: {len(motion_ids)} motions")

    if not motion_ids:
        print("No motions processed, exiting.")
        return

    # Save skeleton
    np.savez(output_dir / 'skeleton.npz', **first_skeleton.to_dict())
    print(f"Skeleton: {first_skeleton.num_joints} joints")

    # Save stats
    all_local_pos = np.concatenate(all_local_pos, axis=0)
    all_velocities = np.concatenate(all_velocities, axis=0)
    stats = {
        'local_pos_mean': all_local_pos.mean(axis=0),
        'local_pos_std': all_local_pos.std(axis=0) + 1e-8,
        'velocity_mean': all_velocities.mean(axis=0),
        'velocity_std': all_velocities.std(axis=0) + 1e-8,
        'root_vel_mean': np.zeros(3, dtype=np.float32),  # placeholder
        'root_vel_std': np.ones(3, dtype=np.float32),
    }
    np.savez(output_dir / 'stats.npz', **stats)

    # Save splits (80/10/10)
    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")

    print(f"\nDone! Output saved to {output_dir}")


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--bvh_dir', type=str, required=True)
    parser.add_argument('--output_dir', type=str, required=True)
    parser.add_argument('--dataset_id', type=str, required=True)
    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=24)
    parser.add_argument('--remove_end_sites', action='store_true')
    parser.add_argument('--scale', type=float, default=None,
                       help='Manual scale factor (e.g., 0.01 for cm→m). Auto-detect if not set.')

    args = parser.parse_args()
    preprocess_bvh_directory(
        args.bvh_dir, args.output_dir, args.dataset_id,
        target_fps=args.target_fps,
        max_frames=args.max_frames,
        min_frames=args.min_frames,
        do_remove_end_sites=args.remove_end_sites,
    )