#!/usr/bin/env python3
"""
Reprocess mesh vertices in existing DROID NPZ files.

FIXES:
1. Wrist view: Interpolate FIXED 2D mesh based on actual gripper_state values
2. Exterior view: (TODO) May need different camera offsets for OPEN vs CLOSED

This script:
- Loads existing NPZ files
- Recalculates mesh vertices with proper gripper state interpolation
- Saves back to NPZ (overwrites mesh_vertices_2d_wrist_fixed)
- Keeps all other data unchanged

Usage:
    python reprocess_mesh_vertices.py --start 0 --end 100  # Test on first 100 episodes
    python reprocess_mesh_vertices.py --all  # Reprocess all episodes
"""
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent))

import argparse
import numpy as np
from tqdm import tqdm

def get_wrist_fixed_mesh_interpolated(gripper_state: float) -> np.ndarray:
    """
    Get wrist mesh vertices with proper gripper state interpolation.

    IMPORTANT: DROID convention (INVERTED):
        gripper_state = 0.0 → OPEN (wide finger spacing)
        gripper_state = 1.0 → CLOSED (narrow finger spacing)

    Args:
        gripper_state: float in [0, 1] from action[6]

    Returns:
        mesh_2d: [7, 2] array of mesh vertex coordinates
    """
    # Manually annotated keypoints from web annotation tool
    # Convention: gripper_state 0.0=OPEN, 1.0=CLOSED

    # OPEN state (gripper_state = 0.0) - wide finger spacing
    mesh_open = np.array([
        [141, 108],  # 0: palm base
        [253, 106],  # 1: RIGHT finger tip (far right)
        [221, 97],   # 2: RIGHT finger joint
        [49, 108],   # 3: LEFT finger tip (far left)
        [86, 101],   # 4: LEFT finger joint
        [195, 141],  # 5: right palm interior
        [95, 138],   # 6: left palm interior
    ], dtype=np.float32)

    # CLOSED state (gripper_state = 1.0) - narrow finger spacing
    mesh_closed = np.array([
        [190, 102],  # 0: palm base (shifted center)
        [198, 102],  # 1: RIGHT finger tip (only 8px from left!)
        [195, 98],   # 2: RIGHT finger joint
        [186, 104],  # 3: LEFT finger tip (only 8px from right!)
        [188, 100],  # 4: LEFT finger joint
        [195, 141],  # 5: right palm interior (same)
        [95, 138],   # 6: left palm interior (same)
    ], dtype=np.float32)

    # Linear interpolation: 0.0 (OPEN) → 1.0 (CLOSED)
    # mesh = (1 - gripper_state) * mesh_open + gripper_state * mesh_closed
    alpha = np.clip(gripper_state, 0.0, 1.0)
    mesh_interpolated = (1.0 - alpha) * mesh_open + alpha * mesh_closed

    return mesh_interpolated


def reprocess_episode(npz_path: Path, dry_run: bool = False) -> dict:
    """
    Reprocess mesh vertices for a single episode.

    Args:
        npz_path: Path to NPZ file
        dry_run: If True, don't save changes

    Returns:
        dict with statistics
    """
    data = np.load(npz_path, allow_pickle=True)

    # Load actions
    actions = data['actions']  # [T, 7]
    gripper_states = actions[:, 6]  # [T]
    T = len(actions)

    # Load existing mesh data (we'll only modify wrist mesh for now)
    mesh_vertices_2d_exterior = data['mesh_vertices_2d_exterior']  # [T, 7, 2]
    mesh_vertices_vis_exterior = data['mesh_vertices_vis_exterior']  # [T, 7]
    mesh_vertices_2d_wrist_old = data['mesh_vertices_2d_wrist_fixed']  # [T, 7, 2]
    mesh_vertices_vis_wrist = data['mesh_vertices_vis_wrist_fixed']  # [T, 7]

    # Recompute wrist mesh with interpolation
    mesh_vertices_2d_wrist_new = np.zeros((T, 7, 2), dtype=np.float32)
    for t in range(T):
        mesh_vertices_2d_wrist_new[t] = get_wrist_fixed_mesh_interpolated(gripper_states[t])

    # Calculate how much changed
    max_change = np.abs(mesh_vertices_2d_wrist_new - mesh_vertices_2d_wrist_old).max()
    mean_change = np.abs(mesh_vertices_2d_wrist_new - mesh_vertices_2d_wrist_old).mean()

    # Calculate finger spread before and after
    def calc_finger_spread(mesh_2d, vis):
        spreads = []
        for t in range(len(mesh_2d)):
            if vis[t, 1] and vis[t, 3]:  # Both finger tips visible
                spread = np.linalg.norm(mesh_2d[t, 1] - mesh_2d[t, 3])
                spreads.append(spread)
        return np.array(spreads)

    spread_old = calc_finger_spread(mesh_vertices_2d_wrist_old, mesh_vertices_vis_wrist)
    spread_new = calc_finger_spread(mesh_vertices_2d_wrist_new, mesh_vertices_vis_wrist)

    stats = {
        'npz_path': npz_path.name,
        'frames': T,
        'gripper_min': gripper_states.min(),
        'gripper_max': gripper_states.max(),
        'gripper_mean': gripper_states.mean(),
        'max_change': max_change,
        'mean_change': mean_change,
        'spread_old_min': spread_old.min() if len(spread_old) > 0 else np.nan,
        'spread_old_max': spread_old.max() if len(spread_old) > 0 else np.nan,
        'spread_new_min': spread_new.min() if len(spread_new) > 0 else np.nan,
        'spread_new_max': spread_new.max() if len(spread_new) > 0 else np.nan,
    }

    # Save if not dry run
    if not dry_run:
        # Create new NPZ with updated wrist mesh
        # Load all arrays from original NPZ
        new_data = {key: data[key] for key in data.keys()}
        # Replace wrist mesh
        new_data['mesh_vertices_2d_wrist_fixed'] = mesh_vertices_2d_wrist_new

        # Save atomically (write to temp file, then rename)
        temp_path = npz_path.with_suffix('.npz.tmp')
        np.savez_compressed(temp_path, **new_data)
        temp_path.replace(npz_path)

    data.close()
    return stats


def main():
    parser = argparse.ArgumentParser(description='Reprocess mesh vertices in DROID NPZ files')
    parser.add_argument('--data-dir', type=str, default='/mnt/kevin/data/droid_preprocessed/data',
                       help='Directory containing NPZ files')
    parser.add_argument('--start', type=int, default=None, help='Start episode index')
    parser.add_argument('--end', type=int, default=None, help='End episode index (exclusive)')
    parser.add_argument('--all', action='store_true', help='Process all episodes')
    parser.add_argument('--dry-run', action='store_true', help='Test without saving changes')
    parser.add_argument('--visualize', type=int, default=0,
                       help='Number of episodes to visualize for verification')
    args = parser.parse_args()

    data_path = Path(args.data_dir)
    if not data_path.exists():
        print(f"Error: Data directory not found: {data_path}")
        return

    # Get episode files
    episode_files = sorted(list(data_path.glob("episode_*.npz")))
    print(f"Found {len(episode_files)} episodes in {data_path}")

    # Determine range
    if args.all:
        start_idx = 0
        end_idx = len(episode_files)
    else:
        start_idx = args.start if args.start is not None else 0
        end_idx = args.end if args.end is not None else min(start_idx + 100, len(episode_files))

    episodes_to_process = episode_files[start_idx:end_idx]
    print(f"\nProcessing episodes {start_idx} to {end_idx-1} ({len(episodes_to_process)} total)")

    if args.dry_run:
        print("DRY RUN MODE - no changes will be saved")

    # Process episodes
    all_stats = []
    for npz_path in tqdm(episodes_to_process, desc="Reprocessing"):
        try:
            stats = reprocess_episode(npz_path, dry_run=args.dry_run)
            all_stats.append(stats)
        except Exception as e:
            print(f"\nError processing {npz_path.name}: {e}")
            continue

    # Print summary
    print("\n" + "=" * 80)
    print("Reprocessing Summary")
    print("=" * 80)
    print(f"Episodes processed: {len(all_stats)}")

    if len(all_stats) > 0:
        max_changes = [s['max_change'] for s in all_stats]
        mean_changes = [s['mean_change'] for s in all_stats]

        print(f"\nMesh vertex changes:")
        print(f"  Max change per episode: {np.max(max_changes):.2f} pixels")
        print(f"  Mean change per episode: {np.mean(mean_changes):.2f} pixels")
        print(f"  Median change per episode: {np.median(mean_changes):.2f} pixels")

        # Show episodes with biggest changes
        sorted_by_change = sorted(all_stats, key=lambda x: x['max_change'], reverse=True)
        print(f"\nTop 5 episodes with biggest changes:")
        for i, s in enumerate(sorted_by_change[:5]):
            print(f"  {i+1}. {s['npz_path']}: max_change={s['max_change']:.2f}px, "
                  f"gripper_range=[{s['gripper_min']:.3f}, {s['gripper_max']:.3f}]")

        # Finger spread statistics
        spread_old_min = np.nanmin([s['spread_old_min'] for s in all_stats])
        spread_old_max = np.nanmax([s['spread_old_max'] for s in all_stats])
        spread_new_min = np.nanmin([s['spread_new_min'] for s in all_stats])
        spread_new_max = np.nanmax([s['spread_new_max'] for s in all_stats])

        print(f"\nFinger spread (wrist view):")
        print(f"  OLD (fixed): [{spread_old_min:.1f}, {spread_old_max:.1f}] pixels")
        print(f"  NEW (interpolated): [{spread_new_min:.1f}, {spread_new_max:.1f}] pixels")

    print("=" * 80)

    if args.dry_run:
        print("\n⚠ DRY RUN - No changes were saved!")
        print("Remove --dry-run flag to actually update the NPZ files.")
    else:
        print(f"\n✓ Successfully updated {len(all_stats)} episodes")
        print(f"  NPZ files in: {data_path}")

    # TODO: Add visualization if requested
    if args.visualize > 0:
        print(f"\n⚠ Visualization not implemented yet")
        print(f"  Use visualize_gripper_mesh_alignment.py to check results")


if __name__ == "__main__":
    main()