#!/usr/bin/env python3
"""
DROID Tracker with Depth Information
Uses the original motion-based tracking but adds depth information to the results
"""

import os
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

import numpy as np
import torch
import h5py
from pathlib import Path
from tqdm import tqdm
import json
import sys
import gc
import cv2
from typing import Dict, Optional, Tuple
from datetime import datetime

# Import the original tracker
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from droid_raw_mp4_tracker import RawDROIDTracker, clear_gpu_memory


class TrackerWithDepth(RawDROIDTracker):
    """
    Minimal extension of RawDROIDTracker that adds depth information
    Uses exact same tracking method, just adds depth data to results
    """
    
    def __init__(self, **kwargs):
        """Initialize tracker - all parameters same as RawDROIDTracker"""
        super().__init__(**kwargs)
        print("  Depth data: Will be added if available (tracking unchanged)")
    
    def load_depth_for_tracks(self, depth_dir: Path, camera_serial: str,
                            tracks: np.ndarray, 
                            scale_factors: Optional[Tuple[float, float]] = None,
                            frame_skip_multiplier: int = 1) -> Optional[np.ndarray]:
        """Load depth values at tracked point locations
        
        Args:
            depth_dir: Directory containing depth maps
            camera_serial: Camera serial number
            tracks: Tracked points of shape (B, T, N, 2)
            scale_factors: Scale factors if tracks are at different resolution
            frame_skip_multiplier: Multiplier for frame indices (2 for 60->30fps)
            
        Returns:
            Track depths of shape (B, T, N) or None if not found
        """
        camera_depth_dir = depth_dir / camera_serial
        
        if not camera_depth_dir.exists():
            print(f"  ⚠️  No depth data found for camera {camera_serial}")
            return None
        
        # Load depth info
        depth_info_path = camera_depth_dir / 'depth_info.json'
        if depth_info_path.exists():
            with open(depth_info_path, 'r') as f:
                depth_info = json.load(f)
                print(f"  Found depth data: {depth_info['extracted_frames']} frames")
                print(f"  Depth range: [{depth_info['depth_range']['global_min']:.0f}, "
                      f"{depth_info['depth_range']['global_max']:.0f}] mm")
        
        B, T, N, _ = tracks.shape
        track_depths = np.zeros((B, T, N))
        missing_frames = 0
        
        # Debug info
        print(f"  Loading depth for {T} frames, {N} points")
        if scale_factors:
            print(f"  Scale factors: {scale_factors}")
            print(f"  save_at_downsample_res: {self.save_at_downsample_res}")
        
        for t in range(T):
            # Map from downsampled frame index to actual video frame
            actual_frame = t * frame_skip_multiplier
            depth_path = camera_depth_dir / f'depth_{actual_frame:06d}.npy'
            
            if depth_path.exists():
                depth_map = np.load(depth_path)
                
                # Downsample depth map if tracks are at downsampled resolution
                if scale_factors is not None and self.save_at_downsample_res:
                    # Tracks are saved at downsampled resolution, need to downsample depth to match
                    h, w = depth_map.shape
                    new_h = int(h * scale_factors[1])
                    new_w = int(w * scale_factors[0])
                    depth_map = cv2.resize(depth_map, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
                    
                    if t == 0:
                        print(f"  Downsampled depth from {h}x{w} to {new_h}x{new_w}")
                
                # Debug: print depth map shape for first frame
                if t == 0:
                    print(f"  Working depth map shape: {depth_map.shape}")
                
                # Extract depth at each track location
                for n in range(N):
                    x, y = tracks[0, t, n]  # Now at same resolution as depth_map
                    
                    # Convert to integer indices
                    x_int = int(np.clip(x, 0, depth_map.shape[1] - 1))
                    y_int = int(np.clip(y, 0, depth_map.shape[0] - 1))
                    
                    # Extract depth value
                    track_depths[0, t, n] = depth_map[y_int, x_int]
            else:
                missing_frames += 1
        
        if missing_frames > 0:
            print(f"  ⚠️  Missing {missing_frames} depth frames")
            if frame_skip_multiplier > 1:
                print(f"     (Note: Looking for every {frame_skip_multiplier} frames due to 60->30fps conversion)")
        
        return track_depths
    
    def process_single_video_with_optional_depth(self, video_path: Path, camera_type: str,
                                               camera_serial: str, depth_dir: Optional[Path],
                                               max_points_per_frame: int = 200) -> Optional[Dict]:
        """Process video using original tracking, add depth if available"""
        
        # Run original tracking
        result = self.process_single_video(video_path, camera_type, max_points_per_frame)
        
        if result is None:
            return None
        
        # Try to add depth information
        if depth_dir and depth_dir.exists():
            print(f"  Adding depth information from: {depth_dir}")
            
            # Get scale factors
            scale_factors = result.get('scale_factors', None)
            
            # Determine frame skip multiplier
            # If we downsampled 60fps to 30fps, we need to skip every other depth frame
            fps = result.get('fps', 30.0)
            effective_fps = result.get('effective_fps', fps)
            frame_skip_multiplier = 2 if (fps >= 59 and effective_fps <= 31) else 1
            
            if frame_skip_multiplier > 1:
                print(f"  Detected 60->30fps downsampling, will load every {frame_skip_multiplier} depth frames")
            
            # Load depth at track locations
            track_depths = self.load_depth_for_tracks(
                depth_dir, camera_serial, result['tracks'], scale_factors, frame_skip_multiplier
            )
            
            if track_depths is not None:
                result['track_depths'] = track_depths
                result['has_depth'] = True
                
                # Also get depth at initial query points
                query_depths = []
                for point in result['query_points']:
                    frame_idx = int(point[0])
                    x, y = point[1], point[2]
                    
                    # Map to actual frame number
                    actual_frame = frame_idx * frame_skip_multiplier
                    depth_path = depth_dir / camera_serial / f'depth_{actual_frame:06d}.npy'
                    if depth_path.exists():
                        depth_map = np.load(depth_path)
                        
                        # Downsample depth map if needed
                        if scale_factors is not None and self.save_at_downsample_res:
                            # Query points are saved at downsampled resolution, need to downsample depth to match
                            h, w = depth_map.shape
                            new_h = int(h * scale_factors[1])
                            new_w = int(w * scale_factors[0])
                            depth_map = cv2.resize(depth_map, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
                        
                        x_int = int(np.clip(x, 0, depth_map.shape[1] - 1))
                        y_int = int(np.clip(y, 0, depth_map.shape[0] - 1))
                        query_depths.append(depth_map[y_int, x_int])
                    else:
                        query_depths.append(0.0)
                
                result['query_depths'] = np.array(query_depths)
            else:
                result['has_depth'] = False
                result['track_depths'] = np.zeros((result['tracks'].shape[0], 
                                                 result['tracks'].shape[1], 
                                                 result['tracks'].shape[2]))
                result['query_depths'] = np.zeros(len(result['query_points']))
        else:
            result['has_depth'] = False
            result['track_depths'] = np.zeros((result['tracks'].shape[0], 
                                             result['tracks'].shape[1], 
                                             result['tracks'].shape[2]))
            result['query_depths'] = np.zeros(len(result['query_points']))
        
        return result


def process_with_optional_depth(data_dir: Path,
                              output_dir: Path = None,
                              depth_dir: Path = None,
                              max_points_per_frame: int = 200,
                              **tracker_kwargs):
    """Process episode using original tracking method, adding depth if available"""
    
    if output_dir is None:
        output_dir = data_dir / 'tracked_results_with_depth'
    output_dir.mkdir(parents=True, exist_ok=True)
    
    if depth_dir is None:
        depth_dir = data_dir / 'depth_maps'
    
    # Check if depth maps exist
    has_depth = depth_dir.exists()
    if has_depth:
        print(f"✓ Depth directory found: {depth_dir}")
    else:
        print(f"⚠️  No depth directory found at {depth_dir}")
        print("  Tracking will proceed without depth information")
    
    # Find metadata file
    metadata_files = list(data_dir.glob('metadata_*.json'))
    if not metadata_files:
        print("No metadata file found!")
        return
    
    metadata_path = metadata_files[0]
    print(f"Using metadata: {metadata_path}")
    
    # Initialize tracker (uses all original parameters)
    tracker = TrackerWithDepth(**tracker_kwargs)
    
    # Load metadata
    metadata = tracker.load_metadata(metadata_path)
    
    # Find MP4 files
    mp4_dir = data_dir / 'recordings' / 'MP4'
    mp4_files = [f for f in mp4_dir.glob('*.mp4') if not f.name.endswith('-stereo.mp4')]
    
    print(f"Found {len(mp4_files)} mono MP4 files")
    print("Using original motion-based tracking method")
    
    # Process each video
    results_by_camera = {}
    
    for mp4_file in mp4_files:
        camera_serial = mp4_file.stem
        camera_type = tracker.identify_camera_type(camera_serial, metadata)
        
        if camera_type == 'unknown':
            print(f"Unknown camera serial: {camera_serial}, skipping")
            continue
        
        try:
            result = tracker.process_single_video_with_optional_depth(
                mp4_file, 
                camera_type,
                camera_serial,
                depth_dir if has_depth else None,
                max_points_per_frame=max_points_per_frame
            )
            
            if result is not None:
                results_by_camera[camera_type] = result
            
            # Force cleanup
            gc.collect()
            clear_gpu_memory()
            
        except Exception as e:
            print(f"Error processing {mp4_file}: {e}")
            import traceback
            traceback.print_exc()
            continue
    
    # Save results
    if results_by_camera:
        timestamp = metadata['timestamp']
        output_path = output_dir / f'tracked_with_depth_{timestamp}.hdf5'
        save_results_with_depth(output_path, results_by_camera, metadata)
        print(f"\nResults saved to {output_path}")
        return output_path
    
    return None


def save_results_with_depth(output_path: Path, results_by_camera: Dict, metadata: Dict):
    """Save tracking results including optional depth information"""
    with h5py.File(output_path, 'w') as f:
        # Metadata
        meta_group = f.create_group('metadata')
        meta_group.attrs['tracker'] = 'TrackerWithDepth'
        meta_group.attrs['tracking_mode'] = 'original_motion_with_optional_depth'
        meta_group.attrs['creation_time'] = datetime.now().isoformat()
        meta_group.attrs['episode_uuid'] = metadata['uuid']
        
        # Results by camera
        views_group = f.create_group('views')
        
        for camera_type, result in results_by_camera.items():
            view_group = views_group.create_group(camera_type)
            
            # Metadata
            view_group.attrs['width'] = result['width']
            view_group.attrs['height'] = result['height']
            view_group.attrs['original_width'] = result['original_width']
            view_group.attrs['original_height'] = result['original_height']
            view_group.attrs['frame_count'] = result['frame_count']
            view_group.attrs['num_points_tracked'] = result['tracks'].shape[2]
            view_group.attrs['camera_type'] = result['camera_type']
            view_group.attrs['video_path'] = result['video_path']
            view_group.attrs['original_fps'] = result['fps']
            view_group.attrs['effective_fps'] = result['effective_fps']
            view_group.attrs['has_depth'] = result['has_depth']
            
            # Data
            compression_opts = {'compression': 'gzip', 'compression_opts': 4}
            
            # Save tracks and visibility
            view_group.create_dataset('tracks', data=result['tracks'], **compression_opts)
            view_group.create_dataset('visibility', data=result['visibility'], **compression_opts)
            view_group.create_dataset('query_points', data=result['query_points'], **compression_opts)
            
            # Save depth information (will be zeros if no depth available)
            view_group.create_dataset('track_depths', data=result['track_depths'], **compression_opts)
            view_group.create_dataset('query_depths', data=result['query_depths'], **compression_opts)
            
            # Also save normalized coordinates for compatibility
            tracks_norm = result['tracks'].copy()
            tracks_norm[:, :, :, 0] = tracks_norm[:, :, :, 0] / result['width']
            tracks_norm[:, :, :, 1] = tracks_norm[:, :, :, 1] / result['height']
            view_group.create_dataset('tracks_normalized', data=tracks_norm, **compression_opts)
            
            print(f"  {camera_type}: {result['tracks'].shape[2]} points tracked "
                  f"({'with' if result['has_depth'] else 'without'} depth)")


def main():
    """Main entry point"""
    import argparse
    
    parser = argparse.ArgumentParser(description='DROID Tracker with Optional Depth')
    parser.add_argument('--data-dir', type=str, required=True,
                       help='Path to raw DROID episode directory')
    parser.add_argument('--output-dir', type=str, default=None,
                       help='Output directory for tracking results')
    parser.add_argument('--depth-dir', type=str, default=None,
                       help='Directory containing depth maps (default: data-dir/depth_maps)')
    
    # All original tracking parameters
    parser.add_argument('--motion-threshold', type=float, default=0.15)
    parser.add_argument('--motion-threshold-exterior', type=float, default=None)
    parser.add_argument('--grid-stride', type=int, default=6)
    parser.add_argument('--target-height', type=int, default=128)
    parser.add_argument('--tracking-batch-size', type=int, default=500)
    parser.add_argument('--max-points-per-frame', type=int, default=200)
    parser.add_argument('--use-online-model', action='store_true')
    parser.add_argument('--frame-skip', type=int, default=0)
    parser.add_argument('--target-points', type=int, default=None)
    parser.add_argument('--noise-scale', type=float, default=5.0)
    parser.add_argument('--disable-downsizing', action='store_true')
    parser.add_argument('--save-at-downsample-res', action='store_true')
    parser.add_argument('--auto-downsample-60fps', action='store_true')
    
    args = parser.parse_args()
    
    # Process episode
    process_with_optional_depth(
        data_dir=Path(args.data_dir),
        output_dir=Path(args.output_dir) if args.output_dir else None,
        depth_dir=Path(args.depth_dir) if args.depth_dir else None,
        motion_threshold=args.motion_threshold,
        motion_threshold_exterior=args.motion_threshold_exterior,
        grid_stride=args.grid_stride,
        tracking_batch_size=args.tracking_batch_size,
        target_height=args.target_height,
        enable_downsizing=not args.disable_downsizing,
        max_points_per_frame=args.max_points_per_frame,
        target_points=args.target_points,
        noise_scale=args.noise_scale,
        use_online_model=args.use_online_model,
        frame_skip=args.frame_skip,
        save_at_downsample_res=args.save_at_downsample_res,
        auto_downsample_60fps=args.auto_downsample_60fps
    )


if __name__ == '__main__':
    main()