"""
Visualize all camera parameter combinations with CoTracker.

Tests all combinations of:
- param_type: refined, measured, vggt
- camera_view: exterior, wrist

Generates videos showing tracked EEF for each combination.
"""

import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent))

import os
import numpy as np

# Import torch first (needs GPU)
import torch
import mediapy as media

# Import TensorFlow and configure it for CPU only
import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
import tensorflow_datasets as tfds

import cv2
import datetime
import re
from tqdm import tqdm

from utils.load_camera_calibration import CameraCalibrationLoader
from utils.franka_mesh_projection import FrankaMeshProjector


def load_cotracker():
    """Load CoTracker v3 model."""
    from cotracker.predictor import CoTrackerPredictor
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = CoTrackerPredictor(checkpoint='/mnt/kevin/vlm_models/cotracker/scaled_offline.pth')
    model = model.to(device)
    model.eval()
    return model, device


def find_closest_calibration(episode, uuid_list):
    """Find closest calibration by timestamp."""
    try:
        recording_path = episode['episode_metadata']['recording_folderpath'].numpy().decode('utf-8')
        match = re.search(r'/([A-Z]+)/success/(\d{4}-\d{2}-\d{2})/\w+_\w+_+\d+_(\d{2}):(\d{2}):(\d{2})_\d{4}/', recording_path)
        if not match:
            return None

        lab, date, hour, minute, second = match.groups()
        episode_time = datetime.datetime.strptime(f"{date} {hour}:{minute}:{second}", "%Y-%m-%d %H:%M:%S")

        matching_calibs = [uuid for uuid in uuid_list if uuid.startswith(f"{lab}+") and f"+{date}-" in uuid]
        if len(matching_calibs) == 0:
            return None

        best_uuid = None
        min_time_diff = float('inf')
        for calib_uuid in matching_calibs:
            parts = calib_uuid.split('+')
            if len(parts) >= 3:
                time_str = parts[2].replace('_cameras', '')
                match_time = re.search(r'(\d{2})h-(\d{2})m-(\d{2})s', time_str)
                if match_time:
                    calib_hour = int(match_time.group(1))
                    calib_min = int(match_time.group(2))
                    calib_sec = int(match_time.group(3))
                    calib_time = datetime.datetime.strptime(
                        f"{date} {calib_hour}:{calib_min}:{calib_sec}",
                        "%Y-%m-%d %H:%M:%S"
                    )
                    time_diff = abs((episode_time - calib_time).total_seconds())
                    if time_diff < min_time_diff:
                        min_time_diff = time_diff
                        best_uuid = calib_uuid
        return best_uuid
    except Exception as e:
        return None


def process_combo(episode, uuid, calib_loader, projector, cotracker, device,
                  extrinsics_type='refined', intrinsics_type='measured',
                  camera_view='exterior', max_frames=16):
    """Process one combination and return video frames."""

    # Get calibration manually to mix intrinsics/extrinsics types
    try:
        calib = calib_loader.load_calibration(uuid)
        available_serials = [k for k in calib.keys() if k not in ['uuid', 'scene_path', 'optimization_summary']]

        if camera_view == 'exterior':
            camera_serial = available_serials[0]
        else:
            camera_serial = available_serials[1] if len(available_serials) > 1 else available_serials[0]

        cam_data = calib[camera_serial]

        # Check if types exist
        if f'{extrinsics_type}_extrinsics' not in cam_data:
            return None
        if f'{intrinsics_type}_intrinsics' not in cam_data:
            return None

        K = np.array(cam_data[f'{intrinsics_type}_intrinsics'])
        E = np.array(cam_data[f'{extrinsics_type}_extrinsics'])

    except Exception as e:
        return None

    # Collect frames and cartesian positions
    frames = []
    cart_positions = []

    for step_idx, step in enumerate(episode['steps']):
        if step_idx >= max_frames:
            break

        cart_pos = step['observation']['cartesian_position'].numpy()
        cart_positions.append(cart_pos)

        if camera_view == 'exterior':
            img = step['observation']['exterior_image_1_left'].numpy()
        else:
            img = step['observation']['wrist_image_left'].numpy()

        if img is None or len(img.shape) != 3:
            return None

        frames.append(img)

    if len(frames) < 10:
        return None

    img_h, img_w = frames[0].shape[:2]

    # Project EEF from first frame
    cart_pos_0 = cart_positions[0]
    eef_pos_3d = cart_pos_0[:3].reshape(1, 3)

    eef_2d, eef_vis = projector._project_3d_to_2d(
        eef_pos_3d, K, E, img_h=img_h, img_w=img_w
    )

    if not eef_vis[0]:
        return None

    print(f"  E:{extrinsics_type:8s} I:{intrinsics_type:8s} | {camera_view:10s} | EEF 3D: {eef_pos_3d[0]} -> 2D: {eef_2d[0]}")

    # Prepare video for CoTracker
    video_np = np.array(frames)
    video_np = video_np.transpose(0, 3, 1, 2)  # [T, H, W, 3] -> [T, 3, H, W]
    video_tensor = torch.from_numpy(video_np).float() / 255.0
    video_tensor = video_tensor.unsqueeze(0).to(device)  # [1, T, 3, H, W]

    # Query point: just the EEF
    queries = np.zeros((1, 3))
    queries[0, 0] = 0  # Start from frame 0
    queries[0, 1] = eef_2d[0, 0]  # x
    queries[0, 2] = eef_2d[0, 1]  # y
    queries_tensor = torch.from_numpy(queries).float().unsqueeze(0).to(device)

    # Run CoTracker
    with torch.no_grad():
        pred_tracks, pred_visibility = cotracker(
            video_tensor,
            queries=queries_tensor,
            backward_tracking=False
        )

    tracks = pred_tracks[0].cpu().numpy()  # [T, 1, 2]
    visibility = pred_visibility[0].cpu().numpy()  # [T, 1]

    # Visualize
    video_frames = []
    for frame_idx, frame in enumerate(frames):
        viz = frame.copy()

        # Draw trajectory (past 10 frames)
        if frame_idx > 0:
            for t in range(max(0, frame_idx-10), frame_idx):
                if visibility[t, 0] and visibility[t+1, 0]:
                    pt1 = tuple(tracks[t, 0].astype(int))
                    pt2 = tuple(tracks[t+1, 0].astype(int))
                    cv2.line(viz, pt1, pt2, (0, 255, 0), 2)

        # Draw current point
        if visibility[frame_idx, 0]:
            pt = tuple(tracks[frame_idx, 0].astype(int))
            cv2.circle(viz, pt, 5, (0, 255, 0), -1)
            # Draw initial EEF projection in red
            init_pt = tuple(eef_2d[0].astype(int))
            cv2.circle(viz, init_pt, 3, (0, 0, 255), -1)

        # Add text
        title = f"E:{extrinsics_type} I:{intrinsics_type} | {camera_view} | {frame_idx}/{len(frames)}"
        cv2.putText(viz, title, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
        cv2.putText(viz, f"2D: [{eef_2d[0,0]:.1f}, {eef_2d[0,1]:.1f}]", (10, 45),
                   cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 0), 1)

        video_frames.append(viz)

    return {
        'extrinsics_type': extrinsics_type,
        'intrinsics_type': intrinsics_type,
        'camera_view': camera_view,
        'frames': video_frames,
        'eef_3d': eef_pos_3d[0],
        'eef_2d': eef_2d[0],
        'img_shape': (img_h, img_w)
    }


def main():
    output_dir = Path('/tmp/droid_all_camera_combos')
    output_dir.mkdir(parents=True, exist_ok=True)

    print("=" * 80)
    print("Visualizing all camera parameter combinations")
    print("=" * 80)

    # Initialize
    calib_dir = '/root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main/vision/u/wenlongh/datasets/droid_v4/cameras'
    calib_loader = CameraCalibrationLoader(calib_dir)
    projector = FrankaMeshProjector(use_gui=False)
    cotracker, device = load_cotracker()

    # Get UUID list
    calib_path = Path(calib_dir)
    calib_files = sorted(calib_path.glob("*_cameras.json"))
    uuid_list = [f.stem.replace('_cameras', '') for f in calib_files]
    print(f"Loaded {len(uuid_list)} camera calibrations")

    # Load dataset
    droid_path = '/mnt/kevin/data/droid/droid/1.0.0'
    print("Loading DROID dataset...")
    builder = tfds.builder_from_directory(droid_path)
    dataset = builder.as_dataset(split='train')

    # Find first valid episode
    episode_found = None
    uuid_found = None

    for episode_idx, episode in enumerate(dataset):
        uuid = find_closest_calibration(episode, uuid_list)
        if uuid is None:
            continue
        if not calib_loader.has_refined_extrinsics(uuid):
            continue

        episode_found = episode
        uuid_found = uuid
        print(f"\nUsing episode {episode_idx}, UUID: {uuid}")
        break

    if episode_found is None:
        print("No valid episode found!")
        return

    # Check which parameter types are available
    print("\nChecking available parameter types...")
    calib = calib_loader.load_calibration(uuid_found)
    available_serials = [k for k in calib.keys() if k not in ['uuid', 'scene_path', 'optimization_summary']]

    available_extrinsics = set()
    available_intrinsics = set()
    for serial in available_serials:
        cam_data = calib[serial]
        for param_type in ['refined', 'measured', 'vggt']:
            if f'{param_type}_extrinsics' in cam_data:
                available_extrinsics.add(param_type)
            if f'{param_type}_intrinsics' in cam_data:
                available_intrinsics.add(param_type)

    print(f"Available extrinsics: {sorted(available_extrinsics)}")
    print(f"Available intrinsics: {sorted(available_intrinsics)}")
    print(f"Available camera serials: {available_serials}")

    # Test all combinations
    print("\nProcessing all combinations...")
    print("-" * 80)

    results = []
    for extrinsics_type in ['refined', 'measured', 'vggt']:
        if extrinsics_type not in available_extrinsics:
            continue

        for intrinsics_type in ['refined', 'measured', 'vggt']:
            if intrinsics_type not in available_intrinsics:
                continue

            for camera_view in ['exterior', 'wrist']:
                result = process_combo(
                    episode_found, uuid_found, calib_loader, projector,
                    cotracker, device, extrinsics_type, intrinsics_type,
                    camera_view, max_frames=16
                )
                if result:
                    results.append(result)
                else:
                    print(f"  Failed: E:{extrinsics_type} I:{intrinsics_type} | {camera_view}")

    # Save videos
    print("\nSaving videos...")
    for r in results:
        video_name = f"E_{r['extrinsics_type']}_I_{r['intrinsics_type']}_{r['camera_view']}.mp4"
        video_path = output_dir / video_name
        media.write_video(str(video_path), r['frames'], fps=10)
        print(f"  Saved: {video_path}")

    print("\n" + "=" * 80)
    print(f"Complete! Videos saved to: {output_dir}")
    print("=" * 80)


if __name__ == "__main__":
    main()