"""
Visualize 7 gripper mesh points on both exterior and wrist views.

The 7 points are offsets from action position (gripper base), transformed by gripper rotation:
1. [0.0, 0.0, 0.0] - gripper base (action position)
2. [0.0, 0.045, 0.161] - finger 1 tip
3. [0.0, -0.045, 0.161] - finger 2 tip
4. [0.0, 0.045, 0.13] - finger 1 end
5. [0.0, -0.045, 0.13] - finger 2 end
6. [0.0, 0.0, 0.13] - gripper center front
7. [0.0, 0.0, 0.065] - gripper center middle
"""

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

import os
import numpy as np
import torch
import mediapy as media
import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
import tensorflow_datasets as tfds
import cv2
import datetime
import re
from scipy.spatial.transform import Rotation as R

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


# 7 gripper offsets in gripper frame (before rotation)
GRIPPER_OFFSETS = np.array([
    [0.0, 0.0, 0.0],      # 0: gripper base (action position)
    [0.0, 0.045, 0.161],  # 1: finger 1 tip
    [0.0, -0.045, 0.161], # 2: finger 2 tip
    [0.0, 0.045, 0.13],   # 3: finger 1 end
    [0.0, -0.045, 0.13],  # 4: finger 2 end
    [0.0, 0.0, 0.13],     # 5: gripper center front
    [0.0, 0.0, 0.065],    # 6: gripper center middle
])

POINT_COLORS = [
    (255, 255, 0),   # 0: cyan - base
    (0, 0, 255),     # 1: red - finger 1 tip
    (0, 0, 255),     # 2: red - finger 2 tip
    (0, 255, 255),   # 3: yellow - finger 1 end
    (0, 255, 255),   # 4: yellow - finger 2 end
    (0, 255, 0),     # 5: green - center front
    (255, 0, 255),   # 6: magenta - center middle
]


def euler_xyz_to_rotation_matrix(euler_xyz):
    """Convert Euler XYZ angles to rotation matrix."""
    return R.from_euler('xyz', euler_xyz).as_matrix()


def transform_gripper_offsets(action):
    """
    Transform gripper offsets using action position and rotation.

    Args:
        action: [x, y, z, rx, ry, rz, gripper] - Euler XYZ rotation

    Returns:
        gripper_points_3d: [7, 3] array of 3D points in world frame
    """
    pos = action[:3]
    rot_euler = action[3:6]

    # Get rotation matrix from Euler XYZ
    rot_matrix = euler_xyz_to_rotation_matrix(rot_euler)

    # Transform offsets: R @ offset + pos
    gripper_points_3d = (rot_matrix @ GRIPPER_OFFSETS.T).T + pos

    return gripper_points_3d


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:
        return None


def process_dual_view_episode(episode, episode_idx, uuid, calib_loader, projector, max_frames=16):
    """
    Process episode with both exterior and wrist views.

    Returns:
        dict with frames and projections for both views, or None if failed
    """
    # Get calibration for both views
    try:
        dual_params = calib_loader.get_dual_view_params(uuid, param_type='refined', require_refined=False)
        if dual_params is None:
            return None
        if not calib_loader.has_refined_extrinsics(uuid):
            return None
    except:
        return None

    K_ext, E_ext = dual_params['exterior_1']
    K_wrist, E_wrist = dual_params['wrist']

    # Collect frames from both views
    frames_ext = []
    frames_wrist = []
    actions = []

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

        img_ext = step['observation']['exterior_image_1_left'].numpy()
        img_wrist = step['observation']['wrist_image_left'].numpy()

        if img_ext is None or len(img_ext.shape) != 3:
            continue
        if img_wrist is None or len(img_wrist.shape) != 3:
            continue

        frames_ext.append(img_ext)
        frames_wrist.append(img_wrist)
        actions.append(step['action'].numpy())

    if len(frames_ext) < 10:
        return None

    img_h_ext, img_w_ext = frames_ext[0].shape[:2]
    img_h_wrist, img_w_wrist = frames_wrist[0].shape[:2]

    # Compute 7 gripper points for all frames
    all_gripper_3d = []
    all_gripper_2d_ext = []
    all_gripper_vis_ext = []
    all_gripper_2d_wrist = []
    all_gripper_vis_wrist = []

    for action in actions:
        # Transform gripper offsets using action
        gripper_3d = transform_gripper_offsets(action)

        # Project to exterior camera
        gripper_2d_ext, gripper_vis_ext = projector._project_3d_to_2d(
            gripper_3d, K_ext, E_ext, img_h=img_h_ext, img_w=img_w_ext
        )

        # Project to wrist camera (NEED TO INVERT EXTRINSICS)
        E_wrist_inv = np.linalg.inv(E_wrist)
        gripper_2d_wrist, gripper_vis_wrist = projector._project_3d_to_2d(
            gripper_3d, K_wrist, E_wrist_inv, img_h=img_h_wrist, img_w=img_w_wrist
        )

        all_gripper_3d.append(gripper_3d)
        all_gripper_2d_ext.append(gripper_2d_ext)
        all_gripper_vis_ext.append(gripper_vis_ext)
        all_gripper_2d_wrist.append(gripper_2d_wrist)
        all_gripper_vis_wrist.append(gripper_vis_wrist)

    all_gripper_3d = np.array(all_gripper_3d)  # [T, 7, 3]
    all_gripper_2d_ext = np.array(all_gripper_2d_ext)  # [T, 7, 2]
    all_gripper_vis_ext = np.array(all_gripper_vis_ext)  # [T, 7]
    all_gripper_2d_wrist = np.array(all_gripper_2d_wrist)  # [T, 7, 2]
    all_gripper_vis_wrist = np.array(all_gripper_vis_wrist)  # [T, 7]

    return {
        'frames_ext': frames_ext,
        'frames_wrist': frames_wrist,
        'gripper_3d': all_gripper_3d,
        'gripper_2d_ext': all_gripper_2d_ext,
        'gripper_vis_ext': all_gripper_vis_ext,
        'gripper_2d_wrist': all_gripper_2d_wrist,
        'gripper_vis_wrist': all_gripper_vis_wrist,
        'actions': np.array(actions),
    }


def create_dual_view_video(result, output_path):
    """Create side-by-side video with exterior (left) and wrist (right) views."""

    frames_ext = result['frames_ext']
    frames_wrist = result['frames_wrist']
    gripper_2d_ext = result['gripper_2d_ext']
    gripper_vis_ext = result['gripper_vis_ext']
    gripper_2d_wrist = result['gripper_2d_wrist']
    gripper_vis_wrist = result['gripper_vis_wrist']

    video_frames = []

    for frame_idx in range(len(frames_ext)):
        # Visualize exterior view
        viz_ext = frames_ext[frame_idx].copy()

        # Draw 7 gripper points
        for pt_idx in range(7):
            if gripper_vis_ext[frame_idx, pt_idx]:
                pt = tuple(gripper_2d_ext[frame_idx, pt_idx].astype(int))
                color = POINT_COLORS[pt_idx]
                cv2.circle(viz_ext, pt, 4, color, -1)
                cv2.putText(viz_ext, str(pt_idx), (pt[0]+6, pt[1]-6),
                           cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)

        # Add title
        cv2.putText(viz_ext, "EXTERIOR VIEW", (10, 20),
                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)

        # Visualize wrist view
        viz_wrist = frames_wrist[frame_idx].copy()

        # Draw 7 gripper points
        for pt_idx in range(7):
            if gripper_vis_wrist[frame_idx, pt_idx]:
                pt = tuple(gripper_2d_wrist[frame_idx, pt_idx].astype(int))
                color = POINT_COLORS[pt_idx]
                cv2.circle(viz_wrist, pt, 4, color, -1)
                cv2.putText(viz_wrist, str(pt_idx), (pt[0]+6, pt[1]-6),
                           cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)

        # Add title
        cv2.putText(viz_wrist, "WRIST VIEW", (10, 20),
                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)

        # Concatenate side by side
        combined = np.concatenate([viz_ext, viz_wrist], axis=1)

        # Add frame counter and legend
        cv2.putText(combined, f"Frame {frame_idx}/{len(frames_ext)}",
                   (combined.shape[1]//2 - 50, 20),
                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 2)

        legend_y = combined.shape[0] - 110
        cv2.putText(combined, "0:Base 1-2:FingerTips 3-4:FingerEnds 5:Front 6:Mid",
                   (10, legend_y), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1)

        video_frames.append(combined)

    # Save video
    media.write_video(str(output_path), video_frames, fps=10)


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

    print("=" * 80)
    print("Visualizing 7 Gripper Points on Dual Views (Exterior + Wrist)")
    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)

    calib_path = Path(calib_dir)
    uuid_list = [f.stem.replace('_cameras', '') for f in sorted(calib_path.glob("*_cameras.json"))]

    # 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')

    # Process episodes
    num_videos = 3
    created_count = 0

    for episode_idx, episode in enumerate(dataset):
        if created_count >= num_videos:
            break

        uuid = find_closest_calibration(episode, uuid_list)
        if uuid is None:
            continue

        if not calib_loader.has_refined_extrinsics(uuid):
            continue

        print(f"\nProcessing episode {episode_idx}...")

        result = process_dual_view_episode(
            episode, episode_idx, uuid, calib_loader, projector, max_frames=16
        )

        if result is None:
            print(f"  Skipped - processing failed")
            continue

        # Create video
        output_path = output_dir / f"dual_view_episode_{episode_idx:04d}.mp4"
        create_dual_view_video(result, output_path)

        # Print statistics
        vis_ext = result['gripper_vis_ext'][0]
        vis_wrist = result['gripper_vis_wrist'][0]
        print(f"  Exterior view - visible points: {vis_ext.sum()}/7")
        print(f"  Wrist view - visible points: {vis_wrist.sum()}/7")
        print(f"  ✓ Saved: {output_path}")

        created_count += 1

    print("\n" + "=" * 80)
    print(f"Created {created_count} dual-view videos")
    print(f"Output directory: {output_dir}")
    print("=" * 80)


if __name__ == "__main__":
    main()