"""
Test the Y-axis scaling fix by comparing old vs new scaling.
"""

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

import numpy as np
import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
import tensorflow_datasets as tfds
import datetime
import re

from utils.load_camera_calibration import CameraCalibrationLoader


def find_closest_calibration(episode, uuid_list):
    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_time = datetime.datetime.strptime(
                        f"{date} {match_time.group(1)}:{match_time.group(2)}:{match_time.group(3)}",
                        "%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 project_manual(point_3d, K, E, img_h, img_w, original_h):
    """Manual projection with specified original height."""
    # Transform to camera frame
    point_3d_hom = np.append(point_3d, 1.0)
    point_cam = (E @ point_3d_hom)[:3]

    # Project to image plane
    point_2d_hom = K @ point_cam
    point_2d = point_2d_hom[:2] / point_2d_hom[2]

    # Scale to target resolution
    original_w = 640
    scale_x = img_w / original_w
    scale_y = img_h / original_h
    point_2d[0] *= scale_x
    point_2d[1] *= scale_y

    return point_2d, point_cam[2] > 0


def main():
    print("=" * 80)
    print("Testing Y-axis scaling fix")
    print("=" * 80)

    # Load data
    calib_dir = '/root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main/vision/u/wenlongh/datasets/droid_v4/cameras'
    calib_loader = CameraCalibrationLoader(calib_dir)
    calib_path = Path(calib_dir)
    uuid_list = [f.stem.replace('_cameras', '') for f in sorted(calib_path.glob("*_cameras.json"))]

    droid_path = '/mnt/kevin/data/droid/droid/1.0.0'
    builder = tfds.builder_from_directory(droid_path)
    dataset = builder.as_dataset(split='train')

    # Find episode
    for episode_idx, episode in enumerate(dataset):
        uuid = find_closest_calibration(episode, uuid_list)
        if uuid and calib_loader.has_refined_extrinsics(uuid):
            break

    print(f"\nUsing episode {episode_idx}, UUID: {uuid}")

    # Get calibration
    calib = calib_loader.load_calibration(uuid)
    serials = [k for k in calib.keys() if k not in ['uuid', 'scene_path', 'optimization_summary']]
    K = np.array(calib[serials[0]]['measured_intrinsics'])
    E = np.array(calib[serials[0]]['refined_extrinsics'])

    # Get first frame
    step0 = next(iter(episode['steps']))
    img = step0['observation']['exterior_image_1_left'].numpy()
    img_h, img_w = img.shape[:2]
    action = step0['action'].numpy()
    point_3d = action[:3]

    print(f"\nImage size: {img_w}x{img_h}")
    print(f"Test point (action xyz): {point_3d}")

    # Test OLD scaling (640x480)
    proj_2d_old, vis_old = project_manual(point_3d, K, E, img_h, img_w, original_h=480)
    print(f"\nOLD scaling (640x480):")
    print(f"  scale_y = {img_h} / 480 = {img_h/480:.4f}")
    print(f"  Projected 2D: [{proj_2d_old[0]:.2f}, {proj_2d_old[1]:.2f}]")

    # Test NEW scaling (640x360)
    proj_2d_new, vis_new = project_manual(point_3d, K, E, img_h, img_w, original_h=360)
    print(f"\nNEW scaling (640x360):")
    print(f"  scale_y = {img_h} / 360 = {img_h/360:.4f}")
    print(f"  Projected 2D: [{proj_2d_new[0]:.2f}, {proj_2d_new[1]:.2f}]")

    # Show difference
    diff = proj_2d_new - proj_2d_old
    print(f"\nDifference (NEW - OLD):")
    print(f"  ΔX: {diff[0]:+.2f} pixels")
    print(f"  ΔY: {diff[1]:+.2f} pixels")
    print(f"\nY scaling ratio: {(img_h/360) / (img_h/480):.4f} = 1.333x")
    print(f"Expected ΔY: {proj_2d_old[1] * (1.333 - 1):.2f} pixels")

    # Now test with the actual library function
    print("\n" + "=" * 80)
    print("Testing with FrankaMeshProjector:")
    print("=" * 80)

    from utils.franka_mesh_projection import FrankaMeshProjector
    projector = FrankaMeshProjector(use_gui=False)

    proj_2d_lib, vis_lib = projector._project_3d_to_2d(
        point_3d.reshape(1, 3), K, E, img_h=img_h, img_w=img_w
    )

    print(f"Library projection: [{proj_2d_lib[0,0]:.2f}, {proj_2d_lib[0,1]:.2f}]")
    print(f"NEW manual:         [{proj_2d_new[0]:.2f}, {proj_2d_new[1]:.2f}]")
    print(f"Match: {np.allclose(proj_2d_lib[0], proj_2d_new, atol=0.1)}")

    if np.allclose(proj_2d_lib[0], proj_2d_new, atol=0.1):
        print("\n✓ Fix is ACTIVE - library uses 640x360 scaling")
    elif np.allclose(proj_2d_lib[0], proj_2d_old, atol=0.1):
        print("\n✗ Fix is NOT ACTIVE - library still uses 640x480 scaling")
    else:
        print("\n⚠ Neither matches - something else is going on")


if __name__ == "__main__":
    main()