"""
Visualize depth predictions from different decoders on KITTI and DDAD datasets
"""
import os
import sys
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')

# Paths
DA2_REPO = '/home/ywan0794/Depth-Anything-V2'
DA3_REPO = '/home/ywan0794/Depth-Anything-3'

# Checkpoints
CHECKPOINTS = {
    'da2_dpt': '/home/ywan0794/Depth-Anything-V2/training/exp/dpt_vitb_both/epoch_007.pth',
    'da2_sdt': '/home/ywan0794/Depth-Anything-V2/training/exp/sdt_vitb_both/epoch_008.pth',
    'da3_dpt': '/home/ywan0794/Depth-Anything-3/training/exp/da3_dpt_vitl_both/epoch_010.pth',
    'da3_sdt': '/home/ywan0794/Depth-Anything-3/training/exp/da3_sdt_vitl_both/epoch_010.pth',
    'da3_dualdpt': '/home/ywan0794/Depth-Anything-3/training/exp/da3_dualdpt_vitl_both/epoch_010.pth',
}

# Dataset paths
KITTI_BASE = '/home/ywan0794/datasets/eval/moge_style_eval/KITTI'
DDAD_BASE = '/home/ywan0794/datasets/eval/moge_style_eval/DDAD/val'


# ============================================
# DA2 Model Loading (same as da2_custom.py)
# ============================================
def load_da2_model(checkpoint_path, encoder='vitb', decoder='dpt'):
    """Load DA2 model with DPT or SDT decoder"""
    repo_path = DA2_REPO
    training_path = os.path.join(repo_path, 'training')

    if repo_path not in sys.path:
        sys.path.insert(0, repo_path)
    if training_path not in sys.path:
        sys.path.insert(0, training_path)

    # Model configurations (same as training)
    model_configs = {
        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
    }

    # Build model based on decoder type
    if decoder == 'dpt':
        from depth_anything_v2.dpt import DepthAnythingV2
        model = DepthAnythingV2(**model_configs[encoder])
    elif decoder == 'sdt':
        from depth_anything_v2.sdt import DepthAnythingV2SDT
        model = DepthAnythingV2SDT(
            encoder=encoder,
            features=model_configs[encoder]['features'],
            out_channels=model_configs[encoder]['out_channels'],
            use_clstoken=True,
            upsampler='dysample'
        )
    else:
        raise ValueError(f"Unknown decoder: {decoder}")

    # Load checkpoint
    ckpt = torch.load(checkpoint_path, map_location='cpu')
    if 'model' in ckpt:
        state_dict = ckpt['model']
    else:
        state_dict = ckpt
    state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
    missing, unexpected = model.load_state_dict(state_dict, strict=False)
    print(f"Loaded DA2 {decoder} from {checkpoint_path}")
    if missing:
        print(f"  Missing keys: {len(missing)}")
    if unexpected:
        print(f"  Unexpected keys: {len(unexpected)}")

    return model


# ============================================
# DA3 Model Loading (same as da3_custom.py)
# ============================================
class DA3Wrapper(torch.nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    def forward(self, x):
        # x: [B, 3, H, W]
        x = x.unsqueeze(1)  # [B, 1, 3, H, W]
        output = self.model(x)
        depth = output.depth.squeeze(1)  # [B, H, W]
        return depth


def load_da3_model(checkpoint_path, decoder='dpt'):
    """Load DA3 model with DPT, SDT, or DualDPT decoder"""
    repo_path = DA3_REPO
    src_path = os.path.join(repo_path, 'src')
    training_path = os.path.join(repo_path, 'training')

    if src_path not in sys.path:
        sys.path.insert(0, src_path)
    if training_path not in sys.path:
        sys.path.insert(0, training_path)

    # Config paths
    config_dir = os.path.join(repo_path, 'src', 'depth_anything_3', 'configs')
    if decoder == 'dpt':
        config_path = os.path.join(config_dir, 'da3dpt-large.yaml')
    elif decoder == 'sdt':
        config_path = os.path.join(config_dir, 'da3sdt-large.yaml')
    elif decoder == 'dualdpt':
        config_path = os.path.join(config_dir, 'da3dualdpt-large.yaml')
    else:
        raise ValueError(f"Unknown decoder: {decoder}")

    from depth_anything_3.cfg import load_config, create_object

    # Build model
    cfg = load_config(config_path)
    base_model = create_object(cfg)
    model = DA3Wrapper(base_model)

    # Load checkpoint
    ckpt = torch.load(checkpoint_path, map_location='cpu')
    if 'model' in ckpt:
        state_dict = ckpt['model']
    else:
        state_dict = ckpt
    state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
    missing, unexpected = model.load_state_dict(state_dict, strict=False)
    print(f"Loaded DA3 {decoder} from {checkpoint_path}")
    if missing:
        print(f"  Missing keys: {len(missing)}")
    if unexpected:
        print(f"  Unexpected keys: {len(unexpected)}")

    return model


# ============================================
# Inference Wrapper
# ============================================
class ModelWrapper:
    def __init__(self, model, device, use_amp=True):
        self.model = model.to(device).eval()
        self.device = device
        self.use_amp = use_amp

    @torch.inference_mode()
    def predict(self, image):
        """image: PIL Image, returns disparity numpy array"""
        # Convert to tensor
        img = TF.to_tensor(image).unsqueeze(0)  # [1, 3, H, W]
        original_height, original_width = img.shape[-2:]

        # Resize to multiple of 14
        resize_factor = 518 / min(original_height, original_width)
        expected_width = round(original_width * resize_factor / 14) * 14
        expected_height = round(original_height * resize_factor / 14) * 14

        img = TF.resize(img, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
        img = TF.normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        img = img.to(self.device)

        # Forward
        if self.use_amp:
            with torch.cuda.amp.autocast(dtype=torch.bfloat16):
                disp = self.model(img)
        else:
            disp = self.model(img)

        # Resize back
        disp = F.interpolate(disp[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False)[:, 0]
        disp = disp.squeeze().cpu().numpy()

        return disp


def colorize_depth(depth, cmap='Spectral', reverse=False, mask_invalid=False):
    """Convert depth/disparity to colorized image using Spectral colormap"""
    depth = depth.copy()

    # Create mask for invalid (zero) regions
    if mask_invalid:
        invalid_mask = depth <= 0

    # Only use valid values for percentile calculation
    if mask_invalid:
        valid_depth = depth[~invalid_mask]
        if len(valid_depth) > 0:
            vmin = np.percentile(valid_depth, 2)
            vmax = np.percentile(valid_depth, 98)
        else:
            vmin, vmax = 0, 1
    else:
        vmin = np.percentile(depth, 2)
        vmax = np.percentile(depth, 98)

    depth = (depth - vmin) / (vmax - vmin + 1e-8)
    depth = np.clip(depth, 0, 1)

    # Reverse if needed
    if reverse:
        depth = 1 - depth

    cm = plt.get_cmap(cmap)
    colored = cm(depth)[:, :, :3]
    colored = (colored * 255).astype(np.uint8)

    # Set invalid regions to black
    if mask_invalid:
        colored[invalid_mask] = 0

    return colored


def load_gt_depth(depth_path):
    """Load ground truth depth from PNG"""
    depth = np.array(Image.open(depth_path))
    if depth.dtype == np.uint16:
        depth = depth.astype(np.float32) / 256.0
    elif depth.dtype == np.uint8:
        depth = depth.astype(np.float32)
    return depth


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--output-dir', type=str, default='/home/ywan0794/MoGe/vis_output')
    parser.add_argument('--num-samples', type=int, default=10)
    parser.add_argument('--device', type=str, default='cuda')
    args = parser.parse_args()

    device = torch.device(args.device)

    # Create output directories
    datasets = ['KITTI', 'DDAD']
    subfolders = ['rgb', 'gt', 'gt_reverse', 'da2_dpt', 'da2_sdt', 'da3_dpt', 'da3_sdt', 'da3_dualdpt']

    for dataset in datasets:
        for subfolder in subfolders:
            os.makedirs(os.path.join(args.output_dir, dataset, subfolder), exist_ok=True)

    print("Loading models...")
    models = {}

    # Load DA2 models
    print("  Loading DA2-DPT...")
    da2_dpt = load_da2_model(CHECKPOINTS['da2_dpt'], encoder='vitb', decoder='dpt')
    models['da2_dpt'] = ModelWrapper(da2_dpt, device, use_amp=False)

    print("  Loading DA2-SDT...")
    da2_sdt = load_da2_model(CHECKPOINTS['da2_sdt'], encoder='vitb', decoder='sdt')
    models['da2_sdt'] = ModelWrapper(da2_sdt, device, use_amp=False)

    # Load DA3 models
    print("  Loading DA3-DPT...")
    da3_dpt = load_da3_model(CHECKPOINTS['da3_dpt'], decoder='dpt')
    models['da3_dpt'] = ModelWrapper(da3_dpt, device, use_amp=True)

    print("  Loading DA3-SDT...")
    da3_sdt = load_da3_model(CHECKPOINTS['da3_sdt'], decoder='sdt')
    models['da3_sdt'] = ModelWrapper(da3_sdt, device, use_amp=True)

    print("  Loading DA3-DualDPT...")
    da3_dualdpt = load_da3_model(CHECKPOINTS['da3_dualdpt'], decoder='dualdpt')
    models['da3_dualdpt'] = ModelWrapper(da3_dualdpt, device, use_amp=True)

    print("All models loaded!")

    # Get KITTI samples
    kitti_samples = []
    for drive in os.listdir(KITTI_BASE):
        drive_path = os.path.join(KITTI_BASE, drive, 'image_02')
        if os.path.isdir(drive_path):
            for frame in sorted(os.listdir(drive_path)):
                sample_dir = os.path.join(drive_path, frame)
                img_path = os.path.join(sample_dir, 'image.jpg')
                gt_path = os.path.join(sample_dir, 'depth.png')
                if os.path.exists(img_path) and os.path.exists(gt_path):
                    kitti_samples.append({
                        'image': img_path,
                        'gt': gt_path,
                        'name': f"{drive}_{frame}"
                    })

    # Get DDAD samples
    ddad_samples = []
    for scene in sorted(os.listdir(DDAD_BASE)):
        scene_path = os.path.join(DDAD_BASE, scene)
        if os.path.isdir(scene_path):
            for cam in sorted(os.listdir(scene_path)):
                sample_dir = os.path.join(scene_path, cam)
                img_path = os.path.join(sample_dir, 'image.jpg')
                gt_path = os.path.join(sample_dir, 'depth.png')
                if os.path.exists(img_path) and os.path.exists(gt_path):
                    ddad_samples.append({
                        'image': img_path,
                        'gt': gt_path,
                        'name': f"{scene}_{cam}"
                    })

    # Select random samples
    np.random.seed(42)
    kitti_selected = np.random.choice(len(kitti_samples), min(args.num_samples, len(kitti_samples)), replace=False)
    ddad_selected = np.random.choice(len(ddad_samples), min(args.num_samples, len(ddad_samples)), replace=False)

    # Process KITTI
    print(f"\nProcessing {len(kitti_selected)} KITTI samples...")
    for idx, i in enumerate(kitti_selected):
        sample = kitti_samples[i]
        print(f"  [{idx+1}/{len(kitti_selected)}] {sample['name']}")

        # Load image
        image = Image.open(sample['image']).convert('RGB')

        # Save RGB
        image.save(os.path.join(args.output_dir, 'KITTI', 'rgb', f"{idx:03d}.png"))

        # Load and save GT (both versions)
        gt_depth = load_gt_depth(sample['gt'])
        gt_colored = colorize_depth(gt_depth, reverse=False, mask_invalid=True)
        gt_colored_rev = colorize_depth(gt_depth, reverse=True, mask_invalid=True)
        Image.fromarray(gt_colored).save(os.path.join(args.output_dir, 'KITTI', 'gt', f"{idx:03d}.png"))
        Image.fromarray(gt_colored_rev).save(os.path.join(args.output_dir, 'KITTI', 'gt_reverse', f"{idx:03d}.png"))

        # Predict and save for each model
        for model_name, wrapper in models.items():
            pred = wrapper.predict(image)
            # DA3 DPT needs reverse
            need_reverse = (model_name == 'da3_dpt')
            pred_colored = colorize_depth(pred, reverse=need_reverse)
            Image.fromarray(pred_colored).save(
                os.path.join(args.output_dir, 'KITTI', model_name, f"{idx:03d}.png")
            )

    # Process DDAD
    print(f"\nProcessing {len(ddad_selected)} DDAD samples...")
    for idx, i in enumerate(ddad_selected):
        sample = ddad_samples[i]
        print(f"  [{idx+1}/{len(ddad_selected)}] {sample['name']}")

        # Load image
        image = Image.open(sample['image']).convert('RGB')

        # Save RGB
        image.save(os.path.join(args.output_dir, 'DDAD', 'rgb', f"{idx:03d}.png"))

        # Load and save GT (both versions)
        gt_depth = load_gt_depth(sample['gt'])
        gt_colored = colorize_depth(gt_depth, reverse=False, mask_invalid=True)
        gt_colored_rev = colorize_depth(gt_depth, reverse=True, mask_invalid=True)
        Image.fromarray(gt_colored).save(os.path.join(args.output_dir, 'DDAD', 'gt', f"{idx:03d}.png"))
        Image.fromarray(gt_colored_rev).save(os.path.join(args.output_dir, 'DDAD', 'gt_reverse', f"{idx:03d}.png"))

        # Predict and save for each model
        for model_name, wrapper in models.items():
            pred = wrapper.predict(image)
            # DA3 DPT needs reverse
            need_reverse = (model_name == 'da3_dpt')
            pred_colored = colorize_depth(pred, reverse=need_reverse)
            Image.fromarray(pred_colored).save(
                os.path.join(args.output_dir, 'DDAD', model_name, f"{idx:03d}.png")
            )

    print(f"\nDone! Results saved to {args.output_dir}")
    print(f"Structure:")
    print(f"  {args.output_dir}/")
    print(f"    KITTI/")
    print(f"      rgb/, gt/, gt_reverse/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/")
    print(f"    DDAD/")
    print(f"      rgb/, gt/, gt_reverse/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/")


if __name__ == '__main__':
    main()