import argparse
import cv2
import numpy as np
import os
import torch
import torch.nn.functional as F
from torchvision.transforms import Compose
from tqdm import tqdm

from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--img-path", type=str)
    parser.add_argument("--outdir", type=str, default="./vis_depth")
    parser.add_argument(
        "--encoder", type=str, default="vitl", choices=["vits", "vitb", "vitl"]
    )

    parser.add_argument(
        "--pred-only",
        dest="pred_only",
        action="store_true",
        help="only display the prediction",
    )
    parser.add_argument(
        "--grayscale",
        dest="grayscale",
        action="store_true",
        help="do not apply colorful palette",
    )

    args = parser.parse_args()

    margin_width = 50
    caption_height = 60

    font = cv2.FONT_HERSHEY_SIMPLEX
    font_scale = 1
    font_thickness = 2

    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

    depth_anything = (
        DepthAnything.from_pretrained(
            "LiheYoung/depth_anything_{}14".format(args.encoder)
        )
        .to(DEVICE)
        .eval()
    )

    total_params = sum(param.numel() for param in depth_anything.parameters())
    print("Total parameters: {:.2f}M".format(total_params / 1e6))

    transform = Compose(
        [
            Resize(
                width=518,
                height=518,
                resize_target=False,
                keep_aspect_ratio=True,
                ensure_multiple_of=14,
                resize_method="lower_bound",
                image_interpolation_method=cv2.INTER_CUBIC,
            ),
            NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
            PrepareForNet(),
        ]
    )

    if os.path.isfile(args.img_path):
        if args.img_path.endswith("txt"):
            with open(args.img_path, "r") as f:
                filenames = f.read().splitlines()
        else:
            filenames = [args.img_path]
    else:
        filenames = os.listdir(args.img_path)
        filenames = [
            os.path.join(args.img_path, filename)
            for filename in filenames
            if not filename.startswith(".")
        ]
        filenames.sort()

    os.makedirs(args.outdir, exist_ok=True)

    for filename in tqdm(filenames):
        raw_image = cv2.imread(filename)
        image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0

        h, w = image.shape[:2]

        image = transform({"image": image})["image"]
        image = torch.from_numpy(image).unsqueeze(0).to(DEVICE)

        with torch.no_grad():
            depth = depth_anything(image)

        depth = F.interpolate(
            depth[None], (h, w), mode="bilinear", align_corners=False
        )[0, 0]
        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0

        depth = depth.cpu().numpy().astype(np.uint8)

        if args.grayscale:
            depth = np.repeat(depth[..., np.newaxis], 3, axis=-1)
        else:
            depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)

        filename = os.path.basename(filename)

        if args.pred_only:
            cv2.imwrite(
                os.path.join(
                    args.outdir, filename[: filename.rfind(".")] + "_depth.png"
                ),
                depth,
            )
        else:
            split_region = (
                np.ones((raw_image.shape[0], margin_width, 3), dtype=np.uint8) * 255
            )
            combined_results = cv2.hconcat([raw_image, split_region, depth])

            caption_space = (
                np.ones((caption_height, combined_results.shape[1], 3), dtype=np.uint8)
                * 255
            )
            captions = ["Raw image", "Depth Anything"]
            segment_width = w + margin_width

            for i, caption in enumerate(captions):
                # Calculate text size
                text_size = cv2.getTextSize(caption, font, font_scale, font_thickness)[
                    0
                ]

                # Calculate x-coordinate to center the text
                text_x = int((segment_width * i) + (w - text_size[0]) / 2)

                # Add text caption
                cv2.putText(
                    caption_space,
                    caption,
                    (text_x, 40),
                    font,
                    font_scale,
                    (0, 0, 0),
                    font_thickness,
                )

            final_result = cv2.vconcat([caption_space, combined_results])

            cv2.imwrite(
                os.path.join(
                    args.outdir, filename[: filename.rfind(".")] + "_img_depth.png"
                ),
                final_result,
            )
