Depth-Anything-V2/run.py at main · qqii/Depth-Anything-V2 · GitHub

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import argparse
import cv2
import glob
import matplotlib
import numpy as np
import os
import torch
from huggingface_hub import hf_hub_download

from depth_anything_v2.dpt import DepthAnythingV2


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Depth Anything V2")

    parser.add_argument("--img-path", type=str)
    parser.add_argument("--input-size", type=int, default=518)
    parser.add_argument("--outdir", type=str, default="./vis_depth")

    parser.add_argument(
        "--encoder", type=str, default="vitl", choices=["vits", "vitb", "vitl", "vitg"]
    )

    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()

    DEVICE = (
        "cuda"
        if torch.cuda.is_available()
        else "mps"
        if torch.backends.mps.is_available()
        else "cpu"
    )
    print(f"Using device: {DEVICE}")

    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],
        },
    }
    checkpoints = {
        "vits": {
            "repo_id": "depth-anything/Depth-Anything-V2-Small",
            "filename": "depth_anything_v2_vits.pth",
        },
        "vitb": {
            "repo_id": "depth-anything/Depth-Anything-V2-Base",
            "filename": "depth_anything_v2_vitb.pth",
        },
        "vitl": {
            "repo_id": "depth-anything/Depth-Anything-V2-Large",
            "filename": "depth_anything_v2_vitl.pth",
        },
    }

    depth_anything = DepthAnythingV2(**model_configs[args.encoder])
    depth_anything.load_state_dict(
        torch.load(
            hf_hub_download(**checkpoints[args.encoder]),
            map_location="cpu",
        )
    )
    depth_anything = depth_anything.to(DEVICE).eval()

    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 = glob.glob(os.path.join(args.img_path, "**/*"), recursive=True)

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

    cmap = matplotlib.colormaps.get_cmap("Spectral_r")

    for k, filename in enumerate(filenames):
        print(f"Progress {k + 1}/{len(filenames)}: {filename}")

        raw_image = cv2.imread(filename)

        depth = depth_anything.infer_image(raw_image, args.input_size)

        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
        depth = depth.astype(np.uint8)

        if args.grayscale:
            depth = np.repeat(depth[..., np.newaxis], 3, axis=-1)
        else:
            depth = (cmap(depth)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8)

        if args.pred_only:
            cv2.imwrite(
                os.path.join(
                    args.outdir,
                    os.path.splitext(os.path.basename(filename))[0] + ".png",
                ),
                depth,
            )
        else:
            split_region = np.ones((raw_image.shape[0], 50, 3), dtype=np.uint8) * 255
            combined_result = cv2.hconcat([raw_image, split_region, depth])

            cv2.imwrite(
                os.path.join(
                    args.outdir,
                    os.path.splitext(os.path.basename(filename))[0] + ".png",
                ),
                combined_result,
            )