run_monodepth.py

"""Compute depth maps for images in the input folder.
"""
import os
import glob
import torch
import cv2
import argparse

import util.io

from torchvision.transforms import Compose

from dpt.models import DPTDepthModel
from dpt.midas_net import MidasNet_large
from dpt.transforms import Resize, NormalizeImage, PrepareForNet

from util.misc import visualize_attention


def run(input_path, output_path, model_path, model_type="dpt_hybrid", optimize=True):
    """Run MonoDepthNN to compute depth maps.

    Args:
        input_path (str): path to input folder
        output_path (str): path to output folder
        model_path (str): path to saved model
    """
    print("initialize")

    # select device
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print("device: %s" % device)

    net_w = net_h = 384

    # load network
    if model_type == "dpt_large":  # DPT-Large
        model = DPTDepthModel(
            path=model_path,
            backbone="vitl16_384",
            non_negative=True,
            enable_attention_hooks=args.vis,
        )
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    elif model_type == "dpt_hybrid":  # DPT-Hybrid
        model = DPTDepthModel(
            path=model_path,
            backbone="vitb_rn50_384",
            non_negative=True,
            enable_attention_hooks=args.vis,
        )
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    elif model_type == "midas_v21":  # Convolutional model
        model = MidasNet_large(model_path, non_negative=True)
        normalization = NormalizeImage(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
        )
    else:
        assert (
            False
        ), f"model_type '{model_type}' not implemented, use: --model_type [dpt_large|dpt_hybrid|midas_v21]"

    transform = Compose(
        [
            Resize(
                net_w,
                net_h,
                resize_target=None,
                keep_aspect_ratio=True,
                ensure_multiple_of=32,
                resize_method="minimal",
                image_interpolation_method=cv2.INTER_CUBIC,
            ),
            normalization,
            PrepareForNet(),
        ]
    )

    model.eval()

    if optimize == True and device == torch.device("cuda"):
        model = model.to(memory_format=torch.channels_last)
        model = model.half()

    model.to(device)

    # get input
    img_names = glob.glob(os.path.join(input_path, "*"))
    num_images = len(img_names)

    # create output folder
    os.makedirs(output_path, exist_ok=True)

    print("start processing")
    for ind, img_name in enumerate(img_names):

        print("  processing {} ({}/{})".format(img_name, ind + 1, num_images))
        # input

        img = util.io.read_image(img_name)
        img_input = transform({"image": img})["image"]

        # compute
        with torch.no_grad():
            sample = torch.from_numpy(img_input).to(device).unsqueeze(0)

            if optimize == True and device == torch.device("cuda"):
                sample = sample.to(memory_format=torch.channels_last)
                sample = sample.half()

            prediction = model.forward(sample)
            prediction = (
                torch.nn.functional.interpolate(
                    prediction.unsqueeze(1),
                    size=img.shape[:2],
                    mode="bicubic",
                    align_corners=False,
                )
                .squeeze()
                .cpu()
                .numpy()
            )

            if args.vis:
                visualize_attention(sample, model, prediction, args.model_type)
        #                exit()

        filename = os.path.join(
            output_path, os.path.splitext(os.path.basename(img_name))[0]
        )
        util.io.write_depth(filename, prediction, bits=2)

    print("finished")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "-i", "--input_path", default="input", help="folder with input images"
    )

    parser.add_argument(
        "-o",
        "--output_path",
        default="output_monodepth",
        help="folder for output images",
    )

    parser.add_argument(
        "-m", "--model_weights", default=None, help="path to model weights"
    )

    parser.add_argument("--show-attention", dest="vis", action="store_true")

    parser.add_argument(
        "-t",
        "--model_type",
        default="dpt_hybrid",
        help="model type [dpt_large|dpt_hybrid|midas_v21]",
    )

    parser.add_argument("--optimize", dest="optimize", action="store_true")
    parser.add_argument("--no-optimize", dest="optimize", action="store_false")
    parser.set_defaults(optimize=True)

    args = parser.parse_args()

    default_models = {
        "midas_v21": "weights/midas_v21-f6b98070.pt",
        "dpt_large": "weights/dpt_large-midas-2f21e586.pt",
        "dpt_hybrid": "weights/dpt_hybrid-midas-501f0c75.pt",
    }

    if args.model_weights is None:
        args.model_weights = default_models[args.model_type]

    # set torch options
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = True

    # compute depth maps
    run(
        args.input_path,
        args.output_path,
        args.model_weights,
        args.model_type,
        args.optimize,
    )