app.py

import gradio as gr
import torch
import copy

from gradio_depth_pred import create_demo as create_depth_pred_demo

from EcoDepth.utils import download_model

download_model("weights_indoor.ckpt")
download_model("weights_outdoor.ckpt")

css = """
#img-display-container {
    max-height: 50vh;
}

/* Center the INPUT image */
#img-display-input {
    display: flex !important;
    align-items: center !important;
    justify-content: center !important;
}

#img-display-input img {
    max-height: 40vh !important;
    width: auto !important;
    object-fit: contain !important;
}

/* Center the OUTPUT image */
#img-display-output {
    display: flex !important;
    align-items: center !important;
    justify-content: center !important;
}

#img-display-output img {
    max-height: 40vh !important;
    width: auto !important;
    object-fit: contain !important;
}
"""

import json
from EcoDepth.model import EcoDepth

class Args:
    def __init__(self):
        with open("infer_config.json", "r") as f:
            config = json.load(f) 
        for n, v in config.items():
           setattr(self, n, v)

# base arguments
args = Args()

args_indoor = copy.deepcopy(args)
args_indoor.no_of_classes = 100
args_indoor.max_depth = 10
model_indoor = EcoDepth(args_indoor).eval()
model_str_indoor = f"{args.ckpt_path}/weights_indoor.ckpt"
model_indoor.load_state_dict(torch.load(model_str_indoor, map_location="cpu", weights_only=True)["state_dict"])

args_outdoor = copy.deepcopy(args)
args_outdoor.no_of_classes = 200
args_outdoor.max_depth = 80
model_outdoor = EcoDepth(args_outdoor).eval()
model_str_outdoor = f"{args.ckpt_path}/weights_outdoor.ckpt"
model_outdoor.load_state_dict(torch.load(model_str_outdoor, map_location="cpu", weights_only=True)["state_dict"])

title = "# ECoDepth"
description = """Official demo for **ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation**.

EcoDepth is a deep learning model for metric depth estimation from a single image.

Please refer to our [paper](https://arxiv.org/abs/2403.18807) or [github](https://github.com/Aradhye2002/EcoDepth) for more details."""

with gr.Blocks(css=css) as demo:
    gr.Markdown(title)
    gr.Markdown(description)
    with gr.Tab("Indoor Depth Prediction (v1)"):
        create_depth_pred_demo(model_indoor, scene="indoor")
    with gr.Tab("Outdoor Depth Prediction (v1)"):
        create_depth_pred_demo(model_outdoor, scene="outdoor")

    gr.HTML('''<br><br><br><center>You can duplicate this Space to skip the queue:<a href="https://huggingface.co/spaces/aradhye/EcoDepth?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a><br></center>''')

if __name__ == '__main__':
    demo.queue().launch()