app.py

import gradio as gr 


import torch
from utils import get_image_from_url, colorize
from PIL import Image
import matplotlib.pyplot as plt

title = "Interactive demo: ZoeDepth"
description = "Unofficial Gradio Demo for using ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth. To use it, simply upload an image or use one of the examples below and click 'Submit'. Results will show up in a few seconds."

repo = "isl-org/ZoeDepth"
# Zoe_N
model_zoe_n = torch.hub.load(repo, "ZoeD_N", pretrained=True)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
zoe = model_zoe_n.to(DEVICE)

def process_image(image):
    depth_numpy = zoe.infer_pil(image)  # as numpy

    depth_pil = zoe.infer_pil(image, output_type="pil")  # as 16-bit PIL Image

    return depth_pil

interface = gr.Interface(fn=process_image, 
                     inputs=[gr.Image(type="pil")],
                     outputs=[gr.Image(type="pil", label ="Depth")
                              ],
                     title=title,
                     description=description,
                     )
                     
interface.launch(debug=True)