app.py

from transformers import GLPNFeatureExtractor, GLPNForDepthEstimation
from transformers import AutoFeatureExtractor, AutoModelForDepthEstimation
import torch
import numpy as np
from PIL import Image
import requests
import gradio as gr
import os

# url = "http://images.cocodataset.org/val2017/000000039769.jpg"
# image = Image.open(requests.get(url, stream=True).raw)

# feature_extractor = GLPNFeatureExtractor.from_pretrained("vinvino02/glpn-nyu")
# model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu")

feature_extractor = AutoFeatureExtractor.from_pretrained("Intel/dpt-large")
model = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-large")

example_list = [["examples/" + example] for example in os.listdir("examples")]

def predict(image):
    inputs = feature_extractor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        predicted_depth = outputs.predicted_depth
        # interpolate to original size
    prediction = torch.nn.functional.interpolate(
        predicted_depth.unsqueeze(1),
        size=image.size[::-1],
        mode="bicubic",
        align_corners=False,
    )
    # visualize the prediction
    output = prediction.squeeze().cpu().numpy()
    formatted = (output * 255 / np.max(output)).astype("uint8")
    depth_image = Image.fromarray(formatted)
    return depth_image


# Gradio App
title="Depth Estimation GAN"
description="This converts a Normal Image to a Depth Image"

demo=gr.Interface(fn=predict,
                  inputs=gr.Image(type='pil'),
                  outputs=gr.Image(type='pil'),
                  title=title ,
                  examples=example_list,
                  description=description)
demo.launch(debug=False)