app.py

import cv2
import torch
import gradio as gr
import numpy as np
from PIL import Image
import time

midas = torch.hub.load("intel-isl/MiDaS", "DPT_Large")

device = "cpu"
midas.to(device)

midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
transform = midas_transforms.dpt_transform

def depth(img):
  original_image = img
  cv_image = np.array(img) 
  img = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)

  input_batch = transform(img).to(device)
  with torch.no_grad():
    prediction = midas(input_batch)

    prediction = torch.nn.functional.interpolate(
        prediction.unsqueeze(1),
        size=img.shape[:2],
        mode="bicubic",
        align_corners=False,
    ).squeeze()
    
  output = prediction.cpu().numpy()
  formatted = (output * 255 / np.max(output)).astype('uint8')
  img = Image.fromarray(formatted)

  # create new image with with original_image and img side by side
  new_im = Image.new('RGB', (original_image.width * 2, original_image.height))
  new_im.paste(original_image, (0,0))
  new_im.paste(img, (original_image.width,0))

  # save the image to a file: (removed for hosting on HF)
  #new_im.save(f'RGBDs/{int(time.time())}_RGBD.png')


  return new_im
    

inputs =  gr.inputs.Image(type='pil', label="Original Image")
outputs = gr.outputs.Image(type="pil",label="Output Image")

title = "RGB to RGBD for Looking Glass (using MiDaS)"
description = "Takes an RGB image and creates the depth + combines to the RGB image. Depth is predicted by MiDaS. This is a demo of the Looking Glass. For more information, visit https://lookingglassfactory.com"
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.01341v3'>Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer</a> | <a href='https://github.com/intel-isl/MiDaS'>Github Repo</a></p>"


gr.Interface(depth, inputs, outputs, title=title, description=description, article=article).launch()