import os import gradio as gr import cv2 import torch import urllib.request from PIL import Image import matplotlib.pyplot as plt def update(slider, img): if not os.path.exists('temp'): os.system('mkdir temp') filename = "temp/image.jpg" img.save(filename, "JPEG") model_type = "DPT_Hybrid" midas = torch.hub.load("intel-isl/MiDaS", model_type) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") midas.to(device) midas.eval() midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms") if model_type == "DPT_Large" or model_type == "DPT_Hybrid": transform = midas_transforms.dpt_transform else: transform = midas_transforms.small_transform img = cv2.imread(filename) img = cv2.cvtColor(img, 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() out_im = Image.fromarray(output) out_im.save("temp/image_depth.jpeg", "JPEG") return f'temp/image_depth.jpeg' with gr.Blocks() as demo: gr.Markdown("Start typing below and then click **Run** to see the output.") inp = [gr.Slider(1,15, default = 2, label='StepCycles',step= 1)] with gr.Row(): inp.append(gr.Image(type="pil", label="Input")) out = gr.Image(type="file", label="Output") btn = gr.Button("Run") btn.click(fn=update, inputs=inp, outputs=out) demo.launch()