Create app.py

app.py

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+import cv2
+import torch
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+torch.hub.download_url_to_file('https://images.unsplash.com/photo-1437622368342-7a3d73a34c8f', 'turtle.jpg')
+torch.hub.download_url_to_file('https://images.unsplash.com/photo-1519066629447-267fffa62d4b', 'lions.jpg')
+
+midas = torch.hub.load("intel-isl/MiDaS", "MiDaS")
+
+use_large_model = True
+
+if use_large_model:
+    midas = torch.hub.load("intel-isl/MiDaS", "MiDaS")
+else:
+    midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
+
+device = "cpu"
+midas.to(device)
+
+midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
+
+if use_large_model:
+    transform = midas_transforms.default_transform
+else:
+    transform = midas_transforms.small_transform
+
+
+def depth(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)
+  return img
+    
+
+inputs =  gr.inputs.Image(type='pil', label="Original Image")
+outputs = gr.outputs.Image(type="pil",label="Output Image")
+
+title = "MiDaS"
+description = "Gradio demo for MiDaS v2.1 which takes in a single image for computing relative depth. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+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>"
+
+examples = [
+    ["turtle.jpg"],
+    ["lions.jpg"]
+]
+
+gr.Interface(depth, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(debug=True)