Create app.py

app.py

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+import gradio as gr
+from transformers import DPTFeatureExtractor, DPTForDepthEstimation
+import torch
+import numpy as np
+import cv2
+
+torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
+
+feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
+model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
+
+def write_depth(depth, bits):
+  depth_min = depth.min()
+  depth_max = depth.max()
+
+  max_val = (2 ** (8 * bits)) - 1
+
+  if depth_max - depth_min > np.finfo("float").eps:
+      out = max_val * (depth - depth_min) / (depth_max - depth_min)
+  else:
+      out = np.zeros(depth.shape, dtype=depth.dtype)
+
+  cv2.imwrite("result.png", out.astype("uint16"), [cv2.IMWRITE_PNG_COMPRESSION, 0])
+  
+  return
+
+def process_image(image):
+    # prepare image for the model
+    encoding = feature_extractor(image, return_tensors="pt")
+    
+    # forward pass
+    with torch.no_grad():
+       outputs = model(**encoding)
+    
+    predicted_depth = outputs.predicted_depth
+    
+    # interpolate to original size
+    predicted_depth = torch.nn.functional.interpolate(
+                        predicted_depth.unsqueeze(1),
+                        size=image.size[::-1],
+                        mode="bicubic",
+                        align_corners=False,
+                 )
+    prediction = prediction.squeeze().cpu().numpy()
+    
+    # write predicted depth to file
+    write_depth(prediction, bits=2)
+    
+    result = Image.open("result.png")
+    
+    return result
+    
+title = "Interactive demo: DPT"
+description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of-the-art dense prediction tasks such as semantic segmentation and depth estimation."
+examples =[['cats.jpg']]
+
+css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
+#css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
+# css = ".output_image, .input_image {height: 600px !important}"
+
+iface = gr.Interface(fn=process_image, 
+                     inputs=gr.inputs.Image(type="pil"), 
+                     outputs=gr.outputs.Image(type="pil", label="predicted depth"),
+                     title=title,
+                     description=description,
+                     examples=examples,
+                     css=css,
+                     enable_queue=True)
+iface.launch(debug=True)