Update app.py

app.py

@@ -60,20 +60,21 @@ def predict_depth(input_image):
         if depth.ndim != 2:
             depth = depth.squeeze()
 
-        # Normalize depth for visualization
-        depth_min = np.min(depth)
-        depth_max = np.max(depth)
-        depth_normalized = (depth - depth_min) / (depth_max - depth_min)
-        
+        # Calculate inverse depth
+        inverse_depth = 1.0 / depth
+
+        # Clip inverse depth to 0-10 range
+        inverse_depth_clipped = np.clip(inverse_depth, 0, 10)
+
         # Create a color map
         plt.figure(figsize=(10, 10))
-        plt.imshow(depth_normalized, cmap='viridis')
-        plt.colorbar(label='Depth')
-        plt.title('Predicted Depth Map')
+        plt.imshow(inverse_depth_clipped, cmap='viridis')
+        plt.colorbar(label='Inverse Depth')
+        plt.title('Predicted Inverse Depth Map')
         plt.axis('off')
         
         # Save the plot to a file
-        output_path = "depth_map.png"
+        output_path = "inverse_depth_map.png"
         plt.savefig(output_path)
         plt.close()
 
@@ -89,9 +90,9 @@ def predict_depth(input_image):
 iface = gr.Interface(
     fn=predict_depth,
     inputs=gr.Image(type="filepath"),
-    outputs=[gr.Image(type="filepath", label="Depth Map"), gr.Textbox(label="Focal Length or Error Message")],
+    outputs=[gr.Image(type="filepath", label="Inverse Depth Map"), gr.Textbox(label="Focal Length or Error Message")],
     title="DepthPro Demo",
-    description="[DepthPro](https://huggingface.co/apple/DepthPro) is a fast metric depth prediction model. Simply upload an image to predict its depth map and focal length. Large images will be automatically resized."
+    description="[DepthPro](https://huggingface.co/apple/DepthPro) is a fast metric depth prediction model. Simply upload an image to predict its inverse depth map and focal length. Large images will be automatically resized."
 )
 
 # Launch the interface