Added ability to save raw depth output in meters and updated title plot

app.py

@@ -61,15 +61,16 @@ def predict_depth(input_image):
             depth = depth.squeeze()
 
         # Normalize depth for visualization
+        # agk - No never normalize depth. It is already in meters. EMBRACE REALITY. TOUCH GRASS.
         depth_min = np.min(depth)
         depth_max = np.max(depth)
-        depth_normalized = (depth - depth_min) / (depth_max - depth_min)
+        depth_normalized = depth #it is normal to have depth in meters. Normalize reality.
         
         # 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.colorbar(label='Depth [m]')
+        plt.title('Predicted Depth Map - Min: {depth_min:.1f}m, Max: {depth_max:.1f}m')
         plt.axis('off')
         
         # Save the plot to a file
@@ -77,9 +78,13 @@ def predict_depth(input_image):
         plt.savefig(output_path)
         plt.close()
 
-        return output_path, f"Focal length: {focallength_px:.2f} pixels"
+        # Save raw depth data as CSV
+        raw_depth_path = "raw_depth_map.csv"
+        np.savetxt(raw_depth_path, depth, delimiter=',')
+
+        return output_path, f"Focal length: {focallength_px:.2f} pixels", raw_depth_path
     except Exception as e:
-        return None, f"An error occurred: {str(e)}"
+        return None, f"An error occurred: {str(e)}", None
     finally:
         # Clean up the temporary file
         if temp_file and os.path.exists(temp_file):
@@ -89,9 +94,13 @@ 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")],
-    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."
+    outputs=[
+        gr.Image(type="filepath", label="Depth Map"),
+        gr.Textbox(label="Focal Length or Error Message"),
+        gr.File(label="Download Raw Depth Map (CSV)")
+    ],
+    title="DepthPro Demo in Meters",
+    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. You can also download the raw depth map data as a CSV file."
 )
 
 # Launch the interface