Update app.py

app.py

@@ -146,22 +146,28 @@ def get_camera_matrix(depth_estimator):
 @spaces.GPU
 def get_detection_data(image):
     """Get structured detection data with depth information"""
+    width, height = 640, 480  # Set default values to avoid UnboundLocalError
+
     try:
         # Resize image to standard size
         image = utils.resize(image)
-        
+
+        # Ensure width and height are properly set
+        if hasattr(image, "shape"):
+            height, width = image.shape[:2]  # Extract actual dimensions
+
         # Get detections and depth
         image_segmentation, objects_data = img_seg.predict(image)
         depthmap, depth_colormap = depth_estimator.make_prediction(image)
-        
+
         # Process each detection
         detections = []
         for data in objects_data:
             cls_id, cls_name, cls_center, cls_mask, cls_clr = data
-            
+
             # Get masked depth for this object
             masked_depth, mean_depth = utils.get_masked_depth(depthmap, cls_mask)
-            
+
             # Get bounding box from mask
             y_indices, x_indices = np.where(cls_mask > 0)
             if len(x_indices) > 0 and len(y_indices) > 0:
@@ -169,64 +175,61 @@ def get_detection_data(image):
                 y1, y2 = np.min(y_indices), np.max(y_indices)
             else:
                 continue
-                
+
             # Normalize coordinates
-            height, width = image.shape[:2]
             bbox_normalized = [
-                float(x1/width),
-                float(y1/height),
-                float(x2/width),
-                float(y2/height)
+                float(x1 / width),
+                float(y1 / height),
+                float(x2 / width),
+                float(y2 / height),
             ]
-            
+
             detection = {
                 "id": int(cls_id),
                 "category": cls_name,
                 "center": [
-                    float(cls_center[0]/width),
-                    float(cls_center[1]/height)
+                    float(cls_center[0] / width),
+                    float(cls_center[1] / height),
                 ],
                 "bbox": bbox_normalized,
                 "depth": float(mean_depth * 10),  # Convert to meters as done in utils
-                "color": [float(c/255) for c in cls_clr],
+                "color": [float(c / 255) for c in cls_clr],
                 "mask": cls_mask.tolist(),
-                "confidence": 1.0  # Add actual confidence if available
+                "confidence": 1.0,  # Add actual confidence if available
             }
             detections.append(detection)
-        
-        # Get camera parameters from depth estimator
-        camera_params = {
-            "fx": depth_estimator.fx_depth,
-            "fy": depth_estimator.fy_depth,
-            "cx": depth_estimator.cx_depth,
-            "cy": depth_estimator.cy_depth
-        }
-        
+
+        # Get camera parameters from depth estimator (check if attributes exist)
+        try:
+            camera_params = {
+                "fx": getattr(depth_estimator, "fx_depth", 0),
+                "fy": getattr(depth_estimator, "fy_depth", 0),
+                "cx": getattr(depth_estimator, "cx_depth", width // 2),
+                "cy": getattr(depth_estimator, "cy_depth", height // 2),
+            }
+        except AttributeError:
+            print("⚠️ Camera parameters are not properly set in depth_estimator.")
+            camera_params = {"fx": 0, "fy": 0, "cx": width // 2, "cy": height // 2}
+
         # Generate point cloud data if needed
         point_clouds = utils.generate_obj_pcd(depthmap, objects_data)
         pcd_data = [
-            {
-                "points": np.asarray(pcd.points).tolist(),
-                "color": [float(c/255) for c in color]
-            }
+            {"points": np.asarray(pcd.points).tolist(), "color": [float(c / 255) for c in color]}
             for pcd, color in point_clouds
         ]
-        
+
         return {
             "detections": detections,
             "depth_map": depthmap.tolist(),
             "camera_params": camera_params,
-            "image_size": {
-                "width": width,
-                "height": height
-            },
-            "point_clouds": pcd_data
+            "image_size": {"width": width, "height": height},
+            "point_clouds": pcd_data,
         }
-        
+
     except Exception as e:
-        print(f"Error in get_detection_data: {str(e)}")
-        raise
-    # ENDS
+        print(f"🚨 Error in get_detection_data: {str(e)}")
+        return {"error": str(e)}
+
 
 def cancel():
     CANCEL_PROCESSING = True