bugfixing wip

code/perception/src/vision_node.py

@@ -356,30 +356,26 @@ def predict_ultralytics(self, image):
         c_boxes = []
         c_labels = []
         c_colors = []
-        if hasattr(output[0], "masks") and output[0].masks is not None:
-            masks = output[0].masks.data
-        else:
-            masks = None
 
         boxes = output[0].boxes
+        masks = output[0].masks.data
         for box in boxes:
             cls = box.cls.item()  # class index of object
             pixels = box.xyxy[0]  # upper left and lower right pixel coords
 
             # only run distance calc when dist_array is available
             # this if is needed because the lidar starts
             # publishing with a delay
-            if self.dist_arrays is None:
-                continue
-
-            # crop bounding box area out of depth image
-            distances = np.asarray(
-                self.dist_arrays[
-                    int(pixels[1]) : int(pixels[3]) : 1,
-                    int(pixels[0]) : int(pixels[2]) : 1,
-                    ::,
-                ]
-            )
+            if self.dist_arrays is not None:
+
+                # crop bounding box area out of depth image
+                distances = np.asarray(
+                    self.dist_arrays[
+                        int(pixels[1]) : int(pixels[3]) : 1,
+                        int(pixels[0]) : int(pixels[2]) : 1,
+                        ::,
+                    ]
+                )
 
             # set all 0 (black) values to np.inf (necessary if
             # you want to search for minimum)
@@ -458,18 +454,18 @@ def predict_ultralytics(self, image):
             width=3,
             font_size=12,
         )
-        if masks is not None:
-            scaled_masks = np.squeeze(
-                scale_masks(masks.unsqueeze(1), cv_image.shape[:2], True).cpu().numpy(),
-                1,
-            )
 
-            drawn_images = draw_segmentation_masks(
-                drawn_images,
-                torch.from_numpy(scaled_masks > 0),
-                alpha=0.6,
-                colors=c_colors,
-            )
+        scaled_masks = np.squeeze(
+            scale_masks(masks.unsqueeze(1), cv_image.shape[:2], True).cpu().numpy(),
+            1,
+        )
+
+        drawn_images = draw_segmentation_masks(
+            drawn_images,
+            torch.from_numpy(scaled_masks > 0),
+            alpha=0.6,
+            colors=c_colors,
+        )
 
         np_image = np.transpose(drawn_images.detach().numpy(), (1, 2, 0))
         return cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB)