Refactor changes in refactor

node_script/wrapper.py

@@ -17,7 +17,6 @@
 
 from detic_ros.msg import SegmentationInfo
 
-
 _cv_bridge = CvBridge()
 
 
@@ -28,7 +27,7 @@ class InferenceRawResult:
     scores: List[float]
     visualization: Optional[VisImage]
     header: Header
-    class_names: List[str]
+    detected_class_names: List[str]
 
     def get_ros_segmentaion_image(self) -> Image:
         seg_img = _cv_bridge.cv2_to_imgmsg(self.segmentation_raw_image, encoding="32SC1")
@@ -47,12 +46,13 @@ def get_ros_debug_segmentation_img(self) -> Image:
         human_friendly_scaling = 255 // self.segmentation_raw_image.max()
         new_data = (self.segmentation_raw_image * human_friendly_scaling).astype(np.uint8)
         debug_seg_img = _cv_bridge.cv2_to_imgmsg(new_data, encoding="mono8")
-        assert self.header is not None
         debug_seg_img.header = self.header
         return debug_seg_img
 
     def get_label_array(self) -> LabelArray:
-        labels = [Label(id=i + 1, name=self.class_names[i]) for i in self.class_indices]
+        labels = [Label(id=i + 1, name=name)
+                  for i, name
+                  in zip(self.class_indices, self.detected_class_names)]
         lab_arr = LabelArray(header=self.header, labels=labels)
         return lab_arr
 
@@ -62,8 +62,7 @@ def get_score_array(self) -> VectorArray:
 
     def get_segmentation_info(self) -> SegmentationInfo:
         seg_img = self.get_ros_segmentaion_image()
-        detected_classes_names = [self.class_names[i] for i in self.class_indices]
-        seg_info = SegmentationInfo(detected_classes=detected_classes_names,
+        seg_info = SegmentationInfo(detected_classes=self.detected_class_names,
                                     scores=self.scores,
                                     segmentation=seg_img,
                                     header=self.header)
@@ -131,13 +130,14 @@ def infer(self, msg: Image) -> InferenceRawResult:
             data[mask] = (i + 1)
 
         # Get class and score arrays
-        class_indexes = instances.pred_classes.tolist()
+        class_indices = instances.pred_classes.tolist()
+        detected_classes_names = [self.class_names[i] for i in class_indices]
         scores = instances.scores.tolist()
         result = InferenceRawResult(
             data,
-            class_indexes,
+            class_indices,
             scores,
             visualized_output,
             msg.header,
-            self.class_names)
+            detected_classes_names)
         return result