feat(PerceptionVisualizer): add support of prediction visualization

Signed-off-by: ktro2828 <[email protected]>

perception_eval/perception_eval/visualization/perception_visualization_config.py

@@ -87,12 +87,12 @@ def __init__(
         self.confidence_threshold_list: Optional[List[float]] = confidence_threshold_list
         self.target_uuids: Optional[List[str]] = target_uuids
 
-        if max_x_position_list is None:
-            self.xlim: float = max(max_distance_list)
-            self.ylim: float = max(max_distance_list)
-        elif max_distance_list is None:
+        if max_x_position_list is not None:
             self.xlim: float = max(max_x_position_list)
             self.ylim: float = max(max_y_position_list)
+        elif max_distance_list is not None:
+            self.xlim: float = max(max_distance_list)
+            self.ylim: float = max(max_distance_list)
         else:
             self.xlim: float = 100.0
             self.ylim: float = 100.0

perception_eval/perception_eval/visualization/perception_visualizer.py

@@ -446,13 +446,20 @@ def plot_objects(
             # tracked path
             if self.config.evaluation_task == EvaluationTask.TRACKING:
                 axes, tracking_artists = self._plot_tracked_path(
-                    object_, is_ground_truth, axes=axes
+                    object_,
+                    is_ground_truth,
+                    axes=axes,
                 )
                 artists += tracking_artists
 
             # predicted path
             if self.config.evaluation_task == EvaluationTask.PREDICTION:
-                pass
+                axes, prediction_artists = self._plot_predicted_path(
+                    object_,
+                    is_ground_truth,
+                    axes=axes,
+                )
+                artists += prediction_artists
 
             box_center_x.append(box_center[0])
             box_center_y.append(box_center[1])
@@ -501,22 +508,36 @@ def _plot_tracked_path(
 
     def _plot_predicted_path(
         self,
-        dynamic_objects: List[DynamicObject],
+        dynamic_object: DynamicObject,
         is_ground_truth: bool,
         axes: Optional[Axes] = None,
     ) -> Axes:
         """[summary]
         Plot predicted paths for one object.
 
         Args:
-            dynamic_objects (List[DynamicObject]): The list of object being visualized.
+            dynamic_object (DynamicObject): The list of object being visualized.
             is_ground_truth (bool): Whether ground truth object is.
             axes (Axes): The Axes instance.
 
         Returns:
             axes (Axes): The Axes instance.
+            artists (List[plt.Artist]): The list of Artist instance.
         """
-        pass
+        if axes is None:
+            axes: Axes = plt.subplot()
+
+        artists: List[plt.Artist] = []
+
+        for i, paths in enumerate(dynamic_object.predicted_paths):
+            path_arr: np.ndarray = np.array([p.position for p in paths])
+            color_: np.ndarray = (
+                self.__cmap.get_simple("red") if is_ground_truth else self.__cmap[i] / 255.0
+            )
+            plot_ = axes.plot(path_arr[:, 0], path_arr[:, 1], "o--", color=color_, markersize=1)
+            artists.append(plot_)
+
+        return axes, artists
 
     def _save_animation(self, file_name: Optional[str] = None):
         """[summary]

perception_eval/test/perception_lsim.py

@@ -361,14 +361,9 @@ def visualize(self, frame_result: PerceptionFrameResult):
         f"{format_class_for_log(prediction_final_metric_score.prediction_scores[0], 100)}"
     )
 
-    # # Visualize all frame results
-    # logging.info("Start visualizing prediction results")
-    # prediction_lsim.evaluator.visualize_all()
-
-    # # Prediction performance report
-    # prediction_analyzer = PerceptionPerformanceAnalyzer(prediction_lsim.evaluator.evaluator_config)
-    # prediction_analyzer.add(prediction_lsim.evaluator.frame_results)
-    # score_df, error_df = prediction_analyzer.analyze()
+    # Visualize all frame results
+    logging.info("Start visualizing prediction results")
+    prediction_lsim.evaluator.visualize_all()
 
     # Clean up tmpdir
     if args.use_tmpdir: