delete ros_critical_ground_truth_objects (#180)

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

README.md

@@ -92,7 +92,6 @@ for frame in datasets:
         unix_time=unix_time,
         ground_truth_now_frame=frame,
         estimated_objects=estimated_objects,
-        ros_critical_ground_truth_objects=frame.objects,
         critical_object_filter_config=critical_object_filter_config,
         frame_pass_fail_config=pass_fail_config,
     )

perception_eval/perception_eval/manager/perception_evaluation_manager.py

@@ -90,15 +90,13 @@ def add_frame_result(
 
         TODO:
         - Arrange `CriticalObjectFilterConfig` and `PerceptionPassFailConfig` to `PerceptionFrameConfig`.
-        - Allow input `PerceptionFrameConfig` and `ros_critical_ground_truth_objects` are None.
+        - Allow input `PerceptionFrameConfig` is None.
 
         Args:
             unix_time (int): Unix timestamp [us].
             ground_truth_now_frame (FrameGroundTruth): FrameGroundTruth instance that has the closest
                 timestamp with `unix_time`.
             estimated_objects (List[ObjectType]): Estimated objects list.
-            ros_critical_ground_truth_objects (List[ObjectType]): Critical ground truth objects filtered by ROS
-                node to evaluate pass fail result.
             critical_object_filter_config (CriticalObjectFilterConfig): Parameter config to filter objects.
             frame_pass_fail_config (PerceptionPassFailConfig):Parameter config to evaluate pass/fail.
 

perception_eval/test/perception_fp_validation_lsim.py

@@ -62,10 +62,6 @@ def __init__(self, dataset_paths: List[int], result_root_directory: str) -> None
     def callback(self, unix_time: int, estimated_objects: List[ObjectType]) -> None:
         ground_truth_now_frame = self.evaluator.get_ground_truth_now_frame(unix_time)
 
-        # Ideally, critical GT should be obtained in each frame.
-        # In this mock, set it as a copy of `ground_truth_now_frame`.
-        ros_critical_ground_truth_objects = ground_truth_now_frame.objects
-
         critical_object_filter_config = CriticalObjectFilterConfig(
             evaluator_config=self.evaluator.evaluator_config,
             target_labels=["car", "bicycle", "pedestrian", "motorbike"],
@@ -83,7 +79,6 @@ def callback(self, unix_time: int, estimated_objects: List[ObjectType]) -> None:
             unix_time=unix_time,
             ground_truth_now_frame=ground_truth_now_frame,
             estimated_objects=estimated_objects,
-            ros_critical_ground_truth_objects=ros_critical_ground_truth_objects,
             critical_object_filter_config=critical_object_filter_config,
             frame_pass_fail_config=frame_pass_fail_config,
         )

perception_eval/test/perception_lsim.py

@@ -101,12 +101,6 @@ def callback(
             unix_time, interpolate_ground_truth=interpolate
         )
 
-        # [Option] ROS側でやる（Map情報・Planning結果を用いる）UC評価objectを選別
-        # ros_critical_ground_truth_objects : List[DynamicObject] = custom_critical_object_filter(
-        #   ground_truth_now_frame.objects
-        # )
-        ros_critical_ground_truth_objects = ground_truth_now_frame.objects
-
         # 1 frameの評価
         # 距離などでUC評価objectを選別するためのインターフェイス（PerceptionEvaluationManager初期化時にConfigを設定せず、関数受け渡しにすることで動的に変更可能なInterface）
         # どれを注目物体とするかのparam

perception_eval/test/perception_lsim2d.py

@@ -119,11 +119,6 @@ def callback(
         else:
             raise ValueError(f"Unexpected label prefix: {self.label_prefix}")
 
-        # [Option] ROS側でやる（Map情報・Planning結果を用いる）UC評価objectを選別
-        # ros_critical_ground_truth_objects : List[DynamicObject] = custom_critical_object_filter(
-        #   ground_truth_now_frame.objects
-        # )
-
         matching_threshold_list = None if self.evaluation_task == "classification2d" else [0.5, 0.5, 0.5, 0.5]
         # 距離などでUC評価objectを選別するためのインターフェイス（PerceptionEvaluationManager初期化時にConfigを設定せず、関数受け渡しにすることで動的に変更可能なInterface）
         # どれを注目物体とするかのparam