feat: rename thresholds into success_thresholds

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

perception_eval/perception_eval/result/perception/perception_frame_config.py

@@ -19,6 +19,7 @@
 from typing import TYPE_CHECKING
 from typing import Union
 
+from perception_eval.common.evaluation_task import EvaluationTask
 from perception_eval.common.label import set_target_lists
 from perception_eval.common.threshold import check_thresholds
 from perception_eval.config.params import PerceptionFilterParam
@@ -62,7 +63,7 @@ def __init__(
         confidence_threshold_list: Optional[List[float]] = None,
         target_uuids: Optional[List[str]] = None,
         ignore_attributes: Optional[List[str]] = None,
-        thresholds: Optional[List[float]] = None,
+        success_thresholds: Optional[List[float]] = None,
     ) -> None:
         """
         Args:
@@ -102,13 +103,22 @@ def __init__(
         )
 
         num_elements: int = len(self.target_labels)
-        if thresholds is None:
-            self.thresholds = None
+        if success_thresholds is None:
+            self.success_thresholds = None
         else:
-            self.thresholds = check_thresholds(thresholds, num_elements)
+            self.success_thresholds = check_thresholds(success_thresholds, num_elements)
 
     @classmethod
     def from_eval_cfg(cls, eval_cfg: PerceptionEvaluationConfig) -> PerceptionFrameConfig:
+        if eval_cfg.evaluation_task.is_3d():
+            success_thresholds = eval_cfg.metrics_param.plane_distance_thresholds
+        else:
+            success_thresholds = (
+                None
+                if eval_cfg.evaluation_task == EvaluationTask.CLASSIFICATION2D
+                else eval_cfg.metrics_param.iou_2d_thresholds
+            )
+
         return cls(
             evaluator_config=eval_cfg,
             target_labels=eval_cfg.target_labels,
@@ -120,5 +130,5 @@ def from_eval_cfg(cls, eval_cfg: PerceptionEvaluationConfig) -> PerceptionFrameC
             confidence_threshold_list=eval_cfg.filter_param.confidence_threshold_list,
             target_uuids=eval_cfg.filter_param.target_uuids,
             ignore_attributes=eval_cfg.filter_param.ignore_attributes,
-            thresholds=eval_cfg.metrics_param.plane_distance_thresholds,
+            success_thresholds=success_thresholds,
         )

perception_eval/perception_eval/result/perception/perception_pass_fail_result.py

@@ -96,7 +96,7 @@ def evaluate(
             object_results,
             self.frame_config.target_labels,
             MatchingMode.IOU2D if self.frame_config.evaluation_task.is_2d() else MatchingMode.PLANEDISTANCE,
-            self.frame_config.thresholds,
+            self.frame_config.success_thresholds,
         )
 
         self.critical_ground_truth_objects = critical_ground_truth_objects
@@ -151,7 +151,7 @@ def __get_positive_object_results(
             matching_mode=MatchingMode.IOU2D
             if self.frame_config.evaluation_task.is_2d()
             else MatchingMode.PLANEDISTANCE,
-            matching_threshold_list=self.frame_config.thresholds,
+            matching_threshold_list=self.frame_config.success_thresholds,
         )
 
         # filter by critical_ground_truth_objects

perception_eval/test/perception_fp_validation_lsim.py

@@ -75,7 +75,7 @@ def callback(self, unix_time: int, estimated_objects: List[ObjectType]) -> None:
             target_labels=["car", "bicycle", "pedestrian", "motorbike"],
             max_x_position_list=[100.0, 100.0, 100.0, 100.0],
             max_y_position_list=[100.0, 100.0, 100.0, 100.0],
-            thresholds=[2.0, 2.0, 2.0, 2.0],
+            success_thresholds=[2.0, 2.0, 2.0, 2.0],
         )
 
         frame_result = self.evaluator.add_frame_result(

perception_eval/test/perception_lsim.py

@@ -116,7 +116,7 @@ def callback(
             ignore_attributes=["cycle_state.without_rider"],
             max_x_position_list=[30.0, 30.0, 30.0, 30.0],
             max_y_position_list=[30.0, 30.0, 30.0, 30.0],
-            thresholds=[2.0, 2.0, 2.0, 2.0],
+            success_thresholds=[2.0, 2.0, 2.0, 2.0],
         )
 
         frame_result = self.evaluator.add_frame_result(

perception_eval/test/perception_lsim2d.py

@@ -115,14 +115,14 @@ def callback(
             else ["car", "bicycle", "pedestrian", "motorbike"]
         )
         ignore_attributes = ["cycle_state.without_rider"] if self.label_prefix == "autoware" else None
-        thresholds = None if self.evaluation_task == "classification2d" else [0.5, 0.5, 0.5, 0.5]
+        success_thresholds = 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
         frame_config = PerceptionFrameConfig(
             evaluator_config=self.evaluator.config,
             target_labels=target_labels,
             ignore_attributes=ignore_attributes,
-            thresholds=thresholds,
+            success_thresholds=success_thresholds,
         )
 
         frame_result = self.evaluator.add_frame_result(