chore: update from_scenario

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

perception_eval/perception_eval/tool/perception_analyzer2d.py

@@ -90,22 +90,20 @@ def from_scenario(
             scenario_obj: Optional[Dict[str, Any]] = yaml.safe_load(scenario_file)
 
         p_cfg: Dict[str, Any] = scenario_obj["Evaluation"]["PerceptionEvaluationConfig"]
-        eval_cfg_dict: Dict[str, Any] = p_cfg["evaluation_config_dict"]
+        config_dict: Dict[str, Any] = p_cfg["evaluation_config_dict"]
 
-        eval_cfg_dict["label_prefix"] = (
-            "traffic_light" if eval_cfg_dict["UseCaseName"] == "traffic_light" else "autoware"
-        )
+        config_dict["label_prefix"] = "traffic_light" if config_dict["UseCaseName"] == "traffic_light" else "autoware"
         camera_types: Dict[str, int] = scenario_obj["Evaluation"]["Conditions"]["TargetCameras"]
 
-        evaluation_config: PerceptionEvaluationConfig = PerceptionEvaluationConfig(
+        config = PerceptionEvaluationConfig(
             dataset_paths=[""],  # dummy path
             frame_id=list(camera_types.keys()),
             result_root_directory=result_root_directory,
-            evaluation_config_dict=eval_cfg_dict,
+            config_dict=config_dict,
             load_raw_data=False,
         )
 
-        return cls(evaluation_config)
+        return cls(config)
 
     @property
     def columns(self) -> List[str]:

perception_eval/perception_eval/tool/perception_analyzer3d.py

@@ -121,19 +121,19 @@ def from_scenario(
             scenario_obj: Optional[Dict[str, Any]] = yaml.safe_load(scenario_file)
 
         p_cfg: Dict[str, Any] = scenario_obj["Evaluation"]["PerceptionEvaluationConfig"]
-        eval_cfg_dict: Dict[str, Any] = p_cfg["evaluation_config_dict"]
+        config_dict: Dict[str, Any] = p_cfg["evaluation_config_dict"]
 
-        eval_cfg_dict["label_prefix"] = "autoware"
+        config_dict["label_prefix"] = "autoware"
 
-        evaluation_config: PerceptionEvaluationConfig = PerceptionEvaluationConfig(
+        config = PerceptionEvaluationConfig(
             dataset_paths=[""],  # dummy path
-            frame_id="base_link" if eval_cfg_dict["evaluation_task"] == "detection" else "map",
+            frame_id="base_link" if config_dict["evaluation_task"] == "detection" else "map",
             result_root_directory=result_root_directory,
-            evaluation_config_dict=eval_cfg_dict,
+            config_dict=config_dict,
             load_raw_data=False,
         )
 
-        return cls(evaluation_config, num_area_division)
+        return cls(config, num_area_division)
 
     @property
     def columns(self) -> List[str]:

perception_eval/perception_eval/visualization/perception_visualizer2d.py

@@ -87,22 +87,20 @@ def from_scenario(
             scenario_obj: Optional[Dict[str, any]] = yaml.safe_load(scenario_file)
 
         p_cfg: Dict[str, any] = scenario_obj["Evaluation"]["PerceptionEvaluationConfig"]
-        eval_cfg_dict: Dict[str, any] = p_cfg["evaluation_config_dict"]
+        config_dict: Dict[str, any] = p_cfg["evaluation_config_dict"]
 
-        eval_cfg_dict["label_prefix"] = (
-            "traffic_light" if eval_cfg_dict["UseCaseName"] == "traffic_light" else "autoware"
-        )
+        config_dict["label_prefix"] = "traffic_light" if config_dict["UseCaseName"] == "traffic_light" else "autoware"
         camera_types: Dict[str, int] = scenario_obj["Evaluation"]["Conditions"]["TargetCameras"]
 
-        evaluation_config: PerceptionEvaluationConfig = PerceptionEvaluationConfig(
+        config = PerceptionEvaluationConfig(
             dataset_paths=[""],  # dummy path
             frame_id=list(camera_types.keys()),
             result_root_directory=result_root_directory,
-            evaluation_config_dict=eval_cfg_dict,
+            config_dict=config_dict,
             load_raw_data=False,
         )
 
-        return cls(evaluation_config, **kwargs)
+        return cls(config, **kwargs)
 
     def init_figure(self) -> Tuple[Figure, np.ndarray]:
         """Initialize figure and axes.

perception_eval/perception_eval/visualization/perception_visualizer3d.py

@@ -106,19 +106,19 @@ def from_scenario(
             scenario_obj: Optional[Dict[str, any]] = yaml.safe_load(scenario_file)
 
         p_cfg: Dict[str, any] = scenario_obj["Evaluation"]["PerceptionEvaluationConfig"]
-        eval_cfg_dict: Dict[str, any] = p_cfg["evaluation_config_dict"]
+        config_dict: Dict[str, any] = p_cfg["evaluation_config_dict"]
 
-        eval_cfg_dict["label_prefix"] = "autoware"
+        config_dict["label_prefix"] = "autoware"
 
-        evaluation_config: PerceptionEvaluationConfig = PerceptionEvaluationConfig(
+        config = PerceptionEvaluationConfig(
             dataset_paths=[""],  # dummy path
-            frame_id="base_link" if eval_cfg_dict["evaluation_task"] == "detection" else "map",
+            frame_id="base_link" if config_dict["evaluation_task"] == "detection" else "map",
             result_root_directory=result_root_directory,
-            evaluation_config_dict=eval_cfg_dict,
+            config_dict=config_dict,
             load_raw_data=False,
         )
 
-        return cls(evaluation_config, **kwargs)
+        return cls(config, **kwargs)
 
     @property
     def config(self) -> PerceptionEvaluationConfig:

perception_eval/perception_eval/visualization/sensing_visualizer.py

@@ -88,19 +88,19 @@ def from_scenario(
             scenario_obj: Optional[Dict[str, any]] = yaml.safe_load(scenario_file)
 
         s_cfg: Dict[str, any] = scenario_obj["Evaluation"]["SensingEvaluationConfig"]
-        eval_cfg_dict: Dict[str, any] = s_cfg["evaluation_config_dict"]
+        config_dict: Dict[str, any] = s_cfg["evaluation_config_dict"]
 
-        eval_cfg_dict["label_prefix"] = "autoware"
+        config_dict["label_prefix"] = "autoware"
 
-        evaluation_config: SensingEvaluationConfig = SensingEvaluationConfig(
+        config: SensingEvaluationConfig = SensingEvaluationConfig(
             dataset_paths=[""],  # dummy path
             frame_id="base_link",
             result_root_directory=result_root_directory,
-            evaluation_config_dict=eval_cfg_dict,
+            config_dict=config_dict,
             load_raw_data=False,
         )
 
-        return cls(evaluation_config, **kwargs)
+        return cls(config, **kwargs)
 
     @property
     def config(self) -> SensingEvaluationConfig: