From b41e74395f029ed03bd39e7ffdd0063877ecca4a Mon Sep 17 00:00:00 2001
From: ktro2828 <kotaro.uetake@tier4.jp>
Date: Tue, 21 Nov 2023 16:57:07 +0900
Subject: [PATCH] revert: remove label of traffic light for crossswalk

Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp>
---
 .../perception_eval/common/dataset_utils.py   |  7 ++-----
 .../perception_eval/common/label.py           | 11 +++-------
 perception_eval/test/perception_lsim2d.py     | 21 ++++---------------
 3 files changed, 9 insertions(+), 30 deletions(-)

diff --git a/perception_eval/perception_eval/common/dataset_utils.py b/perception_eval/perception_eval/common/dataset_utils.py
index 2d2556c0..43000a37 100644
--- a/perception_eval/perception_eval/common/dataset_utils.py
+++ b/perception_eval/perception_eval/common/dataset_utils.py
@@ -576,11 +576,8 @@ def _merge_duplicated_traffic_lights(
                 "If the same regulatory element ID is assigned to multiple traffic lights, "
                 f"it must annotated with only two labels: (unknown, another one). But got, {unique_labels}"
             )
-            semantic_label = [
-                label
-                for label in candidate_labels
-                if label.label != TrafficLightLabel.UNKNOWN and TrafficLightLabel.CROSSWALK_UNKNOWN
-            ][0]
+            semantic_label = [label for label in candidate_labels if label.label != TrafficLightLabel.UNKNOWN][0]
+            assert semantic_label.label != TrafficLightLabel.UNKNOWN
         merged_object = DynamicObject2D(
             unix_time=unix_time,
             frame_id=candidates[0].frame_id,
diff --git a/perception_eval/perception_eval/common/label.py b/perception_eval/perception_eval/common/label.py
index c2b732c0..2bf27ff1 100644
--- a/perception_eval/perception_eval/common/label.py
+++ b/perception_eval/perception_eval/common/label.py
@@ -76,11 +76,6 @@ class TrafficLightLabel(Enum):
     RED_LEFT_DIAGONAL = "red_left_diagonal"
     RED_RIGHT_DIAGONAL = "red_right_diagonal"
 
-    # === for crosswalk ===
-    CROSSWALK_RED = "crosswalk_red"
-    CROSSWALK_GREEN = "crosswalk_green"
-    CROSSWALK_UNKNOWN = "crosswalk_unknown"
-
     # unknown is used in both detection and classification
     UNKNOWN = "unknown"
 
@@ -379,9 +374,9 @@ def _get_traffic_light_paris(
             (TrafficLightLabel.RED_LEFT_DIAGONAL, "red_left_diagonal"),
             (TrafficLightLabel.RED_LEFT_DIAGONAL, "red_leftdiagonal"),
             (TrafficLightLabel.UNKNOWN, "unknown"),
-            (TrafficLightLabel.CROSSWALK_RED, "crosswalk_red"),
-            (TrafficLightLabel.CROSSWALK_GREEN, "crosswalk_green"),
-            (TrafficLightLabel.CROSSWALK_UNKNOWN, "crosswalk_unknown"),
+            (TrafficLightLabel.RED, "crosswalk_red"),
+            (TrafficLightLabel.GREEN, "crosswalk_green"),
+            (TrafficLightLabel.UNKNOWN, "crosswalk_unknown"),
             (TrafficLightLabel.FP, "false_positive"),
         ]
     else:
diff --git a/perception_eval/test/perception_lsim2d.py b/perception_eval/test/perception_lsim2d.py
index b18a54c6..b274571e 100644
--- a/perception_eval/test/perception_lsim2d.py
+++ b/perception_eval/test/perception_lsim2d.py
@@ -45,10 +45,7 @@ def __init__(
         if evaluation_task in ("detection2d", "tracking2d"):
             evaluation_config_dict = {
                 "evaluation_task": evaluation_task,
-                "center_distance_thresholds": [
-                    100,
-                    200,
-                ],  # = [[100, 100, 100, 100], [200, 200, 200, 200]]
+                "center_distance_thresholds": [100, 200],  # = [[100, 100, 100, 100], [200, 200, 200, 200]]
                 "iou_2d_thresholds": [0.5],  # = [[0.5, 0.5, 0.5, 0.5]]
             }
         elif evaluation_task == "classification2d":
@@ -59,15 +56,7 @@ def __init__(
         # If target_labels = None, all labels will be evaluated.
         evaluation_config_dict.update(
             dict(
-                target_labels=[
-                    "green",
-                    "red",
-                    "yellow",
-                    "unknown",
-                    "crosswalk_green",
-                    "crosswalk_red",
-                    "crosswalk_unknown",
-                ]
+                target_labels=["green", "red", "yellow", "unknown"]
                 if label_prefix == "traffic_light"
                 else ["car", "bicycle", "pedestrian", "motorbike"],
                 ignore_attributes=["cycle_state.without_rider"] if label_prefix == "autoware" else None,
@@ -114,14 +103,12 @@ def callback(
 
         # 1 frameの評価
         target_labels = (
-            ["green", "red", "yellow", "unknown", "crosswalk_green", "crosswalk_red", "crosswalk_unknown"]
+            ["green", "red", "yellow", "unknown"]
             if self.label_prefix == "traffic_light"
             else ["car", "bicycle", "pedestrian", "motorbike"]
         )
         ignore_attributes = ["cycle_state.without_rider"] if self.label_prefix == "autoware" else None
-        matching_threshold_list = (
-            None if self.evaluation_task == "classification2d" else [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
-        )
+        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
         critical_object_filter_config: CriticalObjectFilterConfig = CriticalObjectFilterConfig(