diff --git a/driving_log_replayer_v2/CMakeLists.txt b/driving_log_replayer_v2/CMakeLists.txt index 0433e4fc..a695822a 100644 --- a/driving_log_replayer_v2/CMakeLists.txt +++ b/driving_log_replayer_v2/CMakeLists.txt @@ -23,6 +23,7 @@ install(PROGRAMS scripts/perception_database_result.py scripts/perception_2d_evaluator_node.py scripts/traffic_light_evaluator_node.py + scripts/ground_segmentation_evaluator_node.py scripts/obstacle_segmentation_evaluator_node.py scripts/performance_diag_evaluator_node.py scripts/localization_evaluator_node.py diff --git a/driving_log_replayer_v2/driving_log_replayer_v2/ground_segmentation.py b/driving_log_replayer_v2/driving_log_replayer_v2/ground_segmentation.py new file mode 100644 index 00000000..ce033db9 --- /dev/null +++ b/driving_log_replayer_v2/driving_log_replayer_v2/ground_segmentation.py @@ -0,0 +1,100 @@ +# Copyright (c) 2024 TIER IV.inc +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Literal + +from pydantic import BaseModel + +from driving_log_replayer_v2.result import EvaluationItem +from driving_log_replayer_v2.result import ResultBase +from driving_log_replayer_v2.scenario import number +from driving_log_replayer_v2.scenario import Scenario +from driving_log_replayer_v2_msgs.msg import GroundSegmentationEvalResult + + +class Condition(BaseModel): + Method: Literal["annotated_pcd", "annotated_rosbag"] + ground_label: number + obstacle_label: number + accuracy_min: number + accuracy_max: number + PassRate: number + + +class Evaluation(BaseModel): + UseCaseName: Literal["ground_segmentation"] + UseCaseFormatVersion: Literal["0.3.0"] + Conditions: Condition + + +class GroundSegmentationScenario(Scenario): + Evaluation: Evaluation + + +@dataclass +class GroundSegmentation(EvaluationItem): + name: str = "Ground Segmentation" + + def set_frame(self, msg: GroundSegmentationEvalResult) -> dict: + self.condition: Condition + self.total += 1 + + frame_success = self.condition.accuracy_min <= msg.accuracy <= self.condition.accuracy_max + + if frame_success: + self.passed += 1 + + current_rate = self.rate() + self.success = current_rate >= self.condition.PassRate + self.summary = f"{self.name} ({self.success_str()}): {self.passed} / {self.total} -> {current_rate:.2f}" + + return { + "GroundSegmentation": { + "Result": { + "Total": self.success_str(), + "Frame": "Success" if frame_success else "Fail", + }, + "Info": { + "TP": msg.tp, + "FP": msg.fp, + "TN": msg.tn, + "FN": msg.fn, + "Accuracy": msg.accuracy, + "Precision": msg.precision, + "Recall": msg.recall, + "Specificity": msg.specificity, + "F1-score": msg.f1_score, + }, + }, + } + + +class GroundSegmentationResult(ResultBase): + def __init__(self, condition: Condition) -> None: + super().__init__() + self.__ground_segmentation = GroundSegmentation(condition=condition) + + def update(self) -> None: + summary_str = f"{self.__ground_segmentation.summary}" + if self.__ground_segmentation.success: + self._success = True + self._summary = f"Passed: {summary_str}" + else: + self._success = False + self._summary = f"Failed: {summary_str}" + + def set_frame(self, msg: GroundSegmentationEvalResult) -> None: + self._frame = self.__ground_segmentation.set_frame(msg) + self.update() diff --git a/driving_log_replayer_v2/driving_log_replayer_v2/launch/ground_segmentation.py b/driving_log_replayer_v2/driving_log_replayer_v2/launch/ground_segmentation.py new file mode 100644 index 00000000..e004845e --- /dev/null +++ b/driving_log_replayer_v2/driving_log_replayer_v2/launch/ground_segmentation.py @@ -0,0 +1,40 @@ +# Copyright (c) 2024 TIER IV.inc +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from launch.actions import DeclareLaunchArgument +from launch.substitutions import LaunchConfiguration + +RECORD_TOPIC = """^/tf$\ +|^/diagnostics$"\ +""" + +AUTOWARE_DISABLE = { + "localization": "false", + "planning": "false", + "control": "false", +} + +AUTOWARE_ARGS = {"perception_mode": "lidar"} + +NODE_PARAMS = { + "vehicle_model": LaunchConfiguration("vehicle_model"), + "evaluation_target_topic": LaunchConfiguration("evaluation_target_topic"), +} + +USE_CASE_ARGS: list[DeclareLaunchArgument] = [ + DeclareLaunchArgument( + "evaluation_target_topic", + default_value="/perception/obstacle_segmentation/single_frame/pointcloud", + ) +] diff --git a/driving_log_replayer_v2/scripts/ground_segmentation_evaluator_node.py b/driving_log_replayer_v2/scripts/ground_segmentation_evaluator_node.py new file mode 100755 index 00000000..162e76f2 --- /dev/null +++ b/driving_log_replayer_v2/scripts/ground_segmentation_evaluator_node.py @@ -0,0 +1,225 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2024 TIER IV.inc +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +from pathlib import Path + +import message_filters +import numpy as np +from rclpy.qos import qos_profile_sensor_data +import ros2_numpy +from scipy.spatial import cKDTree +from sensor_msgs.msg import PointCloud2 + +from driving_log_replayer_v2.evaluator import DLREvaluatorV2 +from driving_log_replayer_v2.evaluator import evaluator_main +from driving_log_replayer_v2.ground_segmentation import Condition +from driving_log_replayer_v2.ground_segmentation import GroundSegmentationResult +from driving_log_replayer_v2.ground_segmentation import GroundSegmentationScenario +import driving_log_replayer_v2.perception_eval_conversions as eval_conversions +from driving_log_replayer_v2_msgs.msg import GroundSegmentationEvalResult + + +class GroundSegmentationEvaluator(DLREvaluatorV2): + CLOUD_DIM = 6 + TS_DIFF_THRESH = 75000 + + def __init__(self, name: str) -> None: + super().__init__(name, GroundSegmentationScenario, GroundSegmentationResult) + + eval_condition: Condition = self._scenario.Evaluation.Conditions + self.ground_label = eval_condition.ground_label + self.obstacle_label = eval_condition.obstacle_label + self.declare_parameter( + "evaluation_target_topic", + "/perception/obstacle_segmentation/pointcloud", + ) + self.eval_target_topic = ( + self.get_parameter("evaluation_target_topic").get_parameter_value().string_value + ) + + if eval_condition.Method == "annotated_pcd": + # pcd eval mode + sample_data_path = Path(self._t4_dataset_paths[0], "annotation", "sample_data.json") + sample_data = json.load(sample_data_path.open()) + sample_data = list(filter(lambda d: d["filename"].split(".")[-2] == "pcd", sample_data)) + + self.ground_truth: dict[int, np.ndarray] = {} + for i in range(len(sample_data)): + pcd_file_path = Path( + self._t4_dataset_paths[0], + sample_data[i]["filename"], + ).as_posix() + raw_points = np.fromfile(pcd_file_path, dtype=np.float32) + points: np.ndarray = raw_points.reshape((-1, self.CLOUD_DIM)) + self.ground_truth[int(sample_data[i]["timestamp"])] = points + + self.__sub_pointcloud = self.create_subscription( + PointCloud2, + self.eval_target_topic, + self.annotated_pcd_eval_cb, + qos_profile_sensor_data, + ) + elif eval_condition.Method == "annotated_rosbag": + # rosbag (AWSIM) eval mode + + self.__sub_gt_cloud = message_filters.Subscriber( + self, + PointCloud2, + "/sensing/lidar/concatenated/pointcloud", + qos_profile=qos_profile_sensor_data, + ) + self.__sub_eval_target_cloud = message_filters.Subscriber( + self, + PointCloud2, + self.eval_target_topic, + qos_profile=qos_profile_sensor_data, + ) + self.__sync_sub = message_filters.TimeSynchronizer( + [self.__sub_gt_cloud, self.__sub_eval_target_cloud], + 1000, + ) + self.__sync_sub.registerCallback(self.annotated_rosbag_eval_cb) + else: + err = 'The "Method" field must be set to either "annotated_rosbag" or "annotated_pcd"' + raise ValueError(err) + + def annotated_pcd_eval_cb(self, msg: PointCloud2) -> None: + unix_time: int = eval_conversions.unix_time_from_ros_msg(msg.header) + gt_frame_ts = self.__get_gt_frame_ts(unix_time=unix_time) + + if gt_frame_ts < 0: + return + + # get ground truth pointcloud in this frame + # construct kd-tree from gt cloud + gt_frame_cloud: np.ndarray = self.ground_truth[gt_frame_ts] + kdtree = cKDTree(gt_frame_cloud[:, 0:3]) + + # convert ros2 pointcloud to numpy + numpy_pcd = ros2_numpy.numpify(msg) + pointcloud = np.zeros((numpy_pcd.shape[0], 3)) + pointcloud[:, 0] = numpy_pcd["x"] + pointcloud[:, 1] = numpy_pcd["y"] + pointcloud[:, 2] = numpy_pcd["z"] + + # count TP+FN, TN+FP + tp_fn = np.count_nonzero(gt_frame_cloud[:, 5] == self.ground_label) + fp_tn = np.count_nonzero(gt_frame_cloud[:, 5] == self.obstacle_label) + tn: int = 0 + fn: int = 0 + for p in pointcloud: + _, idx = kdtree.query(p, k=1) + if gt_frame_cloud[idx][5] == self.ground_label: + fn += 1 + elif gt_frame_cloud[idx][5] == self.obstacle_label: + tn += 1 + tp = tp_fn - fn + fp = fp_tn - tn + + self.get_logger().info(f"TP {tp}, FP {fp}, TN {tn}, FN {fn}") + + metrics_list = self.__compute_metrics(tp, fp, tn, fn) + + frame_result = GroundSegmentationEvalResult() + frame_result.tp = tp + frame_result.fp = fp + frame_result.tn = tn + frame_result.fn = fn + frame_result.accuracy = metrics_list[0] + frame_result.precision = metrics_list[1] + frame_result.recall = metrics_list[2] + frame_result.specificity = metrics_list[3] + frame_result.f1_score = metrics_list[4] + + self._result.set_frame(frame_result) + self._result_writer.write_result(self._result) + + def annotated_rosbag_eval_cb( + self, + gt_cloud_msg: PointCloud2, + eval_target_cloud_msg: PointCloud2, + ) -> None: + np_gt_cloud: np.array = ros2_numpy.numpify(gt_cloud_msg) + np_target_cloud: np.array = ros2_numpy.numpify(eval_target_cloud_msg) + + # guard + if ( + "entity_id" not in np_gt_cloud.dtype.fields + or "entity_id" not in np_target_cloud.dtype.fields + ): + self.get_logger().warn('The input PointCloud doesn\'t have a field named "entity_id"') + return + + tp_fn = np.count_nonzero(np_gt_cloud["entity_id"] == self.ground_label) + tn_fp = np_gt_cloud.size - tp_fn + fn = np.count_nonzero(np_target_cloud["entity_id"] == self.ground_label) + tn = np_target_cloud.size - fn + + tp = tp_fn - fn + fp = tn_fp - tn + self.get_logger().info(f"TP+FN = {tp_fn}, TN+FP = {tn_fp}") + self.get_logger().info(f"TP {tp}, FP {fp}, TN {tn}, FN {fn}") + metrics_list = self.__compute_metrics(tp, fp, tn, fn) + frame_result = GroundSegmentationEvalResult() + frame_result.tp = tp + frame_result.fp = fp + frame_result.tn = tn + frame_result.fn = fn + frame_result.accuracy = metrics_list[0] + frame_result.precision = metrics_list[1] + frame_result.recall = metrics_list[2] + frame_result.specificity = metrics_list[3] + frame_result.f1_score = metrics_list[4] + + self._result.set_frame(frame_result) + self._result_writer.write_result(self._result) + + def __get_gt_frame_ts(self, unix_time: int) -> int: + ts_itr = iter(self.ground_truth.keys()) + ret_ts: int = int(next(ts_itr)) + min_diff: int = abs(unix_time - ret_ts) + + for _ in range(1, len(self.ground_truth)): + sample_ts = next(ts_itr) + diff_time = abs(unix_time - sample_ts) + if diff_time < min_diff: + min_diff = diff_time + ret_ts = sample_ts + + if min_diff > self.TS_DIFF_THRESH: + self.get_logger().warn("time diff is too big") + return -1 + + return ret_ts + + def __compute_metrics(self, tp: int, fp: int, tn: int, fn: int) -> list[float]: + eps = 1e-10 + accuracy = float(tp + tn) / float(tp + fp + tn + fn + eps) + precision = float(tp) / float(tp + fp + eps) + recall = float(tp) / float(tp + fn + eps) + specificity = float(tn) / float(tn + fp + eps) + f1_score = 2 * (precision * recall) / (precision + recall + eps) + return [accuracy, precision, recall, specificity, f1_score] + + +@evaluator_main +def main() -> DLREvaluatorV2: + return GroundSegmentationEvaluator("ground_segmentation_evaluator") + + +if __name__ == "__main__": + main() diff --git a/driving_log_replayer_v2_msgs/CMakeLists.txt b/driving_log_replayer_v2_msgs/CMakeLists.txt index 7891662b..f8f00747 100644 --- a/driving_log_replayer_v2_msgs/CMakeLists.txt +++ b/driving_log_replayer_v2_msgs/CMakeLists.txt @@ -7,6 +7,7 @@ autoware_package() rosidl_generate_interfaces(${PROJECT_NAME} "msg/ObstacleSegmentationMarker.msg" "msg/ObstacleSegmentationMarkerArray.msg" + "msg/GroundSegmentationEvalResult.msg" DEPENDENCIES std_msgs ) diff --git a/driving_log_replayer_v2_msgs/msg/GroundSegmentationEvalResult.msg b/driving_log_replayer_v2_msgs/msg/GroundSegmentationEvalResult.msg new file mode 100644 index 00000000..af9714ee --- /dev/null +++ b/driving_log_replayer_v2_msgs/msg/GroundSegmentationEvalResult.msg @@ -0,0 +1,10 @@ +uint32 tp +uint32 fp +uint32 tn +uint32 fn + +float32 accuracy +float32 precision +float32 recall +float32 specificity +float32 f1_score diff --git a/sample/ground_segmentation/result_annotated_pcd.json b/sample/ground_segmentation/result_annotated_pcd.json new file mode 100644 index 00000000..6383a7e5 --- /dev/null +++ b/sample/ground_segmentation/result_annotated_pcd.json @@ -0,0 +1,1414 @@ +[ + { + "Condition": { + "Method": "annotated_pcd", + "ground_label": 6, + "obstacle_label": 7, + "accuracy_min": 0.7, + "accuracy_max": 1, + "PassRate": 99 + } + }, + { + "Result": { + "Success": false, + "Summary": "NoData" + }, + "Stamp": { + "System": 1721279626.5128443 + }, + "Frame": {} + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 1 -> 0.00" + }, + "Stamp": { + "System": 1721279635.62191, + "ROS": 1702607140.3340278 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 2 -> 0.00" + }, + "Stamp": { + "System": 1721279636.7386274, + "ROS": 1702607141.4639919 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 3 -> 0.00" + }, + "Stamp": { + "System": 1721279638.6270595, + "ROS": 1702607142.578998 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 4 -> 0.00" + }, + "Stamp": { + "System": 1721279640.200266, + "ROS": 1702607144.469044 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 5 -> 0.00" + }, + "Stamp": { + "System": 1721279641.7985127, + "ROS": 1702607146.0440106 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 6 -> 0.00" + }, + "Stamp": { + "System": 1721279643.244099, + "ROS": 1702607147.639131 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 7 -> 0.00" + }, + "Stamp": { + "System": 1721279644.7044797, + "ROS": 1702607149.0890372 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 8 -> 0.00" + }, + "Stamp": { + "System": 1721279646.079003, + "ROS": 1702607150.5490692 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 9 -> 0.00" + }, + "Stamp": { + "System": 1721279647.483366, + "ROS": 1702607151.919052 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 10 -> 0.00" + }, + "Stamp": { + "System": 1721279648.6291668, + "ROS": 1702607153.3240657 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 11 -> 0.00" + }, + "Stamp": { + "System": 1721279649.5694475, + "ROS": 1702607154.4690607 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + "Precision": 0, + "Recall": 0, + "Specificity": 0, + "F1-score": 0 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 0 / 12 -> 0.00" + }, + "Stamp": { + "System": 1721279650.8749614, + "ROS": 1702607155.4140751 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 0, + "FP": 0, + "TN": 0, + "FN": 0, + "Accuracy": 0, + 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} +] diff --git a/sample/ground_segmentation/result_annotated_rosbag.json b/sample/ground_segmentation/result_annotated_rosbag.json new file mode 100644 index 00000000..8ec1e797 --- /dev/null +++ b/sample/ground_segmentation/result_annotated_rosbag.json @@ -0,0 +1,7011 @@ +[ + { + "Condition": { + "Method": "annotated_rosbag", + "ground_label": 1, + "obstacle_label": 0, + "accuracy_min": 0.7, + "accuracy_max": 1, + "PassRate": 99 + } + }, + { + "Result": { + "Success": false, + "Summary": "NoData" + }, + "Stamp": { + "System": 1721277815.514656 + }, + "Frame": {} + }, + { + "Result": { + "Success": true, + "Summary": "Passed: Ground Segmentation (Success): 1 / 1 -> 100.00" + }, + "Stamp": { + "System": 1721277824.1680663, + "ROS": 1701349187.3502464 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Success", + "Frame": "Success" + }, + "Info": { + "TP": 6438, + "FP": 2129, + "TN": 19956, + "FN": 225, + "Accuracy": 0.918116042855152, + "Precision": 0.751488268938943, 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"Summary": "Failed: Ground Segmentation (Fail): 106 / 240 -> 44.17" + }, + "Stamp": { + "System": 1721277914.099318, + "ROS": 1701349277.320536 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 69609, + "FP": 103552, + "TN": 69696, + "FN": 363, + "Accuracy": 0.572753063070471, + "Precision": 0.4019900554974848, + "Recall": 0.9948122105985236, + "Specificity": 0.40229035833025467, + "F1-score": 0.5726001817525099 + } + } + } + }, + { + "Result": { + "Success": false, + "Summary": "Failed: Ground Segmentation (Fail): 106 / 241 -> 43.98" + }, + "Stamp": { + "System": 1721277914.4195998, + "ROS": 1701349277.5105822 + }, + "Frame": { + "GroundSegmentation": { + "Result": { + "Total": "Fail", + "Frame": "Fail" + }, + "Info": { + "TP": 80169, + "FP": 103895, + "TN": 87672, + "FN": 376, + "Accuracy": 0.6168085200211675, + "Precision": 0.4355495914464532, + "Recall": 0.9953318020982047, + "Specificity": 0.4576571121330916, + "F1-score": 0.6059431084687004 + } + } + } + } +] diff --git a/sample/ground_segmentation/scenario.ja.yaml b/sample/ground_segmentation/scenario.ja.yaml new file mode 100644 index 00000000..1cc77aa4 --- /dev/null +++ b/sample/ground_segmentation/scenario.ja.yaml @@ -0,0 +1,34 @@ +ScenarioFormatVersion: 3.0.0 +ScenarioName: ground_segmentation-sample +ScenarioDescription: ground_segmentation-sample +SensorModel: sample_sensor_kit +VehicleModel: sample_vehicle +Evaluation: + UseCaseName: ground_segmentation + UseCaseFormatVersion: 0.3.0 + Datasets: + - sample_dataset: + VehicleId: default + Conditions: + Method: annotated_pcd # ground truthデータとして使用するデータの種類(annotated_rosbag : アノテーション済み点群が入ったinput_bag，annotated_pcd : アノテーション済みpcd.binファイル(t4_dataset/dataset/data/LIDAR_CONCAT/*.pcd.bin)) + ground_label: 6 # 地面点群のアノテーションラベル + obstacle_label: 7 # 障害物点群のアノテーションラベル + accuracy_min: 0.7 # 精度がこの値以上なら評価成功とみなす + accuracy_max: 1.0 + PassRate: 99.0 # 評価試行回数の内、どの程度(%)評価成功だったら成功とするか + +# Note : 'annotated_rosbag'モードでground_segmentation評価を行なう際は， +# autoware.universeのsensingモジュールを下記のように修正する必要があります: +# +# https://github.com/autowarefoundation/autoware.universe/blob/main/sensing/autoware_pointcloud_preprocessor/src/filter.cpp#L383-L390 +# ... +# if (utils::is_data_layout_compatible_with_point_xyzi(*cloud)) { +# RCLCPP_ERROR( +# get_logger(), +# "The pointcloud layout is compatible with PointXYZI. You may be using legacy " +# "code/data"); +# } +# +# // return; <- コメントアウト +# } +# ... diff --git a/sample/ground_segmentation/scenario.yaml b/sample/ground_segmentation/scenario.yaml new file mode 100644 index 00000000..1421cbbc --- /dev/null +++ b/sample/ground_segmentation/scenario.yaml @@ -0,0 +1,34 @@ +ScenarioFormatVersion: 3.0.0 +ScenarioName: ground_segmentation-sample +ScenarioDescription: ground_segmentation-sample +SensorModel: sample_sensor_kit +VehicleModel: sample_vehicle +Evaluation: + UseCaseName: ground_segmentation + UseCaseFormatVersion: 0.3.0 + Datasets: + - sample_dataset: + VehicleId: default + Conditions: + Method: annotated_pcd # Data types used as GroundTruth data.(annotated_rosbag : input_bag contains annotated pointcloud data, annotated_pcd : annotated pcd.bin file located at t4_dataset/dataset/data/LIDAR_CONCAT/*.pcd.bin) + ground_label: 6 # Annotation label for ground pointcloud. + obstacle_label: 7 # Annotation label for obstacle pointcloud. + accuracy_min: 0.7 # If the accuracy is lower than the threshold value, the evaluation is considered to have failed. + accuracy_max: 1.0 + PassRate: 99.0 # How much (%) of the evaluation attempts are considered successful. + +# Note : When running ground_segmentation in 'annotated_rosbag' mode, +# it is necessary to modify the sensing module of autoware.universe as follows: +# +# https://github.com/autowarefoundation/autoware.universe/blob/main/sensing/autoware_pointcloud_preprocessor/src/filter.cpp#L383-L390 +# ... +# if (utils::is_data_layout_compatible_with_point_xyzi(*cloud)) { +# RCLCPP_ERROR( +# get_logger(), +# "The pointcloud layout is compatible with PointXYZI. You may be using legacy " +# "code/data"); +# } +# +# // return; <- comment out! +# } +# ... diff --git a/sample/ground_segmentation/scenario_annotated_pcd.yaml b/sample/ground_segmentation/scenario_annotated_pcd.yaml new file mode 100644 index 00000000..d5bec6f5 --- /dev/null +++ b/sample/ground_segmentation/scenario_annotated_pcd.yaml @@ -0,0 +1,34 @@ +ScenarioFormatVersion: 3.0.0 +ScenarioName: ground_segmentation-sample +ScenarioDescription: ground_segmentation-sample +SensorModel: sample_sensor_kit +VehicleModel: sample_vehicle +Evaluation: + UseCaseName: ground_segmentation + UseCaseFormatVersion: 0.3.0 + Datasets: + - sample_dataset: + VehicleId: default # Specify VehicleId for each data set + Conditions: + Method: annotated_pcd # Data types used as GroundTruth data.(annotated_rosbag : input_bag contains annotated pointcloud data, annotated_pcd : annotated pcd.bin file located at t4_dataset/dataset/data/LIDAR_CONCAT/*.pcd.bin) + ground_label: 6 # Annotation label for ground pointcloud. + obstacle_label: 7 # Annotation label for obstacle pointcloud. + accuracy_min: 0.7 # If the accuracy is lower than the threshold value, the evaluation is considered to have failed. + accuracy_max: 1.0 + PassRate: 99.0 # How much (%) of the evaluation attempts are considered successful. + +# Note : When running ground_segmentation in 'annotated_rosbag' mode, +# it is necessary to modify the sensing module of autoware.universe as follows: +# +# https://github.com/autowarefoundation/autoware.universe/blob/main/sensing/autoware_pointcloud_preprocessor/src/filter.cpp#L383-L390 +# ... +# if (utils::is_data_layout_compatible_with_point_xyzi(*cloud)) { +# RCLCPP_ERROR( +# get_logger(), +# "The pointcloud layout is compatible with PointXYZI. You may be using legacy " +# "code/data"); +# } +# +# // return; <- comment out! +# } +# ... diff --git a/sample/ground_segmentation/scenario_annotated_rosbag.yaml b/sample/ground_segmentation/scenario_annotated_rosbag.yaml new file mode 100644 index 00000000..b85c53f4 --- /dev/null +++ b/sample/ground_segmentation/scenario_annotated_rosbag.yaml @@ -0,0 +1,34 @@ +ScenarioFormatVersion: 3.0.0 +ScenarioName: ground_segmentation-sample +ScenarioDescription: ground_segmentation-sample +SensorModel: sample_sensor_kit +VehicleModel: sample_vehicle +Evaluation: + UseCaseName: ground_segmentation + UseCaseFormatVersion: 0.3.0 + Datasets: + - sample_dataset: + VehicleId: default # Specify VehicleId for each data set + Conditions: + Method: annotated_rosbag # Data types used as GroundTruth data.(annotated_rosbag : input_bag contains annotated pointcloud data, annotated_pcd : annotated pcd.bin file located at t4_dataset/dataset/data/LIDAR_CONCAT/*.pcd.bin) + ground_label: 1 # Annotation label for ground pointcloud. + obstacle_label: 0 # Annotation label for obstacle pointcloud. + accuracy_min: 0.7 # If the accuracy is lower than the threshold value, the evaluation is considered to have failed. + accuracy_max: 1.0 + PassRate: 99.0 # How much (%) of the evaluation attempts are considered successful. + +# Note : When running ground_segmentation in 'annotated_rosbag' mode, +# it is necessary to modify the sensing module of autoware.universe as follows: +# +# https://github.com/autowarefoundation/autoware.universe/blob/main/sensing/autoware_pointcloud_preprocessor/src/filter.cpp#L383-L390 +# ... +# if (utils::is_data_layout_compatible_with_point_xyzi(*cloud)) { +# RCLCPP_ERROR( +# get_logger(), +# "The pointcloud layout is compatible with PointXYZI. You may be using legacy " +# "code/data"); +# } +# +# // return; <- comment out! +# } +# ...