cicv_evaluate_master-single_eval/custom/center_distance_standard_deviation.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
##################################################################
#
# Copyright (c) 2023 CICV, Inc. All Rights Reserved
#
##################################################################
"""
@Authors:           yangzihao(yangzihao@china-icv.cn)
@Data:              2024/02/21
@Last Modified:     2024/02/21
@Summary:           The template of custom indicator.
"""

import pandas as pd
import numpy as np
import scipy.signal as sg
from common import zip_time_pairs, continuous_group, continous_judge
from log import logger

"""import functions"""


class CustomMetric(object):
    def __init__(self, all_data, case_name):
        self.data = all_data
        self.case_name = case_name
        self.markline_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
        self.result = {
            "name": "ica车道中心线横向距离分布标准差",
            "value": [],
            # "weight": [],
            "tableData": {
                "avg": "",  # 平均值，或指标值
                "max": "",
                "min": ""
            },
            "reportData": {
                "name": "车辆中心线横向距离(m)",
                # "legend": [], # 如果有多个data，则需要增加data对应的说明，如：["横向加速度", "纵向加速度"]
                "data": [],
                "markLine": [],
                "range": [],
            },
            "statusFlag": {}
        }
        self.ego_df = pd.DataFrame()
        self.df_lka = pd.DataFrame()
        self.center_dist_with_nan = list()
        self.center_time_list = list()
        self.center_frame_list = list()
        self.center_dist = list()
        self.run()

    def data_extract(self):
        self.ego_df = self.data.ego_data
        self.df_lka = self.ego_df[self.ego_df['ICA_status'].isin(
            ["Only_Longitudinal_Control", "LLC_Follow_Line", "LLC_Follow_Vehicle"])].copy()

        if self.df_lka.empty:
            self.result['statusFlag']['functionICA'] = False
        else:
            self.result['statusFlag']['functionICA'] = True
        # self.df_lka = self.ego_df[self.ego_df['LKA_status'] == "Active"].copy()
        # self.config = self.data.config
        # self.function_config = self.config.config['function']
        # self.optimal_dict = self.function_config['optimal']

    def data_analyze(self):
        df_lka = self.df_lka
        self.center_dist_with_nan = df_lka['laneOffset'].to_list()
        self.center_time_list = df_lka['simTime'].to_list()
        self.center_frame_list = df_lka['simFrame'].to_list()
        self.center_dist = [x for x in self.center_dist_with_nan if not np.isnan(x)]

        if not self.center_dist:
            self.result['value'].append(0)
        else:
            center_dist = [abs(x) for x in self.center_dist]

            # 车道中心线横向距离分布期望与标准差
            D_lane_center_dist = np.std(center_dist)
            self.result['value'].append(round(D_lane_center_dist, 2))

    def markline_statistic(self):
        unfunc_df = pd.DataFrame(
            {'simTime': self.center_time_list, 'simFrame': self.center_frame_list,
             'center_dist': self.center_dist_with_nan})
        unfunc_df = unfunc_df[unfunc_df['simFrame'] > 1]

        unfunc_df = unfunc_df.dropna(subset=['center_dist'])
        lane_df = unfunc_df[abs(unfunc_df['center_dist']) > 1.5]
        lane_df = lane_df[['simTime', 'simFrame', 'center_dist']]
        dist_lane_df = continuous_group(lane_df)
        dist_lane_df['type'] = "ICA"
        self.markline_df = pd.concat([self.markline_df, dist_lane_df], ignore_index=True)

    def report_data_statistic(self):
        time_list = self.df_lka['simTime'].values.tolist()
        line_dist_list = self.center_dist_with_nan

        self.result['tableData']['avg'] = self.result['value'][0] if not self.df_lka.empty else '-'
        self.result['tableData']['max'] = '-'
        self.result['tableData']['min'] = '-'

        zip_vs_time = zip_time_pairs(time_list, line_dist_list)
        self.result['reportData']['data'] = zip_vs_time

        self.markline_statistic()
        markline_slices = self.markline_df.to_dict('records')
        self.result['reportData']['markLine'] = markline_slices

        self.result['reportData']['range'] = f"[0, 0.5]"

    def run(self):
        # logger.info(f"Custom metric run:[{self.result['name']}].")
        logger.info(
            f"[case:{self.case_name}] Custom metric:[center_distance_standard_deviation:{self.result['name']}] evaluate.")

        try:
            self.data_extract()
        except Exception as e:
            logger.error(f"[case:{self.case_name}] Custom metric:{self.result['name']} data extract ERROR!", e)

        try:
            self.data_analyze()
        except Exception as e:
            logger.error(f"[case:{self.case_name}] Custom metric:{self.result['name']} data analyze ERROR!", e)

        try:
            self.report_data_statistic()
        except Exception as e:
            logger.error(f"[case:{self.case_name}] Custom metric:{self.result['name']} report data statistic ERROR!", e)

# if __name__ == "__main__":
#     pass