test_eval_offline.py

import numpy as np


def eval_at_40points():
    sum = [0, 0, 0]
    for v in range(3):
        for i in range(1, len(data)):
            sum[v] += data[:, v + 1][i]
    print("car_detection_3d AP: {} {} {}".format(sum[0] / 40 * 100, sum[1] / 40 * 100, sum[2] / 40 * 100))


def eval_at_11points():
    sum = [0, 0, 0]
    for v in range(3):
        for i in range(0, len(data), 4):
            sum[v] += data[:, v + 1][i]
    print("car_detection_3d AP: {} {} {}".format(sum[0] / 11 * 100, sum[1] / 11 * 100, sum[2] / 11 * 100))


if __name__ == '__main__':
    # data can be found at http://www.cvlibs.net/datasets/kitti/eval_object_detail.php?&result=672d5d8a671ed0a6a06df16e87aeffe301dcefd1
    data = np.array([0.000000, 0.371295, 1.000000, 1.000000,
                     0.025000, 0.371295, 0.482714, 0.492806,
                     0.050000, 0.371295, 0.454383, 0.472138,
                     0.075000, 0.360524, 0.449523, 0.441424,
                     0.100000, 0.360524, 0.419588, 0.398163,
                     0.125000, 0.360524, 0.383439, 0.353302,
                     0.150000, 0.360524, 0.342580, 0.298475,
                     0.175000, 0.351283, 0.310442, 0.104185,
                     0.200000, 0.332176, 0.243738, 0.000000,
                     0.225000, 0.323867, 0.091153, 0.000000,
                     0.250000, 0.300303, 0.000000, 0.000000,
                     0.275000, 0.267305, 0.000000, 0.000000,
                     0.300000, 0.235226, 0.000000, 0.000000,
                     0.325000, 0.077804, 0.000000, 0.000000,
                     0.350000, 0.075351, 0.000000, 0.000000,
                     0.375000, 0.000000, 0.000000, 0.000000,
                     0.400000, 0.000000, 0.000000, 0.000000,
                     0.425000, 0.000000, 0.000000, 0.000000,
                     0.450000, 0.000000, 0.000000, 0.000000,
                     0.475000, 0.000000, 0.000000, 0.000000,
                     0.500000, 0.000000, 0.000000, 0.000000,
                     0.525000, 0.000000, 0.000000, 0.000000,
                     0.550000, 0.000000, 0.000000, 0.000000,
                     0.575000, 0.000000, 0.000000, 0.000000,
                     0.600000, 0.000000, 0.000000, 0.000000,
                     0.625000, 0.000000, 0.000000, 0.000000,
                     0.650000, 0.000000, 0.000000, 0.000000,
                     0.675000, 0.000000, 0.000000, 0.000000,
                     0.700000, 0.000000, 0.000000, 0.000000,
                     0.725000, 0.000000, 0.000000, 0.000000,
                     0.750000, 0.000000, 0.000000, 0.000000,
                     0.775000, 0.000000, 0.000000, 0.000000,
                     0.800000, 0.000000, 0.000000, 0.000000,
                     0.825000, 0.000000, 0.000000, 0.000000,
                     0.850000, 0.000000, 0.000000, 0.000000,
                     0.875000, 0.000000, 0.000000, 0.000000,
                     0.900000, 0.000000, 0.000000, 0.000000,
                     0.925000, 0.000000, 0.000000, 0.000000,
                     0.950000, 0.000000, 0.000000, 0.000000,
                     0.975000, 0.000000, 0.000000, 0.000000,
                     1.000000, 0.000000, 0.000000, 0.000000, ]).reshape(-1, 4)

    eval_at_40points()
    eval_at_11points()