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import numpy as np
from mmdet.core.evaluation.mean_ap import (eval_map, tpfp_default,
tpfp_imagenet, tpfp_openimages)
det_bboxes = np.array([
[0, 0, 10, 10],
[10, 10, 20, 20],
[32, 32, 38, 42],
])
gt_bboxes = np.array([[0, 0, 10, 20], [0, 10, 10, 19], [10, 10, 20, 20]])
gt_ignore = np.array([[5, 5, 10, 20], [6, 10, 10, 19]])
def test_tpfp_imagenet():
result = tpfp_imagenet(
det_bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_ignore,
use_legacy_coordinate=True)
tp = result[0]
fp = result[1]
assert tp.shape == (1, 3)
assert fp.shape == (1, 3)
assert (tp == np.array([[1, 1, 0]])).all()
assert (fp == np.array([[0, 0, 1]])).all()
result = tpfp_imagenet(
det_bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_ignore,
use_legacy_coordinate=False)
tp = result[0]
fp = result[1]
assert tp.shape == (1, 3)
assert fp.shape == (1, 3)
assert (tp == np.array([[1, 1, 0]])).all()
assert (fp == np.array([[0, 0, 1]])).all()
def test_tpfp_default():
result = tpfp_default(
det_bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_ignore,
use_legacy_coordinate=True)
tp = result[0]
fp = result[1]
assert tp.shape == (1, 3)
assert fp.shape == (1, 3)
assert (tp == np.array([[1, 1, 0]])).all()
assert (fp == np.array([[0, 0, 1]])).all()
result = tpfp_default(
det_bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_ignore,
use_legacy_coordinate=False)
tp = result[0]
fp = result[1]
assert tp.shape == (1, 3)
assert fp.shape == (1, 3)
assert (tp == np.array([[1, 1, 0]])).all()
assert (fp == np.array([[0, 0, 1]])).all()
def test_eval_map():
# 2 image and 2 classes
det_results = [[det_bboxes, det_bboxes], [det_bboxes, det_bboxes]]
labels = np.array([0, 1, 1])
labels_ignore = np.array([0, 1])
gt_info = {
'bboxes': gt_bboxes,
'bboxes_ignore': gt_ignore,
'labels': labels,
'labels_ignore': labels_ignore
}
annotations = [gt_info, gt_info]
mean_ap, eval_results = eval_map(
det_results, annotations, use_legacy_coordinate=True)
assert 0.291 < mean_ap < 0.293
mean_ap, eval_results = eval_map(
det_results, annotations, use_legacy_coordinate=False)
assert 0.291 < mean_ap < 0.293
# 1 image and 2 classes
det_results = [[det_bboxes, det_bboxes]]
labels = np.array([0, 1, 1])
labels_ignore = np.array([0, 1])
gt_info = {
'bboxes': gt_bboxes,
'bboxes_ignore': gt_ignore,
'labels': labels,
'labels_ignore': labels_ignore
}
annotations = [gt_info]
mean_ap, eval_results = eval_map(
det_results, annotations, use_legacy_coordinate=True)
assert 0.291 < mean_ap < 0.293
mean_ap, eval_results = eval_map(
det_results, annotations, use_legacy_coordinate=False)
assert 0.291 < mean_ap < 0.293
def test_tpfp_openimages():
det_bboxes = np.array([[10, 10, 15, 15, 1.0], [15, 15, 30, 30, 0.98],
[10, 10, 25, 25, 0.98], [28, 28, 35, 35, 0.97],
[30, 30, 51, 51, 0.96], [100, 110, 120, 130, 0.15]])
gt_bboxes = np.array([[10., 10., 30., 30.], [30., 30., 50., 50.]])
gt_groups_of = np.array([True, False], dtype=np.bool)
gt_ignore = np.zeros((0, 4))
# Open Images evaluation using group of.
result = tpfp_openimages(
det_bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_ignore,
gt_bboxes_group_of=gt_groups_of,
use_group_of=True,
ioa_thr=0.5)
tp = result[0]
fp = result[1]
cls_dets = result[2]
assert tp.shape == (1, 4)
assert fp.shape == (1, 4)
assert cls_dets.shape == (4, 5)
assert (tp == np.array([[0, 1, 0, 1]])).all()
assert (fp == np.array([[1, 0, 1, 0]])).all()
cls_dets_gt = np.array([[28., 28., 35., 35., 0.97],
[30., 30., 51., 51., 0.96],
[100., 110., 120., 130., 0.15],
[10., 10., 15., 15., 1.]])
assert (cls_dets == cls_dets_gt).all()
# Open Images evaluation not using group of.
result = tpfp_openimages(
det_bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_ignore,
gt_bboxes_group_of=gt_groups_of,
use_group_of=False,
ioa_thr=0.5)
tp = result[0]
fp = result[1]
cls_dets = result[2]
assert tp.shape == (1, 6)
assert fp.shape == (1, 6)
assert cls_dets.shape == (6, 5)
# Open Images evaluation using group of, and gt is all group of bboxes.
gt_groups_of = np.array([True, True], dtype=np.bool)
result = tpfp_openimages(
det_bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_ignore,
gt_bboxes_group_of=gt_groups_of,
use_group_of=True,
ioa_thr=0.5)
tp = result[0]
fp = result[1]
cls_dets = result[2]
assert tp.shape == (1, 3)
assert fp.shape == (1, 3)
assert cls_dets.shape == (3, 5)
# Open Images evaluation with empty gt.
gt_bboxes = np.zeros((0, 4))
gt_groups_of = np.empty((0))
result = tpfp_openimages(
det_bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_ignore,
gt_bboxes_group_of=gt_groups_of,
use_group_of=True,
ioa_thr=0.5)
fp = result[1]
assert (fp == np.array([[1, 1, 1, 1, 1, 1]])).all()
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