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import numpy as np
import pytest
import trackeval
def no_confusion():
num_timesteps = 5
num_gt_ids = 2
num_tracker_ids = 2
# No overlap between pairs (0, 0) and (1, 1).
similarity = np.zeros([num_timesteps, num_gt_ids, num_tracker_ids])
similarity[:, 0, 1] = [0, 0, 0, 1, 1]
similarity[:, 1, 0] = [1, 1, 0, 0, 0]
gt_present = np.zeros([num_timesteps, num_gt_ids])
gt_present[:, 0] = [1, 1, 1, 1, 1]
gt_present[:, 1] = [1, 1, 1, 0, 0]
tracker_present = np.zeros([num_timesteps, num_tracker_ids])
tracker_present[:, 0] = [1, 1, 1, 1, 0]
tracker_present[:, 1] = [1, 1, 1, 1, 1]
expected = {
'clear': {
'CLR_TP': 4,
'CLR_FN': 4,
'CLR_FP': 5,
'IDSW': 0,
'MOTA': 1 - 9 / 8,
},
'identity': {
'IDTP': 4,
'IDFN': 4,
'IDFP': 5,
'IDR': 4 / 8,
'IDP': 4 / 9,
'IDF1': 2 * 4 / 17,
},
'vace': {
'STDA': 2 / 5 + 2 / 4,
'ATA': (2 / 5 + 2 / 4) / 2,
},
}
data = _from_dense(
num_timesteps=num_timesteps,
num_gt_ids=num_gt_ids,
num_tracker_ids=num_tracker_ids,
gt_present=gt_present,
tracker_present=tracker_present,
similarity=similarity,
)
return data, expected
def with_confusion():
num_timesteps = 5
num_gt_ids = 2
num_tracker_ids = 2
similarity = np.zeros([num_timesteps, num_gt_ids, num_tracker_ids])
similarity[:, 0, 1] = [0, 0, 0, 1, 1]
similarity[:, 1, 0] = [1, 1, 0, 0, 0]
# Add some overlap between (0, 0) and (1, 1).
similarity[:, 0, 0] = [0, 0, 1, 0, 0]
similarity[:, 1, 1] = [0, 1, 0, 0, 0]
gt_present = np.zeros([num_timesteps, num_gt_ids])
gt_present[:, 0] = [1, 1, 1, 1, 1]
gt_present[:, 1] = [1, 1, 1, 0, 0]
tracker_present = np.zeros([num_timesteps, num_tracker_ids])
tracker_present[:, 0] = [1, 1, 1, 1, 0]
tracker_present[:, 1] = [1, 1, 1, 1, 1]
expected = {
'clear': {
'CLR_TP': 5,
'CLR_FN': 3, # 8 - 5
'CLR_FP': 4, # 9 - 5
'IDSW': 1,
'MOTA': 1 - 8 / 8,
},
'identity': {
'IDTP': 4,
'IDFN': 4,
'IDFP': 5,
'IDR': 4 / 8,
'IDP': 4 / 9,
'IDF1': 2 * 4 / 17,
},
'vace': {
'STDA': 2 / 5 + 2 / 4,
'ATA': (2 / 5 + 2 / 4) / 2,
},
}
data = _from_dense(
num_timesteps=num_timesteps,
num_gt_ids=num_gt_ids,
num_tracker_ids=num_tracker_ids,
gt_present=gt_present,
tracker_present=tracker_present,
similarity=similarity,
)
return data, expected
def split_tracks():
num_timesteps = 5
num_gt_ids = 2
num_tracker_ids = 5
similarity = np.zeros([num_timesteps, num_gt_ids, num_tracker_ids])
# Split ground-truth 0 between tracks 0, 3.
similarity[:, 0, 0] = [1, 1, 0, 0, 0]
similarity[:, 0, 3] = [0, 0, 0, 1, 1]
# Split ground-truth 1 between tracks 1, 2, 4.
similarity[:, 1, 1] = [0, 0, 1, 1, 0]
similarity[:, 1, 2] = [0, 0, 0, 0, 1]
similarity[:, 1, 4] = [1, 1, 0, 0, 0]
gt_present = np.zeros([num_timesteps, num_gt_ids])
gt_present[:, 0] = [1, 1, 0, 1, 1]
gt_present[:, 1] = [1, 1, 1, 1, 1]
tracker_present = np.zeros([num_timesteps, num_tracker_ids])
tracker_present[:, 0] = [1, 1, 0, 0, 0]
tracker_present[:, 1] = [0, 0, 1, 1, 1]
tracker_present[:, 2] = [0, 0, 0, 0, 1]
tracker_present[:, 3] = [0, 0, 1, 1, 1]
tracker_present[:, 4] = [1, 1, 0, 0, 0]
expected = {
'clear': {
'CLR_TP': 9,
'CLR_FN': 0, # 9 - 9
'CLR_FP': 2, # 11 - 9
'IDSW': 3,
'MOTA': 1 - 5 / 9,
},
'identity': {
'IDTP': 4,
'IDFN': 5, # 9 - 4
'IDFP': 7, # 11 - 4
'IDR': 4 / 9,
'IDP': 4 / 11,
'IDF1': 2 * 4 / 20,
},
'vace': {
'STDA': 2 / 4 + 2 / 5,
'ATA': (2 / 4 + 2 / 5) / (0.5 * (2 + 5)),
},
}
data = _from_dense(
num_timesteps=num_timesteps,
num_gt_ids=num_gt_ids,
num_tracker_ids=num_tracker_ids,
gt_present=gt_present,
tracker_present=tracker_present,
similarity=similarity,
)
return data, expected
def _from_dense(num_timesteps, num_gt_ids, num_tracker_ids, gt_present, tracker_present, similarity):
gt_subset = [np.flatnonzero(gt_present[t, :]) for t in range(num_timesteps)]
tracker_subset = [np.flatnonzero(tracker_present[t, :]) for t in range(num_timesteps)]
similarity_subset = [
similarity[t][gt_subset[t], :][:, tracker_subset[t]]
for t in range(num_timesteps)
]
data = {
'num_timesteps': num_timesteps,
'num_gt_ids': num_gt_ids,
'num_tracker_ids': num_tracker_ids,
'num_gt_dets': np.sum(gt_present),
'num_tracker_dets': np.sum(tracker_present),
'gt_ids': gt_subset,
'tracker_ids': tracker_subset,
'similarity_scores': similarity_subset,
}
return data
METRICS_BY_NAME = {
'clear': trackeval.metrics.CLEAR(),
'identity': trackeval.metrics.Identity(),
'vace': trackeval.metrics.VACE(),
}
SEQUENCE_BY_NAME = {
'no_confusion': no_confusion(),
'with_confusion': with_confusion(),
'split_tracks': split_tracks(),
}
@pytest.mark.parametrize('sequence_name,metric_name', [
('no_confusion', 'clear'),
('no_confusion', 'identity'),
('no_confusion', 'vace'),
('with_confusion', 'clear'),
('with_confusion', 'identity'),
('with_confusion', 'vace'),
('split_tracks', 'clear'),
('split_tracks', 'identity'),
('split_tracks', 'vace'),
])
def test_metric(sequence_name, metric_name):
data, expected = SEQUENCE_BY_NAME[sequence_name]
metric = METRICS_BY_NAME[metric_name]
result = metric.eval_sequence(data)
for key, value in expected[metric_name].items():
assert result[key] == pytest.approx(value), key
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