def test_y_true_equals_y_pred(self):
y_true, _ = _sample1(10, 10, 30, 30, True)
y_pred = y_true.copy()
# Test basic initialization
o = metrics.ObjectMetrics(y_true, y_pred, force_event_links=True)
# Check that object numbers are integers
assert isinstance(o.n_true, int)
assert isinstance(o.n_pred, int)
assert o.n_true == o.n_pred
# metrics should be perfect since y_true == y_pred
assert o.recall == 1
assert o.precision == 1
assert o.f1 == 1
assert o.jaccard == 1
assert o.dice == 1
# test other properties
assert o.correct_detections == o.n_pred
assert o.missed_detections == 0
assert o.gained_detections == 0
assert o.splits == 0
assert o.merges == 0
assert o.catastrophes == 0
assert o.gained_det_from_split == 0
assert o.missed_det_from_merge == 0
assert o.true_det_in_catastrophe == 0
assert o.pred_det_in_catastrophe == 0
assert o.missed_props == []
assert o.merge_props == []
assert o.split_props == []
assert o.gained_props == []
def test_calc_iou(self):
# TODO: test correctness
y_true, y_pred = _sample1(10, 10, 30, 30, True)
o = metrics.ObjectMetrics(y_true, y_pred)
# Check that it is not equal to initial value
assert np.count_nonzero(o.iou) != 0
assert np.count_nonzero(o.seg_thresh) != 0
# check that image without any background passes
y_true, y_pred = _dense_sample()
o = metrics.ObjectMetrics(y_true=y_true, y_pred=y_pred)
# Check that it is not equal to initial value
assert np.count_nonzero(o.iou) != 0
assert np.count_nonzero(o.seg_thresh) != 0
def test_calc_iou_3D(self):
# TODO: test correctness
y_true, y_pred = _sample1_3D(10, 10, 30, 30, True, 8)
o = metrics.ObjectMetrics(y_true, y_pred, is_3d=True)
# Check that it is not equal to initial value
assert np.count_nonzero(o.iou) != 0
assert np.count_nonzero(o.seg_thresh) != 0
def test_init(self):
y_true, _ = _sample1(10, 10, 30, 30, True)
# Test basic initialization
o = metrics.ObjectMetrics(y_true, y_true)
# Test __repr__
print(o)
# Test using float dtype warns but still works
o = metrics.ObjectMetrics(
y_true.astype('float'),
y_true.astype('float'))
# test errors thrown for improper ndim inputs
y_true = np.zeros(shape=(10)) # too few dimensions
with pytest.raises(ValueError):
metrics.ObjectMetrics(y_true, y_true)
y_true = np.zeros(shape=(10, 5, 5, 5)) # too many dimensions
with pytest.raises(ValueError):
metrics.ObjectMetrics(y_true, y_true)
# test errors thrown for improper ndim inputs with 3d data
y_true = np.zeros(shape=(10, 15)) # too few dimensions
with pytest.raises(ValueError):
metrics.ObjectMetrics(y_true, y_true, is_3d=True)
y_true = np.zeros(shape=(10, 15, 15, 10)) # too many dimensions
with pytest.raises(ValueError):
metrics.ObjectMetrics(y_true, y_true, is_3d=True)
# Test mismatched input size
with pytest.raises(ValueError):
metrics.ObjectMetrics(y_true, y_true[0])
def test_multi_split_error(self):
# 1 cell split into 3
# forced event links to ensure accurate assignment
y_true, y_pred = _sample2_3(10, 10, 30, 30,
merge=False, similar_size=False)
o = metrics.ObjectMetrics(y_true, y_pred, force_event_links=True)
assert o.splits == 1
assert o.gained_det_from_split == 2
assert o.split_props != []
def test_multi_merge_error(self):
# 3 cells merged together
# forced event links to ensure accurate assignment
y_true, y_pred = _sample2_3(10, 10, 30, 30,
merge=True, similar_size=False)
o = metrics.ObjectMetrics(y_true, y_pred, force_event_links=True)
assert o.merges == 1
assert o.missed_det_from_merge == 2
assert o.merge_props != []
def test__get_props(self):
y_true, y_pred = _sample1(10, 10, 30, 30, True)
o = metrics.ObjectMetrics(y_true, y_true)
props = o._get_props('correct')
# assert props != []
# Test _get_props with invalid detection type
with pytest.raises(ValueError):
o._get_props('invalid_type')
def test_y_true_empty(self):
y_pred, _ = _sample1(10, 10, 30, 30, True)
# Test basic initialization with empty array y_pred
o = metrics.ObjectMetrics(np.zeros_like(y_pred), y_pred,
force_event_links=True)
assert o.n_true == 0
assert o.correct_detections == 0
assert o.gained_detections == o.n_pred
assert o.recall == 0
assert o.precision == 0
assert o.f1 == 0
assert o.jaccard == 0
assert o.gained_det_from_split == 0
assert o.missed_det_from_merge == 0
assert o.true_det_in_catastrophe == 0
assert o.pred_det_in_catastrophe == 0
def test_split_error(self):
y_true, y_pred = _sample1(10, 10, 30, 30, False)
o = metrics.ObjectMetrics(y_true, y_pred)
assert o.splits == 1
assert o.gained_det_from_split == 1
assert o.split_props != []
def test_merge_error(self):
y_true, y_pred = _sample1(10, 10, 30, 30, True)
o = metrics.ObjectMetrics(y_true, y_pred)
assert o.merges == 1
assert o.missed_det_from_merge == 1
assert o.merge_props != []