def test_contingency_table():
im_true = np.array([1, 2, 3, 4])
im_test = np.array([1, 1, 8, 8])
table1 = np.array([[0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0.25, 0., 0., 0., 0., 0., 0., 0.],
[0., 0.25, 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0.25],
[0., 0., 0., 0., 0., 0., 0., 0., 0.25]])
sparse_table2 = contingency_table(im_true, im_test, normalize=True)
table2 = sparse_table2.toarray()
assert_array_equal(table1, table2)
def _iou_matrix(gt, seg):
# relabel gt and seg for smaller memory footprint of contingency table
gt = _relabel(gt)
seg = _relabel(seg)
# get number of overlapping pixels between GT and SEG
n_inter = contingency_table(gt, seg).A
# number of pixels for GT instances
n_gt = n_inter.sum(axis=1, keepdims=True)
# number of pixels for SEG instances
n_seg = n_inter.sum(axis=0, keepdims=True)
# number of pixels in the union between GT and SEG instances
n_union = n_gt + n_seg - n_inter
iou_matrix = n_inter / n_union
# make sure that the values are within [0,1] range
assert 0 <= np.min(iou_matrix) <= np.max(iou_matrix) <= 1
return iou_matrix