def test_sgm_middle_value_invalid(self):
""" "
Test SGM middle value invalid
"""
p1 = 8
p2 = 32
cv_in = np.array(
[
[[1, 15, 20], [14, 16, 6], [8, 19, 8]],
[[13, 11, 3], [np.nan, np.nan, np.nan], [16, 4, 12]],
[[18, 2, 17], [23, 7, 1], [5, 20, 14]],
]
)
p1_in = p1 * np.ones((3, 3, 8))
p2_in = p2 * np.ones((3, 3, 8))
directions = [[0, 1], [1, 0], [1, 1], [1, -1], [0, -1], [-1, 0], [-1, -1], [-1, 1]]
segmentation = np.ones((3, 3))
cv_out = sgm.run_sgm_parall(cv_in, p1_in, p2_in, directions, segmentation, cost_paths=False, overcounting=False)
# invalid value : nan
self.assertTrue(np.isnan(cv_out["cv"][1, 1, 1]))
def test_sgm_middle_value_lr_0_1(self):
""" "
Test SGM middle value directions 0 1
"""
p1 = 8
p2 = 32
cv_in = common.cv_in
p1_in = p1 * np.ones((3, 3, 8))
p2_in = p2 * np.ones((3, 3, 8))
directions = [[0, 1]]
segmentation = np.ones((3, 3))
cv_out = sgm.run_sgm_parall(cv_in, p1_in, p2_in, directions, segmentation, cost_paths=False, overcounting=False)
# invalid value : nan
self.assertEqual(cv_out["cv"][1, 1, 1], 20)
def test_sgm_middle_value_invalid_overcounting(self):
""" "
Test SGM middle value invalid overcounting
"""
p1 = 8
p2 = 32
cv_in = common.cv_in_nans
p1_in = p1 * np.ones((3, 3, 8))
p2_in = p2 * np.ones((3, 3, 8))
directions = [[0, 1], [1, 0], [1, 1], [1, -1], [0, -1], [-1, 0], [-1, -1], [-1, 1]]
segmentation = np.ones((3, 3))
cv_out = sgm.run_sgm_parall(cv_in, p1_in, p2_in, directions, segmentation, cost_paths=False, overcounting=True)
# invalid value : nan
self.assertTrue(np.isnan(cv_out["cv"][1, 1, 1]))
def test_sgm_middle_value_min_cost(self):
""" "
Test SGM middle value minimum cost
"""
p1 = 8
p2 = 32
cv_in = common.cv_in
p1_in = p1 * np.ones((3, 3, 8))
p2_in = p2 * np.ones((3, 3, 8))
directions = [[0, 1], [1, 0], [1, 1], [1, -1], [0, -1], [-1, 0], [-1, -1], [-1, 1]]
segmentation = np.ones((3, 3))
cv_out = sgm.run_sgm_parall(cv_in, p1_in, p2_in, directions, segmentation, cost_paths=True, overcounting=False)
# invalid value : nan
self.assertEqual(cv_out["cv"][1, 1, 1], 142)
self.assertEqual(cv_out["cv_min"][1, 1, 1], 2)
