def patches_batch_tos_area_p(batch_image, i, j, l):
tree, altitudes = hg.component_tree_tree_of_shapes_image2d(
batch_image[i, :, :, 0])
area = hg.attribute_area(tree)
batch_image[i, :, :,
j + 1] = hg.reconstruct_leaf_data(tree, altitudes, area < l)
return batch_image[i, :, :, j + 1]
def test_type_consistency(self):
tree = hg.Tree((2, 2, 2))
for t in (np.bool, np.int8, np.uint8, np.int16, np.uint16, np.int32,
np.uint32, np.int64, np.uint64, np.float, np.double):
a = np.zeros(3, dtype=t)
res = hg.reconstruct_leaf_data(tree, a)
self.assertTrue(a.dtype == res.dtype)
def test_reconstruct_leaf_data_component_tree_default(self):
g = hg.get_4_adjacency_implicit_graph((1, 6))
vertex_values = np.asarray((1, 5, 4, 3, 3, 6), dtype=np.int32)
tree, altitudes = hg.component_tree_max_tree(g, vertex_values)
area = hg.attribute_area(tree)
output = hg.reconstruct_leaf_data(tree, area)
ref = np.asarray((6, 1, 2, 5, 5, 1), dtype=np.int32)
self.assertTrue(np.all(ref == output))
def test_reconstruct_leaf_data_component_tree(self):
g = hg.get_4_adjacency_implicit_graph((1, 6))
vertex_values = np.asarray((1, 5, 4, 3, 3, 6), dtype=np.int32)
tree, altitudes = hg.component_tree_max_tree(g, vertex_values)
condition = np.asarray((True, False, True, False, True, True, False, True, False, True, False), np.bool_)
output = hg.reconstruct_leaf_data(tree, altitudes, condition)
ref = np.asarray((1, 4, 4, 1, 1, 6), dtype=np.int32)
self.assertTrue(np.all(ref == output))
def patches_batch_tos_area(batch_image, batch_size, lambdas):
for i in range(batch_size):
tree, altitudes = hg.component_tree_tree_of_shapes_image2d(
batch_image[i, :, :, 0])
area = hg.attribute_area(tree)
for j in range(len(lambdas)):
batch_image[i, :, :, j + 1] = hg.reconstruct_leaf_data(
tree, altitudes, area < lambdas[j])
return batch_image
def test_reconstruct_leaf_data_default(self):
tree = hg.Tree(np.asarray((5, 5, 6, 6, 6, 7, 7, 7)))
input = np.asarray(
((1, 8), (2, 7), (3, 6), (4, 5), (5, 4), (6, 3), (7, 2), (8, 1)),
dtype=np.int32)
output = hg.reconstruct_leaf_data(tree, input)
ref = np.asarray(((1, 8), (2, 7), (3, 6), (4, 5), (5, 4)),
dtype=np.int32)
self.assertTrue(np.all(ref == output))
def test_reconstruct_leaf_data(self):
tree = hg.Tree(np.asarray((5, 5, 6, 6, 6, 7, 7, 7)))
input = np.asarray(
((1, 8), (2, 7), (3, 6), (4, 5), (5, 4), (6, 3), (7, 2), (8, 1)),
dtype=np.int32)
condition = np.asarray(
(True, False, True, False, True, True, False, False), np.bool_)
output = hg.reconstruct_leaf_data(tree, input, condition)
ref = np.asarray(((8, 1), (2, 7), (7, 2), (4, 5), (7, 2)),
dtype=np.int32)
self.assertTrue(np.all(ref == output))
def test_simplify_tree_propagate_category(self):
g = hg.get_4_adjacency_implicit_graph((1, 6))
vertex_values = np.asarray((1, 5, 4, 3, 3, 6), dtype=np.int32)
tree, altitudes = hg.component_tree_max_tree(g, vertex_values)
condition = np.asarray((False, False, False, False, False, False,
False, True, False, True, False), np.bool)
new_tree, node_map = hg.simplify_tree(tree, condition)
self.assertTrue(
np.all(new_tree.parents() == (8, 7, 7, 8, 8, 6, 8, 8, 8)))
self.assertTrue(np.all(node_map == (0, 1, 2, 3, 4, 5, 6, 8, 10)))
self.assertTrue(new_tree.category() == hg.TreeCategory.ComponentTree)
rec = hg.reconstruct_leaf_data(new_tree, altitudes[node_map])
self.assertTrue(np.all(rec == (1, 4, 4, 1, 1, 6)))
def test_area_filter_max_tree(self):
graph = hg.get_4_adjacency_implicit_graph((5, 5))
vertex_weights = np.asarray(
((-5, 2, 2, 5, 5), (-4, 2, 2, 6, 5), (3, 3, 3, 3, 3),
(-2, -2, -2, 9, 7), (-1, 0, -2, 8, 9)),
dtype=np.float64)
tree, altitudes = hg.component_tree_max_tree(graph, vertex_weights)
area = hg.attribute_area(tree)
filtered_weights = hg.reconstruct_leaf_data(tree, altitudes, area <= 4)
expected_filtered_weights = \
np.asarray(((-5, 2, 2, 3, 3),
(-4, 2, 2, 3, 3),
(3, 3, 3, 3, 3),
(-2, -2, -2, 3, 3),
(-2, -2, -2, 3, 3)), dtype=np.float64)
self.assertTrue(np.all(filtered_weights == expected_filtered_weights))