def test_hierarchy_alignement(self):
g = hg.get_4_adjacency_graph((3, 3))
fine_labels = np.asarray((0, 1, 2, 3, 4, 2, 3, 4, 2), dtype=np.int32)
aligner = hg.HierarchyAligner.from_labelisation(g, fine_labels)
t = hg.Tree(np.asarray((9, 10, 10, 9, 11, 11, 9, 11, 11, 13, 12, 12, 13, 13), dtype=np.int64))
altitudes = np.asarray((0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2), dtype=np.int32)
sm = aligner.align_hierarchy(t, altitudes)
sm_k = hg.graph_4_adjacency_2_khalimsky(g, sm, (3, 3))
sm_coarse = np.asarray((2, 0, 0, 1, 1, 2, 0, 0, 0, 0, 2, 0), dtype=np.int32)
sm2 = aligner.align_hierarchy(g, sm_coarse)
sm2_k = hg.graph_4_adjacency_2_khalimsky(g, sm2, (3, 3))
sm_k_ref = np.asarray(((0, 2, 0, 1, 0),
(0, 2, 1, 1, 0),
(0, 2, 0, 0, 0),
(0, 2, 0, 0, 0),
(0, 2, 0, 0, 0)), dtype=np.int32)
self.assertTrue(np.all(sm_k == sm_k_ref))
self.assertTrue(np.all(sm2_k == sm_k_ref))
def test_graph_4_adjacency_2_khalimsky(self):
g = hg.get_4_adjacency_graph((2, 3))
data = np.asarray((1, 0, 2, 1, 1, 1, 2))
ref = np.asarray(((0, 1, 0, 2, 0), (0, 1, 1, 2, 1), (0, 1, 0, 2, 0)))
r = hg.graph_4_adjacency_2_khalimsky(g, data)
self.assertTrue(np.allclose(ref, r))
def test_align_hierarchies(self):
g = hg.get_4_adjacency_graph((3, 3))
fine_labels = np.asarray((0, 1, 2, 3, 4, 2, 3, 4, 2), dtype=np.int32)
t = hg.Tree(np.asarray((9, 10, 10, 9, 11, 11, 9, 11, 11, 13, 12, 12, 13, 13), dtype=np.int64))
altitudes = np.asarray((0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2), dtype=np.int32)
t2 = hg.Tree(np.asarray((4, 3, 3, 4, 4), dtype=np.int64))
rag = hg.make_region_adjacency_graph_from_labelisation(g, np.asarray((0, 1, 1,
0, 2, 2,
0, 2, 2)))
hg.CptHierarchy.link(t2, rag)
altitudes2 = np.asarray((0, 0, 0, 1, 2), dtype=np.int32)
sm_coarse = np.asarray((2, 0, 0, 1, 1, 2, 0, 0, 0, 0, 2, 0), dtype=np.int32)
sm_coarse_rag = np.asarray((2, 1, 2), dtype=np.int32)
sm_k_ref = np.asarray(((0, 2, 0, 1, 0),
(0, 2, 1, 1, 0),
(0, 2, 0, 0, 0),
(0, 2, 0, 0, 0),
(0, 2, 0, 0, 0)), dtype=np.int32)
# test only one:
res = hg.align_hierarchies(g, fine_labels, (t, altitudes))
res_k = hg.graph_4_adjacency_2_khalimsky(g, res, (3, 3))
self.assertTrue(np.all(res_k == sm_k_ref))
res = hg.align_hierarchies(g, fine_labels, (t2, altitudes2))
res_k = hg.graph_4_adjacency_2_khalimsky(g, res, (3, 3))
self.assertTrue(np.all(res_k == sm_k_ref))
res = hg.align_hierarchies(g, fine_labels, (g, sm_coarse))
res_k = hg.graph_4_adjacency_2_khalimsky(g, res, (3, 3))
self.assertTrue(np.all(res_k == sm_k_ref))
res = hg.align_hierarchies(g, fine_labels, (rag, sm_coarse_rag))
res_k = hg.graph_4_adjacency_2_khalimsky(g, res, (3, 3))
self.assertTrue(np.all(res_k == sm_k_ref))
# test several
res_all = hg.align_hierarchies(g, fine_labels,
((t2, altitudes2), (g, sm_coarse), (t, altitudes), (rag, sm_coarse_rag)))
for res in res_all:
res_k = hg.graph_4_adjacency_2_khalimsky(g, res, (3, 3))
self.assertTrue(np.all(res_k == sm_k_ref))
def get_contour_masked(self, output, masked):
import numpy as np
import higra as hg
hh = np.copy(masked)
hh[hh > 0] = 255
from scipy import signal
Ix = signal.correlate2d(hh[:, :, 0],
[[1, 2, 1], [0, 0, 0], [-1, -2, -1]],
mode='same',
boundary='symm')
Iy = signal.correlate2d(hh[:, :, 0],
[[1, 0, -1], [2, 0, -2], [1, 0, -1]],
mode='same',
boundary='symm')
G = np.hypot(Ix, Iy)
G = G / G.max()
size = hh.shape[:2]
gradient_coarse = np.array([output[1], G]).max(axis=0)
gradient_fine = np.array([output[0], G]).max(axis=0)
gradient_orientation = output[2]
graph = hg.get_4_adjacency_graph(size)
edge_weights_fine = hg.weight_graph(graph, gradient_fine,
hg.WeightFunction.mean)
edge_weights_coarse = hg.weight_graph(graph, gradient_coarse,
hg.WeightFunction.mean)
edge_weights_hig = hg.weight_graph(graph, G, hg.WeightFunction.mean)
# special handling for angles to wrap around the trigonometric cycle...
edge_orientations_source = hg.weight_graph(graph, gradient_orientation,
hg.WeightFunction.source)
edge_orientations_target = hg.weight_graph(graph, gradient_orientation,
hg.WeightFunction.target)
edge_orientations = hg.mean_angle_mod_pi(edge_orientations_source,
edge_orientations_target)
combined_hierarchy1, altitudes_combined1 = hg.multiscale_mean_pb_hierarchy(
graph,
edge_weights_fine,
others_edge_weights=(edge_weights_coarse, ),
edge_orientations=edge_orientations)
return hg.graph_4_adjacency_2_khalimsky(
graph, hg.saliency(combined_hierarchy1, altitudes_combined1))