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# Copyright ESIEE Paris (2018) #
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# Contributor(s) : Benjamin Perret #
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# Distributed under the terms of the CECILL-B License. #
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# The full license is in the file LICENSE, distributed with this software. #
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import higra as hg
import numpy as np
[docs]@hg.argument_helper(hg.CptGridGraph)
def oriented_watershed(graph, edge_weights, shape, edge_orientations=None):
"""
Creates a region adjacency graph (rag) with the oriented watershed transform.
The method is described in:
P. Arbelaez, M. Maire, C. Fowlkes and J. Malik, "Contour Detection and Hierarchical Image Segmentation,"
in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898-916, May 2011.
doi: 10.1109/TPAMI.2010.161
If no edge orientations are provided, then the weight of a rag edge between region i and j is the mean weight of the
edges linking a vertex of i to a vertex of j.
This does not include gradient estimation.
:param graph: must be a 4 adjacency graph (Concept :class:`~higra.CptGridGraph`)
:param edge_weights: gradient value on edges
:param shape: shape of the graph, i.e. a pair (height, width) (deduced from :class:`~higra.CptGridGraph`)
:param edge_orientations: estimated orientation of the gradient on edges (optional)
:return: a pair (rag, rag_edge_weights): the region adjacency graph (Concept :class:`~higra.CptRegionAdjacencyGraph`) and its estimated edge_weights
"""
shape = hg.normalize_shape(shape)
if edge_orientations is not None:
edge_weights, edge_orientations = hg.cast_to_common_type(edge_weights, edge_orientations)
rag, vertex_map, edge_map, rag_edge_weights = hg.cpp._oriented_watershed(graph, shape, edge_weights,
edge_orientations)
hg.CptRegionAdjacencyGraph.link(rag, graph, vertex_map, edge_map)
return rag, rag_edge_weights
[docs]@hg.argument_helper(hg.CptGridGraph)
def mean_pb_hierarchy(graph, edge_weights, shape, edge_orientations=None):
"""
Mean probability boundary hierarchy.
The method is described in:
P. Arbelaez, M. Maire, C. Fowlkes and J. Malik, "Contour Detection and Hierarchical Image Segmentation,"
in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898-916, May 2011.
doi: 10.1109/TPAMI.2010.161
This does not include gradient estimation.
The returned hierarchy is defined on the gradient watershed super-pixels.
The final sigmoid scaling of the hierarchy altitude is not performed.
:param graph: must be a 4 adjacency graph (Concept :class:`~higra.CptGridGraph`)
:param edge_weights: gradient value on edges
:param shape: shape of the graph, i.e. a pair (height, width) (deduced from :class:`~higra.CptGridGraph`)
:param edge_orientations: estimated orientation of the gradient on edges (optional)
:return: a tree (Concept :class:`~higra.CptHierarchy`) and its node altitudes
"""
shape = hg.normalize_shape(shape)
if edge_orientations is not None:
edge_weights, edge_orientations = hg.cast_to_common_type(edge_weights, edge_orientations)
rag, vertex_map, edge_map, tree, altitudes = hg.cpp._mean_pb_hierarchy(graph, shape, edge_weights,
edge_orientations)
hg.CptRegionAdjacencyGraph.link(rag, graph, vertex_map, edge_map)
hg.CptHierarchy.link(tree, rag)
return tree, altitudes
[docs]@hg.argument_helper(hg.CptGridGraph)
def multiscale_mean_pb_hierarchy(graph, fine_edge_weights, others_edge_weights, shape, edge_orientations=None):
"""
Multiscale mean probability boundary hierarchy.
The method is described in:
J. Pont-Tuset, P. Arbeláez, J. Barron, F. Marques, and J. Malik
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 39, no. 1, pp. 128 - 140, 2017.
and in:
K.K. Maninis, J. Pont-Tuset, P. Arbeláez and L. Van Gool
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 40, no. 4, pp. 819 - 833, 2018.
This does not include gradient estimation.
The returned hierarchy is defined on the gradient watershed super-pixels.
The final sigmoid scaling of the hierarchy altitude is not performed.
:param graph: must be a 4 adjacency graph (Concept :class:`~higra.CptGridGraph`)
:param fine_edge_weights: edge weights of the finest gradient
:param others_edge_weights: tuple of gradient value on edges
:param shape: shape of the graph, i.e. a pair (height, width) (deduced from :class:`~higra.CptGridGraph`)
:param edge_orientations: estimated orientation of the gradient on edges (optional)
:return: a tree (Concept :class:`~higra.CptHierarchy`) and its node altitudes
"""
shape = hg.normalize_shape(shape)
tree_fine, altitudes_fine = hg.mean_pb_hierarchy(graph, fine_edge_weights, shape=shape,
edge_orientations=edge_orientations)
saliency_fine = hg.saliency(tree_fine, altitudes_fine)
super_vertex_fine = hg.labelisation_hierarchy_supervertices(tree_fine, altitudes_fine)
other_hierarchies = []
for edge_weights in others_edge_weights:
tree_coarse, altitudes_coarse = hg.mean_pb_hierarchy(graph, edge_weights, shape=shape,
edge_orientations=edge_orientations)
other_hierarchies.append((tree_coarse, altitudes_coarse))
aligned_saliencies = hg.align_hierarchies(graph, super_vertex_fine, other_hierarchies)
for saliency in aligned_saliencies:
saliency_fine += saliency
saliency_fine *= (1.0 / (1 + len(others_edge_weights)))
return hg.mean_pb_hierarchy(graph, saliency_fine, shape=shape)