.. cpp:function:: auto alphatree(Image f, Neighborhood nbh);
.. cpp:function:: auto alphatree(Image f, Neighborhood nbh, F dist);
Compute the alphatree (also known as quasi-flat zone hierarchy) and returns a pair
Compute the alphatree (also known as quasi-flat zone hierarchy) and returns a pair
`(tree, node_map)`. See :doc:`component_tree` for more information about the
representation of tree.
representation of tree. The implementation is based on the Kruskal algorithm [Naj13]_.
:param input: The input image
:param nbh: The neighborhood
:return: A pair `(tree, node_map)` where *tree* is of type
`component_tree<image_value_t<I>>` and `node_map` is a mapping from each point to the
node *id* in the tree.
:precondition: ``f.domain().has(pinf)``
:param dist: The function weighting the edges between two pixels. For grayscale images, this is generally the absolute difference between two values.
:return: A pair `(tree, node_map)` where *tree* is of type ``component_tree<std::invoke_result_t<F, image_value_t<Image>, image_value_t<Image>>>`` and *node_map* is a mapping between the image pixels and the node of the tree.
.. rubric:: Requirements
* ``image_value_t<I>`` is :cpp:concept:`std::totally_ordered`
* ``std::invoke_result_t<F, image_value_t<Image>, image_value_t<Image>>`` is :cpp:concept:`std::totally_ordered`
Definition
----------
The Alpha tree is the tree of :math:`\alpha`-connected components. An :math:`\alpha`-connected component in an image :math:`f`, for a pixel :math:`p`, is defined as
where :math:`d` is a dissimilarity function between two pixels of the image :math:`f`.
The :math:`\alpha`-connected components form an ordered sequence when :math:`\alpha` is growing, such that for :math:`\alpha_i < \alpha_j`,
:math:`\alpha_i-CC(p) \subseteq \alpha_j-CC(p)`. Thus, the alphatree is the hierarchy where the parenthood relationship represents the inclusion of the
:math:`\alpha`-connected components.
Representation
--------------
.. image:: /figures/morpho/alphatree_repr.svg
:align: center
:width: 30%
The ``alphatree`` function returns a tree and a node map. The tree has two attributes:
* ``parent``: The parenthood relationship of the node :math:`i`, representing the inclusion relationship of the :math:`\alpha`-connected components.
* ``values``: The value of :math:`\alpha` assigned to a node :math:`i`.
.. rubric:: Example
Then, the node map is the relation between a pixel of the image and its related node in the tree, a leaf for the case of the alphatree.
The image above illustrates the representation of the alpha tree in Pylene, the parenthood relationship being illustrated in arrows, the values of alpha, assigned to each node, being in red, and the relation
between a node of the tree and a pixel of the image being represented by blue dashed lines.
Example
-------
This example is used to generate the grayscale lena cut with a threshold of 3 below.
::
#include <mln/accu/accumulators/mean.hpp>
#include <mln/morpho/alphatree.hpp>
#include <mln/core/image/ndimage.hpp>
#include <mln/core/neighborhood/c4.hpp>
mln::image2d<uint8_t> input = ...;
// Compute the alpha tree
auto [tree, node_map] = mln::morpho::alphatree(input, mln::c4);
// Compute an attribute (for example the average pixels value at each node, as below)
auto mean = t.compute_attribute_on_values(node_map, input, mln::accu::accumulators::mean<uint8_t>());
// Making an horizontal cut of the tree
const auto threshold = 3; // Threshold of the horizontal cut, that means the lowest alpha in the cut
auto nodemap_cut = t.horizontal_cut(threshold, node_map); // Return a new nodemap associated to the cut
// Labelizing the cut with the mean values of each node
auto out = t.reconstruct_from(nodemap_cut, ranges::make_span(mean)); // Using range-v3 span
.. list-table::
* - .. figure:: /images/alphatree_cut_color.png
Cut of the alpha tree with a threshold of 10
- .. figure:: /images/alphatree_cut_gray.png
Cut of the alpha tree with a threshold of 3
Notes
-----
...
...
@@ -48,3 +98,5 @@ Complexity
References
----------
.. [Naj13] Laurent Najman, Jean Cousty, and Benjamin Perret (2013). Playing with kruskal: algorithms for morphological trees in edge-weighted graphs. *International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing*. Springer, Berlin, Heidelberg. 135-146
