MorphologicalAttributeFilters

PyPI Python CI Documentation License: GPL-3.0

mmcfilters is an alpha research library for constructing and editing morphological hierarchies, computing node attributes, and applying connected filters and hierarchy operators. It provides a C++20 core and Python bindings.

The library represents hierarchies as rooted trees over finite partial partitions. All supported image-domain hierarchies use the same topology model, with explicit proper-part ownership and optional regular-grid geometry and adjacency.

Use mmcfilters for experiments that require mutable tree topology, direct ownership of hierarchy state, incremental attributes, residual hierarchies, or the filtering operators implemented by this project. For stable, general-purpose hierarchical image processing, Higra will often be the better choice.

Main capabilities

  • construction of max-trees, min-trees, trees of shapes, and self-dual residual trees;
  • generic rooted hierarchies with safe local and staged edits;
  • a broad catalog of node attributes organized into gray-level, shape, moment, boundary, and tree-topology groups;
  • a range of attribute-based filters and hierarchy operators, including pruning and non-pruning strategies;
  • pixel contours and ordered geometric contour traces;
  • import, export, and attribute projection for Higra-style hierarchies;
  • typed altitude handling in C++ and a focused np.uint8 Python API.

Requirements

  • Python 3.9–3.14 for the distributed Python package;
  • NumPy 1.23 or newer;
  • a C++20 compiler and CMake 3.20 or newer for source builds.

Installation

Install the Python package from PyPI:

python -m pip install mmcfilters

Install from a source checkout:

python -m pip install .

For a C++-only build and installation:

cmake -S . -B build -DMMCFILTERS_BUILD_PYTHON=OFF
cmake --build build
cmake --install build --prefix /path/to/prefix

The installed C++ package exports the mmcfilters::core target:

find_package(mmcfilters CONFIG REQUIRED)
target_link_libraries(my_target PRIVATE mmcfilters::core)

Quick start

import numpy as np
import mmcfilters

image = np.ascontiguousarray(
    [
        [3, 3, 2, 2],
        [3, 4, 4, 2],
        [1, 4, 5, 2],
        [1, 1, 5, 0],
    ],
    dtype=np.uint8,
)

# radius=1.5 selects 8-connectivity; use 1.0 for 4-connectivity.
tree = mmcfilters.MorphologicalTreeFactory.createMaxTree(image, radius=1.5)

root = tree.getRoot()
root_children = tree.getChildren(root)

pixel = 10
owner = tree.getProperPartOwner(pixel)
component_pixels = list(tree.getConnectedComponent(owner))
component_mask = tree.reconstructNode(owner)

area = mmcfilters.Attribute.computeSingleTopologyAttribute(
    tree,
    mmcfilters.Attribute.AREA,
)
max_dist = mmcfilters.Attribute.computeSingleAttribute(
    tree,
    mmcfilters.Attribute.MAX_DIST,
)
reconstructed_image = tree.reconstructionImage()

Documentation

User guides

Goal Guide
Understand the tree model and construct hierarchies Morphological trees
Use the Python interface Python API
Compute attributes Attributes
Apply filtering and hierarchy operators Filters
Compute hierarchy and shape-space saliency Saliency maps
Edit a tree safely Editing API
Extract pixel contours or geometric traces Pixel contours and contour traces
Import from or export to Higra Higra interoperability

References

Contributor guides

License

MorphologicalAttributeFilters is distributed under the GNU General Public License v3.0.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (5.1.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for wonderalexandre from gravatar.com wonderalexandre

Credits

Author: Wonder Alexandre Luz Alves

License

GNU GENERAL PUBLIC LICENSE

Requires

Python >=3.9

Tags

morphological trees mathematical morphology image processing computer vision

Classifiers

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5.1.0

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5.0.0

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4.3.0

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3.1.1 This release

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