MorphologicalAttributeFilters

‍Project status: research library for dynamic morphological-tree experiments.

This package focuses on a proper-part tree model for image-domain partial partitions, tree editing, typed altitude contracts, incremental attributes, contours, and project-specific morphology research. It is not intended to be a general-purpose replacement for Higra.

Scope

This repository is a research implementation for morphological-tree workflows that need direct topology ownership, staged edits, typed altitude buffers, and project-specific attribute machinery. Its central model is a tree over image-domain partial partitions: image pixels are explicit proper parts, and internal morphological nodes own those proper parts directly. The implemented models share the MorphologicalTree topology abstraction: rooted inclusion topology, dense internal NodeId values, and explicit proper-part ownership.

Current functionality includes:

• max-tree, min-tree, tree-of-shapes, and self-dual residual tree construction;
• import/export of Higra-style (parent, altitude) hierarchies;
• dynamic topology edits through safe mutators and staged editors;
• gray-level, shape, boundary, topology, and max-distance attributes;
• attribute filters, extinction values, and Ultimate Attribute Opening;
• a C++20 header-oriented core plus a pybind11 Python package.

Higra remains the better fit for stable, general-purpose hierarchical image-analysis workflows. Use this project when the experiment needs mutable tree topology, direct owner-state access, or the local attribute/filter machinery exposed here. See docs/attribute-catalog.md for the public descriptor catalog and docs/higra-interoperability.md for import, export, and attribute-projection contracts.

Python currently follows the canonical 8-bit contract: factory inputs must be C-contiguous np.uint8 arrays and external altitude inputs must stay in [0, 255]. C++ supports typed max/min and SDRT construction through Image<T>, typed Higra imports through createFromHigraParent<T>, and read-only WeightedTreeView<T> altitude spans. Tree of Shapes construction is currently uint8_t.

Installation

From PyPI:

pip install mmcfilters

From source:

cmake -S . -B build -DMMCFILTERS_BUILD_PYTHON=ON
cmake --build build

Installed C++ package:

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

To enable the regression suite or examples:

cmake -S . -B build \
  -DMMCFILTERS_BUILD_PYTHON=ON \
  -DMMCFILTERS_BUILD_TESTS=ON \
  -DMMCFILTERS_BUILD_EXAMPLES=ON
cmake --build build
ctest --test-dir build --output-on-failure

Quick Python example

import numpy as np
import mmcfilters
 
img = np.array(
    [
        [3, 3, 2, 2],
        [3, 4, 4, 2],
        [1, 4, 5, 2],
        [1, 1, 5, 0],
    ],
    dtype=np.uint8,
)
 
# Python factories require C-contiguous np.uint8 images. Use C++ for typed
# int32/float altitude trees.
img = np.ascontiguousarray(img, dtype=np.uint8)
 
# radius=1.5 selects the 8-neighbourhood on a 2D square grid.
# Use radius=1.0 for 4-connectivity.
adjacency_radius = 1.5
 
# Case 1: build a weighted max-tree. Topology queries are available on it.
weighted_tree = mmcfilters.MorphologicalTreeFactory.createMaxTree(
    img,
    radius=adjacency_radius,
)
root_node_id = weighted_tree.getRoot()
root_children = weighted_tree.getChildren(root_node_id)
root_direct_proper_parts = weighted_tree.getProperParts(root_node_id)
 
# Case 2: inspect the component that owns one image pixel.
pixel_index = 10
pixel_component_id = weighted_tree.getProperPartOwner(pixel_index)
pixel_component_pixels = list(weighted_tree.getConnectedComponent(pixel_component_id))
pixel_component_mask = weighted_tree.reconstructNode(pixel_component_id)
 
# Case 3: compute attributes that depend only on tree topology/support.
topology_names, topology_by_node = mmcfilters.Attribute.computeTopologyAttributes(
    weighted_tree,
    [mmcfilters.Attribute.AREA, mmcfilters.Attribute.BOX_HEIGHT],
)
area_by_node = topology_by_node[:, topology_names["AREA"]]
box_height_by_node = topology_by_node[:, topology_names["BOX_HEIGHT"]]
 
# Case 4: compute altitude-dependent attributes and reconstruct the image.
max_dist_by_node = mmcfilters.Attribute.computeSingleAttribute(
    weighted_tree,
    mmcfilters.Attribute.MAX_DIST,
)
reconstructed_image = weighted_tree.reconstructionImage()
 
# Case 5: run Ultimate Attribute Opening, the public UAO API.
uao = mmcfilters.UltimateAttributeOpening(weighted_tree, box_height_by_node)
uao.execute(img.shape[0])
max_contrast_image = uao.getMaxContrastImage()
associated_image = uao.getAssociatedImage()
 
# Case 6: export/import a Higra-style hierarchy for interoperability.
higra_parent, higra_altitude = weighted_tree.exportHigraHierarchy()
max_dist_by_higra = weighted_tree.project_node_values_to_exported_higra(
    max_dist_by_node,
    mmcfilters.Attribute.MAX_DIST,
)
roundtrip_weighted_tree = mmcfilters.MorphologicalTreeFactory.createFromHigraParent(
    higra_parent,
    higra_altitude,
    img.shape[0],
    img.shape[1],
    mmcfilters.MorphologicalTreeKind.MAX_TREE,
    radius=adjacency_radius,
)

Repository guide

Use this map to find the right entry point quickly:

API guides:

• Morphological tree model: docs/trees.md
• Attribute computation: docs/attributes.md
• Attribute catalog: docs/attribute-catalog.md
• Attribute filters, extinction values, and UAO: docs/filters.md
• Editing API and derived-state lifetime: docs/editing-api.md
• Higra interoperability: docs/higra-interoperability.md
• Python interface: docs/python-api.md
• Incremental contours: docs/contours.md

Contributor design notes:

• Attribute computer architecture and extension: docs/attribute-computer-architecture.md
• Contour internals and benchmarks: docs/contours.md

Documentation

The Documentation workflow validates the public and internal Doxygen targets. On pushes to main, it publishes only the public HTML output to GitHub Pages: wonderalexandre.github.io/MorphologicalAttributeFilters. The internal HTML output remains available as a workflow artifact.

Release process

Releases are automated by GitHub Actions. For a production release:

1. Make sure the CI and Package workflows are green on main.
2. Create and push a semantic version tag, for example v1.0.1.
3. The Release workflow validates that the tag matches the resolved package version, builds the source distribution and platform wheels, validates the package metadata, and attaches the artifacts to a GitHub Release.

The release wheel matrix targets Python 3.9 through 3.14 on:

• Linux manylinux x86_64;
• Windows x86_64;
• macOS arm64;
• macOS Intel x86_64.

PyPI publication is intentionally manual. Download the release artifacts from the GitHub Release or from the Release workflow run, then upload them with:

python -m pip install --upgrade twine
python -m twine upload dist/*

Manual runs of the Release workflow also build downloadable artifacts without creating a GitHub Release.