mtlearn 1.0.10

Morphological tree learning utilities with a stable morphology facade

MTLearn

MTLearn (Morphological Tree Learning) is a C++/Python research library for learnable connected operators based on morphological trees. The Python package is published as mtlearn.

The library explores a simple idea: connected morphology can become a structural prior for deep neural networks. Instead of processing images only through local pixel-wise operations, connected operators reason over components, regions, shape, contrast, and hierarchy. This makes them naturally interpretable and well-suited for tasks where structure matters.

Classical connected filters are powerful, but they usually depend on hard keep/discard decisions and manually selected attribute thresholds. This limits their integration into end-to-end trainable neural architectures.

MTLearn provides a stable implementation platform for this research direction. It currently includes Connected Filter Preprocessing (CFP), and is intended to grow toward trainable connected-operator layers, differentiable or learnable attribute criteria, self-dual tree representations, intermediate network insertions, and scalable implementations.

Main Features

• Connected Filter Preprocessing (CFP): the current main model, available as mtlearn.layers.ConnectedFilterPreprocessingLayer. CFP replaces hard connected-filter decisions with a differentiable sigmoid gate over normalized tree-node attributes.

• Stable morphology interface: mtlearn.morphology builds max-trees, min-trees, and tree-of-shapes through a backend-independent API.

• Trainable connected morphology: designed as an implementation platform for connected morphology as a learnable structural prior in deep neural networks.

• Research-ready validation: includes C++ tests, Python tests, gradient checks, reference implementations, notebook validations, and public dataset download helpers.

Install

The Python package is available from PyPI as mtlearn:

pip install mtlearn

See docs/installation.md for installation instructions and docs/development.md for source builds, validation, and releases. For the public scalar attribute and group catalog, see docs/source/concepts/attributes.md.

Quick Start

Build a morphology tree and compute attributes:

import numpy as np
from mtlearn import morphology

image = np.array([[1, 2], [3, 4]], dtype=np.uint8)
tree = morphology.create_max_tree(image)

_, attributes = morphology.compute_attributes(
    tree,
    [morphology.AttributeType.AREA, morphology.AttributeType.COMPACTNESS],
)

print(attributes.shape)

Create a CFP layer and run a forward pass:

import torch
from mtlearn import morphology
from mtlearn.layers import ConnectedFilterPreprocessingLayer

cfp_layer = ConnectedFilterPreprocessingLayer(
    in_channels=1,
    filter_specs=[
        {
            "tree_type": morphology.TreeType.MAX_TREE,
            "attributes": (
                morphology.AttributeType.AREA,
                morphology.AttributeType.CIRCULARITY,
            ),
        }
    ],
    device="cpu",
    scale_mode="none",
)

x = torch.tensor([[[[1, 2], [3, 4]]]], dtype=torch.float32)
y = cfp_layer(x)

assert y.shape == x.shape

For self-dual preprocessing, use the tree-of-shapes backend explicitly:

cfp_tos = ConnectedFilterPreprocessingLayer(
    in_channels=1,
    filter_specs=[
        {
            "tree_type": morphology.TreeType.TREE_OF_SHAPES,
            "attributes": (morphology.AttributeGroup.SHAPE,),
            "tos_interpolation": "self-dual",
        }
    ],
)

CFP Filter Specs

ConnectedFilterPreprocessingLayer is configured with filter_specs. Each specification creates one output channel per input channel and owns the morphology tree and scoring attributes:

from mtlearn import morphology

filter_specs = [
    {
        "name": "max_area_gray",
        "tree_type": morphology.TreeType.MAX_TREE,
        "attributes": (
            morphology.AttributeType.AREA,
            morphology.AttributeType.GRAY_HEIGHT,
        ),
    },
    {
        "name": "tos_boundary",
        "tree_type": morphology.TreeType.TREE_OF_SHAPES,
        "attributes": (morphology.AttributeGroup.BOUNDARY,),
        "tos_interpolation": "self-dual",
    },
    {
        "name": "min_area",
        "tree_type": morphology.TreeType.MIN_TREE,
        "attributes": (morphology.AttributeType.AREA,),
    },
]

name is optional. When provided, it becomes the stable parameter key for that filter spec; when omitted, the layer uses spec_000, spec_001, and so on.

The sigmoid logits are unclamped by default. Pass clamp=12 to clamp beta_f * logits to [-12, 12], or pass an explicit pair such as clamp=(-8, 10).

Examples and Notebooks

Executable examples are available in notebooks/.

Install notebook dependencies with:

pip install "mtlearn[notebooks]"

The main public experiment example is:

notebooks/experiments/Example_screws_filtering.ipynb

Representative ICPR 2026 experiment notebooks are available in notebooks/ICPR2026.

Implementation Notes

ConnectedFilterPreprocessingLayer is the recommended implementation for new CFP experiments.

ConnectedFilterPreprocessingLayerLegacy remains available for loading or reproducing experiments that used the former global tree/output contract.

For PyTorch checkpoints, use the helper functions in mtlearn.layers. CFP trainable weights are regular PyTorch parameters, and the primary layer stores its serializable config and dataset normalization statistics in PyTorch extra state:

from mtlearn.layers import (
    ConnectedFilterPreprocessingLayer,
    load_checkpoint,
    save_checkpoint,
)

save_checkpoint("model.pt", model)

def build_model():
    return Model(
        ConnectedFilterPreprocessingLayer(
            in_channels=1,
            filter_specs=filter_specs,
        ),
        build_backbone(),
    )

model, checkpoint = load_checkpoint("model.pt", build_model, device=device)

The helpers discover CFP layers by module name, save their configs next to the normal model state_dict, and let the CFP extra state validate compatibility and restore dataset normalization statistics during load_state_dict. When the model constructor cannot hard-code the CFP configuration, the load factory may instead accept one cfp_configs argument and call ConnectedFilterPreprocessingLayer.from_config(...).

Checkpoints do not persist per-sample tree, attribute, or normalization caches; those are rebuilt from input data. export_params()/save_params() are manual inspection helpers for CFP weights and metadata, not the recommended full-model checkpoint API.

Tensor operations, trainable parameters, and cached attributes can live on CUDA when device="cuda". Morphology-tree construction is still performed by the C++ backend on CPU.

The main implementation uses an implicit Jacobian formulation. The dense region-pixel matrix is not materialized during normal training; tree-ordering metadata is used to perform the equivalent reconstruction and backward accumulation more compactly. This reduces memory pressure compared with explicit region-pixel Jacobian construction.

Reference implementations based on explicit Jacobians and CPU tree traversals remain available for gradient checks, comparisons, and debugging.

MTLearn uses a C++ morphology backend internally through mtlearn::morphology. User code should interact with morphology through the public Python facade mtlearn.morphology, rather than depending on backend-specific APIs.

The backend is MorphologicalAttributeFilters / mmcfilters, but the top-level Python package mmcfilters is not required as a runtime dependency of mtlearn.

Current Scope

MTLearn is a research-oriented library. CFP is the first validated member of a broader planned family of trainable connected-operator layers. The current implementation supports max-tree, min-tree, and tree-of-shapes CFP workflows, mixed tree types in the same layer, multi-attribute dataset-level attribute normalization, cached preprocessing, and PyTorch forward/backward for CFP parameters on CPU or CUDA tensors.

Citation

If you use the CFP layer in your work, please cite:

Wonder A. L. Alves, Lucas de P. O. Santos, Ronaldo F. Hashimoto, Nicolas Passat, Anderson H. R. Souza, Dennis J. Silva, Yukiko Kenmochi. A trainable connected filter preprocessing layer based on component trees. International Conference on Pattern Recognition (ICPR), 2026, Lyon, France. ⟨hal-05575141⟩