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Implement ilabel: fast pixel-based region labelling (from the MATLAB MVTB C extension)

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  • ¿Algo no está bien?
#119 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Activo
Stack tecnológico
c, python
Área
computer-vision

Línea de trabajo

Start by inspecting wip/ilabel/ilabel.c and wip/ilabel/ilabel.py-unopt, then read the existing blob/label functions and the TODO in tests/test_image_processing.py. Decide and document the Python API and implementation route, with tests comparing shared examples to the contour-based blob code. Done means ilabel is implemented, its return values are tested, and the build and Python-only support implications are addressed.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

tech-debt

Parked work, to be implemented later. The files are on branch feat/fast-label under wip/ilabel/ (not in main).

What it is

A pixel-based, rather than contour-based, approach to segmentation / region labelling, based on the ilabel C extension (Copyright 1995-2009 Peter Corke) from the MATLAB Machine Vision Toolbox. The MATLAB signature was [l, m, parents, color] = ilabel(im).

What exists

  • wip/ilabel/ilabel.c: Cython 3.0.11 output (2024-12), 592 KB. It was generated from an ilabel.pyx that is not in the repo, but Cython embeds the source as /* "ilabel.pyx":NN */ comments, so the .pyx can be reconstructed from this file.
  • wip/ilabel/ilabel.py-unopt: unoptimised pure-Python port, produced with Copilot (2024-12).
  • The Python toolbox has no implementation. The only reference is a TODO in tests/test_image_processing.py (# TODO [l,ml,p,c] = ilabel(im);).

To do

  1. Reconstruct ilabel.pyx from the embedded comments in ilabel.c (or port afresh), and decide the implementation route (Cython extension vs numba vs NumPy/SciPy). Note this package is pure Python and builds with hatchling, so a compiled extension changes the build/wheel story (and Pyodide/JupyterLite support); a pure-Python/NumPy route avoids that.
  2. Define the Python API (Image method, return values), consistent with the existing blob/label functions.
  3. Tests, including a comparison with the existing contour-based blob code on shared examples, and the TODO above.

Related: #45

Lenguaje dominante
Python
Estrellas
223
Forks
30
Merge medio
1 h 4 min
PR fusionados (30 d)
9

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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