grdl-imagej 0.1.1

ImageJ/Fiji algorithm ports for the GRDL framework — 22 classic image processing algorithms as pure-NumPy reimplementations

grdl-imagej

Pure-NumPy reimplementations of 22 classic ImageJ/Fiji image processing algorithms, built as an extension of the GRDL framework.

Selected for relevance to remotely sensed imagery (PAN, MSI, HSI, SAR, thermal). Each class inherits from grdl.image_processing.base.ImageTransform, carries @processor_tags metadata for capability discovery, and declares __gpu_compatible__ for downstream GPU dispatch.

Installation

pip install grdl-imagej

Or install from source:

git clone https://github.com/geoint-org/grdl-imagej.git
cd grdl-imagej
pip install -e ".[dev]"

Dependencies

• grdl >= 0.5.1 (base classes, versioning, Annotated parameter system, vocabulary enums)
• numpy >= 1.20.0
• scipy >= 1.7.0

Components (22 processors)

All processors inherit from ImageTransform and follow the same pattern: instantiate with parameters, call .apply(image). Every processor carries @processor_version and @processor_tags metadata decorators, and declares its tunable parameters via Annotated type hints with Range, Options, and Desc constraint markers from grdl.image_processing.params. This metadata enables grdl-runtime catalog discovery and grdk GUI auto-configuration.

Process > Filters

Class	Description
RankFilters	Median, Min, Max, Mean, Variance, Despeckle
UnsharpMask	Gaussian-based sharpening
GaussianBlur	Isotropic/anisotropic Gaussian smoothing
Convolver	Arbitrary 2D kernel convolution

Process > Subtract Background

Class	Description
RollingBallBackground	Sternberg's rolling-ball background subtraction

Process > Binary

Class	Description
MorphologicalFilter	Erode, Dilate, Open, Close, TopHat, BlackHat, Gradient
DistanceTransform	Euclidean Distance Map (EDM)
Skeletonize	Zhang-Suen binary thinning

Process > Enhance Contrast

Class	Description
CLAHE	Contrast Limited Adaptive Histogram Equalization
GammaCorrection	Power-law intensity transform
ContrastEnhancer	Linear histogram stretching with saturation

Process > Find Edges

Class	Description
EdgeDetector	Sobel, Prewitt, Roberts, LoG, Scharr

Process > FFT

Class	Description
FFTBandpassFilter	Frequency-domain bandpass and stripe suppression

Process > Find Maxima

Class	Description
FindMaxima	Prominence-based peak/target detection

Process > Image Calculator

Class	Description
ImageCalculator	Pixel-wise arithmetic and logical operations

Image > Adjust > Threshold

Class	Description
AutoThreshold	Global thresholding (Otsu, Triangle, Huang, Li, Yen, etc.)
AutoLocalThreshold	Local thresholding (Bernsen, Niblack, Sauvola, Phansalkar, etc.)

Plugins > Segmentation

Class	Description
StatisticalRegionMerging	SRM region-based segmentation
Watershed	EDT-based watershed for splitting touching objects

Image > Stacks

Class	Description
ZProjection	Stack projection (max, mean, median, min, sum, std)

Analyze > Analyze Particles

Class	Description
AnalyzeParticles	Connected component analysis with measurements

Plugins > Anisotropic Diffusion

Class	Description
AnisotropicDiffusion	Perona-Malik edge-preserving smoothing

Quick Start

from grdl_imagej import CLAHE, RankFilters, UnsharpMask

# Enhance contrast
clahe = CLAHE(block_size=127, n_bins=256, max_slope=3.0)
enhanced = clahe.apply(image)

# Denoise with median filter
median = RankFilters(method='median', radius=2)
denoised = median.apply(image)

# Sharpen
usm = UnsharpMask(sigma=2.0, weight=0.6)
sharpened = usm.apply(image)

Pipeline Composition

from grdl_imagej import GammaCorrection, UnsharpMask, EdgeDetector
from grdl.image_processing import Pipeline

pipe = Pipeline([
    GammaCorrection(gamma=0.5),
    UnsharpMask(sigma=2.0, weight=0.6),
    EdgeDetector(method='sobel'),
])
result = pipe.apply(image)

Target Detection

from grdl_imagej import FFTBandpassFilter, FindMaxima

# Remove background drift and high-frequency noise
bp = FFTBandpassFilter(filter_large=40, filter_small=3)
filtered = bp.apply(image)

# Find peaks
fm = FindMaxima(prominence=50.0)
peaks = fm.apply(filtered)  # (N, 2) array of [row, col]

Segmentation

from grdl_imagej import StatisticalRegionMerging, AutoThreshold

# Region segmentation
srm = StatisticalRegionMerging(Q=25)
labels = srm.apply(image)

# Binary thresholding
thresh = AutoThreshold(method='otsu')
binary = thresh.apply(image)

Project Structure

grdl-imagej/
├── grdl_imagej/
│   ├── __init__.py              # Barrel export of all 22 components
│   ├── _taxonomy.py             # ProcessorCategory → ImageJ menu label mapping
│   ├── filters/                 # RankFilters, UnsharpMask, GaussianBlur, Convolver
│   ├── background/              # RollingBallBackground
│   ├── binary/                  # MorphologicalFilter, DistanceTransform, Skeletonize
│   ├── enhance/                 # CLAHE, GammaCorrection, ContrastEnhancer
│   ├── edges/                   # EdgeDetector
│   ├── fft/                     # FFTBandpassFilter
│   ├── find_maxima/             # FindMaxima
│   ├── math/                    # ImageCalculator
│   ├── threshold/               # AutoThreshold, AutoLocalThreshold
│   ├── segmentation/            # StatisticalRegionMerging, Watershed
│   ├── stacks/                  # ZProjection
│   ├── analyze/                 # AnalyzeParticles
│   └── noise/                   # AnisotropicDiffusion
└── tests/
    ├── conftest.py              # Shared fixtures
    ├── test_imagej.py           # Unit tests for all 22 components
    └── test_benchmarks.py       # Performance benchmarks

Testing

# Run all tests
pytest tests/ -v --benchmark-disable

# Run with coverage
pytest tests/ -v --cov=grdl_imagej --cov-report=term-missing --benchmark-disable

# Run benchmarks only
pytest tests/test_benchmarks.py --benchmark-only

Publishing to PyPI

Dependency Management

All dependencies are defined in pyproject.toml. Keep these files synchronized:

• pyproject.toml — source of truth for versions and dependencies
• requirements.txt — regenerate with pip freeze > requirements.txt after updating pyproject.toml
• .github/workflows/publish.yml — automated PyPI publication (do not edit manually)

Releasing a New Version

1. Update the version field in pyproject.toml (semantic versioning: major.minor.patch)
2. Update requirements.txt if dependencies changed: pip install -e ".[all,dev]" && pip freeze > requirements.txt
3. Commit both files
4. Create a git tag: git tag v0.2.0 (matches version in pyproject.toml)
5. Push to GitHub: git push && git push --tags
6. Create a GitHub Release from the tag — this triggers the publish workflow automatically

The workflow:

• Builds wheels and source distribution using python -m build
• Publishes to PyPI with OIDC authentication (secure, no API keys)
• Artifacts are available at pypi.org/p/grdl-imagej

See CLAUDE.md for detailed dependency management guidelines.

Attribution

ImageJ is developed by Wayne Rasband at the U.S. National Institutes of Health. ImageJ 1.x source code is in the public domain.

Fiji plugins (CLAHE, Auto Local Threshold, Statistical Region Merging, Auto Threshold, Anisotropic Diffusion) are distributed under GPL-2. This package provides independent reimplementations in NumPy, not derivative works of the GPL source, but follows the same published algorithms and cites the original authors.

License

MIT License. See LICENSE for full text.