Imgal (IMaGe Algorithm Library) is a fast and open-source scientific image processing and algorithm library. This library is directly inspired by imagej-ops, SciJava Ops, ImgLib2, and the ImageJ2 ecosystem. The imgal library aims to offer users access to fast and well documented image algorithms as a functional programming style library. Imgal is organized as a monorepo with the imgal crate as the core Rust library that contains the algorithm logic while imgal_c, imgal_java and imgal_python serve imgal's C, Java and Python language bindings respectively.

Usage

Using imgal with Rust

To use imgal in your Rust project add it to your crates's dependencies and import the desired algorithm namespaces.

[dependencies]
imgal = "0.2.0"

The example below demonstrates how to create a 3D linear gradient image (with variable offset, scale and size) and perform simple image statistics and thresholding:

use imgal::statistics::{min_max, sum};
use imgal::simulation::gradient;
use imgal::threshold::otsu_value;

fn main() {
    // create 3D linear gradient data
    let offset = 5;
    let scale = 20.0;
    let shape: (usize, usize, usize) = (50, 50, 50);
    let data = gradient::linear_gradient_3d(offset, scale, shape);

    // calculate the Otsu threshold value with an image histogram of 256 bins
    let threshold = otsu_value(&data, Some(256));

    // print image statistics and Otsu threshold
    println!("[INFO] min/max: {:?}", min_max(&data));
    println!("[INFO] sum: {}", sum(&data));
    println!("[INFO] otsu threshold: {}", threshold);
}

Running this example with cargo run returns the following to the console:

[INFO] min/max: (0.0, 880.0)
[INFO] sum: 49500000
[INFO] otsu threshold: 417.65625

Using imgal with Python

You can use imgal with Python by using the imgal_python crate, a PyO3-based Rust bindings for Python. Pre-compiled releases are available on PyPI as the pyimgal package and can be easily installed with pip:

pip install pyimgal

The pyimgal package currently supports the following architectures:

Operating System	Architecture
Linux	amd64, aarch64
macOS	intel, arm64
Windows	amd64

These binaries are compiled for Python 3.9, 3.10, 3.11, 3.12, and 3.13. Alternatively you can build the imgal_python package from source with the Rust toolchain (i.e. rustc and cargo) and the maturin Python package. See the building from source section below for more details.

Once imgal_python has been installed in a compatible Python environment, imgal will be available to import. The example below demonstrates how to obtain a colocalization z-score (i.e. colocalization and anti-colocalization strength) using the Spatially Adaptive Colocalization Analysis (SACA) framework. The two number values after the channels are threshold values for channels a and b respectively.

Note: This example assumes you have 3D data (row, col, ch) to perform colocalization analysis and the tifffile package in your environment.

import imgal.colocalization as coloc
from tifffile import imread

# load some data
image = imread("path/to/data.tif")

# slice channels to perform colocalization analysis
ch_a = image[:, :, 0]
ch_b = image[:, :, 1]

# compute colocalization z-score with SACA 2D
zscore = coloc.saca_2d(ch_a, ch_b, 525, 400)

# apply Bonferroni correction and compute significant pixel mask
mask = coloc.saca_significance_mask(z_score)

Building from source

Although its not particularly useful on its own, you can build the imgal core Rust library from the root of the repository with:

$ cargo build --release

Note

--release is necessary to compile speed optimized libraries and utilize compiler optimizations.

This will compile the entier workspace including the imgal, imgal_c, imgal_java and imgal_python crates.

Building imgal_python from source

To build the pyimgal Python package from source, use the maturin build tool (this requires the Rust toolchain). If you're using uv to manage your Python virtual environments (venv) add maturin to your environment and run the maturin develop --release command in the imgal_python directory of the imgal repository with your venv activated:

$ source ~/path/to/myenv/.venv/bin/activate
$ (myenv) cd imgal_python
$ maturin develop --release

Alternatively if you're using conda or mamba you can do the following:

$ cd imgal_python
$ mamba activate myenv
(myenv) $ mamba install maturin
...
(myenv) $ maturin develop --release

This will install pyimgal in the currently active Python environment.

Documentation

Each function in imgal is documented and published on docs.rs.

License

Imgal is a dual-licensed project with your choice of:

• MIT License (see LICENSE-MIT)
• The Unlicense (see LICENSE-UNLICENSE)