0.2.0

affiners

Fast 3D affine transformations with trilinear interpolation using AVX2/AVX512 SIMD.

Performance

Use u8 for image data (microscopy, CT, MRI) to get 2.2x faster processing with 4x less memory!

Performance vs scipy

Benchmark on AMD Ryzen 9 9950X (32 threads):

Installation

pip install affine-rs

Or build from source:

pip install .

Usage

Python

import numpy as np
import affiners

# Define transformation
matrix = np.array([
    [1.0, 0.25, 0.01],
    [0.0, 1.0, 0.0],
    [0.0, -0.02, 1.0],
], dtype=np.float64)
offset = np.array([-10.0, -5.0, 8.0])

# Float32 data
input_f32 = np.random.rand(512, 512, 512).astype(np.float32)
output_f32 = affiners.affine_transform(input_f32, matrix, offset=offset)

# uint8 data (2.2x faster!)
input_u8 = np.random.randint(0, 256, (512, 512, 512), dtype=np.uint8)
output_u8 = affiners.affine_transform_u8(input_u8, matrix, offset=offset)

Check Build Info

import affiners

print(affiners.__version__)  # '0.1.0'
print(affiners.build_info())
# {'version': '0.1.0', 'simd': {'avx2': True, 'avx512f': True, ...}, 
#  'backend_f32': 'avx512', 'backend_u8': 'avx2', 'num_threads': 32, ...}

Rust

use ndarray::Array3;
use affiners::{affine_transform_3d_f32, affine_transform_3d_u8, AffineMatrix3D};

// Float32
let input_f32 = Array3::<f32>::zeros((100, 100, 100));
let matrix = AffineMatrix3D::identity();
let shift = [10.0, 20.0, 30.0];
let output_f32 = affine_transform_3d_f32(&input_f32.view(), &matrix, &shift, 0.0);

// uint8 (2.2x faster!)
let input_u8 = Array3::<u8>::zeros((100, 100, 100));
let output_u8 = affine_transform_3d_u8(&input_u8.view(), &matrix, &shift, 0);

API Reference

Python

Parameters

• input: 3D numpy array (C-contiguous)
• matrix: 3x3 transformation matrix (float64)
• offset: Translation vector [z, y, x] (optional, default: [0, 0, 0])
• cval: Constant value for out-of-bounds (default: 0)

When to Use Each Type

Memory Requirements

Features

• AVX2/AVX512 SIMD: Processes 8-16 values per iteration
• Multi-threaded: Uses rayon for parallel z-slice processing
• Memory efficient: u8 uses 4x less memory than f32
• Python bindings: Via PyO3 and maturin
• Zero-copy: Works directly with numpy arrays
• Native compilation: Optimized for host CPU features (target-cpu=native)

License

BSD-3-Clause