SplineOps

Projection-based antialiased resizing for N-D scientific arrays.

SplineOps is designed for precise, repeatable downsampling of regular-grid 2-D images and 3-D volumes. It combines explicit coordinate semantics, a readable Python reference implementation, a native CPU backend, and reusable plans for fixed-geometry workloads.

Installation

SplineOps requires Python 3.11 or newer:

python -m pip install splineops

Published wheels include the native resize extension on supported platforms.

Quick start

import numpy as np
from splineops import resize

volume = np.random.default_rng(0).random((64, 192, 192), dtype=np.float32)
coarse = resize(
    volume,
    output_size=(32, 96, 96),
    method="cubic-antialiasing",
)

The *-antialiasing methods project the input spline onto a coarser spline space instead of treating downsampling as interpolation alone. For repeated arrays with the same geometry, ResizePlan reuses setup and workspace state.

When to use SplineOps

SplineOps is a good fit when:

continuous-valued 2-D or 3-D NumPy data must be downsampled;
coordinate, boundary, and output-shape semantics must be explicit;
aliasing matters; or
many arrays share one resize geometry.

Another tool may be a better fit when:

differentiable GPU execution is required;
physical-space image metadata must be managed automatically;
ordinary display-image scaling is the only goal; or
categorical labels are being resampled, where nearest-neighbour semantics are normally appropriate.

Public maturity

Module	Status	Public position
`resize`, `ResizePlan`	Stable	Native N-D interpolation and projection antialiasing with a Python reference path
`TensorSpline`	Stabilizing	Continuous tensor-product models evaluated at arbitrary coordinates
Affine, differentials, smoothing, regression, pyramids, wavelets	Experimental	Available for research while their contracts and reference coverage mature

Experimental describes API and validation maturity, not the importance of the underlying methods. See the project status for exact evidence and remaining graduation work.

Numerical and performance position

SplineOps does not claim to be the fastest generic 2-D image resizer. OpenCV, PyTorch, or other libraries can be faster when their coordinate, boundary, kernel, and antialiasing conventions are acceptable.

SplineOps is strongest when the spline model matters: defined grids and boundaries, N-D projection antialiasing, native/reference parity, and repeated fixed-geometry execution. Cross-library reports include semantic differences as well as timings; numerical difference from SplineOps is not treated as an independent accuracy metric.

Backends

NumPy is supported across the package.
The native C++ backend accelerates resize on CPU.
CuPy interoperability is experimental and limited to parts of TensorSpline.

Set SPLINEOPS_ACCEL=never to force the Python resize reference or SPLINEOPS_ACCEL=always to require the native extension. See the backend contract for exact scope.

Documentation

SplineOps modernizes spline methods developed across the Biomedical Imaging Group at EPFL and its collaborators. Method citations, implementation history, and source provenance are recorded explicitly.

Development

git clone https://github.com/splineops/splineops.git
cd splineops
python -m venv .venv
source .venv/bin/activate
python -m pip install -e '.[dev]'
python -m pytest -q

Reproducible resize evidence commands include:

python scripts/benchmark_resize_pr.py --profile smoke --output-dir /tmp/splineops-smoke
python scripts/benchmark_resize_native.py --backend both --output-csv /tmp/splineops-native.csv
python scripts/benchmark_resize_libraries.py --output-csv /tmp/splineops-libraries.csv

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2.2.0

Jul 17, 2026

This release

2.1.0

Jul 16, 2026

2.0.0

Jul 14, 2026

1.3.0

Jun 19, 2026

1.2.1

Dec 19, 2025

1.2.0

Dec 19, 2025

1.1.1

Dec 10, 2025

1.0.5

Dec 9, 2025

1.0.0

Nov 14, 2025

0.5.5

Oct 21, 2025

0.4.6

May 26, 2025

0.3.6

Sep 23, 2024

splineops 2.1.0

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