kornia-rs
Low level implementations for computer vision in Rust
Decision gist · record as of 2026-08-14
Yes, if you need fast, thread-safe image I/O and basic processing in a Python environment. The Rust backend, prebuilt wheels, and support for modern Python versions make it a low-friction choice for ML pipelines. No runtime dependencies required for core functionality. Consider it especially if you work with depth maps (uint16) or need video capture; for simple image operations, alternatives may suffice.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Optional system dependencies (clang, nasm, libgstreamer) only needed if using v4l, turbojpeg, or gstreamer features; basic image I/O works without them.
- Medium install friction due to compiled wheels; prebuilt binaries available for Python 3.8–3.13 across Linux (amd64/arm64), macOS, and Windows.
- Active maintenance with recent releases; no runtime dependencies required.
License · maintenance · safety
permissive license (permissive) — Permissive license treatment allows use in commercial and proprietary projects without restriction.
last release 2026-05-19 (87 days) · last repo commit 2026-08-14 · 693 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,212,425 downloads/mo, #2,699 on PyPI
Alternatives
Verify before relying
pip install kornia-rs
import kornia_rs as K
img = K.read_image_jpeg("dog.jpeg")
resized = K.resize(img, (128, 128))- Performance benchmarks vs. alternatives for common operations (resize, color conversion).
- Completeness of the Python API surface relative to the full Rust library.
- Free-threaded Python 3.13 build stability and real-world concurrency patterns.
What it is and what it does
kornia-rs is a Rust-based computer vision library with Python bindings that handles image I/O, processing, and video capture. It reads and writes multiple image formats (JPEG via libjpeg-turbo, PNG, WebP, TIFF, and others), performs operations like resizing, cropping, rotation, color conversion, and normalization, and supports video frame capture. The library is designed for efficiency and thread safety, making it suitable for integration into machine learning pipelines and data-science workflows.
The package exposes a subset of its Rust API through Python, including a PIL-style Image class that natively supports uint16 for depth maps and scientific imagery. It includes both high-level convenience functions and lower-level encoder/decoder objects for JPEG workflows. The library is actively maintained, supports Python 3.8 through 3.13 (including the free-threaded build), and provides precompiled wheels for common platforms, reducing installation friction.
Use it for
- Load and preprocess images for deep learning training pipelines with minimal overhead.
- Read and write depth maps or scientific imagery in uint16 format losslessly via PNG.
- Capture video frames from cameras and process them in real time with thread safety.
- Convert image formats and apply basic transformations (resize, grayscale, normalize) in batch workflows.
- Integrate efficient image I/O into machine learning applications.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need fast, thread-safe image I/O and basic processing in a Python environment.
The Rust backend, prebuilt wheels, and support for modern Python versions make it a low-friction choice for ML pipelines. No runtime dependencies required for core functionality. Consider it especially if you work with depth maps (uint16) or need video capture; for simple image operations, alternatives may suffice.
Install
kornia-rs on PyPI
Before you install
Medium install friction due to compiled wheels; prebuilt binaries available for Python 3.8–3.13 across Linux (amd64/arm64), macOS, and Windows. Active maintenance with recent releases; no runtime dependencies required.
Optional system dependencies (clang, nasm, libgstreamer) only needed if using v4l, turbojpeg, or gstreamer features; basic image I/O works without them.
License in practice
Permissive license treatment allows use in commercial and proprietary projects without restriction.
Quickstart
pip install kornia-rs
import kornia_rs as K
img = K.read_image_jpeg("dog.jpeg")
resized = K.resize(img, (128, 128))Verify before relying
- Performance benchmarks vs. alternatives for common operations (resize, color conversion).
- Completeness of the Python API surface relative to the full Rust library.
- Free-threaded Python 3.13 build stability and real-world concurrency patterns.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 87 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,212,425 / month, #2,699 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: GPUIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: Python :: Free Threading :: 3 - StableProgramming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyProgramming Language :: RustTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image Processing |
Evidence: kornia_rs-0.1.14-cp310-cp310-macosx_10_12_x86_64.whl; kornia_rs-0.1.14-cp310-cp310-macosx_11_0_arm64.whl; kornia_rs-0.1.14-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; kornia_rs-0.1.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; kornia_rs-0.1.14-cp310-cp310-win_amd64.whl; kornia_rs-0.1.14-cp311-cp311-macosx_10_12_x86_64.whl; kornia_rs-0.1.14-cp311-cp311-macosx_11_0_arm64.whl; kornia_rs-0.1.14-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; kornia_rs-0.1.14-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; kornia_rs-0.1.14-cp311-cp311-win_amd64.whl; kornia_rs-0.1.14-cp312-cp312-macosx_10_12_x86_64.whl; kornia_rs-0.1.14-cp312-cp312-macosx_11_0_arm64.whl; kornia_rs-0.1.14-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; kornia_rs-0.1.14-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; kornia_rs-0.1.14-cp312-cp312-win_amd64.whl; kornia_rs-0.1.14-cp313-cp313-macosx_10_12_x86_64.whl; kornia_rs-0.1.14-cp313-cp313-macosx_11_0_arm64.whl; kornia_rs-0.1.14-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; kornia_rs-0.1.14-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; kornia_rs-0.1.14-cp313-cp313t-macosx_10_12_x86_64.whl
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