ajpegli 0.1.4

Fast JPEG-to-NumPy image loading powered by Google jpegli

ajpegli

Fast JPEG-to-NumPy image loading powered by Google jpegli.

ajpegli is a dependency-light JPEG loader for Python: pass a file path, get a NumPy array. Decoding is powered by Google jpegli and built for high-throughput data pipelines. The API is cv2.imread-like, but it is not a drop-in OpenCV replacement: color images are returned as RGB by default. Pass mode="BGR" for OpenCV-style pipelines.

Development

Clone with submodules before building native wheels:

git submodule update --init --recursive
uv sync --extra dev
just check
just build
just bench-imread

third_party/jpegli is pinned as a submodule. The pinned commit is exposed at runtime through ajpegli.__jpegli_commit__ and ajpegli.jpegli_commit().

Release and publishing instructions live in releasing.md.

Installation

Install from PyPI:

pip install ajpegli

With uv:

uv add ajpegli

Quickstart

ajpegli ships prebuilt wheels for common Linux, macOS, and Windows CPython builds. NumPy is the only runtime dependency.

import ajpegli

image = ajpegli.imread("image.jpg")
assert image.dtype == "uint8"
assert image.ndim == 3

rgb = ajpegli.imread("image.jpg", mode="RGB")  # default
bgr = ajpegli.imread("image.jpg", mode="BGR")  # for OpenCV-style pipelines
gray = ajpegli.imread("image.jpg", mode="L")

imread() reads the file in the native extension and returns a NumPy array. The first decode slice supports uint8 RGB, BGR, and grayscale output. File I/O and jpegli decode work release the GIL so threaded callers and DataLoader workers do not serialize on Python while the native codec is running.

NumPy is the only runtime dependency. OpenCV, Pillow, and PyTorch are optional benchmark tools and are not required by pip install ajpegli.

encode() and full info() metadata output are still planned API surface, not the release focus yet.

For local source builds, clone with submodules:

git clone --recursive https://github.com/dKosarevsky/ajpegli.git
cd ajpegli
uv sync --extra dev
just check

Benchmarks

The benchmark script keeps comparison tools optional so pip install ajpegli only needs NumPy at runtime. See Benchmarks, Benchmark Results, DataLoader Benchmarking, and DataLoader Results.

just bench-imread path/to/a.jpg 1000 8 RGB ajpegli,cv2,pillow
just bench-imread-dataloader path/to/a.jpg 1000 4 RGB 32

benchmarks/bench_imread.py reports JSON with sequential throughput, threaded throughput, and optional PyTorch DataLoader throughput. Missing optional comparison packages are reported as skipped entries instead of failing the run. Use --thread-workers for threaded reader throughput and --dataloader-workers for PyTorch DataLoader worker count. The checked-in smoke reports are intentionally narrow; broader dataset reports are still required before making project-level speed claims against OpenCV or Pillow.

For local comparison runs, install only what you want to measure in that environment:

uv pip install opencv-python-headless pillow
uv pip install torch  # only for --include-dataloader