albucore 0.2.8

High-performance image processing functions for deep learning and computer vision.

Albucore: High-Performance Image Processing Functions

Albucore is a library of optimized atomic functions designed for efficient image processing. These functions serve as the foundation for AlbumentationsX, an image augmentation library.

Overview

Image processing operations can be implemented in several ways, with performance depending on dtype, size, layout, and channel count. Albucore routes each operation to a benchmark-selected NumPy, OpenCV, NumKong, or StringZilla implementation.

Most image-processing routers support uint8 and float32. The elementwise exp, log, and sqrt routers are float32-only; conversion helpers also support additional integer dtypes.

Key features:

• Optimized atomic image processing functions
• Automatic selection of the fastest implementation based on input image characteristics
• Seamless integration with AlbumentationsX
• Reproducible micro-benchmarks and committed routing reports (see benchmarks/README.md)

Installation

Requires Python 3.10+. Basic installation (you manage OpenCV separately):

pip install albucore

With OpenCV headless (recommended for servers/CI):

pip install albucore[headless]

With OpenCV GUI support (for local development with cv2.imshow):

pip install albucore[gui]

With OpenCV contrib modules:

pip install albucore[contrib]              # GUI version
pip install albucore[contrib-headless]     # Headless version

Note: If you already have opencv-python or opencv-contrib-python installed, just use pip install albucore to avoid package conflicts. Albucore will detect and use your existing OpenCV installation.

Usage

import numpy as np
import albucore

# Create a sample RGB image
image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)

# Apply a function
result = albucore.multiply(image, 1.5)

# For grayscale images, ensure the channel dimension is present
gray_image = np.random.randint(0, 256, (100, 100, 1), dtype=np.uint8)
gray_result = albucore.multiply(gray_image, 1.5)

Albucore automatically selects the most efficient implementation based on the input image type and characteristics.

Shape Conventions

Albucore expects images to follow specific shape conventions, with the channel dimension always present:

• Single image: (H, W, C) - Height, Width, Channels
• Grayscale image: (H, W, 1) - Height, Width, 1 channel
• Batch of images: (N, H, W, C) - Number of images, Height, Width, Channels
• 3D volume: (D, H, W, C) - Depth, Height, Width, Channels
• Batch of volumes: (N, D, H, W, C) - Number of volumes, Depth, Height, Width, Channels

Important Notes:

1. Channel dimension is always required, even for grayscale images (use shape (H, W, 1))
2. Single-channel images should have shape (H, W, 1) not (H, W)
3. Batch vs volume: (N, H, W, C) is N separate images; a single 3D volume is (D, H, W, C) with depth D. Do not confuse N (batch) with D (slices).

Examples:

import numpy as np
import albucore

# Grayscale image - MUST have explicit channel dimension
gray_image = np.random.randint(0, 256, (100, 100, 1), dtype=np.uint8)

# RGB image
rgb_image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)

# Batch of 10 grayscale images
batch_gray = np.random.randint(0, 256, (10, 100, 100, 1), dtype=np.uint8)

# 3D volume with 20 slices
volume = np.random.randint(0, 256, (20, 100, 100, 1), dtype=np.uint8)

# Batch of 5 RGB volumes, each with 20 slices
batch_volumes = np.random.randint(0, 256, (5, 20, 100, 100, 3), dtype=np.uint8)

Functions

The tables below highlight commonly used public routers. They are exported via from albucore import * and can also be imported from albucore.functions. See docs/public-api.md for the complete export list and the distinction between public routers and backend-specific compatibility shims.

Image routers use channel-last inputs with an explicit channel dimension ((H, W, C), never bare (H, W)) and generally support uint8 and float32. Exceptions are stated in the tables.

Arithmetic

Function	Signature	What it does	How it works
multiply	(img, value, inplace=False)	img * value, clipped to dtype range	uint8 scalar/vector → LUT; uint8 array → OpenCV; float32 → NumPy broadcast
add	(img, value, inplace=False)	img + value, clipped to dtype range	uint8 scalar → OpenCV saturate; uint8 vector → LUT; uint8 array → NumKong/OpenCV; float32 → NumPy
power	(img, exponent, inplace=False)	img ** exponent, clipped to dtype range	uint8 → LUT; float32 scalar → cv2.pow; float32 array → NumPy
add_weighted	(img1, weight1, img2, weight2)	img1*w1 + img2*w2, clipped	NumKong SIMD blend; large float32 → cv2.addWeighted
multiply_add	(img, factor, value, inplace=False)	img * factor + value, clipped	uint8 → LUT (fused, one table); large scalar float32 → NumKong scale; other float32 → NumPy broadcast

value / factor / exponent can be a scalar, a length-C 1-D array (per-channel), or a full image-shaped array.

Elementwise math

Function	Signature	What it does	How it works
exp	(array, *, inplace=False)	Elementwise exponential; float32 only	Small arrays → NumPy; large contiguous or strided arrays → OpenCV at benchmark-derived thresholds
log	(array, *, inplace=False)	NumPy-compatible natural logarithm; float32 only	NumPy for special values and small/unsupported layouts; guarded OpenCV path for eligible large arrays
sqrt	(array, *, inplace=False)	NumPy-compatible square root; float32 only	NumPy wins across the benchmark grid

These functions accept float32 arrays of any rank and preserve the exact input shape. With inplace=True, an owned writable buffer may be reused; views and read-only arrays are never mutated. See the elementwise benchmark report for routing thresholds, environment, and NumKong results.

Normalization

Function	Signature	What it does	How it works
normalize	(img, mean, denominator)	(img - mean) * denominator → float32	uint8 → LUT (256-entry float32 table per channel); float32 → NumPy fused. Caller-supplied constants (e.g. ImageNet stats).
normalize_per_image	(img, normalization)	Normalize using stats computed from img → float32	uint8 → LUT (except "min_max" → cv2.normalize); float32 → OpenCV/NumPy. normalization ∈ {"image", "image_per_channel", "min_max", "min_max_per_channel"}

normalize is for fixed per-channel constants (ImageNet-style). normalize_per_image estimates stats from the image at call time.

Statistics

Function	Signature	What it does	How it works
mean	(arr, axis=None, *, keepdims=False, dtype=None)	Population mean	uint8 global → NumKong sum; per-channel routes among NumKong, OpenCV, and NumPy by rank/channel count
std	(arr, axis=None, *, keepdims=False, eps=1e-4, dtype=None)	Population std + eps	uint8 global → NumKong moments; per-channel routes among NumKong, OpenCV, and NumPy
mean_std	(arr, axis=None, *, keepdims=False, eps=1e-4)	Mean and std+eps jointly	Single NumKong moments pass for uint8 global; selected per-channel paths use NumKong or OpenCV
reduce_sum	(arr, axis=None, *, keepdims=False)	Sum with wide accumulator	uint8 and selected float32 per-channel layouts → NumKong; other float32 routes use a float64 NumPy accumulator

axis accepts None/"global" (scalar), "per_channel" (shape (C,)), or any NumPy-style int/tuple[int, ...].

LUT (lookup tables)

Function	Signature	What it does	How it works
apply_uint8_lut	(img, lut, *, inplace=False)	Apply uint8→uint8 LUT; lut shape (256,) or (C, 256)	Shared (256,): StringZilla or cv2.LUT by size heuristic. Per-channel (C, 256): single cv2.LUT with (256,1,C) table on contiguous HWC; else StringZilla per channel
sz_lut	(img, lut, inplace=True)	Apply shared (256,) uint8 LUT via StringZilla translate	Raw byte translation — channel-unaware, fastest for small images and single-channel

Geometric / spatial

Function	Signature	What it does	How it works
hflip	(img)	Mirror left-right	cv2.flip(img, 1); chunked above OpenCV's 128-channel limit
vflip	(img)	Mirror top-bottom	cv2.flip(img, 0) for ≤4 channels; NumPy slice for >4 channels
median_blur	(img, ksize)	Median filter (odd ksize ≥ 3)	uint8 → direct/chunked cv2.medianBlur; float32 ksize 3/5 → native OpenCV; float32 ksize ≥ 7 → uint8 conversion fallback
matmul	(a, b)	Matrix multiply (a @ b)	NumPy @ (BLAS-backed); replaces cv2.gemm which lacks uint8 support
pairwise_distances_squared	(points1, points2)	Squared Euclidean distance matrix (N, M)	Small (N*M < 1000) → NumKong cdist; large → NumPy vectorized ‖a‖²+‖b‖²−2(a·b)

The package also star-exports multi-channel wrappers for copy_make_border, remap, resize, warp_affine, and warp_perspective; see docs/public-api.md and their docstrings for complete signatures.

Type conversion

Function	Signature	What it does	How it works
to_float	(img, max_value=None)	Convert to float32 in [0, 1]	float32 → no-op; uint8 → cv2.LUT (256-entry float32 table); others → NumPy divide
from_float	(img, target_dtype, max_value=None)	Scale float32 → integer dtype (round + clip)	float32 → NumPy rint(img * max_value) then clip; non-float32 → generic NumPy path

Decorators (re-exported)

Decorator	What it does
float32_io	Wrap a function: cast input to float32, cast output back to original dtype
uint8_io	Wrap a function: cast input to uint8, cast output back to original dtype

See docs/decorators.md for @preserve_channel_dim, @contiguous, @clipped, and @batch_transform (used internally, not re-exported).

Array layouts and batch processing

Arithmetic, normalization, statistics, conversion, and elementwise routers operate on channel-last arrays and preserve these layouts where applicable:

• Single images: (H, W, C)
• Batches: (N, H, W, C)
• Volumes: (D, H, W, C)
• Batch of volumes: (N, D, H, W, C)

Spatial routers document their own image-shape requirements. Transform authors can use @batch_transform to adapt an image operation to batches and volumes while restoring the original layout.

See docs/decorators.md for internal decorator documentation (@preserve_channel_dim, @contiguous, @clipped, @batch_transform).

Performance

Albucore uses a combination of techniques to achieve high performance:

1. Multiple Implementations: Each function may have several implementations using NumPy, OpenCV, NumKong, or StringZilla.
2. Automatic Selection: The library chooses a backend from dtype, size, memory layout, channel count, and semantic constraints.
3. Measured Routing: Backend choices and thresholds come from repeatable benchmarks rather than backend preference.
4. NumKong: SIMD blend for uint8 add_weighted and same-shaped add_array; cdist for small pairwise_distances_squared; wide-accumulator moments for selected statistics routes (see docs/numkong-performance.md).

Micro-benchmarks vs NumPy/OpenCV/NumKong: see benchmarks/README.md. Run uv run python benchmarks/benchmark_elementwise.py for exp/log/sqrt, or uv run python benchmarks/benchmark_numkong.py for a smaller NumKong sweep.

See docs/performance-optimization.md for detailed performance guidelines and best practices.

Documentation

• CONTRIBUTING.md - Pull request process and CLA acceptance paths
• AGENTS.md - AI development guidelines for working with this codebase
• docs/image-conventions.md - Image shape conventions and requirements
• docs/decorators.md - Decorator usage and patterns
• docs/performance-optimization.md - Performance optimization guidelines
• docs/numkong-performance.md - NumKong vs OpenCV/NumPy/LUT baselines (benchmark tables; sum/mean/std)
• docs/public-api.md - Star-exported routers vs albucore.functions shims
• benchmarks/README.md - Python micro-benchmarks (uv run python benchmarks/…)
• docs/research/ - Research notes (extra benchmark writeups; see benchmarks/README.md for scripts)

License

Albucore is publicly available under the MIT License, including contributions accepted under the Albucore Contributor License Agreement Version 1.0. The CLA does not change the repository's public license. Historical contributions remain available under MIT and become CLA-covered only through an applicable Version 1.0 Acceptance Record. See CONTRIBUTING.md for the individual CLA Assistant and entity acceptance paths.

Acknowledgements

Albucore provides core image-processing primitives for AlbumentationsX. We'd like to thank all AlbumentationsX contributors and the broader computer vision community for their inspiration and support.