Crate convolve2d[−][src]

Expand description

This crate defines an easy and extensible way to conduct image convolutions, in a way that is free of system dependencies, and works with no_std.

The purpose of convolve2d is to provide a single package that provides everything you need to conduct image convolutions suitable for computer vision or image manipulation. Here’s a breif overview of what’s on offer:

Two convolution functions: allowing you to pass your own buffer if speed is important, or have a buffer allocated and returned for a more idiomatic interface.
Traits: Convolution is defined generically across the Matrix trait. If you have a custom image type, simply define an implementation of Matrix, and you’re good to go!
Built-in image Support: We also offer support for the image library through a feature flag (disabled by default), allowing you to seamlessly use the types you’re already used to!
rayon: Compute convolutions in parallel using the rayon flag. (Enabled by default)
no_std Operation: to suit the needs of specialty systems or WASM.
Kernel Generators: The kernel module provides generation functions for a number of kernels commonly used in image processing.

While other convolution libraries may be more efficient, using a faster algorithm, or running on the GPU, this library’s main focus is providing a complete convolution experience that is portable and easy to use.

Features:

The following features are supported:

Feature	Default	Description
`std`	Yes	Allow access to the standard library, enabling the `DynamicMatrix` type.
`rayon`	Yes	Use rayon to compute convolutions in parallel.
`image`	No	Add extensions for interoperation with the `image` crate.
`full`	No	All features.

To use the library in no_std mode, simply disable all features:

convolve2d = { version = "0.1.0", default-features = false }

Notes on `image` Compatibility

Compatibility with the image library is provided using the image feature flag. This flag provides the following features:

The various pixel formats (Rgb, Luma, etc…) can now be converted to and from the SubPixels type. This allows them to be scaled and added as required for convolutions.
ImageBuffer can be converted to and from DynamicMatrixes using into and from.
ImageBuffers for which the pixel type is Luma can be used as Matrixes directly. This is because each element in the underlying data structure is one pixel. (Whereas in an RGB image, each element is one subpixel, meaning we need to group with SubPixels)