@watercolorizer/convolution

Simple, unoptimized 1D and 2D convolution functions for typed-arrays.

NOTE: This isn't designed to be high-performance or deal with many, if any, edge cases (like Edge handling) in a particularly rigorous way. You probably do not want to use this in production for anything, ever. ⚠️ Use at your own risk. ⚠️

Getting Started

Perform a 3-unit gaussian-blur approximation on a one-dimensional array with edge wrapping:

import { convolution1D, K_GAUSS_BLUR_3 } from '@watercolorizer/convolution';

const data = [1, 2, 3, 4, 5, 6, 7, 8];
const output = [];

convolution2D(K_GAUSS_BLUR_3, data, output);

console.log(output);
/* [3, 2.5, 3.125, 4.03125, 5.0078125, 6.001953125, 7.00048828125, 6.5001220703125] */

Using typed arrays:

import { convolution1D, K_GAUSS_BLUR_3 } from '@watercolorizer/convolution';

const input = Uint8ClampedArray.of(1, 2, 3, 4, 5, 6, 7, 8);
const output = new Uint8ClampedArray(8);

convolution2D(K_GAUSS_BLUR_3, input, output);

console.log(output);
/* [3, 2, 3, 4, 5, 6, 7, 6] */

Kernels

Built-in Kernels:

• K_BOX_BLUR_3 - 3-element 1D box blur

• K_GAUSS_BLUR_3 - 3-element 1D gaussian-approximation blur

• K_GAUSS_BLUR_5 - 5-element 1D gaussian-approximation blur

• K_GAUSS_BLUR_7 - 7-element 1D gaussian-approximation blur

• K_BOX_BLUR_3x3 - 3×3-element 2D box blur

• K_GAUSS_BLUR_3x3 - 3×3-element 2D gaussian-approximation blur

• K_GAUSS_BLUR_5x5 - 5×5-element 2D gaussian-approximation blur

• K_GAUSS_BLUR_7x7 - 7×7-element 2D gaussian-approximation blur

You can also use whatever kernel you want:

import { Kernel1D, Kernel2D } from '@watercolorizer/convolution';

const myKernel: Kernel1D = [[-1, 0, 1]];
const laplacian: Kernel2D = [
  [0, -1, 0, -1, 4, -1, 0, -1, 0],
  [3, 3],
];

Kernels can be either floating point type or integer-with-divisor type kernels... If no divisor is specified in the kernel, it is assumed to be 1:

import { Kernel1D } from '@watercolorizer/convolution';

const fractional: Kernel1D = [Float64Array.of(0.25, 0.5, 0.25)];
const integer: Kernel1D = [Uint8Array.of(1, 2, 1), 4];