jam/js/x11/core/examples/smoketest/blur-convolution.js

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2025-07-21 23:00:54 +02:00
// the code is taken from https://github.com/mattlockyer/iat455/blob/6493c882f1956703133c1bffa1d7ee9a83741cbe/assignment1/assignment/effects/blur-effect-dyn.js
// (c) Matt Lockyer, https://github.com/mattlockyer
function hypotenuse(x1, y1, x2, y2) {
var xSquare = Math.pow(x1 - x2, 2);
var ySquare = Math.pow(y1 - y2, 2);
return Math.sqrt(xSquare + ySquare);
}
/*
* Generates a kernel used for the gaussian blur effect.
*
* @param dimension is an odd integer
* @param sigma is the standard deviation used for our gaussian function.
*
* @returns an array with dimension^2 number of numbers, all less than or equal
* to 1. Represents our gaussian blur kernel.
*/
function generateGaussianKernel(dimension, sigma) {
if (!(dimension % 2) || Math.floor(dimension) !== dimension || dimension<3) {
throw new Error(
'The dimension must be an odd integer greater than or equal to 3'
);
}
var kernel = [];
var twoSigmaSquare = 2 * sigma * sigma;
var centre = (dimension - 1) / 2;
for (var i = 0; i < dimension; i++) {
for (var j = 0; j < dimension; j++) {
var distance = hypotenuse(i, j, centre, centre);
// The following is an algorithm that came from the gaussian blur
// wikipedia page [1].
//
// http://en.wikipedia.org/w/index.php?title=Gaussian_blur&oldid=608793634#Mechanics
var gaussian = (1 / Math.sqrt(
Math.PI * twoSigmaSquare
)) * Math.exp((-1) * (Math.pow(distance, 2) / twoSigmaSquare));
kernel.push(gaussian);
}
}
// Returns the unit vector of the kernel array.
var sum = kernel.reduce(function (c, p) { return c + p; });
return kernel.map(function (e) { return e / sum; });
}
module.exports = generateGaussianKernel;