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