52 lines
		
	
	
		
			1.7 KiB
		
	
	
	
		
			JavaScript
		
	
	
	
	
	
			
		
		
	
	
			52 lines
		
	
	
		
			1.7 KiB
		
	
	
	
		
			JavaScript
		
	
	
	
	
	
// 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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