Mon 21 Jul 22:43:21 CEST 2025

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

@@ -0,0 +1,51 @@
+// 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;