three.js/examples/js/shaders/ConvolutionShader.js

console.warn( "THREE.ConvolutionShader: As part of the transition to ES6 Modules, the files in 'examples/js' were deprecated in May 2020 (r117) and will be deleted in December 2020 (r124). You can find more information about developing using ES6 Modules in https://threejs.org/docs/index.html#manual/en/introduction/Import-via-modules." );
/**
 * @author alteredq / http://alteredqualia.com/
 *
 * Convolution shader
 * ported from o3d sample to WebGL / GLSL
 * http://o3d.googlecode.com/svn/trunk/samples/convolution.html
 */

THREE.ConvolutionShader = {

	defines: {

		"KERNEL_SIZE_FLOAT": "25.0",
		"KERNEL_SIZE_INT": "25"

	},

	uniforms: {

		"tDiffuse": { value: null },
		"uImageIncrement": { value: new THREE.Vector2( 0.001953125, 0.0 ) },
		"cKernel": { value: [] }

	},

	vertexShader: [

		"uniform vec2 uImageIncrement;",

		"varying vec2 vUv;",

		"void main() {",

		"	vUv = uv - ( ( KERNEL_SIZE_FLOAT - 1.0 ) / 2.0 ) * uImageIncrement;",
		"	gl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );",

		"}"

	].join( "\n" ),

	fragmentShader: [

		"uniform float cKernel[ KERNEL_SIZE_INT ];",

		"uniform sampler2D tDiffuse;",
		"uniform vec2 uImageIncrement;",

		"varying vec2 vUv;",

		"void main() {",

		"	vec2 imageCoord = vUv;",
		"	vec4 sum = vec4( 0.0, 0.0, 0.0, 0.0 );",

		"	for( int i = 0; i < KERNEL_SIZE_INT; i ++ ) {",

		"		sum += texture2D( tDiffuse, imageCoord ) * cKernel[ i ];",
		"		imageCoord += uImageIncrement;",

		"	}",

		"	gl_FragColor = sum;",

		"}"


	].join( "\n" ),

	buildKernel: function ( sigma ) {

		// We lop off the sqrt(2 * pi) * sigma term, since we're going to normalize anyway.

		function gauss( x, sigma ) {

			return Math.exp( - ( x * x ) / ( 2.0 * sigma * sigma ) );

		}

		var i, values, sum, halfWidth, kMaxKernelSize = 25, kernelSize = 2 * Math.ceil( sigma * 3.0 ) + 1;

		if ( kernelSize > kMaxKernelSize ) kernelSize = kMaxKernelSize;
		halfWidth = ( kernelSize - 1 ) * 0.5;

		values = new Array( kernelSize );
		sum = 0.0;
		for ( i = 0; i < kernelSize; ++ i ) {

			values[ i ] = gauss( i - halfWidth, sigma );
			sum += values[ i ];

		}

		// normalize the kernel

		for ( i = 0; i < kernelSize; ++ i ) values[ i ] /= sum;

		return values;

	}

};