669 lines
15 KiB
ActionScript
669 lines
15 KiB
ActionScript
/*
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* NeuQuant Neural-Net Quantization Algorithm
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* ------------------------------------------
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*
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* Copyright (c) 1994 Anthony Dekker
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*
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* NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. See
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* "Kohonen neural networks for optimal colour quantization" in "Network:
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* Computation in Neural Systems" Vol. 5 (1994) pp 351-367. for a discussion of
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* the algorithm.
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*
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* Any party obtaining a copy of these files from the author, directly or
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* indirectly, is granted, free of charge, a full and unrestricted irrevocable,
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* world-wide, paid up, royalty-free, nonexclusive right and license to deal in
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* this software and documentation files (the "Software"), including without
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* limitation the rights to use, copy, modify, merge, publish, distribute,
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* sublicense, and/or sell copies of the Software, and to permit persons who
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* receive copies from any such party to do so, with the only requirement being
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* that this copyright notice remain intact.
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*/
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/*
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* This class handles Neural-Net quantization algorithm
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* @author Kevin Weiner (original Java version - kweiner@fmsware.com)
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* @author Thibault Imbert (AS3 version - bytearray.org)
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* @version 0.1 AS3 implementation
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*/
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package org.bytearray.gif.encoder
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{
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import flash.utils.ByteArray;
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public class NeuQuant
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{
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private static var netsize:int = 256; /* number of colours used */
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/* four primes near 500 - assume no image has a length so large */
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/* that it is divisible by all four primes */
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private static var prime1:int = 499;
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private static var prime2:int = 491;
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private static var prime3:int = 487;
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private static var prime4:int = 503;
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private static var minpicturebytes:int = (3 * prime4);
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/* minimum size for input image */
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/*
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* Program Skeleton ---------------- [select samplefac in range 1..30] [read
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* image from input file] pic = (unsigned char*) malloc(3*width*height);
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* initnet(pic,3*width*height,samplefac); learn(); unbiasnet(); [write output
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* image header, using writecolourmap(f)] inxbuild(); write output image using
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* inxsearch(b,g,r)
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*/
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/*
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* Network Definitions -------------------
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*/
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private static var maxnetpos:int = (netsize - 1);
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private static var netbiasshift:int = 4; /* bias for colour values */
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private static var ncycles:int = 100; /* no. of learning cycles */
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/* defs for freq and bias */
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private static var intbiasshift:int = 16; /* bias for fractions */
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private static var intbias:int = (1 << intbiasshift);
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private static var gammashift:int = 10; /* gamma = 1024 */
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private static var gamma:int = (1 << gammashift);
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private static var betashift:int = 10;
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private static var beta:int = (intbias >> betashift); /* beta = 1/1024 */
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private static var betagamma:int = (intbias << (gammashift - betashift));
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/* defs for decreasing radius factor */
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private static var initrad:int = (netsize >> 3); /*
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* for 256 cols, radius
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* starts
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*/
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private static var radiusbiasshift:int = 6; /* at 32.0 biased by 6 bits */
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private static var radiusbias:int = (1 << radiusbiasshift);
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private static var initradius:int = (initrad * radiusbias); /*
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* and
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* decreases
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* by a
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*/
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private static var radiusdec:int = 30; /* factor of 1/30 each cycle */
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/* defs for decreasing alpha factor */
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private static var alphabiasshift:int = 10; /* alpha starts at 1.0 */
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private static var initalpha:int = (1 << alphabiasshift);
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private var alphadec:int /* biased by 10 bits */
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/* radbias and alpharadbias used for radpower calculation */
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private static var radbiasshift:int = 8;
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private static var radbias:int = (1 << radbiasshift);
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private static var alpharadbshift:int = (alphabiasshift + radbiasshift);
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private static var alpharadbias:int = (1 << alpharadbshift);
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/*
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* Types and Global Variables --------------------------
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*/
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private var thepicture:ByteArray/* the input image itself */
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private var lengthcount:int; /* lengthcount = H*W*3 */
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private var samplefac:int; /* sampling factor 1..30 */
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// typedef int pixel[4]; /* BGRc */
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private var network:Array; /* the network itself - [netsize][4] */
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protected var netindex:Array = new Array();
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/* for network lookup - really 256 */
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private var bias:Array = new Array();
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/* bias and freq arrays for learning */
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private var freq:Array = new Array();
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private var radpower:Array = new Array();
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public function NeuQuant(thepic:ByteArray, len:int, sample:int)
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{
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var i:int;
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var p:Array;
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thepicture = thepic;
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lengthcount = len;
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samplefac = sample;
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network = new Array(netsize);
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for (i = 0; i < netsize; i++)
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{
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network[i] = new Array(4);
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p = network[i];
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p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
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freq[i] = intbias / netsize; /* 1/netsize */
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bias[i] = 0;
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}
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}
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private function colorMap():ByteArray
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{
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var map:ByteArray = new ByteArray;
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var index:Array = new Array(netsize);
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for (var i:int = 0; i < netsize; i++)
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index[network[i][3]] = i;
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var k:int = 0;
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for (var l:int = 0; l < netsize; l++) {
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var j:int = index[l];
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map[k++] = (network[j][0]);
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map[k++] = (network[j][1]);
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map[k++] = (network[j][2]);
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}
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return map;
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}
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/*
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* Insertion sort of network and building of netindex[0..255] (to do after
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* unbias)
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* -------------------------------------------------------------------------------
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*/
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private function inxbuild():void
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{
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var i:int;
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var j:int;
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var smallpos:int;
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var smallval:int;
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var p:Array;
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var q:Array;
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var previouscol:int
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var startpos:int
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previouscol = 0;
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startpos = 0;
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for (i = 0; i < netsize; i++)
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{
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p = network[i];
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smallpos = i;
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smallval = p[1]; /* index on g */
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/* find smallest in i..netsize-1 */
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for (j = i + 1; j < netsize; j++)
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{
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q = network[j];
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if (q[1] < smallval)
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{ /* index on g */
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smallpos = j;
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smallval = q[1]; /* index on g */
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}
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}
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q = network[smallpos];
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/* swap p (i) and q (smallpos) entries */
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if (i != smallpos)
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{
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j = q[0];
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q[0] = p[0];
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p[0] = j;
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j = q[1];
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q[1] = p[1];
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p[1] = j;
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j = q[2];
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q[2] = p[2];
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p[2] = j;
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j = q[3];
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q[3] = p[3];
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p[3] = j;
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}
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/* smallval entry is now in position i */
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if (smallval != previouscol)
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{
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netindex[previouscol] = (startpos + i) >> 1;
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for (j = previouscol + 1; j < smallval; j++) netindex[j] = i;
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previouscol = smallval;
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startpos = i;
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}
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}
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netindex[previouscol] = (startpos + maxnetpos) >> 1;
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for (j = previouscol + 1; j < 256; j++) netindex[j] = maxnetpos; /* really 256 */
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}
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/*
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* Main Learning Loop ------------------
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*/
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private function learn():void
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{
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var i:int;
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var j:int;
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var b:int;
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var g:int
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var r:int;
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var radius:int;
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var rad:int;
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var alpha:int;
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var step:int;
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var delta:int;
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var samplepixels:int;
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var p:ByteArray;
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var pix:int;
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var lim:int;
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if (lengthcount < minpicturebytes) samplefac = 1;
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alphadec = 30 + ((samplefac - 1) / 3);
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p = thepicture;
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pix = 0;
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lim = lengthcount;
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samplepixels = lengthcount / (3 * samplefac);
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delta = samplepixels / ncycles;
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alpha = initalpha;
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radius = initradius;
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rad = radius >> radiusbiasshift;
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if (rad <= 1) rad = 0;
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for (i = 0; i < rad; i++) radpower[i] = alpha * (((rad * rad - i * i) * radbias) / (rad * rad));
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if (lengthcount < minpicturebytes) step = 3;
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else if ((lengthcount % prime1) != 0) step = 3 * prime1;
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else
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{
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if ((lengthcount % prime2) != 0) step = 3 * prime2;
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else
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{
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if ((lengthcount % prime3) != 0) step = 3 * prime3;
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else step = 3 * prime4;
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}
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}
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i = 0;
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while (i < samplepixels)
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{
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b = (p[pix + 0] & 0xff) << netbiasshift;
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g = (p[pix + 1] & 0xff) << netbiasshift;
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r = (p[pix + 2] & 0xff) << netbiasshift;
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j = contest(b, g, r);
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altersingle(alpha, j, b, g, r);
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if (rad != 0) alterneigh(rad, j, b, g, r); /* alter neighbours */
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pix += step;
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if (pix >= lim) pix -= lengthcount;
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i++;
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if (delta == 0) delta = 1;
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if (i % delta == 0)
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{
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alpha -= alpha / alphadec;
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radius -= radius / radiusdec;
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rad = radius >> radiusbiasshift;
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if (rad <= 1) rad = 0;
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for (j = 0; j < rad; j++) radpower[j] = alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
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}
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}
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}
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/*
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** Search for BGR values 0..255 (after net is unbiased) and return colour
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* index
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* ----------------------------------------------------------------------------
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*/
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public function map(b:int, g:int, r:int):int
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{
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var i:int;
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var j:int;
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var dist:int
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var a:int;
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var bestd:int;
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var p:Array;
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var best:int;
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bestd = 1000; /* biggest possible dist is 256*3 */
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best = -1;
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i = netindex[g]; /* index on g */
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j = i - 1; /* start at netindex[g] and work outwards */
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while ((i < netsize) || (j >= 0))
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{
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if (i < netsize)
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{
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p = network[i];
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dist = p[1] - g; /* inx key */
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if (dist >= bestd) i = netsize; /* stop iter */
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else
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{
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i++;
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if (dist < 0) dist = -dist;
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a = p[0] - b;
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if (a < 0) a = -a;
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dist += a;
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if (dist < bestd)
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{
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a = p[2] - r;
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if (a < 0) a = -a;
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dist += a;
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if (dist < bestd)
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{
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bestd = dist;
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best = p[3];
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}
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}
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}
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}
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if (j >= 0)
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{
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p = network[j];
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dist = g - p[1]; /* inx key - reverse dif */
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if (dist >= bestd) j = -1; /* stop iter */
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else
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{
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j--;
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if (dist < 0) dist = -dist;
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a = p[0] - b;
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if (a < 0) a = -a;
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dist += a;
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if (dist < bestd)
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{
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a = p[2] - r;
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if (a < 0)a = -a;
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dist += a;
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if (dist < bestd)
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{
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bestd = dist;
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best = p[3];
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}
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}
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}
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}
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}
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return (best);
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}
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public function process():ByteArray
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{
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learn();
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unbiasnet();
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inxbuild();
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return colorMap();
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}
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/*
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* Unbias network to give byte values 0..255 and record position i to prepare
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* for sort
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* -----------------------------------------------------------------------------------
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*/
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private function unbiasnet():void
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{
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var i:int;
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var j:int;
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for (i = 0; i < netsize; i++)
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{
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network[i][0] >>= netbiasshift;
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network[i][1] >>= netbiasshift;
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network[i][2] >>= netbiasshift;
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network[i][3] = i; /* record colour no */
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}
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}
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/*
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* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in
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* radpower[|i-j|]
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* ---------------------------------------------------------------------------------
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*/
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private function alterneigh(rad:int, i:int, b:int, g:int, r:int):void
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{
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var j:int;
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var k:int;
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var lo:int;
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var hi:int;
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var a:int;
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var m:int;
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var p:Array;
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lo = i - rad;
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if (lo < -1) lo = -1;
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hi = i + rad;
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if (hi > netsize) hi = netsize;
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j = i + 1;
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k = i - 1;
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m = 1;
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while ((j < hi) || (k > lo))
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{
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a = radpower[m++];
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if (j < hi)
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{
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p = network[j++];
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try {
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p[0] -= (a * (p[0] - b)) / alpharadbias;
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p[1] -= (a * (p[1] - g)) / alpharadbias;
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p[2] -= (a * (p[2] - r)) / alpharadbias;
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} catch (e:Error) {} // prevents 1.3 miscompilation
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}
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if (k > lo)
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{
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p = network[k--];
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try
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{
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p[0] -= (a * (p[0] - b)) / alpharadbias;
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p[1] -= (a * (p[1] - g)) / alpharadbias;
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p[2] -= (a * (p[2] - r)) / alpharadbias;
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} catch (e:Error) {}
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}
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}
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}
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/*
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* Move neuron i towards biased (b,g,r) by factor alpha
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* ----------------------------------------------------
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*/
|
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private function altersingle(alpha:int, i:int, b:int, g:int, r:int):void
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{
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/* alter hit neuron */
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var n:Array = network[i];
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n[0] -= (alpha * (n[0] - b)) / initalpha;
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n[1] -= (alpha * (n[1] - g)) / initalpha;
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n[2] -= (alpha * (n[2] - r)) / initalpha;
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}
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/*
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* Search for biased BGR values ----------------------------
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*/
|
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private function contest(b:int, g:int, r:int):int
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{
|
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|
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/* finds closest neuron (min dist) and updates freq */
|
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/* finds best neuron (min dist-bias) and returns position */
|
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/* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
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/* bias[i] = gamma*((1/netsize)-freq[i]) */
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|
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var i:int;
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var dist:int;
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var a:int;
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var biasdist:int;
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var betafreq:int;
|
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var bestpos:int;
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var bestbiaspos:int;
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var bestd:int;
|
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var bestbiasd:int;
|
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var n:Array;
|
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|
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bestd = ~(1 << 31);
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bestbiasd = bestd;
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bestpos = -1;
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bestbiaspos = bestpos;
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|
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for (i = 0; i < netsize; i++)
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|
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{
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n = network[i];
|
|
dist = n[0] - b;
|
|
|
|
if (dist < 0) dist = -dist;
|
|
|
|
a = n[1] - g;
|
|
|
|
if (a < 0) a = -a;
|
|
|
|
dist += a;
|
|
|
|
a = n[2] - r;
|
|
|
|
if (a < 0) a = -a;
|
|
|
|
dist += a;
|
|
|
|
if (dist < bestd)
|
|
|
|
{
|
|
|
|
bestd = dist;
|
|
bestpos = i;
|
|
|
|
}
|
|
|
|
biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));
|
|
|
|
if (biasdist < bestbiasd)
|
|
|
|
{
|
|
|
|
bestbiasd = biasdist;
|
|
bestbiaspos = i;
|
|
|
|
}
|
|
|
|
betafreq = (freq[i] >> betashift);
|
|
freq[i] -= betafreq;
|
|
bias[i] += (betafreq << gammashift);
|
|
|
|
}
|
|
|
|
freq[bestpos] += beta;
|
|
bias[bestpos] -= betagamma;
|
|
return (bestbiaspos);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
} |