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2026-09-04 10:46:13 -07:00
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package iron.format.gif;
/*
* Copyright (c) 1994 Anthony Dekker
* Ported to Java by Kevin Weiner, FM Software
* Ported to Haxe by Tilman Schmidt and Sven Bergstr├╢m
*
* NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
* See "Kohonen neural networks for optimal colour quantization"
* in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
* for a discussion of the algorithm.
*
* Any party obtaining a copy of these files from the author, directly or
* indirectly, is granted, free of charge, a full and unrestricted irrevocable,
* world-wide, paid up, royalty-free, nonexclusive right and license to deal
* in this software and documentation files (the "Software"), including without
* limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons who receive
* copies from any such party to do so, with the only requirement being
* that this copyright notice remain intact.
*
*/
import haxe.io.Int32Array;
import haxe.io.UInt8Array;
class NeuQuant {
inline static var netsize : Int = 256; // Number of colours used
// Four primes near 500 - assume no image has a length so large that it is divisible by all four primes
inline static var prime1 : Int = 499;
inline static var prime2 : Int = 491;
inline static var prime3 : Int = 487;
inline static var prime4 : Int = 503;
inline static var minpicturebytes : Int = (3 * prime4); // Minimum size for input image
// Network Definitions
inline static var netbiasshift : Int = 4; // Bias for colour values
inline static var ncycles : Int = 100; // No. of learning cycles
// Defs for freq and bias
inline static var intbiasshift : Int = 16; // Bias for fractions
inline static var intbias : Int = (1 << intbiasshift);
inline static var gammashift : Int = 10; // Gamma = 1024
inline static var gamma : Int = (1 << gammashift);
inline static var betashift : Int = 10;
inline static var beta : Int = (intbias >> betashift); // Beta = 1/1024
inline static var betagamma : Int = (intbias << (gammashift - betashift));
// Defs for decreasing radius factor
inline static var initrad : Int = (netsize >> 3); // For 256 cols, radius starts
inline static var radiusbiasshift : Int = 6; // At 32.0 biased by 6 bits
inline static var radiusbias : Int = (1 << radiusbiasshift);
inline static var initradius : Int = (initrad * radiusbias); // And decreases by a
inline static var radiusdec : Int = 30; // Factor of 1/30 each cycle
// Defs for decreasing alpha factor
inline static var alphabiasshift : Int = 10; /* alpha starts at 1.0 */
inline static var initalpha : Int = (1 << alphabiasshift);
// Radbias and alpharadbias used for radpower calculation
inline static var radbiasshift : Int = 8;
inline static var radbias : Int = (1 << radbiasshift);
inline static var alpharadbshift : Int = (alphabiasshift + radbiasshift);
inline static var alpharadbias : Int = (1 << alpharadbshift);
var alphadec:Int; // Biased by 10 bits
// Types and Global Variables
var thepicture: UInt8Array; // The input image itself
var lengthcount: Int; // Lengthcount = H*W*3
var samplefac: Int; // Sampling factor 1..30
var network: Int32Array; // The network itself - [netsize][4]
var netindex: Int32Array; // For network lookup - really 256
var bias: Int32Array; // Bias array for learning
var freq: Int32Array; // Frequency array for learning
var radpower: Int32Array; // Radpower for precomputation
var colormap_map: UInt8Array; // Cached color map array
var colormap_index: Int32Array; // Cached color map index
public function new()
{
netindex = new Int32Array(256);
bias = new Int32Array(netsize);
freq = new Int32Array(netsize);
radpower = new Int32Array(initrad);
network = new Int32Array(netsize * 4);
colormap_map = new UInt8Array(3 * netsize);
colormap_index = new Int32Array(netsize);
}
// Reset network in range (0,0,0) to (255,255,255) and set parameters
public function reset(thepic:UInt8Array, len:Int, sample:Int):Void {
thepicture = thepic;
lengthcount = len;
samplefac = sample;
for (i in 0...netsize) {
network[i*4 + 0] = network[i*4 + 1] = network[i*4 + 2] = Std.int((i << (netbiasshift + 8)) / netsize);
freq[i] = Std.int(intbias / netsize); // 1 / netsize
bias[i] = 0; // allocated to zero?
}
}
public function colormap():UInt8Array
{
for(i in 0...netsize) {
colormap_index[network[i * 4 + 3]] = i;
}
var k:Int = 0;
for (i in 0...netsize)
{
var j = colormap_index[i];
colormap_map[k++] = network[j * 4];
colormap_map[k++] = network[j * 4 + 1];
colormap_map[k++] = network[j * 4 + 2];
}
return colormap_map;
}
// Insertion sort of network and building of netindex[0..255] (to do after unbias)
public function inxbuild():Void
{
var i:Int;
var j:Int;
var smallpos:Int;
var smallval:Int;
var previouscol:Int;
var startpos:Int;
previouscol = 0;
startpos = 0;
for (i in 0...netsize)
{
smallpos = i;
smallval = network[i*4 + 1]; // Index on g
// Find smallest in i..netsize-1
for (j in (i + 1)...netsize)
{
if (network[j*4 + 1] < smallval)
{
smallpos = j;
smallval = network[j*4 + 1]; // Index on g
}
}
// Swap p (i) and q (smallpos) entries
if (i != smallpos)
{
j = network[smallpos*4 + 0];
network[smallpos*4 + 0] = network[i*4 + 0];
network[i*4 + 0] = j;
j = network[smallpos*4 + 1];
network[smallpos*4 + 1] = network[i*4 + 1];
network[i*4 + 1] = j;
j = network[smallpos*4 + 2];
network[smallpos*4 + 2] = network[i*4 + 2];
network[i*4 + 2] = j;
j = network[smallpos*4 + 3];
network[smallpos*4 + 3] = network[i*4 + 3];
network[i*4 + 3] = j;
}
// Smallval entry is now in position i
if (smallval != previouscol)
{
netindex[previouscol] = (startpos + i) >> 1;
for (j in (previouscol + 1)...smallval)
netindex[j] = i;
previouscol = smallval;
startpos = i;
}
}
var maxnetpos = netsize - 1;
netindex[previouscol] = (startpos + maxnetpos) >> 1;
for (j in (previouscol + 1)...256)
netindex[j] = maxnetpos;
}
// Main learning Loop
public function learn():Void
{
var i:Int;
var j:Int;
var b:Int;
var g:Int;
var r:Int;
var radius:Int;
var rad:Int;
var alpha:Int;
var step:Int;
var delta:Int;
var samplepixels:Int;
var p:UInt8Array;
var pix:Int;
var lim:Int;
if (lengthcount < minpicturebytes)
samplefac = 1;
alphadec = 30 + Std.int((samplefac - 1) / 3);
p = thepicture;
pix = 0;
lim = lengthcount;
samplepixels = Std.int(lengthcount / (3 * samplefac));
delta = Std.int(samplepixels / ncycles);
alpha = initalpha;
radius = initradius;
rad = radius >> radiusbiasshift;
if (rad <= 1)
rad = 0;
for (i in 0...rad)
radpower[i] = Std.int(alpha * (((rad * rad - i * i) * radbias) / (rad * rad)));
if (lengthcount < minpicturebytes)
{
step = 3;
}
else if ((lengthcount % prime1) != 0)
{
step = 3 * prime1;
}
else
{
if ((lengthcount % prime2) != 0)
{
step = 3 * prime2;
}
else
{
if ((lengthcount % prime3) != 0)
step = 3 * prime3;
else
step = 3 * prime4;
}
}
i = 0;
while (i < samplepixels)
{
b = (p[pix + 0] & 0xff) << netbiasshift;
g = (p[pix + 1] & 0xff) << netbiasshift;
r = (p[pix + 2] & 0xff) << netbiasshift;
j = contest(b, g, r);
altersingle(alpha, j, b, g, r);
if (rad != 0)
alterneigh(rad, j, b, g, r); // Alter neighbours
pix += step;
if (pix >= lim)
pix -= lengthcount;
i++;
if (delta == 0)
delta = 1;
if (i % delta == 0)
{
alpha -= Std.int(alpha / alphadec);
radius -= Std.int(radius / radiusdec);
rad = radius >> radiusbiasshift;
if (rad <= 1)
rad = 0;
for (j in 0...rad)
radpower[j] = Std.int(alpha * (((rad * rad - j * j) * radbias) / (rad * rad)));
}
}
}
// Search for BGR values 0..255 (after net is unbiased) and return colour index
public function map(b:Int, g:Int, r:Int):Int
{
var i:Int;
var j:Int;
var dist:Int;
var a:Int;
var bestd:Int;
var best:Int;
bestd = 1000; // Biggest possible dist is 256*3
best = -1;
i = netindex[g]; // Index on g
j = i - 1; // Start at netindex[g] and work outwards
while ((i < netsize) || (j >= 0))
{
if (i < netsize)
{
dist = network[i*4 + 1] - g; // Inx key
if (dist >= bestd)
{
i = netsize; // Stop iter
}
else
{
if (dist < 0)
dist = -dist;
a = network[i*4 + 0] - b;
if (a < 0)
a = -a;
dist += a;
if (dist < bestd)
{
a = network[i*4 + 2] - r;
if (a < 0)
a = -a;
dist += a;
if (dist < bestd)
{
bestd = dist;
best = network[i*4 + 3];
}
}
i++;
}
}
if (j >= 0)
{
dist = g - network[j*4 + 1]; // Inx key - reverse dif
if (dist >= bestd)
{
j = -1; // Stop iter
}
else
{
if (dist < 0)
dist = -dist;
a = network[j*4 + 0] - b;
if (a < 0)
a = -a;
dist += a;
if (dist < bestd)
{
a = network[j*4 + 2] - r;
if (a < 0)
a = -a;
dist += a;
if (dist < bestd)
{
bestd = dist;
best = network[j*4 + 3];
}
}
j--;
}
}
}
return best;
}
public function process():UInt8Array
{
learn();
unbiasnet();
inxbuild();
return colormap();
}
// Unbias network to give byte values 0..255 and record position i to prepare for sort
public function unbiasnet():Void
{
for (i in 0...netsize)
{
network[i*4] >>= netbiasshift;
network[i*4 + 1] >>= netbiasshift;
network[i*4 + 2] >>= netbiasshift;
network[i*4 + 3] = i; // Record colour no
}
}
// Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
function alterneigh(rad:Int, i:Int, b:Int, g:Int, r:Int):Void
{
var j:Int;
var k:Int;
var lo:Int;
var hi:Int;
var a:Int;
var m:Int;
lo = i - rad;
if (lo < -1)
lo = -1;
hi = i + rad;
if (hi > netsize)
hi = netsize;
j = i + 1;
k = i - 1;
m = 1;
while ((j < hi) || (k > lo))
{
a = radpower[m++];
if (j < hi)
{
network[j * 4 + 0] -= Std.int((a * (network[j * 4 + 0] - b)) / alpharadbias);
network[j * 4 + 1] -= Std.int((a * (network[j * 4 + 1] - g)) / alpharadbias);
network[j * 4 + 2] -= Std.int((a * (network[j * 4 + 2] - r)) / alpharadbias);
j++;
}
if (k > lo)
{
network[k * 4 + 0] -= Std.int((a * (network[k * 4 + 0] - b)) / alpharadbias);
network[k * 4 + 1] -= Std.int((a * (network[k * 4 + 1] - g)) / alpharadbias);
network[k * 4 + 2] -= Std.int((a * (network[k * 4 + 2] - r)) / alpharadbias);
k--;
}
}
}
// Move neuron i towards biased (b,g,r) by factor alpha
function altersingle(alpha:Int, i:Int, b:Int, g:Int, r:Int):Void
{
/* Alter hit neuron */
network[i*4 + 0] -= Std.int((alpha * (network[i*4 + 0] - b)) / initalpha);
network[i*4 + 1] -= Std.int((alpha * (network[i*4 + 1] - g)) / initalpha);
network[i*4 + 2] -= Std.int((alpha * (network[i*4 + 2] - r)) / initalpha);
}
inline function make_abs(value:Int) : Int {
var tmp = value >> 31;
value ^= tmp;
value += tmp & 1;
return value;
}
// Search for biased BGR values
static inline var bestd_init = ~(1 << 31);
function contest(b:Int, g:Int, r:Int):Int
{
// Finds closest neuron (min dist) and updates freq
// Finds best neuron (min dist-bias) and returns position
// For frequently chosen neurons, freq[i] is high and bias[i] is negative
// bias[i] = gamma*((1/netsize)-freq[i])
var i:Int;
var dist:Int;
var a:Int;
var biasdist:Int;
var betafreq:Int;
var bestpos:Int;
var bestbiaspos:Int;
var bestd:Int;
var bestbiasd:Int;
bestd = bestd_init;
bestbiasd = bestd;
bestpos = -1;
bestbiaspos = bestpos;
for (i in 0...netsize)
{
var i_n = i * 4;
var b_i = i_n + 0;
var g_i = i_n + 1;
var r_i = i_n + 2;
var b_a = network[b_i];
var g_a = network[g_i];
var r_a = network[r_i];
b_a = make_abs(b_a - b);
g_a = make_abs(g_a - g);
r_a = make_abs(r_a - r);
dist = b_a + g_a + r_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;
}
}