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using UnityEngine;
using System.Collections;
using System.IO;
using System.Collections.Generic;
using System.Threading;
namespace uGIF
{
public class CaptureToGIFCustom : MonoBehaviour
{
public static CaptureToGIFCustom Instance;
public List<Image> Frames = new List<Image>();
public bool stop = false;
[System.NonSerialized]
public byte[] bytes = null;
private void Awake()
{
Instance = this;
}
public IEnumerator Encode ()
{
bytes = null;
stop = false;
Error.Log(Color.yellow, "Encoding...");
yield return new WaitForSeconds(0.1f);
yield return _Encode();
Error.Log(Color.yellow, "Saving gif...");
yield return new WaitForSeconds(0.1f);
yield return WaitForBytes();
}
IEnumerator WaitForBytes() {
while(bytes == null) yield return new WaitForEndOfFrame();
string fileName = string.Format("FateViewer_{0}", System.DateTime.Now.ToString("yyyy-MM-dd_HH-mm-ss-fff"));
WebGLDownload.DownloadFile(bytes, fileName, "gif");
bytes = null;
Error.Log(Color.green, "Gif saved!");
Frames.Clear();
stop = false;
}
public IEnumerator _Encode ()
{
var ge = new GIFEncoder ();
ge.useGlobalColorTable = true;
ge.repeat = 0;
ge.FPS = 30;
ge.transparent = new Color32 (0, 0, 0, 0);
ge.dispose = 2;
var stream = new MemoryStream ();
ge.Start (stream);
while (!stop || Frames.Count > 0)
{
if(Frames[0] != null)
{
Frames[0].Flip();
ge.AddFrame(Frames[0]);
Frames.RemoveAt(0);
}
yield return new WaitForEndOfFrame();
}
ge.Finish ();
bytes = stream.GetBuffer ();
stream.Close ();
yield break;
}
}
}

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fileFormatVersion: 2
guid: fa66f75ff0e8a134b8014ca7a6e65689
timeCreated: 1440680919
licenseType: Pro
MonoImporter:
serializedVersion: 2
defaultReferences: []
executionOrder: 0
icon: {instanceID: 0}
userData:
assetBundleName:
assetBundleVariant:

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using System;
using System.IO;
using UnityEngine;
using System.Collections.Generic;
namespace uGIF
{
public class GIFEncoder
{
public bool useGlobalColorTable = false;
public Color32? transparent = null;
public int repeat = -1;
public int dispose = -1; // disposal code (-1 = use default)
public int quality = 10; // default sample interval for quantizer
public float FPS {
set {
delay = Mathf.RoundToInt (100f / value);
}
}
public void AddFrame (Image im)
{
if (im == null)
throw new ArgumentNullException ("im");
if (!started)
throw new InvalidOperationException ("Start() must be called before AddFrame()");
if (firstFrame) {
width = im.width;
height = im.height;
}
pixels = im.pixels;
RemapPixels (); // build color table & map pixels
pixels = null;
if (firstFrame) {
WriteLSD (); // logical screen descriptior
WritePalette (); // global color table
if (repeat >= 0) {
// use NS app extension to indicate reps
WriteNetscapeExt ();
}
}
WriteGraphicCtrlExt (); // write graphic control extension
WriteImageDesc (); // image descriptor
if (!firstFrame && !useGlobalColorTable) {
WritePalette (); // local color table
}
WritePixels (); // encode and write pixel data
firstFrame = false;
}
public void Finish ()
{
if (!started)
throw new InvalidOperationException ("Start() must be called before Finish()");
started = false;
ms.WriteByte (0x3b); // gif trailer
ms.Flush ();
// reset for subsequent use
transIndex = 0;
pixels = null;
indexedPixels = null;
prevIndexedPixels = null;
colorTab = null;
firstFrame = true;
nq = null;
}
public void Start (MemoryStream os)
{
if (os == null)
throw new ArgumentNullException ("os");
ms = os;
started = true;
WriteString ("GIF89a"); // header
}
void RemapPixels ()
{
int len = pixels.Length;
indexedPixels = new byte[len];
if (firstFrame || !useGlobalColorTable) {
// initialize quantizer
nq = new NeuQuant (pixels, len, quality);
colorTab = nq.Process (); // create reduced palette
}
for (int i = 0; i < len; i++) {
int index = nq.Map (pixels [i].r & 0xff, pixels [i].g & 0xff, pixels [i].b & 0xff);
usedEntry [index] = true;
indexedPixels [i] = (byte)index;
if (dispose == 1 && prevIndexedPixels != null) {
if (indexedPixels [i] == prevIndexedPixels [i]) {
indexedPixels [i] = (byte)transIndex;
} else {
prevIndexedPixels [i] = (byte)index;
}
}
}
colorDepth = 8;
palSize = 7;
// get closest match to transparent color if specified
if (transparent.HasValue) {
var c = transparent.Value;
//transIndex = FindClosest(transparent);
transIndex = nq.Map (c.b, c.g, c.r);
}
if (dispose == 1 && prevIndexedPixels == null)
prevIndexedPixels = indexedPixels.Clone () as byte[];
}
int FindClosest (Color32 c)
{
if (colorTab == null)
return -1;
int r = c.r;
int g = c.g;
int b = c.b;
int minpos = 0;
int dmin = 256 * 256 * 256;
int len = colorTab.Length;
for (int i = 0; i < len;) {
int dr = r - (colorTab [i++] & 0xff);
int dg = g - (colorTab [i++] & 0xff);
int db = b - (colorTab [i] & 0xff);
int d = dr * dr + dg * dg + db * db;
int index = i / 3;
if (usedEntry [index] && (d < dmin)) {
dmin = d;
minpos = index;
}
i++;
}
return minpos;
}
void WriteGraphicCtrlExt ()
{
ms.WriteByte (0x21); // extension introducer
ms.WriteByte (0xf9); // GCE label
ms.WriteByte (4); // data block size
int transp, disp;
if (transparent == null) {
transp = 0;
disp = 0; // dispose = no action
} else {
transp = 1;
disp = 2; // force clear if using transparent color
}
if (dispose >= 0) {
disp = dispose & 7; // user override
}
disp <<= 2;
// packed fields
ms.WriteByte (Convert.ToByte (0 | // 1:3 reserved
disp | // 4:6 disposal
0 | // 7 user input - 0 = none
transp)); // 8 transparency flag
WriteShort (delay); // delay x 1/100 sec
ms.WriteByte (Convert.ToByte (transIndex)); // transparent color index
ms.WriteByte (0); // block terminator
}
void WriteImageDesc ()
{
ms.WriteByte (0x2c); // image separator
WriteShort (0); // image position x,y = 0,0
WriteShort (0);
WriteShort (width); // image size
WriteShort (height);
// no LCT - GCT is used for first (or only) frame
ms.WriteByte (0);
}
void WriteLSD ()
{
// logical screen size
WriteShort (width);
WriteShort (height);
// packed fields
ms.WriteByte (Convert.ToByte (0x80 | // 1 : global color table flag = 1 (gct used)
0x70 | // 2-4 : color resolution = 7
0x00 | // 5 : gct sort flag = 0
palSize)); // 6-8 : gct size
ms.WriteByte (0); // background color index
ms.WriteByte (0); // pixel aspect ratio - assume 1:1
}
void WriteNetscapeExt ()
{
ms.WriteByte (0x21); // extension introducer
ms.WriteByte (0xff); // app extension label
ms.WriteByte (11); // block size
WriteString ("NETSCAPE" + "2.0"); // app id + auth code
ms.WriteByte (3); // sub-block size
ms.WriteByte (1); // loop sub-block id
WriteShort (repeat); // loop count (extra iterations, 0=repeat forever)
ms.WriteByte (0); // block terminator
}
void WritePalette ()
{
ms.Write (colorTab, 0, colorTab.Length);
int n = (3 * 256) - colorTab.Length;
for (int i = 0; i < n; i++) {
ms.WriteByte (0);
}
}
void WritePixels ()
{
LZWEncoder encoder = new LZWEncoder (width, height, indexedPixels, colorDepth);
encoder.Encode (ms);
}
void WriteShort (int value)
{
ms.WriteByte (Convert.ToByte (value & 0xff));
ms.WriteByte (Convert.ToByte ((value >> 8) & 0xff));
}
void WriteString (String s)
{
char[] chars = s.ToCharArray ();
for (int i = 0; i < chars.Length; i++) {
ms.WriteByte ((byte)chars [i]);
}
}
int delay = 0;
int width;
int height;
int transIndex;
bool started = false;
MemoryStream ms;
Color32[] pixels;
byte[] indexedPixels;
byte[] prevIndexedPixels;
int colorDepth;
byte[] colorTab;
bool[] usedEntry = new bool[256]; // active palette entries
int palSize = 7; // color table size (bits-1)
bool firstFrame = true;
NeuQuant nq;
}
}

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fileFormatVersion: 2
guid: f9cefdd8311054a0ca0732d817079222
timeCreated: 1440680959
licenseType: Pro
MonoImporter:
serializedVersion: 2
defaultReferences: []
executionOrder: 0
icon: {instanceID: 0}
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using UnityEngine;
using System.Collections;
namespace uGIF
{
public class Image
{
public int width;
public int height;
public Color32[] pixels;
public Image (Texture2D f)
{
pixels = f.GetPixels32 ();
width = f.width;
height = f.height;
}
public Image (Image image)
{
pixels = image.pixels.Clone () as Color32[];
width = image.width;
height = image.height;
}
public Image (int width, int height)
{
this.width = width;
this.height = height;
pixels = new Color32[width * height];
}
public void DrawImage (Image image, int i, int i2)
{
throw new System.NotImplementedException ();
}
public Color32 GetPixel (int tw, int th)
{
var index = (th * width) + tw;
return pixels [index];
}
public void Flip ()
{
for (var y = 0; y < height/2; y++) {
for (var x = 0; x < width; x++) {
var top = y * width + x;
var bottom = (height - y - 1) * width + x;
var temp = pixels [top];
pixels [top] = pixels [bottom];
pixels [bottom] = temp;
}
}
}
public void Resize (int scale)
{
if (scale <= 1)
return;
var newWidth = width / scale;
var newHeight = height / scale;
var newColors = new Color32[newWidth * newHeight];
for (var y=0; y<newHeight; y++) {
for (var x=0; x<newWidth; x++) {
newColors [(y * newWidth) + x] = pixels [(y * scale) * width + (x * scale)];
}
}
pixels = newColors;
height = newHeight;
width = newWidth;
}
public void ResizeBilinear (int newWidth, int newHeight)
{
if (newWidth == width && newHeight == height)
return;
var texColors = pixels;
var newColors = new Color32[newWidth * newHeight];
var ratioX = 1.0f / ((float)newWidth / (width - 1));
var ratioY = 1.0f / ((float)newHeight / (height - 1));
var w = width;
var w2 = newWidth;
for (var y = 0; y < newHeight; y++) {
var yFloor = Mathf.FloorToInt (y * ratioY);
var y1 = yFloor * w;
var y2 = (yFloor + 1) * w;
var yw = y * w2;
for (var x = 0; x < w2; x++) {
int xFloor = (int)Mathf.Floor (x * ratioX);
var xLerp = x * ratioX - xFloor;
newColors [yw + x] = ColorLerpUnclamped (ColorLerpUnclamped (texColors [y1 + xFloor], texColors [y1 + xFloor + 1], xLerp), ColorLerpUnclamped (texColors [y2 + xFloor], texColors [y2 + xFloor + 1], xLerp), y * ratioY - yFloor);
}
}
pixels = newColors;
height = newHeight;
width = newWidth;
}
Color32 ColorLerpUnclamped (Color A, Color B, float P)
{
return new Color (A.r + (B.r - A.r) * P, A.g + (B.g - A.g) * P, A.b + (B.b - A.b) * P, A.a + (B.a - A.a) * P);
}
}
}

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fileFormatVersion: 2
guid: e6a459d4e9025482c87c0f3bdac39d24
timeCreated: 1440596375
licenseType: Pro
MonoImporter:
serializedVersion: 2
defaultReferences: []
executionOrder: 0
icon: {instanceID: 0}
userData:
assetBundleName:
assetBundleVariant:

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using System;
using System.IO;
namespace uGIF
{
public class LZWEncoder
{
static readonly int EOF = -1;
byte[] pixAry;
int initCodeSize;
int curPixel;
// GIFCOMPR.C - GIF Image compression routines
//
// Lempel-Ziv compression based on 'compress'. GIF modifications by
// David Rowley (mgardi@watdcsu.waterloo.edu)
// General DEFINEs
static readonly int BITS = 12;
static readonly int HSIZE = 5003; // 80% occupancy
// GIF Image compression - modified 'compress'
//
// Based on: compress.c - File compression ala IEEE Computer, June 1984.
//
// By Authors: Spencer W. Thomas (decvax!harpo!utah-cs!utah-gr!thomas)
// Jim McKie (decvax!mcvax!jim)
// Steve Davies (decvax!vax135!petsd!peora!srd)
// Ken Turkowski (decvax!decwrl!turtlevax!ken)
// James A. Woods (decvax!ihnp4!ames!jaw)
// Joe Orost (decvax!vax135!petsd!joe)
int n_bits; // number of bits/code
int maxbits = BITS; // user settable max # bits/code
int maxcode; // maximum code, given n_bits
int maxmaxcode = 1 << BITS; // should NEVER generate this code
int[] htab = new int[HSIZE];
int[] codetab = new int[HSIZE];
int hsize = HSIZE; // for dynamic table sizing
int free_ent = 0; // first unused entry
// block compression parameters -- after all codes are used up,
// and compression rate changes, start over.
bool clear_flg = false;
// Algorithm: use open addressing double hashing (no chaining) on the
// prefix code / next character combination. We do a variant of Knuth's
// algorithm D (vol. 3, sec. 6.4) along with G. Knott's relatively-prime
// secondary probe. Here, the modular division first probe is gives way
// to a faster exclusive-or manipulation. Also do block compression with
// an adaptive reset, whereby the code table is cleared when the compression
// ratio decreases, but after the table fills. The variable-length output
// codes are re-sized at this point, and a special CLEAR code is generated
// for the decompressor. Late addition: construct the table according to
// file size for noticeable speed improvement on small files. Please direct
// questions about this implementation to ames!jaw.
int g_init_bits;
int ClearCode;
int EOFCode;
// output
//
// Output the given code.
// Inputs:
// code: A n_bits-bit integer. If == -1, then EOF. This assumes
// that n_bits =< wordsize - 1.
// Outputs:
// Outputs code to the file.
// Assumptions:
// Chars are 8 bits long.
// Algorithm:
// Maintain a BITS character long buffer (so that 8 codes will
// fit in it exactly). Use the VAX insv instruction to insert each
// code in turn. When the buffer fills up empty it and start over.
int cur_accum = 0;
int cur_bits = 0;
int[] masks =
{
0x0000,
0x0001,
0x0003,
0x0007,
0x000F,
0x001F,
0x003F,
0x007F,
0x00FF,
0x01FF,
0x03FF,
0x07FF,
0x0FFF,
0x1FFF,
0x3FFF,
0x7FFF,
0xFFFF };
// Number of characters so far in this 'packet'
int a_count;
// Define the storage for the packet accumulator
byte[] accum = new byte[256];
//----------------------------------------------------------------------------
public LZWEncoder (int width, int height, byte[] pixels, int color_depth)
{
pixAry = pixels;
initCodeSize = Math.Max (2, color_depth);
}
// Add a character to the end of the current packet, and if it is 254
// characters, flush the packet to disk.
void Add (byte c, Stream outs)
{
accum [a_count++] = c;
if (a_count >= 254)
Flush (outs);
}
// Clear out the hash table
// table clear for block compress
void ClearTable (Stream outs)
{
ResetCodeTable (hsize);
free_ent = ClearCode + 2;
clear_flg = true;
Output (ClearCode, outs);
}
// reset code table
void ResetCodeTable (int hsize)
{
for (int i = 0; i < hsize; ++i)
htab [i] = -1;
}
void Compress (int init_bits, Stream outs)
{
int fcode;
int i /* = 0 */;
int c;
int ent;
int disp;
int hsize_reg;
int hshift;
// Set up the globals: g_init_bits - initial number of bits
g_init_bits = init_bits;
// Set up the necessary values
clear_flg = false;
n_bits = g_init_bits;
maxcode = MaxCode (n_bits);
ClearCode = 1 << (init_bits - 1);
EOFCode = ClearCode + 1;
free_ent = ClearCode + 2;
a_count = 0; // clear packet
ent = NextPixel ();
hshift = 0;
for (fcode = hsize; fcode < 65536; fcode *= 2)
++hshift;
hshift = 8 - hshift; // set hash code range bound
hsize_reg = hsize;
ResetCodeTable (hsize_reg); // clear hash table
Output (ClearCode, outs);
outer_loop :
while ((c = NextPixel()) != EOF) {
fcode = (c << maxbits) + ent;
i = (c << hshift) ^ ent; // xor hashing
if (htab [i] == fcode) {
ent = codetab [i];
continue;
} else if (htab [i] >= 0) { // non-empty slot
disp = hsize_reg - i; // secondary hash (after G. Knott)
if (i == 0)
disp = 1;
do {
if ((i -= disp) < 0)
i += hsize_reg;
if (htab [i] == fcode) {
ent = codetab [i];
goto outer_loop;
}
} while (htab[i] >= 0);
}
Output (ent, outs);
ent = c;
if (free_ent < maxmaxcode) {
codetab [i] = free_ent++; // code -> hashtable
htab [i] = fcode;
} else
ClearTable (outs);
}
// Put out the final code.
Output (ent, outs);
Output (EOFCode, outs);
}
//----------------------------------------------------------------------------
public void Encode (Stream os)
{
os.WriteByte (Convert.ToByte (initCodeSize)); // write "initial code size" byte
curPixel = 0;
Compress (initCodeSize + 1, os); // compress and write the pixel data
os.WriteByte (0); // write block terminator
}
// Flush the packet to disk, and reset the accumulator
void Flush (Stream outs)
{
if (a_count > 0) {
outs.WriteByte (Convert.ToByte (a_count));
outs.Write (accum, 0, a_count);
a_count = 0;
}
}
int MaxCode (int n_bits)
{
return (1 << n_bits) - 1;
}
//----------------------------------------------------------------------------
// Return the next pixel from the image
//----------------------------------------------------------------------------
int NextPixel ()
{
if (curPixel == pixAry.Length)
return EOF;
curPixel++;
return pixAry [curPixel - 1] & 0xff;
}
void Output (int code, Stream outs)
{
cur_accum &= masks [cur_bits];
if (cur_bits > 0)
cur_accum |= (code << cur_bits);
else
cur_accum = code;
cur_bits += n_bits;
while (cur_bits >= 8) {
Add ((byte)(cur_accum & 0xff), outs);
cur_accum >>= 8;
cur_bits -= 8;
}
// If the next entry is going to be too big for the code size,
// then increase it, if possible.
if (free_ent > maxcode || clear_flg) {
if (clear_flg) {
maxcode = MaxCode (n_bits = g_init_bits);
clear_flg = false;
} else {
++n_bits;
if (n_bits == maxbits)
maxcode = maxmaxcode;
else
maxcode = MaxCode (n_bits);
}
}
if (code == EOFCode) {
// At EOF, write the rest of the buffer.
while (cur_bits > 0) {
Add ((byte)(cur_accum & 0xff), outs);
cur_accum >>= 8;
cur_bits -= 8;
}
Flush (outs);
}
}
}
}

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fileFormatVersion: 2
guid: fcb109a1b5481440891b04c6588a6599
timeCreated: 1440596152
licenseType: Pro
MonoImporter:
serializedVersion: 2
defaultReferences: []
executionOrder: 0
icon: {instanceID: 0}
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/* NeuQuant Neural-Net Quantization Algorithm
* ------------------------------------------
*
* Copyright (c) 1994 Anthony Dekker
*
* 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.
*/
// Ported to Java 12/00 K Weiner
using System;
using UnityEngine;
namespace uGIF
{
public class NeuQuant
{
static readonly int netsize = 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 */
static readonly int prime1 = 499;
static readonly int prime2 = 491;
static readonly int prime3 = 487;
static readonly int prime4 = 503;
static readonly int minpicturebytes = (3 * prime4);
/* minimum size for input image */
/* Program Skeleton
----------------
[select samplefac in range 1..30]
[read image from input file]
pic = (unsigned char*) malloc(3*width*height);
initnet(pic,3*width*height,samplefac);
learn();
unbiasnet();
[write output image header, using writecolourmap(f)]
inxbuild();
write output image using inxsearch(b,g,r) */
/* Network Definitions
------------------- */
static readonly int maxnetpos = (netsize - 1);
static readonly int netbiasshift = 4; /* bias for colour values */
static readonly int ncycles = 100; /* no. of learning cycles */
/* defs for freq and bias */
static readonly int intbiasshift = 16; /* bias for fractions */
static readonly int intbias = (((int)1) << intbiasshift);
static readonly int gammashift = 10; /* gamma = 1024 */
static readonly int gamma = (((int)1) << gammashift);
static readonly int betashift = 10;
static readonly int beta = (intbias >> betashift); /* beta = 1/1024 */
static readonly int betagamma = (intbias << (gammashift - betashift));
/* defs for decreasing radius factor */
static readonly int initrad = (netsize >> 3); /* for 256 cols, radius starts */
static readonly int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
static readonly int radiusbias = (((int)1) << radiusbiasshift);
static readonly int initradius = (initrad * radiusbias); /* and decreases by a */
static readonly int radiusdec = 30; /* factor of 1/30 each cycle */
/* defs for decreasing alpha factor */
static readonly int alphabiasshift = 10; /* alpha starts at 1.0 */
static readonly int initalpha = (((int)1) << alphabiasshift);
int alphadec; /* biased by 10 bits */
/* radbias and alpharadbias used for radpower calculation */
static readonly int radbiasshift = 8;
static readonly int radbias = (((int)1) << radbiasshift);
static readonly int alpharadbshift = (alphabiasshift + radbiasshift);
static readonly int alpharadbias = (((int)1) << alpharadbshift);
/* Types and Global Variables
-------------------------- */
Color32[] thepicture; /* the input image itself */
int lengthcount; /* lengthcount = H*W*3 */
int samplefac; /* sampling factor 1..30 */
// typedef int pixel[4]; /* BGRc */
int[][] network; /* the network itself - [netsize][4] */
int[] netindex = new int[256];
/* for network lookup - really 256 */
int[] bias = new int[netsize];
/* bias and freq arrays for learning */
int[] freq = new int[netsize];
int[] radpower = new int[initrad];
/* radpower for precomputation */
/* Initialise network in range (0,0,0) to (255,255,255) and set parameters
----------------------------------------------------------------------- */
public NeuQuant (Color32[] thepic, int len, int sample)
{
int i;
int[] p;
thepicture = thepic;
lengthcount = len;
samplefac = sample;
network = new int[netsize][];
for (i = 0; i < netsize; i++) {
network [i] = new int[4];
p = network [i];
p [0] = p [1] = p [2] = (i << (netbiasshift + 8)) / netsize;
freq [i] = intbias / netsize; /* 1/netsize */
bias [i] = 0;
}
}
byte[] ColorMap ()
{
byte[] map = new byte[3 * netsize];
int[] index = new int[netsize];
for (int i = 0; i < netsize; i++)
index [network [i] [3]] = i;
int k = 0;
for (int i = 0; i < netsize; i++) {
int j = index [i];
map [k++] = (byte)(network [j] [0]);
map [k++] = (byte)(network [j] [1]);
map [k++] = (byte)(network [j] [2]);
}
return map;
}
/* Insertion sort of network and building of netindex[0..255] (to do after unbias)
------------------------------------------------------------------------------- */
void Inxbuild ()
{
int i, j, smallpos, smallval;
int[] p;
int[] q;
int previouscol, startpos;
previouscol = 0;
startpos = 0;
for (i = 0; i < netsize; i++) {
p = network [i];
smallpos = i;
smallval = p [1]; /* index on g */
/* find smallest in i..netsize-1 */
for (j = i + 1; j < netsize; j++) {
q = network [j];
if (q [1] < smallval) { /* index on g */
smallpos = j;
smallval = q [1]; /* index on g */
}
}
q = network [smallpos];
/* swap p (i) and q (smallpos) entries */
if (i != smallpos) {
j = q [0];
q [0] = p [0];
p [0] = j;
j = q [1];
q [1] = p [1];
p [1] = j;
j = q [2];
q [2] = p [2];
p [2] = j;
j = q [3];
q [3] = p [3];
p [3] = j;
}
/* smallval entry is now in position i */
if (smallval != previouscol) {
netindex [previouscol] = (startpos + i) >> 1;
for (j = previouscol + 1; j < smallval; j++)
netindex [j] = i;
previouscol = smallval;
startpos = i;
}
}
netindex [previouscol] = (startpos + maxnetpos) >> 1;
for (j = previouscol + 1; j < 256; j++)
netindex [j] = maxnetpos; /* really 256 */
}
/* Main Learning Loop
------------------ */
void Learn ()
{
int i, j, b, g, r;
int radius, rad, alpha, step, delta, samplepixels;
int pix, lim;
if (lengthcount < minpicturebytes)
samplefac = 1;
alphadec = 30 + ((samplefac - 1) / 3);
var p = thepicture;
pix = 0;
lim = lengthcount;
samplepixels = lengthcount / (3 * samplefac);
delta = samplepixels / ncycles;
alpha = initalpha;
radius = initradius;
rad = radius >> radiusbiasshift;
if (rad <= 1)
rad = 0;
for (i = 0; i < rad; i++)
radpower [i] =
alpha * (((rad * rad - i * i) * radbias) / (rad * rad));
//fprintf(stderr,"beginning 1D learning: initial radius=%d\n", 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].r & 0xff) << netbiasshift;
g = (p [pix].g & 0xff) << netbiasshift;
r = (p [pix].b & 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 -= alpha / alphadec;
radius -= radius / radiusdec;
rad = radius >> radiusbiasshift;
if (rad <= 1)
rad = 0;
for (j = 0; j < rad; j++)
radpower [j] =
alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
}
}
}
/* Search for BGR values 0..255 (after net is unbiased) and return colour index
---------------------------------------------------------------------------- */
public int Map (int b, int g, int r)
{
int i, j, dist, a, bestd;
int[] p;
int best;
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) {
p = network [i];
dist = p [1] - g; /* inx key */
if (dist >= bestd)
i = netsize; /* stop iter */
else {
i++;
if (dist < 0)
dist = -dist;
a = p [0] - b;
if (a < 0)
a = -a;
dist += a;
if (dist < bestd) {
a = p [2] - r;
if (a < 0)
a = -a;
dist += a;
if (dist < bestd) {
bestd = dist;
best = p [3];
}
}
}
}
if (j >= 0) {
p = network [j];
dist = g - p [1]; /* inx key - reverse dif */
if (dist >= bestd)
j = -1; /* stop iter */
else {
j--;
if (dist < 0)
dist = -dist;
a = p [0] - b;
if (a < 0)
a = -a;
dist += a;
if (dist < bestd) {
a = p [2] - r;
if (a < 0)
a = -a;
dist += a;
if (dist < bestd) {
bestd = dist;
best = p [3];
}
}
}
}
}
return (best);
}
public byte[] Process ()
{
Learn ();
Unbiasnet ();
Inxbuild ();
return ColorMap ();
}
/* Unbias network to give byte values 0..255 and record position i to prepare for sort
----------------------------------------------------------------------------------- */
void Unbiasnet ()
{
int i;
for (i = 0; i < netsize; i++) {
network [i] [0] >>= netbiasshift;
network [i] [1] >>= netbiasshift;
network [i] [2] >>= netbiasshift;
network [i] [3] = i; /* record colour no */
}
}
/* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
--------------------------------------------------------------------------------- */
void Alterneigh (int rad, int i, int b, int g, int r)
{
int j, k, lo, hi, a, m;
int[] p;
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) {
p = network [j++];
p [0] -= (a * (p [0] - b)) / alpharadbias;
p [1] -= (a * (p [1] - g)) / alpharadbias;
p [2] -= (a * (p [2] - r)) / alpharadbias;
}
if (k > lo) {
p = network [k--];
p [0] -= (a * (p [0] - b)) / alpharadbias;
p [1] -= (a * (p [1] - g)) / alpharadbias;
p [2] -= (a * (p [2] - r)) / alpharadbias;
}
}
}
/* Move neuron i towards biased (b,g,r) by factor alpha
---------------------------------------------------- */
void Altersingle (int alpha, int i, int b, int g, int r)
{
/* alter hit neuron */
int[] n = network [i];
n [0] -= (alpha * (n [0] - b)) / initalpha;
n [1] -= (alpha * (n [1] - g)) / initalpha;
n [2] -= (alpha * (n [2] - r)) / initalpha;
}
/* Search for biased BGR values
---------------------------- */
int Contest (int b, int g, int r)
{
/* 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]) */
int i, dist, a, biasdist, betafreq;
int bestpos, bestbiaspos, bestd, bestbiasd;
int[] n;
bestd = ~(((int)1) << 31);
bestbiasd = bestd;
bestpos = -1;
bestbiaspos = bestpos;
for (i = 0; i < netsize; i++) {
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);
}
}
}

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