学以致用——Java源码——使用图片文件（.jpg）创建GIF动图(.gif)的Java程序
程序功能：
将多幅静态图片（.jpg）合成为.gif图片。
参考文章：
学以致用——Java源码——使用随机几何图形制作屏保程序（Screen Saver with Shapes Using the Java 2D API），
https://blog.csdn.net/hpdlzu80100/article/details/86678149。
该文章中的动图即使用本文中的方法创建而成。
运行示例：
源码：
（本文中的源码（主体类中的jpgToGif方法、三个辅助类：AnimatedGifEncoder类、LZWEncoder类、NeuQuant类）主要来自互联网）
1. 主体类
import java.awt.image.BufferedImage;
import java.io.File;
import javax.imageio.ImageIO;
/**
*
* @author [email protected]
* @Date Jan 24, 2019, 6:41:50 PM
*
*/
public class MakeGif {
public static void main(String[] args) {
String[] pictures = {"C:\\test\\saveScreen1.jpg","C:\\test\\saveScreen2.jpg",
"C:\\test\\saveScreen3.jpg","C:\\test\\saveScreen4.jpg","C:\\test\\saveScreen5.jpg",
"C:\\test\\saveScreen6.jpg","C:\\test\\saveScreen7.jpg","C:\\test\\saveScreen8.jpg",
"C:\\test\\saveScreen9.jpg"};
String gifCreatd = "C:\\test\\ScreenSaverRandomShapes.gif";
jpgToGif(pictures,gifCreatd);
}
/**
* 把多张jpg图片合成一张
* @param pic String[] 多个jpg文件名 包含路径
* @param newPic String 生成的gif文件名 包含路径
*/
public static synchronized void jpgToGif(String pic[], String newPic) {
try {
AnimatedGifEncoder e = new AnimatedGifEncoder();
e.setRepeat(0);
e.start(newPic);
BufferedImage src[] = new BufferedImage[pic.length];
for (int i = 0; i < src.length; i++) {
e.setDelay(200); //设置播放的延迟时间
src[i] = ImageIO.read(new File(pic[i])); // 读入需要播放的jpg文件
e.addFrame(src[i]); //添加到帧中
}
e.finish();
} catch (Exception e) {
System.out.println( "jpgToGif Failed:");
e.printStackTrace();
}
}
}2. 辅助类
1）AnimatedGifEncoder类
import java.io.*;
import java.awt.*;
import java.awt.image.*;
/**
* Class AnimatedGifEncoder - Encodes a GIF file consisting of one or
* more frames.
*
* Example:
* AnimatedGifEncoder e = new AnimatedGifEncoder();
* e.start(outputFileName);
* e.setDelay(1000); // 1 frame per sec
* e.addFrame(image1);
* e.addFrame(image2);
* e.finish();
*
* No copyright asserted on the source code of this class. May be used
* for any purpose, however, refer to the Unisys LZW patent for restrictions
* on use of the associated LZWEncoder class. Please forward any corrections
* to [email protected].
*
* @author Kevin Weiner, FM Software
* @version 1.03 November 2003
*
*/
public class AnimatedGifEncoder {
protected int width; // image size
protected int height;
protected Color transparent = null; // transparent color if given
protected int transIndex; // transparent index in color table
protected int repeat = -1; // no repeat
protected int delay = 0; // frame delay (hundredths)
protected boolean started = false; // ready to output frames
protected OutputStream out;
protected BufferedImage image; // current frame
protected byte[] pixels; // BGR byte array from frame
protected byte[] indexedPixels; // converted frame indexed to palette
protected int colorDepth; // number of bit planes
protected byte[] colorTab; // RGB palette
protected boolean[] usedEntry = new boolean[256]; // active palette entries
protected int palSize = 7; // color table size (bits-1)
protected int dispose = -1; // disposal code (-1 = use default)
protected boolean closeStream = false; // close stream when finished
protected boolean firstFrame = true;
protected boolean sizeSet = false; // if false, get size from first frame
protected int sample = 10; // default sample interval for quantizer
/**
* Sets the delay time between each frame, or changes it
* for subsequent frames (applies to last frame added).
*
* @param ms int delay time in milliseconds
*/
public void setDelay(int ms) {
delay = Math.round(ms / 10.0f);
}
/**
* Sets the GIF frame disposal code for the last added frame
* and any subsequent frames. Default is 0 if no transparent
* color has been set, otherwise 2.
* @param code int disposal code.
*/
public void setDispose(int code) {
if (code >= 0) {
dispose = code;
}
}
/**
* Sets the number of times the set of GIF frames
* should be played. Default is 1; 0 means play
* indefinitely. Must be invoked before the first
* image is added.
*
* @param iter int number of iterations.
* @return
*/
public void setRepeat(int iter) {
if (iter >= 0) {
repeat = iter;
}
}
/**
* Sets the transparent color for the last added frame
* and any subsequent frames.
* Since all colors are subject to modification
* in the quantization process, the color in the final
* palette for each frame closest to the given color
* becomes the transparent color for that frame.
* May be set to null to indicate no transparent color.
*
* @param c Color to be treated as transparent on display.
*/
public void setTransparent(Color c) {
transparent = c;
}
/**
* Adds next GIF frame. The frame is not written immediately, but is
* actually deferred until the next frame is received so that timing
* data can be inserted. Invoking finish() flushes all
* frames. If setSize was not invoked, the size of the
* first image is used for all subsequent frames.
*
* @param im BufferedImage containing frame to write.
* @return true if successful.
*/
public boolean addFrame(BufferedImage im) {
if ((im == null) || !started) {
return false;
}
boolean ok = true;
try {
if (!sizeSet) {
// use first frame's size
setSize(im.getWidth(), im.getHeight());
}
image = im;
getImagePixels(); // convert to correct format if necessary
analyzePixels(); // build color table & map pixels
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) {
writePalette(); // local color table
}
writePixels(); // encode and write pixel data
firstFrame = false;
} catch (IOException e) {
ok = false;
}
return ok;
}
/**
* Flushes any pending data and closes output file.
* If writing to an OutputStream, the stream is not
* closed.
*/
public boolean finish() {
if (!started) return false;
boolean ok = true;
started = false;
try {
out.write(0x3b); // gif trailer
out.flush();
if (closeStream) {
out.close();
}
} catch (IOException e) {
ok = false;
}
// reset for subsequent use
transIndex = 0;
out = null;
image = null;
pixels = null;
indexedPixels = null;
colorTab = null;
closeStream = false;
firstFrame = true;
return ok;
}
/**
* Sets frame rate in frames per second. Equivalent to
* setDelay(1000/fps).
*
* @param fps float frame rate (frames per second)
*/
public void setFrameRate(float fps) {
if (fps != 0f) {
delay = Math.round(100f / fps);
}
}
/**
* Sets quality of color quantization (conversion of images
* to the maximum 256 colors allowed by the GIF specification).
* Lower values (minimum = 1) produce better colors, but slow
* processing significantly. 10 is the default, and produces
* good color mapping at reasonable speeds. Values greater
* than 20 do not yield significant improvements in speed.
*
* @param quality int greater than 0.
* @return
*/
public void setQuality(int quality) {
if (quality < 1) quality = 1;
sample = quality;
}
/**
* Sets the GIF frame size. The default size is the
* size of the first frame added if this method is
* not invoked.
*
* @param w int frame width.
* @param h int frame width.
*/
public void setSize(int w, int h) {
if (started && !firstFrame) return;
width = w;
height = h;
if (width < 1) width = 320;
if (height < 1) height = 240;
sizeSet = true;
}
/**
* Initiates GIF file creation on the given stream. The stream
* is not closed automatically.
*
* @param os OutputStream on which GIF images are written.
* @return false if initial write failed.
*/
public boolean start(OutputStream os) {
if (os == null) return false;
boolean ok = true;
closeStream = false;
out = os;
try {
writeString("GIF89a"); // header
} catch (IOException e) {
ok = false;
}
return started = ok;
}
/**
* Initiates writing of a GIF file with the specified name.
*
* @param file String containing output file name.
* @return false if open or initial write failed.
*/
public boolean start(String file) {
boolean ok = true;
try {
out = new BufferedOutputStream(new FileOutputStream(file));
ok = start(out);
closeStream = true;
} catch (IOException e) {
ok = false;
}
return started = ok;
}
/**
* Analyzes image colors and creates color map.
*/
protected void analyzePixels() {
int len = pixels.length;
int nPix = len / 3;
indexedPixels = new byte[nPix];
NeuQuant nq = new NeuQuant(pixels, len, sample);
// initialize quantizer
colorTab = nq.process(); // create reduced palette
// convert map from BGR to RGB
for (int i = 0; i < colorTab.length; i += 3) {
byte temp = colorTab[i];
colorTab[i] = colorTab[i + 2];
colorTab[i + 2] = temp;
usedEntry[i / 3] = false;
}
// map image pixels to new palette
int k = 0;
for (int i = 0; i < nPix; i++) {
int index =
nq.map(pixels[k++] & 0xff,
pixels[k++] & 0xff,
pixels[k++] & 0xff);
usedEntry[index] = true;
indexedPixels[i] = (byte) index;
}
pixels = null;
colorDepth = 8;
palSize = 7;
// get closest match to transparent color if specified
if (transparent != null) {
transIndex = findClosest(transparent);
}
}
/**
* Returns index of palette color closest to c
*
*/
protected int findClosest(Color c) {
if (colorTab == null) return -1;
int r = c.getRed();
int g = c.getGreen();
int b = c.getBlue();
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;
}
/**
* Extracts image pixels into byte array "pixels"
*/
protected void getImagePixels() {
int w = image.getWidth();
int h = image.getHeight();
int type = image.getType();
if ((w != width)
|| (h != height)
|| (type != BufferedImage.TYPE_3BYTE_BGR)) {
// create new image with right size/format
BufferedImage temp =
new BufferedImage(width, height, BufferedImage.TYPE_3BYTE_BGR);
Graphics2D g = temp.createGraphics();
g.drawImage(image, 0, 0, null);
image = temp;
}
pixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
}
/**
* Writes Graphic Control Extension
*/
protected void writeGraphicCtrlExt() throws IOException {
out.write(0x21); // extension introducer
out.write(0xf9); // GCE label
out.write(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
out.write(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
out.write(transIndex); // transparent color index
out.write(0); // block terminator
}
/**
* Writes Image Descriptor
*/
protected void writeImageDesc() throws IOException {
out.write(0x2c); // image separator
writeShort(0); // image position x,y = 0,0
writeShort(0);
writeShort(width); // image size
writeShort(height);
// packed fields
if (firstFrame) {
// no LCT - GCT is used for first (or only) frame
out.write(0);
} else {
// specify normal LCT
out.write(0x80 | // 1 local color table 1=yes
0 | // 2 interlace - 0=no
0 | // 3 sorted - 0=no
0 | // 4-5 reserved
palSize); // 6-8 size of color table
}
}
/**
* Writes Logical Screen Descriptor
*/
protected void writeLSD() throws IOException {
// logical screen size
writeShort(width);
writeShort(height);
// packed fields
out.write((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
out.write(0); // background color index
out.write(0); // pixel aspect ratio - assume 1:1
}
/**
* Writes Netscape application extension to define
* repeat count.
*/
protected void writeNetscapeExt() throws IOException {
out.write(0x21); // extension introducer
out.write(0xff); // app extension label
out.write(11); // block size
writeString("NETSCAPE" + "2.0"); // app id + auth code
out.write(3); // sub-block size
out.write(1); // loop sub-block id
writeShort(repeat); // loop count (extra iterations, 0=repeat forever)
out.write(0); // block terminator
}
/**
* Writes color table
*/
protected void writePalette() throws IOException {
out.write(colorTab, 0, colorTab.length);
int n = (3 * 256) - colorTab.length;
for (int i = 0; i < n; i++) {
out.write(0);
}
}
/**
* Encodes and writes pixel data
*/
protected void writePixels() throws IOException {
LZWEncoder encoder =
new LZWEncoder(width, height, indexedPixels, colorDepth);
encoder.encode(out);
}
/**
* Write 16-bit value to output stream, LSB first
*/
protected void writeShort(int value) throws IOException {
out.write(value & 0xff);
out.write((value >> 8) & 0xff);
}
/**
* Writes string to output stream
*/
protected void writeString(String s) throws IOException {
for (int i = 0; i < s.length(); i++) {
out.write((byte) s.charAt(i));
}
}
}2）LZWEncoder类
import java.io.OutputStream;
import java.io.IOException;
//==============================================================================
// Adapted from Jef Poskanzer's Java port by way of J. M. G. Elliott.
// K Weiner 12/00
class LZWEncoder {
private static final int EOF = -1;
private int imgW, imgH;
private byte[] pixAry;
private int initCodeSize;
private int remaining;
private int curPixel;
// GIFCOMPR.C - GIF Image compression routines
//
// Lempel-Ziv compression based on 'compress'. GIF modifications by
// David Rowley ([email protected])
// General DEFINEs
static final int BITS = 12;
static final 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.
boolean 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];
//----------------------------------------------------------------------------
LZWEncoder(int width, int height, byte[] pixels, int color_depth) {
imgW = width;
imgH = height;
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 char_out(byte c, OutputStream outs) throws IOException {
accum[a_count++] = c;
if (a_count >= 254)
flush_char(outs);
}
// Clear out the hash table
// table clear for block compress
void cl_block(OutputStream outs) throws IOException {
cl_hash(hsize);
free_ent = ClearCode + 2;
clear_flg = true;
output(ClearCode, outs);
}
// reset code table
void cl_hash(int hsize) {
for (int i = 0; i < hsize; ++i)
htab[i] = -1;
}
void compress(int init_bits, OutputStream outs) throws IOException {
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;
cl_hash(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];
continue 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
cl_block(outs);
}
// Put out the final code.
output(ent, outs);
output(EOFCode, outs);
}
//----------------------------------------------------------------------------
void encode(OutputStream os) throws IOException {
os.write(initCodeSize); // write "initial code size" byte
remaining = imgW * imgH; // reset navigation variables
curPixel = 0;
compress(initCodeSize + 1, os); // compress and write the pixel data
os.write(0); // write block terminator
}
// Flush the packet to disk, and reset the accumulator
void flush_char(OutputStream outs) throws IOException {
if (a_count > 0) {
outs.write(a_count);
outs.write(accum, 0, a_count);
a_count = 0;
}
}
final int MAXCODE(int n_bits) {
return (1 << n_bits) - 1;
}
//----------------------------------------------------------------------------
// Return the next pixel from the image
//----------------------------------------------------------------------------
private int nextPixel() {
if (remaining == 0)
return EOF;
--remaining;
byte pix = pixAry[curPixel++];
return pix & 0xff;
}
void output(int code, OutputStream outs) throws IOException {
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) {
char_out((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) {
char_out((byte) (cur_accum & 0xff), outs);
cur_accum >>= 8;
cur_bits -= 8;
}
flush_char(outs);
}
}
}3）NeuQuant类
/* 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
public class NeuQuant {
protected static final 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 */
protected static final int prime1 = 499;
protected static final int prime2 = 491;
protected static final int prime3 = 487;
protected static final int prime4 = 503;
protected static final 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
------------------- */
protected static final int maxnetpos = (netsize - 1);
protected static final int netbiasshift = 4; /* bias for colour values */
protected static final int ncycles = 100; /* no. of learning cycles */
/* defs for freq and bias */
protected static final int intbiasshift = 16; /* bias for fractions */
protected static final int intbias = (((int) 1) << intbiasshift);
protected static final int gammashift = 10; /* gamma = 1024 */
protected static final int gamma = (((int) 1) << gammashift);
protected static final int betashift = 10;
protected static final int beta = (intbias >> betashift); /* beta = 1/1024 */
protected static final int betagamma =
(intbias << (gammashift - betashift));
/* defs for decreasing radius factor */
protected static final int initrad = (netsize >> 3); /* for 256 cols, radius starts */
protected static final int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
protected static final int radiusbias = (((int) 1) << radiusbiasshift);
protected static final int initradius = (initrad * radiusbias); /* and decreases by a */
protected static final int radiusdec = 30; /* factor of 1/30 each cycle */
/* defs for decreasing alpha factor */
protected static final int alphabiasshift = 10; /* alpha starts at 1.0 */
protected static final int initalpha = (((int) 1) << alphabiasshift);
protected int alphadec; /* biased by 10 bits */
/* radbias and alpharadbias used for radpower calculation */
protected static final int radbiasshift = 8;
protected static final int radbias = (((int) 1) << radbiasshift);
protected static final int alpharadbshift = (alphabiasshift + radbiasshift);
protected static final int alpharadbias = (((int) 1) << alpharadbshift);
/* Types and Global Variables
-------------------------- */
protected byte[] thepicture; /* the input image itself */
protected int lengthcount; /* lengthcount = H*W*3 */
protected int samplefac; /* sampling factor 1..30 */
// typedef int pixel[4]; /* BGRc */
protected int[][] network; /* the network itself - [netsize][4] */
protected int[] netindex = new int[256];
/* for network lookup - really 256 */
protected int[] bias = new int[netsize];
/* bias and freq arrays for learning */
protected int[] freq = new int[netsize];
protected int[] radpower = new int[initrad];
/* radpower for precomputation */
/* Initialise network in range (0,0,0) to (255,255,255) and set parameters
----------------------------------------------------------------------- */
public NeuQuant(byte[] 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;
}
}
public 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)
------------------------------------------------------------------------------- */
public 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
------------------ */
public void learn() {
int i, j, b, g, r;
int radius, rad, alpha, step, delta, samplepixels;
byte[] p;
int pix, lim;
if (lengthcount < minpicturebytes)
samplefac = 1;
alphadec = 30 + ((samplefac - 1) / 3);
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 + 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 -= 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));
}
}
//fprintf(stderr,"finished 1D learning: final alpha=%f !/n",((float)alpha)/initalpha);
}
/* 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
----------------------------------------------------------------------------------- */
public 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|]
--------------------------------------------------------------------------------- */
protected 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++];
try {
p[0] -= (a * (p[0] - b)) / alpharadbias;
p[1] -= (a * (p[1] - g)) / alpharadbias;
p[2] -= (a * (p[2] - r)) / alpharadbias;
} catch (Exception e) {
} // prevents 1.3 miscompilation
}
if (k > lo) {
p = network[k--];
try {
p[0] -= (a * (p[0] - b)) / alpharadbias;
p[1] -= (a * (p[1] - g)) / alpharadbias;
p[2] -= (a * (p[2] - r)) / alpharadbias;
} catch (Exception e) {
}
}
}
}
/* Move neuron i towards biased (b,g,r) by factor alpha
---------------------------------------------------- */
protected 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
---------------------------- */
protected 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);
}
}