java生成动态gif格式与png格式的验证码(代码3)
2016-10-22 11:49
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Java代码
import java.awt.*;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.BufferedOutputStream;
import java.io.ByteArrayOutputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.OutputStream;
/**
* Class AnimatedGifEncoder - Encodes a GIF file consisting of one or more
* frames.
*
* <pre>
* Example:
* AnimatedGifEncoder e = new AnimatedGifEncoder();
* e.start(outputFileName);
* e.setDelay(1000); // 1 frame per sec
* e.addFrame(image1);
* e.addFrame(image2);
* e.finish();
* </pre>
*
* 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 Encoder class. Please forward any corrections to questions at
* fmsware.com.
*
* @author wzztestin
*
*/
public class GifEncoder {
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 <code>finish()</code> flushes all frames. If
* <code>setSize</code> 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;
}
// added by alvaro
public boolean outFlush() {
boolean ok = true;
try {
out.flush();
return ok;
} catch (IOException e) {
ok = false;
}
return ok;
}
public byte[] getFrameByteArray() {
return ((ByteArrayOutputStream) out).toByteArray();
}
/**
* 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;
}
return ok;
}
public void reset() {
// reset for subsequent use
transIndex = 0;
out = null;
image = null;
pixels = null;
indexedPixels = null;
colorTab = null;
closeStream = false;
firstFrame = true;
}
/**
* Sets frame rate in frames per second. Equivalent to
* <code>setDelay(1000/fps)</code>.
*
* @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];
Quant nq = new Quant(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
20ef
; 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 {
Encoder encoder = new Encoder(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));
}
}
}
Java代码
/**
*
* @author wzztestin
*
*/
public class Quant {
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 Quant(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
* ----------------------------------------------------------
* -------------------------
*/
@SuppressWarnings("unused")
public void unbiasnet() {
int i, j;
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);
}
}
import java.awt.*;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.BufferedOutputStream;
import java.io.ByteArrayOutputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.OutputStream;
/**
* Class AnimatedGifEncoder - Encodes a GIF file consisting of one or more
* frames.
*
* <pre>
* Example:
* AnimatedGifEncoder e = new AnimatedGifEncoder();
* e.start(outputFileName);
* e.setDelay(1000); // 1 frame per sec
* e.addFrame(image1);
* e.addFrame(image2);
* e.finish();
* </pre>
*
* 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 Encoder class. Please forward any corrections to questions at
* fmsware.com.
*
* @author wzztestin
*
*/
public class GifEncoder {
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 <code>finish()</code> flushes all frames. If
* <code>setSize</code> 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;
}
// added by alvaro
public boolean outFlush() {
boolean ok = true;
try {
out.flush();
return ok;
} catch (IOException e) {
ok = false;
}
return ok;
}
public byte[] getFrameByteArray() {
return ((ByteArrayOutputStream) out).toByteArray();
}
/**
* 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;
}
return ok;
}
public void reset() {
// reset for subsequent use
transIndex = 0;
out = null;
image = null;
pixels = null;
indexedPixels = null;
colorTab = null;
closeStream = false;
firstFrame = true;
}
/**
* Sets frame rate in frames per second. Equivalent to
* <code>setDelay(1000/fps)</code>.
*
* @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];
Quant nq = new Quant(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
20ef
; 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 {
Encoder encoder = new Encoder(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));
}
}
}
Java代码
/**
*
* @author wzztestin
*
*/
public class Quant {
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 Quant(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
* ----------------------------------------------------------
* -------------------------
*/
@SuppressWarnings("unused")
public void unbiasnet() {
int i, j;
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);
}
}
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