Java生成动态GIF图片
写selenium自动化时,为了查看运行效果,后给浏览器截图,想到可以生成gif图片来快速预览。看到已经有人实现了,直接拿过来。作者是Kevin Weiner。
共涉及到三个java文件,分别是NeuQuant.java
,LZWEncoder.java
,AnimatedGifEncoder.java
,有了这三个文件,我们可以自己编写方法调用,
代码如下:
BufferedImage src1 = ImageIO.read(new File("Img221785570.jpg")); BufferedImage src2 = ImageIO.read(new File("W.gif")); //BufferedImage src3 = ImageIO.read(new File("c:/ship3.jpg")); AnimatedGifEncoder e = new AnimatedGifEncoder(); e.setRepeat(0); e.start("laoma.gif"); e.setDelay(300); // 1 frame per sec e.addFrame(src1); e.setDelay(100); e.addFrame(src2); e.setDelay(100); // e.addFrame(src2); e.finish();
三个java文件源码如下:
NeuQuant.java
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, 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); } }
LZWEncoder.java
源码如下:
package com.yeetrack.selenium.Image; 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 (mgardi@watdcsu.waterloo.edu) // 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); } } }
AnimatedGifEncoder.java
源码如下: