ネット上で収集したGIF操作の3種類AnimatedGifEncoder,NeuQuant,LZWE(二)

19390 ワード

 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
", 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 !
",((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); } }