关于抖音图片转文字的代码工程分享

最近了解到关于抖音上图片转化为txt文本的视频，自认为IT搬用工我也可以尝试下，从而有以下实验成果

代码工程搬自开源项目，个人加以修改并简单注释了。代码如下：

package cn.xsshome.imagetool.util;
/**
*
*/
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);
   }
}

package cn.xsshome.imagetool;

import java.awt.image.BufferedImage;
import java.io.File;

import javax.imageio.ImageIO;

import cn.xsshome.imagetool.convert.ImageToChar;
/**
* 调用示例
* @author 小帅丶
*
*/
public class Sample {
   public static void main(String[] args) throws Exception {
       ImageToChar.load("G:/sws2.jpg", "G:/sws/woman3.txt");//静态图片转字符保存为txt文件
       ImageToChar.load("G:/sws5.png", "G:/sws/woman6.txt");//静态图片转字符保存为txt文件
       ImageToChar.loadGif("C:/Users/Administrator/Desktop/页面录屏显示.gif", "F:/gif/");//动图转为动态的字符图片
       BufferedImage bi = null;
       bi = ImageIO.read(new File("G:/body.jpg"));
        String bytePic = ImageToChar.txtToImageByBase64(bi);//静态图转字符返回转换后图片的base64
        System.out.println(bytePic);
   }
}

项目工程本人已上传，在我的资源自己下载吧

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