eigsearch.c

FERET人脸库的处理代码。内函预处理

/*----------------------------------------------------------------------
PROGRAM: eigsearch.c  
DATE:    6/16/94
AUTHOR:  Baback Moghaddam, [e-mail]
------------------------------------------------------------------------
NOTE:    This version was adapted from ms_eigsearch.c
------------------------------------------------------------------------


  Local feature search by eigentemplates

  This routine looks  BF files in the datapath (defined below):

  1. features.bf  which is an N-by-2 matrix defining the size (row,col)
     of each of the N templates.

  2. template[n].bf  where n=1...N is the eigenvector ROW matrix where
     the 1st row is the mean feature, and the remaining rows
     are the principal eigenvectors 1:M. 

     NOTE: The eigenvectors are stored in column-order (as in MATLAB)

  3. variances.bf which is an N-by-M matrix, where each row represents
     the rank-ordered eigenvalues associated with that feature

  4. windows.bf

  5. umap_musigma[n].bf

  6. spatial_musigma[n].bf


     NOTE: The set of scales over which the search is to be performed
           is provided in an ASCII file of the format:

	   s1
	   s2
	   .
	   .
	   .
	   sN

----------------------------------------------------------------------

   NOTE: In this version if unitmap'ing is selected it will use
         the Mahalanobis mean/stddev in addition to the standard
	 distance computation.

------------------------------------------------------------------------

     Configure this code using the following defines:
     
     DO_VAR:     will set each pixel whose surrond's variance
                 is < VAR_THRESHOLD to VAR_MAXVALUE

----------------------------------------------------------------------

   NOTE: Fixed the umap_musigma_score problem

---------------------------------------------------------------------- */

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <float.h>
#include "util.h"
#include "io.h"
#include "matrix.h"

struct Points {
  int n;
  int *x;
  int *y;
  float *f;
};

struct Point {
  int x;
  int y;
  float f;
};



/* ----------- CONFIGURES ---------------- */

#define DO_VAR                  0     /*  variance check     */
#define VAR_THRESHOLD           100   /*  threshold for low-var patches */
#define VAR_MAXVALUE            1e7   /*  replacement in distance map   */

#define MAX_NUM_TEMPLATES       5
#define MAX_NUM_EIGENVECTORS    128   /* for static storage in dffs() */
#define MAX_VECTOR_LENGTH       128    /* for static storage in mahalanobis_distance() */
#define MAX_CHARS               512

/* ----------- Command-Line Parsing Stuff ------- */

extern int optind;
extern char *optarg;
char *progname;          /* used to store the name of the program  */
char comline[MAX_CHARS];       /* used to store the entire command line  */

#define OPTIONS "i:l:o:d:n:p:a:b:k:mguefcw"

char *usage = "\t-i indir -l list [-d datadir]\n\
\t\t\t[-o outdir] [-a first_ev] [-b last_ev] [-k feature]\n\
\t\t\t[-g] [-u] [-m] [-e] [-f] [-c] [-w]\n";

char *help = "\
Eigentemplate Search\n\n\
-i indir   \t input directory\n\
-l listfile\t ASCII file listing indir files to process (one per line)\n\
-d datadir \t data dir (holding eigentemplate BF files)\n\
-o outdir  \t output directory (for detection maps)\n\
-a first_ev\t first eigenvector (default = 1)\n\
-b last_ev \t last eigenvector  (default = 5)\n\
-k feature \t search for the k-th feature only\n\
-g         \t pre-process with graymap\n\
-u         \t pre-process with unitmap\n\
-m         \t use Mahalanobis distance instead of DFFS\n\
-c         \t use spatial priors\n\
-w         \t use search windows\n\
-e         \t output detection maps to outdir\n\
-f         \t output ASCII minima files to outdir\n";


/* --------- Function Prototypes ---------- */

float dffs(float *patch, int N, float **eigvectors, int first, int last);
float mahalanobis(float *patch, int N, float **eigvectors, float *eigvalues,
		  int first, int last);
float graymap(float *patch, int N);
float unitmap(float *patch, int N);
void statistics(float *p, int N, float *mean, float *sigma);
float umap_musigma_distance(float mean, float sigma, float **umap_musigma);
float mahalanobis_distance(float *x, int N, float *Mu, float **inv_Sigma);
void minima(float **A, int imin, int imax, int jmin, int jmax,
            struct Points *Ps);


/* -------- Globals ---------------- */

float **template[MAX_NUM_TEMPLATES];
float **errormap[MAX_NUM_TEMPLATES];
float **variances;
float **umap_musigma[MAX_NUM_TEMPLATES];
float **spatial_musigma[MAX_NUM_TEMPLATES];


/* ------- Command Line Defaults ------------- */

int do_dmdumps = 0; /* default is NOT to do errormap dumps  */
int do_graymap = 0; /* default is NOT to graymap each patch */
int do_unitmap = 0; /* default is NOT to unitmap each patch */
int do_mahalanobis = 0; /* default is NOT to do this computation */
int do_spatial = 0; /* default is NOT to add [x,y] Mahalanobis dist */
int do_windows = 0;  /* default is not to limit search to a window */
int do_minima  = 0;  /* default is not to output minima files */

int eig_first = 1;  /* search using first 5 eigenvectors by default */
int eig_last  = 5;




/*----------------------------------------------------------------------*/
/* ---------------------------- MAIN ---------------------------------- */

main(int argc, char *argv[])
{

  register int i,j,k,l,ii,jj;
  int f,s,c,feature=1,nframe,nfeatures,sets, bytes_pixel;
  int nrow, ncol, max_N, xc, yc;
  char command[MAX_CHARS],indir[MAX_CHARS],infile[MAX_CHARS];
  char listfile[MAX_CHARS], scalefile[MAX_CHARS], line[MAX_CHARS];
  char datapath[MAX_CHARS],outdir[MAX_CHARS],filename[MAX_CHARS];

  float **image;
  float fval1, fval2, fval, mean, sigma;
  FILE *fp, *fp1, *fp2;         /* for output values dump */

  int rowl[MAX_NUM_TEMPLATES],rowr[MAX_NUM_TEMPLATES];
  int coll[MAX_NUM_TEMPLATES],colr[MAX_NUM_TEMPLATES];
  int imin[MAX_NUM_TEMPLATES],imax[MAX_NUM_TEMPLATES];
  int jmin[MAX_NUM_TEMPLATES],jmax[MAX_NUM_TEMPLATES];
  int N_dim[MAX_NUM_TEMPLATES]; /* the dimensionality of each template */
  int M_dim[MAX_NUM_TEMPLATES]; /* the # of eigenvectors for each template */

  int rowm[MAX_NUM_TEMPLATES];
  int colm[MAX_NUM_TEMPLATES];
  float error[MAX_NUM_TEMPLATES];
  float *my_vector;
  
  int **templatesize;
  float *patch;

  struct Points myPoints_array[MAX_NUM_TEMPLATES];
  int N_minima;

  /* required input flags */
  
  int errflag   = 0;
  int inflag    = 0;
  int listflag  = 0;
  

  /* command line defaults */

  int singlefeature = 0;  
  strcpy(outdir,".");          /* default output dir is . */
  strcpy(datapath,".");        /* default datapath directory to cwd   */


  /* setup program name and command line strings */
  
  progname = argv[0];
  for (i=0; i<argc; i++)
    strcat(comline, argv[i]),strcat(comline, " ");
  
  
  /* ----------------------  Command Line Parse ------------------------ */
  
  while ((c = getopt(argc, argv, OPTIONS)) != EOF)
    switch (c) {
      
    case 'i':
      strcpy(indir, optarg);
      inflag = 1;
      break;
      
    case 'l':
      strcpy(listfile, optarg);
      listflag = 1;
      break;

    case 'd':
      strcpy(datapath, optarg);
      break;

    case 'k':
      feature = atoi(optarg);
      singlefeature = 1;
      break;

    case 'o':
      strcpy(outdir, optarg);
      break;

    case 'a':
      eig_first  = atoi(optarg);
      break;

    case 'b':
      eig_last = atoi(optarg);
      break;

    case 'g':
      do_graymap = 1;
      break;

    case 'u':
      do_unitmap = 1;
      break;

    case 'e':
      do_dmdumps = 1;
      break;

    case 'f':
      do_minima = 1;
      break;

    case 'm':
      do_mahalanobis = 1;
      break;

    case 'c':
      do_spatial = 1;
      break;

    case 'w':
      do_windows = 1;
      break;

    case '?':
      errflag = 1;
      break;
      
    }
  
  
  /* command line error check */
  
  if (errflag || !inflag || !listflag) {
    fprintf(stderr,"\nUSAGE: %s %s\n%s\n", progname, usage, help);
    exit(1);
  }
  


  /* -------- Load eigentemplate data --------------- */

  sprintf(filename,"%s/features.bf",datapath);
 
  { 
    float **m;
    max_N = 0;
    m = read_BIN(filename, &nfeatures, &ncol);
    templatesize = imatrix(1, nfeatures, 1, 2);
    
    for (i=1; i<=nfeatures; i++) {
      for (j=1; j<=ncol; j++)
	templatesize[i][j] = (int) m[i][j];
      N_dim[i] = templatesize[i][1]*templatesize[i][2];
      if (N_dim[i]>max_N) 
	max_N = N_dim[i];
      free_matrix(m, 1, nfeatures, 1, ncol);
    }
  }

  
  for (i=1; i<=nfeatures; i++) {
    sprintf(filename,"%s/template%d.bf",datapath,i);
    template[i] = read_BIN(filename, &nrow, &ncol);
    M_dim[i] = nrow;
    if (ncol!=N_dim[i]) {
      fprintf(stderr,
	      "Template size in template%d.bf (%d) doesn't match that in features.bf (%d)\n", i, ncol, N_dim[i]);
      exit(3);
    }
  }

  if (do_mahalanobis) {
    sprintf(filename,"%s/variances.bf",datapath);
    variances = read_BIN(filename, &nrow, &ncol);
    if (nrow!=nfeatures) {
      fprintf(stderr,
	      "ERROR: variances.bf has %d rows! (must have %d)\n\n",
	      nrow, nfeatures);
      exit(1);
    }
    if (eig_last>ncol) {
      fprintf(stderr,
	      "ERROR: variances.bf has %d cols! (must have atleast %d)\n\n",
	      ncol, eig_last);
      exit(1);
    }
  }
    
  if (do_unitmap) {
    float **C, **invC;
    int m, n;
    C = matrix(1,2,1,2);
    invC = matrix(1,2,1,2);
    for (k=1; k<=nfeatures; k++) {
      sprintf(filename,"%s/umap_musigma%d.bf",datapath,k);
      umap_musigma[k] = read_BIN(filename, &m, &n );
     
      /* invert the covariance matrix portion of umap_musigma[k] */
      for (i=1; i<=2; i++)
	for (j=1; j<=2; j++)
	  C[i][j] = umap_musigma[k][i+1][j];
      matrix_inverse(C, 2, invC);
      for (i=1; i<=2; i++)
	for (j=1; j<=2; j++)
	  umap_musigma[k][i+1][j] = invC[i][j];

      /* ----------- DEBUG -----------
      for (i=1; i<=3; i++) {
	for (j=1; j<=2; j++)
	  printf("%f ",umap_musigma[k][i][j]);
	printf("\n");
      }
      printf("\n\n");
      C[1][1] = 10;
      C[1][2] = 10;
      printf("dist = %f \n", 
	     mahalanobis_distance(C[1], 2, umap_musigma[k][1], &umap_musigma[k][1]));
      printf("dist = %f \n",
		    umap_musigma_distance(10, 10, umap_musigma[k]));
      exit(1);
      --------------- DEBUG ------------- */
    }  
    free_matrix(C,1,2,1,2);
    free_matrix(invC,1,2,1,2);
  }

   
  if (do_spatial) {
    float **C, **invC;
    int m, n;
    C = matrix(1,2,1,2);
    invC = matrix(1,2,1,2);
    for (k=1; k<=nfeatures; k++) {
      sprintf(filename,"%s/spatial_musigma%d.bf",datapath,k);
      spatial_musigma[k] = read_BIN(filename, &m, &n );
     
      /* invert the covariance matrix portion of spatial_musigma[k] */
      for (i=1; i<=2; i++)
	for (j=1; j<=2; j++)
	  C[i][j] = spatial_musigma[k][i+1][j];
      matrix_inverse(C, 2, invC);
      for (i=1; i<=2; i++)
	for (j=1; j<=2; j++)
	  spatial_musigma[k][i+1][j] = invC[i][j];

      /* ----------- DEBUG -----------
      for (i=1; i<=3; i++) {
	for (j=1; j<=2; j++)
	  printf("%f ",spatial_musigma[k][i][j]);
	printf("\n");
      }
      printf("\n\n");
      C[1][1] = 10;
      C[1][2] = 10;
      printf("dist = %f \n", 
	     mahalanobis_distance(C[1], 2, spatial_musigma[k][1], &spatial_musigma[k][1]));
      printf("dist = %f \n",
		    spatial_musigma_distance(10, 10, spatial_musigma[k]));
      exit(1);
      --------------- DEBUG ------------- */
    }  
    free_matrix(C,1,2,1,2);
    free_matrix(invC,1,2,1,2);
  }

  /* ---- determine left/right boundaries for errormaps ----- */
  for (i=1; i<=nfeatures; i++) {
    rowl[i] = (int) templatesize[i][1]/2.0;
    rowr[i] = templatesize[i][1] - rowl[i] - 1;
    coll[i] = (int) templatesize[i][2]/2.0;
    colr[i] = templatesize[i][2] - coll[i] - 1;
  }

  patch = vector(1, max_N);
  my_vector = vector(1, MAX_VECTOR_LENGTH);


  /* ----  read indir descriptor -------- */
  
  read_descriptor(indir, &nframe, &sets, &bytes_pixel, &ncol, &nrow);
  if (sets>1) 
    myerror("Input files must be single-set DAT files!");
  image = matrix(1, nrow, 1, ncol);


  /* --- allocate errormap tensor and set window boundaries --- */

  for (k=feature; k<=(singlefeature>0 ? feature:nfeatures); k++) 
    errormap[k] = matrix(1, nrow, 1, ncol);
  
  if (do_windows) {
    int m, n;
    float **w;
    sprintf(filename,"%s/windows.bf", datapath);
    w = read_BIN(filename, &m, &n);
    if (m != nfeatures || n!=4) {
      fprintf(stderr,"ERROR: windows.bf has wrong dimensions: %d-by-%d \n",
	      m, n);
      exit(2);
    }
    for (k=feature; k<=(singlefeature>0 ? feature:nfeatures); k++) {
      imin[k] = w[k][1];
      imax[k] = w[k][2];
      jmin[k] = w[k][3];
      jmax[k] = w[k][4];
    }
    free_matrix(w, 1, m, 1, n);
  }
  else {
    for (k=feature; k<=(singlefeature>0 ? feature:nfeatures); k++) {
      imin[k] = rowl[k] + 1;
      imax[k] = nrow - rowr[k];
      jmin[k] = coll[k] + 1;
      jmax[k] = ncol - colr[k];
    }
  }
  
  /* ------- DEBUG ----------------------------------------------------
  for (k=feature; k<=(singlefeature>0 ? feature:nfeatures); k++) 
    printf("templatesize %d: %3d %3d\n",
	   k, templatesize[k][1], templatesize[k][2]);
  for (k=feature; k<=(singlefeature>0 ? feature:nfeatures); k++) 
    printf("window %d: %3d %3d %3d %3d\n",
	   k, imin[k], imax[k], jmin[k], jmax[k]);
  for (k=feature; k<=(singlefeature>0 ? feature:nfeatures); k++)