features_pedro_single.cc

// Original code by Pedro Fe.........
// Switched to single precision by Michael and Bram
#include <math.h>
#include "mex.h"

// small value, used to avoid division by zero
#define eps 0.0001

// unit vectors used to compute gradient orientation
float uu[9] = {1.0000, 
    0.9397, 
    0.7660, 
    0.500, 
    0.1736, 
    -0.1736, 
    -0.5000, 
    -0.7660, 
    -0.9397};
float vv[9] = {0.0000, 
    0.3420, 
    0.6428, 
    0.8660, 
    0.9848, 
    0.9848, 
    0.8660, 
    0.6428, 
    0.3420};

static inline float min(float x, float y) { return (x <= y ? x : y); }
static inline float max(float x, float y) { return (x <= y ? y : x); }

static inline int min(int x, int y) { return (x <= y ? x : y); }
static inline int max(int x, int y) { return (x <= y ? y : x); }

// main function:
// takes a float color image and a bin size 
// returns HOG features
mxArray *process(const mxArray *mximage, const mxArray *mxsbin) {
  float *im = (float *)mxGetPr(mximage);
  const int *dims = mxGetDimensions(mximage);
  if (mxGetNumberOfDimensions(mximage) != 3 ||
      dims[2] != 3 ||
      mxGetClassID(mximage) != mxSINGLE_CLASS)
    mexErrMsgTxt("Invalid input");

  int sbin = (int)mxGetScalar(mxsbin);

  // memory for caching orientation histograms & their norms
  int blocks[2];
  blocks[0] = (int)round((float)dims[0]/(float)sbin);
  blocks[1] = (int)round((float)dims[1]/(float)sbin);
  float *hist = (float *)mxCalloc(blocks[0]*blocks[1]*18, sizeof(float));
  float *norm = (float *)mxCalloc(blocks[0]*blocks[1], sizeof(float));

  // memory for HOG features
  int out[3];
  out[0] = max(blocks[0]-2, 0);
  out[1] = max(blocks[1]-2, 0);
  out[2] = 27+4;
  mxArray *mxfeat = mxCreateNumericArray(3, out, mxSINGLE_CLASS, mxREAL);
  float *feat = (float *)mxGetPr(mxfeat);
  
  int visible[2];
  visible[0] = blocks[0]*sbin;
  visible[1] = blocks[1]*sbin;
  
  for (int x = 1; x < visible[1]-1; x++) {
    for (int y = 1; y < visible[0]-1; y++) {
      // first color channel
      float *s = im + min(x, dims[1]-2)*dims[0] + min(y, dims[0]-2);
      float dy = *(s+1) - *(s-1);
      float dx = *(s+dims[0]) - *(s-dims[0]);
      float v = dx*dx + dy*dy;

      // second color channel
      s += dims[0]*dims[1];
      float dy2 = *(s+1) - *(s-1);
      float dx2 = *(s+dims[0]) - *(s-dims[0]);
      float v2 = dx2*dx2 + dy2*dy2;

      // third color channel
      s += dims[0]*dims[1];
      float dy3 = *(s+1) - *(s-1);
      float dx3 = *(s+dims[0]) - *(s-dims[0]);
      float v3 = dx3*dx3 + dy3*dy3;

      // pick channel with strongest gradient
      if (v2 > v) {
  v = v2;
  dx = dx2;
  dy = dy2;
      } 
      if (v3 > v) {
  v = v3;
  dx = dx3;
  dy = dy3;
      }

      // snap to one of 18 orientations
      float best_dot = 0;
      int best_o = 0;
      for (int o = 0; o < 9; o++) {
  float dot = uu[o]*dx + vv[o]*dy;
  if (dot > best_dot) {
    best_dot = dot;
    best_o = o;
  } else if (-dot > best_dot) {
    best_dot = -dot;
    best_o = o+9;
  }
      }
      
      // add to 4 histograms around pixel using linear interpolation
      float xp = ((float)x+0.5)/(float)sbin - 0.5;
      float yp = ((float)y+0.5)/(float)sbin - 0.5;
      int ixp = (int)floor(xp);
      int iyp = (int)floor(yp);
      float vx0 = xp-ixp;
      float vy0 = yp-iyp;
      float vx1 = 1.0-vx0;
      float vy1 = 1.0-vy0;
      v = sqrt(v);

      if (ixp >= 0 && iyp >= 0) {
  *(hist + ixp*blocks[0] + iyp + best_o*blocks[0]*blocks[1]) += 
    vx1*vy1*v;
      }

      if (ixp+1 < blocks[1] && iyp >= 0) {
  *(hist + (ixp+1)*blocks[0] + iyp + best_o*blocks[0]*blocks[1]) += 
    vx0*vy1*v;
      }

      if (ixp >= 0 && iyp+1 < blocks[0]) {
  *(hist + ixp*blocks[0] + (iyp+1) + best_o*blocks[0]*blocks[1]) += 
    vx1*vy0*v;
      }

      if (ixp+1 < blocks[1] && iyp+1 < blocks[0]) {
  *(hist + (ixp+1)*blocks[0] + (iyp+1) + best_o*blocks[0]*blocks[1]) += 
    vx0*vy0*v;
      }
    }
  }

  // compute energy in each block by summing over orientations
  for (int o = 0; o < 9; o++) {
    float *src1 = hist + o*blocks[0]*blocks[1];
    float *src2 = hist + (o+9)*blocks[0]*blocks[1];
    float *dst = norm;
    float *end = norm + blocks[1]*blocks[0];
    while (dst < end) {
      *(dst++) += (*src1 + *src2) * (*src1 + *src2);
      src1++;
      src2++;
    }
  }

  // compute features
  for (int x = 0; x < out[1]; x++) {
    for (int y = 0; y < out[0]; y++) {
      float *dst = feat + x*out[0] + y;      
      float *src, *p, n1, n2, n3, n4;

      p = norm + (x+1)*blocks[0] + y+1;
      n1 = 1.0 / sqrt(*p + *(p+1) + *(p+blocks[0]) + *(p+blocks[0]+1) + eps);
      p = norm + (x+1)*blocks[0] + y;
      n2 = 1.0 / sqrt(*p + *(p+1) + *(p+blocks[0]) + *(p+blocks[0]+1) + eps);
      p = norm + x*blocks[0] + y+1;
      n3 = 1.0 / sqrt(*p + *(p+1) + *(p+blocks[0]) + *(p+blocks[0]+1) + eps);
      p = norm + x*blocks[0] + y;      
      n4 = 1.0 / sqrt(*p + *(p+1) + *(p+blocks[0]) + *(p+blocks[0]+1) + eps);

      float t1 = 0;
      float t2 = 0;
      float t3 = 0;
      float t4 = 0;

      // contrast-sensitive features
      src = hist + (x+1)*blocks[0] + (y+1);
      for (int o = 0; o < 18; o++) {
  float h1 = min(*src * n1, 0.2);
  float h2 = min(*src * n2, 0.2);
  float h3 = min(*src * n3, 0.2);
  float h4 = min(*src * n4, 0.2);
  *dst = 0.5 * (h1 + h2 + h3 + h4);
  t1 += h1;
  t2 += h2;
  t3 += h3;
  t4 += h4;
  dst += out[0]*out[1];
  src += blocks[0]*blocks[1];
      }

      // contrast-insensitive features
      src = hist + (x+1)*blocks[0] + (y+1);
      for (int o = 0; o < 9; o++) {
        float sum = *src + *(src + 9*blocks[0]*blocks[1]);
        float h1 = min(sum * n1, 0.2);
        float h2 = min(sum * n2, 0.2);
        float h3 = min(sum * n3, 0.2);
        float h4 = min(sum * n4, 0.2);
        *dst = 0.5 * (h1 + h2 + h3 + h4);
        dst += out[0]*out[1];
        src += blocks[0]*blocks[1];
      }

      // texture features
      *dst = 0.2357 * t1;
      dst += out[0]*out[1];
      *dst = 0.2357 * t2;
      dst += out[0]*out[1];
      *dst = 0.2357 * t3;
      dst += out[0]*out[1];
      *dst = 0.2357 * t4;
    }
  }

  mxFree(hist);
  mxFree(norm);
  return mxfeat;
}

// matlab entry point
// F = features_pedro(image, bin)
// image should be color with float values
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { 
  if (nrhs != 2)
    mexErrMsgTxt("Wrong number of inputs"); 
  if (nlhs != 1)
    mexErrMsgTxt("Wrong number of outputs");
  plhs[0] = process(prhs[0], prhs[1]);
}