Matlab_WHOGeneric/features/fconv2D.cc

#include "mex.h"
#include <math.h>
#include <string.h>

/*
 * This code is used for computing filter responses.  It computes the
 * response of a set of filters with a feature map.  
 *
 * Basic version, relatively slow but very compatible.
 */

struct thread_data {
  double *A;
  double *B;
  double *C;
  mxArray *mxC;
  const mwSize *A_dims;
  const mwSize *B_dims;
  mwSize C_dims[2];
};

// convolve A and B
void *process(void *thread_arg)
{
  thread_data *args = (thread_data *)thread_arg;
  
  //input A - e.g., an image
  double *A = args->A;
  
  //input B - e.g., a filter
  double *B = args->B;
  
  //output C - the convolution response map
  double *C = args->C;
  
  const mwSize *A_dims = args->A_dims;
  const mwSize *B_dims = args->B_dims;
  const mwSize *C_dims = args->C_dims;

  //new pointers just for comparability with original fconv.cc
  double *dst = C;
  double *A_src = A;
  double *B_src = B;
  
  //run over possible locations on x dimension
  for (int x = 0; x < C_dims[1]; x++)
  {
    //run over possible locations on y dimension
    for (int y = 0; y < C_dims[0]; y++)
    {
      // compute convolution score for current position
      // loop over filter size
      double val = 0;
      for (int xp = 0; xp < B_dims[1]; xp++)
      {
        double *A_off = A_src + (x+xp)*A_dims[0] + y;
        double *B_off = B_src + xp*B_dims[0];
        
        //hope-to-be efficient version for up to 20 dimensions
        switch(B_dims[0])
        {
          case 20: val += A_off[19] * B_off[19];
          case 19: val += A_off[18] * B_off[18];
          case 18: val += A_off[17] * B_off[17];
          case 17: val += A_off[16] * B_off[16];
          case 16: val += A_off[15] * B_off[15];
          case 15: val += A_off[14] * B_off[14];
          case 14: val += A_off[13] * B_off[13];
          case 13: val += A_off[12] * B_off[12];
          case 12: val += A_off[11] * B_off[11];
          case 11: val += A_off[10] * B_off[10];
          case 10: val += A_off[9]  * B_off[9];
          case 9:  val += A_off[8]  * B_off[8];
          case 8:  val += A_off[7]  * B_off[7];
          case 7:  val += A_off[6]  * B_off[6];
          case 6:  val += A_off[5]  * B_off[5];
          case 5:  val += A_off[4]  * B_off[4];
          case 4:  val += A_off[3]  * B_off[3];
          case 3:  val += A_off[2]  * B_off[2];
          case 2:  val += A_off[1]  * B_off[1];
          case 1:  val += A_off[0]  * B_off[0];
            break;
          // less efficient version with more than 20 dimensions
          default:              
            for (int yp = 0; yp < B_dims[0]; yp++)
            {
              val += *(A_off++) * *(B_off++);
            }
        } // nasty switch-statement
      } //for-loop over filtersize
      
      //write value to current position in output data and increment output pointer
      *(dst++) += val;
    } // for-loop over y-positions
  } // for-loop over x-positions
}

// matlab entry point
// C = fconv(A, cell of B, start, end);
// for convolving a single filter with an image, call fconv2D( A, B, 1, 1)
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { 
  if (nrhs != 4)
    mexErrMsgTxt("Wrong number of inputs"); 
  if (nlhs != 1)
    mexErrMsgTxt("Wrong number of outputs");

  // get input A, e.g., an image
  const mxArray *mxA = prhs[0];
  // safety check for input A
  if ( mxGetNumberOfDimensions(mxA) != 2 /* only single dimension feature matrices supported here*/ || 
       mxGetClassID(mxA) != mxDOUBLE_CLASS
     )
  {
    mexErrMsgTxt("Invalid input: A");
  }

  // get input B, e.g., a cell array of filters,  and start/end indices for cells to use
  const mxArray *cellB = prhs[1];
  mwSize num_bs = mxGetNumberOfElements(cellB);  
  
  // with which filter to start
  int start = (int)mxGetScalar(prhs[2]) - 1;
  // with which filter to end
  int end = (int)mxGetScalar(prhs[3]) - 1;
  
  // safety check for start and end wrt input cell array B
  if ( (start < 0) || (end >= num_bs) || (start > end) )
  {
    mexErrMsgTxt("Invalid input: start/end");
  }
  
  int i_numOfFilters = end-start+1;
  

  // output cell
  plhs[0] = mxCreateCellMatrix(1, i_numOfFilters);

  // do convolutions
  thread_data td;
  const mwSize *A_dims = mxGetDimensions(mxA);
  double *A = (double *)mxGetPr(mxA);
  
  for ( int i = 0; i < i_numOfFilters; i++ )
  {
    const mxArray *mxB = mxGetCell(cellB, i+start);
    
    td.A_dims = A_dims;
    td.A = A;
    
    td.B_dims = mxGetDimensions(mxB);
    td.B = (double *)mxGetPr(mxB);
    
    // safety check for input B
    if ( mxGetNumberOfDimensions(mxB) != 2 /* only single dimension feature matrices supported here*/ ||
         mxGetClassID(mxB) != mxDOUBLE_CLASS
       )
    {
      mexErrMsgTxt("Invalid input: B");
    }

    // compute size of output
    int height = td.A_dims[0] - td.B_dims[0] + 1;
    int width  = td.A_dims[1] - td.B_dims[1] + 1;
    
    if ( (height < 1) || (width < 1) )
      mexErrMsgTxt("Invalid input: B should be smaller than A");
    
    td.C_dims[0] = height;
    td.C_dims[1] = width;
    td.mxC = mxCreateNumericArray(2, td.C_dims, mxDOUBLE_CLASS, mxREAL);
    td.C = (double *)mxGetPr(td.mxC);
    
    // call the main function doing the hacky convolution
    process( (void *)&td );
    mxSetCell(plhs[0], i, td.mxC);
  }
}