void SelfSimDescriptor::compute(const Mat& img, vector<float>& descriptors, Size winStride,
const vector<Point>& locations) const
{
CV_Assert( img.depth() == CV_8U );
winStride.width = std::max(winStride.width, 1);
winStride.height = std::max(winStride.height, 1);
Size gridSize = getGridSize(img.size(), winStride);
int i, nwindows = locations.empty() ? gridSize.width*gridSize.height : (int)locations.size();
int border = largeSize/2 + smallSize/2;
int fsize = (int)getDescriptorSize();
vector<float> tempFeature(fsize+1);
descriptors.resize(fsize*nwindows + 1);
Mat ssd(largeSize, largeSize, CV_32F), mappingMask;
computeLogPolarMapping(mappingMask);
#if 0 //def _OPENMP
int nthreads = cvGetNumThreads();
#pragma omp parallel for num_threads(nthreads)
#endif
for( i = 0; i < nwindows; i++ )
{
Point pt;
float* feature0 = &descriptors[fsize*i];
float* feature = &tempFeature[0];
int x, y, j;
if( !locations.empty() )
{
pt = locations[i];
if( pt.x < border || pt.x >= img.cols - border ||
pt.y < border || pt.y >= img.rows - border )
{
for( j = 0; j < fsize; j++ )
feature0[j] = 0.f;
continue;
}
}
else
pt = Point((i % gridSize.width)*winStride.width + border,
(i / gridSize.width)*winStride.height + border);
SSD(img, pt, ssd);
// Determine in the local neighborhood the largest difference and use for normalization
float var_noise = 1000.f;
for( y = -1; y <= 1 ; y++ )
for( x = -1 ; x <= 1 ; x++ )
var_noise = std::max(var_noise, ssd.at<float>(largeSize/2+y, largeSize/2+x));
for( j = 0; j <= fsize; j++ )
feature[j] = FLT_MAX;
// Derive feature vector before exp(-x) computation
// Idea: for all x,a >= 0, a=const. we have:
// max [ exp( -x / a) ] = exp ( -min(x) / a )
// Thus, determine min(ssd) and store in feature[...]
for( y = 0; y < ssd.rows; y++ )
{
const schar *mappingMaskPtr = mappingMask.ptr<schar>(y);
const float *ssdPtr = ssd.ptr<float>(y);
for( x = 0 ; x < ssd.cols; x++ )
{
int index = mappingMaskPtr[x];
feature[index] = std::min(feature[index], ssdPtr[x]);
}
}
var_noise = -1.f/var_noise;
for( j = 0; j < fsize; j++ )
feature0[j] = feature[j]*var_noise;
Mat _f(1, fsize, CV_32F, feature0);
cv::exp(_f, _f);
}
}
void
icvCrossCorr( const CvArr* _img, const CvArr* _templ, CvArr* _corr,
CvPoint anchor, double delta, int borderType )
{
// disable OpenMP in the case of Visual Studio,
// otherwise the performance drops significantly
#undef USE_OPENMP
#if !defined _MSC_VER || defined CV_ICC
#define USE_OPENMP 1
#endif
const double block_scale = 4.5;
const int min_block_size = 256;
cv::Ptr<CvMat> dft_img[CV_MAX_THREADS];
cv::Ptr<CvMat> dft_templ;
std::vector<uchar> buf[CV_MAX_THREADS];
int k, num_threads = 0;
CvMat istub, *img = (CvMat*)_img;
CvMat tstub, *templ = (CvMat*)_templ;
CvMat cstub, *corr = (CvMat*)_corr;
CvSize dftsize, blocksize;
int depth, templ_depth, corr_depth, max_depth = CV_32F,
cn, templ_cn, corr_cn, buf_size = 0,
tile_count_x, tile_count_y, tile_count;
img = cvGetMat( img, &istub );
templ = cvGetMat( templ, &tstub );
corr = cvGetMat( corr, &cstub );
if( CV_MAT_DEPTH( img->type ) != CV_8U &&
CV_MAT_DEPTH( img->type ) != CV_16U &&
CV_MAT_DEPTH( img->type ) != CV_32F &&
CV_MAT_DEPTH( img->type ) != CV_64F )
CV_Error( CV_StsUnsupportedFormat,
"The function supports only 8u, 16u and 32f data types" );
if( !CV_ARE_DEPTHS_EQ( img, templ ) && CV_MAT_DEPTH( templ->type ) != CV_32F )
CV_Error( CV_StsUnsupportedFormat,
"Template (kernel) must be of the same depth as the input image, or be 32f" );
if( !CV_ARE_DEPTHS_EQ( img, corr ) && CV_MAT_DEPTH( corr->type ) != CV_32F &&
CV_MAT_DEPTH( corr->type ) != CV_64F )
CV_Error( CV_StsUnsupportedFormat,
"The output image must have the same depth as the input image, or be 32f/64f" );
if( (!CV_ARE_CNS_EQ( img, corr ) || CV_MAT_CN(templ->type) > 1) &&
(CV_MAT_CN( corr->type ) > 1 || !CV_ARE_CNS_EQ( img, templ)) )
CV_Error( CV_StsUnsupportedFormat,
"The output must have the same number of channels as the input (when the template has 1 channel), "
"or the output must have 1 channel when the input and the template have the same number of channels" );
depth = CV_MAT_DEPTH(img->type);
cn = CV_MAT_CN(img->type);
templ_depth = CV_MAT_DEPTH(templ->type);
templ_cn = CV_MAT_CN(templ->type);
corr_depth = CV_MAT_DEPTH(corr->type);
corr_cn = CV_MAT_CN(corr->type);
CV_Assert( corr_cn == 1 || delta == 0 );
max_depth = MAX( max_depth, templ_depth );
max_depth = MAX( max_depth, depth );
max_depth = MAX( max_depth, corr_depth );
if( depth > CV_8U )
max_depth = CV_64F;
/*if( img->cols < templ->cols || img->rows < templ->rows )
CV_Error( CV_StsUnmatchedSizes,
"Such a combination of image and template/filter size is not supported" );*/
if( corr->rows > img->rows + templ->rows - 1 ||
corr->cols > img->cols + templ->cols - 1 )
CV_Error( CV_StsUnmatchedSizes,
"output image should not be greater than (W + w - 1)x(H + h - 1)" );
blocksize.width = cvRound(templ->cols*block_scale);
blocksize.width = MAX( blocksize.width, min_block_size - templ->cols + 1 );
blocksize.width = MIN( blocksize.width, corr->cols );
blocksize.height = cvRound(templ->rows*block_scale);
blocksize.height = MAX( blocksize.height, min_block_size - templ->rows + 1 );
blocksize.height = MIN( blocksize.height, corr->rows );
dftsize.width = cvGetOptimalDFTSize(blocksize.width + templ->cols - 1);
if( dftsize.width == 1 )
dftsize.width = 2;
dftsize.height = cvGetOptimalDFTSize(blocksize.height + templ->rows - 1);
if( dftsize.width <= 0 || dftsize.height <= 0 )
CV_Error( CV_StsOutOfRange, "the input arrays are too big" );
// recompute block size
blocksize.width = dftsize.width - templ->cols + 1;
blocksize.width = MIN( blocksize.width, corr->cols );
blocksize.height = dftsize.height - templ->rows + 1;
blocksize.height = MIN( blocksize.height, corr->rows );
dft_templ = cvCreateMat( dftsize.height*templ_cn, dftsize.width, max_depth );
#ifdef USE_OPENMP
num_threads = cvGetNumThreads();
#else
num_threads = 1;
#endif
for( k = 0; k < num_threads; k++ )
dft_img[k] = cvCreateMat( dftsize.height, dftsize.width, max_depth );
if( templ_cn > 1 && templ_depth != max_depth )
buf_size = templ->cols*templ->rows*CV_ELEM_SIZE(templ_depth);
if( cn > 1 && depth != max_depth )
buf_size = MAX( buf_size, (blocksize.width + templ->cols - 1)*
(blocksize.height + templ->rows - 1)*CV_ELEM_SIZE(depth));
if( (corr_cn > 1 || cn > 1) && corr_depth != max_depth )
buf_size = MAX( buf_size, blocksize.width*blocksize.height*CV_ELEM_SIZE(corr_depth));
if( buf_size > 0 )
{
for( k = 0; k < num_threads; k++ )
buf[k].resize(buf_size);
}
// compute DFT of each template plane
for( k = 0; k < templ_cn; k++ )
{
CvMat dstub, *src, *dst, temp;
CvMat* planes[] = { 0, 0, 0, 0 };
int yofs = k*dftsize.height;
src = templ;
dst = cvGetSubRect( dft_templ, &dstub, cvRect(0,yofs,templ->cols,templ->rows));
if( templ_cn > 1 )
{
planes[k] = templ_depth == max_depth ? dst :
cvInitMatHeader( &temp, templ->rows, templ->cols, templ_depth, &buf[0][0] );
cvSplit( templ, planes[0], planes[1], planes[2], planes[3] );
src = planes[k];
planes[k] = 0;
}
if( dst != src )
cvConvert( src, dst );
if( dft_templ->cols > templ->cols )
{
cvGetSubRect( dft_templ, dst, cvRect(templ->cols, yofs,
dft_templ->cols - templ->cols, templ->rows) );
cvZero( dst );
}
cvGetSubRect( dft_templ, dst, cvRect(0,yofs,dftsize.width,dftsize.height) );
cvDFT( dst, dst, CV_DXT_FORWARD + CV_DXT_SCALE, templ->rows );
}
tile_count_x = (corr->cols + blocksize.width - 1)/blocksize.width;
tile_count_y = (corr->rows + blocksize.height - 1)/blocksize.height;
tile_count = tile_count_x*tile_count_y;
#if defined _OPENMP && defined USE_OPENMP
#pragma omp parallel for num_threads(num_threads) schedule(dynamic)
#endif
// calculate correlation by blocks
for( k = 0; k < tile_count; k++ )
{
#ifdef USE_OPENMP
int thread_idx = cvGetThreadNum();
#else
int thread_idx = 0;
#endif
int x = (k%tile_count_x)*blocksize.width;
int y = (k/tile_count_x)*blocksize.height;
int i, yofs;
CvMat sstub, dstub, *src, *dst, temp;
CvMat* planes[] = { 0, 0, 0, 0 };
CvMat* _dft_img = dft_img[thread_idx];
uchar* _buf = buf_size > 0 ? &buf[thread_idx][0] : 0;
CvSize csz = { blocksize.width, blocksize.height }, isz;
int x0 = x - anchor.x, y0 = y - anchor.y;
int x1 = MAX( 0, x0 ), y1 = MAX( 0, y0 ), x2, y2;
csz.width = MIN( csz.width, corr->cols - x );
csz.height = MIN( csz.height, corr->rows - y );
isz.width = csz.width + templ->cols - 1;
isz.height = csz.height + templ->rows - 1;
x2 = MIN( img->cols, x0 + isz.width );
y2 = MIN( img->rows, y0 + isz.height );
for( i = 0; i < cn; i++ )
{
CvMat dstub1, *dst1;
yofs = i*dftsize.height;
src = cvGetSubRect( img, &sstub, cvRect(x1,y1,x2-x1,y2-y1) );
dst = cvGetSubRect( _dft_img, &dstub,
cvRect(0,0,isz.width,isz.height) );
dst1 = dst;
if( x2 - x1 < isz.width || y2 - y1 < isz.height )
dst1 = cvGetSubRect( _dft_img, &dstub1,
cvRect( x1 - x0, y1 - y0, x2 - x1, y2 - y1 ));
if( cn > 1 )
{
planes[i] = dst1;
if( depth != max_depth )
planes[i] = cvInitMatHeader( &temp, y2 - y1, x2 - x1, depth, _buf );
cvSplit( src, planes[0], planes[1], planes[2], planes[3] );
src = planes[i];
planes[i] = 0;
}
if( dst1 != src )
cvConvert( src, dst1 );
if( dst != dst1 )
cvCopyMakeBorder( dst1, dst, cvPoint(x1 - x0, y1 - y0), borderType );
if( dftsize.width > isz.width )
{
cvGetSubRect( _dft_img, dst, cvRect(isz.width, 0,
dftsize.width - isz.width,dftsize.height) );
cvZero( dst );
}
cvDFT( _dft_img, _dft_img, CV_DXT_FORWARD, isz.height );
cvGetSubRect( dft_templ, dst,
cvRect(0,(templ_cn>1?yofs:0),dftsize.width,dftsize.height) );
cvMulSpectrums( _dft_img, dst, _dft_img, CV_DXT_MUL_CONJ );
cvDFT( _dft_img, _dft_img, CV_DXT_INVERSE, csz.height );
src = cvGetSubRect( _dft_img, &sstub, cvRect(0,0,csz.width,csz.height) );
dst = cvGetSubRect( corr, &dstub, cvRect(x,y,csz.width,csz.height) );
if( corr_cn > 1 )
{
planes[i] = src;
if( corr_depth != max_depth )
{
planes[i] = cvInitMatHeader( &temp, csz.height, csz.width,
corr_depth, _buf );
cvConvertScale( src, planes[i], 1, delta );
}
cvMerge( planes[0], planes[1], planes[2], planes[3], dst );
planes[i] = 0;
}
else
{
if( i == 0 )
cvConvertScale( src, dst, 1, delta );
else
{
if( max_depth > corr_depth )
{
cvInitMatHeader( &temp, csz.height, csz.width,
corr_depth, _buf );
cvConvert( src, &temp );
src = &temp;
}
cvAcc( src, dst );
}
}
}
}
}
static void
icvTrueDistTrans( const CvMat* src, CvMat* dst )
{
cv::Ptr<CvMat> buffer = 0;
int i, m, n;
int sstep, dstep;
const float inf = 1e6f;
int thread_count = cvGetNumThreads();
int pass1_sz, pass2_sz;
if( !CV_ARE_SIZES_EQ( src, dst ))
CV_Error( CV_StsUnmatchedSizes, "" );
if( CV_MAT_TYPE(src->type) != CV_8UC1 ||
CV_MAT_TYPE(dst->type) != CV_32FC1 )
CV_Error( CV_StsUnsupportedFormat,
"The input image must have 8uC1 type and the output one must have 32fC1 type" );
m = src->rows;
n = src->cols;
// (see stage 1 below):
// sqr_tab: 2*m, sat_tab: 3*m + 1, d: m*thread_count,
pass1_sz = src->rows*(5 + thread_count) + 1;
// (see stage 2):
// sqr_tab & inv_tab: n each; f & v: n*thread_count each; z: (n+1)*thread_count
pass2_sz = src->cols*(2 + thread_count*3) + thread_count;
buffer = cvCreateMat( 1, MAX(pass1_sz, pass2_sz), CV_32FC1 );
sstep = src->step;
dstep = dst->step / sizeof(float);
// stage 1: compute 1d distance transform of each column
float* sqr_tab = buffer->data.fl;
int* sat_tab = (int*)(sqr_tab + m*2);
const int shift = m*2;
for( i = 0; i < m; i++ )
sqr_tab[i] = (float)(i*i);
for( i = m; i < m*2; i++ )
sqr_tab[i] = inf;
for( i = 0; i < shift; i++ )
sat_tab[i] = 0;
for( ; i <= m*3; i++ )
sat_tab[i] = i - shift;
#ifdef _OPENMP
#pragma omp parallel for num_threads(thread_count)
#endif
for( i = 0; i < n; i++ )
{
const uchar* sptr = src->data.ptr + i + (m-1)*sstep;
float* dptr = dst->data.fl + i;
int* d = (int*)(sat_tab + m*3+1+m*cvGetThreadNum());
int j, dist = m-1;
for( j = m-1; j >= 0; j--, sptr -= sstep )
{
dist = (dist + 1) & (sptr[0] == 0 ? 0 : -1);
d[j] = dist;
}
dist = m-1;
for( j = 0; j < m; j++, dptr += dstep )
{
dist = dist + 1 - sat_tab[dist + 1 - d[j] + shift];
d[j] = dist;
dptr[0] = sqr_tab[dist];
}
}
// stage 2: compute modified distance transform for each row
float* inv_tab = buffer->data.fl;
sqr_tab = inv_tab + n;
inv_tab[0] = sqr_tab[0] = 0.f;
for( i = 1; i < n; i++ )
{
inv_tab[i] = (float)(0.5/i);
sqr_tab[i] = (float)(i*i);
}
#ifdef _OPENMP
#pragma omp parallel for num_threads(thread_count) schedule(dynamic)
#endif
for( i = 0; i < m; i++ )
{
float* d = (float*)(dst->data.ptr + i*dst->step);
float* f = sqr_tab + n + (n*3+1)*cvGetThreadNum();
float* z = f + n;
int* v = (int*)(z + n + 1);
int p, q, k;
v[0] = 0;
z[0] = -inf;
z[1] = inf;
f[0] = d[0];
for( q = 1, k = 0; q < n; q++ )
{
float fq = d[q];
f[q] = fq;
for(;;k--)
{
p = v[k];
float s = (fq + sqr_tab[q] - d[p] - sqr_tab[p])*inv_tab[q - p];
if( s > z[k] )
{
k++;
v[k] = q;
z[k] = s;
z[k+1] = inf;
break;
}
}
}
for( q = 0, k = 0; q < n; q++ )
{
while( z[k+1] < q )
k++;
p = v[k];
d[q] = sqr_tab[abs(q - p)] + f[p];
}
}
cvPow( dst, dst, 0.5 );
}
