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integrated multi-threaded version of SURF (thanks to imahon and yvo2m for the patch; see ticket #275)

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Vadim Pisarevsky 2010-11-22 17:02:51 +00:00
parent 4e52df75a7
commit 17a5e02eca
1 changed files with 327 additions and 209 deletions
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536
modules/features2d/src/surf.cpp
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@ -152,6 +152,56 @@ icvResizeHaarPattern( const int src[][5], CvSurfHF* dst, int n, int oldSize, int
}
}
/*
* Calculate the determinant and trace of the Hessian for a layer of the
* scale-space pyramid
*/
CV_INLINE void
icvCalcLayerDetAndTrace( const CvMat* sum, int size, int sampleStep, CvMat *det, CvMat *trace )
{
const int NX=3, NY=3, NXY=4;
const int dx_s[NX][5] = { {0, 2, 3, 7, 1}, {3, 2, 6, 7, -2}, {6, 2, 9, 7, 1} };
const int dy_s[NY][5] = { {2, 0, 7, 3, 1}, {2, 3, 7, 6, -2}, {2, 6, 7, 9, 1} };
const int dxy_s[NXY][5] = { {1, 1, 4, 4, 1}, {5, 1, 8, 4, -1}, {1, 5, 4, 8, -1}, {5, 5, 8, 8, 1} };
CvSurfHF Dx[NX], Dy[NY], Dxy[NXY];
double dx = 0, dy = 0, dxy = 0;
int i, j, samples_i, samples_j, margin;
int *sum_ptr;
float *det_ptr, *trace_ptr;
if( size>sum->rows-1 || size>sum->cols-1 )
return;
icvResizeHaarPattern( dx_s , Dx , NX , 9, size, sum->cols );
icvResizeHaarPattern( dy_s , Dy , NY , 9, size, sum->cols );
icvResizeHaarPattern( dxy_s, Dxy, NXY, 9, size, sum->cols );
/* The integral image 'sum' is one pixel bigger than the source image */
samples_i = 1+(sum->rows-1-size)/sampleStep;
samples_j = 1+(sum->cols-1-size)/sampleStep;
/* Ignore pixels where some of the kernel is outside the image */
margin = (size/2)/sampleStep;
for( i=0; i<samples_i; i++ )
{
sum_ptr = sum->data.i + (i*sampleStep)*sum->cols;
det_ptr = det->data.fl + (i+margin)*det->cols + margin;
trace_ptr = trace->data.fl + (i+margin)*trace->cols + margin;
for( j=0; j<samples_j; j++ )
{
dx = icvCalcHaarPattern( sum_ptr, Dx , 3 );
dy = icvCalcHaarPattern( sum_ptr, Dy , 3 );
dxy = icvCalcHaarPattern( sum_ptr, Dxy, 4 );
sum_ptr += sampleStep;
*det_ptr++ = (float)(dx*dy - 0.81*dxy*dxy);
*trace_ptr++ = (float)(dx + dy);
}
}
}
/*
* Maxima location interpolation as described in "Invariant Features from
* Interest Point Groups" by Matthew Brown and David Lowe. This is performed by
@ -209,6 +259,185 @@ icvInterpolateKeypoint( float N9[3][9], int dx, int dy, int ds, CvSURFPoint *poi
return solve_ok;
}
/*
* Find the maxima in the determinant of the Hessian in a layer of the
* scale-space pyramid
*/
CV_INLINE void
icvFindMaximaInLayer( const CvMat *sum, const CvMat* mask_sum, const CvSURFParams* params,
CvMat **dets, CvMat **traces, const int *sizes,
int layer, int sampleStep, CvSeq* points )
{
/* Wavelet Data */
const int NM=1;
const int dm[NM][5] = { {0, 0, 9, 9, 1} };
CvSurfHF Dm;
int i, j, size, margin, layer_rows, layer_cols;
float *det_ptr, *trace_ptr;
size = sizes[layer];
/* The integral image 'sum' is one pixel bigger than the source image */
layer_rows = (sum->rows-1)/sampleStep;
layer_cols = (sum->cols-1)/sampleStep;
/* Ignore pixels without a 3x3x3 neighbourhood in the layer above */
margin = (sizes[layer+1]/2)/sampleStep+1;
if( mask_sum )
icvResizeHaarPattern( dm, &Dm, NM, 9, size, mask_sum->cols );
for( i = margin; i < layer_rows-margin; i++ )
{
det_ptr = dets[layer]->data.fl + i*dets[layer]->cols;
trace_ptr = traces[layer]->data.fl + i*traces[layer]->cols;
for( j = margin; j < layer_cols-margin; j++ )
{
float val0 = det_ptr[j];
if( val0 > params->hessianThreshold )
{
/* Coordinates for the start of the wavelet in the sum image. There
is some integer division involved, so don't try to simplify this
(cancel out sampleStep) without checking the result is the same */
int sum_i = sampleStep*(i-(size/2)/sampleStep);
int sum_j = sampleStep*(j-(size/2)/sampleStep);
/* The 3x3x3 neighbouring samples around the maxima.
The maxima is included at N9[1][4] */
int c = dets[layer]->cols;
const float *det1 = dets[layer-1]->data.fl + i*c + j;
const float *det2 = dets[layer]->data.fl + i*c + j;
const float *det3 = dets[layer+1]->data.fl + i*c + j;
float N9[3][9] = { { det1[-c-1], det1[-c], det1[-c+1],
det1[-1] , det1[0] , det1[1],
det1[c-1] , det1[c] , det1[c+1] },
{ det2[-c-1], det2[-c], det2[-c+1],
det2[-1] , det2[0] , det2[1],
det2[c-1] , det2[c] , det2[c+1] },
{ det3[-c-1], det3[-c], det3[-c+1],
det3[-1] , det3[0] , det3[1],
det3[c-1] , det3[c] , det3[c+1] } };
/* Check the mask - why not just check the mask at the center of the wavelet? */
if( mask_sum )
{
const int* mask_ptr = mask_sum->data.i + mask_sum->cols*sum_i + sum_j;
float mval = icvCalcHaarPattern( mask_ptr, &Dm, 1 );
if( mval < 0.5 )
continue;
}
/* Non-maxima suppression. val0 is at N9[1][4]*/
if( val0 > N9[0][0] && val0 > N9[0][1] && val0 > N9[0][2] &&
val0 > N9[0][3] && val0 > N9[0][4] && val0 > N9[0][5] &&
val0 > N9[0][6] && val0 > N9[0][7] && val0 > N9[0][8] &&
val0 > N9[1][0] && val0 > N9[1][1] && val0 > N9[1][2] &&
val0 > N9[1][3] && val0 > N9[1][5] &&
val0 > N9[1][6] && val0 > N9[1][7] && val0 > N9[1][8] &&
val0 > N9[2][0] && val0 > N9[2][1] && val0 > N9[2][2] &&
val0 > N9[2][3] && val0 > N9[2][4] && val0 > N9[2][5] &&
val0 > N9[2][6] && val0 > N9[2][7] && val0 > N9[2][8] )
{
/* Calculate the wavelet center coordinates for the maxima */
double center_i = sum_i + (double)(size-1)/2;
double center_j = sum_j + (double)(size-1)/2;
CvSURFPoint point = cvSURFPoint( cvPoint2D32f(center_j,center_i),
CV_SIGN(trace_ptr[j]), sizes[layer], 0, val0 );
/* Interpolate maxima location within the 3x3x3 neighbourhood */
int ds = size-sizes[layer-1];
int interp_ok = icvInterpolateKeypoint( N9, sampleStep, sampleStep, ds, &point );
/* Sometimes the interpolation step gives a negative size etc. */
if( interp_ok )
{
/*printf( "KeyPoint %f %f %d\n", point.pt.x, point.pt.y, point.size );*/
#ifdef HAVE_TBB
static tbb::mutex m;
tbb::mutex::scoped_lock lock(m);
#endif
cvSeqPush( points, &point );
}
}
}
}
}
}
namespace cv
{
/* Multi-threaded construction of the scale-space pyramid */
struct SURFBuildInvoker
{
SURFBuildInvoker( const CvMat *_sum, const int *_sizes, const int *_sampleSteps,
CvMat** _dets, CvMat** _traces )
{
sum = _sum;
sizes = _sizes;
sampleSteps = _sampleSteps;
dets = _dets;
traces = _traces;
}
void operator()(const BlockedRange& range) const
{
for( int i=range.begin(); i<range.end(); i++ )
icvCalcLayerDetAndTrace( sum, sizes[i], sampleSteps[i], dets[i], traces[i] );
}
const CvMat *sum;
const int *sizes;
const int *sampleSteps;
CvMat** dets;
CvMat** traces;
};
/* Multi-threaded search of the scale-space pyramid for keypoints */
struct SURFFindInvoker
{
SURFFindInvoker( const CvMat *_sum, const CvMat *_mask_sum, const CvSURFParams* _params,
CvMat** _dets, CvMat** _traces, const int *_sizes,
const int *_sampleSteps, const int *_middleIndices, CvSeq* _points )
{
sum = _sum;
mask_sum = _mask_sum;
params = _params;
dets = _dets;
traces = _traces;
sizes = _sizes;
sampleSteps = _sampleSteps;
middleIndices = _middleIndices;
points = _points;
}
void operator()(const BlockedRange& range) const
{
for( int i=range.begin(); i<range.end(); i++ )
{
int layer = middleIndices[i];
icvFindMaximaInLayer( sum, mask_sum, params, dets, traces, sizes, layer,
sampleSteps[layer], points );
}
}
const CvMat *sum;
const CvMat *mask_sum;
const CvSURFParams* params;
CvMat** dets;
CvMat** traces;
const int *sizes;
const int *sampleSteps;
const int *middleIndices;
CvSeq* points;
};
} // namespace cv
/* Wavelet size at first layer of first octave. */
const int HAAR_SIZE0 = 9;
@ -230,152 +459,48 @@ static CvSeq* icvFastHessianDetector( const CvMat* sum, const CvMat* mask_sum,
however keypoint extraction becomes unreliable. */
const int SAMPLE_STEP0 = 1;
int nTotalLayers = (params->nOctaveLayers+2)*params->nOctaves;
int nMiddleLayers = params->nOctaveLayers*params->nOctaves;
/* Wavelet Data */
const int NX=3, NY=3, NXY=4, NM=1;
const int dx_s[NX][5] = { {0, 2, 3, 7, 1}, {3, 2, 6, 7, -2}, {6, 2, 9, 7, 1} };
const int dy_s[NY][5] = { {2, 0, 7, 3, 1}, {2, 3, 7, 6, -2}, {2, 6, 7, 9, 1} };
const int dxy_s[NXY][5] = { {1, 1, 4, 4, 1}, {5, 1, 8, 4, -1}, {1, 5, 4, 8, -1}, {5, 5, 8, 8, 1} };
const int dm[NM][5] = { {0, 0, 9, 9, 1} };
CvSurfHF Dx[NX], Dy[NY], Dxy[NXY], Dm;
CvMat** dets = (CvMat**)cvStackAlloc((params->nOctaveLayers+2)*sizeof(dets[0]));
CvMat** traces = (CvMat**)cvStackAlloc((params->nOctaveLayers+2)*sizeof(traces[0]));
int *sizes = (int*)cvStackAlloc((params->nOctaveLayers+2)*sizeof(sizes[0]));
double dx = 0, dy = 0, dxy = 0;
int octave, layer, sampleStep, size, margin;
int rows, cols;
int i, j, sum_i, sum_j;
const int* s_ptr;
float *det_ptr, *trace_ptr;
/* Allocate enough space for hessian determinant and trace matrices at the
first octave. Clearing these initially or between octaves is not
required, since all values that are accessed are first calculated */
for( layer = 0; layer <= params->nOctaveLayers+1; layer++ )
CvMat** dets = (CvMat**)cvStackAlloc(nTotalLayers*sizeof(dets[0]));
CvMat** traces = (CvMat**)cvStackAlloc(nTotalLayers*sizeof(traces[0]));
int *sizes = (int*)cvStackAlloc(nTotalLayers*sizeof(sizes[0]));
int *sampleSteps = (int*)cvStackAlloc(nTotalLayers*sizeof(sampleSteps[0]));
int *middleIndices = (int*)cvStackAlloc(nMiddleLayers*sizeof(middleIndices[0]));
int octave, layer, step, index, middleIndex;
/* Allocate space and calculate properties of each layer */
index = 0;
middleIndex = 0;
step = SAMPLE_STEP0;
for( octave=0; octave<params->nOctaves; octave++ )
{
dets[layer] = cvCreateMat( (sum->rows-1)/SAMPLE_STEP0, (sum->cols-1)/SAMPLE_STEP0, CV_32FC1 );
traces[layer] = cvCreateMat( (sum->rows-1)/SAMPLE_STEP0, (sum->cols-1)/SAMPLE_STEP0, CV_32FC1 );
for( layer=0; layer<params->nOctaveLayers+2; layer++ )
{
/* The integral image sum is one pixel bigger than the source image*/
dets[index] = cvCreateMat( (sum->rows-1)/step, (sum->cols-1)/step, CV_32FC1 );
traces[index] = cvCreateMat( (sum->rows-1)/step, (sum->cols-1)/step, CV_32FC1 );
sizes[index] = (HAAR_SIZE0+HAAR_SIZE_INC*layer)<<octave;
sampleSteps[index] = step;
if( layer!=0 && layer!=params->nOctaveLayers+1 )
middleIndices[middleIndex++] = index;
index++;
}
step*=2;
}
for( octave = 0, sampleStep=SAMPLE_STEP0; octave < params->nOctaves; octave++, sampleStep*=2 )
{
/* Hessian determinant and trace sample array size in this octave */
rows = (sum->rows-1)/sampleStep;
cols = (sum->cols-1)/sampleStep;
/* Calculate hessian determinant and trace samples in each layer*/
cv::parallel_for( cv::BlockedRange(0, nTotalLayers),
cv::SURFBuildInvoker(sum,sizes,sampleSteps,dets,traces) );
/* Calculate the determinant and trace of the hessian */
for( layer = 0; layer <= params->nOctaveLayers+1; layer++ )
{
sizes[layer] = size = (HAAR_SIZE0+HAAR_SIZE_INC*layer)<<octave;
icvResizeHaarPattern( dx_s, Dx, NX, 9, size, sum->cols );
icvResizeHaarPattern( dy_s, Dy, NY, 9, size, sum->cols );
icvResizeHaarPattern( dxy_s, Dxy, NXY, 9, size, sum->cols );
margin = (size/2)/sampleStep;
for( sum_i=0, i=margin; sum_i<=(sum->rows-1)-size; sum_i+=sampleStep, i++ )
{
s_ptr = sum->data.i + sum_i*sum->cols;
det_ptr = dets[layer]->data.fl + i*dets[layer]->cols + margin;
trace_ptr = traces[layer]->data.fl + i*traces[layer]->cols + margin;
for( sum_j=0, j=margin; sum_j<=(sum->cols-1)-size; sum_j+=sampleStep, j++ )
{
dx = icvCalcHaarPattern( s_ptr, Dx, 3 );
dy = icvCalcHaarPattern( s_ptr, Dy, 3 );
dxy = icvCalcHaarPattern( s_ptr, Dxy, 4 );
s_ptr+=sampleStep;
*det_ptr++ = (float)(dx*dy - 0.81*dxy*dxy);
*trace_ptr++ = (float)(dx + dy);
}
}
}
/* Find maxima in the determinant of the hessian */
for( layer = 1; layer <= params->nOctaveLayers; layer++ )
{
size = sizes[layer];
icvResizeHaarPattern( dm, &Dm, NM, 9, size, mask_sum ? mask_sum->cols : sum->cols );
/* Ignore pixels without a 3x3 neighbourhood in the layer above */
margin = (sizes[layer+1]/2)/sampleStep+1;
for( i = margin; i < rows-margin; i++ )
{
det_ptr = dets[layer]->data.fl + i*dets[layer]->cols;
trace_ptr = traces[layer]->data.fl + i*traces[layer]->cols;
for( j = margin; j < cols-margin; j++ )
{
float val0 = det_ptr[j];
if( val0 > params->hessianThreshold )
{
/* Coordinates for the start of the wavelet in the sum image. There
is some integer division involved, so don't try to simplify this
(cancel out sampleStep) without checking the result is the same */
int sum_i = sampleStep*(i-(size/2)/sampleStep);
int sum_j = sampleStep*(j-(size/2)/sampleStep);
/* The 3x3x3 neighbouring samples around the maxima.
The maxima is included at N9[1][4] */
int c = dets[layer]->cols;
const float *det1 = dets[layer-1]->data.fl + i*c + j;
const float *det2 = dets[layer]->data.fl + i*c + j;
const float *det3 = dets[layer+1]->data.fl + i*c + j;
float N9[3][9] = { { det1[-c-1], det1[-c], det1[-c+1],
det1[-1] , det1[0] , det1[1],
det1[c-1] , det1[c] , det1[c+1] },
{ det2[-c-1], det2[-c], det2[-c+1],
det2[-1] , det2[0] , det2[1],
det2[c-1] , det2[c] , det2[c+1 ] },
{ det3[-c-1], det3[-c], det3[-c+1],
det3[-1 ], det3[0] , det3[1],
det3[c-1] , det3[c] , det3[c+1 ] } };
/* Check the mask - why not just check the mask at the center of the wavelet? */
if( mask_sum )
{
const int* mask_ptr = mask_sum->data.i + mask_sum->cols*sum_i + sum_j;
float mval = icvCalcHaarPattern( mask_ptr, &Dm, 1 );
if( mval < 0.5 )
continue;
}
/* Non-maxima suppression. val0 is at N9[1][4]*/
if( val0 > N9[0][0] && val0 > N9[0][1] && val0 > N9[0][2] &&
val0 > N9[0][3] && val0 > N9[0][4] && val0 > N9[0][5] &&
val0 > N9[0][6] && val0 > N9[0][7] && val0 > N9[0][8] &&
val0 > N9[1][0] && val0 > N9[1][1] && val0 > N9[1][2] &&
val0 > N9[1][3] && val0 > N9[1][5] &&
val0 > N9[1][6] && val0 > N9[1][7] && val0 > N9[1][8] &&
val0 > N9[2][0] && val0 > N9[2][1] && val0 > N9[2][2] &&
val0 > N9[2][3] && val0 > N9[2][4] && val0 > N9[2][5] &&
val0 > N9[2][6] && val0 > N9[2][7] && val0 > N9[2][8] )
{
/* Calculate the wavelet center coordinates for the maxima */
double center_i = sum_i + (double)(size-1)/2;
double center_j = sum_j + (double)(size-1)/2;
CvSURFPoint point = cvSURFPoint( cvPoint2D32f(center_j,center_i),
CV_SIGN(trace_ptr[j]), sizes[layer], 0, val0 );
/* Interpolate maxima location within the 3x3x3 neighbourhood */
int ds = sizes[layer]-sizes[layer-1];
int interp_ok = icvInterpolateKeypoint( N9, sampleStep, sampleStep, ds, &point );
/* Sometimes the interpolation step gives a negative size etc. */
if( interp_ok )
{
/*printf( "KeyPoint %f %f %d\n", point.pt.x, point.pt.y, point.size );*/
cvSeqPush( points, &point );
}
}
}
}
}
}
}
/* Find maxima in the determinant of the hessian */
cv::parallel_for( cv::BlockedRange(0, nMiddleLayers),
cv::SURFFindInvoker(sum,mask_sum,params,dets,traces,sizes,
sampleSteps,middleIndices,points) );
/* Clean-up */
for( layer = 0; layer <= params->nOctaveLayers+1; layer++ )
for( layer = 0; layer < nTotalLayers; layer++ )
{
cvReleaseMat( &dets[layer] );
cvReleaseMat( &traces[layer] );
@ -388,6 +513,10 @@ static CvSeq* icvFastHessianDetector( const CvMat* sum, const CvMat* mask_sum,
namespace cv
{
/* Methods to free data allocated in SURFInvoker constructor */
template<> inline void Ptr<float>::delete_obj(){ cvFree(&obj); }
template<> inline void Ptr<CvPoint>::delete_obj(){ cvFree(&obj); }
struct SURFInvoker
{
enum { ORI_RADIUS = 6, ORI_WIN = 60, PATCH_SZ = 20 };
@ -398,41 +527,69 @@ struct SURFInvoker
SURFInvoker( const CvSURFParams* _params,
CvSeq* _keypoints, CvSeq* _descriptors,
const CvMat* _img, const CvMat* _sum,
const CvPoint* _apt, const float* _aptw,
int _nangle0, const float* _DW )
const CvMat* _img, const CvMat* _sum )
{
params = _params;
keypoints = _keypoints;
descriptors = _descriptors;
img = _img;
sum = _sum;
apt = _apt;
aptw = _aptw;
nangle0 = _nangle0;
DW = _DW;
/* Simple bound for number of grid points in circle of radius ORI_RADIUS */
const int nOriSampleBound = (2*ORI_RADIUS+1)*(2*ORI_RADIUS+1);
/* Allocate arrays */
apt = (CvPoint*)cvAlloc(nOriSampleBound*sizeof(CvPoint));
aptw = (float*)cvAlloc(nOriSampleBound*sizeof(float));
DW = (float*)cvAlloc(PATCH_SZ*PATCH_SZ*sizeof(float));
/* Coordinates and weights of samples used to calculate orientation */
cv::Mat G_ori = cv::getGaussianKernel( 2*ORI_RADIUS+1, ORI_SIGMA, CV_32F );
nOriSamples = 0;
for( int i = -ORI_RADIUS; i <= ORI_RADIUS; i++ )
{
for( int j = -ORI_RADIUS; j <= ORI_RADIUS; j++ )
{
if( i*i + j*j <= ORI_RADIUS*ORI_RADIUS )
{
apt[nOriSamples] = cvPoint(i,j);
aptw[nOriSamples++] = G_ori.at<float>(i+ORI_RADIUS,0) * G_ori.at<float>(j+ORI_RADIUS,0);
}
}
}
CV_Assert( nOriSamples <= nOriSampleBound );
/* Gaussian used to weight descriptor samples */
cv::Mat G_desc = cv::getGaussianKernel( PATCH_SZ, DESC_SIGMA, CV_32F );
for( int i = 0; i < PATCH_SZ; i++ )
{
for( int j = 0; j < PATCH_SZ; j++ )
DW[i*PATCH_SZ+j] = G_desc.at<float>(i,0) * G_desc.at<float>(j,0);
}
}
void operator()(const BlockedRange& range) const
{
/* X and Y gradient wavelet data */
const int NX=2, NY=2;
int dx_s[NX][5] = {{0, 0, 2, 4, -1}, {2, 0, 4, 4, 1}};
int dy_s[NY][5] = {{0, 0, 4, 2, 1}, {0, 2, 4, 4, -1}};
const int dx_s[NX][5] = {{0, 0, 2, 4, -1}, {2, 0, 4, 4, 1}};
const int dy_s[NY][5] = {{0, 0, 4, 2, 1}, {0, 2, 4, 4, -1}};
const int descriptor_size = params->extended ? 128 : 64;
const int max_ori_samples = (2*ORI_RADIUS+1)*(2*ORI_RADIUS+1);
float X[max_ori_samples], Y[max_ori_samples], angle[max_ori_samples];
/* Optimisation is better using nOriSampleBound than nOriSamples for
array lengths. Maybe because it is a constant known at compile time */
const int nOriSampleBound =(2*ORI_RADIUS+1)*(2*ORI_RADIUS+1);
float X[nOriSampleBound], Y[nOriSampleBound], angle[nOriSampleBound];
uchar PATCH[PATCH_SZ+1][PATCH_SZ+1];
float DX[PATCH_SZ][PATCH_SZ], DY[PATCH_SZ][PATCH_SZ];
CvMat matX = cvMat(1, max_ori_samples, CV_32F, X);
CvMat matY = cvMat(1, max_ori_samples, CV_32F, Y);
CvMat _angle = cvMat(1, max_ori_samples, CV_32F, angle);
CvMat matX = cvMat(1, nOriSampleBound, CV_32F, X);
CvMat matY = cvMat(1, nOriSampleBound, CV_32F, Y);
CvMat _angle = cvMat(1, nOriSampleBound, CV_32F, angle);
CvMat _patch = cvMat(PATCH_SZ+1, PATCH_SZ+1, CV_8U, PATCH);
int k, k1 = range.begin(), k2 = range.end();
int k, k1 = range.begin(), k2 = range.end();
int maxSize = 0;
for( k = k1; k < k2; k++ )
@ -475,7 +632,7 @@ struct SURFInvoker
}
icvResizeHaarPattern( dx_s, dx_t, NX, 4, grad_wav_size, sum->cols );
icvResizeHaarPattern( dy_s, dy_t, NY, 4, grad_wav_size, sum->cols );
for( kk = 0, nangle = 0; kk < nangle0; kk++ )
for( kk = 0, nangle = 0; kk < nOriSamples; kk++ )
{
const int* ptr;
float vx, vy;
@ -649,33 +806,32 @@ struct SURFInvoker
}
}
/* Parameters */
const CvSURFParams* params;
const CvMat* img;
const CvMat* sum;
CvSeq* keypoints;
CvSeq* descriptors;
const CvPoint* apt;
const float* aptw;
int nangle0;
const float* DW;
/* Pre-calculated values */
int nOriSamples;
cv::Ptr<CvPoint> apt;
cv::Ptr<float> aptw;
cv::Ptr<float> DW;
};
const int SURFInvoker::ORI_SEARCH_INC = 5;
const float SURFInvoker::ORI_SIGMA = 2.5f;
const float SURFInvoker::DESC_SIGMA = 3.3f;
}
CV_IMPL void
cvExtractSURF( const CvArr* _img, const CvArr* _mask,
CvSeq** _keypoints, CvSeq** _descriptors,
CvMemStorage* storage, CvSURFParams params,
int useProvidedKeyPts)
{
const int ORI_RADIUS = cv::SURFInvoker::ORI_RADIUS;
const float ORI_SIGMA = cv::SURFInvoker::ORI_SIGMA;
const float DESC_SIGMA = cv::SURFInvoker::DESC_SIGMA;
CvMat *sum = 0, *mask1 = 0, *mask_sum = 0;
if( _keypoints && !useProvidedKeyPts ) // If useProvidedKeyPts!=0 we'll use current contents of "*_keypoints"
@ -687,15 +843,9 @@ cvExtractSURF( const CvArr* _img, const CvArr* _mask,
CvMat imghdr, *img = cvGetMat(_img, &imghdr);
CvMat maskhdr, *mask = _mask ? cvGetMat(_mask, &maskhdr) : 0;
const int max_ori_samples = (2*ORI_RADIUS+1)*(2*ORI_RADIUS+1);
int descriptor_size = params.extended ? 128 : 64;
const int descriptor_data_type = CV_32F;
const int PATCH_SZ = 20;
float DW[PATCH_SZ][PATCH_SZ];
CvMat _DW = cvMat(PATCH_SZ, PATCH_SZ, CV_32F, DW);
CvPoint apt[max_ori_samples];
float aptw[max_ori_samples];
int i, j, nangle0 = 0, N;
int i, N;
CV_Assert(img != 0);
CV_Assert(CV_MAT_TYPE(img->type) == CV_8UC1);
@ -734,44 +884,12 @@ cvExtractSURF( const CvArr* _img, const CvArr* _mask,
cvSeqPushMulti( descriptors, 0, N );
}
/* Coordinates and weights of samples used to calculate orientation */
cv::Mat matG = cv::getGaussianKernel( 2*ORI_RADIUS+1, ORI_SIGMA, CV_32F );
const float* G = (const float*)matG.data;
for( i = -ORI_RADIUS; i <= ORI_RADIUS; i++ )
{
for( j = -ORI_RADIUS; j <= ORI_RADIUS; j++ )
{
if( i*i + j*j <= ORI_RADIUS*ORI_RADIUS )
{
apt[nangle0] = cvPoint(j,i);
aptw[nangle0++] = G[i+ORI_RADIUS]*G[j+ORI_RADIUS];
}
}
}
/* Gaussian used to weight descriptor samples */
double c2 = 1./(DESC_SIGMA*DESC_SIGMA*2);
double gs = 0;
for( i = 0; i < PATCH_SZ; i++ )
{
for( j = 0; j < PATCH_SZ; j++ )
{
double x = j - (float)(PATCH_SZ-1)/2, y = i - (float)(PATCH_SZ-1)/2;
double val = exp(-(x*x+y*y)*c2);
DW[i][j] = (float)val;
gs += val;
}
}
cvScale( &_DW, &_DW, 1./gs );
if ( N > 0 )
cv::parallel_for(cv::BlockedRange(0, N),
cv::SURFInvoker(&params, keypoints, descriptors, img, sum,
apt, aptw, nangle0, &DW[0][0]));
//cv::SURFInvoker(&params, keypoints, descriptors, img, sum,
// apt, aptw, nangle0, &DW[0][0])(cv::BlockedRange(0, N));
cv::parallel_for(cv::BlockedRange(0, N),
cv::SURFInvoker(&params, keypoints, descriptors, img, sum) );
/* remove keypoints that were marked for deletion */
for ( i = 0; i < N; i++ )
{