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opencv/modules/features2d/src/stardetector.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2008, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
static void
icvComputeIntegralImages( const CvMat* matI, CvMat* matS, CvMat* matT, CvMat* _FT )
{
int x, y, rows = matI->rows, cols = matI->cols;
const uchar* I = matI->data.ptr;
int *S = matS->data.i, *T = matT->data.i, *FT = _FT->data.i;
int istep = matI->step, step = matS->step/sizeof(S[0]);
assert( CV_MAT_TYPE(matI->type) == CV_8UC1 &&
CV_MAT_TYPE(matS->type) == CV_32SC1 &&
CV_ARE_TYPES_EQ(matS, matT) && CV_ARE_TYPES_EQ(matS, _FT) &&
CV_ARE_SIZES_EQ(matS, matT) && CV_ARE_SIZES_EQ(matS, _FT) &&
matS->step == matT->step && matS->step == _FT->step &&
matI->rows+1 == matS->rows && matI->cols+1 == matS->cols );
for( x = 0; x <= cols; x++ )
S[x] = T[x] = FT[x] = 0;
S += step; T += step; FT += step;
S[0] = T[0] = 0;
FT[0] = I[0];
for( x = 1; x < cols; x++ )
{
S[x] = S[x-1] + I[x-1];
T[x] = I[x-1];
FT[x] = I[x] + I[x-1];
}
S[cols] = S[cols-1] + I[cols-1];
T[cols] = FT[cols] = I[cols-1];
for( y = 2; y <= rows; y++ )
{
I += istep, S += step, T += step, FT += step;
S[0] = S[-step]; S[1] = S[-step+1] + I[0];
T[0] = T[-step + 1];
T[1] = FT[0] = T[-step + 2] + I[-istep] + I[0];
FT[1] = FT[-step + 2] + I[-istep] + I[1] + I[0];
for( x = 2; x < cols; x++ )
{
S[x] = S[x - 1] + S[-step + x] - S[-step + x - 1] + I[x - 1];
T[x] = T[-step + x - 1] + T[-step + x + 1] - T[-step*2 + x] + I[-istep + x - 1] + I[x - 1];
FT[x] = FT[-step + x - 1] + FT[-step + x + 1] - FT[-step*2 + x] + I[x] + I[x-1];
}
S[cols] = S[cols - 1] + S[-step + cols] - S[-step + cols - 1] + I[cols - 1];
T[cols] = FT[cols] = T[-step + cols - 1] + I[-istep + cols - 1] + I[cols - 1];
}
}
typedef struct CvStarFeature
{
int area;
int* p[8];
}
CvStarFeature;
static int
icvStarDetectorComputeResponses( const CvMat* img, CvMat* responses, CvMat* sizes,
const CvStarDetectorParams* params )
{
const int MAX_PATTERN = 17;
static const int sizes0[] = {1, 2, 3, 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128, -1};
static const int pairs[][2] = {{1, 0}, {3, 1}, {4, 2}, {5, 3}, {7, 4}, {8, 5}, {9, 6},
{11, 8}, {13, 10}, {14, 11}, {15, 12}, {16, 14}, {-1, -1}};
float invSizes[MAX_PATTERN][2];
int sizes1[MAX_PATTERN];
#if CV_SSE2
__m128 invSizes4[MAX_PATTERN][2];
__m128 sizes1_4[MAX_PATTERN];
Cv32suf absmask;
absmask.i = 0x7fffffff;
volatile bool useSIMD = cv::checkHardwareSupport(CV_CPU_SSE2);
#endif
CvStarFeature f[MAX_PATTERN];
CvMat *sum = 0, *tilted = 0, *flatTilted = 0;
int y, i=0, rows = img->rows, cols = img->cols, step;
int border, npatterns=0, maxIdx=0;
#ifdef _OPENMP
int nthreads = cvGetNumThreads();
#endif
assert( CV_MAT_TYPE(img->type) == CV_8UC1 &&
CV_MAT_TYPE(responses->type) == CV_32FC1 &&
CV_MAT_TYPE(sizes->type) == CV_16SC1 &&
CV_ARE_SIZES_EQ(responses, sizes) );
while( pairs[i][0] >= 0 && !
( sizes0[pairs[i][0]] >= params->maxSize
|| sizes0[pairs[i+1][0]] + sizes0[pairs[i+1][0]]/2 >= std::min(rows, cols) ) )
{
++i;
}
npatterns = i;
npatterns += (pairs[npatterns-1][0] >= 0);
maxIdx = pairs[npatterns-1][0];
sum = cvCreateMat( rows + 1, cols + 1, CV_32SC1 );
tilted = cvCreateMat( rows + 1, cols + 1, CV_32SC1 );
flatTilted = cvCreateMat( rows + 1, cols + 1, CV_32SC1 );
step = sum->step/CV_ELEM_SIZE(sum->type);
icvComputeIntegralImages( img, sum, tilted, flatTilted );
for( i = 0; i <= maxIdx; i++ )
{
int ur_size = sizes0[i], t_size = sizes0[i] + sizes0[i]/2;
int ur_area = (2*ur_size + 1)*(2*ur_size + 1);
int t_area = t_size*t_size + (t_size + 1)*(t_size + 1);
f[i].p[0] = sum->data.i + (ur_size + 1)*step + ur_size + 1;
f[i].p[1] = sum->data.i - ur_size*step + ur_size + 1;
f[i].p[2] = sum->data.i + (ur_size + 1)*step - ur_size;
f[i].p[3] = sum->data.i - ur_size*step - ur_size;
f[i].p[4] = tilted->data.i + (t_size + 1)*step + 1;
f[i].p[5] = flatTilted->data.i - t_size;
f[i].p[6] = flatTilted->data.i + t_size + 1;
f[i].p[7] = tilted->data.i - t_size*step + 1;
f[i].area = ur_area + t_area;
sizes1[i] = sizes0[i];
}
// negate end points of the size range
// for a faster rejection of very small or very large features in non-maxima suppression.
sizes1[0] = -sizes1[0];
sizes1[1] = -sizes1[1];
sizes1[maxIdx] = -sizes1[maxIdx];
border = sizes0[maxIdx] + sizes0[maxIdx]/2;
for( i = 0; i < npatterns; i++ )
{
int innerArea = f[pairs[i][1]].area;
int outerArea = f[pairs[i][0]].area - innerArea;
invSizes[i][0] = 1.f/outerArea;
invSizes[i][1] = 1.f/innerArea;
}
#if CV_SSE2
if( useSIMD )
{
for( i = 0; i < npatterns; i++ )
{
_mm_store_ps((float*)&invSizes4[i][0], _mm_set1_ps(invSizes[i][0]));
_mm_store_ps((float*)&invSizes4[i][1], _mm_set1_ps(invSizes[i][1]));
}
for( i = 0; i <= maxIdx; i++ )
_mm_store_ps((float*)&sizes1_4[i], _mm_set1_ps((float)sizes1[i]));
}
#endif
for( y = 0; y < border; y++ )
{
float* r_ptr = (float*)(responses->data.ptr + responses->step*y);
float* r_ptr2 = (float*)(responses->data.ptr + responses->step*(rows - 1 - y));
short* s_ptr = (short*)(sizes->data.ptr + sizes->step*y);
short* s_ptr2 = (short*)(sizes->data.ptr + sizes->step*(rows - 1 - y));
memset( r_ptr, 0, cols*sizeof(r_ptr[0]));
memset( r_ptr2, 0, cols*sizeof(r_ptr2[0]));
memset( s_ptr, 0, cols*sizeof(s_ptr[0]));
memset( s_ptr2, 0, cols*sizeof(s_ptr2[0]));
}
#ifdef _OPENMP
#pragma omp parallel for num_threads(nthreads) schedule(static)
#endif
for( y = border; y < rows - border; y++ )
{
int x = border, i;
float* r_ptr = (float*)(responses->data.ptr + responses->step*y);
short* s_ptr = (short*)(sizes->data.ptr + sizes->step*y);
memset( r_ptr, 0, border*sizeof(r_ptr[0]));
memset( s_ptr, 0, border*sizeof(s_ptr[0]));
memset( r_ptr + cols - border, 0, border*sizeof(r_ptr[0]));
memset( s_ptr + cols - border, 0, border*sizeof(s_ptr[0]));
#if CV_SSE2
if( useSIMD )
{
__m128 absmask4 = _mm_set1_ps(absmask.f);
for( ; x <= cols - border - 4; x += 4 )
{
int ofs = y*step + x;
__m128 vals[MAX_PATTERN];
__m128 bestResponse = _mm_setzero_ps();
__m128 bestSize = _mm_setzero_ps();
for( i = 0; i <= maxIdx; i++ )
{
const int** p = (const int**)&f[i].p[0];
__m128i r0 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[0]+ofs)),
_mm_loadu_si128((const __m128i*)(p[1]+ofs)));
__m128i r1 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[3]+ofs)),
_mm_loadu_si128((const __m128i*)(p[2]+ofs)));
__m128i r2 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[4]+ofs)),
_mm_loadu_si128((const __m128i*)(p[5]+ofs)));
__m128i r3 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[7]+ofs)),
_mm_loadu_si128((const __m128i*)(p[6]+ofs)));
r0 = _mm_add_epi32(_mm_add_epi32(r0,r1), _mm_add_epi32(r2,r3));
_mm_store_ps((float*)&vals[i], _mm_cvtepi32_ps(r0));
}
for( i = 0; i < npatterns; i++ )
{
__m128 inner_sum = vals[pairs[i][1]];
__m128 outer_sum = _mm_sub_ps(vals[pairs[i][0]], inner_sum);
__m128 response = _mm_sub_ps(_mm_mul_ps(inner_sum, invSizes4[i][1]),
_mm_mul_ps(outer_sum, invSizes4[i][0]));
__m128 swapmask = _mm_cmpgt_ps(_mm_and_ps(response,absmask4),
_mm_and_ps(bestResponse,absmask4));
bestResponse = _mm_xor_ps(bestResponse,
_mm_and_ps(_mm_xor_ps(response,bestResponse), swapmask));
bestSize = _mm_xor_ps(bestSize,
_mm_and_ps(_mm_xor_ps(sizes1_4[pairs[i][0]], bestSize), swapmask));
}
_mm_storeu_ps(r_ptr + x, bestResponse);
_mm_storel_epi64((__m128i*)(s_ptr + x),
_mm_packs_epi32(_mm_cvtps_epi32(bestSize),_mm_setzero_si128()));
}
}
#endif
for( ; x < cols - border; x++ )
{
int ofs = y*step + x;
int vals[MAX_PATTERN];
float bestResponse = 0;
int bestSize = 0;
for( i = 0; i <= maxIdx; i++ )
{
const int** p = (const int**)&f[i].p[0];
vals[i] = p[0][ofs] - p[1][ofs] - p[2][ofs] + p[3][ofs] +
p[4][ofs] - p[5][ofs] - p[6][ofs] + p[7][ofs];
}
for( i = 0; i < npatterns; i++ )
{
int inner_sum = vals[pairs[i][1]];
int outer_sum = vals[pairs[i][0]] - inner_sum;
float response = inner_sum*invSizes[i][1] - outer_sum*invSizes[i][0];
if( fabs(response) > fabs(bestResponse) )
{
bestResponse = response;
bestSize = sizes1[pairs[i][0]];
}
}
r_ptr[x] = bestResponse;
s_ptr[x] = (short)bestSize;
}
}
cvReleaseMat(&sum);
cvReleaseMat(&tilted);
cvReleaseMat(&flatTilted);
return border;
}
static bool
icvStarDetectorSuppressLines( const CvMat* responses, const CvMat* sizes, CvPoint pt,
const CvStarDetectorParams* params )
{
const float* r_ptr = responses->data.fl;
int rstep = responses->step/sizeof(r_ptr[0]);
const short* s_ptr = sizes->data.s;
int sstep = sizes->step/sizeof(s_ptr[0]);
int sz = s_ptr[pt.y*sstep + pt.x];
int x, y, delta = sz/4, radius = delta*4;
float Lxx = 0, Lyy = 0, Lxy = 0;
int Lxxb = 0, Lyyb = 0, Lxyb = 0;
for( y = pt.y - radius; y <= pt.y + radius; y += delta )
for( x = pt.x - radius; x <= pt.x + radius; x += delta )
{
float Lx = r_ptr[y*rstep + x + 1] - r_ptr[y*rstep + x - 1];
float Ly = r_ptr[(y+1)*rstep + x] - r_ptr[(y-1)*rstep + x];
Lxx += Lx*Lx; Lyy += Ly*Ly; Lxy += Lx*Ly;
}
if( (Lxx + Lyy)*(Lxx + Lyy) >= params->lineThresholdProjected*(Lxx*Lyy - Lxy*Lxy) )
return true;
for( y = pt.y - radius; y <= pt.y + radius; y += delta )
for( x = pt.x - radius; x <= pt.x + radius; x += delta )
{
int Lxb = (s_ptr[y*sstep + x + 1] == sz) - (s_ptr[y*sstep + x - 1] == sz);
int Lyb = (s_ptr[(y+1)*sstep + x] == sz) - (s_ptr[(y-1)*sstep + x] == sz);
Lxxb += Lxb * Lxb; Lyyb += Lyb * Lyb; Lxyb += Lxb * Lyb;
}
if( (Lxxb + Lyyb)*(Lxxb + Lyyb) >= params->lineThresholdBinarized*(Lxxb*Lyyb - Lxyb*Lxyb) )
return true;
return false;
}
static void
icvStarDetectorSuppressNonmax( const CvMat* responses, const CvMat* sizes,
CvSeq* keypoints, int border,
const CvStarDetectorParams* params )
{
int x, y, x1, y1, delta = params->suppressNonmaxSize/2;
int rows = responses->rows, cols = responses->cols;
const float* r_ptr = responses->data.fl;
int rstep = responses->step/sizeof(r_ptr[0]);
const short* s_ptr = sizes->data.s;
int sstep = sizes->step/sizeof(s_ptr[0]);
short featureSize = 0;
for( y = border; y < rows - border; y += delta+1 )
for( x = border; x < cols - border; x += delta+1 )
{
float maxResponse = (float)params->responseThreshold;
float minResponse = (float)-params->responseThreshold;
CvPoint maxPt = {-1,-1}, minPt = {-1,-1};
int tileEndY = MIN(y + delta, rows - border - 1);
int tileEndX = MIN(x + delta, cols - border - 1);
for( y1 = y; y1 <= tileEndY; y1++ )
for( x1 = x; x1 <= tileEndX; x1++ )
{
float val = r_ptr[y1*rstep + x1];
if( maxResponse < val )
{
maxResponse = val;
maxPt = cvPoint(x1, y1);
}
else if( minResponse > val )
{
minResponse = val;
minPt = cvPoint(x1, y1);
}
}
if( maxPt.x >= 0 )
{
for( y1 = maxPt.y - delta; y1 <= maxPt.y + delta; y1++ )
for( x1 = maxPt.x - delta; x1 <= maxPt.x + delta; x1++ )
{
float val = r_ptr[y1*rstep + x1];
if( val >= maxResponse && (y1 != maxPt.y || x1 != maxPt.x))
goto skip_max;
}
if( (featureSize = s_ptr[maxPt.y*sstep + maxPt.x]) >= 4 &&
!icvStarDetectorSuppressLines( responses, sizes, maxPt, params ))
{
CvStarKeypoint kpt = cvStarKeypoint( maxPt, featureSize, maxResponse );
cvSeqPush( keypoints, &kpt );
}
}
skip_max:
if( minPt.x >= 0 )
{
for( y1 = minPt.y - delta; y1 <= minPt.y + delta; y1++ )
for( x1 = minPt.x - delta; x1 <= minPt.x + delta; x1++ )
{
float val = r_ptr[y1*rstep + x1];
if( val <= minResponse && (y1 != minPt.y || x1 != minPt.x))
goto skip_min;
}
if( (featureSize = s_ptr[minPt.y*sstep + minPt.x]) >= 4 &&
!icvStarDetectorSuppressLines( responses, sizes, minPt, params ))
{
CvStarKeypoint kpt = cvStarKeypoint( minPt, featureSize, minResponse );
cvSeqPush( keypoints, &kpt );
}
}
skip_min:
;
}
}
CV_IMPL CvSeq*
cvGetStarKeypoints( const CvArr* _img, CvMemStorage* storage,
CvStarDetectorParams params )
{
CvMat stub, *img = cvGetMat(_img, &stub);
CvSeq* keypoints = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvStarKeypoint), storage );
CvMat* responses = cvCreateMat( img->rows, img->cols, CV_32FC1 );
CvMat* sizes = cvCreateMat( img->rows, img->cols, CV_16SC1 );
int border = icvStarDetectorComputeResponses( img, responses, sizes, &params );
if( border >= 0 )
icvStarDetectorSuppressNonmax( responses, sizes, keypoints, border, &params );
cvReleaseMat( &responses );
cvReleaseMat( &sizes );
return border >= 0 ? keypoints : 0;
}
namespace cv
{
StarDetector::StarDetector()
{
*(CvStarDetectorParams*)this = cvStarDetectorParams();
}
StarDetector::StarDetector(int _maxSize, int _responseThreshold,
int _lineThresholdProjected,
int _lineThresholdBinarized,
int _suppressNonmaxSize)
{
*(CvStarDetectorParams*)this = cvStarDetectorParams(_maxSize, _responseThreshold,
_lineThresholdProjected, _lineThresholdBinarized, _suppressNonmaxSize);
}
void StarDetector::operator()(const Mat& image, vector<KeyPoint>& keypoints) const
{
CvMat _image = image;
MemStorage storage(cvCreateMemStorage(0));
Seq<CvStarKeypoint> kp = cvGetStarKeypoints( &_image, storage, *(const CvStarDetectorParams*)this);
Seq<CvStarKeypoint>::iterator it = kp.begin();
keypoints.resize(kp.size());
size_t i, n = kp.size();
for( i = 0; i < n; i++, ++it )
{
const CvStarKeypoint& kpt = *it;
keypoints[i] = KeyPoint(kpt.pt, (float)kpt.size, -1.f, kpt.response, 0);
}
}
}
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