--- /dev/null
+/*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.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// 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 "_cv.h"
+
+static void
+icvComputeIntegralImages( const CvMat* _I, CvMat* _S, CvMat* _T, CvMat* _FT )
+{
+ int x, y, rows = _I->rows, cols = _I->cols;
+ const uchar* I = _I->data.ptr;
+ int *S = _S->data.i, *T = _T->data.i, *FT = _FT->data.i;
+ int istep = _I->step, step = _S->step/sizeof(S[0]);
+
+ assert( CV_MAT_TYPE(_I->type) == CV_8UC1 &&
+ CV_MAT_TYPE(_S->type) == CV_32SC1 &&
+ CV_ARE_TYPES_EQ(_S, _T) && CV_ARE_TYPES_EQ(_S, _FT) &&
+ CV_ARE_SIZES_EQ(_S, _T) && CV_ARE_SIZES_EQ(_S, _FT) &&
+ _S->step == _T->step && _S->step == _FT->step &&
+ _I->rows+1 == _S->rows && _I->cols+1 == _S->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;
+ __m128 absmask4 = _mm_set1_ps(absmask.f);
+#endif
+ CvStarFeature f[MAX_PATTERN];
+
+ CvMat *sum = 0, *tilted = 0, *flatTilted = 0;
+ int x, 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) );
+
+ for(; pairs[i][0] >= 0; i++ )
+ {
+ if( sizes0[pairs[i][0]] >= params->maxSize )
+ break;
+ }
+ 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
+ _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]));
+#else
+ }
+#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));
+ for( x = 0; x < cols; x++ )
+ {
+ r_ptr[x] = r_ptr2[x] = 0;
+ s_ptr[x] = s_ptr2[x] = 0;
+ }
+ }
+
+#ifdef _OPENMP
+ #pragma omp parallel for num_threads(nthreads) schedule(static)
+#endif
+ for( y = border; y < rows - border; y++ )
+ {
+ int x, i;
+ float* r_ptr = (float*)(responses->data.ptr + responses->step*y);
+ short* s_ptr = (short*)(sizes->data.ptr + sizes->step*y);
+ for( x = 0; x < border; x++ )
+ {
+ r_ptr[x] = r_ptr[cols - 1 - x] = 0;
+ s_ptr[x] = s_ptr[cols - 1 - x] = 0;
+ }
+
+#if CV_SSE2
+ 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, ¶ms );
+ if( border >= 0 )
+ icvStarDetectorSuppressNonmax( responses, sizes, keypoints, border, ¶ms );
+
+ 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);
+ }
+}
+
+}
projects / opencv / blobdiff