提交 e0489cb4 编写于 作者: A Alexander Alekhin

akaze: fix T-API interfaces, disable OpenCL code

- OpenCL kernels don't handle matrices properly. Assumptions are not checked.
- OpenCL/T-API integration is not correct.
上级 922ac1a1
...@@ -15,6 +15,10 @@ ...@@ -15,6 +15,10 @@
#include <iostream> #include <iostream>
#ifdef HAVE_OPENCL // OpenCL is not well supported
#undef HAVE_OPENCL
#endif
// Namespaces // Namespaces
namespace cv namespace cv
{ {
...@@ -251,38 +255,41 @@ private: ...@@ -251,38 +255,41 @@ private:
#ifdef HAVE_OPENCL #ifdef HAVE_OPENCL
static inline bool static inline bool
ocl_non_linear_diffusion_step(const UMat& Lt, const UMat& Lf, UMat& Lstep, float step_size) ocl_non_linear_diffusion_step(InputArray Lt_, InputArray Lf_, OutputArray Lstep_, float step_size)
{ {
if(!Lt.isContinuous()) if (!Lt_.isContinuous())
return false; return false;
UMat Lt = Lt_.getUMat(), Lf = Lf_.getUMat(), Lstep = Lstep_.getUMat();
size_t globalSize[] = {(size_t)Lt.cols, (size_t)Lt.rows}; size_t globalSize[] = {(size_t)Lt.cols, (size_t)Lt.rows};
ocl::Kernel ker("AKAZE_nld_step_scalar", ocl::features2d::akaze_oclsrc); ocl::Kernel ker("AKAZE_nld_step_scalar", ocl::features2d::akaze_oclsrc);
if( ker.empty() ) if (ker.empty())
return false; return false;
return ker.args( return ker.args(
ocl::KernelArg::ReadOnly(Lt), ocl::KernelArg::ReadOnly(Lt),
ocl::KernelArg::PtrReadOnly(Lf), ocl::KernelArg::PtrReadOnly(Lf),
ocl::KernelArg::PtrWriteOnly(Lstep), ocl::KernelArg::PtrWriteOnly(Lstep),
step_size).run(2, globalSize, 0, true); step_size)
.run(2, globalSize, 0, true);
} }
#endif // HAVE_OPENCL #endif // HAVE_OPENCL
static inline void static inline void
non_linear_diffusion_step(const UMat& Lt, const UMat& Lf, UMat& Lstep, float step_size) non_linear_diffusion_step(InputArray Lt, InputArray Lf, OutputArray Lstep, float step_size)
{ {
CV_INSTRUMENT_REGION() CV_INSTRUMENT_REGION()
Lstep.create(Lt.size(), Lt.type()); Lstep.create(Lt.size(), Lt.type());
CV_OCL_RUN(true, ocl_non_linear_diffusion_step(Lt, Lf, Lstep, step_size)); #ifdef HAVE_OPENCL
CV_OCL_RUN(OCL_PERFORMANCE_CHECK(Lstep.isUMat()), ocl_non_linear_diffusion_step(Lt, Lf, Lstep, step_size));
#endif
// when on CPU UMats should be already allocated on CPU so getMat here is basicallly no-op Mat Mstep = Lstep.getMat();
Mat Mstep = Lstep.getMat(ACCESS_WRITE); parallel_for_(Range(0, Lt.rows()), NonLinearScalarDiffusionStep(Lt.getMat(), Lf.getMat(), Mstep, step_size));
parallel_for_(Range(0, Lt.rows), NonLinearScalarDiffusionStep(Lt.getMat(ACCESS_READ),
Lf.getMat(ACCESS_READ), Mstep, step_size));
} }
/** /**
...@@ -347,25 +354,28 @@ compute_kcontrast(const cv::Mat& Lx, const cv::Mat& Ly, float perc, int nbins) ...@@ -347,25 +354,28 @@ compute_kcontrast(const cv::Mat& Lx, const cv::Mat& Ly, float perc, int nbins)
#ifdef HAVE_OPENCL #ifdef HAVE_OPENCL
static inline bool static inline bool
ocl_pm_g2(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast) ocl_pm_g2(InputArray Lx_, InputArray Ly_, OutputArray Lflow_, float kcontrast)
{ {
int total = Lx.rows * Lx.cols; UMat Lx = Lx_.getUMat(), Ly = Ly_.getUMat(), Lflow = Lflow_.getUMat();
size_t globalSize[] = {(size_t)total};
int total = Lx.rows * Lx.cols;
ocl::Kernel ker("AKAZE_pm_g2", ocl::features2d::akaze_oclsrc); size_t globalSize[] = {(size_t)total};
if( ker.empty() )
return false; ocl::Kernel ker("AKAZE_pm_g2", ocl::features2d::akaze_oclsrc);
if (ker.empty())
return ker.args( return false;
ocl::KernelArg::PtrReadOnly(Lx),
ocl::KernelArg::PtrReadOnly(Ly), return ker.args(
ocl::KernelArg::PtrWriteOnly(Lflow), ocl::KernelArg::PtrReadOnly(Lx),
kcontrast, total).run(1, globalSize, 0, true); ocl::KernelArg::PtrReadOnly(Ly),
ocl::KernelArg::PtrWriteOnly(Lflow),
kcontrast, total)
.run(1, globalSize, 0, true);
} }
#endif // HAVE_OPENCL #endif // HAVE_OPENCL
static inline void static inline void
compute_diffusivity(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast, int diffusivity) compute_diffusivity(InputArray Lx, InputArray Ly, OutputArray Lflow, float kcontrast, int diffusivity)
{ {
CV_INSTRUMENT_REGION() CV_INSTRUMENT_REGION()
...@@ -376,7 +386,9 @@ compute_diffusivity(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast ...@@ -376,7 +386,9 @@ compute_diffusivity(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast
pm_g1(Lx, Ly, Lflow, kcontrast); pm_g1(Lx, Ly, Lflow, kcontrast);
break; break;
case KAZE::DIFF_PM_G2: case KAZE::DIFF_PM_G2:
CV_OCL_RUN(true, ocl_pm_g2(Lx, Ly, Lflow, kcontrast)); #ifdef HAVE_OPENCL
CV_OCL_RUN(OCL_PERFORMANCE_CHECK(Lflow.isUMat()), ocl_pm_g2(Lx, Ly, Lflow, kcontrast));
#endif
pm_g2(Lx, Ly, Lflow, kcontrast); pm_g2(Lx, Ly, Lflow, kcontrast);
break; break;
case KAZE::DIFF_WEICKERT: case KAZE::DIFF_WEICKERT:
...@@ -391,32 +403,6 @@ compute_diffusivity(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast ...@@ -391,32 +403,6 @@ compute_diffusivity(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast
} }
} }
/**
* @brief Fetches pyramid from the gpu.
* @details Setups mapping for matrices that might be probably on the GPU, if the
* code executes with OpenCL. This will setup MLx, MLy, Mdet members in the pyramid with
* mapping to respective UMats. This must be called before CPU-only parts of AKAZE, that work
* only on these Mats.
*
* This prevents mapping/unmapping overhead (and possible uploads/downloads) that would occur, if
* we just create Mats from UMats each time we need it later. This has devastating effects on OCL
* performace.
*
* @param evolution Pyramid to download
*/
static inline void downloadPyramid(std::vector<Evolution>& evolution)
{
CV_INSTRUMENT_REGION()
for (size_t i = 0; i < evolution.size(); ++i) {
Evolution& e = evolution[i];
e.Mx = e.Lx.getMat(ACCESS_READ);
e.My = e.Ly.getMat(ACCESS_READ);
e.Mt = e.Lt.getMat(ACCESS_READ);
e.Mdet = e.Ldet.getMat(ACCESS_READ);
}
}
/** /**
* @brief This method creates the nonlinear scale space for a given image * @brief This method creates the nonlinear scale space for a given image
* @param img Input image for which the nonlinear scale space needs to be created * @param img Input image for which the nonlinear scale space needs to be created
...@@ -435,12 +421,11 @@ void AKAZEFeatures::Create_Nonlinear_Scale_Space(InputArray img) ...@@ -435,12 +421,11 @@ void AKAZEFeatures::Create_Nonlinear_Scale_Space(InputArray img)
if (evolution_.size() == 1) { if (evolution_.size() == 1) {
// we don't need to compute kcontrast factor // we don't need to compute kcontrast factor
Compute_Determinant_Hessian_Response(); Compute_Determinant_Hessian_Response();
downloadPyramid(evolution_);
return; return;
} }
// derivatives, flow and diffusion step // derivatives, flow and diffusion step
UMat Lx, Ly, Lsmooth, Lflow, Lstep; Mat Lx, Ly, Lsmooth, Lflow, Lstep;
// compute derivatives for computing k contrast // compute derivatives for computing k contrast
GaussianBlur(img, Lsmooth, Size(5, 5), 1.0f, 1.0f, BORDER_REPLICATE); GaussianBlur(img, Lsmooth, Size(5, 5), 1.0f, 1.0f, BORDER_REPLICATE);
...@@ -448,8 +433,7 @@ void AKAZEFeatures::Create_Nonlinear_Scale_Space(InputArray img) ...@@ -448,8 +433,7 @@ void AKAZEFeatures::Create_Nonlinear_Scale_Space(InputArray img)
Scharr(Lsmooth, Ly, CV_32F, 0, 1, 1, 0, BORDER_DEFAULT); Scharr(Lsmooth, Ly, CV_32F, 0, 1, 1, 0, BORDER_DEFAULT);
Lsmooth.release(); Lsmooth.release();
// compute the kcontrast factor // compute the kcontrast factor
float kcontrast = compute_kcontrast(Lx.getMat(ACCESS_READ), Ly.getMat(ACCESS_READ), float kcontrast = compute_kcontrast(Lx, Ly, options_.kcontrast_percentile, options_.kcontrast_nbins);
options_.kcontrast_percentile, options_.kcontrast_nbins);
// Now generate the rest of evolution levels // Now generate the rest of evolution levels
for (size_t i = 1; i < evolution_.size(); i++) { for (size_t i = 1; i < evolution_.size(); i++) {
...@@ -483,31 +467,30 @@ void AKAZEFeatures::Create_Nonlinear_Scale_Space(InputArray img) ...@@ -483,31 +467,30 @@ void AKAZEFeatures::Create_Nonlinear_Scale_Space(InputArray img)
} }
Compute_Determinant_Hessian_Response(); Compute_Determinant_Hessian_Response();
downloadPyramid(evolution_);
return;
} }
/* ************************************************************************* */ /* ************************************************************************* */
#ifdef HAVE_OPENCL #ifdef HAVE_OPENCL
static inline bool static inline bool
ocl_compute_determinant(const UMat& Lxx, const UMat& Lxy, const UMat& Lyy, ocl_compute_determinant(InputArray Lxx_, InputArray Lxy_, InputArray Lyy_, OutputArray Ldet_, float sigma)
UMat& Ldet, float sigma)
{ {
const int total = Lxx.rows * Lxx.cols; UMat Lxx = Lxx_.getUMat(), Lxy = Lxy_.getUMat(), Lyy = Lyy_.getUMat(), Ldet = Ldet_.getUMat();
size_t globalSize[] = {(size_t)total};
const int total = Lxx.rows * Lxx.cols;
ocl::Kernel ker("AKAZE_compute_determinant", ocl::features2d::akaze_oclsrc); size_t globalSize[] = {(size_t)total};
if( ker.empty() )
return false; ocl::Kernel ker("AKAZE_compute_determinant", ocl::features2d::akaze_oclsrc);
if (ker.empty())
return ker.args( return false;
ocl::KernelArg::PtrReadOnly(Lxx),
ocl::KernelArg::PtrReadOnly(Lxy), return ker.args(
ocl::KernelArg::PtrReadOnly(Lyy), ocl::KernelArg::PtrReadOnly(Lxx),
ocl::KernelArg::PtrWriteOnly(Ldet), ocl::KernelArg::PtrReadOnly(Lxy),
sigma, total).run(1, globalSize, 0, true); ocl::KernelArg::PtrReadOnly(Lyy),
ocl::KernelArg::PtrWriteOnly(Ldet),
sigma, total)
.run(1, globalSize, 0, true);
} }
#endif // HAVE_OPENCL #endif // HAVE_OPENCL
...@@ -521,27 +504,30 @@ ocl_compute_determinant(const UMat& Lxx, const UMat& Lxy, const UMat& Lyy, ...@@ -521,27 +504,30 @@ ocl_compute_determinant(const UMat& Lxx, const UMat& Lxy, const UMat& Lyy,
* @param Ldet output determinant * @param Ldet output determinant
* @param sigma determinant will be scaled by this sigma * @param sigma determinant will be scaled by this sigma
*/ */
static inline void compute_determinant(const UMat& Lxx, const UMat& Lxy, const UMat& Lyy, static inline void compute_determinant(InputArray Lxx, InputArray Lxy, InputArray Lyy, OutputArray Ldet, float sigma)
UMat& Ldet, float sigma)
{ {
CV_INSTRUMENT_REGION() CV_INSTRUMENT_REGION()
Ldet.create(Lxx.size(), Lxx.type()); Ldet.create(Lxx.size(), Lxx.type());
CV_OCL_RUN(true, ocl_compute_determinant(Lxx, Lxy, Lyy, Ldet, sigma)); #ifdef HAVE_OPENCL
CV_OCL_RUN(OCL_PERFORMANCE_CHECK(Ldet.isUMat()), ocl_compute_determinant(Lxx, Lxy, Lyy, Ldet, sigma));
// output determinant #endif
Mat Mxx = Lxx.getMat(ACCESS_READ), Mxy = Lxy.getMat(ACCESS_READ), Myy = Lyy.getMat(ACCESS_READ);
Mat Mdet = Ldet.getMat(ACCESS_WRITE);
float *lxx = Mxx.ptr<float>();
float *lxy = Mxy.ptr<float>();
float *lyy = Myy.ptr<float>();
float *ldet = Mdet.ptr<float>();
const int total = Lxx.cols * Lxx.rows;
for (int j = 0; j < total; j++) {
ldet[j] = (lxx[j] * lyy[j] - lxy[j] * lxy[j]) * sigma;
}
// output determinant
Mat Mxx = Lxx.getMat(), Mxy = Lxy.getMat(), Myy = Lyy.getMat(), Mdet = Ldet.getMat();
const int W = Mxx.cols, H = Mxx.rows;
for (int y = 0; y < H; y++)
{
float *lxx = Mxx.ptr<float>(y);
float *lxy = Mxy.ptr<float>(y);
float *lyy = Myy.ptr<float>(y);
float *ldet = Mdet.ptr<float>(y);
for (int x = 0; x < W; x++)
{
ldet[x] = (lxx[x] * lyy[x] - lxy[x] * lxy[x]) * sigma;
}
}
} }
class DeterminantHessianResponse : public ParallelLoopBody class DeterminantHessianResponse : public ParallelLoopBody
...@@ -554,7 +540,7 @@ public: ...@@ -554,7 +540,7 @@ public:
void operator()(const Range& range) const void operator()(const Range& range) const
{ {
UMat Lxx, Lxy, Lyy; Mat Lxx, Lxy, Lyy;
for (int i = range.start; i < range.end; i++) for (int i = range.start; i < range.end; i++)
{ {
...@@ -670,16 +656,16 @@ public: ...@@ -670,16 +656,16 @@ public:
const Evolution &e = (*evolution_)[i]; const Evolution &e = (*evolution_)[i];
Mat &kpts = (*keypoints_by_layers_)[i]; Mat &kpts = (*keypoints_by_layers_)[i];
// this mask will hold positions of keypoints in this level // this mask will hold positions of keypoints in this level
kpts = Mat::zeros(e.Mdet.size(), CV_8UC1); kpts = Mat::zeros(e.Ldet.size(), CV_8UC1);
// if border is too big we shouldn't search any keypoints // if border is too big we shouldn't search any keypoints
if (e.border + 1 >= e.Ldet.rows) if (e.border + 1 >= e.Ldet.rows)
continue; continue;
const float * prev = e.Mdet.ptr<float>(e.border - 1); const float * prev = e.Ldet.ptr<float>(e.border - 1);
const float * curr = e.Mdet.ptr<float>(e.border ); const float * curr = e.Ldet.ptr<float>(e.border );
const float * next = e.Mdet.ptr<float>(e.border + 1); const float * next = e.Ldet.ptr<float>(e.border + 1);
const float * ldet = e.Mdet.ptr<float>(); const float * ldet = e.Ldet.ptr<float>();
uchar *mask = kpts.ptr<uchar>(); uchar *mask = kpts.ptr<uchar>();
const int search_radius = e.sigma_size; // size of keypoint in this level const int search_radius = e.sigma_size; // size of keypoint in this level
...@@ -743,8 +729,8 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<Mat>& keypoints_by_laye ...@@ -743,8 +729,8 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<Mat>& keypoints_by_laye
const Mat &keypoints = keypoints_by_layers[i]; const Mat &keypoints = keypoints_by_layers[i];
const uchar *const kpts = keypoints_by_layers[i].ptr<uchar>(); const uchar *const kpts = keypoints_by_layers[i].ptr<uchar>();
uchar *const kpts_prev = keypoints_by_layers[i-1].ptr<uchar>(); uchar *const kpts_prev = keypoints_by_layers[i-1].ptr<uchar>();
const float *const ldet = evolution_[i].Mdet.ptr<float>(); const float *const ldet = evolution_[i].Ldet.ptr<float>();
const float *const ldet_prev = evolution_[i-1].Mdet.ptr<float>(); const float *const ldet_prev = evolution_[i-1].Ldet.ptr<float>();
// ratios are just powers of 2 // ratios are just powers of 2
const int diff_ratio = (int)evolution_[i].octave_ratio / (int)evolution_[i-1].octave_ratio; const int diff_ratio = (int)evolution_[i].octave_ratio / (int)evolution_[i-1].octave_ratio;
const int search_radius = evolution_[i].sigma_size * diff_ratio; // size of keypoint in this level const int search_radius = evolution_[i].sigma_size * diff_ratio; // size of keypoint in this level
...@@ -775,8 +761,8 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<Mat>& keypoints_by_laye ...@@ -775,8 +761,8 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<Mat>& keypoints_by_laye
const Mat &keypoints = keypoints_by_layers[i]; const Mat &keypoints = keypoints_by_layers[i];
const uchar *const kpts = keypoints_by_layers[i].ptr<uchar>(); const uchar *const kpts = keypoints_by_layers[i].ptr<uchar>();
uchar *const kpts_next = keypoints_by_layers[i+1].ptr<uchar>(); uchar *const kpts_next = keypoints_by_layers[i+1].ptr<uchar>();
const float *const ldet = evolution_[i].Mdet.ptr<float>(); const float *const ldet = evolution_[i].Ldet.ptr<float>();
const float *const ldet_next = evolution_[i+1].Mdet.ptr<float>(); const float *const ldet_next = evolution_[i+1].Ldet.ptr<float>();
// ratios are just powers of 2, i+1 ratio is always greater or equal to i // ratios are just powers of 2, i+1 ratio is always greater or equal to i
const int diff_ratio = (int)evolution_[i+1].octave_ratio / (int)evolution_[i].octave_ratio; const int diff_ratio = (int)evolution_[i+1].octave_ratio / (int)evolution_[i].octave_ratio;
const int search_radius = evolution_[i+1].sigma_size; // size of keypoints in upper level const int search_radius = evolution_[i+1].sigma_size; // size of keypoints in upper level
...@@ -814,7 +800,7 @@ void AKAZEFeatures::Do_Subpixel_Refinement( ...@@ -814,7 +800,7 @@ void AKAZEFeatures::Do_Subpixel_Refinement(
for (size_t i = 0; i < keypoints_by_layers.size(); i++) { for (size_t i = 0; i < keypoints_by_layers.size(); i++) {
const Evolution &e = evolution_[i]; const Evolution &e = evolution_[i];
const float * const ldet = e.Mdet.ptr<float>(); const float * const ldet = e.Ldet.ptr<float>();
const float ratio = e.octave_ratio; const float ratio = e.octave_ratio;
const int cols = e.Ldet.cols; const int cols = e.Ldet.cols;
const Mat& keypoints = keypoints_by_layers[i]; const Mat& keypoints = keypoints_by_layers[i];
...@@ -1308,7 +1294,7 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu ...@@ -1308,7 +1294,7 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
// Sample derivatives responses for the points within radius of 6*scale // Sample derivatives responses for the points within radius of 6*scale
const int ang_size = 109; const int ang_size = 109;
float resX[ang_size], resY[ang_size]; float resX[ang_size], resY[ang_size];
Sample_Derivative_Response_Radius6(e.Mx, e.My, x0, y0, scale, resX, resY); Sample_Derivative_Response_Radius6(e.Lx, e.Ly, x0, y0, scale, resX, resY);
// Compute the angle of each gradient vector // Compute the angle of each gradient vector
float Ang[ang_size]; float Ang[ang_size];
...@@ -1445,8 +1431,8 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const ...@@ -1445,8 +1431,8 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
ratio = (float)(1 << kpt.octave); ratio = (float)(1 << kpt.octave);
scale = cvRound(0.5f*kpt.size / ratio); scale = cvRound(0.5f*kpt.size / ratio);
const int level = kpt.class_id; const int level = kpt.class_id;
Mat Lx = evolution[level].Mx; const Mat Lx = evolution[level].Lx;
Mat Ly = evolution[level].My; const Mat Ly = evolution[level].Ly;
yf = kpt.pt.y / ratio; yf = kpt.pt.y / ratio;
xf = kpt.pt.x / ratio; xf = kpt.pt.x / ratio;
...@@ -1575,8 +1561,8 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f ...@@ -1575,8 +1561,8 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
scale = cvRound(0.5f*kpt.size / ratio); scale = cvRound(0.5f*kpt.size / ratio);
angle = kpt.angle * static_cast<float>(CV_PI / 180.f); angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
const int level = kpt.class_id; const int level = kpt.class_id;
Mat Lx = evolution[level].Mx; const Mat Lx = evolution[level].Lx;
Mat Ly = evolution[level].My; const Mat Ly = evolution[level].Ly;
yf = kpt.pt.y / ratio; yf = kpt.pt.y / ratio;
xf = kpt.pt.x / ratio; xf = kpt.pt.x / ratio;
co = cos(angle); co = cos(angle);
...@@ -1708,9 +1694,9 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons ...@@ -1708,9 +1694,9 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
ratio = (float)(1 << kpt.octave); ratio = (float)(1 << kpt.octave);
scale = cvRound(0.5f*kpt.size / ratio); scale = cvRound(0.5f*kpt.size / ratio);
const int level = kpt.class_id; const int level = kpt.class_id;
Mat Lx = evolution[level].Mx; const Mat Lx = evolution[level].Lx;
Mat Ly = evolution[level].My; const Mat Ly = evolution[level].Ly;
Mat Lt = evolution[level].Mt; const Mat Lt = evolution[level].Lt;
yf = kpt.pt.y / ratio; yf = kpt.pt.y / ratio;
xf = kpt.pt.x / ratio; xf = kpt.pt.x / ratio;
...@@ -1795,9 +1781,9 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Fill_Values(float* values, int sample_st ...@@ -1795,9 +1781,9 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Fill_Values(float* values, int sample_st
int pattern_size = options_->descriptor_pattern_size; int pattern_size = options_->descriptor_pattern_size;
int chan = options_->descriptor_channels; int chan = options_->descriptor_channels;
int valpos = 0; int valpos = 0;
Mat Lx = evolution[level].Mx; const Mat Lx = evolution[level].Lx;
Mat Ly = evolution[level].My; const Mat Ly = evolution[level].Ly;
Mat Lt = evolution[level].Mt; const Mat Lt = evolution[level].Lt;
for (int i = -pattern_size; i < pattern_size; i += sample_step) { for (int i = -pattern_size; i < pattern_size; i += sample_step) {
for (int j = -pattern_size; j < pattern_size; j += sample_step) { for (int j = -pattern_size; j < pattern_size; j += sample_step) {
...@@ -1944,9 +1930,9 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint& ...@@ -1944,9 +1930,9 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
int scale = cvRound(0.5f*kpt.size / ratio); int scale = cvRound(0.5f*kpt.size / ratio);
float angle = kpt.angle * static_cast<float>(CV_PI / 180.f); float angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
const int level = kpt.class_id; const int level = kpt.class_id;
Mat Lx = evolution[level].Mx; const Mat Lx = evolution[level].Lx;
Mat Ly = evolution[level].My; const Mat Ly = evolution[level].Ly;
Mat Lt = evolution[level].Mt; const Mat Lt = evolution[level].Lt;
float yf = kpt.pt.y / ratio; float yf = kpt.pt.y / ratio;
float xf = kpt.pt.x / ratio; float xf = kpt.pt.x / ratio;
float co = cos(angle); float co = cos(angle);
...@@ -2051,9 +2037,9 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset( ...@@ -2051,9 +2037,9 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
float ratio = (float)(1 << kpt.octave); float ratio = (float)(1 << kpt.octave);
int scale = cvRound(0.5f*kpt.size / ratio); int scale = cvRound(0.5f*kpt.size / ratio);
const int level = kpt.class_id; const int level = kpt.class_id;
Mat Lx = evolution[level].Mx; const Mat Lx = evolution[level].Lx;
Mat Ly = evolution[level].My; const Mat Ly = evolution[level].Ly;
Mat Lt = evolution[level].Mt; const Mat Lt = evolution[level].Lt;
float yf = kpt.pt.y / ratio; float yf = kpt.pt.y / ratio;
float xf = kpt.pt.x / ratio; float xf = kpt.pt.x / ratio;
......
...@@ -29,15 +29,10 @@ struct Evolution ...@@ -29,15 +29,10 @@ struct Evolution
border = 0; border = 0;
} }
UMat Lx, Ly; ///< First order spatial derivatives Mat Lx, Ly; ///< First order spatial derivatives
UMat Lt; ///< Evolution image Mat Lt; ///< Evolution image
UMat Lsmooth; ///< Smoothed image, used only for computing determinant, released afterwards Mat Lsmooth; ///< Smoothed image, used only for computing determinant, released afterwards
UMat Ldet; ///< Detector response Mat Ldet; ///< Detector response
// the same as above, holding CPU mapping to UMats above
Mat Mx, My;
Mat Mt;
Mat Mdet;
Size size; ///< Size of the layer Size size; ///< Size of the layer
float etime; ///< Evolution time float etime; ///< Evolution time
......
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