From: Ievgen Khvedchenia Date: Mon, 5 May 2014 18:48:54 +0000 (+0300) Subject: Clean-up from the dead code X-Git-Tag: submit/tizen_ivi/20141117.190038~2^2~400^2~10 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=220de14077c6164891ff9e4f97719dc598ceb050;p=profile%2Fivi%2Fopencv.git Clean-up from the dead code --- diff --git a/modules/features2d/src/akaze/AKAZEFeatures.cpp b/modules/features2d/src/akaze/AKAZEFeatures.cpp index 7400b2a..b1a4ba5 100644 --- a/modules/features2d/src/akaze/AKAZEFeatures.cpp +++ b/modules/features2d/src/akaze/AKAZEFeatures.cpp @@ -93,17 +93,9 @@ void AKAZEFeatures::Allocate_Memory_Evolution(void) { * @param img Input image for which the nonlinear scale space needs to be created * @return 0 if the nonlinear scale space was created successfully, -1 otherwise */ -int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img) { - - //double t1 = 0.0, t2 = 0.0; +int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img) +{ CV_Assert(evolution_.size() > 0); - //if (evolution_.size() == 0) { - // cerr << "Error generating the nonlinear scale space!!" << endl; - // cerr << "Firstly you need to call AKAZEFeatures::Allocate_Memory_Evolution()" << endl; - // return -1; - //} - - //t1 = cv::getTickCount(); // Copy the original image to the first level of the evolution img.copyTo(evolution_[0].Lt); @@ -113,9 +105,6 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img) { // First compute the kcontrast factor options_.kcontrast = compute_k_percentile(img, options_.kcontrast_percentile, 1.0f, options_.kcontrast_nbins, 0, 0); - //t2 = cv::getTickCount(); - //timing_.kcontrast = 1000.0*(t2 - t1) / cv::getTickFrequency(); - // Now generate the rest of evolution levels for (size_t i = 1; i < evolution_.size(); i++) { @@ -158,9 +147,6 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img) { } } - //t2 = cv::getTickCount(); - //timing_.scale = 1000.0*(t2 - t1) / cv::getTickFrequency(); - return 0; } @@ -169,20 +155,13 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img) { * @brief This method selects interesting keypoints through the nonlinear scale space * @param kpts Vector of detected keypoints */ -void AKAZEFeatures::Feature_Detection(std::vector& kpts) { - - //double t1 = 0.0, t2 = 0.0; - - //t1 = cv::getTickCount(); - +void AKAZEFeatures::Feature_Detection(std::vector& kpts) +{ kpts.clear(); Compute_Determinant_Hessian_Response(); Find_Scale_Space_Extrema(kpts); Do_Subpixel_Refinement(kpts); - - //t2 = cv::getTickCount(); - //timing_.detector = 1000.0*(t2 - t1) / cv::getTickFrequency(); } /* ************************************************************************* */ @@ -228,34 +207,10 @@ private: /** * @brief This method computes the multiscale derivatives for the nonlinear scale space */ -void AKAZEFeatures::Compute_Multiscale_Derivatives(void) { - - //double t1 = 0.0, t2 = 0.0; - - //t1 = cv::getTickCount(); - - cv::parallel_for_(cv::Range(0, (int)evolution_.size()), MultiscaleDerivativesInvoker(evolution_, options_)); - /* - for (int i = 0; i < (int)(evolution_.size()); i++) { - - float ratio = pow(2.f, (float)evolution_[i].octave); - int sigma_size_ = fRound(evolution_[i].esigma*options_.derivative_factor / ratio); - - compute_scharr_derivatives(evolution_[i].Lsmooth, evolution_[i].Lx, 1, 0, sigma_size_); - compute_scharr_derivatives(evolution_[i].Lsmooth, evolution_[i].Ly, 0, 1, sigma_size_); - compute_scharr_derivatives(evolution_[i].Lx, evolution_[i].Lxx, 1, 0, sigma_size_); - compute_scharr_derivatives(evolution_[i].Ly, evolution_[i].Lyy, 0, 1, sigma_size_); - compute_scharr_derivatives(evolution_[i].Lx, evolution_[i].Lxy, 0, 1, sigma_size_); - - evolution_[i].Lx = evolution_[i].Lx*((sigma_size_)); - evolution_[i].Ly = evolution_[i].Ly*((sigma_size_)); - evolution_[i].Lxx = evolution_[i].Lxx*((sigma_size_)*(sigma_size_)); - evolution_[i].Lxy = evolution_[i].Lxy*((sigma_size_)*(sigma_size_)); - evolution_[i].Lyy = evolution_[i].Lyy*((sigma_size_)*(sigma_size_)); - } - */ - //t2 = cv::getTickCount(); - //timing_.derivatives = 1000.0*(t2 - t1) / cv::getTickFrequency(); +void AKAZEFeatures::Compute_Multiscale_Derivatives(void) +{ + cv::parallel_for_(cv::Range(0, (int)evolution_.size()), + MultiscaleDerivativesInvoker(evolution_, options_)); } /* ************************************************************************* */ @@ -268,14 +223,12 @@ void AKAZEFeatures::Compute_Determinant_Hessian_Response(void) { // Firstly compute the multiscale derivatives Compute_Multiscale_Derivatives(); - for (size_t i = 0; i < evolution_.size(); i++) { - - //if (options_.verbosity == true) { - // cout << "Computing detector response. Determinant of Hessian. Evolution time: " << evolution_[i].etime << endl; - //} - - for (int ix = 0; ix < evolution_[i].Ldet.rows; ix++) { - for (int jx = 0; jx < evolution_[i].Ldet.cols; jx++) { + for (size_t i = 0; i < evolution_.size(); i++) + { + for (int ix = 0; ix < evolution_[i].Ldet.rows; ix++) + { + for (int jx = 0; jx < evolution_[i].Ldet.cols; jx++) + { float lxx = *(evolution_[i].Lxx.ptr(ix)+jx); float lxy = *(evolution_[i].Lxy.ptr(ix)+jx); float lyy = *(evolution_[i].Lyy.ptr(ix)+jx); @@ -290,9 +243,9 @@ void AKAZEFeatures::Compute_Determinant_Hessian_Response(void) { * @brief This method finds extrema in the nonlinear scale space * @param kpts Vector of detected keypoints */ -void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector& kpts) { +void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector& kpts) +{ - //double t1 = 0.0, t2 = 0.0; float value = 0.0; float dist = 0.0, ratio = 0.0, smax = 0.0; int npoints = 0, id_repeated = 0; @@ -310,8 +263,6 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector& kpts) { smax = 12.0f*sqrtf(2.0f); } - //t1 = cv::getTickCount(); - for (size_t i = 0; i < evolution_.size(); i++) { for (int ix = 1; ix < evolution_[i].Ldet.rows - 1; ix++) { for (int jx = 1; jx < evolution_[i].Ldet.cols - 1; jx++) { @@ -415,9 +366,6 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector& kpts) { if (is_repeated == false) kpts.push_back(pt); } - - //t2 = cv::getTickCount(); - //timing_.extrema = 1000.0*(t2 - t1) / cv::getTickFrequency(); } /* ************************************************************************* */ @@ -425,9 +373,8 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector& kpts) { * @brief This method performs subpixel refinement of the detected keypoints * @param kpts Vector of detected keypoints */ -void AKAZEFeatures::Do_Subpixel_Refinement(std::vector& kpts) { - - //double t1 = 0.0, t2 = 0.0; +void AKAZEFeatures::Do_Subpixel_Refinement(std::vector& kpts) +{ float Dx = 0.0, Dy = 0.0, ratio = 0.0; float Dxx = 0.0, Dyy = 0.0, Dxy = 0.0; int x = 0, y = 0; @@ -435,8 +382,6 @@ void AKAZEFeatures::Do_Subpixel_Refinement(std::vector& kpts) { cv::Mat b = cv::Mat::zeros(2, 1, CV_32F); cv::Mat dst = cv::Mat::zeros(2, 1, CV_32F); - //t1 = cv::getTickCount(); - for (size_t i = 0; i < kpts.size(); i++) { ratio = pow(2.f, kpts[i].octave); x = fRound(kpts[i].pt.x / ratio); @@ -487,9 +432,6 @@ void AKAZEFeatures::Do_Subpixel_Refinement(std::vector& kpts) { i--; } } - - //t2 = cv::getTickCount(); - //timing_.subpixel = 1000.0*(t2 - t1) / cv::getTickFrequency(); } /* ************************************************************************* */ @@ -739,12 +681,8 @@ private: * @param kpts Vector of detected keypoints * @param desc Matrix to store the descriptors */ -void AKAZEFeatures::Compute_Descriptors(std::vector& kpts, cv::Mat& desc) { - - //double t1 = 0.0, t2 = 0.0; - - //t1 = cv::getTickCount(); - +void AKAZEFeatures::Compute_Descriptors(std::vector& kpts, cv::Mat& desc) +{ // Allocate memory for the matrix with the descriptors if (options_.descriptor < MLDB_UPRIGHT) { desc = cv::Mat::zeros((int)kpts.size(), 64, CV_32FC1); @@ -766,39 +704,21 @@ void AKAZEFeatures::Compute_Descriptors(std::vector& kpts, cv::Mat case SURF_UPRIGHT: // Upright descriptors, not invariant to rotation { cv::parallel_for_(cv::Range(0, (int)kpts.size()), SURF_Descriptor_Upright_64_Invoker(kpts, desc, evolution_)); - - //for (int i = 0; i < (int)(kpts.size()); i++) { - // Get_SURF_Descriptor_Upright_64(kpts[i], desc.ptr(i)); - //} } break; case SURF: { cv::parallel_for_(cv::Range(0, (int)kpts.size()), SURF_Descriptor_64_Invoker(kpts, desc, evolution_)); - - //for (int i = 0; i < (int)(kpts.size()); i++) { - // Compute_Main_Orientation(kpts[i]); - // Get_SURF_Descriptor_64(kpts[i], desc.ptr(i)); - //} } break; case MSURF_UPRIGHT: // Upright descriptors, not invariant to rotation { cv::parallel_for_(cv::Range(0, (int)kpts.size()), MSURF_Upright_Descriptor_64_Invoker(kpts, desc, evolution_)); - - //for (int i = 0; i < (int)(kpts.size()); i++) { - // Get_MSURF_Upright_Descriptor_64(kpts[i], desc.ptr(i)); - //} } break; case MSURF: { cv::parallel_for_(cv::Range(0, (int)kpts.size()), MSURF_Descriptor_64_Invoker(kpts, desc, evolution_)); - - //for (int i = 0; i < (int)(kpts.size()); i++) { - // Compute_Main_Orientation(kpts[i]); - // Get_MSURF_Descriptor_64(kpts[i], desc.ptr(i)); - //} } break; case MLDB_UPRIGHT: // Upright descriptors, not invariant to rotation @@ -807,13 +727,6 @@ void AKAZEFeatures::Compute_Descriptors(std::vector& kpts, cv::Mat cv::parallel_for_(cv::Range(0, (int)kpts.size()), Upright_MLDB_Full_Descriptor_Invoker(kpts, desc, evolution_, options_)); else cv::parallel_for_(cv::Range(0, (int)kpts.size()), Upright_MLDB_Descriptor_Subset_Invoker(kpts, desc, evolution_, options_, descriptorSamples_, descriptorBits_)); - - //for (int i = 0; i < (int)(kpts.size()); i++) { - // if (options_.descriptor_size == 0) - // Get_Upright_MLDB_Full_Descriptor(kpts[i], desc.ptr(i)); - // else - // Get_Upright_MLDB_Descriptor_Subset(kpts[i], desc.ptr(i)); - //} } break; case MLDB: @@ -822,20 +735,9 @@ void AKAZEFeatures::Compute_Descriptors(std::vector& kpts, cv::Mat cv::parallel_for_(cv::Range(0, (int)kpts.size()), MLDB_Full_Descriptor_Invoker(kpts, desc, evolution_, options_)); else cv::parallel_for_(cv::Range(0, (int)kpts.size()), MLDB_Descriptor_Subset_Invoker(kpts, desc, evolution_, options_, descriptorSamples_, descriptorBits_)); - - //for (int i = 0; i < (int)(kpts.size()); i++) { - // Compute_Main_Orientation(kpts[i]); - // if (options_.descriptor_size == 0) - // Get_MLDB_Full_Descriptor(kpts[i], desc.ptr(i)); - // else - // Get_MLDB_Descriptor_Subset(kpts[i], desc.ptr(i)); - //} } break; } - - //t2 = cv::getTickCount(); - //timing_.descriptor = 1000.0*(t2 - t1) / cv::getTickFrequency(); } /* ************************************************************************* */ @@ -2047,22 +1949,6 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset( } } - - -/* ************************************************************************* */ -/** - * @brief This method displays the computation times - */ -//void AKAZEFeatures::Show_Computation_Times() const { -// cout << "(*) Time Scale Space: " << timing_.scale << endl; -// cout << "(*) Time Detector: " << timing_.detector << endl; -// cout << " - Time Derivatives: " << timing_.derivatives << endl; -// cout << " - Time Extrema: " << timing_.extrema << endl; -// cout << " - Time Subpixel: " << timing_.subpixel << endl; -// cout << "(*) Time Descriptor: " << timing_.descriptor << endl; -// cout << endl; -//} - /* ************************************************************************* */ /** * @brief This function computes a (quasi-random) list of bits to be taken diff --git a/modules/features2d/src/akaze/AKAZEFeatures.h b/modules/features2d/src/akaze/AKAZEFeatures.h index 4bebc16..302ef0d 100644 --- a/modules/features2d/src/akaze/AKAZEFeatures.h +++ b/modules/features2d/src/akaze/AKAZEFeatures.h @@ -51,30 +51,6 @@ public: void Compute_Descriptors(std::vector& kpts, cv::Mat& desc); static void Compute_Main_Orientation(cv::KeyPoint& kpt, const std::vector& evolution_); - - // SURF Pattern Descriptor - //void Get_SURF_Descriptor_Upright_64(const cv::KeyPoint& kpt, float* desc) const; - //void Get_SURF_Descriptor_64(const cv::KeyPoint& kpt, float* desc) const; - - // M-SURF Pattern Descriptor - //void Get_MSURF_Upright_Descriptor_64(const cv::KeyPoint& kpt, float* desc) const; - //void Get_MSURF_Descriptor_64(const cv::KeyPoint& kpt, float* desc) const; - - // M-LDB Pattern Descriptor - //void Get_Upright_MLDB_Full_Descriptor(const cv::KeyPoint& kpt, unsigned char* desc) const; - //void Get_MLDB_Full_Descriptor(const cv::KeyPoint& kpt, unsigned char* desc) const; - //void Get_Upright_MLDB_Descriptor_Subset(const cv::KeyPoint& kpt, unsigned char* desc); - //void Get_MLDB_Descriptor_Subset(const cv::KeyPoint& kpt, unsigned char* desc); - - // Methods for saving some results and showing computation times - //void Save_Scale_Space(); - //void Save_Detector_Responses(); - //void Show_Computation_Times() const; - - /// Return the computation times - //AKAZETiming Get_Computation_Times() const { - // return timing_; - //} }; /* ************************************************************************* */ diff --git a/modules/features2d/src/kaze/KAZEFeatures.cpp b/modules/features2d/src/kaze/KAZEFeatures.cpp index 8d1b726..15c003e 100644 --- a/modules/features2d/src/kaze/KAZEFeatures.cpp +++ b/modules/features2d/src/kaze/KAZEFeatures.cpp @@ -135,18 +135,9 @@ void KAZEFeatures::Allocate_Memory_Evolution(void) { * @param img Input image for which the nonlinear scale space needs to be created * @return 0 if the nonlinear scale space was created successfully. -1 otherwise */ -int KAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat &img) { - - //double t2 = 0.0, t1 = 0.0; - +int KAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat &img) +{ CV_Assert(evolution_.size() > 0); - //if (evolution_.size() == 0) { - // cout << "Error generating the nonlinear scale space!!" << endl; - // cout << "Firstly you need to call KAZE::Allocate_Memory_Evolution()" << endl; - // return -1; - //} - - //t1 = getTickCount(); // Copy the original image to the first level of the evolution img.copyTo(evolution_[0].Lt); @@ -156,14 +147,6 @@ int KAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat &img) { // Firstly compute the kcontrast factor Compute_KContrast(evolution_[0].Lt, KCONTRAST_PERCENTILE); - //t2 = getTickCount(); - //tkcontrast_ = 1000.0*(t2 - t1) / getTickFrequency(); - - //if (verbosity_ == true) { - // cout << "Computed image evolution step. Evolution time: " << evolution_[0].etime << - // " Sigma: " << evolution_[0].esigma << endl; - //} - // Now generate the rest of evolution levels for (size_t i = 1; i < evolution_.size(); i++) { @@ -196,16 +179,8 @@ int KAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat &img) { AOS_Step_Scalar(evolution_[i].Lt, evolution_[i - 1].Lt, evolution_[i].Lflow, evolution_[i].etime - evolution_[i - 1].etime); } - - //if (verbosity_ == true) { - // cout << "Computed image evolution step " << i << " Evolution time: " << evolution_[i].etime << - // " Sigma: " << evolution_[i].esigma << endl; - //} } - //t2 = getTickCount(); - //tnlscale_ = 1000.0*(t2 - t1) / getTickFrequency(); - return 0; } @@ -217,20 +192,9 @@ int KAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat &img) { * @param img Input image * @param kpercentile Percentile of the gradient histogram */ -void KAZEFeatures::Compute_KContrast(const cv::Mat &img, const float &kpercentile) { - - //if (verbosity_ == true) { - // cout << "Computing Kcontrast factor." << endl; - //} - - //if (COMPUTE_KCONTRAST) { - kcontrast_ = compute_k_percentile(img, kpercentile, sderivatives_, KCONTRAST_NBINS, 0, 0); - //} - - //if (verbosity_ == true) { - // cout << "kcontrast = " << kcontrast_ << endl; - // cout << endl << "Now computing the nonlinear scale space!!" << endl; - //} +void KAZEFeatures::Compute_KContrast(const cv::Mat &img, const float &kpercentile) +{ + kcontrast_ = compute_k_percentile(img, kpercentile, sderivatives_, KCONTRAST_NBINS, 0, 0); } //************************************************************************************* @@ -241,19 +205,9 @@ void KAZEFeatures::Compute_KContrast(const cv::Mat &img, const float &kpercentil */ void KAZEFeatures::Compute_Multiscale_Derivatives(void) { - //double t2 = 0.0, t1 = 0.0; - //t1 = getTickCount(); - -#ifdef _OPENMP -#pragma omp parallel for -#endif - for (size_t i = 0; i < evolution_.size(); i++) { - - //if (verbosity_ == true) { - // cout << "Computing multiscale derivatives. Evolution time: " << evolution_[i].etime - // << " Step (pixels): " << evolution_[i].sigma_size << endl; - //} - + // TODO: use cv::parallel_for_ + for (size_t i = 0; i < evolution_.size(); i++) + { // Compute multiscale derivatives for the detector compute_scharr_derivatives(evolution_[i].Lsmooth, evolution_[i].Lx, 1, 0, evolution_[i].sigma_size); compute_scharr_derivatives(evolution_[i].Lsmooth, evolution_[i].Ly, 0, 1, evolution_[i].sigma_size); @@ -267,9 +221,6 @@ void KAZEFeatures::Compute_Multiscale_Derivatives(void) evolution_[i].Lxy = evolution_[i].Lxy*((evolution_[i].sigma_size)*(evolution_[i].sigma_size)); evolution_[i].Lyy = evolution_[i].Lyy*((evolution_[i].sigma_size)*(evolution_[i].sigma_size)); } - - //t2 = getTickCount(); - //tmderivatives_ = 1000.0*(t2 - t1) / getTickFrequency(); } //************************************************************************************* @@ -279,25 +230,19 @@ void KAZEFeatures::Compute_Multiscale_Derivatives(void) * @brief This method computes the feature detector response for the nonlinear scale space * @note We use the Hessian determinant as feature detector */ -void KAZEFeatures::Compute_Detector_Response(void) { - - //double t2 = 0.0, t1 = 0.0; +void KAZEFeatures::Compute_Detector_Response(void) +{ float lxx = 0.0, lxy = 0.0, lyy = 0.0; - //t1 = getTickCount(); - // Firstly compute the multiscale derivatives Compute_Multiscale_Derivatives(); - for (size_t i = 0; i < evolution_.size(); i++) { - - // Determinant of the Hessian - //if (verbosity_ == true) { - // cout << "Computing detector response. Determinant of Hessian. Evolution time: " << evolution_[i].etime << endl; - //} - - for (int ix = 0; ix < img_height_; ix++) { - for (int jx = 0; jx < img_width_; jx++) { + for (size_t i = 0; i < evolution_.size(); i++) + { + for (int ix = 0; ix < img_height_; ix++) + { + for (int jx = 0; jx < img_width_; jx++) + { lxx = *(evolution_[i].Lxx.ptr(ix)+jx); lxy = *(evolution_[i].Lxy.ptr(ix)+jx); lyy = *(evolution_[i].Lyy.ptr(ix)+jx); @@ -305,9 +250,6 @@ void KAZEFeatures::Compute_Detector_Response(void) { } } } - - //t2 = getTickCount(); - //tdresponse_ = 1000.0*(t2 - t1) / getTickFrequency(); } //************************************************************************************* @@ -317,11 +259,8 @@ void KAZEFeatures::Compute_Detector_Response(void) { * @brief This method selects interesting keypoints through the nonlinear scale space * @param kpts Vector of keypoints */ -void KAZEFeatures::Feature_Detection(std::vector& kpts) { - - //double t2 = 0.0, t1 = 0.0; - //t1 = getTickCount(); - +void KAZEFeatures::Feature_Detection(std::vector& kpts) +{ kpts.clear(); // Firstly compute the detector response for each pixel and scale level @@ -332,9 +271,6 @@ void KAZEFeatures::Feature_Detection(std::vector& kpts) { // Perform some subpixel refinement Do_Subpixel_Refinement(kpts); - - //t2 = getTickCount(); - //tdetector_ = 1000.0*(t2 - t1) / getTickFrequency(); } //************************************************************************************* @@ -346,8 +282,8 @@ void KAZEFeatures::Feature_Detection(std::vector& kpts) { * @param kpts Vector of keypoints * @note We compute features for each of the nonlinear scale space level in a different processing thread */ -void KAZEFeatures::Determinant_Hessian_Parallel(std::vector& kpts) { - +void KAZEFeatures::Determinant_Hessian_Parallel(std::vector& kpts) +{ int level = 0; float dist = 0.0, smax = 3.0; int npoints = 0, id_repeated = 0; @@ -367,9 +303,7 @@ void KAZEFeatures::Determinant_Hessian_Parallel(std::vector& kpts) kpts_par_.push_back(aux); } -#ifdef _OPENMP -#pragma omp parallel for -#endif + // TODO: Use cv::parallel_for_ for (int i = 1; i < (int)evolution_.size() - 1; i++) { Find_Extremum_Threading(i); } @@ -499,9 +433,7 @@ void KAZEFeatures::Do_Subpixel_Refinement(std::vector &kpts) { Mat A = Mat::zeros(3, 3, CV_32F); Mat b = Mat::zeros(3, 1, CV_32F); Mat dst = Mat::zeros(3, 1, CV_32F); - //double t2 = 0.0, t1 = 0.0; - //t1 = cv::getTickCount(); vector kpts_(kpts); for (size_t i = 0; i < kpts_.size(); i++) { @@ -583,9 +515,6 @@ void KAZEFeatures::Do_Subpixel_Refinement(std::vector &kpts) { kpts.push_back(kpts_[i]); } } - - //t2 = getTickCount(); - //tsubpixel_ = 1000.0*(t2 - t1) / getTickFrequency(); } //************************************************************************************* @@ -596,11 +525,8 @@ void KAZEFeatures::Do_Subpixel_Refinement(std::vector &kpts) { * @param kpts Vector of keypoints * @param desc Matrix with the feature descriptors */ -void KAZEFeatures::Feature_Description(std::vector &kpts, cv::Mat &desc) { - - //double t2 = 0.0, t1 = 0.0; - //t1 = getTickCount(); - +void KAZEFeatures::Feature_Description(std::vector &kpts, cv::Mat &desc) +{ // Allocate memory for the matrix of descriptors if (use_extended_ == true) { desc = Mat::zeros((int)kpts.size(), 128, CV_32FC1); @@ -730,9 +656,6 @@ void KAZEFeatures::Feature_Description(std::vector &kpts, cv::Mat } } } - - //t2 = getTickCount(); - //tdescriptor_ = 1000.0*(t2 - t1) / getTickFrequency(); } //*************************************************************************************