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KAZE and AKAZE integration initial commit

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Ievgen Khvedchenia 2014-04-04 14:25:38 +03:00
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modules/features2d/src/akaze/AKAZE.cpp Normal file
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/**
* @file AKAZE.h
* @brief Main class for detecting and computing binary descriptors in an
* accelerated nonlinear scale space
* @date Mar 27, 2013
* @author Pablo F. Alcantarilla, Jesus Nuevo
*/
#ifndef _AKAZE_H_
#define _AKAZE_H_
//*************************************************************************************
//*************************************************************************************
// Includes
#include "config.h"
#include "fed.h"
#include "utils.h"
#include "nldiffusion_functions.h"
//*************************************************************************************
//*************************************************************************************
// AKAZE Class Declaration
class AKAZE {
private:
// Parameters of the AKAZE class
int omax_; // Maximum octave level
int noctaves_; // Number of octaves
int nsublevels_; // Number of sublevels per octave level
int img_width_; // Width of the original image
int img_height_; // Height of the original image
float soffset_; // Base scale offset
float factor_size_; // Factor for the multiscale derivatives
float sderivatives_; // Standard deviation of the Gaussian for the nonlinear diff. derivatives
float kcontrast_; // The contrast parameter for the scalar nonlinear diffusion
float dthreshold_; // Feature detector threshold response
int diffusivity_; // Diffusivity type, 0->PM G1, 1->PM G2, 2-> Weickert, 3->Charbonnier
int descriptor_; // Descriptor mode:
// 0-> SURF_UPRIGHT, 1->SURF
// 2-> M-SURF_UPRIGHT, 3->M-SURF
// 4-> M-LDB_UPRIGHT, 5->M-LDB
int descriptor_size_; // Size of the descriptor in bits. Use 0 for the full descriptor
int descriptor_pattern_size_; // Size of the pattern. Actual size sampled is 2*pattern_size
int descriptor_channels_; // Number of channels to consider in the M-LDB descriptor
bool save_scale_space_; // For saving scale space images
bool verbosity_; // Verbosity level
std::vector<tevolution> evolution_; // Vector of nonlinear diffusion evolution
// FED parameters
int ncycles_; // Number of cycles
bool reordering_; // Flag for reordering time steps
std::vector<std::vector<float > > tsteps_; // Vector of FED dynamic time steps
std::vector<int> nsteps_; // Vector of number of steps per cycle
// Some matrices for the M-LDB descriptor computation
cv::Mat descriptorSamples_; // List of positions in the grids to sample LDB bits from.
cv::Mat descriptorBits_;
cv::Mat bitMask_;
// Computation times variables in ms
double tkcontrast_; // Kcontrast factor computation
double tscale_; // Nonlinear Scale space generation
double tderivatives_; // Multiscale derivatives
double tdetector_; // Feature detector
double textrema_; // Scale Space extrema
double tsubpixel_; // Subpixel refinement
double tdescriptor_; // Feature descriptors
public:
// Constructor
AKAZE(const AKAZEOptions &options);
// Destructor
~AKAZE(void);
// Setters
void Set_Octave_Max(const int& omax) {
omax_ = omax;
}
void Set_NSublevels(const int& nsublevels) {
nsublevels_ = nsublevels;
}
void Set_Save_Scale_Space_Flag(const bool& save_scale_space) {
save_scale_space_ = save_scale_space;
}
void Set_Image_Width(const int& img_width) {
img_width_ = img_width;
}
void Set_Image_Height(const int& img_height) {
img_height_ = img_height;
}
// Getters
int Get_Image_Width(void) {
return img_width_;
}
int Get_Image_Height(void) {
return img_height_;
}
double Get_Time_KContrast(void) {
return tkcontrast_;
}
double Get_Time_Scale_Space(void) {
return tscale_;
}
double Get_Time_Derivatives(void) {
return tderivatives_;
}
double Get_Time_Detector(void) {
return tdetector_;
}
double Get_Time_Descriptor(void) {
return tdescriptor_;
}
// Scale Space methods
void Allocate_Memory_Evolution(void);
int Create_Nonlinear_Scale_Space(const cv::Mat& img);
void Feature_Detection(std::vector<cv::KeyPoint>& kpts);
void Compute_Determinant_Hessian_Response(void);
void Compute_Multiscale_Derivatives(void);
void Find_Scale_Space_Extrema(std::vector<cv::KeyPoint>& kpts);
void Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts);
void Feature_Suppression_Distance(std::vector<cv::KeyPoint>& kpts, float mdist);
// Feature description methods
void Compute_Descriptors(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc);
void Compute_Main_Orientation_SURF(cv::KeyPoint& kpt);
// SURF Pattern Descriptor
void Get_SURF_Descriptor_Upright_64(const cv::KeyPoint& kpt, float *desc);
void Get_SURF_Descriptor_64(const cv::KeyPoint& kpt, float *desc);
// M-SURF Pattern Descriptor
void Get_MSURF_Upright_Descriptor_64(const cv::KeyPoint& kpt, float *desc);
void Get_MSURF_Descriptor_64(const cv::KeyPoint& kpt, float *desc);
// M-LDB Pattern Descriptor
void Get_Upright_MLDB_Full_Descriptor(const cv::KeyPoint& kpt, unsigned char *desc);
void Get_MLDB_Full_Descriptor(const cv::KeyPoint& kpt, unsigned char *desc);
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);
void Save_Detector_Responses(void);
void Show_Computation_Times(void);
};
//*************************************************************************************
//*************************************************************************************
// Inline functions
/**
* @brief This function sets default parameters for the A-KAZE detector.
* @param options AKAZE options
*/
void setDefaultAKAZEOptions(AKAZEOptions& options);
// Inline functions
void generateDescriptorSubsample(cv::Mat& sampleList, cv::Mat& comparisons,
int nbits, int pattern_size, int nchannels);
float get_angle(float x, float y);
float gaussian(float x, float y, float sigma);
void check_descriptor_limits(int& x, int& y, const int width, const int height);
int fRound(float flt);
//*************************************************************************************
//*************************************************************************************
#endif

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#ifndef _CONFIG_H_
#define _CONFIG_H_
// STL
#include <string>
#include <vector>
#include <cmath>
#include <bitset>
#include <iomanip>
// OpenCV
#include "precomp.hpp"
// OpenMP
#ifdef _OPENMP
# include <omp.h>
#endif
// Lookup table for 2d gaussian (sigma = 2.5) where (0,0) is top left and (6,6) is bottom right
const float gauss25[7][7] = {
{0.02546481f, 0.02350698f, 0.01849125f, 0.01239505f, 0.00708017f, 0.00344629f, 0.00142946f},
{0.02350698f, 0.02169968f, 0.01706957f, 0.01144208f, 0.00653582f, 0.00318132f, 0.00131956f},
{0.01849125f, 0.01706957f, 0.01342740f, 0.00900066f, 0.00514126f, 0.00250252f, 0.00103800f},
{0.01239505f, 0.01144208f, 0.00900066f, 0.00603332f, 0.00344629f, 0.00167749f, 0.00069579f},
{0.00708017f, 0.00653582f, 0.00514126f, 0.00344629f, 0.00196855f, 0.00095820f, 0.00039744f},
{0.00344629f, 0.00318132f, 0.00250252f, 0.00167749f, 0.00095820f, 0.00046640f, 0.00019346f},
{0.00142946f, 0.00131956f, 0.00103800f, 0.00069579f, 0.00039744f, 0.00019346f, 0.00008024f}
};
// Scale Space parameters
const float DEFAULT_SCALE_OFFSET = 1.60f; // Base scale offset (sigma units)
const float DEFAULT_FACTOR_SIZE = 1.5f; // Factor for the multiscale derivatives
const int DEFAULT_OCTAVE_MIN = 0; // Initial octave level (-1 means that the size of the input image is duplicated)
const int DEFAULT_OCTAVE_MAX = 4; // Maximum octave evolution of the image 2^sigma (coarsest scale sigma units)
const int DEFAULT_NSUBLEVELS = 4; // Default number of sublevels per scale level
const int DEFAULT_DIFFUSIVITY_TYPE = 1;
const float KCONTRAST_PERCENTILE = 0.7f;
const int KCONTRAST_NBINS = 300;
const float DEFAULT_SIGMA_SMOOTHING_DERIVATIVES = 1.0f;
const float DEFAULT_KCONTRAST = .01f;
// Detector Parameters
const float DEFAULT_DETECTOR_THRESHOLD = 0.001f; // Detector response threshold to accept point
const float DEFAULT_MIN_DETECTOR_THRESHOLD = 0.00001f; // Minimum Detector response threshold to accept point
const int DEFAULT_LDB_DESCRIPTOR_SIZE = 0; // Use 0 for the full descriptor, or the number of bits
const int DEFAULT_LDB_PATTERN_SIZE = 10; // Actual patch size is 2*pattern_size*point.scale;
const int DEFAULT_LDB_CHANNELS = 3;
// Descriptor Parameters
enum DESCRIPTOR_TYPE
{
SURF_UPRIGHT = 0, // Upright descriptors, not invariant to rotation
SURF = 1,
MSURF_UPRIGHT = 2, // Upright descriptors, not invariant to rotation
MSURF = 3,
MLDB_UPRIGHT = 4, // Upright descriptors, not invariant to rotation
MLDB = 5
};
const int DEFAULT_DESCRIPTOR = MLDB;
// Some debugging options
const bool DEFAULT_SAVE_SCALE_SPACE = false; // For saving the scale space images
const bool DEFAULT_VERBOSITY = false; // Verbosity level (0->no verbosity)
const bool DEFAULT_SHOW_RESULTS = true; // For showing the output image with the detected features plus some ratios
const bool DEFAULT_SAVE_KEYPOINTS = false; // For saving the list of keypoints
// Options structure
struct AKAZEOptions
{
int omin;
int omax;
int nsublevels;
int img_width;
int img_height;
int diffusivity;
float soffset;
float sderivatives;
float dthreshold;
float dthreshold2;
int descriptor;
int descriptor_size;
int descriptor_channels;
int descriptor_pattern_size;
bool save_scale_space;
bool save_keypoints;
bool verbosity;
AKAZEOptions()
{
// Load the default options
soffset = DEFAULT_SCALE_OFFSET;
omax = DEFAULT_OCTAVE_MAX;
nsublevels = DEFAULT_NSUBLEVELS;
dthreshold = DEFAULT_DETECTOR_THRESHOLD;
diffusivity = DEFAULT_DIFFUSIVITY_TYPE;
descriptor = DEFAULT_DESCRIPTOR;
descriptor_size = DEFAULT_LDB_DESCRIPTOR_SIZE;
descriptor_channels = DEFAULT_LDB_CHANNELS;
descriptor_pattern_size = DEFAULT_LDB_PATTERN_SIZE;
sderivatives = DEFAULT_SIGMA_SMOOTHING_DERIVATIVES;
save_scale_space = DEFAULT_SAVE_SCALE_SPACE;
save_keypoints = DEFAULT_SAVE_KEYPOINTS;
verbosity = DEFAULT_VERBOSITY;
}
friend std::ostream& operator<<(std::ostream& os,
const AKAZEOptions& akaze_options)
{
os << std::left;
#define CHECK_AKAZE_OPTION(option) \
os << std::setw(33) << #option << " = " << option << std::endl
// Scale-space parameters.
CHECK_AKAZE_OPTION(akaze_options.omax);
CHECK_AKAZE_OPTION(akaze_options.nsublevels);
CHECK_AKAZE_OPTION(akaze_options.soffset);
CHECK_AKAZE_OPTION(akaze_options.sderivatives);
CHECK_AKAZE_OPTION(akaze_options.diffusivity);
// Detection parameters.
CHECK_AKAZE_OPTION(akaze_options.dthreshold);
// Descriptor parameters.
CHECK_AKAZE_OPTION(akaze_options.descriptor);
CHECK_AKAZE_OPTION(akaze_options.descriptor_channels);
CHECK_AKAZE_OPTION(akaze_options.descriptor_size);
// Save scale-space
CHECK_AKAZE_OPTION(akaze_options.save_scale_space);
// Verbose option for debug.
CHECK_AKAZE_OPTION(akaze_options.verbosity);
#undef CHECK_AKAZE_OPTIONS
return os;
}
};
struct tevolution
{
cv::Mat Lx, Ly; // First order spatial derivatives
cv::Mat Lxx, Lxy, Lyy; // Second order spatial derivatives
cv::Mat Lflow; // Diffusivity image
cv::Mat Lt; // Evolution image
cv::Mat Lsmooth; // Smoothed image
cv::Mat Lstep; // Evolution step update
cv::Mat Ldet; // Detector response
float etime; // Evolution time
float esigma; // Evolution sigma. For linear diffusion t = sigma^2 / 2
int octave; // Image octave
int sublevel; // Image sublevel in each octave
int sigma_size; // Integer sigma. For computing the feature detector responses
};
#endif

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#ifndef FED_H
#define FED_H
//******************************************************************************
//******************************************************************************
// Includes
#include <iostream>
#include <vector>
//*************************************************************************************
//*************************************************************************************
// Declaration of functions
int fed_tau_by_process_time(const float& T, const int& M, const float& tau_max,
const bool& reordering, std::vector<float>& tau);
int fed_tau_by_cycle_time(const float& t, const float& tau_max,
const bool& reordering, std::vector<float> &tau) ;
int fed_tau_internal(const int& n, const float& scale, const float& tau_max,
const bool& reordering, std::vector<float> &tau);
bool fed_is_prime_internal(const int& number);
//*************************************************************************************
//*************************************************************************************
#endif // FED_H

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//=============================================================================
//
// nldiffusion_functions.cpp
// Authors: Pablo F. Alcantarilla (1), Jesus Nuevo (2)
// Institutions: Georgia Institute of Technology (1)
// TrueVision Solutions (2)
// Date: 15/09/2013
// Email: pablofdezalc@gmail.com
//
// AKAZE Features Copyright 2013, Pablo F. Alcantarilla, Jesus Nuevo
// All Rights Reserved
// See LICENSE for the license information
//=============================================================================
/**
* @file nldiffusion_functions.cpp
* @brief Functions for nonlinear diffusion filtering applications
* @date Sep 15, 2013
* @author Pablo F. Alcantarilla, Jesus Nuevo
*/
#include "nldiffusion_functions.h"
using namespace std;
using namespace cv;
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function smoothes an image with a Gaussian kernel
* @param src Input image
* @param dst Output image
* @param ksize_x Kernel size in X-direction (horizontal)
* @param ksize_y Kernel size in Y-direction (vertical)
* @param sigma Kernel standard deviation
*/
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, const size_t& ksize_x,
const size_t& ksize_y, const float& sigma) {
size_t ksize_x_ = 0, ksize_y_ = 0;
// Compute an appropriate kernel size according to the specified sigma
if (sigma > ksize_x || sigma > ksize_y || ksize_x == 0 || ksize_y == 0) {
ksize_x_ = ceil(2.0*(1.0 + (sigma-0.8)/(0.3)));
ksize_y_ = ksize_x_;
}
// The kernel size must be and odd number
if ((ksize_x_ % 2) == 0) {
ksize_x_ += 1;
}
if ((ksize_y_ % 2) == 0) {
ksize_y_ += 1;
}
// Perform the Gaussian Smoothing with border replication
GaussianBlur(src,dst,Size(ksize_x_,ksize_y_),sigma,sigma,BORDER_REPLICATE);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes image derivatives with Scharr kernel
* @param src Input image
* @param dst Output image
* @param xorder Derivative order in X-direction (horizontal)
* @param yorder Derivative order in Y-direction (vertical)
* @note Scharr operator approximates better rotation invariance than
* other stencils such as Sobel. See Weickert and Scharr,
* A Scheme for Coherence-Enhancing Diffusion Filtering with Optimized Rotation Invariance,
* Journal of Visual Communication and Image Representation 2002
*/
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst,
const size_t& xorder, const size_t& yorder) {
Scharr(src,dst,CV_32F,xorder,yorder,1.0,0,BORDER_DEFAULT);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes the Perona and Malik conductivity coefficient g1
* g1 = exp(-|dL|^2/k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
*/
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float& k) {
exp(-(Lx.mul(Lx)+Ly.mul(Ly))/(k*k),dst);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes the Perona and Malik conductivity coefficient g2
* g2 = 1 / (1 + dL^2 / k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
*/
void pm_g2(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float& k) {
dst = 1.0/(1.0+(Lx.mul(Lx)+Ly.mul(Ly))/(k*k));
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes Weickert conductivity coefficient gw
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
* @note For more information check the following paper: J. Weickert
* Applications of nonlinear diffusion in image processing and computer vision,
* Proceedings of Algorithmy 2000
*/
void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float& k) {
Mat modg;
pow((Lx.mul(Lx) + Ly.mul(Ly))/(k*k),4,modg);
cv::exp(-3.315/modg, dst);
dst = 1.0 - dst;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes Charbonnier conductivity coefficient gc
* gc = 1 / sqrt(1 + dL^2 / k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
* @note For more information check the following paper: J. Weickert
* Applications of nonlinear diffusion in image processing and computer vision,
* Proceedings of Algorithmy 2000
*/
void charbonnier_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float& k) {
Mat den;
cv::sqrt(1.0+(Lx.mul(Lx)+Ly.mul(Ly))/(k*k),den);
dst = 1.0/ den;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes a good empirical value for the k contrast factor
* given an input image, the percentile (0-1), the gradient scale and the number of
* bins in the histogram
* @param img Input image
* @param perc Percentile of the image gradient histogram (0-1)
* @param gscale Scale for computing the image gradient histogram
* @param nbins Number of histogram bins
* @param ksize_x Kernel size in X-direction (horizontal) for the Gaussian smoothing kernel
* @param ksize_y Kernel size in Y-direction (vertical) for the Gaussian smoothing kernel
* @return k contrast factor
*/
float compute_k_percentile(const cv::Mat& img, const float& perc, const float& gscale,
const size_t& nbins, const size_t& ksize_x, const size_t& ksize_y) {
size_t nbin = 0, nelements = 0, nthreshold = 0, k = 0;
float kperc = 0.0, modg = 0.0, lx = 0.0, ly = 0.0;
float npoints = 0.0;
float hmax = 0.0;
// Create the array for the histogram
float *hist = new float[nbins];
// Create the matrices
Mat gaussian = Mat::zeros(img.rows,img.cols,CV_32F);
Mat Lx = Mat::zeros(img.rows,img.cols,CV_32F);
Mat Ly = Mat::zeros(img.rows,img.cols,CV_32F);
// Set the histogram to zero, just in case
for (size_t i = 0; i < nbins; i++) {
hist[i] = 0.0;
}
// Perform the Gaussian convolution
gaussian_2D_convolution(img,gaussian,ksize_x,ksize_y,gscale);
// Compute the Gaussian derivatives Lx and Ly
image_derivatives_scharr(gaussian,Lx,1,0);
image_derivatives_scharr(gaussian,Ly,0,1);
// Skip the borders for computing the histogram
for (int i = 1; i < gaussian.rows-1; i++) {
for (int j = 1; j < gaussian.cols-1; j++) {
lx = *(Lx.ptr<float>(i)+j);
ly = *(Ly.ptr<float>(i)+j);
modg = sqrt(lx*lx + ly*ly);
// Get the maximum
if (modg > hmax) {
hmax = modg;
}
}
}
// Skip the borders for computing the histogram
for (int i = 1; i < gaussian.rows-1; i++) {
for (int j = 1; j < gaussian.cols-1; j++) {
lx = *(Lx.ptr<float>(i)+j);
ly = *(Ly.ptr<float>(i)+j);
modg = sqrt(lx*lx + ly*ly);
// Find the correspondent bin
if (modg != 0.0) {
nbin = floor(nbins*(modg/hmax));
if (nbin == nbins) {
nbin--;
}
hist[nbin]++;
npoints++;
}
}
}
// Now find the perc of the histogram percentile
nthreshold = (size_t)(npoints*perc);
for (k = 0; nelements < nthreshold && k < nbins; k++) {
nelements = nelements + hist[k];
}
if (nelements < nthreshold) {
kperc = 0.03;
}
else {
kperc = hmax*((float)(k)/(float)nbins);
}
delete [] hist;
return kperc;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes Scharr image derivatives
* @param src Input image
* @param dst Output image
* @param xorder Derivative order in X-direction (horizontal)
* @param yorder Derivative order in Y-direction (vertical)
* @param scale Scale factor for the derivative size
*/
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, const size_t& xorder,
const size_t& yorder, const size_t& scale) {
Mat kx, ky;
compute_derivative_kernels(kx, ky, xorder,yorder,scale);
sepFilter2D(src,dst,CV_32F,kx,ky);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function performs a scalar non-linear diffusion step
* @param Ld2 Output image in the evolution
* @param c Conductivity image
* @param Lstep Previous image in the evolution
* @param stepsize The step size in time units
* @note Forward Euler Scheme 3x3 stencil
* The function c is a scalar value that depends on the gradient norm
* dL_by_ds = d(c dL_by_dx)_by_dx + d(c dL_by_dy)_by_dy
*/
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, const float& stepsize) {
#ifdef _OPENMP
#pragma omp parallel for schedule(dynamic)
#endif
for (int i = 1; i < Lstep.rows-1; i++) {
for (int j = 1; j < Lstep.cols-1; j++) {
float xpos = ((*(c.ptr<float>(i)+j))+(*(c.ptr<float>(i)+j+1)))*((*(Ld.ptr<float>(i)+j+1))-(*(Ld.ptr<float>(i)+j)));
float xneg = ((*(c.ptr<float>(i)+j-1))+(*(c.ptr<float>(i)+j)))*((*(Ld.ptr<float>(i)+j))-(*(Ld.ptr<float>(i)+j-1)));
float ypos = ((*(c.ptr<float>(i)+j))+(*(c.ptr<float>(i+1)+j)))*((*(Ld.ptr<float>(i+1)+j))-(*(Ld.ptr<float>(i)+j)));
float yneg = ((*(c.ptr<float>(i-1)+j))+(*(c.ptr<float>(i)+j)))*((*(Ld.ptr<float>(i)+j))-(*(Ld.ptr<float>(i-1)+j)));
*(Lstep.ptr<float>(i)+j) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
}
for (int j = 1; j < Lstep.cols-1; j++) {
float xpos = ((*(c.ptr<float>(0)+j))+(*(c.ptr<float>(0)+j+1)))*((*(Ld.ptr<float>(0)+j+1))-(*(Ld.ptr<float>(0)+j)));
float xneg = ((*(c.ptr<float>(0)+j-1))+(*(c.ptr<float>(0)+j)))*((*(Ld.ptr<float>(0)+j))-(*(Ld.ptr<float>(0)+j-1)));
float ypos = ((*(c.ptr<float>(0)+j))+(*(c.ptr<float>(1)+j)))*((*(Ld.ptr<float>(1)+j))-(*(Ld.ptr<float>(0)+j)));
float yneg = ((*(c.ptr<float>(0)+j))+(*(c.ptr<float>(0)+j)))*((*(Ld.ptr<float>(0)+j))-(*(Ld.ptr<float>(0)+j)));
*(Lstep.ptr<float>(0)+j) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
for (int j = 1; j < Lstep.cols-1; j++) {
float xpos = ((*(c.ptr<float>(Lstep.rows-1)+j))+(*(c.ptr<float>(Lstep.rows-1)+j+1)))*((*(Ld.ptr<float>(Lstep.rows-1)+j+1))-(*(Ld.ptr<float>(Lstep.rows-1)+j)));
float xneg = ((*(c.ptr<float>(Lstep.rows-1)+j-1))+(*(c.ptr<float>(Lstep.rows-1)+j)))*((*(Ld.ptr<float>(Lstep.rows-1)+j))-(*(Ld.ptr<float>(Lstep.rows-1)+j-1)));
float ypos = ((*(c.ptr<float>(Lstep.rows-1)+j))+(*(c.ptr<float>(Lstep.rows-1)+j)))*((*(Ld.ptr<float>(Lstep.rows-1)+j))-(*(Ld.ptr<float>(Lstep.rows-1)+j)));
float yneg = ((*(c.ptr<float>(Lstep.rows-2)+j))+(*(c.ptr<float>(Lstep.rows-1)+j)))*((*(Ld.ptr<float>(Lstep.rows-1)+j))-(*(Ld.ptr<float>(Lstep.rows-2)+j)));
*(Lstep.ptr<float>(Lstep.rows-1)+j) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
for (int i = 1; i < Lstep.rows-1; i++) {
float xpos = ((*(c.ptr<float>(i)))+(*(c.ptr<float>(i)+1)))*((*(Ld.ptr<float>(i)+1))-(*(Ld.ptr<float>(i))));
float xneg = ((*(c.ptr<float>(i)))+(*(c.ptr<float>(i))))*((*(Ld.ptr<float>(i)))-(*(Ld.ptr<float>(i))));
float ypos = ((*(c.ptr<float>(i)))+(*(c.ptr<float>(i+1))))*((*(Ld.ptr<float>(i+1)))-(*(Ld.ptr<float>(i))));
float yneg = ((*(c.ptr<float>(i-1)))+(*(c.ptr<float>(i))))*((*(Ld.ptr<float>(i)))-(*(Ld.ptr<float>(i-1))));
*(Lstep.ptr<float>(i)) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
for (int i = 1; i < Lstep.rows-1; i++) {
float xpos = ((*(c.ptr<float>(i)+Lstep.cols-1))+(*(c.ptr<float>(i)+Lstep.cols-1)))*((*(Ld.ptr<float>(i)+Lstep.cols-1))-(*(Ld.ptr<float>(i)+Lstep.cols-1)));
float xneg = ((*(c.ptr<float>(i)+Lstep.cols-2))+(*(c.ptr<float>(i)+Lstep.cols-1)))*((*(Ld.ptr<float>(i)+Lstep.cols-1))-(*(Ld.ptr<float>(i)+Lstep.cols-2)));
float ypos = ((*(c.ptr<float>(i)+Lstep.cols-1))+(*(c.ptr<float>(i+1)+Lstep.cols-1)))*((*(Ld.ptr<float>(i+1)+Lstep.cols-1))-(*(Ld.ptr<float>(i)+Lstep.cols-1)));
float yneg = ((*(c.ptr<float>(i-1)+Lstep.cols-1))+(*(c.ptr<float>(i)+Lstep.cols-1)))*((*(Ld.ptr<float>(i)+Lstep.cols-1))-(*(Ld.ptr<float>(i-1)+Lstep.cols-1)));
*(Lstep.ptr<float>(i)+Lstep.cols-1) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
Ld = Ld + Lstep;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function downsamples the input image with the kernel [1/4,1/2,1/4]
* @param img Input image to be downsampled
* @param dst Output image with half of the resolution of the input image
*/
void downsample_image(const cv::Mat& src, cv::Mat& dst) {
int i1 = 0, j1 = 0, i2 = 0, j2 = 0;
for (i1 = 1; i1 < src.rows; i1+=2) {
j2 = 0;
for (j1 = 1; j1 < src.cols; j1+=2) {
*(dst.ptr<float>(i2)+j2) = 0.5*(*(src.ptr<float>(i1)+j1))+0.25*(*(src.ptr<float>(i1)+j1-1) + *(src.ptr<float>(i1)+j1+1));
j2++;
}
i2++;
}
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function downsamples the input image using OpenCV resize
* @param img Input image to be downsampled
* @param dst Output image with half of the resolution of the input image
*/
void halfsample_image(const cv::Mat& src, cv::Mat& dst) {
// Make sure the destination image is of the right size
CV_Assert(src.cols/2==dst.cols);
CV_Assert(src.rows / 2 == dst.rows);
resize(src,dst,dst.size(),0,0,cv::INTER_AREA);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief Compute Scharr derivative kernels for sizes different than 3
* @param kx_ The derivative kernel in x-direction
* @param ky_ The derivative kernel in y-direction
* @param dx The derivative order in x-direction
* @param dy The derivative order in y-direction
* @param scale The kernel size
*/
void compute_derivative_kernels(cv::OutputArray kx_, cv::OutputArray ky_,
const size_t& dx, const size_t& dy, const size_t& scale) {
const int ksize = 3 + 2*(scale-1);
// The usual Scharr kernel
if (scale == 1) {
getDerivKernels(kx_,ky_,dx,dy,0,true,CV_32F);
return;
}
kx_.create(ksize,1,CV_32F,-1,true);
ky_.create(ksize,1,CV_32F,-1,true);
Mat kx = kx_.getMat();
Mat ky = ky_.getMat();
float w = 10.0/3.0;
float norm = 1.0/(2.0*scale*(w+2.0));
for (int k = 0; k < 2; k++) {
Mat* kernel = k == 0 ? &kx : &ky;
int order = k == 0 ? dx : dy;
float kerI[1000];
for (int t = 0; t<ksize; t++) {
kerI[t] = 0;
}
if (order == 0) {
kerI[0] = norm;
kerI[ksize/2] = w*norm;
kerI[ksize-1] = norm;
}
else if (order == 1) {
kerI[0] = -1;
kerI[ksize/2] = 0;
kerI[ksize-1] = 1;
}
Mat temp(kernel->rows, kernel->cols, CV_32F, &kerI[0]);
temp.copyTo(*kernel);
}
}

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#ifndef _NLDIFFUSION_FUNCTIONS_H_
#define _NLDIFFUSION_FUNCTIONS_H_
//******************************************************************************
//******************************************************************************
// Includes
#include "precomp.hpp"
// OpenMP Includes
#ifdef _OPENMP
# include <omp.h>
#endif
//*************************************************************************************
//*************************************************************************************
// Declaration of functions
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, const size_t& ksize_x,
const size_t& ksize_y, const float& sigma);
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst,
const size_t& xorder, const size_t& yorder);
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float& k);
void pm_g2(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float& k);
void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float& k);
void charbonnier_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float& k);
float compute_k_percentile(const cv::Mat& img, const float& perc, const float& gscale,
const size_t& nbins, const size_t& ksize_x, const size_t& ksize_y);
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, const size_t& xorder,
const size_t& yorder, const size_t& scale);
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, const float& stepsize);
void downsample_image(const cv::Mat& src, cv::Mat& dst);
void halfsample_image(const cv::Mat& src, cv::Mat& dst);
void compute_derivative_kernels(cv::OutputArray kx_, cv::OutputArray ky_,
const size_t& dx, const size_t& dy, const size_t& scale);
//*************************************************************************************
//*************************************************************************************
#endif

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//=============================================================================
//
// utils.cpp
// Authors: Pablo F. Alcantarilla (1), Jesus Nuevo (2)
// Institutions: Georgia Institute of Technology (1)
// TrueVision Solutions (2)
//
// Date: 15/09/2013
// Email: pablofdezalc@gmail.com
//
// AKAZE Features Copyright 2013, Pablo F. Alcantarilla, Jesus Nuevo
// All Rights Reserved
// See LICENSE for the license information
//=============================================================================
/**
* @file utils.cpp
* @brief Some utilities functions
* @date Sep 15, 2013
* @author Pablo F. Alcantarilla, Jesus Nuevo
*/
#include "precomp.hpp"
#include "utils.h"
// Namespaces
using namespace std;
using namespace cv;
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes the minimum value of a float image
* @param src Input image
* @param value Minimum value
*/
void compute_min_32F(const cv::Mat &src, float &value) {
float aux = 1000.0;
for (int i = 0; i < src.rows; i++) {
for (int j = 0; j < src.cols; j++) {
if (src.at<float>(i,j) < aux) {
aux = src.at<float>(i,j);
}
}
}
value = aux;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes the maximum value of a float image
* @param src Input image
* @param value Maximum value
*/
void compute_max_32F(const cv::Mat &src, float &value) {
float aux = 0.0;
for (int i = 0; i < src.rows; i++) {
for (int j = 0; j < src.cols; j++) {
if (src.at<float>(i,j) > aux) {
aux = src.at<float>(i,j);
}
}
}
value = aux;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function converts the scale of the input image prior to visualization
* @param src Input/Output image
* @param value Maximum value
*/
void convert_scale(cv::Mat &src) {
float min_val = 0, max_val = 0;
compute_min_32F(src,min_val);
src = src - min_val;
compute_max_32F(src,max_val);
src = src / max_val;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function copies the input image and converts the scale of the copied
* image prior visualization
* @param src Input image
* @param dst Output image
*/
void copy_and_convert_scale(const cv::Mat &src, cv::Mat dst) {
float min_val = 0, max_val = 0;
src.copyTo(dst);
compute_min_32F(dst,min_val);
dst = dst - min_val;
compute_max_32F(dst,max_val);
dst = dst / max_val;
}
//*************************************************************************************
//*************************************************************************************
const size_t length = string("--descriptor_channels").size() + 2;
static inline std::ostream& cout_help()
{ cout << setw(length); return cout; }
static inline std::string toUpper(std::string s)
{
std::transform(s.begin(), s.end(), s.begin(), ::toupper);
return s;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function shows the possible command line configuration options
*/
void show_input_options_help(int example) {
fflush(stdout);
cout << "A-KAZE Features" << endl;
cout << "Usage: ";
if (example == 0) {
cout << "./akaze_features -i img.jpg [options]" << endl;
}
else if (example == 1) {
cout << "./akaze_match img1.jpg img2.pgm homography.txt [options]" << endl;
}
else if (example == 2) {
cout << "./akaze_compare img1.jpg img2.pgm homography.txt [options]" << endl;
}
cout << endl;
cout_help() << "Options below are not mandatory. Unless specified, default arguments are used." << endl << endl;
// Justify on the left
cout << left;
// Generalities
cout_help() << "--help" << "Show the command line options" << endl;
cout_help() << "--verbose " << "Verbosity is required" << endl;
cout_help() << endl;
// Scale-space parameters
cout_help() << "--soffset" << "Base scale offset (sigma units)" << endl;
cout_help() << "--omax" << "Maximum octave of image evolution" << endl;
cout_help() << "--nsublevels" << "Number of sublevels per octave" << endl;
cout_help() << "--diffusivity" << "Diffusivity function. Possible values:" << endl;
cout_help() << " " << "0 -> Perona-Malik, g1 = exp(-|dL|^2/k^2)" << endl;
cout_help() << " " << "1 -> Perona-Malik, g2 = 1 / (1 + dL^2 / k^2)" << endl;
cout_help() << " " << "2 -> Weickert diffusivity" << endl;
cout_help() << " " << "3 -> Charbonnier diffusivity" << endl;
cout_help() << endl;
// Feature detection parameters.
cout_help() << "--dthreshold" << "Feature detector threshold response for keypoints" << endl;
cout_help() << " " << "(0.001 can be a good value)" << endl;
cout_help() << endl;
// Descriptor parameters.
cout_help() << "--descriptor" << "Descriptor Type. Possible values:" << endl;
cout_help() << " " << "0 -> SURF_UPRIGHT" << endl;
cout_help() << " " << "1 -> SURF" << endl;
cout_help() << " " << "2 -> M-SURF_UPRIGHT," << endl;
cout_help() << " " << "3 -> M-SURF" << endl;
cout_help() << " " << "4 -> M-LDB_UPRIGHT" << endl;
cout_help() << " " << "5 -> M-LDB" << endl;
cout_help() << "--descriptor_channels " << "Descriptor Channels for M-LDB. Valid values: " << endl;
cout_help() << " " << "1 -> intensity" << endl;
cout_help() << " " << "2 -> intensity + gradient magnitude" << endl;
cout_help() << " " << "3 -> intensity + X and Y gradients" <<endl;
cout_help() << "--descriptor_size" << "Descriptor size for M-LDB in bits." << endl;
cout_help() << " " << "0: means the full length descriptor (486)!!" << endl;
cout_help() << endl;
// Save results?
cout_help() << "--show_results" << "Possible values below:" << endl;
cout_help() << " " << "1 -> show detection results." << endl;
cout_help() << " " << "0 -> don't show detection results" << endl;
cout_help() << endl;
}

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#ifndef _UTILS_H_
#define _UTILS_H_
//******************************************************************************
//******************************************************************************
// OpenCV Includes
#include "precomp.hpp"
// System Includes
#include <stdlib.h>
#include <stdio.h>
#include <cstdlib>
#include <vector>
#include <fstream>
#include <iostream>
#include <iomanip>
//******************************************************************************
//******************************************************************************
// Stringify common types such as int, double and others.
template <typename T>
inline std::string to_string(const T& x) {
std::stringstream oss;
oss << x;
return oss.str();
}
//******************************************************************************
//******************************************************************************
// Stringify and format integral types as follows:
// to_formatted_string( 1, 2) produces string: '01'
// to_formatted_string( 5, 2) produces string: '05'
// to_formatted_string( 19, 2) produces string: '19'
// to_formatted_string( 19, 3) produces string: '019'
template <typename Integer>
inline std::string to_formatted_string(Integer x, int num_digits) {
std::stringstream oss;
oss << std::setfill('0') << std::setw(num_digits) << x;
return oss.str();
}
//******************************************************************************
//******************************************************************************
void compute_min_32F(const cv::Mat& src, float& value);
void compute_max_32F(const cv::Mat& src, float& value);
void convert_scale(cv::Mat& src);
void copy_and_convert_scale(const cv::Mat& src, cv::Mat& dst);
#endif

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/**
* @file KAZE.h
* @brief Main program for detecting and computing descriptors in a nonlinear
* scale space
* @date Jan 21, 2012
* @author Pablo F. Alcantarilla
*/
#ifndef KAZE_H_
#define KAZE_H_
//*************************************************************************************
//*************************************************************************************
// Includes
#include "config.h"
#include "nldiffusion_functions.h"
#include "fed.h"
#include "utils.h"
//*************************************************************************************
//*************************************************************************************
// KAZE Class Declaration
class KAZE {
private:
// Parameters of the Nonlinear diffusion class
float soffset_; // Base scale offset
float sderivatives_; // Standard deviation of the Gaussian for the nonlinear diff. derivatives
int omax_; // Maximum octave level
int nsublevels_; // Number of sublevels per octave level
int img_width_; // Width of the original image
int img_height_; // Height of the original image
bool save_scale_space_; // For saving scale space images
bool verbosity_; // Verbosity level
std::vector<TEvolution> evolution_; // Vector of nonlinear diffusion evolution
float kcontrast_; // The contrast parameter for the scalar nonlinear diffusion
float dthreshold_; // Feature detector threshold response
int diffusivity_; // Diffusivity type, 0->PM G1, 1->PM G2, 2-> Weickert
int descriptor_mode_; // Descriptor mode
bool use_fed_; // Set to true in case we want to use FED for the nonlinear diffusion filtering. Set false for using AOS
bool use_upright_; // Set to true in case we want to use the upright version of the descriptors
bool use_extended_; // Set to true in case we want to use the extended version of the descriptors
// Vector of keypoint vectors for finding extrema in multiple threads
std::vector<std::vector<cv::KeyPoint> > kpts_par_;
// FED parameters
int ncycles_; // Number of cycles
bool reordering_; // Flag for reordering time steps
std::vector<std::vector<float > > tsteps_; // Vector of FED dynamic time steps
std::vector<int> nsteps_; // Vector of number of steps per cycle
// Computation times variables in ms
double tkcontrast_; // Kcontrast factor computation
double tnlscale_; // Nonlinear Scale space generation
double tdetector_; // Feature detector
double tmderivatives_; // Multiscale derivatives computation
double tdresponse_; // Detector response computation
double tdescriptor_; // Feature descriptor
double tsubpixel_; // Subpixel refinement
// Some auxiliary variables used in the AOS step
cv::Mat Ltx_, Lty_, px_, py_, ax_, ay_, bx_, by_, qr_, qc_;
public:
// Constructor
KAZE(KAZEOptions& options);
// Destructor
~KAZE(void);
// Public methods for KAZE interface
void Allocate_Memory_Evolution(void);
int Create_Nonlinear_Scale_Space(const cv::Mat& img);
void Feature_Detection(std::vector<cv::KeyPoint>& kpts);
void Feature_Description(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc);
// Methods for saving the scale space set of images and detector responses
void Save_Nonlinear_Scale_Space(void);
void Save_Detector_Responses(void);
void Save_Flow_Responses(void);
private:
// Feature Detection Methods
void Compute_KContrast(const cv::Mat& img, const float& kper);
void Compute_Multiscale_Derivatives(void);
void Compute_Detector_Response(void);
void Determinant_Hessian_Parallel(std::vector<cv::KeyPoint>& kpts);
void Find_Extremum_Threading(const int& level);
void Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts);
void Feature_Suppression_Distance(std::vector<cv::KeyPoint>& kpts, const float& mdist);
// AOS Methods
void AOS_Step_Scalar(cv::Mat &Ld, const cv::Mat &Ldprev, const cv::Mat &c, const float& stepsize);
void AOS_Rows(const cv::Mat &Ldprev, const cv::Mat &c, const float& stepsize);
void AOS_Columns(const cv::Mat &Ldprev, const cv::Mat &c, const float& stepsize);
void Thomas(const cv::Mat &a, const cv::Mat &b, const cv::Mat &Ld, cv::Mat &x);
// Feature Description methods
void Compute_Main_Orientation_SURF(cv::KeyPoint& kpt);
// Descriptor Mode -> 0 SURF 64
void Get_SURF_Upright_Descriptor_64(const cv::KeyPoint& kpt, float* desc);
void Get_SURF_Descriptor_64(const cv::KeyPoint& kpt, float* desc);
// Descriptor Mode -> 0 SURF 128
void Get_SURF_Upright_Descriptor_128(const cv::KeyPoint& kpt, float* desc);
void Get_SURF_Descriptor_128(const cv::KeyPoint& kpt, float* desc);
// Descriptor Mode -> 1 M-SURF 64
void Get_MSURF_Upright_Descriptor_64(const cv::KeyPoint& kpt, float* desc);
void Get_MSURF_Descriptor_64(const cv::KeyPoint& kpt, float* desc);
// Descriptor Mode -> 1 M-SURF 128
void Get_MSURF_Upright_Descriptor_128(const cv::KeyPoint& kpt, float* desc);
void Get_MSURF_Descriptor_128(const cv::KeyPoint& kpt, float *desc);
// Descriptor Mode -> 2 G-SURF 64
void Get_GSURF_Upright_Descriptor_64(const cv::KeyPoint& kpt, float* desc);
void Get_GSURF_Descriptor_64(const cv::KeyPoint& kpt, float *desc);
// Descriptor Mode -> 2 G-SURF 128
void Get_GSURF_Upright_Descriptor_128(const cv::KeyPoint& kpt, float* desc);
void Get_GSURF_Descriptor_128(const cv::KeyPoint& kpt, float* desc);
public:
// Setters
void Set_Scale_Offset(float soffset) {
soffset_ = soffset;
}
void Set_SDerivatives(float sderivatives) {
sderivatives_ = sderivatives;
}
void Set_Octave_Max(int omax) {
omax_ = omax;
}
void Set_NSublevels(int nsublevels) {
nsublevels_ = nsublevels;
}
void Set_Save_Scale_Space_Flag(bool save_scale_space) {
save_scale_space_ = save_scale_space;
}
void Set_Image_Width(int img_width) {
img_width_ = img_width;
}
void Set_Image_Height(int img_height) {
img_height_ = img_height;
}
void Set_Verbosity_Level(bool verbosity) {
verbosity_ = verbosity;
}
void Set_KContrast(float kcontrast) {
kcontrast_ = kcontrast;
}
void Set_Detector_Threshold(float dthreshold) {
dthreshold_ = dthreshold;
}
void Set_Diffusivity_Type(int diffusivity) {
diffusivity_ = diffusivity;
}
void Set_Descriptor_Mode(int descriptor_mode) {
descriptor_mode_ = descriptor_mode;
}
void Set_Use_FED(bool use_fed) {
use_fed_ = use_fed;
}
void Set_Upright(bool use_upright) {
use_upright_ = use_upright;
}
void Set_Extended(bool use_extended) {
use_extended_ = use_extended;
}
// Getters
float Get_Scale_Offset(void) {
return soffset_;
}
float Get_SDerivatives(void) {
return sderivatives_;
}
int Get_Octave_Max(void) {
return omax_;
}
int Get_NSublevels(void) {
return nsublevels_;
}
bool Get_Save_Scale_Space_Flag(void) {
return save_scale_space_;
}
int Get_Image_Width(void) {
return img_width_;
}
int Get_Image_Height(void) {
return img_height_;
}
bool Get_Verbosity_Level(void) {
return verbosity_;
}
float Get_KContrast(void) {
return kcontrast_;
}
float Get_Detector_Threshold(void) {
return dthreshold_;
}
int Get_Diffusivity_Type(void) {
return diffusivity_;
}
int Get_Descriptor_Mode(void) {
return descriptor_mode_;
}
bool Get_Upright(void) {
return use_upright_;
}
bool Get_Extended(void) {
return use_extended_;
}
float Get_Time_KContrast(void) {
return tkcontrast_;
}
float Get_Time_NLScale(void) {
return tnlscale_;
}
float Get_Time_Detector(void) {
return tdetector_;
}
float Get_Time_Multiscale_Derivatives(void) {
return tmderivatives_;
}
float Get_Time_Detector_Response(void) {
return tdresponse_;
}
float Get_Time_Descriptor(void) {
return tdescriptor_;
}
float Get_Time_Subpixel(void) {
return tsubpixel_;
}
};
//*************************************************************************************
//*************************************************************************************
// Inline functions
float getAngle(const float& x, const float& y);
float gaussian(const float& x, const float& y, const float& sig);
void checkDescriptorLimits(int &x, int &y, const int& width, const int& height);
void clippingDescriptor(float *desc, const int& dsize, const int& niter, const float& ratio);
int fRound(const float& flt);
//*************************************************************************************
//*************************************************************************************
#endif // KAZE_H_

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/**
* @file config.h
* @brief Configuration file
* @date Dec 27, 2011
* @author Pablo F. Alcantarilla
*/
#ifndef _CONFIG_H_
#define _CONFIG_H_
//******************************************************************************
//******************************************************************************
// System Includes
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <cstdlib>
#include <string>
#include <vector>
#include <math.h>
// OpenCV Includes
#include "precomp.hpp"
// OpenMP Includes
#ifdef _OPENMP
#include <omp.h>
#else
#define omp_get_thread_num() 0
#endif
//*************************************************************************************
//*************************************************************************************
// Some defines
#define NMAX_CHAR 400
// Some default options
const float DEFAULT_SCALE_OFFSET = 1.60; // Base scale offset (sigma units)
const float DEFAULT_OCTAVE_MAX = 4.0; // Maximum octave evolution of the image 2^sigma (coarsest scale sigma units)
const int DEFAULT_NSUBLEVELS = 4; // Default number of sublevels per scale level
const float DEFAULT_DETECTOR_THRESHOLD = 0.001; // Detector response threshold to accept point
const float DEFAULT_MIN_DETECTOR_THRESHOLD = 0.00001; // Minimum Detector response threshold to accept point
const int DEFAULT_DESCRIPTOR_MODE = 1; // Descriptor Mode 0->SURF, 1->M-SURF
const bool DEFAULT_USE_FED = true; // 0->AOS, 1->FED
const bool DEFAULT_UPRIGHT = false; // Upright descriptors, not invariant to rotation
const bool DEFAULT_EXTENDED = false; // Extended descriptor, dimension 128
const bool DEFAULT_SAVE_SCALE_SPACE = false; // For saving the scale space images
const bool DEFAULT_VERBOSITY = false; // Verbosity level (0->no verbosity)
const bool DEFAULT_SHOW_RESULTS = true; // For showing the output image with the detected features plus some ratios
const bool DEFAULT_SAVE_KEYPOINTS = false; // For saving the list of keypoints
// Some important configuration variables
const float DEFAULT_SIGMA_SMOOTHING_DERIVATIVES = 1.0;
const float DEFAULT_KCONTRAST = .01;
const float KCONTRAST_PERCENTILE = 0.7;
const int KCONTRAST_NBINS = 300;
const bool COMPUTE_KCONTRAST = true;
const int DEFAULT_DIFFUSIVITY_TYPE = 1; // 0 -> PM G1, 1 -> PM G2, 2 -> Weickert
const bool USE_CLIPPING_NORMALIZATION = false;
const float CLIPPING_NORMALIZATION_RATIO = 1.6;
const int CLIPPING_NORMALIZATION_NITER = 5;
//*************************************************************************************
//*************************************************************************************
struct KAZEOptions {
KAZEOptions() {
// Load the default options
soffset = DEFAULT_SCALE_OFFSET;
omax = DEFAULT_OCTAVE_MAX;
nsublevels = DEFAULT_NSUBLEVELS;
dthreshold = DEFAULT_DETECTOR_THRESHOLD;
use_fed = DEFAULT_USE_FED;
upright = DEFAULT_UPRIGHT;
extended = DEFAULT_EXTENDED;
descriptor = DEFAULT_DESCRIPTOR_MODE;
diffusivity = DEFAULT_DIFFUSIVITY_TYPE;
sderivatives = DEFAULT_SIGMA_SMOOTHING_DERIVATIVES;
save_scale_space = DEFAULT_SAVE_SCALE_SPACE;
save_keypoints = DEFAULT_SAVE_KEYPOINTS;
verbosity = DEFAULT_VERBOSITY;
show_results = DEFAULT_SHOW_RESULTS;
}
float soffset;
int omax;
int nsublevels;
int img_width;
int img_height;
int diffusivity;
float sderivatives;
float dthreshold;
bool use_fed;
bool upright;
bool extended;
int descriptor;
bool save_scale_space;
bool save_keypoints;
bool verbosity;
bool show_results;
};
struct TEvolution {
cv::Mat Lx, Ly; // First order spatial derivatives
cv::Mat Lxx, Lxy, Lyy; // Second order spatial derivatives
cv::Mat Lflow; // Diffusivity image
cv::Mat Lt; // Evolution image
cv::Mat Lsmooth; // Smoothed image
cv::Mat Lstep; // Evolution step update
cv::Mat Ldet; // Detector response
float etime; // Evolution time
float esigma; // Evolution sigma. For linear diffusion t = sigma^2 / 2
float octave; // Image octave
float sublevel; // Image sublevel in each octave
int sigma_size; // Integer esigma. For computing the feature detector responses
};
//*************************************************************************************
//*************************************************************************************
#endif

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//=============================================================================
//
// fed.cpp
// Authors: Pablo F. Alcantarilla (1), Jesus Nuevo (2)
// Institutions: Georgia Institute of Technology (1)
// TrueVision Solutions (2)
// Date: 15/09/2013
// Email: pablofdezalc@gmail.com
//
// AKAZE Features Copyright 2013, Pablo F. Alcantarilla, Jesus Nuevo
// All Rights Reserved
// See LICENSE for the license information
//=============================================================================
/**
* @file fed.cpp
* @brief Functions for performing Fast Explicit Diffusion and building the
* nonlinear scale space
* @date Sep 15, 2013
* @author Pablo F. Alcantarilla, Jesus Nuevo
* @note This code is derived from FED/FJ library from Grewenig et al.,
* The FED/FJ library allows solving more advanced problems
* Please look at the following papers for more information about FED:
* [1] S. Grewenig, J. Weickert, C. Schroers, A. Bruhn. Cyclic Schemes for
* PDE-Based Image Analysis. Technical Report No. 327, Department of Mathematics,
* Saarland University, Saarbrücken, Germany, March 2013
* [2] S. Grewenig, J. Weickert, A. Bruhn. From box filtering to fast explicit diffusion.
* DAGM, 2010
*
*/
#include "fed.h"
using namespace std;
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function allocates an array of the least number of time steps such
* that a certain stopping time for the whole process can be obtained and fills
* it with the respective FED time step sizes for one cycle
* The function returns the number of time steps per cycle or 0 on failure
* @param T Desired process stopping time
* @param M Desired number of cycles
* @param tau_max Stability limit for the explicit scheme
* @param reordering Reordering flag
* @param tau The vector with the dynamic step sizes
*/
int fed_tau_by_process_time(const float& T, const int& M, const float& tau_max,
const bool& reordering, std::vector<float>& tau) {
// All cycles have the same fraction of the stopping time
return fed_tau_by_cycle_time(T/(float)M,tau_max,reordering,tau);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function allocates an array of the least number of time steps such
* that a certain stopping time for the whole process can be obtained and fills it
* it with the respective FED time step sizes for one cycle
* The function returns the number of time steps per cycle or 0 on failure
* @param t Desired cycle stopping time
* @param tau_max Stability limit for the explicit scheme
* @param reordering Reordering flag
* @param tau The vector with the dynamic step sizes
*/
int fed_tau_by_cycle_time(const float& t, const float& tau_max,
const bool& reordering, std::vector<float> &tau) {
int n = 0; // Number of time steps
float scale = 0.0; // Ratio of t we search to maximal t
// Compute necessary number of time steps
n = (int)(ceilf(sqrtf(3.0*t/tau_max+0.25f)-0.5f-1.0e-8f)+ 0.5f);
scale = 3.0*t/(tau_max*(float)(n*(n+1)));
// Call internal FED time step creation routine
return fed_tau_internal(n,scale,tau_max,reordering,tau);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function allocates an array of time steps and fills it with FED
* time step sizes
* The function returns the number of time steps per cycle or 0 on failure
* @param n Number of internal steps
* @param scale Ratio of t we search to maximal t
* @param tau_max Stability limit for the explicit scheme
* @param reordering Reordering flag
* @param tau The vector with the dynamic step sizes
*/
int fed_tau_internal(const int& n, const float& scale, const float& tau_max,
const bool& reordering, std::vector<float> &tau) {
float c = 0.0, d = 0.0; // Time savers
vector<float> tauh; // Helper vector for unsorted taus
if (n <= 0) {
return 0;
}
// Allocate memory for the time step size
tau = vector<float>(n);
if (reordering) {
tauh = vector<float>(n);
}
// Compute time saver
c = 1.0f / (4.0f * (float)n + 2.0f);
d = scale * tau_max / 2.0f;
// Set up originally ordered tau vector
for (int k = 0; k < n; ++k) {
float h = cosf(CV_PI * (2.0f * (float)k + 1.0f) * c);
if (reordering) {
tauh[k] = d / (h * h);
}
else {
tau[k] = d / (h * h);
}
}
// Permute list of time steps according to chosen reordering function
int kappa = 0, prime = 0;
if (reordering == true) {
// Choose kappa cycle with k = n/2
// This is a heuristic. We can use Leja ordering instead!!
kappa = n / 2;
// Get modulus for permutation
prime = n + 1;
while (!fed_is_prime_internal(prime)) {
prime++;
}
// Perform permutation
for (int k = 0, l = 0; l < n; ++k, ++l) {
int index = 0;
while ((index = ((k+1)*kappa) % prime - 1) >= n) {
k++;
}
tau[l] = tauh[index];
}
}
return n;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function checks if a number is prime or not
* @param number Number to check if it is prime or not
* @return true if the number is prime
*/
bool fed_is_prime_internal(const int& number) {
bool is_prime = false;
if (number <= 1) {
return false;
}
else if (number == 1 || number == 2 || number == 3 || number == 5 || number == 7) {
return true;
}
else if ((number % 2) == 0 || (number % 3) == 0 || (number % 5) == 0 || (number % 7) == 0) {
return false;
}
else {
is_prime = true;
int upperLimit = sqrt(number+1.0);
int divisor = 11;
while (divisor <= upperLimit ) {
if (number % divisor == 0)
{
is_prime = false;
}
divisor +=2;
}
return is_prime;
}
}

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#ifndef FED_H
#define FED_H
//******************************************************************************
//******************************************************************************
// Includes
#include <iostream>
#include <stdlib.h>
#include <stdio.h>
#include <cstdlib>
#include <math.h>
#include <vector>
//*************************************************************************************
//*************************************************************************************
// Declaration of functions
int fed_tau_by_process_time(const float& T, const int& M, const float& tau_max,
const bool& reordering, std::vector<float>& tau);
int fed_tau_by_cycle_time(const float& t, const float& tau_max,
const bool& reordering, std::vector<float> &tau) ;
int fed_tau_internal(const int& n, const float& scale, const float& tau_max,
const bool& reordering, std::vector<float> &tau);
bool fed_is_prime_internal(const int& number);
//*************************************************************************************
//*************************************************************************************
#endif // FED_H

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//=============================================================================
//
// nldiffusion_functions.cpp
// Author: Pablo F. Alcantarilla
// Institution: University d'Auvergne
// Address: Clermont Ferrand, France
// Date: 27/12/2011
// Email: pablofdezalc@gmail.com
//
// KAZE Features Copyright 2012, Pablo F. Alcantarilla
// All Rights Reserved
// See LICENSE for the license information
//=============================================================================
/**
* @file nldiffusion_functions.cpp
* @brief Functions for non-linear diffusion applications:
* 2D Gaussian Derivatives
* Perona and Malik conductivity equations
* Perona and Malik evolution
* @date Dec 27, 2011
* @author Pablo F. Alcantarilla
*/
#include "nldiffusion_functions.h"
// Namespaces
using namespace std;
using namespace cv;
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function smoothes an image with a Gaussian kernel
* @param src Input image
* @param dst Output image
* @param ksize_x Kernel size in X-direction (horizontal)
* @param ksize_y Kernel size in Y-direction (vertical)
* @param sigma Kernel standard deviation
*/
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst,
int ksize_x, int ksize_y, float sigma) {
size_t ksize_x_ = 0, ksize_y_ = 0;
// Compute an appropriate kernel size according to the specified sigma
if (sigma > ksize_x || sigma > ksize_y || ksize_x == 0 || ksize_y == 0) {
ksize_x_ = ceil(2.0*(1.0 + (sigma-0.8)/(0.3)));
ksize_y_ = ksize_x_;
}
// The kernel size must be and odd number
if ((ksize_x_ % 2) == 0) {
ksize_x_ += 1;
}
if ((ksize_y_ % 2) == 0) {
ksize_y_ += 1;
}
// Perform the Gaussian Smoothing with border replication
GaussianBlur(src,dst,Size(ksize_x_,ksize_y_),sigma,sigma,cv::BORDER_REPLICATE);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes the Perona and Malik conductivity coefficient g1
* g1 = exp(-|dL|^2/k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
*/
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
cv::exp(-(Lx.mul(Lx) + Ly.mul(Ly))/(k*k),dst);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes the Perona and Malik conductivity coefficient g2
* g2 = 1 / (1 + dL^2 / k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
*/
void pm_g2(const cv::Mat &Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
dst = 1./(1. + (Lx.mul(Lx) + Ly.mul(Ly))/(k*k));
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes Weickert conductivity coefficient g3
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
* @note For more information check the following paper: J. Weickert
* Applications of nonlinear diffusion in image processing and computer vision,
* Proceedings of Algorithmy 2000
*/
void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
Mat modg;
cv::pow((Lx.mul(Lx) + Ly.mul(Ly))/(k*k),4,modg);
cv::exp(-3.315/modg, dst);
dst = 1.0 - dst;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes a good empirical value for the k contrast factor
* given an input image, the percentile (0-1), the gradient scale and the number of
* bins in the histogram
* @param img Input image
* @param perc Percentile of the image gradient histogram (0-1)
* @param gscale Scale for computing the image gradient histogram
* @param nbins Number of histogram bins
* @param ksize_x Kernel size in X-direction (horizontal) for the Gaussian smoothing kernel
* @param ksize_y Kernel size in Y-direction (vertical) for the Gaussian smoothing kernel
* @return k contrast factor
*/
float compute_k_percentile(const cv::Mat& img, float perc, float gscale,
int nbins, int ksize_x, int ksize_y) {
int nbin = 0, nelements = 0, nthreshold = 0, k = 0;
float kperc = 0.0, modg = 0.0, lx = 0.0, ly = 0.0;
float npoints = 0.0;
float hmax = 0.0;
// Create the array for the histogram
float *hist = new float[nbins];
// Create the matrices
Mat gaussian = Mat::zeros(img.rows,img.cols,CV_32F);
Mat Lx = Mat::zeros(img.rows,img.cols,CV_32F);
Mat Ly = Mat::zeros(img.rows,img.cols,CV_32F);
// Set the histogram to zero, just in case
for (int i = 0; i < nbins; i++) {
hist[i] = 0.0;
}
// Perform the Gaussian convolution
gaussian_2D_convolution(img,gaussian,ksize_x,ksize_y,gscale);
// Compute the Gaussian derivatives Lx and Ly
Scharr(gaussian,Lx,CV_32F,1,0,1,0,cv::BORDER_DEFAULT);
Scharr(gaussian,Ly,CV_32F,0,1,1,0,cv::BORDER_DEFAULT);
// Skip the borders for computing the histogram
for (int i = 1; i < gaussian.rows-1; i++) {
for (int j = 1; j < gaussian.cols-1; j++) {
lx = *(Lx.ptr<float>(i)+j);
ly = *(Ly.ptr<float>(i)+j);
modg = sqrt(lx*lx + ly*ly);
// Get the maximum
if (modg > hmax) {
hmax = modg;
}
}
}
// Skip the borders for computing the histogram
for (int i = 1; i < gaussian.rows-1; i++) {
for (int j = 1; j < gaussian.cols-1; j++) {
lx = *(Lx.ptr<float>(i)+j);
ly = *(Ly.ptr<float>(i)+j);
modg = sqrt(lx*lx + ly*ly);
// Find the correspondent bin
if (modg != 0.0) {
nbin = floor(nbins*(modg/hmax));
if (nbin == nbins) {
nbin--;
}
hist[nbin]++;
npoints++;
}
}
}
// Now find the perc of the histogram percentile
nthreshold = (size_t)(npoints*perc);
for (k = 0; nelements < nthreshold && k < nbins; k++) {
nelements = nelements + hist[k];
}
if (nelements < nthreshold) {
kperc = 0.03;
}
else {
kperc = hmax*((float)(k)/(float)nbins);
}
delete hist;
return kperc;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function computes Scharr image derivatives
* @param src Input image
* @param dst Output image
* @param xorder Derivative order in X-direction (horizontal)
* @param yorder Derivative order in Y-direction (vertical)
* @param scale Scale factor or derivative size
*/
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst,
int xorder, int yorder, int scale) {
Mat kx, ky;
compute_derivative_kernels(kx,ky,xorder,yorder,scale);
sepFilter2D(src,dst,CV_32F,kx,ky);
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief Compute derivative kernels for sizes different than 3
* @param _kx Horizontal kernel values
* @param _ky Vertical kernel values
* @param dx Derivative order in X-direction (horizontal)
* @param dy Derivative order in Y-direction (vertical)
* @param scale_ Scale factor or derivative size
*/
void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky,
int dx, int dy, int scale) {
int ksize = 3 + 2*(scale-1);
// The standard Scharr kernel
if (scale == 1) {
getDerivKernels(_kx,_ky,dx,dy,0,true,CV_32F);
return;
}
_kx.create(ksize,1,CV_32F,-1,true);
_ky.create(ksize,1,CV_32F,-1,true);
Mat kx = _kx.getMat();
Mat ky = _ky.getMat();
float w = 10.0/3.0;
float norm = 1.0/(2.0*scale*(w+2.0));
for (int k = 0; k < 2; k++) {
Mat* kernel = k == 0 ? &kx : &ky;
int order = k == 0 ? dx : dy;
std::vector<float> kerI(ksize);
for (int t=0; t<ksize; t++) {
kerI[t] = 0;
}
if (order == 0) {
kerI[0] = norm, kerI[ksize/2] = w*norm, kerI[ksize-1] = norm;
}
else if (order == 1) {
kerI[0] = -1, kerI[ksize/2] = 0, kerI[ksize-1] = 1;
}
Mat temp(kernel->rows,kernel->cols,CV_32F,&kerI[0]);
temp.copyTo(*kernel);
}
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function performs a scalar non-linear diffusion step
* @param Ld2 Output image in the evolution
* @param c Conductivity image
* @param Lstep Previous image in the evolution
* @param stepsize The step size in time units
* @note Forward Euler Scheme 3x3 stencil
* The function c is a scalar value that depends on the gradient norm
* dL_by_ds = d(c dL_by_dx)_by_dx + d(c dL_by_dy)_by_dy
*/
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize) {
#ifdef _OPENMP
#pragma omp parallel for schedule(dynamic)
#endif
for (int i = 1; i < Lstep.rows-1; i++) {
for (int j = 1; j < Lstep.cols-1; j++) {
float xpos = ((*(c.ptr<float>(i)+j))+(*(c.ptr<float>(i)+j+1)))*((*(Ld.ptr<float>(i)+j+1))-(*(Ld.ptr<float>(i)+j)));
float xneg = ((*(c.ptr<float>(i)+j-1))+(*(c.ptr<float>(i)+j)))*((*(Ld.ptr<float>(i)+j))-(*(Ld.ptr<float>(i)+j-1)));
float ypos = ((*(c.ptr<float>(i)+j))+(*(c.ptr<float>(i+1)+j)))*((*(Ld.ptr<float>(i+1)+j))-(*(Ld.ptr<float>(i)+j)));
float yneg = ((*(c.ptr<float>(i-1)+j))+(*(c.ptr<float>(i)+j)))*((*(Ld.ptr<float>(i)+j))-(*(Ld.ptr<float>(i-1)+j)));
*(Lstep.ptr<float>(i)+j) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
}
for (int j = 1; j < Lstep.cols-1; j++) {
float xpos = ((*(c.ptr<float>(0)+j))+(*(c.ptr<float>(0)+j+1)))*((*(Ld.ptr<float>(0)+j+1))-(*(Ld.ptr<float>(0)+j)));
float xneg = ((*(c.ptr<float>(0)+j-1))+(*(c.ptr<float>(0)+j)))*((*(Ld.ptr<float>(0)+j))-(*(Ld.ptr<float>(0)+j-1)));
float ypos = ((*(c.ptr<float>(0)+j))+(*(c.ptr<float>(1)+j)))*((*(Ld.ptr<float>(1)+j))-(*(Ld.ptr<float>(0)+j)));
float yneg = ((*(c.ptr<float>(0)+j))+(*(c.ptr<float>(0)+j)))*((*(Ld.ptr<float>(0)+j))-(*(Ld.ptr<float>(0)+j)));
*(Lstep.ptr<float>(0)+j) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
for (int j = 1; j < Lstep.cols-1; j++) {
float xpos = ((*(c.ptr<float>(Lstep.rows-1)+j))+(*(c.ptr<float>(Lstep.rows-1)+j+1)))*((*(Ld.ptr<float>(Lstep.rows-1)+j+1))-(*(Ld.ptr<float>(Lstep.rows-1)+j)));
float xneg = ((*(c.ptr<float>(Lstep.rows-1)+j-1))+(*(c.ptr<float>(Lstep.rows-1)+j)))*((*(Ld.ptr<float>(Lstep.rows-1)+j))-(*(Ld.ptr<float>(Lstep.rows-1)+j-1)));
float ypos = ((*(c.ptr<float>(Lstep.rows-1)+j))+(*(c.ptr<float>(Lstep.rows-1)+j)))*((*(Ld.ptr<float>(Lstep.rows-1)+j))-(*(Ld.ptr<float>(Lstep.rows-1)+j)));
float yneg = ((*(c.ptr<float>(Lstep.rows-2)+j))+(*(c.ptr<float>(Lstep.rows-1)+j)))*((*(Ld.ptr<float>(Lstep.rows-1)+j))-(*(Ld.ptr<float>(Lstep.rows-2)+j)));
*(Lstep.ptr<float>(Lstep.rows-1)+j) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
for (int i = 1; i < Lstep.rows-1; i++) {
float xpos = ((*(c.ptr<float>(i)))+(*(c.ptr<float>(i)+1)))*((*(Ld.ptr<float>(i)+1))-(*(Ld.ptr<float>(i))));
float xneg = ((*(c.ptr<float>(i)))+(*(c.ptr<float>(i))))*((*(Ld.ptr<float>(i)))-(*(Ld.ptr<float>(i))));
float ypos = ((*(c.ptr<float>(i)))+(*(c.ptr<float>(i+1))))*((*(Ld.ptr<float>(i+1)))-(*(Ld.ptr<float>(i))));
float yneg = ((*(c.ptr<float>(i-1)))+(*(c.ptr<float>(i))))*((*(Ld.ptr<float>(i)))-(*(Ld.ptr<float>(i-1))));
*(Lstep.ptr<float>(i)) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
for (int i = 1; i < Lstep.rows-1; i++) {
float xpos = ((*(c.ptr<float>(i)+Lstep.cols-1))+(*(c.ptr<float>(i)+Lstep.cols-1)))*((*(Ld.ptr<float>(i)+Lstep.cols-1))-(*(Ld.ptr<float>(i)+Lstep.cols-1)));
float xneg = ((*(c.ptr<float>(i)+Lstep.cols-2))+(*(c.ptr<float>(i)+Lstep.cols-1)))*((*(Ld.ptr<float>(i)+Lstep.cols-1))-(*(Ld.ptr<float>(i)+Lstep.cols-2)));
float ypos = ((*(c.ptr<float>(i)+Lstep.cols-1))+(*(c.ptr<float>(i+1)+Lstep.cols-1)))*((*(Ld.ptr<float>(i+1)+Lstep.cols-1))-(*(Ld.ptr<float>(i)+Lstep.cols-1)));
float yneg = ((*(c.ptr<float>(i-1)+Lstep.cols-1))+(*(c.ptr<float>(i)+Lstep.cols-1)))*((*(Ld.ptr<float>(i)+Lstep.cols-1))-(*(Ld.ptr<float>(i-1)+Lstep.cols-1)));
*(Lstep.ptr<float>(i)+Lstep.cols-1) = 0.5*stepsize*(xpos-xneg + ypos-yneg);
}
Ld = Ld + Lstep;
}
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function checks if a given pixel is a maximum in a local neighbourhood
* @param img Input image where we will perform the maximum search
* @param dsize Half size of the neighbourhood
* @param value Response value at (x,y) position
* @param row Image row coordinate
* @param col Image column coordinate
* @param same_img Flag to indicate if the image value at (x,y) is in the input image
* @return 1->is maximum, 0->otherwise
*/
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value,
int row, int col, bool same_img) {
bool response = true;
for (int i = row-dsize; i <= row+dsize; i++) {
for (int j = col-dsize; j <= col+dsize; j++) {
if (i >= 0 && i < img.rows && j >= 0 && j < img.cols) {
if (same_img == true) {
if (i != row || j != col) {
if ((*(img.ptr<float>(i)+j)) > value) {
response = false;
return response;
}
}
}
else {
if ((*(img.ptr<float>(i)+j)) > value) {
response = false;
return response;
}
}
}
}
}
return response;
}

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/**
* @file nldiffusion_functions.h
* @brief Functions for non-linear diffusion applications:
* 2D Gaussian Derivatives
* Perona and Malik conductivity equations
* Perona and Malik evolution
* @date Dec 27, 2011
* @author Pablo F. Alcantarilla
*/
#ifndef NLDIFFUSION_FUNCTIONS_H_
#define NLDIFFUSION_FUNCTIONS_H_
//******************************************************************************
//******************************************************************************
// Includes
#include "config.h"
//*************************************************************************************
//*************************************************************************************
// Gaussian 2D convolution
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst,
int ksize_x, int ksize_y, float sigma);
// Diffusivity functions
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
void pm_g2(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
float compute_k_percentile(const cv::Mat& img, float perc, float gscale,
int nbins, int ksize_x, int ksize_y);
// Image derivatives
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst,
int xorder, int yorder, int scale);
void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky,
int dx, int dy, int scale);
// Nonlinear diffusion filtering scalar step
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
// For non-maxima suppresion
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value,
int row, int col, bool same_img);
//*************************************************************************************
//*************************************************************************************
#endif // NLDIFFUSION_FUNCTIONS_H_

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//=============================================================================
//
// utils.cpp
// Author: Pablo F. Alcantarilla
// Institution: University d'Auvergne
// Address: Clermont Ferrand, France
// Date: 29/12/2011
// Email: pablofdezalc@gmail.com
//
// KAZE Features Copyright 2012, Pablo F. Alcantarilla
// All Rights Reserved
// See LICENSE for the license information
//=============================================================================
/**
* @file utils.cpp
* @brief Some useful functions
* @date Dec 29, 2011
* @author Pablo F. Alcantarilla
*/
#include "utils.h"
using namespace std;
using namespace cv;
//*************************************************************************************
//*************************************************************************************
/**
* @brief This function copies the input image and converts the scale of the copied
* image prior visualization
* @param src Input image
* @param dst Output image
*/
void copy_and_convert_scale(const cv::Mat& src, cv::Mat& dst) {
float min_val = 0, max_val = 0;
src.copyTo(dst);
compute_min_32F(dst,min_val);
dst = dst - min_val;
compute_max_32F(dst,max_val);
dst = dst / max_val;
}
//*************************************************************************************
//*************************************************************************************
/*
void show_input_options_help(int example) {
fflush(stdout);
cout << endl;
cout << endl;
cout << "KAZE Features" << endl;
cout << "***********************************************************" << endl;
cout << "For running the program you need to type in the command line the following arguments: " << endl;
if (example == 0) {
cout << "./kaze_features img.jpg [options]" << endl;
}
else if (example == 1) {
cout << "./kaze_match img1.jpg img2.pgm homography.txt [options]" << endl;
}
else if (example == 2) {
cout << "./kaze_compare img1.jpg img2.pgm homography.txt [options]" << endl;
}
cout << endl;
cout << "The options are not mandatory. In case you do not specify additional options, default arguments will be used" << endl << endl;
cout << "Here is a description of the additional options: " << endl;
cout << "--verbose " << "\t\t if verbosity is required" << endl;
cout << "--help" << "\t\t for showing the command line options" << endl;
cout << "--soffset" << "\t\t the base scale offset (sigma units)" << endl;
cout << "--omax" << "\t\t maximum octave evolution of the image 2^sigma (coarsest scale)" << endl;
cout << "--nsublevels" << "\t\t number of sublevels per octave" << endl;
cout << "--dthreshold" << "\t\t Feature detector threshold response for accepting points (0.001 can be a good value)" << endl;
cout << "--descriptor" << "\t\t Descriptor Type 0 -> SURF, 1 -> M-SURF, 2 -> G-SURF" << endl;
cout << "--use_fed" "\t\t 1 -> Use FED, 0 -> Use AOS for the nonlinear diffusion filtering" << endl;
cout << "--upright" << "\t\t 0 -> Rotation Invariant, 1 -> No Rotation Invariant" << endl;
cout << "--extended" << "\t\t 0 -> Normal Descriptor (64), 1 -> Extended Descriptor (128)" << endl;
cout << "--output keypoints.txt" << "\t\t For saving the detected keypoints into a .txt file" << endl;
cout << "--save_scale_space" << "\t\t 1 in case we want to save the nonlinear scale space images. 0 otherwise" << endl;
cout << "--show_results" << "\t\t 1 in case we want to show detection results. 0 otherwise" << endl;
cout << endl;
}
*/

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/**
* @file utils.h
* @brief Some useful functions
* @date Dec 29, 2011
* @author Pablo F. Alcantarilla
*/
#ifndef UTILS_H_
#define UTILS_H_
//******************************************************************************
//******************************************************************************
// OPENCV Includes
#include "precomp.hpp"
// System Includes
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <cstdlib>
#include <string>
#include <vector>
#include <fstream>
#include <assert.h>
#include <math.h>
//*************************************************************************************
//*************************************************************************************
// Declaration of Functions
void compute_min_32F(const cv::Mat& src, float& value);
void compute_max_32F(const cv::Mat& src, float& value);
void convert_scale(cv::Mat& src);
void copy_and_convert_scale(const cv::Mat &src, cv::Mat& dst);
//*************************************************************************************
//*************************************************************************************
#endif // UTILS_H_