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Merged nldiffusion functions into one module with removal of duplicate functions

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This commit is contained in:
Ievgen Khvedchenia 2014-05-01 22:24:15 +03:00
parent 2df7242646
commit 9fc90f4069
6 changed files with 77 additions and 437 deletions
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6
modules/features2d/src/akaze/AKAZEFeatures.cpp
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@ -8,11 +8,11 @@
#include "AKAZEFeatures.h" #include "AKAZEFeatures.h"
#include "../kaze/fed.h" #include "../kaze/fed.h"
#include "nldiffusion_functions.h" #include "../kaze/nldiffusion_functions.h"
using namespace std; using namespace std;
using namespace cv; using namespace cv;
using namespace cv::details::akaze; using namespace cv::details::kaze;
/* ************************************************************************* */ /* ************************************************************************* */
/** /**
@ -154,7 +154,7 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img) {
// Perform FED n inner steps // Perform FED n inner steps
for (int j = 0; j < nsteps_[i - 1]; j++) { for (int j = 0; j < nsteps_[i - 1]; j++) {
nld_step_scalar(evolution_[i].Lt, evolution_[i].Lflow, evolution_[i].Lstep, tsteps_[i - 1][j]); cv::details::kaze::nld_step_scalar(evolution_[i].Lt, evolution_[i].Lflow, evolution_[i].Lstep, tsteps_[i - 1][j]);
} }
} }

371
modules/features2d/src/akaze/nldiffusion_functions.cpp
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@ -1,371 +0,0 @@
//=============================================================================
//
// 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 "akaze/nldiffusion_functions.h"
using namespace std;
using namespace cv;
namespace cv {
namespace details {
namespace akaze {
/* ************************************************************************* */
/**
* @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) {
int 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_ = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
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, int xorder, int 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, 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
std::vector<int> hist(nbins, 0);
// Create the matrices
cv::Mat gaussian = cv::Mat::zeros(img.rows, img.cols, CV_32F);
cv::Mat Lx = cv::Mat::zeros(img.rows, img.cols, CV_32F);
cv::Mat Ly = cv::Mat::zeros(img.rows, img.cols, CV_32F);
// 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 = (int)floor(nbins*(modg / hmax));
if (nbin == nbins) {
nbin--;
}
hist[nbin]++;
npoints++;
}
}
}
// Now find the perc of the histogram percentile
nthreshold = (int)(npoints*perc);
for (k = 0; nelements < nthreshold && k < nbins; k++) {
nelements = nelements + hist[k];
}
if (nelements < nthreshold) {
kperc = 0.03f;
}
else {
kperc = hmax*((float)(k) / (float)nbins);
}
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, 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 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_, int dx, int dy, int 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 = 10.0f / 3.0f;
float norm = 1.0f / (2.0f*scale*(w + 2.0f));
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);
}
}
/* ************************************************************************* */
/**
* @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) = 0.5f*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)));
*(Lstep.ptr<float>(0) + j) = 0.5f*stepsize*(xpos - xneg + ypos);
}
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) = 0.5f*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)) = 0.5f*stepsize*(xpos - xneg + ypos - yneg);
}
for (int i = 1; i < Lstep.rows - 1; i++) {
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) = 0.5f*stepsize*(-xneg + ypos - yneg);
}
Ld = Ld + Lstep;
}
/* ************************************************************************* */
/**
* @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);
}
}
}
}

39
modules/features2d/src/akaze/nldiffusion_functions.h
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@ -1,39 +0,0 @@
/**
* @file nldiffusion_functions.h
* @brief Functions for nonlinear diffusion filtering applications
* @date Sep 15, 2013
* @author Pablo F. Alcantarilla, Jesus Nuevo
*/
#ifndef AKAZE_NLDIFFUSION_FUNCTIONS_H
#define AKAZE_NLDIFFUSION_FUNCTIONS_H
/* ************************************************************************* */
// Includes
#include "precomp.hpp"
/* ************************************************************************* */
// Declaration of functions
namespace cv {
namespace details {
namespace akaze {
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma);
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int 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, float perc, float gscale, int nbins, int ksize_x, int ksize_y);
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int, int scale);
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, const float& stepsize);
void halfsample_image(const cv::Mat& src, cv::Mat& dst);
void compute_derivative_kernels(cv::OutputArray kx_, cv::OutputArray ky_, int dx, int dy, int scale);
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
}
}
}
#endif

86
modules/features2d/src/kaze/nldiffusion_functions.cpp
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@ -1,4 +1,3 @@
//============================================================================= //=============================================================================
// //
// nldiffusion_functions.cpp // nldiffusion_functions.cpp
@ -64,7 +63,23 @@ namespace cv {
} }
// Perform the Gaussian Smoothing with border replication // Perform the Gaussian Smoothing with border replication
GaussianBlur(src, dst, Size(ksize_x_, ksize_y_), sigma, sigma, cv::BORDER_REPLICATE); 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, int xorder, int yorder) {
Scharr(src, dst, CV_32F, xorder, yorder, 1.0, 0, BORDER_DEFAULT);
} }
/* ************************************************************************* */ /* ************************************************************************* */
@ -90,12 +105,12 @@ namespace cv {
* @param k Contrast factor parameter * @param k Contrast factor parameter
*/ */
void pm_g2(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) {
dst = 1. / (1. + (Lx.mul(Lx) + Ly.mul(Ly)) / (k*k)); dst = 1.0f / (1.0f + (Lx.mul(Lx) + Ly.mul(Ly)) / (k*k));
} }
/* ************************************************************************* */ /* ************************************************************************* */
/** /**
* @brief This function computes Weickert conductivity coefficient g3 * @brief This function computes Weickert conductivity coefficient gw
* @param Lx First order image derivative in X-direction (horizontal) * @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical) * @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image * @param dst Output image
@ -107,10 +122,28 @@ namespace cv {
void weickert_diffusivity(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) {
Mat modg; Mat modg;
cv::pow((Lx.mul(Lx) + Ly.mul(Ly)) / (k*k), 4, modg); cv::pow((Lx.mul(Lx) + Ly.mul(Ly)) / (k*k), 4, modg);
cv::exp(-3.315 / modg, dst); cv::exp(-3.315f / modg, dst);
dst = 1.0f - dst; dst = 1.0f - 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, float k) {
Mat den;
cv::sqrt(1.0f + (Lx.mul(Lx) + Ly.mul(Ly)) / (k*k), den);
dst = 1.0f / den;
}
/* ************************************************************************* */ /* ************************************************************************* */
/** /**
* @brief This function computes a good empirical value for the k contrast factor * @brief This function computes a good empirical value for the k contrast factor
@ -182,8 +215,7 @@ namespace cv {
} }
// Now find the perc of the histogram percentile // Now find the perc of the histogram percentile
nthreshold = (size_t)(npoints*perc); nthreshold = (int)(npoints*perc);
for (k = 0; nelements < nthreshold && k < nbins; k++) { for (k = 0; nelements < nthreshold && k < nbins; k++) {
nelements = nelements + hist[k]; nelements = nelements + hist[k];
@ -206,7 +238,7 @@ namespace cv {
* @param dst Output image * @param dst Output image
* @param xorder Derivative order in X-direction (horizontal) * @param xorder Derivative order in X-direction (horizontal)
* @param yorder Derivative order in Y-direction (vertical) * @param yorder Derivative order in Y-direction (vertical)
* @param scale Scale factor or derivative size * @param scale Scale factor for the derivative size
*/ */
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale) { void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale) {
Mat kx, ky; Mat kx, ky;
@ -260,15 +292,15 @@ namespace cv {
/* ************************************************************************* */ /* ************************************************************************* */
/** /**
* @brief This function performs a scalar non-linear diffusion step * @brief This function performs a scalar non-linear diffusion step
* @param Ld2 Output image in the evolution * @param Ld2 Output image in the evolution
* @param c Conductivity image * @param c Conductivity image
* @param Lstep Previous image in the evolution * @param Lstep Previous image in the evolution
* @param stepsize The step size in time units * @param stepsize The step size in time units
* @note Forward Euler Scheme 3x3 stencil * @note Forward Euler Scheme 3x3 stencil
* The function c is a scalar value that depends on the gradient norm * 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 * 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) { void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize) {
#ifdef _OPENMP #ifdef _OPENMP
@ -288,8 +320,7 @@ namespace cv {
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 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 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 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) = 0.5f*stepsize*(xpos - xneg + ypos);
*(Lstep.ptr<float>(0) + j) = 0.5f*stepsize*(xpos - xneg + ypos - yneg);
} }
for (int j = 1; j < Lstep.cols - 1; j++) { for (int j = 1; j < Lstep.cols - 1; j++) {
@ -309,16 +340,29 @@ namespace cv {
} }
for (int i = 1; i < Lstep.rows - 1; i++) { 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 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 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))); 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) = 0.5f*stepsize*(xpos - xneg + ypos - yneg); *(Lstep.ptr<float>(i)+Lstep.cols - 1) = 0.5f*stepsize*(-xneg + ypos - yneg);
} }
Ld = Ld + Lstep; Ld = Ld + Lstep;
} }
/* ************************************************************************* */
/**
* @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 This function checks if a given pixel is a maximum in a local neighbourhood * @brief This function checks if a given pixel is a maximum in a local neighbourhood

10
modules/features2d/src/kaze/nldiffusion_functions.h
View File

@ -11,11 +11,12 @@
#ifndef KAZE_NLDIFFUSION_FUNCTIONS_H #ifndef KAZE_NLDIFFUSION_FUNCTIONS_H
#define KAZE_NLDIFFUSION_FUNCTIONS_H #define KAZE_NLDIFFUSION_FUNCTIONS_H
/* ************************************************************************* */
// Includes // Includes
#include "precomp.hpp" #include "precomp.hpp"
//************************************************************************************* /* ************************************************************************* */
//************************************************************************************* // Declaration of functions
namespace cv { namespace cv {
namespace details { namespace details {
@ -28,11 +29,14 @@ namespace cv {
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k); 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 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); void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
void charbonnier_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); float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y);
// Image derivatives // Image derivatives
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale); 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); void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale);
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder);
// Nonlinear diffusion filtering scalar step // Nonlinear diffusion filtering scalar step
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize); void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
@ -40,6 +44,8 @@ namespace cv {
// For non-maxima suppresion // For non-maxima suppresion
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img); bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
// Image downsampling
void halfsample_image(const cv::Mat& src, cv::Mat& dst);
} }
} }
} }

2
modules/features2d/test/test_keypoints.cpp
View File

@ -177,4 +177,4 @@ TEST(Features2d_Detector_Keypoints_AKAZE, validation)
{ {
CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.AKAZE")); CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.AKAZE"));
test.safe_run(); test.safe_run();
} }