*/
#include "AKAZEFeatures.h"
-#include "fed.h"
+#include "../kaze/fed.h"
#include "nldiffusion_functions.h"
using namespace std;
+++ /dev/null
-#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
* @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
/* ************************************************************************* */
/**
- * @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) = 0.5f*(*(src.ptr<float>(i1)+j1)) + 0.25f*(*(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
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_, 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);
- }
- }
}
}
}
\ No newline at end of file
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 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_, 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);
//******************************************************************************
// Includes
-#include <iostream>
-#include <stdlib.h>
-#include <stdio.h>
-#include <cstdlib>
-#include <math.h>
#include <vector>
//*************************************************************************************
// Namespaces
using namespace std;
using namespace cv;
-using namespace cv::details::kaze;
-//*************************************************************************************
-//*************************************************************************************
+/* ************************************************************************* */
namespace cv {
namespace details {
namespace kaze {
+
+ /* ************************************************************************* */
/**
* @brief This function smoothes an image with a Gaussian kernel
* @param src Input image
* @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) {
+ 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;
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)
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)
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)
dst = 1.0f - 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
return kperc;
}
- //*************************************************************************************
- //*************************************************************************************
-
+ /* ************************************************************************* */
/**
* @brief This function computes Scharr image derivatives
* @param src Input image
* @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) {
+ 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 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) {
+ void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale) {
int ksize = 3 + 2 * (scale - 1);
}
}
- //*************************************************************************************
- //*************************************************************************************
-
+ /* ************************************************************************* */
/**
* @brief This function performs a scalar non-linear diffusion step
* @param Ld2 Output image in the evolution
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 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 check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img) {
bool response = true;
projects / profile / ivi / opencv.git / commitdiff