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opencv
Commit a941d25f authored by Ievgen Khvedchenia's avatar Ievgen Khvedchenia
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Fix size_t to int conversion

parent 56622943
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Showing with 18 additions and 23 deletions
modules/features2d/src/akaze/nldiffusion_functions.cpp
View file @ a941d25f
......@@ -36,11 +36,11 @@ using namespace cv;
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;
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_ = ceil(2.0*(1.0 + (sigma - 0.8) / (0.3)));
ksize_x_ = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
ksize_y_ = ksize_x_;
}
......@@ -158,17 +158,13 @@ float compute_k_percentile(const cv::Mat& img, float perc, float gscale,
float hmax = 0.0;
// Create the array for the histogram
float *hist = new float[nbins];
std::vector<size_t> 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);
// Set the histogram to zero
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);
......@@ -199,7 +195,7 @@ float compute_k_percentile(const cv::Mat& img, float perc, float gscale,
// Find the correspondent bin
if (modg != 0.0) {
nbin = floor(nbins*(modg / hmax));
nbin = (size_t)floor(nbins*(modg / hmax));
if (nbin == nbins) {
nbin--;
......@@ -219,13 +215,12 @@ float compute_k_percentile(const cv::Mat& img, float perc, float gscale,
}
if (nelements < nthreshold) {
kperc = 0.03;
kperc = 0.03f;
}
else {
kperc = hmax*((float)(k) / (float)nbins);
}
delete[] hist;
return kperc;
}
......@@ -268,7 +263,7 @@ void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, const float&
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);
*(Lstep.ptr<float>(i)+j) = 0.5f*stepsize*(xpos - xneg + ypos - yneg);
}
}
......@@ -276,7 +271,7 @@ void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, const float&
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) = 0.5*stepsize*(xpos - xneg + ypos);
*(Lstep.ptr<float>(0) + j) = 0.5f*stepsize*(xpos - xneg + ypos);
}
for (int j = 1; j < Lstep.cols - 1; j++) {
......@@ -284,7 +279,7 @@ void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, const float&
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);
*(Lstep.ptr<float>(Lstep.rows - 1) + j) = 0.5f*stepsize*(xpos - xneg + ypos - yneg);
}
for (int i = 1; i < Lstep.rows - 1; i++) {
......@@ -292,14 +287,14 @@ void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, const float&
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);
*(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) = 0.5*stepsize*(-xneg + ypos - yneg);
*(Lstep.ptr<float>(i)+Lstep.cols - 1) = 0.5f*stepsize*(-xneg + ypos - yneg);
}
Ld = Ld + Lstep;
......@@ -318,7 +313,7 @@ void downsample_image(const cv::Mat& src, cv::Mat& dst) {
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));
*(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++;
}
......@@ -352,7 +347,7 @@ 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) {
const int ksize = 3 + 2 * (scale - 1);
const int ksize = 3 + 2 * ( (int)scale - 1);
// The usual Scharr kernel
if (scale == 1) {
......@@ -365,8 +360,8 @@ void compute_derivative_kernels(cv::OutputArray kx_, cv::OutputArray ky_,
Mat kx = kx_.getMat();
Mat ky = ky_.getMat();
float w = 10.0 / 3.0;
float norm = 1.0 / (2.0*scale*(w + 2.0));
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;
......
modules/features2d/src/kaze/fed.cpp
View file @ a941d25f
......@@ -72,8 +72,8 @@ int fed_tau_by_cycle_time(const float& t, const float& tau_max,
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)));
n = (int)(ceilf(sqrtf(3.0f*t/tau_max+0.25f)-0.5f-1.0e-8f)+ 0.5f);
scale = 3.0f*t/(tau_max*(float)(n*(n+1)));
// Call internal FED time step creation routine
return fed_tau_internal(n,scale,tau_max,reordering,tau);
......@@ -114,7 +114,7 @@ int fed_tau_internal(const int& n, const float& scale, const float& tau_max,
// 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);
float h = cosf((float)CV_PI * (2.0f * (float)k + 1.0f) * c);
if (reordering) {
tauh[k] = d / (h * h);
......@@ -175,7 +175,7 @@ bool fed_is_prime_internal(const int& number) {
}
else {
is_prime = true;
int upperLimit = sqrt(number+1.0);
int upperLimit = (int)sqrt(1.0f + number);
int divisor = 11;
while (divisor <= upperLimit ) {
......
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