potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 00:03:03 +04:00
Code Packages Releases Wiki Activity

replace some wrapped functions with direct calls to OpenCV functions

Browse Source 
* improved readability for people familiar with opencv
* do not same image twice in base level
This commit is contained in:
Jiri Horner
2017-06-28 14:05:01 +02:00
parent 8015cfd22f
commit 7f8e9c25a5
1 changed files with 28 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/features2d/src/kaze/AKAZEFeatures.cpp
+28 -8
View File
@@ -99,6 +99,18 @@ void AKAZEFeatures::Allocate_Memory_Evolution(void) {
}
/* ************************************************************************* */
/**
* @brief Computes kernel size for Gaussian smoothing if the image
* @param sigma Kernel standard deviation
* @returns kernel size
*/
static inline int getGaussianKernelSize(float sigma) {
// Compute an appropriate kernel size according to the specified sigma
int ksize = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
ksize |= 1; // kernel should be odd
return ksize;
}
/**
* @brief This method creates the nonlinear scale space for a given image
* @param img Input image for which the nonlinear scale space needs to be created
@@ -109,10 +121,15 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const Mat& img)
CV_INSTRUMENT_REGION()
CV_Assert(evolution_.size() > 0);
// Copy the original image to the first level of the evolution
img.copyTo(evolution_[0].Lt);
gaussian_2D_convolution(evolution_[0].Lt, evolution_[0].Lt, 0, 0, options_.soffset);
evolution_[0].Lt.copyTo(evolution_[0].Lsmooth);
// create first level of the evolution
int ksize = getGaussianKernelSize(options_.soffset);
GaussianBlur(img, evolution_[0].Lt, Size(ksize, ksize), options_.soffset, options_.soffset, BORDER_REPLICATE);
evolution_[0].Lsmooth = evolution_[0].Lt;
if (evolution_.size() == 1) {
// we don't need to compute kcontrast factor
return 0;
}
// First compute the kcontrast factor
options_.kcontrast = compute_k_percentile(img, options_.kcontrast_percentile, 1.0f, options_.kcontrast_nbins, 0, 0);
@@ -121,18 +138,21 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const Mat& img)
for (size_t i = 1; i < evolution_.size(); i++) {
if (evolution_[i].octave > evolution_[i - 1].octave) {
halfsample_image(evolution_[i - 1].Lt, evolution_[i].Lt);
Size half_size = evolution_[i - 1].Lt.size();
half_size.width /= 2;
half_size.height /= 2;
resize(evolution_[i - 1].Lt, evolution_[i].Lt, half_size, 0, 0, INTER_AREA);
options_.kcontrast = options_.kcontrast*0.75f;
}
else {
evolution_[i - 1].Lt.copyTo(evolution_[i].Lt);
}
gaussian_2D_convolution(evolution_[i].Lt, evolution_[i].Lsmooth, 0, 0, 1.0f);
GaussianBlur(evolution_[i].Lt, evolution_[i].Lsmooth, Size(5, 5), 1.0f, 1.0f, BORDER_REPLICATE);
// Compute the Gaussian derivatives Lx and Ly
image_derivatives_scharr(evolution_[i].Lsmooth, evolution_[i].Lx, 1, 0);
image_derivatives_scharr(evolution_[i].Lsmooth, evolution_[i].Ly, 0, 1);
Scharr(evolution_[i].Lsmooth, evolution_[i].Lx, CV_32F, 1, 0, 1.0, 0, BORDER_DEFAULT);
Scharr(evolution_[i].Lsmooth, evolution_[i].Ly, CV_32F, 0, 1, 1.0, 0, BORDER_DEFAULT);
// Compute the conductivity equation
switch (options_.diffusivity) {