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Merge branch 4.x

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This commit is contained in:
Alexander Smorkalov
2023-10-16 21:25:56 +03:00
parent d789cb459c 2f63c58380
commit 97620c053f
221 changed files with 3584 additions and 2215 deletions
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modules/features2d/src/akaze.cpp
+17 -3
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@@ -61,7 +61,7 @@ namespace cv
{
public:
AKAZE_Impl(DescriptorType _descriptor_type, int _descriptor_size, int _descriptor_channels,
float _threshold, int _octaves, int _sublevels, KAZE::DiffusivityType _diffusivity)
float _threshold, int _octaves, int _sublevels, KAZE::DiffusivityType _diffusivity, int _max_points)
: descriptor(_descriptor_type)
, descriptor_channels(_descriptor_channels)
, descriptor_size(_descriptor_size)
@@ -69,6 +69,7 @@ namespace cv
, octaves(_octaves)
, sublevels(_sublevels)
, diffusivity(_diffusivity)
, max_points(_max_points)
{
}
@@ -98,6 +99,9 @@ namespace cv
void setDiffusivity(KAZE::DiffusivityType diff_) CV_OVERRIDE{ diffusivity = diff_; }
KAZE::DiffusivityType getDiffusivity() const CV_OVERRIDE{ return diffusivity; }
void setMaxPoints(int max_points_) CV_OVERRIDE { max_points = max_points_; }
int getMaxPoints() const CV_OVERRIDE { return max_points; }
// returns the descriptor size in bytes
int descriptorSize() const CV_OVERRIDE
{
@@ -195,6 +199,12 @@ namespace cv
KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
}
if (max_points > 0 && (int)keypoints.size() > max_points) {
std::partial_sort(keypoints.begin(), keypoints.begin() + max_points, keypoints.end(),
[](const cv::KeyPoint& k1, const cv::KeyPoint& k2) {return k1.response > k2.response;});
keypoints.erase(keypoints.begin() + max_points, keypoints.end());
}
if(descriptors.needed())
{
impl.Compute_Descriptors(keypoints, descriptors);
@@ -215,6 +225,7 @@ namespace cv
fs << "octaves" << octaves;
fs << "sublevels" << sublevels;
fs << "diffusivity" << diffusivity;
fs << "max_points" << max_points;
}
void read(const FileNode& fn) CV_OVERRIDE
@@ -234,6 +245,8 @@ namespace cv
sublevels = (int)fn["sublevels"];
if (!fn["diffusivity"].empty())
diffusivity = static_cast<KAZE::DiffusivityType>((int)fn["diffusivity"]);
if (!fn["max_points"].empty())
max_points = (int)fn["max_points"];
}
DescriptorType descriptor;
@@ -243,15 +256,16 @@ namespace cv
int octaves;
int sublevels;
KAZE::DiffusivityType diffusivity;
int max_points;
};
Ptr<AKAZE> AKAZE::create(DescriptorType descriptor_type,
int descriptor_size, int descriptor_channels,
float threshold, int octaves,
int sublevels, KAZE::DiffusivityType diffusivity)
int sublevels, KAZE::DiffusivityType diffusivity, int max_points)
{
return makePtr<AKAZE_Impl>(descriptor_type, descriptor_size, descriptor_channels,
threshold, octaves, sublevels, diffusivity);
threshold, octaves, sublevels, diffusivity, max_points);
}
String AKAZE::getDefaultName() const
modules/features2d/src/hal_replacement.hpp
+10 -5
View File
@@ -64,9 +64,12 @@
//! @{
/**
@brief Detects corners using the FAST algorithm, returns mask.
@param src_data,src_step Source image
@param dst_data,dst_step Destination mask
@param width,height Source image dimensions
@param src_data Source image data
@param src_step Source image step
@param dst_data Destination mask data
@param dst_step Destination mask step
@param width Source image width
@param height Source image height
@param type FAST type
*/
inline int hal_ni_FAST_dense(const uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step, int width, int height, cv::FastFeatureDetector::DetectorType type) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
@@ -89,8 +92,10 @@ inline int hal_ni_FAST_NMS(const uchar* src_data, size_t src_step, uchar* dst_da
/**
@brief Detects corners using the FAST algorithm.
@param src_data,src_step Source image
@param width,height Source image dimensions
@param src_data Source image data
@param src_step Source image step
@param width Source image width
@param height Source image height
@param keypoints_data Pointer to keypoints
@param keypoints_count Count of keypoints
@param threshold Threshold for keypoint
modules/features2d/src/kaze/nldiffusion_functions.cpp
+15 -15
View File
@@ -86,9 +86,9 @@ void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int
/**
* @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 _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(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
@@ -117,9 +117,9 @@ void pm_g1(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
/**
* @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 _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(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
@@ -146,9 +146,9 @@ void pm_g2(InputArray _Lx, InputArray _Ly, OutputArray _dst, float 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 _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,
@@ -183,9 +183,9 @@ void weickert_diffusivity(InputArray _Lx, InputArray _Ly, OutputArray _dst, floa
/**
* @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 _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,
@@ -323,7 +323,7 @@ void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, in
* @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
* @param scale Scale factor or derivative size
*/
void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale) {
CV_INSTRUMENT_REGION();
@@ -415,7 +415,7 @@ private:
/* ************************************************************************* */
/**
* @brief This function performs a scalar non-linear diffusion step
* @param Ld2 Output image in the evolution
* @param Ld Output image in the evolution
* @param c Conductivity image
* @param Lstep Previous image in the evolution
* @param stepsize The step size in time units
@@ -490,7 +490,7 @@ void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsi
/* ************************************************************************* */
/**
* @brief This function downsamples the input image using OpenCV resize
* @param img Input image to be downsampled
* @param src 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) {
modules/features2d/src/kaze/utils.h
+1 -1
View File
@@ -6,7 +6,7 @@
* @brief This function computes the value of a 2D Gaussian function
* @param x X Position
* @param y Y Position
* @param sig Standard Deviation
* @param sigma Standard Deviation
*/
inline float gaussian(float x, float y, float sigma) {
return expf(-(x*x + y*y) / (2.0f*sigma*sigma));