opencv/modules/features2d/src/akaze.cpp

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/*
OpenCV wrapper of reference implementation of
[1] Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces.
Pablo F. Alcantarilla, J. Nuevo and Adrien Bartoli.
In British Machine Vision Conference (BMVC), Bristol, UK, September 2013
http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla13bmvc.pdf
@author Eugene Khvedchenya <ekhvedchenya@gmail.com>
*/
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#include "precomp.hpp"
#include "akaze/AKAZEFeatures.h"
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namespace cv
{
AKAZE::AKAZE(int _descriptor, int _descriptor_size, int _descriptor_channels)
: descriptor_channels(_descriptor_channels)
, descriptor(_descriptor)
, descriptor_size(_descriptor_size)
{
}
AKAZE::~AKAZE()
{
}
// returns the descriptor size in bytes
int AKAZE::descriptorSize() const
{
if (descriptor < MLDB_UPRIGHT)
{
return 64;
}
else
{
// We use the full length binary descriptor -> 486 bits
if (descriptor_size == 0)
{
int t = (6 + 36 + 120) * descriptor_channels;
return (int)ceil(t / 8.);
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}
else
{
// We use the random bit selection length binary descriptor
return (int)ceil(descriptor_size / 8.);
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}
}
}
// returns the descriptor type
int AKAZE::descriptorType() const
{
if (descriptor < MLDB_UPRIGHT)
{
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return CV_32F;
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}
else
{
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return CV_8U;
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}
}
// returns the default norm type
int AKAZE::defaultNorm() const
{
if (descriptor < MLDB_UPRIGHT)
{
return NORM_L2;
}
else
{
return NORM_HAMMING;
}
}
void AKAZE::operator()(InputArray image, InputArray mask,
std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints) const
{
cv::Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
cv::Mat& desc = descriptors.getMatRef();
AKAZEOptions options;
options.img_width = img.cols;
options.img_height = img.rows;
AKAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
if (!useProvidedKeypoints)
{
impl.Feature_Detection(keypoints);
}
if (!mask.empty())
{
cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
}
impl.Compute_Descriptors(keypoints, desc);
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CV_Assert((!desc.rows || desc.cols == descriptorSize()));
CV_Assert((!desc.rows || (desc.type() & descriptorType())));
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}
void AKAZE::detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
{
cv::Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
AKAZEOptions options;
options.img_width = img.cols;
options.img_height = img.rows;
AKAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
impl.Feature_Detection(keypoints);
if (!mask.empty())
{
cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
}
}
void AKAZE::computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
{
cv::Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
cv::Mat& desc = descriptors.getMatRef();
AKAZEOptions options;
options.img_width = img.cols;
options.img_height = img.rows;
AKAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
impl.Compute_Descriptors(keypoints, desc);
CV_Assert((!desc.rows || desc.cols == descriptorSize()));
CV_Assert((!desc.rows || (desc.type() & descriptorType())));
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}
}