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"
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#include "kaze/AKAZEFeatures.h"
#include <iostream>
using namespace std;
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namespace cv
{
AKAZE::AKAZE()
: descriptor(DESCRIPTOR_MLDB)
, descriptor_channels(3)
, descriptor_size(0)
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, threshold(0.001f)
, octaves(4)
, sublevels(4)
, diffusivity(DIFF_PM_G2)
{
}
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AKAZE::AKAZE(int _descriptor_type, int _descriptor_size, int _descriptor_channels,
float _threshold, int _octaves, int _sublevels, int _diffusivity)
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: descriptor(_descriptor_type)
, descriptor_channels(_descriptor_channels)
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, descriptor_size(_descriptor_size)
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, threshold(_threshold)
, octaves(_octaves)
, sublevels(_sublevels)
, diffusivity(_diffusivity)
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{
}
AKAZE::~AKAZE()
{
}
// returns the descriptor size in bytes
int AKAZE::descriptorSize() const
{
switch (descriptor)
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{
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case cv::DESCRIPTOR_KAZE:
case cv::DESCRIPTOR_KAZE_UPRIGHT:
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return 64;
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case cv::DESCRIPTOR_MLDB:
case cv::DESCRIPTOR_MLDB_UPRIGHT:
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// 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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}
default:
return -1;
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}
}
// returns the descriptor type
int AKAZE::descriptorType() const
{
switch (descriptor)
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{
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case cv::DESCRIPTOR_KAZE:
case cv::DESCRIPTOR_KAZE_UPRIGHT:
return CV_32F;
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case cv::DESCRIPTOR_MLDB:
case cv::DESCRIPTOR_MLDB_UPRIGHT:
return CV_8U;
default:
return -1;
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}
}
// returns the default norm type
int AKAZE::defaultNorm() const
{
switch (descriptor)
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{
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case cv::DESCRIPTOR_KAZE:
case cv::DESCRIPTOR_KAZE_UPRIGHT:
return cv::NORM_L2;
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case cv::DESCRIPTOR_MLDB:
case cv::DESCRIPTOR_MLDB_UPRIGHT:
return cv::NORM_HAMMING;
default:
return -1;
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}
}
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;
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options.descriptor = descriptor;
options.descriptor_channels = descriptor_channels;
options.descriptor_size = descriptor_size;
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options.img_width = img.cols;
options.img_height = img.rows;
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options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
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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;
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options.descriptor = descriptor;
options.descriptor_channels = descriptor_channels;
options.descriptor_size = descriptor_size;
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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;
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options.descriptor = descriptor;
options.descriptor_channels = descriptor_channels;
options.descriptor_size = descriptor_size;
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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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}
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}