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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2008, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
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>
*/
#include "precomp.hpp"
#include "kaze/AKAZEFeatures.h"
#include <iostream>
namespace cv
{
using namespace std;
class AKAZE_Impl : public AKAZE
{
public:
AKAZE_Impl(int _descriptor_type, int _descriptor_size, int _descriptor_channels,
float _threshold, int _octaves, int _sublevels, int _diffusivity)
: descriptor(_descriptor_type)
, descriptor_channels(_descriptor_channels)
, descriptor_size(_descriptor_size)
, threshold(_threshold)
, octaves(_octaves)
, sublevels(_sublevels)
, diffusivity(_diffusivity)
{
}
virtual ~AKAZE_Impl()
{
}
void setDescriptorType(int dtype) { descriptor = dtype; }
int getDescriptorType() const { return descriptor; }
void setDescriptorSize(int dsize) { descriptor_size = dsize; }
int getDescriptorSize() const { return descriptor_size; }
void setDescriptorChannels(int dch) { descriptor_channels = dch; }
int getDescriptorChannels() const { return descriptor_channels; }
void setThreshold(double threshold_) { threshold = (float)threshold_; }
double getThreshold() const { return threshold; }
void setNOctaves(int octaves_) { octaves = octaves_; }
int getNOctaves() const { return octaves; }
void setNOctaveLayers(int octaveLayers_) { sublevels = octaveLayers_; }
int getNOctaveLayers() const { return sublevels; }
void setDiffusivity(int diff_) { diffusivity = diff_; }
int getDiffusivity() const { return diffusivity; }
// returns the descriptor size in bytes
int descriptorSize() const
{
switch (descriptor)
{
case DESCRIPTOR_KAZE:
case DESCRIPTOR_KAZE_UPRIGHT:
return 64;
case DESCRIPTOR_MLDB:
case DESCRIPTOR_MLDB_UPRIGHT:
// 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.);
}
else
{
// We use the random bit selection length binary descriptor
return (int)ceil(descriptor_size / 8.);
}
default:
return -1;
}
}
// returns the descriptor type
int descriptorType() const
{
switch (descriptor)
{
case DESCRIPTOR_KAZE:
case DESCRIPTOR_KAZE_UPRIGHT:
return CV_32F;
case DESCRIPTOR_MLDB:
case DESCRIPTOR_MLDB_UPRIGHT:
return CV_8U;
default:
return -1;
}
}
// returns the default norm type
int defaultNorm() const
{
switch (descriptor)
{
case DESCRIPTOR_KAZE:
case DESCRIPTOR_KAZE_UPRIGHT:
return NORM_L2;
case DESCRIPTOR_MLDB:
case DESCRIPTOR_MLDB_UPRIGHT:
return NORM_HAMMING;
default:
return -1;
}
}
void detectAndCompute(InputArray image, InputArray mask,
std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints)
{
CV_INSTRUMENT_REGION()
Mat img = image.getMat();
if (img.channels() > 1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
if ( img.depth() == CV_32F )
img1_32 = img;
else if ( img.depth() == CV_8U )
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
else if ( img.depth() == CV_16U )
img.convertTo(img1_32, CV_32F, 1.0 / 65535.0, 0);
CV_Assert( ! img1_32.empty() );
AKAZEOptions options;
options.descriptor = descriptor;
options.descriptor_channels = descriptor_channels;
options.descriptor_size = descriptor_size;
options.img_width = img.cols;
options.img_height = img.rows;
options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
AKAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
if (!useProvidedKeypoints)
{
impl.Feature_Detection(keypoints);
impl.Compute_Keypoints_Orientation(keypoints);
}
if (!mask.empty())
{
KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
}
if( descriptors.needed() )
{
Mat desc;
impl.Compute_Descriptors(keypoints, desc);
// TODO optimize this copy
desc.copyTo(descriptors);
CV_Assert((!desc.rows || desc.cols == descriptorSize()));
CV_Assert((!desc.rows || (desc.type() == descriptorType())));
}
}
void write(FileStorage& fs) const
{
writeFormat(fs);
fs << "descriptor" << descriptor;
fs << "descriptor_channels" << descriptor_channels;
fs << "descriptor_size" << descriptor_size;
fs << "threshold" << threshold;
fs << "octaves" << octaves;
fs << "sublevels" << sublevels;
fs << "diffusivity" << diffusivity;
}
void read(const FileNode& fn)
{
descriptor = (int)fn["descriptor"];
descriptor_channels = (int)fn["descriptor_channels"];
descriptor_size = (int)fn["descriptor_size"];
threshold = (float)fn["threshold"];
octaves = (int)fn["octaves"];
sublevels = (int)fn["sublevels"];
diffusivity = (int)fn["diffusivity"];
}
int descriptor;
int descriptor_channels;
int descriptor_size;
float threshold;
int octaves;
int sublevels;
int diffusivity;
};
Ptr<AKAZE> AKAZE::create(int descriptor_type,
int descriptor_size, int descriptor_channels,
float threshold, int octaves,
int sublevels, int diffusivity)
{
return makePtr<AKAZE_Impl>(descriptor_type, descriptor_size, descriptor_channels,
threshold, octaves, sublevels, diffusivity);
}
}