akaze.cpp 8.92 KB
Newer Older
wester committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115
/*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;
a  
Kai Westerkamp committed
116
                    return (int)ceil(t / 8.);
wester committed
117 118 119 120
                }
                else
                {
                    // We use the random bit selection length binary descriptor
a  
Kai Westerkamp committed
121
                    return (int)ceil(descriptor_size / 8.);
wester committed
122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209
                }

            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)
        {
            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);
            }

            if (!mask.empty())
            {
                KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
            }

            if( descriptors.needed() )
            {
a  
Kai Westerkamp committed
210 211
                Mat& desc = descriptors.getMatRef();
                impl.Compute_Descriptors(keypoints, desc);
wester committed
212

a  
Kai Westerkamp committed
213 214
                CV_Assert((!desc.rows || desc.cols == descriptorSize()));
                CV_Assert((!desc.rows || (desc.type() == descriptorType())));
wester committed
215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257
            }
        }

        void write(FileStorage& fs) const
        {
            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);
    }
}