1 /*M/////////////////////////////////////////////////////////////////////////////////////// 2 // 3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4 // 5 // By downloading, copying, installing or using the software you agree to this license. 6 // If you do not agree to this license, do not download, install, 7 // copy or use the software. 8 // 9 // 10 // License Agreement 11 // For Open Source Computer Vision Library 12 // 13 // Copyright (C) 2008, Willow Garage Inc., all rights reserved. 14 // Third party copyrights are property of their respective owners. 15 // 16 // Redistribution and use in source and binary forms, with or without modification, 17 // are permitted provided that the following conditions are met: 18 // 19 // * Redistribution's of source code must retain the above copyright notice, 20 // this list of conditions and the following disclaimer. 21 // 22 // * Redistribution's in binary form must reproduce the above copyright notice, 23 // this list of conditions and the following disclaimer in the documentation 24 // and/or other materials provided with the distribution. 25 // 26 // * The name of Intel Corporation may not be used to endorse or promote products 27 // derived from this software without specific prior written permission. 28 // 29 // This software is provided by the copyright holders and contributors "as is" and 30 // any express or implied warranties, including, but not limited to, the implied 31 // warranties of merchantability and fitness for a particular purpose are disclaimed. 32 // In no event shall the Intel Corporation or contributors be liable for any direct, 33 // indirect, incidental, special, exemplary, or consequential damages 34 // (including, but not limited to, procurement of substitute goods or services; 35 // loss of use, data, or profits; or business interruption) however caused 36 // and on any theory of liability, whether in contract, strict liability, 37 // or tort (including negligence or otherwise) arising in any way out of 38 // the use of this software, even if advised of the possibility of such damage. 39 // 40 //M*/ 41 42 /* 43 OpenCV wrapper of reference implementation of 44 [1] Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. 45 Pablo F. Alcantarilla, J. Nuevo and Adrien Bartoli. 46 In British Machine Vision Conference (BMVC), Bristol, UK, September 2013 47 http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla13bmvc.pdf 48 @author Eugene Khvedchenya <ekhvedchenya (at) gmail.com> 49 */ 50 51 #include "precomp.hpp" 52 #include "kaze/AKAZEFeatures.h" 53 54 #include <iostream> 55 56 namespace cv 57 { 58 using namespace std; 59 60 class AKAZE_Impl : public AKAZE 61 { 62 public: 63 AKAZE_Impl(int _descriptor_type, int _descriptor_size, int _descriptor_channels, 64 float _threshold, int _octaves, int _sublevels, int _diffusivity) 65 : descriptor(_descriptor_type) 66 , descriptor_channels(_descriptor_channels) 67 , descriptor_size(_descriptor_size) 68 , threshold(_threshold) 69 , octaves(_octaves) 70 , sublevels(_sublevels) 71 , diffusivity(_diffusivity) 72 { 73 } 74 75 virtual ~AKAZE_Impl() 76 { 77 78 } 79 80 void setDescriptorType(int dtype) { descriptor = dtype; } 81 int getDescriptorType() const { return descriptor; } 82 83 void setDescriptorSize(int dsize) { descriptor_size = dsize; } 84 int getDescriptorSize() const { return descriptor_size; } 85 86 void setDescriptorChannels(int dch) { descriptor_channels = dch; } 87 int getDescriptorChannels() const { return descriptor_channels; } 88 89 void setThreshold(double threshold_) { threshold = (float)threshold_; } 90 double getThreshold() const { return threshold; } 91 92 void setNOctaves(int octaves_) { octaves = octaves_; } 93 int getNOctaves() const { return octaves; } 94 95 void setNOctaveLayers(int octaveLayers_) { sublevels = octaveLayers_; } 96 int getNOctaveLayers() const { return sublevels; } 97 98 void setDiffusivity(int diff_) { diffusivity = diff_; } 99 int getDiffusivity() const { return diffusivity; } 100 101 // returns the descriptor size in bytes 102 int descriptorSize() const 103 { 104 switch (descriptor) 105 { 106 case DESCRIPTOR_KAZE: 107 case DESCRIPTOR_KAZE_UPRIGHT: 108 return 64; 109 110 case DESCRIPTOR_MLDB: 111 case DESCRIPTOR_MLDB_UPRIGHT: 112 // We use the full length binary descriptor -> 486 bits 113 if (descriptor_size == 0) 114 { 115 int t = (6 + 36 + 120) * descriptor_channels; 116 return (int)ceil(t / 8.); 117 } 118 else 119 { 120 // We use the random bit selection length binary descriptor 121 return (int)ceil(descriptor_size / 8.); 122 } 123 124 default: 125 return -1; 126 } 127 } 128 129 // returns the descriptor type 130 int descriptorType() const 131 { 132 switch (descriptor) 133 { 134 case DESCRIPTOR_KAZE: 135 case DESCRIPTOR_KAZE_UPRIGHT: 136 return CV_32F; 137 138 case DESCRIPTOR_MLDB: 139 case DESCRIPTOR_MLDB_UPRIGHT: 140 return CV_8U; 141 142 default: 143 return -1; 144 } 145 } 146 147 // returns the default norm type 148 int defaultNorm() const 149 { 150 switch (descriptor) 151 { 152 case DESCRIPTOR_KAZE: 153 case DESCRIPTOR_KAZE_UPRIGHT: 154 return NORM_L2; 155 156 case DESCRIPTOR_MLDB: 157 case DESCRIPTOR_MLDB_UPRIGHT: 158 return NORM_HAMMING; 159 160 default: 161 return -1; 162 } 163 } 164 165 void detectAndCompute(InputArray image, InputArray mask, 166 std::vector<KeyPoint>& keypoints, 167 OutputArray descriptors, 168 bool useProvidedKeypoints) 169 { 170 Mat img = image.getMat(); 171 if (img.type() != CV_8UC1) 172 cvtColor(image, img, COLOR_BGR2GRAY); 173 174 Mat img1_32; 175 if ( img.depth() == CV_32F ) 176 img1_32 = img; 177 else if ( img.depth() == CV_8U ) 178 img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0); 179 else if ( img.depth() == CV_16U ) 180 img.convertTo(img1_32, CV_32F, 1.0 / 65535.0, 0); 181 182 CV_Assert( ! img1_32.empty() ); 183 184 AKAZEOptions options; 185 options.descriptor = descriptor; 186 options.descriptor_channels = descriptor_channels; 187 options.descriptor_size = descriptor_size; 188 options.img_width = img.cols; 189 options.img_height = img.rows; 190 options.dthreshold = threshold; 191 options.omax = octaves; 192 options.nsublevels = sublevels; 193 options.diffusivity = diffusivity; 194 195 AKAZEFeatures impl(options); 196 impl.Create_Nonlinear_Scale_Space(img1_32); 197 198 if (!useProvidedKeypoints) 199 { 200 impl.Feature_Detection(keypoints); 201 } 202 203 if (!mask.empty()) 204 { 205 KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat()); 206 } 207 208 if( descriptors.needed() ) 209 { 210 Mat& desc = descriptors.getMatRef(); 211 impl.Compute_Descriptors(keypoints, desc); 212 213 CV_Assert((!desc.rows || desc.cols == descriptorSize())); 214 CV_Assert((!desc.rows || (desc.type() == descriptorType()))); 215 } 216 } 217 218 void write(FileStorage& fs) const 219 { 220 fs << "descriptor" << descriptor; 221 fs << "descriptor_channels" << descriptor_channels; 222 fs << "descriptor_size" << descriptor_size; 223 fs << "threshold" << threshold; 224 fs << "octaves" << octaves; 225 fs << "sublevels" << sublevels; 226 fs << "diffusivity" << diffusivity; 227 } 228 229 void read(const FileNode& fn) 230 { 231 descriptor = (int)fn["descriptor"]; 232 descriptor_channels = (int)fn["descriptor_channels"]; 233 descriptor_size = (int)fn["descriptor_size"]; 234 threshold = (float)fn["threshold"]; 235 octaves = (int)fn["octaves"]; 236 sublevels = (int)fn["sublevels"]; 237 diffusivity = (int)fn["diffusivity"]; 238 } 239 240 int descriptor; 241 int descriptor_channels; 242 int descriptor_size; 243 float threshold; 244 int octaves; 245 int sublevels; 246 int diffusivity; 247 }; 248 249 Ptr<AKAZE> AKAZE::create(int descriptor_type, 250 int descriptor_size, int descriptor_channels, 251 float threshold, int octaves, 252 int sublevels, int diffusivity) 253 { 254 return makePtr<AKAZE_Impl>(descriptor_type, descriptor_size, descriptor_channels, 255 threshold, octaves, sublevels, diffusivity); 256 } 257 } 258
