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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 /* autopano-sift, Automatic panorama image creation * Copyright (C) 2004 -- Sebastian Nowozin * * This program is free software released under the GNU General Public * License, which is included in this software package (doc/LICENSE). */ /* LoweDetector.cs * * Lowe scale-invariant keypoint feature detector (SIFT) interface. * * (C) Copyright 2004 -- Sebastian Nowozin (nowozin@cs.tu-berlin.de) * * Implementation of the SIFT algorithm as specified in this research paper by * David Lowe: http://www.cs.ubc.ca/~lowe/papers/ijcv03-abs.html * * "The University of British Columbia has applied for a patent on the SIFT * algorithm in the United States. Commercial applications of this software * may require a license from the University of British Columbia." * For more information, see the LICENSE file supplied with the distribution. */ #include "AutoPanoSift.h" // Detection parameters, suggested by Lowe's research paper. // Initial parameters double octaveSigma = 1.6; // Sigma for gaussian filter applied to double-scaled input image. double preprocSigma = 1.5; double LoweFeatureDetector_SetPreprocSigma( double newsigma ){ double d = preprocSigma; if( newsigma < 0 ) newsigma = 0; preprocSigma = newsigma; return d; } // Once one of the downscaled image's dimension falls below this, // downscaling is stopped. int minimumRequiredPixelsize = 32; // How many DoG levels for each octave. int scaleSpaceLevels = 3; bool printWarning = true; // Disable the patent warning. Used so far only in autopano's subcalls to // the SIFT algorithm in the refining step. void LoweFeatureDetector_SetPrintWarning(bool value) { printWarning = value; } bool verbose = true; void LoweFeatureDetector_SetVerbose(bool value) { verbose = value; } // *** Peak related parameters // Tweak here to reduce/increase keypoint density // Minimum absolute DoG value of a pixel to be allowed as minimum/maximum // peak. This control how much general non-differing areas, such as the // sky is filtered. Higher value = less peaks, lower value = more peaks. // Good values from 0.005 to 0.01. Note this is related to // 'dValueLowThresh', which should be a bit larger, factor 1.0 to 1.5. double dogThresh = 0.0075; // D-value filter highcap value, higher = less keypoints, lower = more. // Lower: only keep keypoints with good localization properties, i.e. // those that are precisely and easily to localize (high contrast, see // Lowe, page 11. He recommends 0.03, but this seems way too high to // me.) double dValueLowThresh = 0.008; // Required cornerness ratio level, higher = more keypoints, lower = less. double maximumEdgeRatio = 20.0; // The exact sub-pixel localization is done on just one DoG plane. Even // when the scale adjustment exceeds +/- 0.5, the plane is not changed. // With this value you can discard peaks that are localized to be too far // from the plane. A high value will allow for peaks to be used that are // more far away from the plane used for localization, while a low value // will sort out more peaks, that drifted too far away. // // Be very careful with this value, as a too large value will lead to a // high number of keypoints in hard to localize areas such as in photos of // the sky. // // Good values seem to lie between 0.30 and 0.6. double scaleAdjustThresh = 0.50; // Number of maximum steps a single keypoint can make in its space. int relocationMaximum = 4; // Results ArrayList* LoweFeatureDetector_GlobalKeypoints(LoweFeatureDetector* self) { return (self->globalKeypoints); } // The Integer-normalized version of the globalKeypoints. ArrayList* LoweFeatureDetector_GlobalNaturalKeypoints(LoweFeatureDetector* self) { int i; if (self->globalNaturalKeypoints != NULL) return (self->globalNaturalKeypoints); if (self->globalKeypoints == NULL) WriteError ("No keypoints generated yet."); self->globalNaturalKeypoints = ArrayList_new0 (KeypointN_delete); for(i=0; iglobalKeypoints); i++) { Keypoint* kp = (Keypoint *) ArrayList_GetItem(self->globalKeypoints, i); ArrayList_AddItem (self->globalNaturalKeypoints, KeypointN_new(kp)); } return (self->globalNaturalKeypoints); } LoweFeatureDetector* LoweFeatureDetector_new0() { LoweFeatureDetector* self = (LoweFeatureDetector*)malloc(sizeof(LoweFeatureDetector)); self->globalKeypoints = NULL; self->globalNaturalKeypoints = NULL; self->pyr = NULL; return self; } void LoweFeatureDetector_delete(LoweFeatureDetector* self) { if (self) { ArrayList_delete(self->globalKeypoints); ArrayList_delete(self->globalNaturalKeypoints); OctavePyramid_delete(self->pyr); free( self); } } // Return the number of detected features. int LoweFeatureDetector_DetectFeatures (LoweFeatureDetector* self, ImageMap* img) { return (LoweFeatureDetector_DetectFeaturesDownscaled (self, img, -1, 1.0)); } // Scale down the images down so that both dimensions are smaller than // 'bothDimHi'. If 'bothDimHi' is < 0, the image is doubled before // processing, if it is zero, nothing is done to the image. int LoweFeatureDetector_DetectFeaturesDownscaled (LoweFeatureDetector* self, ImageMap* img, int bothDimHi, double startScale) { if (printWarning) { // Print license restriction WriteLine (""); WriteLine ("==============================================================================="); WriteLine ("The use of this software is restricted by certain conditions."); WriteLine ("See the \"LICENSE\" file distributed with the program for details."); WriteLine (""); WriteLine ("The University of British Columbia has applied for a patent on the SIFT"); WriteLine ("algorithm in the United States. Commercial applications of this software may"); WriteLine ("require a license from the University of British Columbia."); WriteLine ("==============================================================================="); WriteLine (""); } // Double the image size, as this way more features are detected. The // scale is reduced to 0.5. if (bothDimHi < 0) { ImageMap* tmp = ImageMap_ScaleDouble (img); ImageMap_delete(img); img = tmp; startScale *= 0.5; } else if (bothDimHi > 0) { while (img->xDim > bothDimHi || img->yDim > bothDimHi) { ImageMap* tmp = ImageMap_ScaleHalf (img); ImageMap_delete(img); img = tmp; startScale *= 2.0; } } // XXX: Maybe the blurring has to be before double-sizing? // better not, if we would lose more information then? // (Lowe03, p10, "We assume that the original image has a blur of at // least \sigma = 0.5 ...") // So, do one initial image smoothing pass. if (preprocSigma > 0.0) { ImageMap* tmp = ImageMap_GaussianConvolution (img, preprocSigma); ImageMap_delete(img); img = tmp; } self->pyr = OctavePyramid_new0 (); self->pyr->Verbose = verbose; OctavePyramid_BuildOctaves (self->pyr, img, startScale, scaleSpaceLevels, octaveSigma, minimumRequiredPixelsize); self->globalKeypoints = ArrayList_new0 (Keypoint_delete); // Generate keypoints from each scalespace. int on; for (on = 0 ; on < OctavePyramid_Count(self->pyr) ; ++on) { DScaleSpace* dsp = OctavePyramid_GetScaleSpace(self->pyr, on); ArrayList* peaks = DScaleSpace_FindPeaks (dsp, dogThresh); if (verbose) WriteLine ("Octave %d has %d raw peaks", on, ArrayList_Count(peaks)); int oldCount = ArrayList_Count(peaks); ArrayList* peaksFilt = DScaleSpace_FilterAndLocalizePeaks (dsp, peaks, maximumEdgeRatio, dValueLowThresh, scaleAdjustThresh, relocationMaximum); if (verbose) { WriteLine (" filtered: %d remaining from %d, thats %2.2f%%", ArrayList_Count(peaksFilt), oldCount, (100.0 * ArrayList_Count(peaksFilt)) / oldCount); WriteLine ("generating keypoints from peaks"); } // Generate the actual keypoint descriptors, using pre-computed // values for the gradient magnitude and direction. DScaleSpace_GenerateMagnitudeAndDirectionMaps (dsp); ArrayList* keypoints = DScaleSpace_GenerateKeypoints (dsp, peaksFilt, scaleSpaceLevels, octaveSigma); // dangelo if( verbose ){ WriteLine (" %d keypoints generated", ArrayList_Count(keypoints)); } ArrayList_delete(peaksFilt); ArrayList_delete(peaks); DScaleSpace_ClearMagnitudeAndDirectionMaps (dsp); ArrayList_AddRange (self->globalKeypoints, keypoints); ArrayList_delete(keypoints); } return (ArrayList_Count(self->globalKeypoints)); }