001// 002// This file is auto-generated. Please don't modify it! 003// 004package org.opencv.features2d; 005 006import org.opencv.features2d.Feature2D; 007import org.opencv.features2d.SIFT; 008 009// C++: class SIFT 010/** 011 * Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform 012 * (SIFT) algorithm by D. Lowe CITE: Lowe04 . 013 */ 014public class SIFT extends Feature2D { 015 016 protected SIFT(long addr) { super(addr); } 017 018 // internal usage only 019 public static SIFT __fromPtr__(long addr) { return new SIFT(addr); } 020 021 // 022 // C++: static Ptr_SIFT cv::SIFT::create(int nfeatures = 0, int nOctaveLayers = 3, double contrastThreshold = 0.04, double edgeThreshold = 10, double sigma = 1.6, bool enable_precise_upscale = false) 023 // 024 025 /** 026 * @param nfeatures The number of best features to retain. The features are ranked by their scores 027 * (measured in SIFT algorithm as the local contrast) 028 * 029 * @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The 030 * number of octaves is computed automatically from the image resolution. 031 * 032 * @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform 033 * (low-contrast) regions. The larger the threshold, the less features are produced by the detector. 034 * 035 * <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When 036 * nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set 037 * this argument to 0.09. 038 * 039 * @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning 040 * is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are 041 * filtered out (more features are retained). 042 * 043 * @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image 044 * is captured with a weak camera with soft lenses, you might want to reduce the number. 045 * 046 * @param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps 047 * index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option 048 * is disabled by default. 049 * @return automatically generated 050 */ 051 public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, boolean enable_precise_upscale) { 052 return SIFT.__fromPtr__(create_0(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, enable_precise_upscale)); 053 } 054 055 /** 056 * @param nfeatures The number of best features to retain. The features are ranked by their scores 057 * (measured in SIFT algorithm as the local contrast) 058 * 059 * @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The 060 * number of octaves is computed automatically from the image resolution. 061 * 062 * @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform 063 * (low-contrast) regions. The larger the threshold, the less features are produced by the detector. 064 * 065 * <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When 066 * nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set 067 * this argument to 0.09. 068 * 069 * @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning 070 * is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are 071 * filtered out (more features are retained). 072 * 073 * @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image 074 * is captured with a weak camera with soft lenses, you might want to reduce the number. 075 * 076 * index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option 077 * is disabled by default. 078 * @return automatically generated 079 */ 080 public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma) { 081 return SIFT.__fromPtr__(create_1(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma)); 082 } 083 084 /** 085 * @param nfeatures The number of best features to retain. The features are ranked by their scores 086 * (measured in SIFT algorithm as the local contrast) 087 * 088 * @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The 089 * number of octaves is computed automatically from the image resolution. 090 * 091 * @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform 092 * (low-contrast) regions. The larger the threshold, the less features are produced by the detector. 093 * 094 * <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When 095 * nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set 096 * this argument to 0.09. 097 * 098 * @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning 099 * is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are 100 * filtered out (more features are retained). 101 * 102 * is captured with a weak camera with soft lenses, you might want to reduce the number. 103 * 104 * index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option 105 * is disabled by default. 106 * @return automatically generated 107 */ 108 public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold) { 109 return SIFT.__fromPtr__(create_2(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold)); 110 } 111 112 /** 113 * @param nfeatures The number of best features to retain. The features are ranked by their scores 114 * (measured in SIFT algorithm as the local contrast) 115 * 116 * @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The 117 * number of octaves is computed automatically from the image resolution. 118 * 119 * @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform 120 * (low-contrast) regions. The larger the threshold, the less features are produced by the detector. 121 * 122 * <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When 123 * nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set 124 * this argument to 0.09. 125 * 126 * is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are 127 * filtered out (more features are retained). 128 * 129 * is captured with a weak camera with soft lenses, you might want to reduce the number. 130 * 131 * index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option 132 * is disabled by default. 133 * @return automatically generated 134 */ 135 public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold) { 136 return SIFT.__fromPtr__(create_3(nfeatures, nOctaveLayers, contrastThreshold)); 137 } 138 139 /** 140 * @param nfeatures The number of best features to retain. The features are ranked by their scores 141 * (measured in SIFT algorithm as the local contrast) 142 * 143 * @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The 144 * number of octaves is computed automatically from the image resolution. 145 * 146 * (low-contrast) regions. The larger the threshold, the less features are produced by the detector. 147 * 148 * <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When 149 * nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set 150 * this argument to 0.09. 151 * 152 * is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are 153 * filtered out (more features are retained). 154 * 155 * is captured with a weak camera with soft lenses, you might want to reduce the number. 156 * 157 * index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option 158 * is disabled by default. 159 * @return automatically generated 160 */ 161 public static SIFT create(int nfeatures, int nOctaveLayers) { 162 return SIFT.__fromPtr__(create_4(nfeatures, nOctaveLayers)); 163 } 164 165 /** 166 * @param nfeatures The number of best features to retain. The features are ranked by their scores 167 * (measured in SIFT algorithm as the local contrast) 168 * 169 * number of octaves is computed automatically from the image resolution. 170 * 171 * (low-contrast) regions. The larger the threshold, the less features are produced by the detector. 172 * 173 * <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When 174 * nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set 175 * this argument to 0.09. 176 * 177 * is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are 178 * filtered out (more features are retained). 179 * 180 * is captured with a weak camera with soft lenses, you might want to reduce the number. 181 * 182 * index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option 183 * is disabled by default. 184 * @return automatically generated 185 */ 186 public static SIFT create(int nfeatures) { 187 return SIFT.__fromPtr__(create_5(nfeatures)); 188 } 189 190 /** 191 * (measured in SIFT algorithm as the local contrast) 192 * 193 * number of octaves is computed automatically from the image resolution. 194 * 195 * (low-contrast) regions. The larger the threshold, the less features are produced by the detector. 196 * 197 * <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When 198 * nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set 199 * this argument to 0.09. 200 * 201 * is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are 202 * filtered out (more features are retained). 203 * 204 * is captured with a weak camera with soft lenses, you might want to reduce the number. 205 * 206 * index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option 207 * is disabled by default. 208 * @return automatically generated 209 */ 210 public static SIFT create() { 211 return SIFT.__fromPtr__(create_6()); 212 } 213 214 215 // 216 // C++: static Ptr_SIFT cv::SIFT::create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, bool enable_precise_upscale = false) 217 // 218 219 /** 220 * Create SIFT with specified descriptorType. 221 * @param nfeatures The number of best features to retain. The features are ranked by their scores 222 * (measured in SIFT algorithm as the local contrast) 223 * 224 * @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The 225 * number of octaves is computed automatically from the image resolution. 226 * 227 * @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform 228 * (low-contrast) regions. The larger the threshold, the less features are produced by the detector. 229 * 230 * <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When 231 * nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set 232 * this argument to 0.09. 233 * 234 * @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning 235 * is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are 236 * filtered out (more features are retained). 237 * 238 * @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image 239 * is captured with a weak camera with soft lenses, you might want to reduce the number. 240 * 241 * @param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported. 242 * 243 * @param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps 244 * index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option 245 * is disabled by default. 246 * @return automatically generated 247 */ 248 public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, boolean enable_precise_upscale) { 249 return SIFT.__fromPtr__(create_7(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType, enable_precise_upscale)); 250 } 251 252 /** 253 * Create SIFT with specified descriptorType. 254 * @param nfeatures The number of best features to retain. The features are ranked by their scores 255 * (measured in SIFT algorithm as the local contrast) 256 * 257 * @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The 258 * number of octaves is computed automatically from the image resolution. 259 * 260 * @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform 261 * (low-contrast) regions. The larger the threshold, the less features are produced by the detector. 262 * 263 * <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When 264 * nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set 265 * this argument to 0.09. 266 * 267 * @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning 268 * is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are 269 * filtered out (more features are retained). 270 * 271 * @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image 272 * is captured with a weak camera with soft lenses, you might want to reduce the number. 273 * 274 * @param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported. 275 * 276 * index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option 277 * is disabled by default. 278 * @return automatically generated 279 */ 280 public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType) { 281 return SIFT.__fromPtr__(create_8(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType)); 282 } 283 284 285 // 286 // C++: String cv::SIFT::getDefaultName() 287 // 288 289 public String getDefaultName() { 290 return getDefaultName_0(nativeObj); 291 } 292 293 294 // 295 // C++: void cv::SIFT::setNFeatures(int maxFeatures) 296 // 297 298 public void setNFeatures(int maxFeatures) { 299 setNFeatures_0(nativeObj, maxFeatures); 300 } 301 302 303 // 304 // C++: int cv::SIFT::getNFeatures() 305 // 306 307 public int getNFeatures() { 308 return getNFeatures_0(nativeObj); 309 } 310 311 312 // 313 // C++: void cv::SIFT::setNOctaveLayers(int nOctaveLayers) 314 // 315 316 public void setNOctaveLayers(int nOctaveLayers) { 317 setNOctaveLayers_0(nativeObj, nOctaveLayers); 318 } 319 320 321 // 322 // C++: int cv::SIFT::getNOctaveLayers() 323 // 324 325 public int getNOctaveLayers() { 326 return getNOctaveLayers_0(nativeObj); 327 } 328 329 330 // 331 // C++: void cv::SIFT::setContrastThreshold(double contrastThreshold) 332 // 333 334 public void setContrastThreshold(double contrastThreshold) { 335 setContrastThreshold_0(nativeObj, contrastThreshold); 336 } 337 338 339 // 340 // C++: double cv::SIFT::getContrastThreshold() 341 // 342 343 public double getContrastThreshold() { 344 return getContrastThreshold_0(nativeObj); 345 } 346 347 348 // 349 // C++: void cv::SIFT::setEdgeThreshold(double edgeThreshold) 350 // 351 352 public void setEdgeThreshold(double edgeThreshold) { 353 setEdgeThreshold_0(nativeObj, edgeThreshold); 354 } 355 356 357 // 358 // C++: double cv::SIFT::getEdgeThreshold() 359 // 360 361 public double getEdgeThreshold() { 362 return getEdgeThreshold_0(nativeObj); 363 } 364 365 366 // 367 // C++: void cv::SIFT::setSigma(double sigma) 368 // 369 370 public void setSigma(double sigma) { 371 setSigma_0(nativeObj, sigma); 372 } 373 374 375 // 376 // C++: double cv::SIFT::getSigma() 377 // 378 379 public double getSigma() { 380 return getSigma_0(nativeObj); 381 } 382 383 384 @Override 385 protected void finalize() throws Throwable { 386 delete(nativeObj); 387 } 388 389 390 391 // C++: static Ptr_SIFT cv::SIFT::create(int nfeatures = 0, int nOctaveLayers = 3, double contrastThreshold = 0.04, double edgeThreshold = 10, double sigma = 1.6, bool enable_precise_upscale = false) 392 private static native long create_0(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, boolean enable_precise_upscale); 393 private static native long create_1(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma); 394 private static native long create_2(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold); 395 private static native long create_3(int nfeatures, int nOctaveLayers, double contrastThreshold); 396 private static native long create_4(int nfeatures, int nOctaveLayers); 397 private static native long create_5(int nfeatures); 398 private static native long create_6(); 399 400 // C++: static Ptr_SIFT cv::SIFT::create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, bool enable_precise_upscale = false) 401 private static native long create_7(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, boolean enable_precise_upscale); 402 private static native long create_8(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType); 403 404 // C++: String cv::SIFT::getDefaultName() 405 private static native String getDefaultName_0(long nativeObj); 406 407 // C++: void cv::SIFT::setNFeatures(int maxFeatures) 408 private static native void setNFeatures_0(long nativeObj, int maxFeatures); 409 410 // C++: int cv::SIFT::getNFeatures() 411 private static native int getNFeatures_0(long nativeObj); 412 413 // C++: void cv::SIFT::setNOctaveLayers(int nOctaveLayers) 414 private static native void setNOctaveLayers_0(long nativeObj, int nOctaveLayers); 415 416 // C++: int cv::SIFT::getNOctaveLayers() 417 private static native int getNOctaveLayers_0(long nativeObj); 418 419 // C++: void cv::SIFT::setContrastThreshold(double contrastThreshold) 420 private static native void setContrastThreshold_0(long nativeObj, double contrastThreshold); 421 422 // C++: double cv::SIFT::getContrastThreshold() 423 private static native double getContrastThreshold_0(long nativeObj); 424 425 // C++: void cv::SIFT::setEdgeThreshold(double edgeThreshold) 426 private static native void setEdgeThreshold_0(long nativeObj, double edgeThreshold); 427 428 // C++: double cv::SIFT::getEdgeThreshold() 429 private static native double getEdgeThreshold_0(long nativeObj); 430 431 // C++: void cv::SIFT::setSigma(double sigma) 432 private static native void setSigma_0(long nativeObj, double sigma); 433 434 // C++: double cv::SIFT::getSigma() 435 private static native double getSigma_0(long nativeObj); 436 437 // native support for java finalize() 438 private static native void delete(long nativeObj); 439 440}