//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.features2d;
// C++: class SIFT
/**
* Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform
* (SIFT) algorithm by D. Lowe CITE: Lowe04 .
*/
public class SIFT extends Feature2D {
protected SIFT(long addr) { super(addr); }
// internal usage only
public static SIFT __fromPtr__(long addr) { return new SIFT(addr); }
//
// 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)
//
/**
* @param nfeatures The number of best features to retain. The features are ranked by their scores
* (measured in SIFT algorithm as the local contrast)
*
* @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
* number of octaves is computed automatically from the image resolution.
*
* @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
* (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
*
* <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
* nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
* this argument to 0.09.
*
* @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
* is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
* filtered out (more features are retained).
*
* @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
* is captured with a weak camera with soft lenses, you might want to reduce the number.
*
* @param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps
* index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option
* is disabled by default.
* @return automatically generated
*/
public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, boolean enable_precise_upscale) {
return SIFT.__fromPtr__(create_0(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, enable_precise_upscale));
}
/**
* @param nfeatures The number of best features to retain. The features are ranked by their scores
* (measured in SIFT algorithm as the local contrast)
*
* @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
* number of octaves is computed automatically from the image resolution.
*
* @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
* (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
*
* <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
* nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
* this argument to 0.09.
*
* @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
* is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
* filtered out (more features are retained).
*
* @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
* is captured with a weak camera with soft lenses, you might want to reduce the number.
*
* index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option
* is disabled by default.
* @return automatically generated
*/
public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma) {
return SIFT.__fromPtr__(create_1(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma));
}
/**
* @param nfeatures The number of best features to retain. The features are ranked by their scores
* (measured in SIFT algorithm as the local contrast)
*
* @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
* number of octaves is computed automatically from the image resolution.
*
* @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
* (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
*
* <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
* nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
* this argument to 0.09.
*
* @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
* is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
* filtered out (more features are retained).
*
* is captured with a weak camera with soft lenses, you might want to reduce the number.
*
* index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option
* is disabled by default.
* @return automatically generated
*/
public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold) {
return SIFT.__fromPtr__(create_2(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold));
}
/**
* @param nfeatures The number of best features to retain. The features are ranked by their scores
* (measured in SIFT algorithm as the local contrast)
*
* @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
* number of octaves is computed automatically from the image resolution.
*
* @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
* (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
*
* <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
* nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
* this argument to 0.09.
*
* is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
* filtered out (more features are retained).
*
* is captured with a weak camera with soft lenses, you might want to reduce the number.
*
* index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option
* is disabled by default.
* @return automatically generated
*/
public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold) {
return SIFT.__fromPtr__(create_3(nfeatures, nOctaveLayers, contrastThreshold));
}
/**
* @param nfeatures The number of best features to retain. The features are ranked by their scores
* (measured in SIFT algorithm as the local contrast)
*
* @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
* number of octaves is computed automatically from the image resolution.
*
* (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
*
* <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
* nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
* this argument to 0.09.
*
* is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
* filtered out (more features are retained).
*
* is captured with a weak camera with soft lenses, you might want to reduce the number.
*
* index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option
* is disabled by default.
* @return automatically generated
*/
public static SIFT create(int nfeatures, int nOctaveLayers) {
return SIFT.__fromPtr__(create_4(nfeatures, nOctaveLayers));
}
/**
* @param nfeatures The number of best features to retain. The features are ranked by their scores
* (measured in SIFT algorithm as the local contrast)
*
* number of octaves is computed automatically from the image resolution.
*
* (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
*
* <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
* nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
* this argument to 0.09.
*
* is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
* filtered out (more features are retained).
*
* is captured with a weak camera with soft lenses, you might want to reduce the number.
*
* index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option
* is disabled by default.
* @return automatically generated
*/
public static SIFT create(int nfeatures) {
return SIFT.__fromPtr__(create_5(nfeatures));
}
/**
* (measured in SIFT algorithm as the local contrast)
*
* number of octaves is computed automatically from the image resolution.
*
* (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
*
* <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
* nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
* this argument to 0.09.
*
* is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
* filtered out (more features are retained).
*
* is captured with a weak camera with soft lenses, you might want to reduce the number.
*
* index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option
* is disabled by default.
* @return automatically generated
*/
public static SIFT create() {
return SIFT.__fromPtr__(create_6());
}
//
// C++: static Ptr_SIFT cv::SIFT::create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, bool enable_precise_upscale = false)
//
/**
* Create SIFT with specified descriptorType.
* @param nfeatures The number of best features to retain. The features are ranked by their scores
* (measured in SIFT algorithm as the local contrast)
*
* @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
* number of octaves is computed automatically from the image resolution.
*
* @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
* (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
*
* <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
* nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
* this argument to 0.09.
*
* @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
* is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
* filtered out (more features are retained).
*
* @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
* is captured with a weak camera with soft lenses, you might want to reduce the number.
*
* @param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported.
*
* @param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps
* index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option
* is disabled by default.
* @return automatically generated
*/
public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, boolean enable_precise_upscale) {
return SIFT.__fromPtr__(create_7(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType, enable_precise_upscale));
}
/**
* Create SIFT with specified descriptorType.
* @param nfeatures The number of best features to retain. The features are ranked by their scores
* (measured in SIFT algorithm as the local contrast)
*
* @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
* number of octaves is computed automatically from the image resolution.
*
* @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
* (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
*
* <b>Note:</b> The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
* nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
* this argument to 0.09.
*
* @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
* is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
* filtered out (more features are retained).
*
* @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
* is captured with a weak camera with soft lenses, you might want to reduce the number.
*
* @param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported.
*
* index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option
* is disabled by default.
* @return automatically generated
*/
public static SIFT create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType) {
return SIFT.__fromPtr__(create_8(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType));
}
//
// C++: String cv::SIFT::getDefaultName()
//
public String getDefaultName() {
return getDefaultName_0(nativeObj);
}
//
// C++: void cv::SIFT::setNFeatures(int maxFeatures)
//
public void setNFeatures(int maxFeatures) {
setNFeatures_0(nativeObj, maxFeatures);
}
//
// C++: int cv::SIFT::getNFeatures()
//
public int getNFeatures() {
return getNFeatures_0(nativeObj);
}
//
// C++: void cv::SIFT::setNOctaveLayers(int nOctaveLayers)
//
public void setNOctaveLayers(int nOctaveLayers) {
setNOctaveLayers_0(nativeObj, nOctaveLayers);
}
//
// C++: int cv::SIFT::getNOctaveLayers()
//
public int getNOctaveLayers() {
return getNOctaveLayers_0(nativeObj);
}
//
// C++: void cv::SIFT::setContrastThreshold(double contrastThreshold)
//
public void setContrastThreshold(double contrastThreshold) {
setContrastThreshold_0(nativeObj, contrastThreshold);
}
//
// C++: double cv::SIFT::getContrastThreshold()
//
public double getContrastThreshold() {
return getContrastThreshold_0(nativeObj);
}
//
// C++: void cv::SIFT::setEdgeThreshold(double edgeThreshold)
//
public void setEdgeThreshold(double edgeThreshold) {
setEdgeThreshold_0(nativeObj, edgeThreshold);
}
//
// C++: double cv::SIFT::getEdgeThreshold()
//
public double getEdgeThreshold() {
return getEdgeThreshold_0(nativeObj);
}
//
// C++: void cv::SIFT::setSigma(double sigma)
//
public void setSigma(double sigma) {
setSigma_0(nativeObj, sigma);
}
//
// C++: double cv::SIFT::getSigma()
//
public double getSigma() {
return getSigma_0(nativeObj);
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// 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)
private static native long create_0(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, boolean enable_precise_upscale);
private static native long create_1(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma);
private static native long create_2(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold);
private static native long create_3(int nfeatures, int nOctaveLayers, double contrastThreshold);
private static native long create_4(int nfeatures, int nOctaveLayers);
private static native long create_5(int nfeatures);
private static native long create_6();
// C++: static Ptr_SIFT cv::SIFT::create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, bool enable_precise_upscale = false)
private static native long create_7(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, boolean enable_precise_upscale);
private static native long create_8(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType);
// C++: String cv::SIFT::getDefaultName()
private static native String getDefaultName_0(long nativeObj);
// C++: void cv::SIFT::setNFeatures(int maxFeatures)
private static native void setNFeatures_0(long nativeObj, int maxFeatures);
// C++: int cv::SIFT::getNFeatures()
private static native int getNFeatures_0(long nativeObj);
// C++: void cv::SIFT::setNOctaveLayers(int nOctaveLayers)
private static native void setNOctaveLayers_0(long nativeObj, int nOctaveLayers);
// C++: int cv::SIFT::getNOctaveLayers()
private static native int getNOctaveLayers_0(long nativeObj);
// C++: void cv::SIFT::setContrastThreshold(double contrastThreshold)
private static native void setContrastThreshold_0(long nativeObj, double contrastThreshold);
// C++: double cv::SIFT::getContrastThreshold()
private static native double getContrastThreshold_0(long nativeObj);
// C++: void cv::SIFT::setEdgeThreshold(double edgeThreshold)
private static native void setEdgeThreshold_0(long nativeObj, double edgeThreshold);
// C++: double cv::SIFT::getEdgeThreshold()
private static native double getEdgeThreshold_0(long nativeObj);
// C++: void cv::SIFT::setSigma(double sigma)
private static native void setSigma_0(long nativeObj, double sigma);
// C++: double cv::SIFT::getSigma()
private static native double getSigma_0(long nativeObj);
// native support for java finalize()
private static native void delete(long nativeObj);
}
