using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;
namespace OpenCVForUnity.Features2dModule
{
// 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 : Feature2D
{
protected override void Dispose(bool disposing)
{
try
{
if (disposing)
{
}
if (IsEnabledDispose)
{
if (nativeObj != IntPtr.Zero)
features2d_SIFT_delete(nativeObj);
nativeObj = IntPtr.Zero;
}
}
finally
{
base.Dispose(disposing);
}
}
protected internal SIFT(IntPtr addr) : base(addr) { }
// internal usage only
public static new SIFT __fromPtr__(IntPtr 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.
*
* Note: 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, bool enable_precise_upscale)
{
return SIFT.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_create_10(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.
*
* Note: 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__(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_create_11(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.
*
* Note: 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__(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_create_12(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.
*
* Note: 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__(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_create_13(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.
*
* Note: 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__(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_create_14(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.
*
* Note: 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__(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_create_15(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.
*
* Note: 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__(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_create_16()));
}
//
// 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.
*
* Note: 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, bool enable_precise_upscale)
{
return SIFT.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_create_17(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.
*
* Note: 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__(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_create_18(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType)));
}
//
// C++: String cv::SIFT::getDefaultName()
//
public override string getDefaultName()
{
ThrowIfDisposed();
string retVal = Marshal.PtrToStringAnsi(DisposableObject.ThrowIfNullIntPtr(features2d_SIFT_getDefaultName_10(nativeObj)));
return retVal;
}
//
// C++: void cv::SIFT::setNFeatures(int maxFeatures)
//
public void setNFeatures(int maxFeatures)
{
ThrowIfDisposed();
features2d_SIFT_setNFeatures_10(nativeObj, maxFeatures);
}
//
// C++: int cv::SIFT::getNFeatures()
//
public int getNFeatures()
{
ThrowIfDisposed();
return features2d_SIFT_getNFeatures_10(nativeObj);
}
//
// C++: void cv::SIFT::setNOctaveLayers(int nOctaveLayers)
//
public void setNOctaveLayers(int nOctaveLayers)
{
ThrowIfDisposed();
features2d_SIFT_setNOctaveLayers_10(nativeObj, nOctaveLayers);
}
//
// C++: int cv::SIFT::getNOctaveLayers()
//
public int getNOctaveLayers()
{
ThrowIfDisposed();
return features2d_SIFT_getNOctaveLayers_10(nativeObj);
}
//
// C++: void cv::SIFT::setContrastThreshold(double contrastThreshold)
//
public void setContrastThreshold(double contrastThreshold)
{
ThrowIfDisposed();
features2d_SIFT_setContrastThreshold_10(nativeObj, contrastThreshold);
}
//
// C++: double cv::SIFT::getContrastThreshold()
//
public double getContrastThreshold()
{
ThrowIfDisposed();
return features2d_SIFT_getContrastThreshold_10(nativeObj);
}
//
// C++: void cv::SIFT::setEdgeThreshold(double edgeThreshold)
//
public void setEdgeThreshold(double edgeThreshold)
{
ThrowIfDisposed();
features2d_SIFT_setEdgeThreshold_10(nativeObj, edgeThreshold);
}
//
// C++: double cv::SIFT::getEdgeThreshold()
//
public double getEdgeThreshold()
{
ThrowIfDisposed();
return features2d_SIFT_getEdgeThreshold_10(nativeObj);
}
//
// C++: void cv::SIFT::setSigma(double sigma)
//
public void setSigma(double sigma)
{
ThrowIfDisposed();
features2d_SIFT_setSigma_10(nativeObj, sigma);
}
//
// C++: double cv::SIFT::getSigma()
//
public double getSigma()
{
ThrowIfDisposed();
return features2d_SIFT_getSigma_10(nativeObj);
}
#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
const string LIBNAME = "__Internal";
#else
const string LIBNAME = "opencvforunity";
#endif
// 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)
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_create_10(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, [MarshalAs(UnmanagedType.U1)] bool enable_precise_upscale);
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_create_11(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma);
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_create_12(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold);
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_create_13(int nfeatures, int nOctaveLayers, double contrastThreshold);
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_create_14(int nfeatures, int nOctaveLayers);
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_create_15(int nfeatures);
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_create_16();
// C++: static Ptr_SIFT cv::SIFT::create(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, bool enable_precise_upscale = false)
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_create_17(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, [MarshalAs(UnmanagedType.U1)] bool enable_precise_upscale);
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_create_18(int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType);
// C++: String cv::SIFT::getDefaultName()
[DllImport(LIBNAME)]
private static extern IntPtr features2d_SIFT_getDefaultName_10(IntPtr nativeObj);
// C++: void cv::SIFT::setNFeatures(int maxFeatures)
[DllImport(LIBNAME)]
private static extern void features2d_SIFT_setNFeatures_10(IntPtr nativeObj, int maxFeatures);
// C++: int cv::SIFT::getNFeatures()
[DllImport(LIBNAME)]
private static extern int features2d_SIFT_getNFeatures_10(IntPtr nativeObj);
// C++: void cv::SIFT::setNOctaveLayers(int nOctaveLayers)
[DllImport(LIBNAME)]
private static extern void features2d_SIFT_setNOctaveLayers_10(IntPtr nativeObj, int nOctaveLayers);
// C++: int cv::SIFT::getNOctaveLayers()
[DllImport(LIBNAME)]
private static extern int features2d_SIFT_getNOctaveLayers_10(IntPtr nativeObj);
// C++: void cv::SIFT::setContrastThreshold(double contrastThreshold)
[DllImport(LIBNAME)]
private static extern void features2d_SIFT_setContrastThreshold_10(IntPtr nativeObj, double contrastThreshold);
// C++: double cv::SIFT::getContrastThreshold()
[DllImport(LIBNAME)]
private static extern double features2d_SIFT_getContrastThreshold_10(IntPtr nativeObj);
// C++: void cv::SIFT::setEdgeThreshold(double edgeThreshold)
[DllImport(LIBNAME)]
private static extern void features2d_SIFT_setEdgeThreshold_10(IntPtr nativeObj, double edgeThreshold);
// C++: double cv::SIFT::getEdgeThreshold()
[DllImport(LIBNAME)]
private static extern double features2d_SIFT_getEdgeThreshold_10(IntPtr nativeObj);
// C++: void cv::SIFT::setSigma(double sigma)
[DllImport(LIBNAME)]
private static extern void features2d_SIFT_setSigma_10(IntPtr nativeObj, double sigma);
// C++: double cv::SIFT::getSigma()
[DllImport(LIBNAME)]
private static extern double features2d_SIFT_getSigma_10(IntPtr nativeObj);
// native support for java finalize()
[DllImport(LIBNAME)]
private static extern void features2d_SIFT_delete(IntPtr nativeObj);
}
}