Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [Simonyan14]. [詳解]
#include <xfeatures2d.hpp>
cv::Feature2Dを継承しています。
公開型 | |
| enum | { VGG_120 = 100 , VGG_80 = 101 , VGG_64 = 102 , VGG_48 = 103 } |
公開メンバ関数 | |
| virtual CV_WRAP void | setSigma (const float isigma)=0 |
| virtual CV_WRAP float | getSigma () const =0 |
| virtual CV_WRAP void | setUseNormalizeImage (const bool img_normalize)=0 |
| virtual CV_WRAP bool | getUseNormalizeImage () const =0 |
| virtual CV_WRAP void | setUseScaleOrientation (const bool use_scale_orientation)=0 |
| virtual CV_WRAP bool | getUseScaleOrientation () const =0 |
| virtual CV_WRAP void | setScaleFactor (const float scale_factor)=0 |
| virtual CV_WRAP float | getScaleFactor () const =0 |
| virtual CV_WRAP void | setUseNormalizeDescriptor (const bool dsc_normalize)=0 |
| virtual CV_WRAP bool | getUseNormalizeDescriptor () const =0 |
 基底クラス cv::Feature2D に属する継承公開メンバ関数 | |
| virtual CV_WRAP void | detect (InputArray image, CV_OUT std::vector< KeyPoint > &keypoints, InputArray mask=noArray()) |
| Detects keypoints in an image (first variant) or image set (second variant). [詳解] | |
| virtual CV_WRAP void | detect (InputArrayOfArrays images, CV_OUT std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray()) |
| virtual CV_WRAP void | compute (InputArray image, CV_OUT CV_IN_OUT std::vector< KeyPoint > &keypoints, OutputArray descriptors) |
| Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant). [詳解] | |
| virtual CV_WRAP void | compute (InputArrayOfArrays images, CV_OUT CV_IN_OUT std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors) |
| virtual CV_WRAP void | detectAndCompute (InputArray image, InputArray mask, CV_OUT std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false) |
| virtual CV_WRAP int | descriptorSize () const |
| virtual CV_WRAP int | descriptorType () const |
| virtual CV_WRAP int | defaultNorm () const |
| CV_WRAP void | write (const String &fileName) const |
| CV_WRAP void | read (const String &fileName) |
| virtual void | write (FileStorage &) const CV_OVERRIDE |
| Stores algorithm parameters in a file storage [詳解] | |
| virtual CV_WRAP void | read (const FileNode &) CV_OVERRIDE |
| Reads algorithm parameters from a file storage [詳解] | |
| virtual CV_WRAP bool | empty () const CV_OVERRIDE |
| Return true if detector object is empty [詳解] | |
| virtual CV_WRAP String | getDefaultName () const CV_OVERRIDE |
| CV_WRAP void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
| 基底クラス cv::Algorithm に属する継承公開メンバ関数 | |
| virtual CV_WRAP void | clear () |
| Clears the algorithm state [詳解] | |
| CV_WRAP void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
| simplified API for language bindings これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。 | |
| virtual CV_WRAP void | save (const String &filename) const |
静的公開メンバ関数 | |
| static CV_WRAP Ptr< VGG > | create (int desc=VGG::VGG_120, float isigma=1.4f, bool img_normalize=true, bool use_scale_orientation=true, float scale_factor=6.25f, bool dsc_normalize=false) |
| 基底クラス cv::Algorithm に属する継承静的公開メンバ関数 | |
| template<typename _Tp > | |
| static Ptr< _Tp > | read (const FileNode &fn) |
| Reads algorithm from the file node [詳解] | |
| template<typename _Tp > | |
| static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| Loads algorithm from the file [詳解] | |
| template<typename _Tp > | |
| static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| Loads algorithm from a String [詳解] | |
その他の継承メンバ | |
| 基底クラス cv::Algorithm に属する継承限定公開メンバ関数 | |
| void | writeFormat (FileStorage &fs) const |
詳解
Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [Simonyan14].
- 引数
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desc type of descriptor to use, VGG::VGG_120 is default (120 dimensions float) Available types are VGG::VGG_120, VGG::VGG_80, VGG::VGG_64, VGG::VGG_48 isigma gaussian kernel value for image blur (default is 1.4f) img_normalize use image sample intensity normalization (enabled by default) use_orientation sample patterns using keypoints orientation, enabled by default scale_factor adjust the sampling window of detected keypoints to 64.0f (VGG sampling window) 6.25f is default and fits for KAZE, SURF detected keypoints window ratio 6.75f should be the scale for SIFT detected keypoints window ratio 5.00f should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints window ratio 0.75f should be the scale for ORB keypoints ratio dsc_normalize clamp descriptors to 255 and convert to uchar CV_8UC1 (disabled by default)
このクラス詳解は次のファイルから抽出されました:
