Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [Trzcinski13a] and [Trzcinski13b]. [詳解]
#include <xfeatures2d.hpp>
cv::Feature2Dを継承しています。
公開型 | |
| enum | { BGM = 100 , BGM_HARD = 101 , BGM_BILINEAR = 102 , LBGM = 200 , BINBOOST_64 = 300 , BINBOOST_128 = 301 , BINBOOST_256 = 302 } |
公開メンバ関数 | |
| 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 |
 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< BoostDesc > | create (int desc=BoostDesc::BINBOOST_256, bool use_scale_orientation=true, float scale_factor=6.25f) |
| 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 BoostDesc (Learning Image Descriptors with Boosting), described in [Trzcinski13a] and [Trzcinski13b].
- 引数
-
desc type of descriptor to use, BoostDesc::BINBOOST_256 is default (256 bit long dimension) Available types are: BoostDesc::BGM, BoostDesc::BGM_HARD, BoostDesc::BGM_BILINEAR, BoostDesc::LBGM, BoostDesc::BINBOOST_64, BoostDesc::BINBOOST_128, BoostDesc::BINBOOST_256 use_orientation sample patterns using keypoints orientation, enabled by default scale_factor adjust the sampling window of detected keypoints 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 1.50f was the default in original implementation
- 覚え書き
- BGM is the base descriptor where each binary dimension is computed as the output of a single weak learner. BGM_HARD and BGM_BILINEAR refers to same BGM but use different type of gradient binning. In the BGM_HARD that use ASSIGN_HARD binning type the gradient is assigned to the nearest orientation bin. In the BGM_BILINEAR that use ASSIGN_BILINEAR binning type the gradient is assigned to the two neighbouring bins. In the BGM and all other modes that use ASSIGN_SOFT binning type the gradient is assigned to 8 nearest bins according to the cosine value between the gradient angle and the bin center. LBGM (alias FP-Boost) is the floating point extension where each dimension is computed as a linear combination of the weak learner responses. BINBOOST and subvariants are the binary extensions of LBGM where each bit is computed as a thresholded linear combination of a set of weak learners. BoostDesc header files (boostdesc_*.i) was exported from original binaries with export-boostdesc.py script from samples subfolder.
このクラス詳解は次のファイルから抽出されました:
