Important changes to repositories hosted on mbed.com
Mbed hosted mercurial repositories are deprecated and are due to be permanently deleted in July 2026.
To keep a copy of this software download the repository Zip archive or clone locally using Mercurial.
It is also possible to export all your personal repositories from the account settings page.
Dependents: RZ_A2M_Mbed_samples
AKAZE Class Reference
[Feature Detection and Description]
Class implementing the AKAZE keypoint detector and descriptor extractor, described in ANB13 . More...
#include <features2d.hpp>
Inherits cv::Feature2D.
Public Types | |
| enum | { DESCRIPTOR_KAZE_UPRIGHT = 2 , DESCRIPTOR_MLDB_UPRIGHT = 4 } |
Public Member Functions | |
| 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). | |
| virtual CV_WRAP void | detect (InputArrayOfArrays images, CV_OUT std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray()) |
| This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. | |
| 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). | |
| virtual CV_WRAP void | compute (InputArrayOfArrays images, CV_OUT CV_IN_OUT std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors) |
| This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. | |
| virtual CV_WRAP void | detectAndCompute (InputArray image, InputArray mask, CV_OUT std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false) |
| Detects keypoints and computes the descriptors. | |
| virtual void | write (FileStorage &) const |
| Stores algorithm parameters in a file storage. | |
| virtual void | read (const FileNode &) |
| Reads algorithm parameters from a file storage. | |
| virtual CV_WRAP bool | empty () const |
| Return true if detector object is empty. | |
| virtual CV_WRAP void | clear () |
| Clears the algorithm state. | |
| virtual CV_WRAP void | save (const String &filename) const |
| Saves the algorithm to a file. | |
| virtual CV_WRAP String | getDefaultName () const |
| Returns the algorithm string identifier. | |
Static Public Member Functions | |
| static CV_WRAP Ptr< AKAZE > | create (int descriptor_type=AKAZE::DESCRIPTOR_MLDB, int descriptor_size=0, int descriptor_channels=3, float threshold=0.001f, int nOctaves=4, int nOctaveLayers=4, int diffusivity=KAZE::DIFF_PM_G2) |
| The AKAZE constructor. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| Loads algorithm from the file. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| Loads algorithm from a String. | |
Detailed Description
Class implementing the AKAZE keypoint detector and descriptor extractor, described in ANB13 .
:
- Note:
- AKAZE descriptors can only be used with KAZE or AKAZE keypoints. Try to avoid using *extract* and *detect* instead of *operator()* due to performance reasons. .. [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
Definition at line 651 of file features2d.hpp.
Member Enumeration Documentation
| anonymous enum |
- Enumerator:
DESCRIPTOR_KAZE_UPRIGHT Upright descriptors, not invariant to rotation.
DESCRIPTOR_MLDB_UPRIGHT Upright descriptors, not invariant to rotation.
Definition at line 655 of file features2d.hpp.
Member Function Documentation
| virtual CV_WRAP void clear | ( | ) | [virtual, inherited] |
Clears the algorithm state.
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
| virtual CV_WRAP void compute | ( | InputArrayOfArrays | images, |
| CV_OUT CV_IN_OUT std::vector< std::vector< KeyPoint > > & | keypoints, | ||
| OutputArrayOfArrays | descriptors | ||
| ) | [virtual, inherited] |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters:
-
images Image set. keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation). descriptors Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.
| virtual CV_WRAP void compute | ( | InputArray | image, |
| CV_OUT CV_IN_OUT std::vector< KeyPoint > & | keypoints, | ||
| OutputArray | descriptors | ||
| ) | [virtual, inherited] |
Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).
- Parameters:
-
image Image. keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation). descriptors Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.
| static CV_WRAP Ptr<AKAZE> create | ( | int | descriptor_type = AKAZE::DESCRIPTOR_MLDB, |
| int | descriptor_size = 0, |
||
| int | descriptor_channels = 3, |
||
| float | threshold = 0.001f, |
||
| int | nOctaves = 4, |
||
| int | nOctaveLayers = 4, |
||
| int | diffusivity = KAZE::DIFF_PM_G2 |
||
| ) | [static] |
The AKAZE constructor.
- Parameters:
-
descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE, DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT. descriptor_size Size of the descriptor in bits. 0 -> Full size descriptor_channels Number of channels in the descriptor (1, 2, 3) threshold Detector response threshold to accept point nOctaves Maximum octave evolution of the image nOctaveLayers Default number of sublevels per scale level diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER
| virtual CV_WRAP void detect | ( | InputArray | image, |
| CV_OUT std::vector< KeyPoint > & | keypoints, | ||
| InputArray | mask = noArray() |
||
| ) | [virtual, inherited] |
Detects keypoints in an image (first variant) or image set (second variant).
- Parameters:
-
image Image. keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] . mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer matrix with non-zero values in the region of interest.
| virtual CV_WRAP void detect | ( | InputArrayOfArrays | images, |
| CV_OUT std::vector< std::vector< KeyPoint > > & | keypoints, | ||
| InputArrayOfArrays | masks = noArray() |
||
| ) | [virtual, inherited] |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters:
-
images Image set. keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] . masks Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].
| virtual CV_WRAP void detectAndCompute | ( | InputArray | image, |
| InputArray | mask, | ||
| CV_OUT std::vector< KeyPoint > & | keypoints, | ||
| OutputArray | descriptors, | ||
| bool | useProvidedKeypoints = false |
||
| ) | [virtual, inherited] |
Detects keypoints and computes the descriptors.
| virtual CV_WRAP bool empty | ( | ) | const [virtual, inherited] |
Return true if detector object is empty.
Reimplemented from Algorithm.
| virtual CV_WRAP String getDefaultName | ( | ) | const [virtual, inherited] |
Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string.
| static Ptr<_Tp> load | ( | const String & | filename, |
| const String & | objname = String() |
||
| ) | [static, inherited] |
Loads algorithm from the file.
- Parameters:
-
filename Name of the file to read. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).
| static Ptr<_Tp> loadFromString | ( | const String & | strModel, |
| const String & | objname = String() |
||
| ) | [static, inherited] |
Loads algorithm from a String.
- Parameters:
-
strModel The string variable containing the model you want to load. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
| virtual void read | ( | const FileNode & | fn ) | [virtual, inherited] |
Reads algorithm parameters from a file storage.
Reimplemented from Algorithm.
| virtual CV_WRAP void save | ( | const String & | filename ) | const [virtual, inherited] |
Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
| virtual void write | ( | FileStorage & | fs ) | const [virtual, inherited] |
Stores algorithm parameters in a file storage.
Reimplemented from Algorithm.
Generated on Tue Jul 12 2022 18:20:23 by
1.7.2