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virtual | ~Feature2D () |
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virtual void | compute (InputArray image, 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). More...
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virtual void | compute (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors) |
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virtual int | defaultNorm () const |
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virtual int | descriptorSize () const |
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virtual int | descriptorType () const |
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virtual void | detect (InputArray image, std::vector< KeyPoint > &keypoints, InputArray mask=noArray()) |
| Detects keypoints in an image (first variant) or image set (second variant). More...
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virtual void | detect (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray()) |
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virtual void | detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false) |
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virtual bool | empty () const CV_OVERRIDE |
| Return true if detector object is empty. More...
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virtual String | getDefaultName () const CV_OVERRIDE |
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void | read (const String &fileName) |
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virtual void | read (const FileNode &) CV_OVERRIDE |
| Reads algorithm parameters from a file storage. More...
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void | write (const String &fileName) const |
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virtual void | write (FileStorage &) const CV_OVERRIDE |
| Stores algorithm parameters in a file storage. More...
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void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
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| Algorithm () |
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virtual | ~Algorithm () |
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virtual void | clear () |
| Clears the algorithm state. More...
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virtual void | save (const String &filename) const |
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void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
| simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...
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void | writeFormat (FileStorage &fs) const |
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Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [8] .
The algorithm propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Key- point (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are competitive alternatives to existing keypoints in particular for embedded applications.
- Note
- An example on how to use the FREAK descriptor can be found at opencv_source_code/samples/cpp/freak_demo.cpp