Feature Detection and Description ================================= SIFT ---- .. ocv:class:: SIFT : public Feature2D Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [Lowe04]_. .. [Lowe04] Lowe, D. G., “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60, 2, pp. 91-110, 2004. SIFT::SIFT ---------- The SIFT constructors. .. ocv:function:: SIFT::SIFT( int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6) :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. :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. SIFT::operator () ----------------- Extract features and computes their descriptors using SIFT algorithm .. ocv:function:: void SIFT::operator()(InputArray img, InputArray mask, vector& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false) :param img: Input 8-bit grayscale image :param mask: Optional input mask that marks the regions where we should detect features. :param keypoints: The input/output vector of keypoints :param descriptors: The output matrix of descriptors. Pass ``cv::noArray()`` if you do not need them. :param useProvidedKeypoints: Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors. SURF ---- .. ocv:class:: SURF : public Feature2D Class for extracting Speeded Up Robust Features from an image [Bay06]_. The class is derived from ``CvSURFParams`` structure, which specifies the algorithm parameters: .. ocv:member:: int extended * 0 means that the basic descriptors (64 elements each) shall be computed * 1 means that the extended descriptors (128 elements each) shall be computed .. ocv:member:: int upright * 0 means that detector computes orientation of each feature. * 1 means that the orientation is not computed (which is much, much faster). For example, if you match images from a stereo pair, or do image stitching, the matched features likely have very similar angles, and you can speed up feature extraction by setting ``upright=1``. .. ocv:member:: double hessianThreshold Threshold for the keypoint detector. Only features, whose hessian is larger than ``hessianThreshold`` are retained by the detector. Therefore, the larger the value, the less keypoints you will get. A good default value could be from 300 to 500, depending from the image contrast. .. ocv:member:: int nOctaves The number of a gaussian pyramid octaves that the detector uses. It is set to 4 by default. If you want to get very large features, use the larger value. If you want just small features, decrease it. .. ocv:member:: int nOctaveLayers The number of images within each octave of a gaussian pyramid. It is set to 2 by default. .. [Bay06] Bay, H. and Tuytelaars, T. and Van Gool, L. "SURF: Speeded Up Robust Features", 9th European Conference on Computer Vision, 2006 SURF::SURF ---------- The SURF extractor constructors. .. ocv:function:: SURF::SURF() .. ocv:function:: SURF::SURF( double hessianThreshold, int nOctaves=4, int nOctaveLayers=2, bool extended=true, bool upright=false ) .. ocv:pyfunction:: cv2.SURF([hessianThreshold[, nOctaves[, nOctaveLayers[, extended[, upright]]]]]) -> :param hessianThreshold: Threshold for hessian keypoint detector used in SURF. :param nOctaves: Number of pyramid octaves the keypoint detector will use. :param nOctaveLayers: Number of octave layers within each octave. :param extended: Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors). :param upright: Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation). SURF::operator() ---------------- Detects keypoints and computes SURF descriptors for them. .. ocv:function:: void SURF::operator()(InputArray img, InputArray mask, vector& keypoints) const .. ocv:function:: void SURF::operator()(InputArray img, InputArray mask, vector& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false) .. ocv:pyfunction:: cv2.SURF.detect(image[, mask]) -> keypoints .. ocv:cfunction:: void cvExtractSURF( const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params ) :param image: Input 8-bit grayscale image :param mask: Optional input mask that marks the regions where we should detect features. :param keypoints: The input/output vector of keypoints :param descriptors: The output matrix of descriptors. Pass ``cv::noArray()`` if you do not need them. :param useProvidedKeypoints: Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors. :param storage: Memory storage for the output keypoints and descriptors in OpenCV 1.x API. :param params: SURF algorithm parameters in OpenCV 1.x API. The function is parallelized with the TBB library. If you are using the C version, make sure you call ``cv::initModule_nonfree()`` from ``nonfree/nonfree.hpp``. gpu::SURF_GPU ------------- .. ocv:class:: gpu::SURF_GPU Class used for extracting Speeded Up Robust Features (SURF) from an image. :: class SURF_GPU { public: enum KeypointLayout { X_ROW = 0, Y_ROW, LAPLACIAN_ROW, OCTAVE_ROW, SIZE_ROW, ANGLE_ROW, HESSIAN_ROW, ROWS_COUNT }; //! the default constructor SURF_GPU(); //! the full constructor taking all the necessary parameters explicit SURF_GPU(double _hessianThreshold, int _nOctaves=4, int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f); //! returns the descriptor size in float's (64 or 128) int descriptorSize() const; //! upload host keypoints to device memory void uploadKeypoints(const vector& keypoints, GpuMat& keypointsGPU); //! download keypoints from device to host memory void downloadKeypoints(const GpuMat& keypointsGPU, vector& keypoints); //! download descriptors from device to host memory void downloadDescriptors(const GpuMat& descriptorsGPU, vector& descriptors); void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints); void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors, bool useProvidedKeypoints = false, bool calcOrientation = true); void operator()(const GpuMat& img, const GpuMat& mask, std::vector& keypoints); void operator()(const GpuMat& img, const GpuMat& mask, std::vector& keypoints, GpuMat& descriptors, bool useProvidedKeypoints = false, bool calcOrientation = true); void operator()(const GpuMat& img, const GpuMat& mask, std::vector& keypoints, std::vector& descriptors, bool useProvidedKeypoints = false, bool calcOrientation = true); void releaseMemory(); // SURF parameters double hessianThreshold; int nOctaves; int nOctaveLayers; bool extended; bool upright; //! max keypoints = keypointsRatio * img.size().area() float keypointsRatio; GpuMat sum, mask1, maskSum, intBuffer; GpuMat det, trace; GpuMat maxPosBuffer; }; The class ``SURF_GPU`` implements Speeded Up Robust Features descriptor. There is a fast multi-scale Hessian keypoint detector that can be used to find the keypoints (which is the default option). But the descriptors can also be computed for the user-specified keypoints. Only 8-bit grayscale images are supported. The class ``SURF_GPU`` can store results in the GPU and CPU memory. It provides functions to convert results between CPU and GPU version ( ``uploadKeypoints``, ``downloadKeypoints``, ``downloadDescriptors`` ). The format of CPU results is the same as ``SURF`` results. GPU results are stored in ``GpuMat``. The ``keypoints`` matrix is :math:`\texttt{nFeatures} \times 7` matrix with the ``CV_32FC1`` type. * ``keypoints.ptr(X_ROW)[i]`` contains x coordinate of the i-th feature. * ``keypoints.ptr(Y_ROW)[i]`` contains y coordinate of the i-th feature. * ``keypoints.ptr(LAPLACIAN_ROW)[i]`` contains the laplacian sign of the i-th feature. * ``keypoints.ptr(OCTAVE_ROW)[i]`` contains the octave of the i-th feature. * ``keypoints.ptr(SIZE_ROW)[i]`` contains the size of the i-th feature. * ``keypoints.ptr(ANGLE_ROW)[i]`` contain orientation of the i-th feature. * ``keypoints.ptr(HESSIAN_ROW)[i]`` contains the response of the i-th feature. The ``descriptors`` matrix is :math:`\texttt{nFeatures} \times \texttt{descriptorSize}` matrix with the ``CV_32FC1`` type. The class ``SURF_GPU`` uses some buffers and provides access to it. All buffers can be safely released between function calls. .. seealso:: :ocv:class:`SURF` ocl::SURF_OCL ------------- .. ocv:class:: ocl::SURF_OCL Class used for extracting Speeded Up Robust Features (SURF) from an image. :: class SURF_OCL { public: enum KeypointLayout { X_ROW = 0, Y_ROW, LAPLACIAN_ROW, OCTAVE_ROW, SIZE_ROW, ANGLE_ROW, HESSIAN_ROW, ROWS_COUNT }; //! the default constructor SURF_OCL(); //! the full constructor taking all the necessary parameters explicit SURF_OCL(double _hessianThreshold, int _nOctaves=4, int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false); //! returns the descriptor size in float's (64 or 128) int descriptorSize() const; //! upload host keypoints to device memory void uploadKeypoints(const vector& keypoints, oclMat& keypointsocl); //! download keypoints from device to host memory void downloadKeypoints(const oclMat& keypointsocl, vector& keypoints); //! download descriptors from device to host memory void downloadDescriptors(const oclMat& descriptorsocl, vector& descriptors); void operator()(const oclMat& img, const oclMat& mask, oclMat& keypoints); void operator()(const oclMat& img, const oclMat& mask, oclMat& keypoints, oclMat& descriptors, bool useProvidedKeypoints = false); void operator()(const oclMat& img, const oclMat& mask, std::vector& keypoints); void operator()(const oclMat& img, const oclMat& mask, std::vector& keypoints, oclMat& descriptors, bool useProvidedKeypoints = false); void operator()(const oclMat& img, const oclMat& mask, std::vector& keypoints, std::vector& descriptors, bool useProvidedKeypoints = false); void releaseMemory(); // SURF parameters double hessianThreshold; int nOctaves; int nOctaveLayers; bool extended; bool upright; //! max keypoints = min(keypointsRatio * img.size().area(), 65535) float keypointsRatio; oclMat sum, mask1, maskSum, intBuffer; oclMat det, trace; oclMat maxPosBuffer; }; The class ``SURF_OCL`` implements Speeded Up Robust Features descriptor. There is a fast multi-scale Hessian keypoint detector that can be used to find the keypoints (which is the default option). But the descriptors can also be computed for the user-specified keypoints. Only 8-bit grayscale images are supported. The class ``SURF_OCL`` can store results in the GPU and CPU memory. It provides functions to convert results between CPU and GPU version ( ``uploadKeypoints``, ``downloadKeypoints``, ``downloadDescriptors`` ). The format of CPU results is the same as ``SURF`` results. GPU results are stored in ``oclMat``. The ``keypoints`` matrix is :math:`\texttt{nFeatures} \times 7` matrix with the ``CV_32FC1`` type. * ``keypoints.ptr(X_ROW)[i]`` contains x coordinate of the i-th feature. * ``keypoints.ptr(Y_ROW)[i]`` contains y coordinate of the i-th feature. * ``keypoints.ptr(LAPLACIAN_ROW)[i]`` contains the laplacian sign of the i-th feature. * ``keypoints.ptr(OCTAVE_ROW)[i]`` contains the octave of the i-th feature. * ``keypoints.ptr(SIZE_ROW)[i]`` contains the size of the i-th feature. * ``keypoints.ptr(ANGLE_ROW)[i]`` contain orientation of the i-th feature. * ``keypoints.ptr(HESSIAN_ROW)[i]`` contains the response of the i-th feature. The ``descriptors`` matrix is :math:`\texttt{nFeatures} \times \texttt{descriptorSize}` matrix with the ``CV_32FC1`` type. The class ``SURF_OCL`` uses some buffers and provides access to it. All buffers can be safely released between function calls. .. seealso:: :ocv:class:`SURF`