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# Feature Detection and Description

## SIFT

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.

C++:`` `SIFT::``SIFT`(int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6)

  Parameters: | nfeatures – The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast) 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. 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. 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). 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

C++:`` void `SIFT::``operator()`(InputArray img, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)

  Parameters: | img – Input 8-bit grayscale image mask – Optional input mask that marks the regions where we should detect features. keypoints – The input/output vector of keypoints descriptors – The output matrix of descriptors. Pass `cv::noArray()` if you do not need them. 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

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:

  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
  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`.
  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.

  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.

  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

Note

- An example using the SURF feature detector can be found at opencv_source_code/samples/cpp/generic_descriptor_match.cpp
- Another example using the SURF feature detector, extractor and matcher can be found at opencv_source_code/samples/cpp/matcher_simple.cpp

## SURF::SURF

The SURF extractor constructors.

C++:`` `SURF::``SURF`()

C++:`` `SURF::``SURF`(double hessianThreshold, int nOctaves=4, int nOctaveLayers=2, bool extended=true, bool upright=false )

Python:`` `cv2.``SURF`([hessianThreshold[, nOctaves[, nOctaveLayers[, extended[, upright]]]]]) → <SURF object>

  Parameters: | hessianThreshold – Threshold for hessian keypoint detector used in SURF. nOctaves – Number of pyramid octaves the keypoint detector will use. nOctaveLayers – Number of octave layers within each octave. extended – Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors). 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.

C++:`` void `SURF::``operator()`(InputArray img, InputArray mask, vector<KeyPoint>& keypoints)`const`

C++:`` void `SURF::``operator()`(InputArray img, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)

Python:`` `cv2.SURF.``detect`(image[, mask]) → keypoints

Python:`` `cv2.SURF.``detectAndCompute`(image[, mask]) → keypoints, descriptors

C:`` void `cvExtractSURF`(const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params)

Python:`` `cv.``ExtractSURF`(image, mask, storage, params)-> (keypoints, descriptors)

  Parameters: | image – Input 8-bit grayscale image mask – Optional input mask that marks the regions where we should detect features. keypoints – The input/output vector of keypoints descriptors – The output matrix of descriptors. Pass `cv::noArray()` if you do not need them. 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. storage – Memory storage for the output keypoints and descriptors in OpenCV 1.x API. 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

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<KeyPoint>& keypoints,
            GpuMat& keypointsGPU);
        //! download keypoints from device to host memory
        void downloadKeypoints(const GpuMat& keypointsGPU,
            vector<KeyPoint>& keypoints);

        //! download descriptors from device to host memory
        void downloadDescriptors(const GpuMat& descriptorsGPU,
            vector<float>& 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<KeyPoint>& keypoints);

        void operator()(const GpuMat& img, const GpuMat& mask,
            std::vector<KeyPoint>& keypoints, GpuMat& descriptors,
            bool useProvidedKeypoints = false,
            bool calcOrientation = true);

        void operator()(const GpuMat& img, const GpuMat& mask,
            std::vector<KeyPoint>& keypoints,
            std::vector<float>& 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 \texttt{nFeatures} \times 7 matrix with the `CV_32FC1` type.

- `keypoints.ptr<float>(X_ROW)[i]` contains x coordinate of the i-th feature.
- `keypoints.ptr<float>(Y_ROW)[i]` contains y coordinate of the i-th feature.
- `keypoints.ptr<float>(LAPLACIAN_ROW)[i]` contains the laplacian sign of the i-th feature.
- `keypoints.ptr<float>(OCTAVE_ROW)[i]` contains the octave of the i-th feature.
- `keypoints.ptr<float>(SIZE_ROW)[i]` contains the size of the i-th feature.
- `keypoints.ptr<float>(ANGLE_ROW)[i]` contain orientation of the i-th feature.
- `keypoints.ptr<float>(HESSIAN_ROW)[i]` contains the response of the i-th feature.

The `descriptors` matrix is \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.

See also

`SURF`

Note

- An example for using the SURF keypoint matcher on GPU can be found at opencv_source_code/samples/gpu/surf_keypoint_matcher.cpp

## ocl::SURF_OCL

class `ocl::``SURF_OCL` : public `Feature2D`

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<KeyPoint>& keypoints,
            oclMat& keypointsocl);
        //! download keypoints from device to host memory
        void downloadKeypoints(const oclMat& keypointsocl,
            vector<KeyPoint>& keypoints);

        //! download descriptors from device to host memory
        void downloadDescriptors(const oclMat& descriptorsocl,
            vector<float>& 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<KeyPoint>& keypoints);

        void operator()(const oclMat& img, const oclMat& mask,
            std::vector<KeyPoint>& keypoints, oclMat& descriptors,
            bool useProvidedKeypoints = false);

        void operator()(const oclMat& img, const oclMat& mask,
            std::vector<KeyPoint>& keypoints,
            std::vector<float>& 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 \texttt{nFeatures} \times 7 matrix with the `CV_32FC1` type.

- `keypoints.ptr<float>(X_ROW)[i]` contains x coordinate of the i-th feature.
- `keypoints.ptr<float>(Y_ROW)[i]` contains y coordinate of the i-th feature.
- `keypoints.ptr<float>(LAPLACIAN_ROW)[i]` contains the laplacian sign of the i-th feature.
- `keypoints.ptr<float>(OCTAVE_ROW)[i]` contains the octave of the i-th feature.
- `keypoints.ptr<float>(SIZE_ROW)[i]` contains the size of the i-th feature.
- `keypoints.ptr<float>(ANGLE_ROW)[i]` contain orientation of the i-th feature.
- `keypoints.ptr<float>(HESSIAN_ROW)[i]` contains the response of the i-th feature.

The `descriptors` matrix is \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.

See also

`SURF`

Note

- OCL : An example of the SURF detector can be found at opencv_source_code/samples/ocl/surf_matcher.cpp

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