\section{Object Detection} \cvclass{gpu::HOGDescriptor} Histogram of Oriented Gradients descriptor and detector. \begin{lstlisting} struct CV_EXPORTS HOGDescriptor { enum { DEFAULT_WIN_SIGMA = -1 }; enum { DEFAULT_NLEVELS = 64 }; enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL }; HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16), Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA, double threshold_L2hys=0.2, bool gamma_correction=true, int nlevels=DEFAULT_NLEVELS); size_t getDescriptorSize() const; size_t getBlockHistogramSize() const; void setSVMDetector(const vector& detector); static vector getDefaultPeopleDetector(); static vector getPeopleDetector48x96(); static vector getPeopleDetector64x128(); void detect(const GpuMat& img, vector& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size()); void detectMultiScale(const GpuMat& img, vector& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size(), double scale0=1.05, int group_threshold=2); void getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors, int descr_format=DESCR_FORMAT_COL_BY_COL); Size win_size; Size block_size; Size block_stride; Size cell_size; int nbins; double win_sigma; double threshold_L2hys; bool gamma_correction; int nlevels; private: // Hidden } \end{lstlisting} Interfaces of all methods are kept similar to CPU HOG descriptor and detector analogues as much as possible. \cvCppFunc{gpu::HOGDescriptor::HOGDescriptor} Creates HOG descriptor and detector. \cvdefCpp{HOGDescriptor(Size win\_size=Size(64, 128), Size block\_size=Size(16, 16),\par Size block\_stride=Size(8, 8), Size cell\_size=Size(8, 8),\par int nbins=9, double win\_sigma=DEFAULT\_WIN\_SIGMA,\par double threshold\_L2hys=0.2, bool gamma\_correction=true,\par int nlevels=DEFAULT\_NLEVELS);} \begin{description} \cvarg{win\_size}{Detection window size. Must be aligned to block size and block stride.} \cvarg{block\_size}{Block size in pixels. Must be aligned to cell size. Only (16,16) is supported for now.} \cvarg{block\_stride}{Block stride. Must be a multiple of cell size.} \cvarg{cell\_size}{Cell size. Only (8, 8) is supported for now.} \cvarg{nbins}{Number of bins. Only 9 bins per cell is supported for now.} \cvarg{win\_sigma}{Gaussian smoothing window parameter.} \cvarg{threshold\_L2Hys}{L2-Hys normalization method shrinkage.} \cvarg{gamma\_correction}{Do gamma correction preprocessing or not.} \cvarg{nlevels}{Maximum number of detection window increases.} \end{description} \cvCppFunc{gpu::HOGDescriptor::getDescriptorSize} Returns number of coefficients required for the classification. \cvdefCpp{size\_t getDescriptorSize() const;} \cvCppFunc{gpu::HOGDescriptor::getBlockHistogramSize} Returns block histogram size. \cvdefCpp{size\_t getBlockHistogramSize() const;} \cvCppFunc{gpu::HOGDescriptor::setSVMDetector} Sets coefficients for the linear SVM classifier. \cvdefCpp{void setSVMDetector(const vector\& detector);} \cvCppFunc{gpu::HOGDescriptor::getDefaultPeopleDetector} Returns coefficients of the classifier trained for people detection (for default window size). \cvdefCpp{static vector getDefaultPeopleDetector();} \cvCppFunc{gpu::HOGDescriptor::getPeopleDetector48x96} Returns coefficients of the classifier trained for people detection (for 48x96 windows). \cvdefCpp{static vector getPeopleDetector48x96();} \cvCppFunc{gpu::HOGDescriptor::getPeopleDetector64x128} Returns coefficients of the classifier trained for people detection (for 64x128 windows). \cvdefCpp{static vector getPeopleDetector64x128();} \cvCppFunc{gpu::HOGDescriptor::detect} Perfroms object detection without multiscale window. \cvdefCpp{void detect(const GpuMat\& img, vector\& found\_locations,\par double hit\_threshold=0, Size win\_stride=Size(),\par Size padding=Size());} \begin{description} \cvarg{img}{Source image. \texttt{CV\_8UC1} and \texttt{CV\_8UC4}types are supported for now.} \cvarg{found\_locations}{Will contain left-top corner points of detected objects boundaries.} \cvarg{hit\_threshold}{Threshold for the distance between features and SVM classifying plane. Usually it's 0 and should be specfied in the detector coefficients (as the last free coefficient), but if the free coefficient is omitted (it's allowed) you can specify it manually here.} \cvarg{win\_stride}{Window stride. Must be a multiple of block stride.} \cvarg{padding}{Mock parameter to keep CPU interface compatibility. Must be (0,0).} \end{description} \cvCppFunc{gpu::HOGDescriptor::detectMultiScale} Perfroms object detection with multiscale window. \cvdefCpp{void detectMultiScale(const GpuMat\& img, vector\& found\_locations,\par double hit\_threshold=0, Size win\_stride=Size(),\par Size padding=Size(), double scale0=1.05,\par int group\_threshold=2);} \begin{description} \cvarg{img}{Source image. See \cvCppCross{gpu::HOGDescriptor::detect} for type limitations.} \cvarg{found\_locations}{Will contain detected objects boundaries.} \cvarg{hit\_threshold}{The threshold for the distance between features and SVM classifying plane. See \cvCppCross{gpu::HOGDescriptor::detect} for details.} \cvarg{win\_stride}{Window stride. Must be a multiple of block stride.} \cvarg{padding}{Mock parameter to keep CPU interface compatibility. Must be (0,0).} \cvarg{scale0}{Coefficient of the detection window increase.} \cvarg{group\_threshold}{After detection some objects could be covered by many rectangles. This coefficient regulates similarity threshold. 0 means don't perform grouping.\newline See \cvCppCross{groupRectangles}.} \end{description} \cvCppFunc{gpu::HOGDescriptor::getDescriptors} Returns block descriptors computed for the whole image. It's mainly used for classifier learning purposes. \cvdefCpp{void getDescriptors(const GpuMat\& img, Size win\_stride,\par GpuMat\& descriptors,\par int descr\_format=DESCR\_FORMAT\_COL\_BY\_COL);} \begin{description} \cvarg{img}{Source image. See \cvCppCross{gpu::HOGDescriptor::detect} for type limitations.} \cvarg{win\_stride}{Window stride. Must be a multiple of block stride.} \cvarg{descriptors}{2D array of descriptors.} \cvarg{descr\_format}{Descriptor storage format: \begin{description} \cvarg{DESCR\_FORMAT\_ROW\_BY\_ROW}{Row-major order.} \cvarg{DESCR\_FORMAT\_COL\_BY\_COL}{Column-major order.} \end{description}} \end{description}