// // This file is auto-generated. Please don't modify it! // #pragma once #ifdef __cplusplus //#import "opencv.hpp" #import "opencv2/objdetect.hpp" #else #define CV_EXPORTS #endif #import @class DoubleVector; @class FloatVector; @class Mat; @class Point2i; @class Rect2i; @class Size2i; // C++: enum DescriptorStorageFormat (cv.HOGDescriptor.DescriptorStorageFormat) typedef NS_ENUM(int, DescriptorStorageFormat) { DESCR_FORMAT_COL_BY_COL = 0, DESCR_FORMAT_ROW_BY_ROW = 1 }; // C++: enum HistogramNormType (cv.HOGDescriptor.HistogramNormType) typedef NS_ENUM(int, HistogramNormType) { L2Hys = 0 }; NS_ASSUME_NONNULL_BEGIN // C++: class HOGDescriptor /** * Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector. * * the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs CITE: Dalal2005 . * * useful links: * * https://hal.inria.fr/inria-00548512/document/ * * https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients * * https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor * * http://www.learnopencv.com/histogram-of-oriented-gradients * * http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial * * Member of `Objdetect` */ CV_EXPORTS @interface HOGDescriptor : NSObject #ifdef __cplusplus @property(readonly)cv::Ptr nativePtr; #endif #ifdef __cplusplus - (instancetype)initWithNativePtr:(cv::Ptr)nativePtr; + (instancetype)fromNative:(cv::Ptr)nativePtr; #endif #pragma mark - Class Constants @property (class, readonly) int DEFAULT_NLEVELS NS_SWIFT_NAME(DEFAULT_NLEVELS); #pragma mark - Methods // // cv::HOGDescriptor::HOGDescriptor() // /** * Creates the HOG descriptor and detector with default parameters. * * aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 ) */ - (instancetype)init; // // cv::HOGDescriptor::HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture = 1, double _winSigma = -1, HOGDescriptor_HistogramNormType _histogramNormType = HOGDescriptor::L2Hys, double _L2HysThreshold = 0.2, bool _gammaCorrection = false, int _nlevels = HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient = false) // /** * * @param _winSize sets winSize with given value. * @param _blockSize sets blockSize with given value. * @param _blockStride sets blockStride with given value. * @param _cellSize sets cellSize with given value. * @param _nbins sets nbins with given value. * @param _derivAperture sets derivAperture with given value. * @param _winSigma sets winSigma with given value. * @param _histogramNormType sets histogramNormType with given value. * @param _L2HysThreshold sets L2HysThreshold with given value. * @param _gammaCorrection sets gammaCorrection with given value. * @param _nlevels sets nlevels with given value. * @param _signedGradient sets signedGradient with given value. */ - (instancetype)initWith_winSize:(Size2i*)_winSize _blockSize:(Size2i*)_blockSize _blockStride:(Size2i*)_blockStride _cellSize:(Size2i*)_cellSize _nbins:(int)_nbins _derivAperture:(int)_derivAperture _winSigma:(double)_winSigma _histogramNormType:(HistogramNormType)_histogramNormType _L2HysThreshold:(double)_L2HysThreshold _gammaCorrection:(BOOL)_gammaCorrection _nlevels:(int)_nlevels _signedGradient:(BOOL)_signedGradient; /** * * @param _winSize sets winSize with given value. * @param _blockSize sets blockSize with given value. * @param _blockStride sets blockStride with given value. * @param _cellSize sets cellSize with given value. * @param _nbins sets nbins with given value. * @param _derivAperture sets derivAperture with given value. * @param _winSigma sets winSigma with given value. * @param _histogramNormType sets histogramNormType with given value. * @param _L2HysThreshold sets L2HysThreshold with given value. * @param _gammaCorrection sets gammaCorrection with given value. * @param _nlevels sets nlevels with given value. */ - (instancetype)initWith_winSize:(Size2i*)_winSize _blockSize:(Size2i*)_blockSize _blockStride:(Size2i*)_blockStride _cellSize:(Size2i*)_cellSize _nbins:(int)_nbins _derivAperture:(int)_derivAperture _winSigma:(double)_winSigma _histogramNormType:(HistogramNormType)_histogramNormType _L2HysThreshold:(double)_L2HysThreshold _gammaCorrection:(BOOL)_gammaCorrection _nlevels:(int)_nlevels; /** * * @param _winSize sets winSize with given value. * @param _blockSize sets blockSize with given value. * @param _blockStride sets blockStride with given value. * @param _cellSize sets cellSize with given value. * @param _nbins sets nbins with given value. * @param _derivAperture sets derivAperture with given value. * @param _winSigma sets winSigma with given value. * @param _histogramNormType sets histogramNormType with given value. * @param _L2HysThreshold sets L2HysThreshold with given value. * @param _gammaCorrection sets gammaCorrection with given value. */ - (instancetype)initWith_winSize:(Size2i*)_winSize _blockSize:(Size2i*)_blockSize _blockStride:(Size2i*)_blockStride _cellSize:(Size2i*)_cellSize _nbins:(int)_nbins _derivAperture:(int)_derivAperture _winSigma:(double)_winSigma _histogramNormType:(HistogramNormType)_histogramNormType _L2HysThreshold:(double)_L2HysThreshold _gammaCorrection:(BOOL)_gammaCorrection; /** * * @param _winSize sets winSize with given value. * @param _blockSize sets blockSize with given value. * @param _blockStride sets blockStride with given value. * @param _cellSize sets cellSize with given value. * @param _nbins sets nbins with given value. * @param _derivAperture sets derivAperture with given value. * @param _winSigma sets winSigma with given value. * @param _histogramNormType sets histogramNormType with given value. * @param _L2HysThreshold sets L2HysThreshold with given value. */ - (instancetype)initWith_winSize:(Size2i*)_winSize _blockSize:(Size2i*)_blockSize _blockStride:(Size2i*)_blockStride _cellSize:(Size2i*)_cellSize _nbins:(int)_nbins _derivAperture:(int)_derivAperture _winSigma:(double)_winSigma _histogramNormType:(HistogramNormType)_histogramNormType _L2HysThreshold:(double)_L2HysThreshold; /** * * @param _winSize sets winSize with given value. * @param _blockSize sets blockSize with given value. * @param _blockStride sets blockStride with given value. * @param _cellSize sets cellSize with given value. * @param _nbins sets nbins with given value. * @param _derivAperture sets derivAperture with given value. * @param _winSigma sets winSigma with given value. * @param _histogramNormType sets histogramNormType with given value. */ - (instancetype)initWith_winSize:(Size2i*)_winSize _blockSize:(Size2i*)_blockSize _blockStride:(Size2i*)_blockStride _cellSize:(Size2i*)_cellSize _nbins:(int)_nbins _derivAperture:(int)_derivAperture _winSigma:(double)_winSigma _histogramNormType:(HistogramNormType)_histogramNormType; /** * * @param _winSize sets winSize with given value. * @param _blockSize sets blockSize with given value. * @param _blockStride sets blockStride with given value. * @param _cellSize sets cellSize with given value. * @param _nbins sets nbins with given value. * @param _derivAperture sets derivAperture with given value. * @param _winSigma sets winSigma with given value. */ - (instancetype)initWith_winSize:(Size2i*)_winSize _blockSize:(Size2i*)_blockSize _blockStride:(Size2i*)_blockStride _cellSize:(Size2i*)_cellSize _nbins:(int)_nbins _derivAperture:(int)_derivAperture _winSigma:(double)_winSigma; /** * * @param _winSize sets winSize with given value. * @param _blockSize sets blockSize with given value. * @param _blockStride sets blockStride with given value. * @param _cellSize sets cellSize with given value. * @param _nbins sets nbins with given value. * @param _derivAperture sets derivAperture with given value. */ - (instancetype)initWith_winSize:(Size2i*)_winSize _blockSize:(Size2i*)_blockSize _blockStride:(Size2i*)_blockStride _cellSize:(Size2i*)_cellSize _nbins:(int)_nbins _derivAperture:(int)_derivAperture; /** * * @param _winSize sets winSize with given value. * @param _blockSize sets blockSize with given value. * @param _blockStride sets blockStride with given value. * @param _cellSize sets cellSize with given value. * @param _nbins sets nbins with given value. */ - (instancetype)initWith_winSize:(Size2i*)_winSize _blockSize:(Size2i*)_blockSize _blockStride:(Size2i*)_blockStride _cellSize:(Size2i*)_cellSize _nbins:(int)_nbins; // // cv::HOGDescriptor::HOGDescriptor(String filename) // /** * * * Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file. * @param filename The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier. */ - (instancetype)initWithFilename:(NSString*)filename; // // size_t cv::HOGDescriptor::getDescriptorSize() // /** * Returns the number of coefficients required for the classification. */ - (size_t)getDescriptorSize NS_SWIFT_NAME(getDescriptorSize()); // // bool cv::HOGDescriptor::checkDetectorSize() // /** * Checks if detector size equal to descriptor size. */ - (BOOL)checkDetectorSize NS_SWIFT_NAME(checkDetectorSize()); // // double cv::HOGDescriptor::getWinSigma() // /** * Returns winSigma value */ - (double)getWinSigma NS_SWIFT_NAME(getWinSigma()); // // void cv::HOGDescriptor::setSVMDetector(Mat svmdetector) // /** * Sets coefficients for the linear SVM classifier. * @param svmdetector coefficients for the linear SVM classifier. */ - (void)setSVMDetector:(Mat*)svmdetector NS_SWIFT_NAME(setSVMDetector(svmdetector:)); // // bool cv::HOGDescriptor::load(String filename, String objname = String()) // /** * loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file * @param filename Name of the file to read. * @param objname The optional name of the node to read (if empty, the first top-level node will be used). */ - (BOOL)load:(NSString*)filename objname:(NSString*)objname NS_SWIFT_NAME(load(filename:objname:)); /** * loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file * @param filename Name of the file to read. */ - (BOOL)load:(NSString*)filename NS_SWIFT_NAME(load(filename:)); // // void cv::HOGDescriptor::save(String filename, String objname = String()) // /** * saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file * @param filename File name * @param objname Object name */ - (void)save:(NSString*)filename objname:(NSString*)objname NS_SWIFT_NAME(save(filename:objname:)); /** * saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file * @param filename File name */ - (void)save:(NSString*)filename NS_SWIFT_NAME(save(filename:)); // // void cv::HOGDescriptor::compute(Mat img, vector_float& descriptors, Size winStride = Size(), Size padding = Size(), vector_Point locations = std::vector()) // /** * Computes HOG descriptors of given image. * @param img Matrix of the type CV_8U containing an image where HOG features will be calculated. * @param descriptors Matrix of the type CV_32F * @param winStride Window stride. It must be a multiple of block stride. * @param padding Padding * @param locations Vector of Point */ - (void)compute:(Mat*)img descriptors:(FloatVector*)descriptors winStride:(Size2i*)winStride padding:(Size2i*)padding locations:(NSArray*)locations NS_SWIFT_NAME(compute(img:descriptors:winStride:padding:locations:)); /** * Computes HOG descriptors of given image. * @param img Matrix of the type CV_8U containing an image where HOG features will be calculated. * @param descriptors Matrix of the type CV_32F * @param winStride Window stride. It must be a multiple of block stride. * @param padding Padding */ - (void)compute:(Mat*)img descriptors:(FloatVector*)descriptors winStride:(Size2i*)winStride padding:(Size2i*)padding NS_SWIFT_NAME(compute(img:descriptors:winStride:padding:)); /** * Computes HOG descriptors of given image. * @param img Matrix of the type CV_8U containing an image where HOG features will be calculated. * @param descriptors Matrix of the type CV_32F * @param winStride Window stride. It must be a multiple of block stride. */ - (void)compute:(Mat*)img descriptors:(FloatVector*)descriptors winStride:(Size2i*)winStride NS_SWIFT_NAME(compute(img:descriptors:winStride:)); /** * Computes HOG descriptors of given image. * @param img Matrix of the type CV_8U containing an image where HOG features will be calculated. * @param descriptors Matrix of the type CV_32F */ - (void)compute:(Mat*)img descriptors:(FloatVector*)descriptors NS_SWIFT_NAME(compute(img:descriptors:)); // // void cv::HOGDescriptor::detect(Mat img, vector_Point& foundLocations, vector_double& weights, double hitThreshold = 0, Size winStride = Size(), Size padding = Size(), vector_Point searchLocations = std::vector()) // /** * Performs object detection without a multi-scale window. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. * @param weights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * @param winStride Window stride. It must be a multiple of block stride. * @param padding Padding * @param searchLocations Vector of Point includes set of requested locations to be evaluated. */ - (void)detect:(Mat*)img foundLocations:(NSMutableArray*)foundLocations weights:(DoubleVector*)weights hitThreshold:(double)hitThreshold winStride:(Size2i*)winStride padding:(Size2i*)padding searchLocations:(NSArray*)searchLocations NS_SWIFT_NAME(detect(img:foundLocations:weights:hitThreshold:winStride:padding:searchLocations:)); /** * Performs object detection without a multi-scale window. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. * @param weights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * @param winStride Window stride. It must be a multiple of block stride. * @param padding Padding */ - (void)detect:(Mat*)img foundLocations:(NSMutableArray*)foundLocations weights:(DoubleVector*)weights hitThreshold:(double)hitThreshold winStride:(Size2i*)winStride padding:(Size2i*)padding NS_SWIFT_NAME(detect(img:foundLocations:weights:hitThreshold:winStride:padding:)); /** * Performs object detection without a multi-scale window. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. * @param weights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * @param winStride Window stride. It must be a multiple of block stride. */ - (void)detect:(Mat*)img foundLocations:(NSMutableArray*)foundLocations weights:(DoubleVector*)weights hitThreshold:(double)hitThreshold winStride:(Size2i*)winStride NS_SWIFT_NAME(detect(img:foundLocations:weights:hitThreshold:winStride:)); /** * Performs object detection without a multi-scale window. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. * @param weights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. */ - (void)detect:(Mat*)img foundLocations:(NSMutableArray*)foundLocations weights:(DoubleVector*)weights hitThreshold:(double)hitThreshold NS_SWIFT_NAME(detect(img:foundLocations:weights:hitThreshold:)); /** * Performs object detection without a multi-scale window. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. * @param weights Vector that will contain confidence values for each detected object. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. */ - (void)detect:(Mat*)img foundLocations:(NSMutableArray*)foundLocations weights:(DoubleVector*)weights NS_SWIFT_NAME(detect(img:foundLocations:weights:)); // // void cv::HOGDescriptor::detectMultiScale(Mat img, vector_Rect& foundLocations, vector_double& foundWeights, double hitThreshold = 0, Size winStride = Size(), Size padding = Size(), double scale = 1.05, double groupThreshold = 2.0, bool useMeanshiftGrouping = false) // /** * Detects objects of different sizes in the input image. The detected objects are returned as a list * of rectangles. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of rectangles where each rectangle contains the detected object. * @param foundWeights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * @param winStride Window stride. It must be a multiple of block stride. * @param padding Padding * @param scale Coefficient of the detection window increase. * @param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered * by many rectangles. 0 means not to perform grouping. * @param useMeanshiftGrouping indicates grouping algorithm */ - (void)detectMultiScale:(Mat*)img foundLocations:(NSMutableArray*)foundLocations foundWeights:(DoubleVector*)foundWeights hitThreshold:(double)hitThreshold winStride:(Size2i*)winStride padding:(Size2i*)padding scale:(double)scale groupThreshold:(double)groupThreshold useMeanshiftGrouping:(BOOL)useMeanshiftGrouping NS_SWIFT_NAME(detectMultiScale(img:foundLocations:foundWeights:hitThreshold:winStride:padding:scale:groupThreshold:useMeanshiftGrouping:)); /** * Detects objects of different sizes in the input image. The detected objects are returned as a list * of rectangles. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of rectangles where each rectangle contains the detected object. * @param foundWeights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * @param winStride Window stride. It must be a multiple of block stride. * @param padding Padding * @param scale Coefficient of the detection window increase. * @param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered * by many rectangles. 0 means not to perform grouping. */ - (void)detectMultiScale:(Mat*)img foundLocations:(NSMutableArray*)foundLocations foundWeights:(DoubleVector*)foundWeights hitThreshold:(double)hitThreshold winStride:(Size2i*)winStride padding:(Size2i*)padding scale:(double)scale groupThreshold:(double)groupThreshold NS_SWIFT_NAME(detectMultiScale(img:foundLocations:foundWeights:hitThreshold:winStride:padding:scale:groupThreshold:)); /** * Detects objects of different sizes in the input image. The detected objects are returned as a list * of rectangles. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of rectangles where each rectangle contains the detected object. * @param foundWeights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * @param winStride Window stride. It must be a multiple of block stride. * @param padding Padding * @param scale Coefficient of the detection window increase. * by many rectangles. 0 means not to perform grouping. */ - (void)detectMultiScale:(Mat*)img foundLocations:(NSMutableArray*)foundLocations foundWeights:(DoubleVector*)foundWeights hitThreshold:(double)hitThreshold winStride:(Size2i*)winStride padding:(Size2i*)padding scale:(double)scale NS_SWIFT_NAME(detectMultiScale(img:foundLocations:foundWeights:hitThreshold:winStride:padding:scale:)); /** * Detects objects of different sizes in the input image. The detected objects are returned as a list * of rectangles. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of rectangles where each rectangle contains the detected object. * @param foundWeights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * @param winStride Window stride. It must be a multiple of block stride. * @param padding Padding * by many rectangles. 0 means not to perform grouping. */ - (void)detectMultiScale:(Mat*)img foundLocations:(NSMutableArray*)foundLocations foundWeights:(DoubleVector*)foundWeights hitThreshold:(double)hitThreshold winStride:(Size2i*)winStride padding:(Size2i*)padding NS_SWIFT_NAME(detectMultiScale(img:foundLocations:foundWeights:hitThreshold:winStride:padding:)); /** * Detects objects of different sizes in the input image. The detected objects are returned as a list * of rectangles. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of rectangles where each rectangle contains the detected object. * @param foundWeights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * @param winStride Window stride. It must be a multiple of block stride. * by many rectangles. 0 means not to perform grouping. */ - (void)detectMultiScale:(Mat*)img foundLocations:(NSMutableArray*)foundLocations foundWeights:(DoubleVector*)foundWeights hitThreshold:(double)hitThreshold winStride:(Size2i*)winStride NS_SWIFT_NAME(detectMultiScale(img:foundLocations:foundWeights:hitThreshold:winStride:)); /** * Detects objects of different sizes in the input image. The detected objects are returned as a list * of rectangles. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of rectangles where each rectangle contains the detected object. * @param foundWeights Vector that will contain confidence values for each detected object. * @param hitThreshold Threshold for the distance between features and SVM classifying plane. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * by many rectangles. 0 means not to perform grouping. */ - (void)detectMultiScale:(Mat*)img foundLocations:(NSMutableArray*)foundLocations foundWeights:(DoubleVector*)foundWeights hitThreshold:(double)hitThreshold NS_SWIFT_NAME(detectMultiScale(img:foundLocations:foundWeights:hitThreshold:)); /** * Detects objects of different sizes in the input image. The detected objects are returned as a list * of rectangles. * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. * @param foundLocations Vector of rectangles where each rectangle contains the detected object. * @param foundWeights Vector that will contain confidence values for each detected object. * Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). * But if the free coefficient is omitted (which is allowed), you can specify it manually here. * by many rectangles. 0 means not to perform grouping. */ - (void)detectMultiScale:(Mat*)img foundLocations:(NSMutableArray*)foundLocations foundWeights:(DoubleVector*)foundWeights NS_SWIFT_NAME(detectMultiScale(img:foundLocations:foundWeights:)); // // void cv::HOGDescriptor::computeGradient(Mat img, Mat& grad, Mat& angleOfs, Size paddingTL = Size(), Size paddingBR = Size()) // /** * Computes gradients and quantized gradient orientations. * @param img Matrix contains the image to be computed * @param grad Matrix of type CV_32FC2 contains computed gradients * @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations * @param paddingTL Padding from top-left * @param paddingBR Padding from bottom-right */ - (void)computeGradient:(Mat*)img grad:(Mat*)grad angleOfs:(Mat*)angleOfs paddingTL:(Size2i*)paddingTL paddingBR:(Size2i*)paddingBR NS_SWIFT_NAME(computeGradient(img:grad:angleOfs:paddingTL:paddingBR:)); /** * Computes gradients and quantized gradient orientations. * @param img Matrix contains the image to be computed * @param grad Matrix of type CV_32FC2 contains computed gradients * @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations * @param paddingTL Padding from top-left */ - (void)computeGradient:(Mat*)img grad:(Mat*)grad angleOfs:(Mat*)angleOfs paddingTL:(Size2i*)paddingTL NS_SWIFT_NAME(computeGradient(img:grad:angleOfs:paddingTL:)); /** * Computes gradients and quantized gradient orientations. * @param img Matrix contains the image to be computed * @param grad Matrix of type CV_32FC2 contains computed gradients * @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations */ - (void)computeGradient:(Mat*)img grad:(Mat*)grad angleOfs:(Mat*)angleOfs NS_SWIFT_NAME(computeGradient(img:grad:angleOfs:)); // // static vector_float cv::HOGDescriptor::getDefaultPeopleDetector() // /** * Returns coefficients of the classifier trained for people detection (for 64x128 windows). */ + (FloatVector*)getDefaultPeopleDetector NS_SWIFT_NAME(getDefaultPeopleDetector()); // // static vector_float cv::HOGDescriptor::getDaimlerPeopleDetector() // /** * Returns coefficients of the classifier trained for people detection (for 48x96 windows). */ + (FloatVector*)getDaimlerPeopleDetector NS_SWIFT_NAME(getDaimlerPeopleDetector()); // // C++: Size cv::HOGDescriptor::winSize // @property (readonly) Size2i* winSize; // // C++: Size cv::HOGDescriptor::blockSize // @property (readonly) Size2i* blockSize; // // C++: Size cv::HOGDescriptor::blockStride // @property (readonly) Size2i* blockStride; // // C++: Size cv::HOGDescriptor::cellSize // @property (readonly) Size2i* cellSize; // // C++: int cv::HOGDescriptor::nbins // @property (readonly) int nbins; // // C++: int cv::HOGDescriptor::derivAperture // @property (readonly) int derivAperture; // // C++: double cv::HOGDescriptor::winSigma // @property (readonly) double winSigma; // // C++: HOGDescriptor_HistogramNormType cv::HOGDescriptor::histogramNormType // @property (readonly) HistogramNormType histogramNormType; // // C++: double cv::HOGDescriptor::L2HysThreshold // @property (readonly) double L2HysThreshold; // // C++: bool cv::HOGDescriptor::gammaCorrection // @property (readonly) BOOL gammaCorrection; // // C++: vector_float cv::HOGDescriptor::svmDetector // @property (readonly) FloatVector* svmDetector; // // C++: int cv::HOGDescriptor::nlevels // @property (readonly) int nlevels; // // C++: bool cv::HOGDescriptor::signedGradient // @property (readonly) BOOL signedGradient; @end NS_ASSUME_NONNULL_END