import type { bool, double, FileNode, FileStorage, float, InputArray, InputOutputArray, int, Point, Size, size_t, UMat } from "./_types";
/**
 * the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs Dalal2005 .
 *
 * useful links:
 *
 * Source:
 * [opencv2/objdetect.hpp](https://github.com/opencv/opencv/tree/master/modules/core/include/opencv2/objdetect.hpp#L377).
 *
 */
export declare class HOGDescriptor {
    blockSize: Size;
    blockStride: Size;
    cellSize: Size;
    derivAperture: int;
    free_coef: float;
    gammaCorrection: bool;
    histogramNormType: any;
    L2HysThreshold: double;
    nbins: int;
    nlevels: int;
    oclSvmDetector: UMat;
    signedGradient: bool;
    svmDetector: any;
    winSigma: double;
    winSize: Size;
    /**
     *   aqual to [HOGDescriptor](Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
     */
    constructor();
    /**
     *   This is an overloaded member function, provided for convenience. It differs from the above
     * function only in what argument(s) it accepts.
     *
     * @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.
     */
    constructor(_winSize: Size, _blockSize: Size, _blockStride: Size, _cellSize: Size, _nbins: int, _derivAperture?: int, _winSigma?: double, _histogramNormType?: any, _L2HysThreshold?: double, _gammaCorrection?: bool, _nlevels?: int, _signedGradient?: bool);
    /**
     *   This is an overloaded member function, provided for convenience. It differs from the above
     * function only in what argument(s) it accepts.
     *
     * @param filename The file name containing HOGDescriptor properties and coefficients for the linear
     * SVM classifier.
     */
    constructor(filename: String);
    /**
     *   This is an overloaded member function, provided for convenience. It differs from the above
     * function only in what argument(s) it accepts.
     *
     * @param d the HOGDescriptor which cloned to create a new one.
     */
    constructor(d: HOGDescriptor);
    checkDetectorSize(): bool;
    /**
     * @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
     */
    compute(img: InputArray, descriptors: any, winStride?: Size, padding?: Size, locations?: Point): InputArray;
    /**
     * @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
     */
    computeGradient(img: InputArray, grad: InputOutputArray, angleOfs: InputOutputArray, paddingTL?: Size, paddingBR?: Size): InputArray;
    /**
     * @param c cloned HOGDescriptor
     */
    copyTo(c: HOGDescriptor): HOGDescriptor;
    /**
     * @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.
     */
    detect(img: InputArray, foundLocations: any, weights: any, hitThreshold?: double, winStride?: Size, padding?: Size, searchLocations?: Point): InputArray;
    /**
     * @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 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 locations to search.
     */
    detect(img: InputArray, foundLocations: any, hitThreshold?: double, winStride?: Size, padding?: Size, searchLocations?: Point): InputArray;
    /**
     * @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 finalThreshold Final threshold
     *
     * @param useMeanshiftGrouping indicates grouping algorithm
     */
    detectMultiScale(img: InputArray, foundLocations: any, foundWeights: any, hitThreshold?: double, winStride?: Size, padding?: Size, scale?: double, finalThreshold?: double, useMeanshiftGrouping?: bool): InputArray;
    /**
     * @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 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 finalThreshold Final threshold
     *
     * @param useMeanshiftGrouping indicates grouping algorithm
     */
    detectMultiScale(img: InputArray, foundLocations: any, hitThreshold?: double, winStride?: Size, padding?: Size, scale?: double, finalThreshold?: double, useMeanshiftGrouping?: bool): InputArray;
    /**
     * @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 locations Vector of DetectionROI
     *
     * @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 groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a
     * group of rectangles to retain it.
     */
    detectMultiScaleROI(img: InputArray, foundLocations: any, locations: any, hitThreshold?: double, groupThreshold?: int): InputArray;
    /**
     * @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
     *
     * @param locations Vector of Point
     *
     * @param foundLocations Vector of Point where each Point is detected object's top-left point.
     *
     * @param confidences confidences
     *
     * @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 winStride
     *
     * @param padding padding
     */
    detectROI(img: InputArray, locations: any, foundLocations: any, confidences: any, hitThreshold?: double, winStride?: any, padding?: any): InputArray;
    getDescriptorSize(): size_t;
    getWinSigma(): double;
    /**
     * @param rectList Input/output vector of rectangles. Output vector includes retained and grouped
     * rectangles. (The Python list is not modified in place.)
     *
     * @param weights Input/output vector of weights of rectangles. Output vector includes weights of
     * retained and grouped rectangles. (The Python list is not modified in place.)
     *
     * @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a
     * group of rectangles to retain it.
     *
     * @param eps Relative difference between sides of the rectangles to merge them into a group.
     */
    groupRectangles(rectList: any, weights: any, groupThreshold: int, eps: double): any;
    /**
     * @param filename Path 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).
     */
    load(filename: String, objname?: String): String;
    /**
     * @param fn File node
     */
    read(fn: FileNode): FileNode;
    /**
     * @param filename File name
     *
     * @param objname Object name
     */
    save(filename: String, objname?: String): String;
    /**
     * @param svmdetector coefficients for the linear SVM classifier.
     */
    setSVMDetector(svmdetector: InputArray): InputArray;
    /**
     * @param fs File storage
     *
     * @param objname Object name
     */
    write(fs: FileStorage, objname: String): FileStorage;
    static getDaimlerPeopleDetector(): any;
    static getDefaultPeopleDetector(): any;
}
export declare const DEFAULT_NLEVELS: any;
export declare const DESCR_FORMAT_COL_BY_COL: DescriptorStorageFormat;
export declare const DESCR_FORMAT_ROW_BY_ROW: DescriptorStorageFormat;
export declare const L2Hys: HistogramNormType;
export type DescriptorStorageFormat = any;
export type HistogramNormType = any;