java.lang.Object
- org.opencv.objdetect.HOGDescriptor

```
public class HOGDescriptor
extends java.lang.Object
```
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

Field Summary

Fields
Modifier and Type Field Description

static int DEFAULT_NLEVELS

static int L2Hys

protected long nativeObj

Constructor Summary

Constructors
Modifier	Constructor	Description
	`HOGDescriptor()`	Creates the HOG descriptor and detector with default parameters.
`protected`	`HOGDescriptor(long addr)`
	`HOGDescriptor(java.lang.String filename)`	Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
	`HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins)`
	`HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture)`
	`HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma)`
	`HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType)`
	`HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold)`
	`HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection)`
	`HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection, int _nlevels)`
	`HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection, int _nlevels, boolean _signedGradient)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`static HOGDescriptor`	`__fromPtr__(long addr)`
`boolean`	`checkDetectorSize()`	Checks if detector size equal to descriptor size.
`void`	`compute(Mat img, MatOfFloat descriptors)`	Computes HOG descriptors of given image.
`void`	`compute(Mat img, MatOfFloat descriptors, Size winStride)`	Computes HOG descriptors of given image.
`void`	`compute(Mat img, MatOfFloat descriptors, Size winStride, Size padding)`	Computes HOG descriptors of given image.
`void`	`compute(Mat img, MatOfFloat descriptors, Size winStride, Size padding, MatOfPoint locations)`	Computes HOG descriptors of given image.
`void`	`computeGradient(Mat img, Mat grad, Mat angleOfs)`	Computes gradients and quantized gradient orientations.
`void`	`computeGradient(Mat img, Mat grad, Mat angleOfs, Size paddingTL)`	Computes gradients and quantized gradient orientations.
`void`	`computeGradient(Mat img, Mat grad, Mat angleOfs, Size paddingTL, Size paddingBR)`	Computes gradients and quantized gradient orientations.
`void`	`detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights)`	Performs object detection without a multi-scale window.
`void`	`detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold)`	Performs object detection without a multi-scale window.
`void`	`detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride)`	Performs object detection without a multi-scale window.
`void`	`detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride, Size padding)`	Performs object detection without a multi-scale window.
`void`	`detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride, Size padding, MatOfPoint searchLocations)`	Performs object detection without a multi-scale window.
`void`	`detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights)`	Detects objects of different sizes in the input image.
`void`	`detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold)`	Detects objects of different sizes in the input image.
`void`	`detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride)`	Detects objects of different sizes in the input image.
`void`	`detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding)`	Detects objects of different sizes in the input image.
`void`	`detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale)`	Detects objects of different sizes in the input image.
`void`	`detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale, double groupThreshold)`	Detects objects of different sizes in the input image.
`void`	`detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale, double groupThreshold, boolean useMeanshiftGrouping)`	Detects objects of different sizes in the input image.
`protected void`	`finalize()`
`Size`	`get_blockSize()`
`Size`	`get_blockStride()`
`Size`	`get_cellSize()`
`int`	`get_derivAperture()`
`boolean`	`get_gammaCorrection()`
`int`	`get_histogramNormType()`
`double`	`get_L2HysThreshold()`
`int`	`get_nbins()`
`int`	`get_nlevels()`
`boolean`	`get_signedGradient()`
`MatOfFloat`	`get_svmDetector()`
`double`	`get_winSigma()`
`Size`	`get_winSize()`
`static MatOfFloat`	`getDaimlerPeopleDetector()`	Returns coefficients of the classifier trained for people detection (for 48x96 windows).
`static MatOfFloat`	`getDefaultPeopleDetector()`	Returns coefficients of the classifier trained for people detection (for 64x128 windows).
`long`	`getDescriptorSize()`	Returns the number of coefficients required for the classification.
`long`	`getNativeObjAddr()`
`double`	`getWinSigma()`	Returns winSigma value
`boolean`	`load(java.lang.String filename)`	loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
`boolean`	`load(java.lang.String filename, java.lang.String objname)`	loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
`void`	`save(java.lang.String filename)`	saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
`void`	`save(java.lang.String filename, java.lang.String objname)`	saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
`void`	`setSVMDetector(Mat _svmdetector)`	Sets coefficients for the linear SVM classifier.

Methods inherited from class java.lang.Object
clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - nativeObj
```
protected final long nativeObj
```
  - L2Hys
```
public static final int L2Hys
```
    See Also:
    
    Constant Field Values
  - DEFAULT_NLEVELS
```
public static final int DEFAULT_NLEVELS
```
    See Also:
    
    Constant Field Values
- Constructor Detail
  - HOGDescriptor
```
protected HOGDescriptor(long addr)
```
  - HOGDescriptor
```
public 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, 1 )
  - HOGDescriptor
```
public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType,
                     double _L2HysThreshold,
                     boolean _gammaCorrection,
                     int _nlevels,
                     boolean _signedGradient)
```
    Parameters:
    
    _winSize - sets winSize with given value.
    
    _blockSize - sets blockSize with given value.
    
    _blockStride - sets blockStride with given value.
    
    _cellSize - sets cellSize with given value.
    
    _nbins - sets nbins with given value.
    
    _derivAperture - sets derivAperture with given value.
    
    _winSigma - sets winSigma with given value.
    
    _histogramNormType - sets histogramNormType with given value.
    
    _L2HysThreshold - sets L2HysThreshold with given value.
    
    _gammaCorrection - sets gammaCorrection with given value.
    
    _nlevels - sets nlevels with given value.
    
    _signedGradient - sets signedGradient with given value.
  - HOGDescriptor
```
public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType,
                     double _L2HysThreshold,
                     boolean _gammaCorrection,
                     int _nlevels)
```
    Parameters:
    
    _winSize - sets winSize with given value.
    
    _blockSize - sets blockSize with given value.
    
    _blockStride - sets blockStride with given value.
    
    _cellSize - sets cellSize with given value.
    
    _nbins - sets nbins with given value.
    
    _derivAperture - sets derivAperture with given value.
    
    _winSigma - sets winSigma with given value.
    
    _histogramNormType - sets histogramNormType with given value.
    
    _L2HysThreshold - sets L2HysThreshold with given value.
    
    _gammaCorrection - sets gammaCorrection with given value.
    
    _nlevels - sets nlevels with given value.
  - HOGDescriptor
```
public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType,
                     double _L2HysThreshold,
                     boolean _gammaCorrection)
```
    Parameters:
    
    _winSize - sets winSize with given value.
    
    _blockSize - sets blockSize with given value.
    
    _blockStride - sets blockStride with given value.
    
    _cellSize - sets cellSize with given value.
    
    _nbins - sets nbins with given value.
    
    _derivAperture - sets derivAperture with given value.
    
    _winSigma - sets winSigma with given value.
    
    _histogramNormType - sets histogramNormType with given value.
    
    _L2HysThreshold - sets L2HysThreshold with given value.
    
    _gammaCorrection - sets gammaCorrection with given value.
  - HOGDescriptor
```
public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType,
                     double _L2HysThreshold)
```
    Parameters:
    
    _winSize - sets winSize with given value.
    
    _blockSize - sets blockSize with given value.
    
    _blockStride - sets blockStride with given value.
    
    _cellSize - sets cellSize with given value.
    
    _nbins - sets nbins with given value.
    
    _derivAperture - sets derivAperture with given value.
    
    _winSigma - sets winSigma with given value.
    
    _histogramNormType - sets histogramNormType with given value.
    
    _L2HysThreshold - sets L2HysThreshold with given value.
  - HOGDescriptor
```
public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma,
                     int _histogramNormType)
```
    Parameters:
    
    _winSize - sets winSize with given value.
    
    _blockSize - sets blockSize with given value.
    
    _blockStride - sets blockStride with given value.
    
    _cellSize - sets cellSize with given value.
    
    _nbins - sets nbins with given value.
    
    _derivAperture - sets derivAperture with given value.
    
    _winSigma - sets winSigma with given value.
    
    _histogramNormType - sets histogramNormType with given value.
  - HOGDescriptor
```
public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture,
                     double _winSigma)
```
    Parameters:
    
    _winSize - sets winSize with given value.
    
    _blockSize - sets blockSize with given value.
    
    _blockStride - sets blockStride with given value.
    
    _cellSize - sets cellSize with given value.
    
    _nbins - sets nbins with given value.
    
    _derivAperture - sets derivAperture with given value.
    
    _winSigma - sets winSigma with given value.
  - HOGDescriptor
```
public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins,
                     int _derivAperture)
```
    Parameters:
    
    _winSize - sets winSize with given value.
    
    _blockSize - sets blockSize with given value.
    
    _blockStride - sets blockStride with given value.
    
    _cellSize - sets cellSize with given value.
    
    _nbins - sets nbins with given value.
    
    _derivAperture - sets derivAperture with given value.
  - HOGDescriptor
```
public HOGDescriptor(Size _winSize,
                     Size _blockSize,
                     Size _blockStride,
                     Size _cellSize,
                     int _nbins)
```
    Parameters:
    
    _winSize - sets winSize with given value.
    
    _blockSize - sets blockSize with given value.
    
    _blockStride - sets blockStride with given value.
    
    _cellSize - sets cellSize with given value.
    
    _nbins - sets nbins with given value.
  - HOGDescriptor
```
public HOGDescriptor(java.lang.String filename)
```
    Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
    
    Parameters:
    
    filename - the file name containing HOGDescriptor properties and coefficients of the trained classifier
- Method Detail
  - getNativeObjAddr
```
public long getNativeObjAddr()
```
  - __fromPtr__
```
public static HOGDescriptor __fromPtr__(long addr)
```
  - getDescriptorSize
```
public long getDescriptorSize()
```
    Returns the number of coefficients required for the classification.
    
    Returns:
    
    automatically generated
  - checkDetectorSize
```
public boolean checkDetectorSize()
```
    Checks if detector size equal to descriptor size.
    
    Returns:
    
    automatically generated
  - getWinSigma
```
public double getWinSigma()
```
    Returns winSigma value
    
    Returns:
    
    automatically generated
  - setSVMDetector
```
public void setSVMDetector(Mat _svmdetector)
```
    Sets coefficients for the linear SVM classifier.
    
    Parameters:
    
    _svmdetector - coefficients for the linear SVM classifier.
  - load
```
public boolean load(java.lang.String filename,
                    java.lang.String objname)
```
    loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
    
    Parameters:
    
    filename - Name of the file to read.
    
    objname - The optional name of the node to read (if empty, the first top-level node will be used).
    
    Returns:
    
    automatically generated
  - load
```
public boolean load(java.lang.String filename)
```
    loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
    
    Parameters:
    
    filename - Name of the file to read.
    
    Returns:
    
    automatically generated
  - save
```
public void save(java.lang.String filename,
                 java.lang.String objname)
```
    saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
    
    Parameters:
    
    filename - File name
    
    objname - Object name
  - save
```
public void save(java.lang.String filename)
```
    saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
    
    Parameters:
    
    filename - File name
  - compute
```
public void compute(Mat img,
                    MatOfFloat descriptors,
                    Size winStride,
                    Size padding,
                    MatOfPoint locations)
```
    Computes HOG descriptors of given image.
    
    Parameters:
    
    img - Matrix of the type CV_8U containing an image where HOG features will be calculated.
    
    descriptors - Matrix of the type CV_32F
    
    winStride - Window stride. It must be a multiple of block stride.
    
    padding - Padding
    
    locations - Vector of Point
  - compute
```
public void compute(Mat img,
                    MatOfFloat descriptors,
                    Size winStride,
                    Size padding)
```
    Computes HOG descriptors of given image.
    
    Parameters:
    
    img - Matrix of the type CV_8U containing an image where HOG features will be calculated.
    
    descriptors - Matrix of the type CV_32F
    
    winStride - Window stride. It must be a multiple of block stride.
    
    padding - Padding
  - compute
```
public void compute(Mat img,
                    MatOfFloat descriptors,
                    Size winStride)
```
    Computes HOG descriptors of given image.
    
    Parameters:
    
    img - Matrix of the type CV_8U containing an image where HOG features will be calculated.
    
    descriptors - Matrix of the type CV_32F
    
    winStride - Window stride. It must be a multiple of block stride.
  - compute
```
public void compute(Mat img,
                    MatOfFloat descriptors)
```
    Computes HOG descriptors of given image.
    
    Parameters:
    
    img - Matrix of the type CV_8U containing an image where HOG features will be calculated.
    
    descriptors - Matrix of the type CV_32F
  - detect
```
public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights,
                   double hitThreshold,
                   Size winStride,
                   Size padding,
                   MatOfPoint searchLocations)
```
    Performs object detection without a multi-scale window.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.
    
    weights - Vector that will contain confidence values for each detected object.
    
    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.
    
    winStride - Window stride. It must be a multiple of block stride.
    
    padding - Padding
    
    searchLocations - Vector of Point includes set of requested locations to be evaluated.
  - detect
```
public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights,
                   double hitThreshold,
                   Size winStride,
                   Size padding)
```
    Performs object detection without a multi-scale window.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.
    
    weights - Vector that will contain confidence values for each detected object.
    
    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.
    
    winStride - Window stride. It must be a multiple of block stride.
    
    padding - Padding
  - detect
```
public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights,
                   double hitThreshold,
                   Size winStride)
```
    Performs object detection without a multi-scale window.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.
    
    weights - Vector that will contain confidence values for each detected object.
    
    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.
    
    winStride - Window stride. It must be a multiple of block stride.
  - detect
```
public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights,
                   double hitThreshold)
```
    Performs object detection without a multi-scale window.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.
    
    weights - Vector that will contain confidence values for each detected object.
    
    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.
  - detect
```
public void detect(Mat img,
                   MatOfPoint foundLocations,
                   MatOfDouble weights)
```
    Performs object detection without a multi-scale window.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of point where each point contains left-top corner point of detected object boundaries.
    
    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.
  - detectMultiScale
```
public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride,
                             Size padding,
                             double scale,
                             double groupThreshold,
                             boolean useMeanshiftGrouping)
```
    Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of rectangles where each rectangle contains the detected object.
    
    foundWeights - Vector that will contain confidence values for each detected object.
    
    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.
    
    winStride - Window stride. It must be a multiple of block stride.
    
    padding - Padding
    
    scale - Coefficient of the detection window increase.
    
    groupThreshold - Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.
    
    useMeanshiftGrouping - indicates grouping algorithm
  - detectMultiScale
```
public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride,
                             Size padding,
                             double scale,
                             double groupThreshold)
```
    Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of rectangles where each rectangle contains the detected object.
    
    foundWeights - Vector that will contain confidence values for each detected object.
    
    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.
    
    winStride - Window stride. It must be a multiple of block stride.
    
    padding - Padding
    
    scale - Coefficient of the detection window increase.
    
    groupThreshold - Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.
  - detectMultiScale
```
public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride,
                             Size padding,
                             double scale)
```
    Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of rectangles where each rectangle contains the detected object.
    
    foundWeights - Vector that will contain confidence values for each detected object.
    
    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.
    
    winStride - Window stride. It must be a multiple of block stride.
    
    padding - Padding
    
    scale - Coefficient of the detection window increase. by many rectangles. 0 means not to perform grouping.
  - detectMultiScale
```
public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride,
                             Size padding)
```
    Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of rectangles where each rectangle contains the detected object.
    
    foundWeights - Vector that will contain confidence values for each detected object.
    
    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.
    
    winStride - Window stride. It must be a multiple of block stride.
    
    padding - Padding by many rectangles. 0 means not to perform grouping.
  - detectMultiScale
```
public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold,
                             Size winStride)
```
    Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of rectangles where each rectangle contains the detected object.
    
    foundWeights - Vector that will contain confidence values for each detected object.
    
    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.
    
    winStride - Window stride. It must be a multiple of block stride. by many rectangles. 0 means not to perform grouping.
  - detectMultiScale
```
public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights,
                             double hitThreshold)
```
    Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of rectangles where each rectangle contains the detected object.
    
    foundWeights - Vector that will contain confidence values for each detected object.
    
    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.
  - detectMultiScale
```
public void detectMultiScale(Mat img,
                             MatOfRect foundLocations,
                             MatOfDouble foundWeights)
```
    Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
    
    Parameters:
    
    img - Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    
    foundLocations - Vector of rectangles where each rectangle contains the detected object.
    
    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.
  - computeGradient
```
public void computeGradient(Mat img,
                            Mat grad,
                            Mat angleOfs,
                            Size paddingTL,
                            Size paddingBR)
```
    Computes gradients and quantized gradient orientations.
    
    Parameters:
    
    img - Matrix contains the image to be computed
    
    grad - Matrix of type CV_32FC2 contains computed gradients
    
    angleOfs - Matrix of type CV_8UC2 contains quantized gradient orientations
    
    paddingTL - Padding from top-left
    
    paddingBR - Padding from bottom-right
  - computeGradient
```
public void computeGradient(Mat img,
                            Mat grad,
                            Mat angleOfs,
                            Size paddingTL)
```
    Computes gradients and quantized gradient orientations.
    
    Parameters:
    
    img - Matrix contains the image to be computed
    
    grad - Matrix of type CV_32FC2 contains computed gradients
    
    angleOfs - Matrix of type CV_8UC2 contains quantized gradient orientations
    
    paddingTL - Padding from top-left
  - computeGradient
```
public void computeGradient(Mat img,
                            Mat grad,
                            Mat angleOfs)
```
    Computes gradients and quantized gradient orientations.
    
    Parameters:
    
    img - Matrix contains the image to be computed
    
    grad - Matrix of type CV_32FC2 contains computed gradients
    
    angleOfs - Matrix of type CV_8UC2 contains quantized gradient orientations
  - getDefaultPeopleDetector
```
public static MatOfFloat getDefaultPeopleDetector()
```
    Returns coefficients of the classifier trained for people detection (for 64x128 windows).
    
    Returns:
    
    automatically generated
  - getDaimlerPeopleDetector
```
public static MatOfFloat getDaimlerPeopleDetector()
```
    Returns coefficients of the classifier trained for people detection (for 48x96 windows).
    
    Returns:
    
    automatically generated
  - get_winSize
```
public Size get_winSize()
```
  - get_blockSize
```
public Size get_blockSize()
```
  - get_blockStride
```
public Size get_blockStride()
```
  - get_cellSize
```
public Size get_cellSize()
```
  - get_nbins
```
public int get_nbins()
```
  - get_derivAperture
```
public int get_derivAperture()
```
  - get_winSigma
```
public double get_winSigma()
```
  - get_histogramNormType
```
public int get_histogramNormType()
```
  - get_L2HysThreshold
```
public double get_L2HysThreshold()
```
  - get_gammaCorrection
```
public boolean get_gammaCorrection()
```
  - get_svmDetector
```
public MatOfFloat get_svmDetector()
```
  - get_nlevels
```
public int get_nlevels()
```
  - get_signedGradient
```
public boolean get_signedGradient()
```
  - finalize
```
protected void finalize()
                 throws java.lang.Throwable
```
    Overrides:
    
    finalize in class java.lang.Object
    
    Throws:
    
    java.lang.Throwable

Modifier and Type	Field	Description
`static int`	`DEFAULT_NLEVELS`
`static int`	`L2Hys`
`protected long`	`nativeObj`

Class HOGDescriptor

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

nativeObj

L2Hys

DEFAULT_NLEVELS

Constructor Detail

HOGDescriptor

HOGDescriptor

HOGDescriptor

HOGDescriptor

HOGDescriptor

HOGDescriptor

HOGDescriptor

HOGDescriptor

HOGDescriptor

HOGDescriptor

HOGDescriptor

Method Detail

getNativeObjAddr

__fromPtr__

getDescriptorSize

checkDetectorSize

getWinSigma

setSVMDetector

load

load

save

save

compute

compute

compute

compute

detect

detect

detect

detect

detect

detectMultiScale

detectMultiScale

detectMultiScale

detectMultiScale

detectMultiScale

detectMultiScale

detectMultiScale

computeGradient

computeGradient

computeGradient

getDefaultPeopleDetector

getDaimlerPeopleDetector

get_winSize

get_blockSize

get_blockStride

get_cellSize

get_nbins

get_derivAperture

get_winSigma

get_histogramNormType

get_L2HysThreshold

get_gammaCorrection

get_svmDetector

get_nlevels

get_signedGradient

finalize

fromPtr