//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.objdetect;
import org.opencv.core.Mat;
import org.opencv.core.MatOfDouble;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfPoint;
import org.opencv.core.MatOfRect;
import org.opencv.core.Size;
// 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
*/
public class HOGDescriptor {
protected final long nativeObj;
protected HOGDescriptor(long addr) { nativeObj = addr; }
public long getNativeObjAddr() { return nativeObj; }
// internal usage only
public static HOGDescriptor __fromPtr__(long addr) { return new HOGDescriptor(addr); }
// C++: enum <unnamed>
public static final int
DEFAULT_NLEVELS = 64;
// C++: enum DescriptorStorageFormat (cv.HOGDescriptor.DescriptorStorageFormat)
public static final int
DESCR_FORMAT_COL_BY_COL = 0,
DESCR_FORMAT_ROW_BY_ROW = 1;
// C++: enum HistogramNormType (cv.HOGDescriptor.HistogramNormType)
public static final int
L2Hys = 0;
//
// C++: 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 )
*/
public HOGDescriptor() {
nativeObj = HOGDescriptor_0();
}
//
// C++: 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.
*/
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) {
nativeObj = HOGDescriptor_1(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins, _derivAperture, _winSigma, _histogramNormType, _L2HysThreshold, _gammaCorrection, _nlevels, _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.
*/
public HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection, int _nlevels) {
nativeObj = HOGDescriptor_2(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins, _derivAperture, _winSigma, _histogramNormType, _L2HysThreshold, _gammaCorrection, _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.
*/
public HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection) {
nativeObj = HOGDescriptor_3(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins, _derivAperture, _winSigma, _histogramNormType, _L2HysThreshold, _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.
*/
public HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold) {
nativeObj = HOGDescriptor_4(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins, _derivAperture, _winSigma, _histogramNormType, _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.
*/
public HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType) {
nativeObj = HOGDescriptor_5(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins, _derivAperture, _winSigma, _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.
*/
public HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma) {
nativeObj = HOGDescriptor_6(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins, _derivAperture, _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.
*/
public HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture) {
nativeObj = HOGDescriptor_7(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins, _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.
*/
public HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins) {
nativeObj = HOGDescriptor_8(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins);
}
//
// C++: 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.
*/
public HOGDescriptor(String filename) {
nativeObj = HOGDescriptor_9(filename);
}
//
// C++: size_t cv::HOGDescriptor::getDescriptorSize()
//
/**
* Returns the number of coefficients required for the classification.
* @return automatically generated
*/
public long getDescriptorSize() {
return getDescriptorSize_0(nativeObj);
}
//
// C++: bool cv::HOGDescriptor::checkDetectorSize()
//
/**
* Checks if detector size equal to descriptor size.
* @return automatically generated
*/
public boolean checkDetectorSize() {
return checkDetectorSize_0(nativeObj);
}
//
// C++: double cv::HOGDescriptor::getWinSigma()
//
/**
* Returns winSigma value
* @return automatically generated
*/
public double getWinSigma() {
return getWinSigma_0(nativeObj);
}
//
// C++: void cv::HOGDescriptor::setSVMDetector(Mat svmdetector)
//
/**
* Sets coefficients for the linear SVM classifier.
* @param svmdetector coefficients for the linear SVM classifier.
*/
public void setSVMDetector(Mat svmdetector) {
setSVMDetector_0(nativeObj, svmdetector.nativeObj);
}
//
// C++: 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).
* @return automatically generated
*/
public boolean load(String filename, String objname) {
return load_0(nativeObj, filename, objname);
}
/**
* loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
* @param filename Name of the file to read.
* @return automatically generated
*/
public boolean load(String filename) {
return load_1(nativeObj, filename);
}
//
// C++: 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
*/
public void save(String filename, String objname) {
save_0(nativeObj, filename, objname);
}
/**
* saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
* @param filename File name
*/
public void save(String filename) {
save_1(nativeObj, filename);
}
//
// C++: void cv::HOGDescriptor::compute(Mat img, vector_float& descriptors, Size winStride = Size(), Size padding = Size(), vector_Point locations = std::vector<Point>())
//
/**
* 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
*/
public void compute(Mat img, MatOfFloat descriptors, Size winStride, Size padding, MatOfPoint locations) {
Mat descriptors_mat = descriptors;
Mat locations_mat = locations;
compute_0(nativeObj, img.nativeObj, descriptors_mat.nativeObj, winStride.width, winStride.height, padding.width, padding.height, locations_mat.nativeObj);
}
/**
* 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
*/
public void compute(Mat img, MatOfFloat descriptors, Size winStride, Size padding) {
Mat descriptors_mat = descriptors;
compute_1(nativeObj, img.nativeObj, descriptors_mat.nativeObj, winStride.width, winStride.height, padding.width, padding.height);
}
/**
* 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.
*/
public void compute(Mat img, MatOfFloat descriptors, Size winStride) {
Mat descriptors_mat = descriptors;
compute_2(nativeObj, img.nativeObj, descriptors_mat.nativeObj, winStride.width, winStride.height);
}
/**
* 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
*/
public void compute(Mat img, MatOfFloat descriptors) {
Mat descriptors_mat = descriptors;
compute_3(nativeObj, img.nativeObj, descriptors_mat.nativeObj);
}
//
// C++: 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<Point>())
//
/**
* 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.
*/
public void detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride, Size padding, MatOfPoint searchLocations) {
Mat foundLocations_mat = foundLocations;
Mat weights_mat = weights;
Mat searchLocations_mat = searchLocations;
detect_0(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, weights_mat.nativeObj, hitThreshold, winStride.width, winStride.height, padding.width, padding.height, searchLocations_mat.nativeObj);
}
/**
* 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
*/
public void detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride, Size padding) {
Mat foundLocations_mat = foundLocations;
Mat weights_mat = weights;
detect_1(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, weights_mat.nativeObj, hitThreshold, winStride.width, winStride.height, padding.width, padding.height);
}
/**
* 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.
*/
public void detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride) {
Mat foundLocations_mat = foundLocations;
Mat weights_mat = weights;
detect_2(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, weights_mat.nativeObj, hitThreshold, winStride.width, winStride.height);
}
/**
* 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.
*/
public void detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold) {
Mat foundLocations_mat = foundLocations;
Mat weights_mat = weights;
detect_3(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, weights_mat.nativeObj, 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.
*/
public void detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights) {
Mat foundLocations_mat = foundLocations;
Mat weights_mat = weights;
detect_4(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, weights_mat.nativeObj);
}
//
// C++: 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
*/
public void detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale, double groupThreshold, boolean useMeanshiftGrouping) {
Mat foundLocations_mat = foundLocations;
Mat foundWeights_mat = foundWeights;
detectMultiScale_0(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, foundWeights_mat.nativeObj, hitThreshold, winStride.width, winStride.height, padding.width, padding.height, 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.
*/
public void detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale, double groupThreshold) {
Mat foundLocations_mat = foundLocations;
Mat foundWeights_mat = foundWeights;
detectMultiScale_1(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, foundWeights_mat.nativeObj, hitThreshold, winStride.width, winStride.height, padding.width, padding.height, 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.
*/
public void detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale) {
Mat foundLocations_mat = foundLocations;
Mat foundWeights_mat = foundWeights;
detectMultiScale_2(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, foundWeights_mat.nativeObj, hitThreshold, winStride.width, winStride.height, padding.width, padding.height, 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.
*/
public void detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding) {
Mat foundLocations_mat = foundLocations;
Mat foundWeights_mat = foundWeights;
detectMultiScale_3(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, foundWeights_mat.nativeObj, hitThreshold, winStride.width, winStride.height, padding.width, padding.height);
}
/**
* 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.
*/
public void detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride) {
Mat foundLocations_mat = foundLocations;
Mat foundWeights_mat = foundWeights;
detectMultiScale_4(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, foundWeights_mat.nativeObj, hitThreshold, winStride.width, winStride.height);
}
/**
* 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.
*/
public void detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold) {
Mat foundLocations_mat = foundLocations;
Mat foundWeights_mat = foundWeights;
detectMultiScale_5(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, foundWeights_mat.nativeObj, 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.
*/
public void detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights) {
Mat foundLocations_mat = foundLocations;
Mat foundWeights_mat = foundWeights;
detectMultiScale_6(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, foundWeights_mat.nativeObj);
}
//
// C++: 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
*/
public void computeGradient(Mat img, Mat grad, Mat angleOfs, Size paddingTL, Size paddingBR) {
computeGradient_0(nativeObj, img.nativeObj, grad.nativeObj, angleOfs.nativeObj, paddingTL.width, paddingTL.height, paddingBR.width, paddingBR.height);
}
/**
* 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
*/
public void computeGradient(Mat img, Mat grad, Mat angleOfs, Size paddingTL) {
computeGradient_1(nativeObj, img.nativeObj, grad.nativeObj, angleOfs.nativeObj, paddingTL.width, paddingTL.height);
}
/**
* 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
*/
public void computeGradient(Mat img, Mat grad, Mat angleOfs) {
computeGradient_2(nativeObj, img.nativeObj, grad.nativeObj, angleOfs.nativeObj);
}
//
// C++: static vector_float cv::HOGDescriptor::getDefaultPeopleDetector()
//
/**
* Returns coefficients of the classifier trained for people detection (for 64x128 windows).
* @return automatically generated
*/
public static MatOfFloat getDefaultPeopleDetector() {
return MatOfFloat.fromNativeAddr(getDefaultPeopleDetector_0());
}
//
// C++: static vector_float cv::HOGDescriptor::getDaimlerPeopleDetector()
//
/**
* Returns coefficients of the classifier trained for people detection (for 48x96 windows).
* @return automatically generated
*/
public static MatOfFloat getDaimlerPeopleDetector() {
return MatOfFloat.fromNativeAddr(getDaimlerPeopleDetector_0());
}
//
// C++: Size HOGDescriptor::winSize
//
public Size get_winSize() {
return new Size(get_winSize_0(nativeObj));
}
//
// C++: Size HOGDescriptor::blockSize
//
public Size get_blockSize() {
return new Size(get_blockSize_0(nativeObj));
}
//
// C++: Size HOGDescriptor::blockStride
//
public Size get_blockStride() {
return new Size(get_blockStride_0(nativeObj));
}
//
// C++: Size HOGDescriptor::cellSize
//
public Size get_cellSize() {
return new Size(get_cellSize_0(nativeObj));
}
//
// C++: int HOGDescriptor::nbins
//
public int get_nbins() {
return get_nbins_0(nativeObj);
}
//
// C++: int HOGDescriptor::derivAperture
//
public int get_derivAperture() {
return get_derivAperture_0(nativeObj);
}
//
// C++: double HOGDescriptor::winSigma
//
public double get_winSigma() {
return get_winSigma_0(nativeObj);
}
//
// C++: HOGDescriptor_HistogramNormType HOGDescriptor::histogramNormType
//
public int get_histogramNormType() {
return get_histogramNormType_0(nativeObj);
}
//
// C++: double HOGDescriptor::L2HysThreshold
//
public double get_L2HysThreshold() {
return get_L2HysThreshold_0(nativeObj);
}
//
// C++: bool HOGDescriptor::gammaCorrection
//
public boolean get_gammaCorrection() {
return get_gammaCorrection_0(nativeObj);
}
//
// C++: vector_float HOGDescriptor::svmDetector
//
public MatOfFloat get_svmDetector() {
return MatOfFloat.fromNativeAddr(get_svmDetector_0(nativeObj));
}
//
// C++: int HOGDescriptor::nlevels
//
public int get_nlevels() {
return get_nlevels_0(nativeObj);
}
//
// C++: bool HOGDescriptor::signedGradient
//
public boolean get_signedGradient() {
return get_signedGradient_0(nativeObj);
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: cv::HOGDescriptor::HOGDescriptor()
private static native long HOGDescriptor_0();
// C++: 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)
private static native long HOGDescriptor_1(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection, int _nlevels, boolean _signedGradient);
private static native long HOGDescriptor_2(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection, int _nlevels);
private static native long HOGDescriptor_3(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection);
private static native long HOGDescriptor_4(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold);
private static native long HOGDescriptor_5(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType);
private static native long HOGDescriptor_6(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins, int _derivAperture, double _winSigma);
private static native long HOGDescriptor_7(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins, int _derivAperture);
private static native long HOGDescriptor_8(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins);
// C++: cv::HOGDescriptor::HOGDescriptor(String filename)
private static native long HOGDescriptor_9(String filename);
// C++: size_t cv::HOGDescriptor::getDescriptorSize()
private static native long getDescriptorSize_0(long nativeObj);
// C++: bool cv::HOGDescriptor::checkDetectorSize()
private static native boolean checkDetectorSize_0(long nativeObj);
// C++: double cv::HOGDescriptor::getWinSigma()
private static native double getWinSigma_0(long nativeObj);
// C++: void cv::HOGDescriptor::setSVMDetector(Mat svmdetector)
private static native void setSVMDetector_0(long nativeObj, long svmdetector_nativeObj);
// C++: bool cv::HOGDescriptor::load(String filename, String objname = String())
private static native boolean load_0(long nativeObj, String filename, String objname);
private static native boolean load_1(long nativeObj, String filename);
// C++: void cv::HOGDescriptor::save(String filename, String objname = String())
private static native void save_0(long nativeObj, String filename, String objname);
private static native void save_1(long nativeObj, String filename);
// C++: void cv::HOGDescriptor::compute(Mat img, vector_float& descriptors, Size winStride = Size(), Size padding = Size(), vector_Point locations = std::vector<Point>())
private static native void compute_0(long nativeObj, long img_nativeObj, long descriptors_mat_nativeObj, double winStride_width, double winStride_height, double padding_width, double padding_height, long locations_mat_nativeObj);
private static native void compute_1(long nativeObj, long img_nativeObj, long descriptors_mat_nativeObj, double winStride_width, double winStride_height, double padding_width, double padding_height);
private static native void compute_2(long nativeObj, long img_nativeObj, long descriptors_mat_nativeObj, double winStride_width, double winStride_height);
private static native void compute_3(long nativeObj, long img_nativeObj, long descriptors_mat_nativeObj);
// C++: 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<Point>())
private static native void detect_0(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long weights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height, double padding_width, double padding_height, long searchLocations_mat_nativeObj);
private static native void detect_1(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long weights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height, double padding_width, double padding_height);
private static native void detect_2(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long weights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height);
private static native void detect_3(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long weights_mat_nativeObj, double hitThreshold);
private static native void detect_4(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long weights_mat_nativeObj);
// C++: 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)
private static native void detectMultiScale_0(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long foundWeights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height, double padding_width, double padding_height, double scale, double groupThreshold, boolean useMeanshiftGrouping);
private static native void detectMultiScale_1(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long foundWeights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height, double padding_width, double padding_height, double scale, double groupThreshold);
private static native void detectMultiScale_2(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long foundWeights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height, double padding_width, double padding_height, double scale);
private static native void detectMultiScale_3(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long foundWeights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height, double padding_width, double padding_height);
private static native void detectMultiScale_4(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long foundWeights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height);
private static native void detectMultiScale_5(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long foundWeights_mat_nativeObj, double hitThreshold);
private static native void detectMultiScale_6(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long foundWeights_mat_nativeObj);
// C++: void cv::HOGDescriptor::computeGradient(Mat img, Mat& grad, Mat& angleOfs, Size paddingTL = Size(), Size paddingBR = Size())
private static native void computeGradient_0(long nativeObj, long img_nativeObj, long grad_nativeObj, long angleOfs_nativeObj, double paddingTL_width, double paddingTL_height, double paddingBR_width, double paddingBR_height);
private static native void computeGradient_1(long nativeObj, long img_nativeObj, long grad_nativeObj, long angleOfs_nativeObj, double paddingTL_width, double paddingTL_height);
private static native void computeGradient_2(long nativeObj, long img_nativeObj, long grad_nativeObj, long angleOfs_nativeObj);
// C++: static vector_float cv::HOGDescriptor::getDefaultPeopleDetector()
private static native long getDefaultPeopleDetector_0();
// C++: static vector_float cv::HOGDescriptor::getDaimlerPeopleDetector()
private static native long getDaimlerPeopleDetector_0();
// C++: Size HOGDescriptor::winSize
private static native double[] get_winSize_0(long nativeObj);
// C++: Size HOGDescriptor::blockSize
private static native double[] get_blockSize_0(long nativeObj);
// C++: Size HOGDescriptor::blockStride
private static native double[] get_blockStride_0(long nativeObj);
// C++: Size HOGDescriptor::cellSize
private static native double[] get_cellSize_0(long nativeObj);
// C++: int HOGDescriptor::nbins
private static native int get_nbins_0(long nativeObj);
// C++: int HOGDescriptor::derivAperture
private static native int get_derivAperture_0(long nativeObj);
// C++: double HOGDescriptor::winSigma
private static native double get_winSigma_0(long nativeObj);
// C++: HOGDescriptor_HistogramNormType HOGDescriptor::histogramNormType
private static native int get_histogramNormType_0(long nativeObj);
// C++: double HOGDescriptor::L2HysThreshold
private static native double get_L2HysThreshold_0(long nativeObj);
// C++: bool HOGDescriptor::gammaCorrection
private static native boolean get_gammaCorrection_0(long nativeObj);
// C++: vector_float HOGDescriptor::svmDetector
private static native long get_svmDetector_0(long nativeObj);
// C++: int HOGDescriptor::nlevels
private static native int get_nlevels_0(long nativeObj);
// C++: bool HOGDescriptor::signedGradient
private static native boolean get_signedGradient_0(long nativeObj);
// native support for java finalize()
private static native void delete(long nativeObj);
}
