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Merge pull request #9744 from sturkmen72:Update_HOGDescriptor_Documentation

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Vadim Pisarevsky 8 years ago
parent ab748188b1 20c11d3b7b
commit 49afb28738
1 changed files with 212 additions and 15 deletions
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  1. 227
      modules/objdetect/include/opencv2/objdetect.hpp

227
modules/objdetect/include/opencv2/objdetect.hpp
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@ -352,22 +352,54 @@ struct DetectionROI
std::vector<double> confidences;
};
/**@example peopledetect.cpp
/**@brief 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
*/
struct CV_EXPORTS_W HOGDescriptor
{
public:
enum { L2Hys = 0
enum { L2Hys = 0 //!< Default histogramNormType
};
enum { DEFAULT_NLEVELS = 64
enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value.
};
/**@brief Creates the HOG descriptor and detector with default params.
aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9, 1 )
*/
CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false)
{}
/** @overload
@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.
*/
CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
int _histogramNormType=HOGDescriptor::L2Hys,
@ -379,97 +411,262 @@ public:
gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
{}
/** @overload
@param filename the file name containing HOGDescriptor properties and coefficients of the trained classifier
*/
CV_WRAP HOGDescriptor(const String& filename)
{
load(filename);
}
/** @overload
@param d the HOGDescriptor which cloned to create a new one.
*/
HOGDescriptor(const HOGDescriptor& d)
{
d.copyTo(*this);
}
/**@brief Default destructor.
*/
virtual ~HOGDescriptor() {}
/**@brief Returns the number of coefficients required for the classification.
*/
CV_WRAP size_t getDescriptorSize() const;
/** @brief Checks if detector size equal to descriptor size.
*/
CV_WRAP bool checkDetectorSize() const;
/** @brief Returns winSigma value
*/
CV_WRAP double getWinSigma() const;
/**@example peopledetect.cpp
*/
/**@brief Sets coefficients for the linear SVM classifier.
@param _svmdetector coefficients for the linear SVM classifier.
*/
CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
/** @brief Reads HOGDescriptor parameters from a file node.
@param fn File node
*/
virtual bool read(FileNode& fn);
/** @brief Stores HOGDescriptor parameters in a file storage.
@param fs File storage
@param objname Object name
*/
virtual void write(FileStorage& fs, const String& objname) const;
/** @brief loads 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).
*/
CV_WRAP virtual bool load(const String& filename, const String& objname = String());
/** @brief saves coefficients for the linear SVM classifier to a file
@param filename File name
@param objname Object name
*/
CV_WRAP virtual void save(const String& filename, const String& objname = String()) const;
/** @brief clones the HOGDescriptor
@param c cloned HOGDescriptor
*/
virtual void copyTo(HOGDescriptor& c) const;
/**@example train_HOG.cpp
*/
/** @brief 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
*/
CV_WRAP virtual void compute(InputArray img,
CV_OUT std::vector<float>& descriptors,
Size winStride = Size(), Size padding = Size(),
const std::vector<Point>& locations = std::vector<Point>()) const;
//! with found weights output
/** @brief 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 specfied 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 requrested locations to be evaluated.
*/
CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
CV_OUT std::vector<double>& weights,
double hitThreshold = 0, Size winStride = Size(),
Size padding = Size(),
const std::vector<Point>& searchLocations = std::vector<Point>()) const;
//! without found weights output
/** @brief 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 hitThreshold Threshold for the distance between features and SVM classifying plane.
Usually it is 0 and should be specfied 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.
*/
virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
double hitThreshold = 0, Size winStride = Size(),
Size padding = Size(),
const std::vector<Point>& searchLocations=std::vector<Point>()) const;
//! with result weights output
/** @brief 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 specfied 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
*/
CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
Size winStride = Size(), Size padding = Size(), double scale = 1.05,
double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
//! without found weights output
/** @brief 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 hitThreshold Threshold for the distance between features and SVM classifying plane.
Usually it is 0 and should be specfied 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
*/
virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
double hitThreshold = 0, Size winStride = Size(),
Size padding = Size(), double scale = 1.05,
double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
/** @brief 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
*/
CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
Size paddingTL = Size(), Size paddingBR = Size()) const;
/** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows).
*/
CV_WRAP static std::vector<float> getDefaultPeopleDetector();
/**@example hog.cpp
*/
/** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows).
*/
CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
//! Detection window size. Align to block size and block stride. Default value is Size(64,128).
CV_PROP Size winSize;
//! Block size in pixels. Align to cell size. Default value is Size(16,16).
CV_PROP Size blockSize;
//! Block stride. It must be a multiple of cell size. Default value is Size(8,8).
CV_PROP Size blockStride;
//! Cell size. Default value is Size(8,8).
CV_PROP Size cellSize;
//! Number of bins used in the calculation of histogram of gradients. Default value is 9.
CV_PROP int nbins;
//! not documented
CV_PROP int derivAperture;
//! Gaussian smoothing window parameter.
CV_PROP double winSigma;
//! histogramNormType
CV_PROP int histogramNormType;
//! L2-Hys normalization method shrinkage.
CV_PROP double L2HysThreshold;
//! Flag to specify whether the gamma correction preprocessing is required or not.
CV_PROP bool gammaCorrection;
//! coefficients for the linear SVM classifier.
CV_PROP std::vector<float> svmDetector;
//! coefficients for the linear SVM classifier used when OpenCL is enabled
UMat oclSvmDetector;
//! not documented
float free_coef;
//! Maximum number of detection window increases. Default value is 64
CV_PROP int nlevels;
CV_PROP bool signedGradient;
//! Indicates signed gradient will be used or not
CV_PROP bool signedGradient;
//! evaluate specified ROI and return confidence value for each location
/** @brief evaluate specified ROI and return confidence value for each location
@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 specfied 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
*/
virtual void detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
double hitThreshold = 0, cv::Size winStride = Size(),
cv::Size padding = Size()) const;
//! evaluate specified ROI and return confidence value for each location in multiple scales
/** @brief evaluate specified ROI and return confidence value for each location in multiple scales
@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 specfied
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.
*/
virtual void detectMultiScaleROI(const cv::Mat& img,
CV_OUT std::vector<cv::Rect>& foundLocations,
std::vector<DetectionROI>& locations,
double hitThreshold = 0,
int groupThreshold = 0) const;
CV_OUT std::vector<cv::Rect>& foundLocations,
std::vector<DetectionROI>& locations,
double hitThreshold = 0,
int groupThreshold = 0) const;
//! read/parse Dalal's alt model file
/** @brief read/parse Dalal's alt model file
@param modelfile Path of Dalal's alt model file.
*/
void readALTModel(String modelfile);
/** @brief Groups the object candidate rectangles.
@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.
*/
void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
};

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