init - 初始化项目

modules/objdetect/CMakeLists.txt

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+set(the_description "Object Detection")
+ocv_define_module(objdetect opencv_core opencv_imgproc opencv_calib3d WRAP java objc python js)
+
+if(HAVE_QUIRC)
+    get_property(QUIRC_INCLUDE GLOBAL PROPERTY QUIRC_INCLUDE_DIR)
+    ocv_include_directories(${QUIRC_INCLUDE})
+    ocv_target_link_libraries(${the_module} quirc)
+endif()

modules/objdetect/include/opencv2/objdetect.hpp

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+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_OBJDETECT_HPP
+#define OPENCV_OBJDETECT_HPP
+
+#include "opencv2/core.hpp"
+
+/**
+@defgroup objdetect Object Detection
+
+Haar Feature-based Cascade Classifier for Object Detection
+----------------------------------------------------------
+
+The object detector described below has been initially proposed by Paul Viola @cite Viola01 and
+improved by Rainer Lienhart @cite Lienhart02 .
+
+First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is
+trained with a few hundred sample views of a particular object (i.e., a face or a car), called
+positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary
+images of the same size.
+
+After a classifier is trained, it can be applied to a region of interest (of the same size as used
+during the training) in an input image. The classifier outputs a "1" if the region is likely to show
+the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can
+move the search window across the image and check every location using the classifier. The
+classifier is designed so that it can be easily "resized" in order to be able to find the objects of
+interest at different sizes, which is more efficient than resizing the image itself. So, to find an
+object of an unknown size in the image the scan procedure should be done several times at different
+scales.
+
+The word "cascade" in the classifier name means that the resultant classifier consists of several
+simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some
+stage the candidate is rejected or all the stages are passed. The word "boosted" means that the
+classifiers at every stage of the cascade are complex themselves and they are built out of basic
+classifiers using one of four different boosting techniques (weighted voting). Currently Discrete
+Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are
+decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic
+classifiers, and are calculated as described below. The current algorithm uses the following
+Haar-like features:
+
+![image](pics/haarfeatures.png)
+
+The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within
+the region of interest and the scale (this scale is not the same as the scale used at the detection
+stage, though these two scales are multiplied). For example, in the case of the third line feature
+(2c) the response is calculated as the difference between the sum of image pixels under the
+rectangle covering the whole feature (including the two white stripes and the black stripe in the
+middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to
+compensate for the differences in the size of areas. The sums of pixel values over a rectangular
+regions are calculated rapidly using integral images (see below and the integral description).
+
+To see the object detector at work, have a look at the facedetect demo:
+<https://github.com/opencv/opencv/tree/master/samples/cpp/dbt_face_detection.cpp>
+
+The following reference is for the detection part only. There is a separate application called
+opencv_traincascade that can train a cascade of boosted classifiers from a set of samples.
+
+@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in
+addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection
+using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at
+<http://research.microsoft.com/en-us/um/people/viola/Pubs/Detect/violaJones_CVPR2001.pdf>
+
+@{
+    @defgroup objdetect_c C API
+@}
+ */
+
+typedef struct CvHaarClassifierCascade CvHaarClassifierCascade;
+
+namespace cv
+{
+
+//! @addtogroup objdetect
+//! @{
+
+///////////////////////////// Object Detection ////////////////////////////
+
+//! class for grouping object candidates, detected by Cascade Classifier, HOG etc.
+//! instance of the class is to be passed to cv::partition (see cxoperations.hpp)
+class CV_EXPORTS SimilarRects
+{
+public:
+    SimilarRects(double _eps) : eps(_eps) {}
+    inline bool operator()(const Rect& r1, const Rect& r2) const
+    {
+        double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5;
+        return std::abs(r1.x - r2.x) <= delta &&
+            std::abs(r1.y - r2.y) <= delta &&
+            std::abs(r1.x + r1.width - r2.x - r2.width) <= delta &&
+            std::abs(r1.y + r1.height - r2.y - r2.height) <= delta;
+    }
+    double eps;
+};
+
+/** @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 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.
+
+The function is a wrapper for the generic function partition . It clusters all the input rectangles
+using the rectangle equivalence criteria that combines rectangles with similar sizes and similar
+locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If
+\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small
+clusters containing less than or equal to groupThreshold rectangles are rejected. In each other
+cluster, the average rectangle is computed and put into the output rectangle list.
+ */
+CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps = 0.2);
+/** @overload */
+CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights,
+                                  int groupThreshold, double eps = 0.2);
+/** @overload */
+CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, int groupThreshold,
+                                  double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
+/** @overload */
+CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
+                                  std::vector<double>& levelWeights, int groupThreshold, double eps = 0.2);
+/** @overload */
+CV_EXPORTS   void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
+                                            std::vector<double>& foundScales,
+                                            double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
+
+template<> struct DefaultDeleter<CvHaarClassifierCascade>{ CV_EXPORTS void operator ()(CvHaarClassifierCascade* obj) const; };
+
+enum { CASCADE_DO_CANNY_PRUNING    = 1,
+       CASCADE_SCALE_IMAGE         = 2,
+       CASCADE_FIND_BIGGEST_OBJECT = 4,
+       CASCADE_DO_ROUGH_SEARCH     = 8
+     };
+
+class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm
+{
+public:
+    virtual ~BaseCascadeClassifier();
+    virtual bool empty() const CV_OVERRIDE = 0;
+    virtual bool load( const String& filename ) = 0;
+    virtual void detectMultiScale( InputArray image,
+                           CV_OUT std::vector<Rect>& objects,
+                           double scaleFactor,
+                           int minNeighbors, int flags,
+                           Size minSize, Size maxSize ) = 0;
+
+    virtual void detectMultiScale( InputArray image,
+                           CV_OUT std::vector<Rect>& objects,
+                           CV_OUT std::vector<int>& numDetections,
+                           double scaleFactor,
+                           int minNeighbors, int flags,
+                           Size minSize, Size maxSize ) = 0;
+
+    virtual void detectMultiScale( InputArray image,
+                                   CV_OUT std::vector<Rect>& objects,
+                                   CV_OUT std::vector<int>& rejectLevels,
+                                   CV_OUT std::vector<double>& levelWeights,
+                                   double scaleFactor,
+                                   int minNeighbors, int flags,
+                                   Size minSize, Size maxSize,
+                                   bool outputRejectLevels ) = 0;
+
+    virtual bool isOldFormatCascade() const = 0;
+    virtual Size getOriginalWindowSize() const = 0;
+    virtual int getFeatureType() const = 0;
+    virtual void* getOldCascade() = 0;
+
+    class CV_EXPORTS MaskGenerator
+    {
+    public:
+        virtual ~MaskGenerator() {}
+        virtual Mat generateMask(const Mat& src)=0;
+        virtual void initializeMask(const Mat& /*src*/) { }
+    };
+    virtual void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator) = 0;
+    virtual Ptr<MaskGenerator> getMaskGenerator() = 0;
+};
+
+/** @example samples/cpp/facedetect.cpp
+This program demonstrates usage of the Cascade classifier class
+\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254
+*/
+/** @brief Cascade classifier class for object detection.
+ */
+class CV_EXPORTS_W CascadeClassifier
+{
+public:
+    CV_WRAP CascadeClassifier();
+    /** @brief Loads a classifier from a file.
+
+    @param filename Name of the file from which the classifier is loaded.
+     */
+    CV_WRAP CascadeClassifier(const String& filename);
+    ~CascadeClassifier();
+    /** @brief Checks whether the classifier has been loaded.
+    */
+    CV_WRAP bool empty() const;
+    /** @brief Loads a classifier from a file.
+
+    @param filename Name of the file from which the classifier is loaded. The file may contain an old
+    HAAR classifier trained by the haartraining application or a new cascade classifier trained by the
+    traincascade application.
+     */
+    CV_WRAP bool load( const String& filename );
+    /** @brief Reads a classifier from a FileStorage node.
+
+    @note The file may contain a new cascade classifier (trained traincascade application) only.
+     */
+    CV_WRAP bool read( const FileNode& node );
+
+    /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
+    of rectangles.
+
+    @param image Matrix of the type CV_8U containing an image where objects are detected.
+    @param objects Vector of rectangles where each rectangle contains the detected object, the
+    rectangles may be partially outside the original image.
+    @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
+    @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
+    to retain it.
+    @param flags Parameter with the same meaning for an old cascade as in the function
+    cvHaarDetectObjects. It is not used for a new cascade.
+    @param minSize Minimum possible object size. Objects smaller than that are ignored.
+    @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
+
+    The function is parallelized with the TBB library.
+
+    @note
+       -   (Python) A face detection example using cascade classifiers can be found at
+            opencv_source_code/samples/python/facedetect.py
+    */
+    CV_WRAP void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          double scaleFactor = 1.1,
+                          int minNeighbors = 3, int flags = 0,
+                          Size minSize = Size(),
+                          Size maxSize = Size() );
+
+    /** @overload
+    @param image Matrix of the type CV_8U containing an image where objects are detected.
+    @param objects Vector of rectangles where each rectangle contains the detected object, the
+    rectangles may be partially outside the original image.
+    @param numDetections Vector of detection numbers for the corresponding objects. An object's number
+    of detections is the number of neighboring positively classified rectangles that were joined
+    together to form the object.
+    @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
+    @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
+    to retain it.
+    @param flags Parameter with the same meaning for an old cascade as in the function
+    cvHaarDetectObjects. It is not used for a new cascade.
+    @param minSize Minimum possible object size. Objects smaller than that are ignored.
+    @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
+    */
+    CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          CV_OUT std::vector<int>& numDetections,
+                          double scaleFactor=1.1,
+                          int minNeighbors=3, int flags=0,
+                          Size minSize=Size(),
+                          Size maxSize=Size() );
+
+    /** @overload
+    This function allows you to retrieve the final stage decision certainty of classification.
+    For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
+    For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
+    This value can then be used to separate strong from weaker classifications.
+
+    A code sample on how to use it efficiently can be found below:
+    @code
+    Mat img;
+    vector<double> weights;
+    vector<int> levels;
+    vector<Rect> detections;
+    CascadeClassifier model("/path/to/your/model.xml");
+    model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
+    cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
+    @endcode
+    */
+    CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image,
+                                  CV_OUT std::vector<Rect>& objects,
+                                  CV_OUT std::vector<int>& rejectLevels,
+                                  CV_OUT std::vector<double>& levelWeights,
+                                  double scaleFactor = 1.1,
+                                  int minNeighbors = 3, int flags = 0,
+                                  Size minSize = Size(),
+                                  Size maxSize = Size(),
+                                  bool outputRejectLevels = false );
+
+    CV_WRAP bool isOldFormatCascade() const;
+    CV_WRAP Size getOriginalWindowSize() const;
+    CV_WRAP int getFeatureType() const;
+    void* getOldCascade();
+
+    CV_WRAP static bool convert(const String& oldcascade, const String& newcascade);
+
+    void setMaskGenerator(const Ptr<BaseCascadeClassifier::MaskGenerator>& maskGenerator);
+    Ptr<BaseCascadeClassifier::MaskGenerator> getMaskGenerator();
+
+    Ptr<BaseCascadeClassifier> cc;
+};
+
+CV_EXPORTS Ptr<BaseCascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator();
+
+//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
+
+//! struct for detection region of interest (ROI)
+struct DetectionROI
+{
+   //! scale(size) of the bounding box
+   double scale;
+   //! set of requested locations to be evaluated
+   std::vector<cv::Point> locations;
+   //! vector that will contain confidence values for each location
+   std::vector<double> confidences;
+};
+
+/**@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 HistogramNormType { L2Hys = 0 //!< Default histogramNormType
+         };
+    enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value.
+         };
+    enum DescriptorStorageFormat { DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW };
+
+    /**@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 )
+    */
+    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,
+                  HOGDescriptor::HistogramNormType _histogramNormType=HOGDescriptor::L2Hys,
+                  double _L2HysThreshold=0.2, bool _gammaCorrection=false,
+                  int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false)
+    : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
+    nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),
+    histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
+    gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
+    {}
+
+    /** @overload
+    @param filename The file name containing HOGDescriptor properties and coefficients for the linear SVM 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 samples/cpp/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 cv::FileNode.
+    @param fn File node
+    */
+    virtual bool read(FileNode& fn);
+
+    /** @brief Stores HOGDescriptor parameters in a cv::FileStorage.
+    @param fs File storage
+    @param objname Object name
+    */
+    virtual void write(FileStorage& fs, const String& objname) const;
+
+    /** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
+    @param filename Path of the file to read.
+    @param objname The optional name of the node to read (if empty, the first top-level node will be used).
+    */
+    CV_WRAP virtual bool load(const String& filename, const String& objname = String());
+
+    /** @brief saves HOGDescriptor parameters and 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 samples/cpp/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;
+
+    /** @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 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.
+    */
+    CV_WRAP virtual void detect(InputArray 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;
+
+    /** @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 specified in the detector coefficients (as the last free coefficient).
+    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
+    @param winStride Window stride. It must be a multiple of block stride.
+    @param padding Padding
+    @param searchLocations Vector of Point includes locations to search.
+    */
+    virtual void detect(InputArray 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;
+
+    /** @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 specified in the detector coefficients (as the last free coefficient).
+    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
+    @param winStride Window stride. It must be a multiple of block stride.
+    @param padding Padding
+    @param scale Coefficient of the detection window increase.
+    @param finalThreshold Final threshold
+    @param useMeanshiftGrouping indicates grouping algorithm
+    */
+    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;
+
+    /** @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 specified in the detector coefficients (as the last free coefficient).
+    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
+    @param winStride Window stride. It must be a multiple of block stride.
+    @param padding Padding
+    @param scale Coefficient of the detection window increase.
+    @param finalThreshold Final threshold
+    @param useMeanshiftGrouping indicates grouping algorithm
+    */
+    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(InputArray img, InputOutputArray grad, InputOutputArray 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 samples/tapi/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 HOGDescriptor::HistogramNormType 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;
+
+    //! Indicates signed gradient will be used or not
+    CV_PROP bool signedGradient;
+
+    /** @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 specified in the detector coefficients (as the last free coefficient). But if
+    the free coefficient is omitted (which is allowed), you can specify it manually here
+    @param winStride winStride
+    @param padding padding
+    */
+    virtual void detectROI(InputArray 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;
+
+    /** @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 specified
+    in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.
+    @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
+    */
+    virtual void detectMultiScaleROI(InputArray img,
+                                     CV_OUT std::vector<cv::Rect>& foundLocations,
+                                     std::vector<DetectionROI>& locations,
+                                     double hitThreshold = 0,
+                                     int groupThreshold = 0) const;
+
+    /** @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;
+};
+
+class CV_EXPORTS_W QRCodeDetector
+{
+public:
+    CV_WRAP QRCodeDetector();
+    ~QRCodeDetector();
+
+    /** @brief sets the epsilon used during the horizontal scan of QR code stop marker detection.
+     @param epsX Epsilon neighborhood, which allows you to determine the horizontal pattern
+     of the scheme 1:1:3:1:1 according to QR code standard.
+    */
+    CV_WRAP void setEpsX(double epsX);
+    /** @brief sets the epsilon used during the vertical scan of QR code stop marker detection.
+     @param epsY Epsilon neighborhood, which allows you to determine the vertical pattern
+     of the scheme 1:1:3:1:1 according to QR code standard.
+     */
+    CV_WRAP void setEpsY(double epsY);
+
+    /** @brief Detects QR code in image and returns the quadrangle containing the code.
+     @param img grayscale or color (BGR) image containing (or not) QR code.
+     @param points Output vector of vertices of the minimum-area quadrangle containing the code.
+     */
+    CV_WRAP bool detect(InputArray img, OutputArray points) const;
+
+    /** @brief Decodes QR code in image once it's found by the detect() method.
+
+     Returns UTF8-encoded output string or empty string if the code cannot be decoded.
+     @param img grayscale or color (BGR) image containing QR code.
+     @param points Quadrangle vertices found by detect() method (or some other algorithm).
+     @param straight_qrcode The optional output image containing rectified and binarized QR code
+     */
+    CV_WRAP std::string decode(InputArray img, InputArray points, OutputArray straight_qrcode = noArray());
+
+    /** @brief Decodes QR code on a curved surface in image once it's found by the detect() method.
+
+     Returns UTF8-encoded output string or empty string if the code cannot be decoded.
+     @param img grayscale or color (BGR) image containing QR code.
+     @param points Quadrangle vertices found by detect() method (or some other algorithm).
+     @param straight_qrcode The optional output image containing rectified and binarized QR code
+     */
+    CV_WRAP cv::String decodeCurved(InputArray img, InputArray points, OutputArray straight_qrcode = noArray());
+
+    /** @brief Both detects and decodes QR code
+
+     @param img grayscale or color (BGR) image containing QR code.
+     @param points optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
+     @param straight_qrcode The optional output image containing rectified and binarized QR code
+     */
+    CV_WRAP std::string detectAndDecode(InputArray img, OutputArray points=noArray(),
+                                        OutputArray straight_qrcode = noArray());
+
+    /** @brief Both detects and decodes QR code on a curved surface
+
+     @param img grayscale or color (BGR) image containing QR code.
+     @param points optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
+     @param straight_qrcode The optional output image containing rectified and binarized QR code
+     */
+    CV_WRAP std::string detectAndDecodeCurved(InputArray img, OutputArray points=noArray(),
+                                              OutputArray straight_qrcode = noArray());
+
+    /** @brief Detects QR codes in image and returns the vector of the quadrangles containing the codes.
+     @param img grayscale or color (BGR) image containing (or not) QR codes.
+     @param points Output vector of vector of vertices of the minimum-area quadrangle containing the codes.
+     */
+    CV_WRAP
+    bool detectMulti(InputArray img, OutputArray points) const;
+
+    /** @brief Decodes QR codes in image once it's found by the detect() method.
+     @param img grayscale or color (BGR) image containing QR codes.
+     @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded.
+     @param points vector of Quadrangle vertices found by detect() method (or some other algorithm).
+     @param straight_qrcode The optional output vector of images containing rectified and binarized QR codes
+     */
+    CV_WRAP
+    bool decodeMulti(
+            InputArray img, InputArray points,
+            CV_OUT std::vector<std::string>& decoded_info,
+            OutputArrayOfArrays straight_qrcode = noArray()
+    ) const;
+
+    /** @brief Both detects and decodes QR codes
+    @param img grayscale or color (BGR) image containing QR codes.
+    @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded.
+    @param points optional output vector of vertices of the found QR code quadrangles. Will be empty if not found.
+    @param straight_qrcode The optional output vector of images containing rectified and binarized QR codes
+    */
+    CV_WRAP
+    bool detectAndDecodeMulti(
+            InputArray img, CV_OUT std::vector<std::string>& decoded_info,
+            OutputArray points = noArray(),
+            OutputArrayOfArrays straight_qrcode = noArray()
+    ) const;
+
+protected:
+    struct Impl;
+    Ptr<Impl> p;
+};
+
+//! @} objdetect
+}
+
+#include "opencv2/objdetect/detection_based_tracker.hpp"
+
+#endif

modules/objdetect/include/opencv2/objdetect/detection_based_tracker.hpp

@@ -0,0 +1,222 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_OBJDETECT_DBT_HPP
+#define OPENCV_OBJDETECT_DBT_HPP
+
+#include <opencv2/core.hpp>
+
+#include <vector>
+
+namespace cv
+{
+
+//! @addtogroup objdetect
+//! @{
+
+class CV_EXPORTS DetectionBasedTracker
+{
+    public:
+        struct CV_EXPORTS Parameters
+        {
+            int maxTrackLifetime;
+            int minDetectionPeriod; //the minimal time between run of the big object detector (on the whole frame) in ms (1000 mean 1 sec), default=0
+
+            Parameters();
+        };
+
+        class IDetector
+        {
+            public:
+                IDetector():
+                    minObjSize(96, 96),
+                    maxObjSize(INT_MAX, INT_MAX),
+                    minNeighbours(2),
+                    scaleFactor(1.1f)
+                {}
+
+                virtual void detect(const cv::Mat& image, std::vector<cv::Rect>& objects) = 0;
+
+                void setMinObjectSize(const cv::Size& min)
+                {
+                    minObjSize = min;
+                }
+                void setMaxObjectSize(const cv::Size& max)
+                {
+                    maxObjSize = max;
+                }
+                cv::Size getMinObjectSize() const
+                {
+                    return minObjSize;
+                }
+                cv::Size getMaxObjectSize() const
+                {
+                    return maxObjSize;
+                }
+                float getScaleFactor()
+                {
+                    return scaleFactor;
+                }
+                void setScaleFactor(float value)
+                {
+                    scaleFactor = value;
+                }
+                int getMinNeighbours()
+                {
+                    return minNeighbours;
+                }
+                void setMinNeighbours(int value)
+                {
+                    minNeighbours = value;
+                }
+                virtual ~IDetector() {}
+
+            protected:
+                cv::Size minObjSize;
+                cv::Size maxObjSize;
+                int minNeighbours;
+                float scaleFactor;
+        };
+
+        DetectionBasedTracker(cv::Ptr<IDetector> mainDetector, cv::Ptr<IDetector> trackingDetector, const Parameters& params);
+        virtual ~DetectionBasedTracker();
+
+        virtual bool run();
+        virtual void stop();
+        virtual void resetTracking();
+
+        virtual void process(const cv::Mat& imageGray);
+
+        bool setParameters(const Parameters& params);
+        const Parameters& getParameters() const;
+
+
+        typedef std::pair<cv::Rect, int> Object;
+        virtual void getObjects(std::vector<cv::Rect>& result) const;
+        virtual void getObjects(std::vector<Object>& result) const;
+
+        enum ObjectStatus
+        {
+            DETECTED_NOT_SHOWN_YET,
+            DETECTED,
+            DETECTED_TEMPORARY_LOST,
+            WRONG_OBJECT
+        };
+        struct ExtObject
+        {
+            int id;
+            cv::Rect location;
+            ObjectStatus status;
+            ExtObject(int _id, cv::Rect _location, ObjectStatus _status)
+                :id(_id), location(_location), status(_status)
+            {
+            }
+        };
+        virtual void getObjects(std::vector<ExtObject>& result) const;
+
+
+        virtual int addObject(const cv::Rect& location); //returns id of the new object
+
+    protected:
+        class SeparateDetectionWork;
+        cv::Ptr<SeparateDetectionWork> separateDetectionWork;
+        friend void* workcycleObjectDetectorFunction(void* p);
+
+        struct InnerParameters
+        {
+            int numLastPositionsToTrack;
+            int numStepsToWaitBeforeFirstShow;
+            int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown;
+            int numStepsToShowWithoutDetecting;
+
+            float coeffTrackingWindowSize;
+            float coeffObjectSizeToTrack;
+            float coeffObjectSpeedUsingInPrediction;
+
+            InnerParameters();
+        };
+        Parameters parameters;
+        InnerParameters innerParameters;
+
+        struct TrackedObject
+        {
+            typedef std::vector<cv::Rect> PositionsVector;
+
+            PositionsVector lastPositions;
+
+            int numDetectedFrames;
+            int numFramesNotDetected;
+            int id;
+
+            TrackedObject(const cv::Rect& rect):numDetectedFrames(1), numFramesNotDetected(0)
+            {
+                lastPositions.push_back(rect);
+                id=getNextId();
+            };
+
+            static int getNextId()
+            {
+                static int _id=0;
+                return _id++;
+            }
+        };
+
+        int numTrackedSteps;
+        std::vector<TrackedObject> trackedObjects;
+
+        std::vector<float> weightsPositionsSmoothing;
+        std::vector<float> weightsSizesSmoothing;
+
+        cv::Ptr<IDetector> cascadeForTracking;
+
+        void updateTrackedObjects(const std::vector<cv::Rect>& detectedObjects);
+        cv::Rect calcTrackedObjectPositionToShow(int i) const;
+        cv::Rect calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const;
+        void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector<cv::Rect>& detectedObjectsInRegions);
+};
+
+//! @} objdetect
+
+} //end of cv namespace
+
+#endif

modules/objdetect/include/opencv2/objdetect/objdetect.hpp

@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/objdetect.hpp"

modules/objdetect/misc/java/test/CascadeClassifierTest.java

@@ -0,0 +1,104 @@
+package org.opencv.test.objdetect;
+
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfRect;
+import org.opencv.core.Size;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.objdetect.CascadeClassifier;
+import org.opencv.objdetect.Objdetect;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+
+public class CascadeClassifierTest extends OpenCVTestCase {
+
+    private CascadeClassifier cc;
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+
+        cc = null;
+    }
+
+    public void testCascadeClassifier() {
+        cc = new CascadeClassifier();
+        assertNotNull(cc);
+    }
+
+    public void testCascadeClassifierString() {
+        cc = new CascadeClassifier(OpenCVTestRunner.LBPCASCADE_FRONTALFACE_PATH);
+        assertNotNull(cc);
+    }
+
+    public void testDetectMultiScaleMatListOfRect() {
+        CascadeClassifier cc = new CascadeClassifier(OpenCVTestRunner.LBPCASCADE_FRONTALFACE_PATH);
+        MatOfRect faces = new MatOfRect();
+
+        Mat greyLena = new Mat();
+        Imgproc.cvtColor(rgbLena, greyLena, Imgproc.COLOR_RGB2GRAY);
+        Imgproc.equalizeHist(greyLena, greyLena);
+
+        cc.detectMultiScale(greyLena, faces, 1.1, 3, Objdetect.CASCADE_SCALE_IMAGE, new Size(30, 30), new Size());
+        assertEquals(1, faces.total());
+    }
+
+    public void testDetectMultiScaleMatListOfRectDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleIntInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleIntIntSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleIntIntSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleIntInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleIntIntSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleIntIntSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleIntIntSizeSizeBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testEmpty() {
+        cc = new CascadeClassifier();
+        assertTrue(cc.empty());
+    }
+
+    public void testLoad() {
+        cc = new CascadeClassifier();
+        cc.load(OpenCVTestRunner.LBPCASCADE_FRONTALFACE_PATH);
+        assertFalse(cc.empty());
+    }
+
+}

modules/objdetect/misc/java/test/HOGDescriptorTest.java

@@ -0,0 +1,259 @@
+package org.opencv.test.objdetect;
+
+import org.opencv.objdetect.HOGDescriptor;
+import org.opencv.test.OpenCVTestCase;
+
+public class HOGDescriptorTest extends OpenCVTestCase {
+
+    public void testCheckDetectorSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeGradientMatMatMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeGradientMatMatMatSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeGradientMatMatMatSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfFloatSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfFloatSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfFloatSizeSizeListOfPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointDoubleSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointDoubleSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointDoubleSizeSizeListOfPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDoubleDoubleSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDoubleDoubleSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDoubleDoubleSizeSizeListOfPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRect() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSizeSizeDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSizeSizeDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSizeSizeDoubleDoubleBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSizeSizeDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSizeSizeDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSizeSizeDoubleDoubleBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_blockSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_blockStride() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_cellSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_derivAperture() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_gammaCorrection() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_histogramNormType() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_L2HysThreshold() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_nbins() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_nlevels() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_svmDetector() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_winSigma() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_winSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testGetDaimlerPeopleDetector() {
+        fail("Not yet implemented");
+    }
+
+    public void testGetDefaultPeopleDetector() {
+        fail("Not yet implemented");
+    }
+
+    public void testGetDescriptorSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testGetWinSigma() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptor() {
+        HOGDescriptor hog = new HOGDescriptor();
+
+        assertNotNull(hog);
+        assertEquals(HOGDescriptor.DEFAULT_NLEVELS, hog.get_nlevels());
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDoubleInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDoubleIntDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDoubleIntDoubleBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDoubleIntDoubleBooleanInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorString() {
+        fail("Not yet implemented");
+    }
+
+    public void testLoadString() {
+        fail("Not yet implemented");
+    }
+
+    public void testLoadStringString() {
+        fail("Not yet implemented");
+    }
+
+    public void testSaveString() {
+        fail("Not yet implemented");
+    }
+
+    public void testSaveStringString() {
+        fail("Not yet implemented");
+    }
+
+    public void testSetSVMDetector() {
+        fail("Not yet implemented");
+    }
+
+}

modules/objdetect/misc/java/test/ObjdetectTest.java

@@ -0,0 +1,42 @@
+package org.opencv.test.objdetect;
+
+import org.opencv.test.OpenCVTestCase;
+
+public class ObjdetectTest extends OpenCVTestCase {
+
+    public void testGroupRectanglesListOfRectListOfIntegerInt() {
+        fail("Not yet implemented");
+        /*
+        final int NUM = 10;
+        MatOfRect rects = new MatOfRect();
+        rects.alloc(NUM);
+
+        for (int i = 0; i < NUM; i++)
+            rects.put(i, 0, 10, 10, 20, 20);
+
+        int groupThreshold = 1;
+        Objdetect.groupRectangles(rects, null, groupThreshold);//TODO: second parameter should not be null
+        assertEquals(1, rects.total());
+        */
+    }
+
+    public void testGroupRectanglesListOfRectListOfIntegerIntDouble() {
+        fail("Not yet implemented");
+        /*
+        final int NUM = 10;
+        MatOfRect rects = new MatOfRect();
+        rects.alloc(NUM);
+
+        for (int i = 0; i < NUM; i++)
+            rects.put(i, 0, 10, 10, 20, 20);
+
+        for (int i = 0; i < NUM; i++)
+            rects.put(i, 0, 10, 10, 25, 25);
+
+        int groupThreshold = 1;
+        double eps = 0.2;
+        Objdetect.groupRectangles(rects, null, groupThreshold, eps);//TODO: second parameter should not be null
+        assertEquals(2, rects.size());
+        */
+    }
+}

modules/objdetect/misc/java/test/QRCodeDetectorTest.java

@@ -0,0 +1,49 @@
+package org.opencv.test.objdetect;
+
+import java.util.List;
+import org.opencv.core.Mat;
+import org.opencv.objdetect.QRCodeDetector;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.test.OpenCVTestCase;
+import java.util.ArrayList;
+
+public class QRCodeDetectorTest extends OpenCVTestCase {
+
+    private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";
+    private String testDataPath;
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+
+        testDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);
+        if (testDataPath == null)
+            throw new Exception(ENV_OPENCV_TEST_DATA_PATH + " has to be defined!");
+    }
+
+    public void testDetectAndDecode() {
+        Mat img = Imgcodecs.imread(testDataPath + "/cv/qrcode/link_ocv.jpg");
+        assertFalse(img.empty());
+        QRCodeDetector detector = new QRCodeDetector();
+        assertNotNull(detector);
+        String output = detector.detectAndDecode(img);
+        assertEquals(output, "https://opencv.org/");
+    }
+
+    public void testDetectAndDecodeMulti() {
+        Mat img = Imgcodecs.imread(testDataPath + "/cv/qrcode/multiple/6_qrcodes.png");
+        assertFalse(img.empty());
+        QRCodeDetector detector = new QRCodeDetector();
+        assertNotNull(detector);
+        List < String > output = new ArrayList< String >();
+        boolean result = detector.detectAndDecodeMulti(img, output);
+        assertTrue(result);
+        assertEquals(output.size(), 6);
+        assertEquals(output.get(0), "SKIP");
+        assertEquals(output.get(1), "EXTRA");
+        assertEquals(output.get(2), "TWO STEPS FORWARD");
+        assertEquals(output.get(3), "STEP BACK");
+        assertEquals(output.get(4), "QUESTION");
+        assertEquals(output.get(5), "STEP FORWARD");
+    }
+}

modules/objdetect/misc/python/pyopencv_objdetect.hpp

@@ -0,0 +1,8 @@
+#ifdef HAVE_OPENCV_OBJDETECT
+
+#include "opencv2/objdetect.hpp"
+
+typedef HOGDescriptor::HistogramNormType HOGDescriptor_HistogramNormType;
+typedef HOGDescriptor::DescriptorStorageFormat HOGDescriptor_DescriptorStorageFormat;
+
+#endif

modules/objdetect/misc/python/test/test_facedetect.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python
+
+'''
+face detection using haar cascades
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+
+def detect(img, cascade):
+    rects = cascade.detectMultiScale(img, scaleFactor=1.275, minNeighbors=4, minSize=(30, 30),
+                                     flags=cv.CASCADE_SCALE_IMAGE)
+    if len(rects) == 0:
+        return []
+    rects[:,2:] += rects[:,:2]
+    return rects
+
+from tests_common import NewOpenCVTests, intersectionRate
+
+class facedetect_test(NewOpenCVTests):
+
+    def test_facedetect(self):
+        cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
+        nested_fn  = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'
+
+        cascade = cv.CascadeClassifier(cascade_fn)
+        nested = cv.CascadeClassifier(nested_fn)
+
+        samples = ['samples/data/lena.jpg', 'cv/cascadeandhog/images/mona-lisa.png']
+
+        faces = []
+        eyes = []
+
+        testFaces = [
+        #lena
+        [[218, 200, 389, 371],
+        [ 244, 240, 294, 290],
+        [ 309, 246, 352, 289]],
+
+        #lisa
+        [[167, 119, 307, 259],
+        [188, 153, 229, 194],
+        [236, 153, 277, 194]]
+        ]
+
+        for sample in samples:
+
+            img = self.get_sample(  sample)
+            gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+            gray = cv.GaussianBlur(gray, (5, 5), 0)
+
+            rects = detect(gray, cascade)
+            faces.append(rects)
+
+            if not nested.empty():
+                for x1, y1, x2, y2 in rects:
+                    roi = gray[y1:y2, x1:x2]
+                    subrects = detect(roi.copy(), nested)
+
+                    for rect in subrects:
+                        rect[0] += x1
+                        rect[2] += x1
+                        rect[1] += y1
+                        rect[3] += y1
+
+                    eyes.append(subrects)
+
+        faces_matches = 0
+        eyes_matches = 0
+
+        eps = 0.8
+
+        for i in range(len(faces)):
+            for j in range(len(testFaces)):
+                if intersectionRate(faces[i][0], testFaces[j][0]) > eps:
+                    faces_matches += 1
+                    #check eyes
+                    if len(eyes[i]) == 2:
+                        if intersectionRate(eyes[i][0], testFaces[j][1]) > eps and intersectionRate(eyes[i][1] , testFaces[j][2]) > eps:
+                            eyes_matches += 1
+                        elif intersectionRate(eyes[i][1], testFaces[j][1]) > eps and intersectionRate(eyes[i][0], testFaces[j][2]) > eps:
+                            eyes_matches += 1
+
+        self.assertEqual(faces_matches, 2)
+        self.assertEqual(eyes_matches, 2)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/objdetect/misc/python/test/test_peopledetect.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+
+'''
+example to detect upright people in images using HOG features
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+
+
+def inside(r, q):
+    rx, ry, rw, rh = r
+    qx, qy, qw, qh = q
+    return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
+
+from tests_common import NewOpenCVTests, intersectionRate
+
+class peopledetect_test(NewOpenCVTests):
+    def test_peopledetect(self):
+
+        hog = cv.HOGDescriptor()
+        hog.setSVMDetector( cv.HOGDescriptor_getDefaultPeopleDetector() )
+
+        dirPath = 'samples/data/'
+        samples = ['basketball1.png', 'basketball2.png']
+
+        testPeople = [
+        [[23, 76, 164, 477], [440, 22, 637, 478]],
+        [[23, 76, 164, 477], [440, 22, 637, 478]]
+        ]
+
+        eps = 0.5
+
+        for sample in samples:
+
+            img = self.get_sample(dirPath + sample, 0)
+
+            found, _w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
+            found_filtered = []
+            for ri, r in enumerate(found):
+                for qi, q in enumerate(found):
+                    if ri != qi and inside(r, q):
+                        break
+                else:
+                    found_filtered.append(r)
+
+            matches = 0
+
+            for i in range(len(found_filtered)):
+                for j in range(len(testPeople)):
+
+                    found_rect = (found_filtered[i][0], found_filtered[i][1],
+                        found_filtered[i][0] + found_filtered[i][2],
+                        found_filtered[i][1] + found_filtered[i][3])
+
+                    if intersectionRate(found_rect, testPeople[j][0]) > eps or intersectionRate(found_rect, testPeople[j][1]) > eps:
+                        matches += 1
+
+            self.assertGreater(matches, 0)
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/objdetect/misc/python/test/test_qrcode_detect.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+'''
+===============================================================================
+QR code detect and decode pipeline.
+===============================================================================
+'''
+import os
+import numpy as np
+import cv2 as cv
+
+from tests_common import NewOpenCVTests
+
+class qrcode_detector_test(NewOpenCVTests):
+
+    def test_detect(self):
+        img = cv.imread(os.path.join(self.extraTestDataPath, 'cv/qrcode/link_ocv.jpg'))
+        self.assertFalse(img is None)
+        detector = cv.QRCodeDetector()
+        retval, points = detector.detect(img)
+        self.assertTrue(retval)
+        self.assertEqual(points.shape, (1, 4, 2))
+
+    def test_detect_and_decode(self):
+        img = cv.imread(os.path.join(self.extraTestDataPath, 'cv/qrcode/link_ocv.jpg'))
+        self.assertFalse(img is None)
+        detector = cv.QRCodeDetector()
+        retval, points, straight_qrcode = detector.detectAndDecode(img)
+        self.assertEqual(retval, "https://opencv.org/")
+        self.assertEqual(points.shape, (1, 4, 2))
+
+    def test_detect_multi(self):
+        img = cv.imread(os.path.join(self.extraTestDataPath, 'cv/qrcode/multiple/6_qrcodes.png'))
+        self.assertFalse(img is None)
+        detector = cv.QRCodeDetector()
+        retval, points = detector.detectMulti(img)
+        self.assertTrue(retval)
+        self.assertEqual(points.shape, (6, 4, 2))
+
+    def test_detect_and_decode_multi(self):
+        img = cv.imread(os.path.join(self.extraTestDataPath, 'cv/qrcode/multiple/6_qrcodes.png'))
+        self.assertFalse(img is None)
+        detector = cv.QRCodeDetector()
+        retval, decoded_data, points, straight_qrcode = detector.detectAndDecodeMulti(img)
+        self.assertTrue(retval)
+        self.assertEqual(len(decoded_data), 6)
+        self.assertEqual(decoded_data[0], "TWO STEPS FORWARD")
+        self.assertEqual(decoded_data[1], "EXTRA")
+        self.assertEqual(decoded_data[2], "SKIP")
+        self.assertEqual(decoded_data[3], "STEP FORWARD")
+        self.assertEqual(decoded_data[4], "STEP BACK")
+        self.assertEqual(decoded_data[5], "QUESTION")
+        self.assertEqual(points.shape, (6, 4, 2))

modules/objdetect/perf/opencl/perf_cascades.cpp

@@ -0,0 +1,61 @@
+#include "../perf_precomp.hpp"
+#include <opencv2/imgproc.hpp>
+
+#include "opencv2/ts/ocl_perf.hpp"
+
+namespace opencv_test
+{
+using namespace perf;
+
+typedef tuple<std::string, std::string, int> Cascade_Image_MinSize_t;
+typedef perf::TestBaseWithParam<Cascade_Image_MinSize_t> Cascade_Image_MinSize;
+
+#ifdef HAVE_OPENCL
+
+OCL_PERF_TEST_P(Cascade_Image_MinSize, CascadeClassifier,
+                 testing::Combine(
+                    testing::Values( string("cv/cascadeandhog/cascades/haarcascade_frontalface_alt.xml"),
+                                     string("cv/cascadeandhog/cascades/haarcascade_frontalface_alt2.xml"),
+                                     string("cv/cascadeandhog/cascades/lbpcascade_frontalface.xml") ),
+                    testing::Values( string("cv/shared/lena.png"),
+                                     string("cv/cascadeandhog/images/bttf301.png"),
+                                     string("cv/cascadeandhog/images/class57.png") ),
+                    testing::Values(30, 64, 90) ) )
+{
+    const string cascadePath = get<0>(GetParam());
+    const string imagePath   = get<1>(GetParam());
+    int min_size = get<2>(GetParam());
+    Size minSize(min_size, min_size);
+
+    CascadeClassifier cc( getDataPath(cascadePath) );
+    if (cc.empty())
+        FAIL() << "Can't load cascade file: " << getDataPath(cascadePath);
+
+    Mat img = imread(getDataPath(imagePath), IMREAD_GRAYSCALE);
+    if (img.empty())
+        FAIL() << "Can't load source image: " << getDataPath(imagePath);
+
+    vector<Rect> faces;
+
+    equalizeHist(img, img);
+    declare.in(img).time(60);
+
+    UMat uimg = img.getUMat(ACCESS_READ);
+
+    while(next())
+    {
+        faces.clear();
+        cvtest::ocl::perf::safeFinish();
+
+        startTimer();
+        cc.detectMultiScale(uimg, faces, 1.1, 3, 0, minSize);
+        stopTimer();
+    }
+
+    sort(faces.begin(), faces.end(), comparators::RectLess());
+    SANITY_CHECK(faces, min_size/5);
+}
+
+#endif //HAVE_OPENCL
+
+} // namespace

modules/objdetect/perf/opencl/perf_hogdetect.cpp

@@ -0,0 +1,93 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//    Fangfang Bai, fangfang@multicorewareinc.com
+//    Jin Ma,       jin@multicorewareinc.com
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors as is and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "../perf_precomp.hpp"
+#include "opencv2/ts/ocl_perf.hpp"
+
+#ifdef HAVE_OPENCL
+
+namespace opencv_test {
+namespace ocl {
+///////////// HOG////////////////////////
+
+struct RectLess
+{
+    bool operator()(const cv::Rect& a,
+        const cv::Rect& b) const
+    {
+        if (a.x != b.x)
+            return a.x < b.x;
+        else if (a.y != b.y)
+            return a.y < b.y;
+        else if (a.width != b.width)
+            return a.width < b.width;
+        else
+            return a.height < b.height;
+    }
+};
+
+OCL_PERF_TEST(HOGFixture, HOG)
+{
+    UMat src;
+    imread(getDataPath("gpu/hog/road.png"), cv::IMREAD_GRAYSCALE).copyTo(src);
+    ASSERT_FALSE(src.empty());
+
+    vector<cv::Rect> found_locations;
+    declare.in(src);
+
+    HOGDescriptor hog;
+    hog.setSVMDetector(hog.getDefaultPeopleDetector());
+
+    OCL_TEST_CYCLE() hog.detectMultiScale(src, found_locations);
+
+    std::sort(found_locations.begin(), found_locations.end(), RectLess());
+    SANITY_CHECK(found_locations, 3);
+}
+
+}
+}
+
+#endif

modules/objdetect/perf/perf_main.cpp

@@ -0,0 +1,7 @@
+#include "perf_precomp.hpp"
+
+#if defined(HAVE_HPX)
+    #include <hpx/hpx_main.hpp>
+#endif
+
+CV_PERF_TEST_MAIN(objdetect)

modules/objdetect/perf/perf_precomp.hpp

@@ -0,0 +1,7 @@
+#ifndef __OPENCV_PERF_PRECOMP_HPP__
+#define __OPENCV_PERF_PRECOMP_HPP__
+
+#include "opencv2/ts.hpp"
+#include "opencv2/objdetect.hpp"
+
+#endif

modules/objdetect/perf/perf_qrcode_pipeline.cpp

@@ -0,0 +1,193 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "perf_precomp.hpp"
+
+namespace opencv_test
+{
+namespace
+{
+
+typedef ::perf::TestBaseWithParam< std::string > Perf_Objdetect_QRCode;
+
+PERF_TEST_P_(Perf_Objdetect_QRCode, detect)
+{
+    const std::string name_current_image = GetParam();
+    const std::string root = "cv/qrcode/";
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path, IMREAD_GRAYSCALE), straight_barcode;
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+
+    std::vector< Point > corners;
+    QRCodeDetector qrcode;
+    TEST_CYCLE() ASSERT_TRUE(qrcode.detect(src, corners));
+    SANITY_CHECK(corners);
+}
+
+#ifdef HAVE_QUIRC
+PERF_TEST_P_(Perf_Objdetect_QRCode, decode)
+{
+    const std::string name_current_image = GetParam();
+    const std::string root = "cv/qrcode/";
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path, IMREAD_GRAYSCALE), straight_barcode;
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+
+    std::vector< Point > corners;
+    std::string decoded_info;
+    QRCodeDetector qrcode;
+    ASSERT_TRUE(qrcode.detect(src, corners));
+    TEST_CYCLE()
+    {
+        decoded_info = qrcode.decode(src, corners, straight_barcode);
+        ASSERT_FALSE(decoded_info.empty());
+    }
+
+    std::vector<uint8_t> decoded_info_uint8_t(decoded_info.begin(), decoded_info.end());
+    SANITY_CHECK(decoded_info_uint8_t);
+    SANITY_CHECK(straight_barcode);
+
+}
+#endif
+
+typedef ::perf::TestBaseWithParam< std::string > Perf_Objdetect_QRCode_Multi;
+
+PERF_TEST_P_(Perf_Objdetect_QRCode_Multi, detectMulti)
+{
+    const std::string name_current_image = GetParam();
+    const std::string root = "cv/qrcode/multiple/";
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path);
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+    std::vector<Point2f> corners;
+    QRCodeDetector qrcode;
+    TEST_CYCLE() ASSERT_TRUE(qrcode.detectMulti(src, corners));
+    SANITY_CHECK(corners);
+}
+
+#ifdef HAVE_QUIRC
+PERF_TEST_P_(Perf_Objdetect_QRCode_Multi, decodeMulti)
+{
+    const std::string name_current_image = GetParam();
+    const std::string root = "cv/qrcode/multiple/";
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path);
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+    QRCodeDetector qrcode;
+    std::vector<Point2f> corners;
+    ASSERT_TRUE(qrcode.detectMulti(src, corners));
+    std::vector<Mat> straight_barcode;
+    std::vector< cv::String > decoded_info;
+    TEST_CYCLE()
+    {
+        ASSERT_TRUE(qrcode.decodeMulti(src, corners, decoded_info, straight_barcode));
+        for(size_t i = 0; i < decoded_info.size(); i++)
+        {
+            ASSERT_FALSE(decoded_info[i].empty());
+        }
+    }
+    std::vector < std::vector< uint8_t > > decoded_info_uint8_t;
+    for(size_t i = 0; i < decoded_info.size(); i++)
+    {
+        std::vector< uint8_t > tmp(decoded_info[i].begin(), decoded_info[i].end());
+        decoded_info_uint8_t.push_back(tmp);
+    }
+    SANITY_CHECK(decoded_info_uint8_t);
+    SANITY_CHECK(straight_barcode);
+
+}
+#endif
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, Perf_Objdetect_QRCode,
+    ::testing::Values(
+        "version_1_down.jpg", "version_1_left.jpg", "version_1_right.jpg", "version_1_up.jpg", "version_1_top.jpg",
+        "version_5_down.jpg", "version_5_left.jpg", "version_5_right.jpg", "version_5_up.jpg", "version_5_top.jpg",
+        "russian.jpg", "kanji.jpg", "link_github_ocv.jpg", "link_ocv.jpg", "link_wiki_cv.jpg"
+    )
+);
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, Perf_Objdetect_QRCode_Multi,
+    ::testing::Values(
+      "2_qrcodes.png", "3_close_qrcodes.png", "3_qrcodes.png", "4_qrcodes.png",
+      "5_qrcodes.png", "6_qrcodes.png", "7_qrcodes.png", "8_close_qrcodes.png"
+    )
+);
+
+typedef ::perf::TestBaseWithParam< tuple< std::string, Size > > Perf_Objdetect_Not_QRCode;
+
+PERF_TEST_P_(Perf_Objdetect_Not_QRCode, detect)
+{
+    std::vector<Point> corners;
+    std::string type_gen = get<0>(GetParam());
+    Size resolution = get<1>(GetParam());
+    Mat not_qr_code(resolution, CV_8UC1, Scalar(0));
+    if (type_gen == "random")
+    {
+        RNG rng;
+        rng.fill(not_qr_code, RNG::UNIFORM, Scalar(0), Scalar(1));
+    }
+    if (type_gen == "chessboard")
+    {
+        uint8_t next_pixel = 0;
+        for (int r = 0; r < not_qr_code.rows * not_qr_code.cols; r++)
+        {
+            int i = r / not_qr_code.cols;
+            int j = r % not_qr_code.cols;
+            not_qr_code.ptr<uchar>(i)[j] = next_pixel;
+            next_pixel = 255 - next_pixel;
+        }
+    }
+
+    QRCodeDetector qrcode;
+    TEST_CYCLE() ASSERT_FALSE(qrcode.detect(not_qr_code, corners));
+    SANITY_CHECK_NOTHING();
+}
+
+#ifdef HAVE_QUIRC
+PERF_TEST_P_(Perf_Objdetect_Not_QRCode, decode)
+{
+    Mat straight_barcode;
+    std::vector< Point > corners;
+    corners.push_back(Point( 0, 0)); corners.push_back(Point( 0,  5));
+    corners.push_back(Point(10, 0)); corners.push_back(Point(15, 15));
+
+    std::string type_gen = get<0>(GetParam());
+    Size resolution = get<1>(GetParam());
+    Mat not_qr_code(resolution, CV_8UC1, Scalar(0));
+    if (type_gen == "random")
+    {
+        RNG rng;
+        rng.fill(not_qr_code, RNG::UNIFORM, Scalar(0), Scalar(1));
+    }
+    if (type_gen == "chessboard")
+    {
+        uint8_t next_pixel = 0;
+        for (int r = 0; r < not_qr_code.rows * not_qr_code.cols; r++)
+        {
+            int i = r / not_qr_code.cols;
+            int j = r % not_qr_code.cols;
+            not_qr_code.ptr<uchar>(i)[j] = next_pixel;
+            next_pixel = 255 - next_pixel;
+        }
+    }
+
+    QRCodeDetector qrcode;
+    TEST_CYCLE() ASSERT_TRUE(qrcode.decode(not_qr_code, corners, straight_barcode).empty());
+    SANITY_CHECK_NOTHING();
+}
+#endif
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, Perf_Objdetect_Not_QRCode,
+      ::testing::Combine(
+            ::testing::Values("zero", "random", "chessboard"),
+            ::testing::Values(Size(640, 480),   Size(1280, 720),
+                              Size(1920, 1080), Size(3840, 2160))
+      ));
+
+}
+} // namespace

modules/objdetect/src/cascadedetect.hpp

@@ -0,0 +1,656 @@
+#pragma once
+
+#include "opencv2/core/ocl.hpp"
+
+namespace cv
+{
+
+void clipObjects(Size sz, std::vector<Rect>& objects,
+                 std::vector<int>* a, std::vector<double>* b);
+
+class FeatureEvaluator
+{
+public:
+    enum
+    {
+        HAAR = 0,
+        LBP  = 1,
+        HOG  = 2
+    };
+
+    struct ScaleData
+    {
+        ScaleData() { scale = 0.f; layer_ofs = ystep = 0; }
+        Size getWorkingSize(Size winSize) const
+        {
+            return Size(std::max(szi.width - winSize.width, 0),
+                        std::max(szi.height - winSize.height, 0));
+        }
+
+        float scale;
+        Size szi;
+        int layer_ofs, ystep;
+    };
+
+    virtual ~FeatureEvaluator();
+
+    virtual bool read(const FileNode& node, Size origWinSize);
+    virtual Ptr<FeatureEvaluator> clone() const;
+    virtual int getFeatureType() const;
+    int getNumChannels() const { return nchannels; }
+
+    virtual bool setImage(InputArray img, const std::vector<float>& scales);
+    virtual bool setWindow(Point p, int scaleIdx);
+    const ScaleData& getScaleData(int scaleIdx) const
+    {
+        CV_Assert( 0 <= scaleIdx && scaleIdx < (int)scaleData->size());
+        return scaleData->at(scaleIdx);
+    }
+    virtual void getUMats(std::vector<UMat>& bufs);
+    virtual void getMats();
+
+    Size getLocalSize() const { return localSize; }
+    Size getLocalBufSize() const { return lbufSize; }
+
+    virtual float calcOrd(int featureIdx) const;
+    virtual int calcCat(int featureIdx) const;
+
+    static Ptr<FeatureEvaluator> create(int type);
+
+protected:
+    enum { SBUF_VALID=1, USBUF_VALID=2 };
+    int sbufFlag;
+
+    bool updateScaleData( Size imgsz, const std::vector<float>& _scales );
+    virtual void computeChannels( int, InputArray ) {}
+    virtual void computeOptFeatures() {}
+
+    Size origWinSize, sbufSize, localSize, lbufSize;
+    int nchannels;
+    Mat sbuf, rbuf;
+    UMat urbuf, usbuf, ufbuf, uscaleData;
+
+    Ptr<std::vector<ScaleData> > scaleData;
+};
+
+
+class CascadeClassifierImpl CV_FINAL : public BaseCascadeClassifier
+{
+public:
+    CascadeClassifierImpl();
+    virtual ~CascadeClassifierImpl() CV_OVERRIDE;
+
+    bool empty() const CV_OVERRIDE;
+    bool load( const String& filename ) CV_OVERRIDE;
+    void read( const FileNode& node ) CV_OVERRIDE;
+    bool read_( const FileNode& node );
+    void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          double scaleFactor = 1.1,
+                          int minNeighbors = 3, int flags = 0,
+                          Size minSize = Size(),
+                          Size maxSize = Size() ) CV_OVERRIDE;
+
+    void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          CV_OUT std::vector<int>& numDetections,
+                          double scaleFactor=1.1,
+                          int minNeighbors=3, int flags=0,
+                          Size minSize=Size(),
+                          Size maxSize=Size() ) CV_OVERRIDE;
+
+    void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          CV_OUT std::vector<int>& rejectLevels,
+                          CV_OUT std::vector<double>& levelWeights,
+                          double scaleFactor = 1.1,
+                          int minNeighbors = 3, int flags = 0,
+                          Size minSize = Size(),
+                          Size maxSize = Size(),
+                          bool outputRejectLevels = false ) CV_OVERRIDE;
+
+
+    bool isOldFormatCascade() const CV_OVERRIDE;
+    Size getOriginalWindowSize() const CV_OVERRIDE;
+    int getFeatureType() const CV_OVERRIDE;
+    void* getOldCascade() CV_OVERRIDE;
+
+    void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator) CV_OVERRIDE;
+    Ptr<MaskGenerator> getMaskGenerator() CV_OVERRIDE;
+
+protected:
+    enum { SUM_ALIGN = 64 };
+
+    bool detectSingleScale( InputArray image, Size processingRectSize,
+                            int yStep, double factor, std::vector<Rect>& candidates,
+                            std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
+                            Size sumSize0, bool outputRejectLevels = false );
+#ifdef HAVE_OPENCL
+    bool ocl_detectMultiScaleNoGrouping( const std::vector<float>& scales,
+                                         std::vector<Rect>& candidates );
+#endif
+    void detectMultiScaleNoGrouping( InputArray image, std::vector<Rect>& candidates,
+                                    std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
+                                    double scaleFactor, Size minObjectSize, Size maxObjectSize,
+                                    bool outputRejectLevels = false );
+
+    enum { MAX_FACES = 10000 };
+    enum { BOOST = 0 };
+    enum { DO_CANNY_PRUNING    = CASCADE_DO_CANNY_PRUNING,
+        SCALE_IMAGE         = CASCADE_SCALE_IMAGE,
+        FIND_BIGGEST_OBJECT = CASCADE_FIND_BIGGEST_OBJECT,
+        DO_ROUGH_SEARCH     = CASCADE_DO_ROUGH_SEARCH
+    };
+
+    friend class CascadeClassifierInvoker;
+    friend class SparseCascadeClassifierInvoker;
+
+    template<class FEval>
+    friend int predictOrdered( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
+
+    template<class FEval>
+    friend int predictCategorical( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
+
+    template<class FEval>
+    friend int predictOrderedStump( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
+
+    template<class FEval>
+    friend int predictCategoricalStump( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
+
+    int runAt( Ptr<FeatureEvaluator>& feval, Point pt, int scaleIdx, double& weight );
+
+    class Data
+    {
+    public:
+        struct DTreeNode
+        {
+            int featureIdx;
+            float threshold; // for ordered features only
+            int left;
+            int right;
+        };
+
+        struct DTree
+        {
+            int nodeCount;
+        };
+
+        struct Stage
+        {
+            int first;
+            int ntrees;
+            float threshold;
+        };
+
+        struct Stump
+        {
+            Stump() : featureIdx(0), threshold(0), left(0), right(0) { }
+            Stump(int _featureIdx, float _threshold, float _left, float _right)
+            : featureIdx(_featureIdx), threshold(_threshold), left(_left), right(_right) {}
+
+            int featureIdx;
+            float threshold;
+            float left;
+            float right;
+        };
+
+        Data();
+
+        bool read(const FileNode &node);
+
+        int stageType;
+        int featureType;
+        int ncategories;
+        int minNodesPerTree, maxNodesPerTree;
+        Size origWinSize;
+
+        std::vector<Stage> stages;
+        std::vector<DTree> classifiers;
+        std::vector<DTreeNode> nodes;
+        std::vector<float> leaves;
+        std::vector<int> subsets;
+        std::vector<Stump> stumps;
+    };
+
+    Data data;
+    Ptr<FeatureEvaluator> featureEvaluator;
+    Ptr<CvHaarClassifierCascade> oldCascade;
+
+    Ptr<MaskGenerator> maskGenerator;
+    UMat ugrayImage;
+    UMat ufacepos, ustages, unodes, uleaves, usubsets;
+#ifdef HAVE_OPENCL
+    ocl::Kernel haarKernel, lbpKernel;
+    bool tryOpenCL;
+#endif
+
+    Mutex mtx;
+};
+
+#define CC_CASCADE_PARAMS "cascadeParams"
+#define CC_STAGE_TYPE     "stageType"
+#define CC_FEATURE_TYPE   "featureType"
+#define CC_HEIGHT         "height"
+#define CC_WIDTH          "width"
+
+#define CC_STAGE_NUM    "stageNum"
+#define CC_STAGES       "stages"
+#define CC_STAGE_PARAMS "stageParams"
+
+#define CC_BOOST            "BOOST"
+#define CC_MAX_DEPTH        "maxDepth"
+#define CC_WEAK_COUNT       "maxWeakCount"
+#define CC_STAGE_THRESHOLD  "stageThreshold"
+#define CC_WEAK_CLASSIFIERS "weakClassifiers"
+#define CC_INTERNAL_NODES   "internalNodes"
+#define CC_LEAF_VALUES      "leafValues"
+
+#define CC_FEATURES       "features"
+#define CC_FEATURE_PARAMS "featureParams"
+#define CC_MAX_CAT_COUNT  "maxCatCount"
+
+#define CC_HAAR   "HAAR"
+#define CC_RECTS  "rects"
+#define CC_TILTED "tilted"
+
+#define CC_LBP  "LBP"
+#define CC_RECT "rect"
+
+#define CC_HOG  "HOG"
+
+#define CV_SUM_PTRS( p0, p1, p2, p3, sum, rect, step )                    \
+    /* (x, y) */                                                          \
+    (p0) = sum + (rect).x + (step) * (rect).y,                            \
+    /* (x + w, y) */                                                      \
+    (p1) = sum + (rect).x + (rect).width + (step) * (rect).y,             \
+    /* (x, y + h) */                                                      \
+    (p2) = sum + (rect).x + (step) * ((rect).y + (rect).height),          \
+    /* (x + w, y + h) */                                                  \
+    (p3) = sum + (rect).x + (rect).width + (step) * ((rect).y + (rect).height)
+
+#define CV_TILTED_PTRS( p0, p1, p2, p3, tilted, rect, step )                        \
+    /* (x, y) */                                                                    \
+    (p0) = tilted + (rect).x + (step) * (rect).y,                                   \
+    /* (x - h, y + h) */                                                            \
+    (p1) = tilted + (rect).x - (rect).height + (step) * ((rect).y + (rect).height), \
+    /* (x + w, y + w) */                                                            \
+    (p2) = tilted + (rect).x + (rect).width + (step) * ((rect).y + (rect).width),   \
+    /* (x + w - h, y + w + h) */                                                    \
+    (p3) = tilted + (rect).x + (rect).width - (rect).height                         \
+           + (step) * ((rect).y + (rect).width + (rect).height)
+
+#define CALC_SUM_(p0, p1, p2, p3, offset) \
+    ((p0)[offset] - (p1)[offset] - (p2)[offset] + (p3)[offset])
+
+#define CALC_SUM(rect,offset) CALC_SUM_((rect)[0], (rect)[1], (rect)[2], (rect)[3], offset)
+
+#define CV_SUM_OFS( p0, p1, p2, p3, sum, rect, step )                 \
+/* (x, y) */                                                          \
+(p0) = sum + (rect).x + (step) * (rect).y,                            \
+/* (x + w, y) */                                                      \
+(p1) = sum + (rect).x + (rect).width + (step) * (rect).y,             \
+/* (x, y + h) */                                                      \
+(p2) = sum + (rect).x + (step) * ((rect).y + (rect).height),          \
+/* (x + w, y + h) */                                                  \
+(p3) = sum + (rect).x + (rect).width + (step) * ((rect).y + (rect).height)
+
+#define CV_TILTED_OFS( p0, p1, p2, p3, tilted, rect, step )                     \
+/* (x, y) */                                                                    \
+(p0) = tilted + (rect).x + (step) * (rect).y,                                   \
+/* (x - h, y + h) */                                                            \
+(p1) = tilted + (rect).x - (rect).height + (step) * ((rect).y + (rect).height), \
+/* (x + w, y + w) */                                                            \
+(p2) = tilted + (rect).x + (rect).width + (step) * ((rect).y + (rect).width),   \
+/* (x + w - h, y + w + h) */                                                    \
+(p3) = tilted + (rect).x + (rect).width - (rect).height                         \
++ (step) * ((rect).y + (rect).width + (rect).height)
+
+#define CALC_SUM_OFS_(p0, p1, p2, p3, ptr) \
+((ptr)[p0] - (ptr)[p1] - (ptr)[p2] + (ptr)[p3])
+
+#define CALC_SUM_OFS(rect, ptr) CALC_SUM_OFS_((rect)[0], (rect)[1], (rect)[2], (rect)[3], ptr)
+
+//----------------------------------------------  HaarEvaluator ---------------------------------------
+class HaarEvaluator CV_FINAL : public FeatureEvaluator
+{
+public:
+    struct Feature
+    {
+        Feature();
+        bool read(const FileNode& node, const Size& origWinSize);
+
+        bool tilted;
+
+        enum { RECT_NUM = 3 };
+        struct RectWeigth
+        {
+            Rect r;
+            float weight;
+        } rect[RECT_NUM];
+    };
+
+    struct OptFeature
+    {
+        OptFeature();
+
+        enum { RECT_NUM = Feature::RECT_NUM };
+        float calc( const int* pwin ) const;
+        void setOffsets( const Feature& _f, int step, int tofs );
+
+        int ofs[RECT_NUM][4];
+        float weight[4];
+    };
+
+    HaarEvaluator();
+    virtual ~HaarEvaluator() CV_OVERRIDE;
+
+    virtual bool read( const FileNode& node, Size origWinSize) CV_OVERRIDE;
+    virtual Ptr<FeatureEvaluator> clone() const CV_OVERRIDE;
+    virtual int getFeatureType() const CV_OVERRIDE { return FeatureEvaluator::HAAR; }
+
+    virtual bool setWindow(Point p, int scaleIdx) CV_OVERRIDE;
+    Rect getNormRect() const;
+    int getSquaresOffset() const;
+
+    float operator()(int featureIdx) const
+    { return optfeaturesPtr[featureIdx].calc(pwin) * varianceNormFactor; }
+    virtual float calcOrd(int featureIdx) const CV_OVERRIDE
+    { return (*this)(featureIdx); }
+
+protected:
+    virtual void computeChannels( int i, InputArray img ) CV_OVERRIDE;
+    virtual void computeOptFeatures() CV_OVERRIDE;
+
+    Ptr<std::vector<Feature> > features;
+    Ptr<std::vector<OptFeature> > optfeatures;
+    Ptr<std::vector<OptFeature> > optfeatures_lbuf;
+    bool hasTiltedFeatures;
+
+    int tofs, sqofs;
+    Vec4i nofs;
+    Rect normrect;
+    const int* pwin;
+    OptFeature* optfeaturesPtr; // optimization
+    float varianceNormFactor;
+};
+
+inline HaarEvaluator::Feature :: Feature()
+{
+    tilted = false;
+    rect[0].r = rect[1].r = rect[2].r = Rect();
+    rect[0].weight = rect[1].weight = rect[2].weight = 0;
+}
+
+inline HaarEvaluator::OptFeature :: OptFeature()
+{
+    weight[0] = weight[1] = weight[2] = 0.f;
+
+    ofs[0][0] = ofs[0][1] = ofs[0][2] = ofs[0][3] =
+    ofs[1][0] = ofs[1][1] = ofs[1][2] = ofs[1][3] =
+    ofs[2][0] = ofs[2][1] = ofs[2][2] = ofs[2][3] = 0;
+}
+
+inline float HaarEvaluator::OptFeature :: calc( const int* ptr ) const
+{
+    float ret = weight[0] * CALC_SUM_OFS(ofs[0], ptr) +
+                weight[1] * CALC_SUM_OFS(ofs[1], ptr);
+
+    if( weight[2] != 0.0f )
+        ret += weight[2] * CALC_SUM_OFS(ofs[2], ptr);
+
+    return ret;
+}
+
+//----------------------------------------------  LBPEvaluator -------------------------------------
+
+class LBPEvaluator CV_FINAL : public FeatureEvaluator
+{
+public:
+    struct Feature
+    {
+        Feature();
+        Feature( int x, int y, int _block_w, int _block_h  ) :
+                 rect(x, y, _block_w, _block_h) {}
+
+        bool read(const FileNode& node, const Size& origWinSize);
+
+        Rect rect; // weight and height for block
+    };
+
+    struct OptFeature
+    {
+        OptFeature();
+
+        int calc( const int* pwin ) const;
+        void setOffsets( const Feature& _f, int step );
+        int ofs[16];
+    };
+
+    LBPEvaluator();
+    virtual ~LBPEvaluator() CV_OVERRIDE;
+
+    virtual bool read( const FileNode& node, Size origWinSize ) CV_OVERRIDE;
+    virtual Ptr<FeatureEvaluator> clone() const CV_OVERRIDE;
+    virtual int getFeatureType() const CV_OVERRIDE { return FeatureEvaluator::LBP; }
+
+    virtual bool setWindow(Point p, int scaleIdx) CV_OVERRIDE;
+
+    int operator()(int featureIdx) const
+    { return optfeaturesPtr[featureIdx].calc(pwin); }
+    virtual int calcCat(int featureIdx) const CV_OVERRIDE
+    { return (*this)(featureIdx); }
+protected:
+    virtual void computeChannels( int i, InputArray img ) CV_OVERRIDE;
+    virtual void computeOptFeatures() CV_OVERRIDE;
+
+    Ptr<std::vector<Feature> > features;
+    Ptr<std::vector<OptFeature> > optfeatures;
+    Ptr<std::vector<OptFeature> > optfeatures_lbuf;
+    OptFeature* optfeaturesPtr; // optimization
+
+    const int* pwin;
+};
+
+
+inline LBPEvaluator::Feature :: Feature()
+{
+    rect = Rect();
+}
+
+inline LBPEvaluator::OptFeature :: OptFeature()
+{
+    for( int i = 0; i < 16; i++ )
+        ofs[i] = 0;
+}
+
+inline int LBPEvaluator::OptFeature :: calc( const int* p ) const
+{
+    int cval = CALC_SUM_OFS_( ofs[5], ofs[6], ofs[9], ofs[10], p );
+
+    return (CALC_SUM_OFS_( ofs[0], ofs[1], ofs[4], ofs[5], p ) >= cval ? 128 : 0) |   // 0
+           (CALC_SUM_OFS_( ofs[1], ofs[2], ofs[5], ofs[6], p ) >= cval ? 64 : 0) |    // 1
+           (CALC_SUM_OFS_( ofs[2], ofs[3], ofs[6], ofs[7], p ) >= cval ? 32 : 0) |    // 2
+           (CALC_SUM_OFS_( ofs[6], ofs[7], ofs[10], ofs[11], p ) >= cval ? 16 : 0) |  // 5
+           (CALC_SUM_OFS_( ofs[10], ofs[11], ofs[14], ofs[15], p ) >= cval ? 8 : 0)|  // 8
+           (CALC_SUM_OFS_( ofs[9], ofs[10], ofs[13], ofs[14], p ) >= cval ? 4 : 0)|   // 7
+           (CALC_SUM_OFS_( ofs[8], ofs[9], ofs[12], ofs[13], p ) >= cval ? 2 : 0)|    // 6
+           (CALC_SUM_OFS_( ofs[4], ofs[5], ofs[8], ofs[9], p ) >= cval ? 1 : 0);
+}
+
+
+//----------------------------------------------  predictor functions -------------------------------------
+
+template<class FEval>
+inline int predictOrdered( CascadeClassifierImpl& cascade,
+                           Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
+{
+    CV_INSTRUMENT_REGION();
+
+    int nstages = (int)cascade.data.stages.size();
+    int nodeOfs = 0, leafOfs = 0;
+    FEval& featureEvaluator = (FEval&)*_featureEvaluator;
+    float* cascadeLeaves = &cascade.data.leaves[0];
+    CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
+    CascadeClassifierImpl::Data::DTree* cascadeWeaks = &cascade.data.classifiers[0];
+    CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
+
+    for( int si = 0; si < nstages; si++ )
+    {
+        CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
+        int wi, ntrees = stage.ntrees;
+        sum = 0;
+
+        for( wi = 0; wi < ntrees; wi++ )
+        {
+            CascadeClassifierImpl::Data::DTree& weak = cascadeWeaks[stage.first + wi];
+            int idx = 0, root = nodeOfs;
+
+            do
+            {
+                CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[root + idx];
+                double val = featureEvaluator(node.featureIdx);
+                idx = val < node.threshold ? node.left : node.right;
+            }
+            while( idx > 0 );
+            sum += cascadeLeaves[leafOfs - idx];
+            nodeOfs += weak.nodeCount;
+            leafOfs += weak.nodeCount + 1;
+        }
+        if( sum < stage.threshold )
+            return -si;
+    }
+    return 1;
+}
+
+template<class FEval>
+inline int predictCategorical( CascadeClassifierImpl& cascade,
+                               Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
+{
+    CV_INSTRUMENT_REGION();
+
+    int nstages = (int)cascade.data.stages.size();
+    int nodeOfs = 0, leafOfs = 0;
+    FEval& featureEvaluator = (FEval&)*_featureEvaluator;
+    size_t subsetSize = (cascade.data.ncategories + 31)/32;
+    int* cascadeSubsets = &cascade.data.subsets[0];
+    float* cascadeLeaves = &cascade.data.leaves[0];
+    CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
+    CascadeClassifierImpl::Data::DTree* cascadeWeaks = &cascade.data.classifiers[0];
+    CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
+
+    for(int si = 0; si < nstages; si++ )
+    {
+        CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
+        int wi, ntrees = stage.ntrees;
+        sum = 0;
+
+        for( wi = 0; wi < ntrees; wi++ )
+        {
+            CascadeClassifierImpl::Data::DTree& weak = cascadeWeaks[stage.first + wi];
+            int idx = 0, root = nodeOfs;
+            do
+            {
+                CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[root + idx];
+                int c = featureEvaluator(node.featureIdx);
+                const int* subset = &cascadeSubsets[(root + idx)*subsetSize];
+                idx = (subset[c>>5] & (1 << (c & 31))) ? node.left : node.right;
+            }
+            while( idx > 0 );
+            sum += cascadeLeaves[leafOfs - idx];
+            nodeOfs += weak.nodeCount;
+            leafOfs += weak.nodeCount + 1;
+        }
+        if( sum < stage.threshold )
+            return -si;
+    }
+    return 1;
+}
+
+template<class FEval>
+inline int predictOrderedStump( CascadeClassifierImpl& cascade,
+                                Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
+{
+    CV_INSTRUMENT_REGION();
+
+    CV_Assert(!cascade.data.stumps.empty());
+    FEval& featureEvaluator = (FEval&)*_featureEvaluator;
+    const CascadeClassifierImpl::Data::Stump* cascadeStumps = &cascade.data.stumps[0];
+    const CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
+
+    int nstages = (int)cascade.data.stages.size();
+    double tmp = 0;
+
+    for( int stageIdx = 0; stageIdx < nstages; stageIdx++ )
+    {
+        const CascadeClassifierImpl::Data::Stage& stage = cascadeStages[stageIdx];
+        tmp = 0;
+
+        int ntrees = stage.ntrees;
+        for( int i = 0; i < ntrees; i++ )
+        {
+            const CascadeClassifierImpl::Data::Stump& stump = cascadeStumps[i];
+            double value = featureEvaluator(stump.featureIdx);
+            tmp += value < stump.threshold ? stump.left : stump.right;
+        }
+
+        if( tmp < stage.threshold )
+        {
+            sum = (double)tmp;
+            return -stageIdx;
+        }
+        cascadeStumps += ntrees;
+    }
+
+    sum = (double)tmp;
+    return 1;
+}
+
+template<class FEval>
+inline int predictCategoricalStump( CascadeClassifierImpl& cascade,
+                                    Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
+{
+    CV_INSTRUMENT_REGION();
+
+    CV_Assert(!cascade.data.stumps.empty());
+    int nstages = (int)cascade.data.stages.size();
+    FEval& featureEvaluator = (FEval&)*_featureEvaluator;
+    size_t subsetSize = (cascade.data.ncategories + 31)/32;
+    const int* cascadeSubsets = &cascade.data.subsets[0];
+    const CascadeClassifierImpl::Data::Stump* cascadeStumps = &cascade.data.stumps[0];
+    const CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
+
+    double tmp = 0;
+    for( int si = 0; si < nstages; si++ )
+    {
+        const CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
+        int wi, ntrees = stage.ntrees;
+        tmp = 0;
+
+        for( wi = 0; wi < ntrees; wi++ )
+        {
+            const CascadeClassifierImpl::Data::Stump& stump = cascadeStumps[wi];
+            int c = featureEvaluator(stump.featureIdx);
+            const int* subset = &cascadeSubsets[wi*subsetSize];
+            tmp += (subset[c>>5] & (1 << (c & 31))) ? stump.left : stump.right;
+        }
+
+        if( tmp < stage.threshold )
+        {
+            sum = tmp;
+            return -si;
+        }
+
+        cascadeStumps += ntrees;
+        cascadeSubsets += ntrees*subsetSize;
+    }
+
+    sum = (double)tmp;
+    return 1;
+}
+
+namespace haar_cvt
+{
+bool convert(const FileNode& oldcascade_root, FileStorage& newfs);
+}
+
+}

modules/objdetect/src/cascadedetect_convert.cpp

@@ -0,0 +1,275 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, Itseez Inc, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+/* Haar features calculation */
+
+#include "precomp.hpp"
+#include "cascadedetect.hpp"
+#include <stdio.h>
+
+namespace cv
+{
+
+/* field names */
+
+#define ICV_HAAR_SIZE_NAME            "size"
+#define ICV_HAAR_STAGES_NAME          "stages"
+#define ICV_HAAR_TREES_NAME           "trees"
+#define ICV_HAAR_FEATURE_NAME         "feature"
+#define ICV_HAAR_RECTS_NAME           "rects"
+#define ICV_HAAR_TILTED_NAME          "tilted"
+#define ICV_HAAR_THRESHOLD_NAME       "threshold"
+#define ICV_HAAR_LEFT_NODE_NAME       "left_node"
+#define ICV_HAAR_LEFT_VAL_NAME        "left_val"
+#define ICV_HAAR_RIGHT_NODE_NAME      "right_node"
+#define ICV_HAAR_RIGHT_VAL_NAME       "right_val"
+#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"
+#define ICV_HAAR_PARENT_NAME          "parent"
+#define ICV_HAAR_NEXT_NAME            "next"
+
+namespace haar_cvt
+{
+
+struct HaarFeature
+{
+    enum { RECT_NUM = 3 };
+
+    HaarFeature()
+    {
+        tilted = false;
+        for( int i = 0; i < RECT_NUM; i++ )
+        {
+            rect[i].r = Rect(0,0,0,0);
+            rect[i].weight = 0.f;
+        }
+    }
+    bool tilted;
+    struct
+    {
+        Rect r;
+        float weight;
+    } rect[RECT_NUM];
+};
+
+struct HaarClassifierNode
+{
+    HaarClassifierNode()
+    {
+        f = left = right = 0;
+        threshold = 0.f;
+    }
+    int f, left, right;
+    float threshold;
+};
+
+struct HaarClassifier
+{
+    std::vector<HaarClassifierNode> nodes;
+    std::vector<float> leaves;
+};
+
+struct HaarStageClassifier
+{
+    HaarStageClassifier() : threshold(0) {}
+
+    double threshold;
+    std::vector<HaarClassifier> weaks;
+};
+
+bool convert(const FileNode& oldroot, FileStorage& newfs)
+{
+    FileNode sznode = oldroot[ICV_HAAR_SIZE_NAME];
+    if( sznode.empty() )
+        return false;
+    Size cascadesize;
+    cascadesize.width = (int)sznode[0];
+    cascadesize.height = (int)sznode[1];
+    std::vector<HaarFeature> features;
+
+    int i, j, k, n;
+
+    FileNode stages_seq = oldroot[ICV_HAAR_STAGES_NAME];
+    int nstages = (int)stages_seq.size();
+    std::vector<HaarStageClassifier> stages(nstages);
+
+    for( i = 0; i < nstages; i++ )
+    {
+        FileNode stagenode = stages_seq[i];
+        HaarStageClassifier& stage = stages[i];
+        stage.threshold = (double)stagenode[ICV_HAAR_STAGE_THRESHOLD_NAME];
+        FileNode weaks_seq = stagenode[ICV_HAAR_TREES_NAME];
+        int nweaks = (int)weaks_seq.size();
+        stage.weaks.resize(nweaks);
+
+        for( j = 0; j < nweaks; j++ )
+        {
+            HaarClassifier& weak = stage.weaks[j];
+            FileNode weaknode = weaks_seq[j];
+            int nnodes = (int)weaknode.size();
+
+            for( n = 0; n < nnodes; n++ )
+            {
+                FileNode nnode = weaknode[n];
+                FileNode fnode = nnode[ICV_HAAR_FEATURE_NAME];
+                HaarFeature f;
+                HaarClassifierNode node;
+                node.f = (int)features.size();
+                f.tilted = (int)fnode[ICV_HAAR_TILTED_NAME] != 0;
+                FileNode rects_seq = fnode[ICV_HAAR_RECTS_NAME];
+                int nrects = (int)rects_seq.size();
+
+                for( k = 0; k < nrects; k++ )
+                {
+                    FileNode rnode = rects_seq[k];
+                    f.rect[k].r.x = (int)rnode[0];
+                    f.rect[k].r.y = (int)rnode[1];
+                    f.rect[k].r.width = (int)rnode[2];
+                    f.rect[k].r.height = (int)rnode[3];
+                    f.rect[k].weight = (float)rnode[4];
+                }
+                features.push_back(f);
+                node.threshold = nnode[ICV_HAAR_THRESHOLD_NAME];
+                FileNode leftValNode = nnode[ICV_HAAR_LEFT_VAL_NAME];
+                if( !leftValNode.empty() )
+                {
+                    node.left = -(int)weak.leaves.size();
+                    weak.leaves.push_back((float)leftValNode);
+                }
+                else
+                {
+                    node.left = (int)nnode[ICV_HAAR_LEFT_NODE_NAME];
+                }
+                FileNode rightValNode = nnode[ICV_HAAR_RIGHT_VAL_NAME];
+                if( !rightValNode.empty() )
+                {
+                    node.right = -(int)weak.leaves.size();
+                    weak.leaves.push_back((float)rightValNode);
+                }
+                else
+                {
+                    node.right = (int)nnode[ICV_HAAR_RIGHT_NODE_NAME];
+                }
+                weak.nodes.push_back(node);
+            }
+        }
+    }
+
+    int maxWeakCount = 0, nfeatures = (int)features.size();
+    for( i = 0; i < nstages; i++ )
+        maxWeakCount = std::max(maxWeakCount, (int)stages[i].weaks.size());
+
+    newfs << "cascade" << "{:opencv-cascade-classifier"
+    << "stageType" << "BOOST"
+    << "featureType" << "HAAR"
+    << "width" << cascadesize.width
+    << "height" << cascadesize.height
+    << "stageParams" << "{"
+        << "maxWeakCount" << (int)maxWeakCount
+    << "}"
+    << "featureParams" << "{"
+        << "maxCatCount" << 0
+    << "}"
+    << "stageNum" << (int)nstages
+    << "stages" << "[";
+
+    for( i = 0; i < nstages; i++ )
+    {
+        int nweaks = (int)stages[i].weaks.size();
+        newfs << "{" << "maxWeakCount" << (int)nweaks
+            << "stageThreshold" << stages[i].threshold
+            << "weakClassifiers" << "[";
+        for( j = 0; j < nweaks; j++ )
+        {
+            const HaarClassifier& c = stages[i].weaks[j];
+            newfs << "{" << "internalNodes" << "[:";
+            int nnodes = (int)c.nodes.size(), nleaves = (int)c.leaves.size();
+            for( k = 0; k < nnodes; k++ )
+                newfs << c.nodes[k].left << c.nodes[k].right
+                    << c.nodes[k].f << c.nodes[k].threshold;
+            newfs << "]" << "leafValues" << "[:";
+            for( k = 0; k < nleaves; k++ )
+                newfs << c.leaves[k];
+            newfs << "]" << "}";
+        }
+        newfs << "]" << "}";
+    }
+
+    newfs << "]"
+        << "features" << "[";
+
+    for( i = 0; i < nfeatures; i++ )
+    {
+        const HaarFeature& f = features[i];
+        newfs << "{" << "rects" << "[";
+        for( j = 0; j < HaarFeature::RECT_NUM; j++ )
+        {
+            if( j >= 2 && fabs(f.rect[j].weight) < FLT_EPSILON )
+                break;
+            newfs << "[:" << f.rect[j].r.x << f.rect[j].r.y <<
+                f.rect[j].r.width << f.rect[j].r.height << f.rect[j].weight << "]";
+        }
+        newfs << "]";
+        if( f.tilted )
+            newfs << "tilted" << 1;
+        newfs << "}";
+    }
+
+    newfs << "]" << "}";
+    return true;
+}
+
+}
+
+bool CascadeClassifier::convert(const String& oldcascade, const String& newcascade)
+{
+    FileStorage oldfs(oldcascade, FileStorage::READ);
+    FileStorage newfs(newcascade, FileStorage::WRITE);
+    if( !oldfs.isOpened() || !newfs.isOpened() )
+        return false;
+    FileNode oldroot = oldfs.getFirstTopLevelNode();
+
+    bool ok = haar_cvt::convert(oldroot, newfs);
+    if( !ok && newcascade.size() > 0 )
+        remove(newcascade.c_str());
+    return ok;
+}
+
+}

modules/objdetect/src/detection_based_tracker.cpp

@@ -0,0 +1,885 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+#include "opencv2/core/utility.hpp"
+
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+
+#if defined(DEBUG) || defined(_DEBUG)
+#undef DEBUGLOGS
+#define DEBUGLOGS 1
+#endif
+
+#ifndef DEBUGLOGS
+#define DEBUGLOGS 0
+#endif
+
+#ifdef __ANDROID__
+#include <android/log.h>
+#define LOG_TAG "OBJECT_DETECTOR"
+#define LOGD0(...) ((void)__android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__))
+#define LOGI0(...) ((void)__android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__))
+#define LOGW0(...) ((void)__android_log_print(ANDROID_LOG_WARN, LOG_TAG, __VA_ARGS__))
+#define LOGE0(...) ((void)__android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__))
+#else
+
+#include <stdio.h>
+
+#define LOGD0(_str, ...) (printf(_str , ## __VA_ARGS__), printf("\n"), fflush(stdout))
+#define LOGI0(_str, ...) (printf(_str , ## __VA_ARGS__), printf("\n"), fflush(stdout))
+#define LOGW0(_str, ...) (printf(_str , ## __VA_ARGS__), printf("\n"), fflush(stdout))
+#define LOGE0(_str, ...) (printf(_str , ## __VA_ARGS__), printf("\n"), fflush(stdout))
+#endif //__ANDROID__
+
+#if DEBUGLOGS
+#define LOGD(_str, ...) LOGD0(_str , ## __VA_ARGS__)
+#define LOGI(_str, ...) LOGI0(_str , ## __VA_ARGS__)
+#define LOGW(_str, ...) LOGW0(_str , ## __VA_ARGS__)
+#define LOGE(_str, ...) LOGE0(_str , ## __VA_ARGS__)
+#else
+#define LOGD(...)
+#define LOGI(...)
+#define LOGW(...)
+#define LOGE(...)
+#endif //DEBUGLOGS
+
+
+using namespace cv;
+
+static inline cv::Point2f centerRect(const cv::Rect& r)
+{
+    return cv::Point2f(r.x+((float)r.width)/2, r.y+((float)r.height)/2);
+}
+
+static inline cv::Rect scale_rect(const cv::Rect& r, float scale)
+{
+    cv::Point2f m=centerRect(r);
+    float width  = r.width  * scale;
+    float height = r.height * scale;
+    int x=cvRound(m.x - width/2);
+    int y=cvRound(m.y - height/2);
+
+    return cv::Rect(x, y, cvRound(width), cvRound(height));
+}
+
+namespace cv
+{
+    void* workcycleObjectDetectorFunction(void* p);
+}
+
+class cv::DetectionBasedTracker::SeparateDetectionWork
+{
+    public:
+        SeparateDetectionWork(cv::DetectionBasedTracker& _detectionBasedTracker, cv::Ptr<DetectionBasedTracker::IDetector> _detector,
+                              const cv::DetectionBasedTracker::Parameters& params);
+        virtual ~SeparateDetectionWork();
+        bool communicateWithDetectingThread(const Mat& imageGray, std::vector<Rect>& rectsWhereRegions);
+        bool run();
+        void stop();
+        void resetTracking();
+
+        inline bool isWorking()
+        {
+            return (stateThread==STATE_THREAD_WORKING_SLEEPING) || (stateThread==STATE_THREAD_WORKING_WITH_IMAGE);
+        }
+        void setParameters(const cv::DetectionBasedTracker::Parameters& params)
+        {
+            std::unique_lock<std::mutex> mtx_lock(mtx);
+            parameters = params;
+        }
+
+        inline void init()
+        {
+            std::unique_lock<std::mutex> mtx_lock(mtx);
+            stateThread = STATE_THREAD_STOPPED;
+            isObjectDetectingReady = false;
+            shouldObjectDetectingResultsBeForgot = false;
+            objectDetectorThreadStartStop.notify_one();
+        }
+    protected:
+
+        DetectionBasedTracker& detectionBasedTracker;
+        cv::Ptr<DetectionBasedTracker::IDetector> cascadeInThread;
+        std::thread second_workthread;
+        std::mutex mtx;
+        std::condition_variable objectDetectorRun;
+        std::condition_variable objectDetectorThreadStartStop;
+        std::vector<cv::Rect> resultDetect;
+        volatile bool isObjectDetectingReady;
+        volatile bool shouldObjectDetectingResultsBeForgot;
+
+        enum StateSeparatedThread {
+            STATE_THREAD_STOPPED=0,
+            STATE_THREAD_WORKING_SLEEPING,
+            STATE_THREAD_WORKING_WITH_IMAGE,
+            STATE_THREAD_WORKING,
+            STATE_THREAD_STOPPING
+        };
+        volatile StateSeparatedThread stateThread;
+
+        cv::Mat imageSeparateDetecting;
+
+        void workcycleObjectDetector();
+        friend void* workcycleObjectDetectorFunction(void* p);
+
+        long long  timeWhenDetectingThreadStartedWork;
+        cv::DetectionBasedTracker::Parameters parameters;
+};
+
+cv::DetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork(DetectionBasedTracker& _detectionBasedTracker, cv::Ptr<DetectionBasedTracker::IDetector> _detector,
+                                                                        const cv::DetectionBasedTracker::Parameters& params)
+    :detectionBasedTracker(_detectionBasedTracker),
+    cascadeInThread(),
+    isObjectDetectingReady(false),
+    shouldObjectDetectingResultsBeForgot(false),
+    stateThread(STATE_THREAD_STOPPED),
+    timeWhenDetectingThreadStartedWork(-1),
+    parameters(params)
+{
+    CV_Assert(_detector);
+
+    cascadeInThread = _detector;
+}
+
+cv::DetectionBasedTracker::SeparateDetectionWork::~SeparateDetectionWork()
+{
+    if(stateThread!=STATE_THREAD_STOPPED) {
+        LOGE("\n\n\nATTENTION!!! dangerous algorithm error: destructor DetectionBasedTracker::DetectionBasedTracker::~SeparateDetectionWork is called before stopping the workthread");
+    }
+    second_workthread.join();
+}
+bool cv::DetectionBasedTracker::SeparateDetectionWork::run()
+{
+    LOGD("DetectionBasedTracker::SeparateDetectionWork::run() --- start");
+    std::unique_lock<std::mutex> mtx_lock(mtx);
+    // unlocked when leaving scope
+    if (stateThread != STATE_THREAD_STOPPED) {
+        LOGE("DetectionBasedTracker::SeparateDetectionWork::run is called while the previous run is not stopped");
+        return false;
+    }
+    stateThread=STATE_THREAD_WORKING_SLEEPING;
+    second_workthread = std::thread(workcycleObjectDetectorFunction, (void*)this); //TODO: add attributes?
+    objectDetectorThreadStartStop.wait(mtx_lock);
+    LOGD("DetectionBasedTracker::SeparateDetectionWork::run --- end");
+    return true;
+}
+
+#define CATCH_ALL_AND_LOG(_block)                                                           \
+    try {                                                                                   \
+        _block;                                                                             \
+    }                                                                                       \
+    catch(const cv::Exception& e) {                                                         \
+        LOGE0("\n %s: ERROR: OpenCV Exception caught: \n'%s'\n\n", CV_Func, e.what());      \
+    } catch(const std::exception& e) {                                                      \
+        LOGE0("\n %s: ERROR: Exception caught: \n'%s'\n\n", CV_Func, e.what());             \
+    } catch(...) {                                                                          \
+        LOGE0("\n %s: ERROR: UNKNOWN Exception caught\n\n", CV_Func);                       \
+    }
+
+void* cv::workcycleObjectDetectorFunction(void* p)
+{
+    CATCH_ALL_AND_LOG({ ((cv::DetectionBasedTracker::SeparateDetectionWork*)p)->workcycleObjectDetector(); });
+    try{
+        ((cv::DetectionBasedTracker::SeparateDetectionWork*)p)->init();
+    } catch(...) {
+        LOGE0("DetectionBasedTracker: workcycleObjectDetectorFunction: ERROR concerning pointer, received as the function parameter");
+    }
+    return NULL;
+}
+
+void cv::DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector()
+{
+    static double freq = getTickFrequency();
+    LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- start");
+    std::vector<Rect> objects;
+
+    CV_Assert(stateThread==STATE_THREAD_WORKING_SLEEPING);
+    std::unique_lock<std::mutex> mtx_lock(mtx);
+    {
+        objectDetectorThreadStartStop.notify_one();
+        LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- before waiting");
+        CV_Assert(stateThread==STATE_THREAD_WORKING_SLEEPING);
+        objectDetectorRun.wait(mtx_lock);
+        if (isWorking()) {
+            stateThread=STATE_THREAD_WORKING_WITH_IMAGE;
+        }
+        LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- after waiting");
+    }
+    mtx_lock.unlock();
+
+    bool isFirstStep=true;
+
+    isObjectDetectingReady=false;
+
+    while(isWorking())
+    {
+        LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- next step");
+
+        if (! isFirstStep) {
+            LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- before waiting");
+            CV_Assert(stateThread==STATE_THREAD_WORKING_SLEEPING);
+            mtx_lock.lock();
+            if (!isWorking()) {//it is a rare case, but may cause a crash
+                LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- go out from the workcycle from inner part of lock just before waiting");
+                mtx_lock.unlock();
+                break;
+            }
+            CV_Assert(stateThread==STATE_THREAD_WORKING_SLEEPING);
+            objectDetectorRun.wait(mtx_lock);
+            if (isWorking()) {
+                stateThread=STATE_THREAD_WORKING_WITH_IMAGE;
+            }
+            mtx_lock.unlock();
+
+            LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- after waiting");
+        } else {
+            isFirstStep=false;
+        }
+
+        if (!isWorking()) {
+            LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- go out from the workcycle just after waiting");
+            break;
+        }
+
+
+        if (imageSeparateDetecting.empty()) {
+            LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- imageSeparateDetecting is empty, continue");
+            continue;
+        }
+        LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- start handling imageSeparateDetecting, img.size=%dx%d, img.data=0x%p",
+                imageSeparateDetecting.size().width, imageSeparateDetecting.size().height, (void*)imageSeparateDetecting.data);
+
+
+        int64 t1_detect=getTickCount();
+
+        cascadeInThread->detect(imageSeparateDetecting, objects);
+
+        /*cascadeInThread.detectMultiScale( imageSeparateDetecting, objects,
+                detectionBasedTracker.parameters.scaleFactor, detectionBasedTracker.parameters.minNeighbors, 0
+                |CV_HAAR_SCALE_IMAGE
+                ,
+                min_objectSize,
+                max_objectSize
+                );
+        */
+
+        LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- end handling imageSeparateDetecting");
+
+        if (!isWorking()) {
+            LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- go out from the workcycle just after detecting");
+            break;
+        }
+
+        int64 t2_detect = getTickCount();
+        int64 dt_detect = t2_detect-t1_detect;
+        double dt_detect_ms=((double)dt_detect)/freq * 1000.0;
+        (void)(dt_detect_ms);
+
+        LOGI("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- objects num==%d, t_ms=%.4f", (int)objects.size(), dt_detect_ms);
+        mtx_lock.lock();
+        if (!shouldObjectDetectingResultsBeForgot) {
+            resultDetect=objects;
+            isObjectDetectingReady=true;
+        } else { //shouldObjectDetectingResultsBeForgot==true
+            resultDetect.clear();
+            isObjectDetectingReady=false;
+            shouldObjectDetectingResultsBeForgot=false;
+        }
+        if(isWorking()) {
+            stateThread=STATE_THREAD_WORKING_SLEEPING;
+        }
+        mtx_lock.unlock();
+
+        objects.clear();
+    }// while(isWorking())
+
+    LOGI("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector: Returning");
+}
+
+void cv::DetectionBasedTracker::SeparateDetectionWork::stop()
+{
+    //FIXME: TODO: should add quickStop functionality
+  std::unique_lock<std::mutex> mtx_lock(mtx);
+    if (!isWorking()) {
+        mtx_lock.unlock();
+        LOGE("SimpleHighguiDemoCore::stop is called but the SimpleHighguiDemoCore pthread is not active");
+        stateThread = STATE_THREAD_STOPPING;
+        return;
+    }
+    stateThread=STATE_THREAD_STOPPING;
+    LOGD("DetectionBasedTracker::SeparateDetectionWork::stop: before going to sleep to wait for the signal from the workthread");
+    objectDetectorRun.notify_one();
+    objectDetectorThreadStartStop.wait(mtx_lock);
+    LOGD("DetectionBasedTracker::SeparateDetectionWork::stop: after receiving the signal from the workthread, stateThread=%d", (int)stateThread);
+    mtx_lock.unlock();
+}
+
+void cv::DetectionBasedTracker::SeparateDetectionWork::resetTracking()
+{
+    LOGD("DetectionBasedTracker::SeparateDetectionWork::resetTracking");
+    std::unique_lock<std::mutex> mtx_lock(mtx);
+
+    if (stateThread == STATE_THREAD_WORKING_WITH_IMAGE) {
+        LOGD("DetectionBasedTracker::SeparateDetectionWork::resetTracking: since workthread is detecting objects at the moment, we should make cascadeInThread stop detecting and forget the detecting results");
+        shouldObjectDetectingResultsBeForgot=true;
+        //cascadeInThread.setStopFlag();//FIXME: TODO: this feature also should be contributed to OpenCV
+    } else {
+        LOGD("DetectionBasedTracker::SeparateDetectionWork::resetTracking: since workthread is NOT detecting objects at the moment, we should NOT make any additional actions");
+    }
+
+    resultDetect.clear();
+    isObjectDetectingReady=false;
+
+    mtx_lock.unlock();
+}
+
+bool cv::DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread(const Mat& imageGray, std::vector<Rect>& rectsWhereRegions)
+{
+    static double freq = getTickFrequency();
+
+    bool shouldCommunicateWithDetectingThread = (stateThread==STATE_THREAD_WORKING_SLEEPING);
+    LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: shouldCommunicateWithDetectingThread=%d", (shouldCommunicateWithDetectingThread?1:0));
+
+    if (!shouldCommunicateWithDetectingThread) {
+        return false;
+    }
+
+    bool shouldHandleResult = false;
+
+    std::unique_lock<std::mutex> mtx_lock(mtx);
+
+    if (isObjectDetectingReady) {
+        shouldHandleResult=true;
+        rectsWhereRegions = resultDetect;
+        isObjectDetectingReady=false;
+
+        double lastBigDetectionDuration = 1000.0 * (((double)(getTickCount()  - timeWhenDetectingThreadStartedWork )) / freq);
+        (void)(lastBigDetectionDuration);
+        LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: lastBigDetectionDuration=%f ms", (double)lastBigDetectionDuration);
+    }
+
+    bool shouldSendNewDataToWorkThread = true;
+    if (timeWhenDetectingThreadStartedWork > 0) {
+        double time_from_previous_launch_in_ms=1000.0 * (((double)(getTickCount()  - timeWhenDetectingThreadStartedWork )) / freq); //the same formula as for lastBigDetectionDuration
+        shouldSendNewDataToWorkThread = (time_from_previous_launch_in_ms >= detectionBasedTracker.parameters.minDetectionPeriod);
+        LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: shouldSendNewDataToWorkThread was 1, now it is %d, since time_from_previous_launch_in_ms=%.2f, minDetectionPeriod=%d",
+                (shouldSendNewDataToWorkThread?1:0), time_from_previous_launch_in_ms, detectionBasedTracker.parameters.minDetectionPeriod);
+    }
+
+    if (shouldSendNewDataToWorkThread) {
+
+        imageSeparateDetecting.create(imageGray.size(), CV_8UC1);
+
+        imageGray.copyTo(imageSeparateDetecting);//may change imageSeparateDetecting ptr. But should not.
+
+
+        timeWhenDetectingThreadStartedWork = getTickCount() ;
+
+        objectDetectorRun.notify_one();
+    }
+
+    mtx_lock.unlock();
+    LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: result: shouldHandleResult=%d", (shouldHandleResult?1:0));
+
+    return shouldHandleResult;
+}
+
+cv::DetectionBasedTracker::Parameters::Parameters()
+{
+  maxTrackLifetime = 5;
+  minDetectionPeriod = 0;
+}
+
+cv::DetectionBasedTracker::InnerParameters::InnerParameters()
+{
+    numLastPositionsToTrack=4;
+    numStepsToWaitBeforeFirstShow=6;
+    numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown=3;
+    numStepsToShowWithoutDetecting=3;
+
+    coeffTrackingWindowSize=2.0;
+    coeffObjectSizeToTrack=0.85f;
+    coeffObjectSpeedUsingInPrediction=0.8f;
+
+}
+
+cv::DetectionBasedTracker::DetectionBasedTracker(cv::Ptr<IDetector> mainDetector, cv::Ptr<IDetector> trackingDetector, const Parameters& params)
+    :separateDetectionWork(),
+    parameters(params),
+    innerParameters(),
+    numTrackedSteps(0),
+    cascadeForTracking(trackingDetector)
+{
+    CV_Assert( (params.maxTrackLifetime >= 0)
+//            && mainDetector
+            && trackingDetector );
+
+    if (mainDetector) {
+        Ptr<SeparateDetectionWork> tmp(new SeparateDetectionWork(*this, mainDetector, params));
+        separateDetectionWork.swap(tmp);
+    }
+
+    weightsPositionsSmoothing.push_back(1);
+    weightsSizesSmoothing.push_back(0.5);
+    weightsSizesSmoothing.push_back(0.3f);
+    weightsSizesSmoothing.push_back(0.2f);
+}
+
+cv::DetectionBasedTracker::~DetectionBasedTracker()
+{
+}
+
+void DetectionBasedTracker::process(const Mat& imageGray)
+{
+    CV_INSTRUMENT_REGION();
+
+    CV_Assert(imageGray.type()==CV_8UC1);
+
+    if ( separateDetectionWork && !separateDetectionWork->isWorking() ) {
+        separateDetectionWork->run();
+    }
+
+    static double freq = getTickFrequency();
+    static long long time_when_last_call_started=getTickCount();
+
+    {
+        double delta_time_from_prev_call=1000.0 * (((double)(getTickCount()  - time_when_last_call_started)) / freq);
+        (void)(delta_time_from_prev_call);
+        LOGD("DetectionBasedTracker::process: time from the previous call is %f ms", (double)delta_time_from_prev_call);
+        time_when_last_call_started=getTickCount();
+    }
+
+    Mat imageDetect=imageGray;
+
+    std::vector<Rect> rectsWhereRegions;
+    bool shouldHandleResult=false;
+    if (separateDetectionWork) {
+        shouldHandleResult = separateDetectionWork->communicateWithDetectingThread(imageGray, rectsWhereRegions);
+    }
+
+    if (shouldHandleResult) {
+        LOGD("DetectionBasedTracker::process: get _rectsWhereRegions were got from resultDetect");
+    } else {
+        LOGD("DetectionBasedTracker::process: get _rectsWhereRegions from previous positions");
+        for(size_t i = 0; i < trackedObjects.size(); i++) {
+            size_t n = trackedObjects[i].lastPositions.size();
+            CV_Assert(n > 0);
+
+            Rect r = trackedObjects[i].lastPositions[n-1];
+            if(r.empty()) {
+                LOGE("DetectionBasedTracker::process: ERROR: ATTENTION: strange algorithm's behavior: trackedObjects[i].rect() is empty");
+                continue;
+            }
+
+            //correction by speed of rectangle
+            if (n > 1) {
+                Point2f center = centerRect(r);
+                Point2f center_prev = centerRect(trackedObjects[i].lastPositions[n-2]);
+                Point2f shift = (center - center_prev) * innerParameters.coeffObjectSpeedUsingInPrediction;
+
+                r.x += cvRound(shift.x);
+                r.y += cvRound(shift.y);
+            }
+
+
+            rectsWhereRegions.push_back(r);
+        }
+    }
+    LOGI("DetectionBasedTracker::process: tracked objects num==%d", (int)trackedObjects.size());
+
+    std::vector<Rect> detectedObjectsInRegions;
+
+    LOGD("DetectionBasedTracker::process: rectsWhereRegions.size()=%d", (int)rectsWhereRegions.size());
+    for(size_t i=0; i < rectsWhereRegions.size(); i++) {
+        Rect r = rectsWhereRegions[i];
+
+        detectInRegion(imageDetect, r, detectedObjectsInRegions);
+    }
+    LOGD("DetectionBasedTracker::process: detectedObjectsInRegions.size()=%d", (int)detectedObjectsInRegions.size());
+
+    updateTrackedObjects(detectedObjectsInRegions);
+}
+
+void cv::DetectionBasedTracker::getObjects(std::vector<cv::Rect>& result) const
+{
+    result.clear();
+
+    for(size_t i=0; i < trackedObjects.size(); i++) {
+        Rect r=calcTrackedObjectPositionToShow((int)i);
+        if (r.empty()) {
+            continue;
+        }
+        result.push_back(r);
+        LOGD("DetectionBasedTracker::process: found a object with SIZE %d x %d, rect={%d, %d, %d x %d}", r.width, r.height, r.x, r.y, r.width, r.height);
+    }
+}
+
+void cv::DetectionBasedTracker::getObjects(std::vector<Object>& result) const
+{
+    result.clear();
+
+    for(size_t i=0; i < trackedObjects.size(); i++) {
+        Rect r=calcTrackedObjectPositionToShow((int)i);
+        if (r.empty()) {
+            continue;
+        }
+        result.push_back(Object(r, trackedObjects[i].id));
+        LOGD("DetectionBasedTracker::process: found a object with SIZE %d x %d, rect={%d, %d, %d x %d}", r.width, r.height, r.x, r.y, r.width, r.height);
+    }
+}
+void cv::DetectionBasedTracker::getObjects(std::vector<ExtObject>& result) const
+{
+    result.clear();
+
+    for(size_t i=0; i < trackedObjects.size(); i++) {
+        ObjectStatus status;
+        Rect r=calcTrackedObjectPositionToShow((int)i, status);
+        result.push_back(ExtObject(trackedObjects[i].id, r, status));
+        LOGD("DetectionBasedTracker::process: found a object with SIZE %d x %d, rect={%d, %d, %d x %d}, status = %d", r.width, r.height, r.x, r.y, r.width, r.height, (int)status);
+    }
+}
+
+bool cv::DetectionBasedTracker::run()
+{
+    if (separateDetectionWork) {
+        return separateDetectionWork->run();
+    }
+    return false;
+}
+
+void cv::DetectionBasedTracker::stop()
+{
+    if (separateDetectionWork) {
+        separateDetectionWork->stop();
+    }
+}
+
+void cv::DetectionBasedTracker::resetTracking()
+{
+    if (separateDetectionWork) {
+        separateDetectionWork->resetTracking();
+    }
+    trackedObjects.clear();
+}
+
+void cv::DetectionBasedTracker::updateTrackedObjects(const std::vector<Rect>& detectedObjects)
+{
+    enum {
+        NEW_RECTANGLE=-1,
+        INTERSECTED_RECTANGLE=-2
+    };
+
+    int N1=(int)trackedObjects.size();
+    int N2=(int)detectedObjects.size();
+    LOGD("DetectionBasedTracker::updateTrackedObjects: N1=%d, N2=%d", N1, N2);
+
+    for(int i=0; i < N1; i++) {
+        trackedObjects[i].numDetectedFrames++;
+    }
+
+    std::vector<int> correspondence(detectedObjects.size(), NEW_RECTANGLE);
+    correspondence.clear();
+    correspondence.resize(detectedObjects.size(), NEW_RECTANGLE);
+
+    for(int i=0; i < N1; i++) {
+        LOGD("DetectionBasedTracker::updateTrackedObjects: i=%d", i);
+        TrackedObject& curObject=trackedObjects[i];
+
+        int bestIndex=-1;
+        int bestArea=-1;
+
+        int numpositions=(int)curObject.lastPositions.size();
+        CV_Assert(numpositions > 0);
+        Rect prevRect=curObject.lastPositions[numpositions-1];
+        LOGD("DetectionBasedTracker::updateTrackedObjects: prevRect[%d]={%d, %d, %d x %d}", i, prevRect.x, prevRect.y, prevRect.width, prevRect.height);
+
+        for(int j=0; j < N2; j++) {
+            LOGD("DetectionBasedTracker::updateTrackedObjects: j=%d", j);
+            if (correspondence[j] >= 0) {
+                LOGD("DetectionBasedTracker::updateTrackedObjects: j=%d is rejected, because it has correspondence=%d", j, correspondence[j]);
+                continue;
+            }
+            if (correspondence[j] !=NEW_RECTANGLE) {
+                LOGD("DetectionBasedTracker::updateTrackedObjects: j=%d is rejected, because it is intersected with another rectangle", j);
+                continue;
+            }
+            LOGD("DetectionBasedTracker::updateTrackedObjects: detectedObjects[%d]={%d, %d, %d x %d}",
+                    j, detectedObjects[j].x, detectedObjects[j].y, detectedObjects[j].width, detectedObjects[j].height);
+
+            Rect r=prevRect & detectedObjects[j];
+            if ( (r.width > 0) && (r.height > 0) ) {
+                LOGD("DetectionBasedTracker::updateTrackedObjects: There is intersection between prevRect and detectedRect, r={%d, %d, %d x %d}",
+                        r.x, r.y, r.width, r.height);
+                correspondence[j]=INTERSECTED_RECTANGLE;
+
+                if ( r.area() > bestArea) {
+                    LOGD("DetectionBasedTracker::updateTrackedObjects: The area of intersection is %d, it is better than bestArea=%d", r.area(), bestArea);
+                    bestIndex=j;
+                    bestArea=r.area();
+                }
+            }
+        }
+        if (bestIndex >= 0) {
+            LOGD("DetectionBasedTracker::updateTrackedObjects: The best correspondence for i=%d is j=%d", i, bestIndex);
+            correspondence[bestIndex]=i;
+
+            for(int j=0; j < N2; j++) {
+                if (correspondence[j] >= 0)
+                    continue;
+
+                Rect r=detectedObjects[j] & detectedObjects[bestIndex];
+                if ( (r.width > 0) && (r.height > 0) ) {
+                    LOGD("DetectionBasedTracker::updateTrackedObjects: Found intersection between "
+                            "rectangles j=%d and bestIndex=%d, rectangle j=%d is marked as intersected", j, bestIndex, j);
+                    correspondence[j]=INTERSECTED_RECTANGLE;
+                }
+            }
+        } else {
+            LOGD("DetectionBasedTracker::updateTrackedObjects: There is no correspondence for i=%d ", i);
+            curObject.numFramesNotDetected++;
+        }
+    }
+
+    LOGD("DetectionBasedTracker::updateTrackedObjects: start second cycle");
+    for(int j=0; j < N2; j++) {
+        LOGD("DetectionBasedTracker::updateTrackedObjects: j=%d", j);
+        int i=correspondence[j];
+        if (i >= 0) {//add position
+            LOGD("DetectionBasedTracker::updateTrackedObjects: add position");
+            trackedObjects[i].lastPositions.push_back(detectedObjects[j]);
+            while ((int)trackedObjects[i].lastPositions.size() > (int) innerParameters.numLastPositionsToTrack) {
+                trackedObjects[i].lastPositions.erase(trackedObjects[i].lastPositions.begin());
+            }
+            trackedObjects[i].numFramesNotDetected=0;
+        } else if (i==NEW_RECTANGLE){ //new object
+            LOGD("DetectionBasedTracker::updateTrackedObjects: new object");
+            trackedObjects.push_back(detectedObjects[j]);
+        } else {
+            LOGD("DetectionBasedTracker::updateTrackedObjects: was auxiliary intersection");
+        }
+    }
+
+    std::vector<TrackedObject>::iterator it=trackedObjects.begin();
+    while( it != trackedObjects.end() ) {
+        if ( (it->numFramesNotDetected > parameters.maxTrackLifetime)
+                ||
+                (
+                 (it->numDetectedFrames <= innerParameters.numStepsToWaitBeforeFirstShow)
+                 &&
+                 (it->numFramesNotDetected > innerParameters.numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown)
+                )
+           )
+        {
+            int numpos=(int)it->lastPositions.size();
+            CV_Assert(numpos > 0);
+            Rect r = it->lastPositions[numpos-1];
+            (void)(r);
+            LOGD("DetectionBasedTracker::updateTrackedObjects: deleted object {%d, %d, %d x %d}",
+                    r.x, r.y, r.width, r.height);
+            it=trackedObjects.erase(it);
+        } else {
+            it++;
+        }
+    }
+}
+
+int cv::DetectionBasedTracker::addObject(const Rect& location)
+{
+    LOGD("DetectionBasedTracker::addObject: new object {%d, %d %dx%d}",location.x, location.y, location.width, location.height);
+    trackedObjects.push_back(TrackedObject(location));
+    int newId = trackedObjects.back().id;
+    LOGD("DetectionBasedTracker::addObject: newId = %d", newId);
+    return newId;
+}
+
+Rect cv::DetectionBasedTracker::calcTrackedObjectPositionToShow(int i) const
+{
+    ObjectStatus status;
+    return calcTrackedObjectPositionToShow(i, status);
+}
+Rect cv::DetectionBasedTracker::calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const
+{
+    if ( (i < 0) || (i >= (int)trackedObjects.size()) ) {
+        LOGE("DetectionBasedTracker::calcTrackedObjectPositionToShow: ERROR: wrong i=%d", i);
+        status = WRONG_OBJECT;
+        return Rect();
+    }
+    if (trackedObjects[i].numDetectedFrames <= innerParameters.numStepsToWaitBeforeFirstShow){
+        LOGI("DetectionBasedTracker::calcTrackedObjectPositionToShow: trackedObjects[%d].numDetectedFrames=%d <= numStepsToWaitBeforeFirstShow=%d --- return empty Rect()",
+                i, trackedObjects[i].numDetectedFrames, innerParameters.numStepsToWaitBeforeFirstShow);
+        status = DETECTED_NOT_SHOWN_YET;
+        return Rect();
+    }
+    if (trackedObjects[i].numFramesNotDetected > innerParameters.numStepsToShowWithoutDetecting) {
+        status = DETECTED_TEMPORARY_LOST;
+        return Rect();
+    }
+
+    const TrackedObject::PositionsVector& lastPositions=trackedObjects[i].lastPositions;
+
+    int N=(int)lastPositions.size();
+    if (N<=0) {
+        LOGE("DetectionBasedTracker::calcTrackedObjectPositionToShow: ERROR: no positions for i=%d", i);
+        status = WRONG_OBJECT;
+        return Rect();
+    }
+
+    int Nsize=std::min(N, (int)weightsSizesSmoothing.size());
+    int Ncenter= std::min(N, (int)weightsPositionsSmoothing.size());
+
+    Point2f center;
+    double w=0, h=0;
+    if (Nsize > 0) {
+        double sum=0;
+        for(int j=0; j < Nsize; j++) {
+            int k=N-j-1;
+            w += lastPositions[k].width  * weightsSizesSmoothing[j];
+            h += lastPositions[k].height * weightsSizesSmoothing[j];
+            sum+=weightsSizesSmoothing[j];
+        }
+        w /= sum;
+        h /= sum;
+    } else {
+        w=lastPositions[N-1].width;
+        h=lastPositions[N-1].height;
+    }
+
+    if (Ncenter > 0) {
+        double sum=0;
+        for(int j=0; j < Ncenter; j++) {
+            int k=N-j-1;
+            Point tl(lastPositions[k].tl());
+            Point br(lastPositions[k].br());
+            Point2f c1;
+            c1=tl;
+            c1=c1* 0.5f;
+            Point2f c2;
+            c2=br;
+            c2=c2*0.5f;
+            c1=c1+c2;
+
+            center=center+  (c1  * weightsPositionsSmoothing[j]);
+            sum+=weightsPositionsSmoothing[j];
+        }
+        center *= (float)(1 / sum);
+    } else {
+        int k=N-1;
+        Point tl(lastPositions[k].tl());
+        Point br(lastPositions[k].br());
+        Point2f c1;
+        c1=tl;
+        c1=c1* 0.5f;
+        Point2f c2;
+        c2=br;
+        c2=c2*0.5f;
+
+        center=c1+c2;
+    }
+    Point2f tl=center-Point2f((float)w*0.5f,(float)h*0.5f);
+    Rect res(cvRound(tl.x), cvRound(tl.y), cvRound(w), cvRound(h));
+    LOGD("DetectionBasedTracker::calcTrackedObjectPositionToShow: Result for i=%d: {%d, %d, %d x %d}", i, res.x, res.y, res.width, res.height);
+
+    status = DETECTED;
+    return res;
+}
+
+void cv::DetectionBasedTracker::detectInRegion(const Mat& img, const Rect& r, std::vector<Rect>& detectedObjectsInRegions)
+{
+    Rect r0(Point(), img.size());
+    Rect r1 = scale_rect(r, innerParameters.coeffTrackingWindowSize);
+    r1 = r1 & r0;
+
+    if ( (r1.width <=0) || (r1.height <= 0) ) {
+        LOGD("DetectionBasedTracker::detectInRegion: Empty intersection");
+        return;
+    }
+
+    int d = cvRound(std::min(r.width, r.height) * innerParameters.coeffObjectSizeToTrack);
+
+    std::vector<Rect> tmpobjects;
+
+    Mat img1(img, r1);//subimage for rectangle -- without data copying
+    LOGD("DetectionBasedTracker::detectInRegion: img1.size()=%d x %d, d=%d",
+            img1.size().width, img1.size().height, d);
+
+    cascadeForTracking->setMinObjectSize(Size(d, d));
+    cascadeForTracking->detect(img1, tmpobjects);
+            /*
+            detectMultiScale( img1, tmpobjects,
+            parameters.scaleFactor, parameters.minNeighbors, 0
+            |CV_HAAR_FIND_BIGGEST_OBJECT
+            |CV_HAAR_SCALE_IMAGE
+            ,
+            Size(d,d),
+            max_objectSize
+            );*/
+
+    for(size_t i=0; i < tmpobjects.size(); i++) {
+        Rect curres(tmpobjects[i].tl() + r1.tl(), tmpobjects[i].size());
+        detectedObjectsInRegions.push_back(curres);
+    }
+}
+
+bool cv::DetectionBasedTracker::setParameters(const Parameters& params)
+{
+    if ( params.maxTrackLifetime < 0 )
+    {
+        LOGE("DetectionBasedTracker::setParameters: ERROR: wrong parameters value");
+        return false;
+    }
+
+    if (separateDetectionWork) {
+        separateDetectionWork->setParameters(params);
+    }
+    parameters=params;
+    return true;
+}
+
+const cv::DetectionBasedTracker::Parameters& DetectionBasedTracker::getParameters() const
+{
+    return parameters;
+}

modules/objdetect/src/main.cpp

@@ -0,0 +1,52 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+//
+// Library initialization file
+//
+
+#include "precomp.hpp"
+
+IPP_INITIALIZER_AUTO
+
+/* End of file. */

modules/objdetect/src/opencl/cascadedetect.cl

@@ -0,0 +1,661 @@
+///////////////////////////// OpenCL kernels for face detection //////////////////////////////
+////////////////////////////// see the opencv/doc/license.txt ///////////////////////////////
+
+//
+// the code has been derived from the OpenCL Haar cascade kernel by
+//
+//    Niko Li, newlife20080214@gmail.com
+//    Wang Weiyan, wangweiyanster@gmail.com
+//    Jia Haipeng, jiahaipeng95@gmail.com
+//    Nathan, liujun@multicorewareinc.com
+//    Peng Xiao, pengxiao@outlook.com
+//    Erping Pang, erping@multicorewareinc.com
+//
+
+#ifdef HAAR
+typedef struct __attribute__((aligned(4))) OptHaarFeature
+{
+    int4 ofs[3] __attribute__((aligned (4)));
+    float4 weight __attribute__((aligned (4)));
+}
+OptHaarFeature;
+#endif
+
+#ifdef LBP
+typedef struct __attribute__((aligned(4))) OptLBPFeature
+{
+    int16 ofs __attribute__((aligned (4)));
+}
+OptLBPFeature;
+#endif
+
+typedef struct __attribute__((aligned(4))) Stump
+{
+    float4 st __attribute__((aligned (4)));
+}
+Stump;
+
+typedef struct __attribute__((aligned(4))) Node
+{
+    int4 n __attribute__((aligned (4)));
+}
+Node;
+
+typedef struct __attribute__((aligned (4))) Stage
+{
+    int first __attribute__((aligned (4)));
+    int ntrees __attribute__((aligned (4)));
+    float threshold __attribute__((aligned (4)));
+}
+Stage;
+
+typedef struct __attribute__((aligned (4))) ScaleData
+{
+    float scale __attribute__((aligned (4)));
+    int szi_width __attribute__((aligned (4)));
+    int szi_height __attribute__((aligned (4)));
+    int layer_ofs __attribute__((aligned (4)));
+    int ystep __attribute__((aligned (4)));
+}
+ScaleData;
+
+#ifndef SUM_BUF_SIZE
+#define SUM_BUF_SIZE 0
+#endif
+
+#ifndef NODE_COUNT
+#define NODE_COUNT 1
+#endif
+
+#ifdef HAAR
+__kernel __attribute__((reqd_work_group_size(LOCAL_SIZE_X,LOCAL_SIZE_Y,1)))
+void runHaarClassifier(
+    int nscales, __global const ScaleData* scaleData,
+    __global const int* sum,
+    int _sumstep, int sumoffset,
+    __global const OptHaarFeature* optfeatures,
+    __global const Stage* stages,
+    __global const Node* nodes,
+    __global const float* leaves0,
+
+    volatile __global int* facepos,
+    int4 normrect, int sqofs, int2 windowsize)
+{
+    int lx = get_local_id(0);
+    int ly = get_local_id(1);
+    int groupIdx = get_group_id(0);
+    int i, ngroups = get_global_size(0)/LOCAL_SIZE_X;
+    int scaleIdx, tileIdx, stageIdx;
+    int sumstep = (int)(_sumstep/sizeof(int));
+    int4 nofs0 = (int4)(mad24(normrect.y, sumstep, normrect.x),
+                        mad24(normrect.y, sumstep, normrect.x + normrect.z),
+                        mad24(normrect.y + normrect.w, sumstep, normrect.x),
+                        mad24(normrect.y + normrect.w, sumstep, normrect.x + normrect.z));
+    int normarea = normrect.z * normrect.w;
+    float invarea = 1.f/normarea;
+    int lidx = ly*LOCAL_SIZE_X + lx;
+
+    #if SUM_BUF_SIZE > 0
+    int4 nofs = (int4)(mad24(normrect.y, SUM_BUF_STEP, normrect.x),
+                       mad24(normrect.y, SUM_BUF_STEP, normrect.x + normrect.z),
+                       mad24(normrect.y + normrect.w, SUM_BUF_STEP, normrect.x),
+                       mad24(normrect.y + normrect.w, SUM_BUF_STEP, normrect.x + normrect.z));
+    #else
+    int4 nofs = nofs0;
+    #endif
+    #define LOCAL_SIZE (LOCAL_SIZE_X*LOCAL_SIZE_Y)
+    __local int lstore[SUM_BUF_SIZE + LOCAL_SIZE*5/2+1];
+    #if SUM_BUF_SIZE > 0
+    __local int* ibuf = lstore;
+    __local int* lcount = ibuf + SUM_BUF_SIZE;
+    #else
+    __local int* lcount = lstore;
+    #endif
+    __local float* lnf = (__local float*)(lcount + 1);
+    __local float* lpartsum = lnf + LOCAL_SIZE;
+    __local short* lbuf = (__local short*)(lpartsum + LOCAL_SIZE);
+
+    for( scaleIdx = nscales-1; scaleIdx >= 0; scaleIdx-- )
+    {
+        __global const ScaleData* s = scaleData + scaleIdx;
+        int ystep = s->ystep;
+        int2 worksize = (int2)(max(s->szi_width - windowsize.x, 0), max(s->szi_height - windowsize.y, 0));
+        int2 ntiles = (int2)((worksize.x + LOCAL_SIZE_X-1)/LOCAL_SIZE_X,
+                             (worksize.y + LOCAL_SIZE_Y-1)/LOCAL_SIZE_Y);
+        int totalTiles = ntiles.x*ntiles.y;
+
+        for( tileIdx = groupIdx; tileIdx < totalTiles; tileIdx += ngroups )
+        {
+            int ix0 = (tileIdx % ntiles.x)*LOCAL_SIZE_X;
+            int iy0 = (tileIdx / ntiles.x)*LOCAL_SIZE_Y;
+            int ix = lx, iy = ly;
+            __global const int* psum0 = sum + mad24(iy0, sumstep, ix0) + s->layer_ofs;
+            __global const int* psum1 = psum0 + mad24(iy, sumstep, ix);
+
+            if( ix0 >= worksize.x || iy0 >= worksize.y )
+                continue;
+            #if SUM_BUF_SIZE > 0
+            for( i = lidx*4; i < SUM_BUF_SIZE; i += LOCAL_SIZE_X*LOCAL_SIZE_Y*4 )
+            {
+                int dy = i/SUM_BUF_STEP, dx = i - dy*SUM_BUF_STEP;
+                vstore4(vload4(0, psum0 + mad24(dy, sumstep, dx)), 0, ibuf+i);
+            }
+            #endif
+
+            if( lidx == 0 )
+                lcount[0] = 0;
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            if( ix0 + ix < worksize.x && iy0 + iy < worksize.y )
+            {
+                #if NODE_COUNT==1
+                __global const Stump* stump = (__global const Stump*)nodes;
+                #else
+                __global const Node* node = nodes;
+                __global const float* leaves = leaves0;
+                #endif
+                #if SUM_BUF_SIZE > 0
+                __local const int* psum = ibuf + mad24(iy, SUM_BUF_STEP, ix);
+                #else
+                __global const int* psum = psum1;
+                #endif
+
+                __global const int* psqsum = (__global const int*)(psum1 + sqofs);
+                float sval = (psum[nofs.x] - psum[nofs.y] - psum[nofs.z] + psum[nofs.w])*invarea;
+                float sqval = (psqsum[nofs0.x] - psqsum[nofs0.y] - psqsum[nofs0.z] + psqsum[nofs0.w])*invarea;
+                float nf = (float)normarea * sqrt(max(sqval - sval * sval, 0.f));
+                nf = nf > 0 ? nf : 1.f;
+
+                for( stageIdx = 0; stageIdx < SPLIT_STAGE; stageIdx++ )
+                {
+                    int ntrees = stages[stageIdx].ntrees;
+                    float s = 0.f;
+                    #if NODE_COUNT==1
+                    for( i = 0; i < ntrees; i++ )
+                    {
+                        float4 st = stump[i].st;
+                        __global const OptHaarFeature* f = optfeatures + as_int(st.x);
+                        float4 weight = f->weight;
+
+                        int4 ofs = f->ofs[0];
+                        sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x;
+                        ofs = f->ofs[1];
+                        sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval);
+                        if( weight.z > 0 )
+                        {
+                            ofs = f->ofs[2];
+                            sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval);
+                        }
+
+                        s += (sval < st.y*nf) ? st.z : st.w;
+                    }
+                    stump += ntrees;
+                    #else
+                    for( i = 0; i < ntrees; i++, node += NODE_COUNT, leaves += NODE_COUNT+1 )
+                    {
+                        int idx = 0;
+                        do
+                        {
+                            int4 n = node[idx].n;
+                            __global const OptHaarFeature* f = optfeatures + n.x;
+                            float4 weight = f->weight;
+
+                            int4 ofs = f->ofs[0];
+
+                            sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x;
+                            ofs = f->ofs[1];
+                            sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval);
+                            if( weight.z > 0 )
+                            {
+                                ofs = f->ofs[2];
+                                sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval);
+                            }
+
+                            idx = (sval < as_float(n.y)*nf) ? n.z : n.w;
+                        }
+                        while(idx > 0);
+                        s += leaves[-idx];
+                    }
+                    #endif
+
+                    if( s < stages[stageIdx].threshold )
+                        break;
+                }
+
+                if( stageIdx == SPLIT_STAGE && (ystep == 1 || ((ix | iy) & 1) == 0) )
+                {
+                    int count = atomic_inc(lcount);
+                    lbuf[count] = (int)(ix | (iy << 8));
+                    lnf[count] = nf;
+                }
+            }
+
+            for( stageIdx = SPLIT_STAGE; stageIdx < N_STAGES; stageIdx++ )
+            {
+                barrier(CLK_LOCAL_MEM_FENCE);
+                int nrects = lcount[0];
+
+                if( nrects == 0 )
+                    break;
+                barrier(CLK_LOCAL_MEM_FENCE);
+                if( lidx == 0 )
+                    lcount[0] = 0;
+
+                {
+                    #if NODE_COUNT == 1
+                    __global const Stump* stump = (__global const Stump*)nodes + stages[stageIdx].first;
+                    #else
+                    __global const Node* node = nodes + stages[stageIdx].first*NODE_COUNT;
+                    __global const float* leaves = leaves0 + stages[stageIdx].first*(NODE_COUNT+1);
+                    #endif
+                    int nparts = LOCAL_SIZE / nrects;
+                    int ntrees = stages[stageIdx].ntrees;
+                    int ntrees_p = (ntrees + nparts - 1)/nparts;
+                    int nr = lidx / nparts;
+                    int partidx = -1, idxval = 0;
+                    float partsum = 0.f, nf = 0.f;
+
+                    if( nr < nrects )
+                    {
+                        partidx = lidx % nparts;
+                        idxval = lbuf[nr];
+                        nf = lnf[nr];
+
+                        {
+                        int ntrees0 = ntrees_p*partidx;
+                        int ntrees1 = min(ntrees0 + ntrees_p, ntrees);
+                        int ix1 = idxval & 255, iy1 = idxval >> 8;
+                        #if SUM_BUF_SIZE > 0
+                        __local const int* psum = ibuf + mad24(iy1, SUM_BUF_STEP, ix1);
+                        #else
+                        __global const int* psum = psum0 + mad24(iy1, sumstep, ix1);
+                        #endif
+
+                        #if NODE_COUNT == 1
+                        for( i = ntrees0; i < ntrees1; i++ )
+                        {
+                            float4 st = stump[i].st;
+                            __global const OptHaarFeature* f = optfeatures + as_int(st.x);
+                            float4 weight = f->weight;
+
+                            int4 ofs = f->ofs[0];
+                            float sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x;
+                            ofs = f->ofs[1];
+                            sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval);
+                            //if( weight.z > 0 )
+                            if( fabs(weight.z) > 0 )
+                            {
+                                ofs = f->ofs[2];
+                                sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval);
+                            }
+
+                            partsum += (sval < st.y*nf) ? st.z : st.w;
+                        }
+                        #else
+                        for( i = ntrees0; i < ntrees1; i++ )
+                        {
+                            int idx = 0;
+                            do
+                            {
+                                int4 n = node[i*2 + idx].n;
+                                __global const OptHaarFeature* f = optfeatures + n.x;
+                                float4 weight = f->weight;
+                                int4 ofs = f->ofs[0];
+
+                                float sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x;
+                                ofs = f->ofs[1];
+                                sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval);
+                                if( weight.z > 0 )
+                                {
+                                    ofs = f->ofs[2];
+                                    sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval);
+                                }
+
+                                idx = (sval < as_float(n.y)*nf) ? n.z : n.w;
+                            }
+                            while(idx > 0);
+                            partsum += leaves[i*3-idx];
+                        }
+                        #endif
+                        }
+                    }
+                    lpartsum[lidx] = partsum;
+                    barrier(CLK_LOCAL_MEM_FENCE);
+
+                    if( partidx == 0 )
+                    {
+                        float s = lpartsum[nr*nparts];
+                        for( i = 1; i < nparts; i++ )
+                            s += lpartsum[i + nr*nparts];
+                        if( s >= stages[stageIdx].threshold )
+                        {
+                            int count = atomic_inc(lcount);
+                            lbuf[count] = idxval;
+                            lnf[count] = nf;
+                        }
+                    }
+                }
+            }
+
+            barrier(CLK_LOCAL_MEM_FENCE);
+            if( stageIdx == N_STAGES )
+            {
+                int nrects = lcount[0];
+                if( lidx < nrects )
+                {
+                    int nfaces = atomic_inc(facepos);
+                    if( nfaces < MAX_FACES )
+                    {
+                        volatile __global int* face = facepos + 1 + nfaces*3;
+                        int val = lbuf[lidx];
+                        face[0] = scaleIdx;
+                        face[1] = ix0 + (val & 255);
+                        face[2] = iy0 + (val >> 8);
+                    }
+                }
+            }
+        }
+    }
+}
+#endif
+
+#ifdef LBP
+#undef CALC_SUM_OFS_
+#define CALC_SUM_OFS_(p0, p1, p2, p3, ptr) \
+    ((ptr)[p0] - (ptr)[p1] - (ptr)[p2] + (ptr)[p3])
+
+__kernel void runLBPClassifierStumpSimple(
+    int nscales, __global const ScaleData* scaleData,
+    __global const int* sum,
+    int _sumstep, int sumoffset,
+    __global const OptLBPFeature* optfeatures,
+    __global const Stage* stages,
+    __global const Stump* stumps,
+    __global const int* bitsets,
+    int bitsetSize,
+
+    volatile __global int* facepos,
+    int2 windowsize)
+{
+    int lx = get_local_id(0);
+    int ly = get_local_id(1);
+    int local_size_x = get_local_size(0);
+    int local_size_y = get_local_size(1);
+    int groupIdx = get_group_id(1)*get_num_groups(0) + get_group_id(0);
+    int ngroups = get_num_groups(0)*get_num_groups(1);
+    int scaleIdx, tileIdx, stageIdx;
+    int sumstep = (int)(_sumstep/sizeof(int));
+
+    for( scaleIdx = nscales-1; scaleIdx >= 0; scaleIdx-- )
+    {
+        __global const ScaleData* s = scaleData + scaleIdx;
+        int ystep = s->ystep;
+        int2 worksize = (int2)(max(s->szi_width - windowsize.x, 0), max(s->szi_height - windowsize.y, 0));
+        int2 ntiles = (int2)((worksize.x/ystep + local_size_x-1)/local_size_x,
+                             (worksize.y/ystep + local_size_y-1)/local_size_y);
+        int totalTiles = ntiles.x*ntiles.y;
+
+        for( tileIdx = groupIdx; tileIdx < totalTiles; tileIdx += ngroups )
+        {
+            int iy = mad24((tileIdx / ntiles.x), local_size_y, ly) * ystep;
+            int ix = mad24((tileIdx % ntiles.x), local_size_x, lx) * ystep;
+
+            if( ix < worksize.x && iy < worksize.y )
+            {
+                __global const int* p = sum + mad24(iy, sumstep, ix) + s->layer_ofs;
+                __global const Stump* stump = stumps;
+                __global const int* bitset = bitsets;
+
+                for( stageIdx = 0; stageIdx < N_STAGES; stageIdx++ )
+                {
+                    int i, ntrees = stages[stageIdx].ntrees;
+                    float s = 0.f;
+                    for( i = 0; i < ntrees; i++, stump++, bitset += bitsetSize )
+                    {
+                        float4 st = stump->st;
+                        __global const OptLBPFeature* f = optfeatures + as_int(st.x);
+                        int16 ofs = f->ofs;
+
+                        int cval = CALC_SUM_OFS_( ofs.s5, ofs.s6, ofs.s9, ofs.sa, p );
+
+                        int mask, idx = (CALC_SUM_OFS_( ofs.s0, ofs.s1, ofs.s4, ofs.s5, p ) >= cval ? 4 : 0); // 0
+                        idx |= (CALC_SUM_OFS_( ofs.s1, ofs.s2, ofs.s5, ofs.s6, p ) >= cval ? 2 : 0); // 1
+                        idx |= (CALC_SUM_OFS_( ofs.s2, ofs.s3, ofs.s6, ofs.s7, p ) >= cval ? 1 : 0); // 2
+
+                        mask = (CALC_SUM_OFS_( ofs.s6, ofs.s7, ofs.sa, ofs.sb, p ) >= cval ? 16 : 0); // 5
+                        mask |= (CALC_SUM_OFS_( ofs.sa, ofs.sb, ofs.se, ofs.sf, p ) >= cval ? 8 : 0);  // 8
+                        mask |= (CALC_SUM_OFS_( ofs.s9, ofs.sa, ofs.sd, ofs.se, p ) >= cval ? 4 : 0);  // 7
+                        mask |= (CALC_SUM_OFS_( ofs.s8, ofs.s9, ofs.sc, ofs.sd, p ) >= cval ? 2 : 0);  // 6
+                        mask |= (CALC_SUM_OFS_( ofs.s4, ofs.s5, ofs.s8, ofs.s9, p ) >= cval ? 1 : 0);  // 7
+
+                        s += (bitset[idx] & (1 << mask)) ? st.z : st.w;
+                    }
+
+                    if( s < stages[stageIdx].threshold )
+                        break;
+                }
+
+                if( stageIdx == N_STAGES )
+                {
+                    int nfaces = atomic_inc(facepos);
+                    if( nfaces < MAX_FACES )
+                    {
+                        volatile __global int* face = facepos + 1 + nfaces*3;
+                        face[0] = scaleIdx;
+                        face[1] = ix;
+                        face[2] = iy;
+                    }
+                }
+            }
+        }
+    }
+}
+
+__kernel __attribute__((reqd_work_group_size(LOCAL_SIZE_X,LOCAL_SIZE_Y,1)))
+void runLBPClassifierStump(
+    int nscales, __global const ScaleData* scaleData,
+    __global const int* sum,
+    int _sumstep, int sumoffset,
+    __global const OptLBPFeature* optfeatures,
+    __global const Stage* stages,
+    __global const Stump* stumps,
+    __global const int* bitsets,
+    int bitsetSize,
+
+    volatile __global int* facepos,
+    int2 windowsize)
+{
+    int lx = get_local_id(0);
+    int ly = get_local_id(1);
+    int groupIdx = get_group_id(0);
+    int i, ngroups = get_global_size(0)/LOCAL_SIZE_X;
+    int scaleIdx, tileIdx, stageIdx;
+    int sumstep = (int)(_sumstep/sizeof(int));
+    int lidx = ly*LOCAL_SIZE_X + lx;
+
+    #define LOCAL_SIZE (LOCAL_SIZE_X*LOCAL_SIZE_Y)
+    __local int lstore[SUM_BUF_SIZE + LOCAL_SIZE*3/2+1];
+    #if SUM_BUF_SIZE > 0
+    __local int* ibuf = lstore;
+    __local int* lcount = ibuf + SUM_BUF_SIZE;
+    #else
+    __local int* lcount = lstore;
+    #endif
+    __local float* lpartsum = (__local float*)(lcount + 1);
+    __local short* lbuf = (__local short*)(lpartsum + LOCAL_SIZE);
+
+    for( scaleIdx = nscales-1; scaleIdx >= 0; scaleIdx-- )
+    {
+        __global const ScaleData* s = scaleData + scaleIdx;
+        int ystep = s->ystep;
+        int2 worksize = (int2)(max(s->szi_width - windowsize.x, 0), max(s->szi_height - windowsize.y, 0));
+        int2 ntiles = (int2)((worksize.x + LOCAL_SIZE_X-1)/LOCAL_SIZE_X,
+                             (worksize.y + LOCAL_SIZE_Y-1)/LOCAL_SIZE_Y);
+        int totalTiles = ntiles.x*ntiles.y;
+
+        for( tileIdx = groupIdx; tileIdx < totalTiles; tileIdx += ngroups )
+        {
+            int ix0 = (tileIdx % ntiles.x)*LOCAL_SIZE_X;
+            int iy0 = (tileIdx / ntiles.x)*LOCAL_SIZE_Y;
+            int ix = lx, iy = ly;
+            __global const int* psum0 = sum + mad24(iy0, sumstep, ix0) + s->layer_ofs;
+
+            if( ix0 >= worksize.x || iy0 >= worksize.y )
+                continue;
+            #if SUM_BUF_SIZE > 0
+            for( i = lidx*4; i < SUM_BUF_SIZE; i += LOCAL_SIZE_X*LOCAL_SIZE_Y*4 )
+            {
+                int dy = i/SUM_BUF_STEP, dx = i - dy*SUM_BUF_STEP;
+                vstore4(vload4(0, psum0 + mad24(dy, sumstep, dx)), 0, ibuf+i);
+            }
+            barrier(CLK_LOCAL_MEM_FENCE);
+            #endif
+
+            if( lidx == 0 )
+                lcount[0] = 0;
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            if( ix0 + ix < worksize.x && iy0 + iy < worksize.y )
+            {
+                __global const Stump* stump = stumps;
+                __global const int* bitset = bitsets;
+                #if SUM_BUF_SIZE > 0
+                __local const int* p = ibuf + mad24(iy, SUM_BUF_STEP, ix);
+                #else
+                __global const int* p = psum0 + mad24(iy, sumstep, ix);
+                #endif
+
+                for( stageIdx = 0; stageIdx < SPLIT_STAGE; stageIdx++ )
+                {
+                    int ntrees = stages[stageIdx].ntrees;
+                    float s = 0.f;
+                    for( i = 0; i < ntrees; i++, stump++, bitset += bitsetSize )
+                    {
+                        float4 st = stump->st;
+                        __global const OptLBPFeature* f = optfeatures + as_int(st.x);
+                        int16 ofs = f->ofs;
+
+                        int cval = CALC_SUM_OFS_( ofs.s5, ofs.s6, ofs.s9, ofs.sa, p );
+
+                        int mask, idx = (CALC_SUM_OFS_( ofs.s0, ofs.s1, ofs.s4, ofs.s5, p ) >= cval ? 4 : 0); // 0
+                        idx |= (CALC_SUM_OFS_( ofs.s1, ofs.s2, ofs.s5, ofs.s6, p ) >= cval ? 2 : 0); // 1
+                        idx |= (CALC_SUM_OFS_( ofs.s2, ofs.s3, ofs.s6, ofs.s7, p ) >= cval ? 1 : 0); // 2
+
+                        mask = (CALC_SUM_OFS_( ofs.s6, ofs.s7, ofs.sa, ofs.sb, p ) >= cval ? 16 : 0); // 5
+                        mask |= (CALC_SUM_OFS_( ofs.sa, ofs.sb, ofs.se, ofs.sf, p ) >= cval ? 8 : 0);  // 8
+                        mask |= (CALC_SUM_OFS_( ofs.s9, ofs.sa, ofs.sd, ofs.se, p ) >= cval ? 4 : 0);  // 7
+                        mask |= (CALC_SUM_OFS_( ofs.s8, ofs.s9, ofs.sc, ofs.sd, p ) >= cval ? 2 : 0);  // 6
+                        mask |= (CALC_SUM_OFS_( ofs.s4, ofs.s5, ofs.s8, ofs.s9, p ) >= cval ? 1 : 0);  // 7
+
+                        s += (bitset[idx] & (1 << mask)) ? st.z : st.w;
+                    }
+
+                    if( s < stages[stageIdx].threshold )
+                        break;
+                }
+
+                if( stageIdx == SPLIT_STAGE && (ystep == 1 || ((ix | iy) & 1) == 0) )
+                {
+                    int count = atomic_inc(lcount);
+                    lbuf[count] = (int)(ix | (iy << 8));
+                }
+            }
+
+            for( stageIdx = SPLIT_STAGE; stageIdx < N_STAGES; stageIdx++ )
+            {
+                int nrects = lcount[0];
+
+                barrier(CLK_LOCAL_MEM_FENCE);
+                if( nrects == 0 )
+                    break;
+                if( lidx == 0 )
+                    lcount[0] = 0;
+
+                {
+                    __global const Stump* stump = stumps + stages[stageIdx].first;
+                    __global const int* bitset = bitsets + stages[stageIdx].first*bitsetSize;
+                    int nparts = LOCAL_SIZE / nrects;
+                    int ntrees = stages[stageIdx].ntrees;
+                    int ntrees_p = (ntrees + nparts - 1)/nparts;
+                    int nr = lidx / nparts;
+                    int partidx = -1, idxval = 0;
+                    float partsum = 0.f, nf = 0.f;
+
+                    if( nr < nrects )
+                    {
+                        partidx = lidx % nparts;
+                        idxval = lbuf[nr];
+
+                        {
+                            int ntrees0 = ntrees_p*partidx;
+                            int ntrees1 = min(ntrees0 + ntrees_p, ntrees);
+                            int ix1 = idxval & 255, iy1 = idxval >> 8;
+                            #if SUM_BUF_SIZE > 0
+                            __local const int* p = ibuf + mad24(iy1, SUM_BUF_STEP, ix1);
+                            #else
+                            __global const int* p = psum0 + mad24(iy1, sumstep, ix1);
+                            #endif
+
+                            for( i = ntrees0; i < ntrees1; i++ )
+                            {
+                                float4 st = stump[i].st;
+                                __global const OptLBPFeature* f = optfeatures + as_int(st.x);
+                                int16 ofs = f->ofs;
+
+                                #define CALC_SUM_OFS_(p0, p1, p2, p3, ptr) \
+                                    ((ptr)[p0] - (ptr)[p1] - (ptr)[p2] + (ptr)[p3])
+
+                                int cval = CALC_SUM_OFS_( ofs.s5, ofs.s6, ofs.s9, ofs.sa, p );
+
+                                int mask, idx = (CALC_SUM_OFS_( ofs.s0, ofs.s1, ofs.s4, ofs.s5, p ) >= cval ? 4 : 0); // 0
+                                idx |= (CALC_SUM_OFS_( ofs.s1, ofs.s2, ofs.s5, ofs.s6, p ) >= cval ? 2 : 0); // 1
+                                idx |= (CALC_SUM_OFS_( ofs.s2, ofs.s3, ofs.s6, ofs.s7, p ) >= cval ? 1 : 0); // 2
+
+                                mask = (CALC_SUM_OFS_( ofs.s6, ofs.s7, ofs.sa, ofs.sb, p ) >= cval ? 16 : 0); // 5
+                                mask |= (CALC_SUM_OFS_( ofs.sa, ofs.sb, ofs.se, ofs.sf, p ) >= cval ? 8 : 0);  // 8
+                                mask |= (CALC_SUM_OFS_( ofs.s9, ofs.sa, ofs.sd, ofs.se, p ) >= cval ? 4 : 0);  // 7
+                                mask |= (CALC_SUM_OFS_( ofs.s8, ofs.s9, ofs.sc, ofs.sd, p ) >= cval ? 2 : 0);  // 6
+                                mask |= (CALC_SUM_OFS_( ofs.s4, ofs.s5, ofs.s8, ofs.s9, p ) >= cval ? 1 : 0);  // 7
+
+                                partsum += (bitset[i*bitsetSize + idx] & (1 << mask)) ? st.z : st.w;
+                            }
+                        }
+                    }
+                    lpartsum[lidx] = partsum;
+                    barrier(CLK_LOCAL_MEM_FENCE);
+
+                    if( partidx == 0 )
+                    {
+                        float s = lpartsum[nr*nparts];
+                        for( i = 1; i < nparts; i++ )
+                            s += lpartsum[i + nr*nparts];
+                        if( s >= stages[stageIdx].threshold )
+                        {
+                            int count = atomic_inc(lcount);
+                            lbuf[count] = idxval;
+                        }
+                    }
+                }
+            }
+
+            barrier(CLK_LOCAL_MEM_FENCE);
+            if( stageIdx == N_STAGES )
+            {
+                int nrects = lcount[0];
+                if( lidx < nrects )
+                {
+                    int nfaces = atomic_inc(facepos);
+                    if( nfaces < MAX_FACES )
+                    {
+                        volatile __global int* face = facepos + 1 + nfaces*3;
+                        int val = lbuf[lidx];
+                        face[0] = scaleIdx;
+                        face[1] = ix0 + (val & 255);
+                        face[2] = iy0 + (val >> 8);
+                    }
+                }
+            }
+        }
+    }
+}
+#endif

modules/objdetect/src/opencl/objdetect_hog.cl

@@ -0,0 +1,633 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//    Wenju He, wenju@multicorewareinc.com
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors as is and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#define CELL_WIDTH 8
+#define CELL_HEIGHT 8
+#define CELLS_PER_BLOCK_X 2
+#define CELLS_PER_BLOCK_Y 2
+#define NTHREADS 256
+#define CV_PI_F M_PI_F
+
+#ifdef INTEL_DEVICE
+#define QANGLE_TYPE     int
+#define QANGLE_TYPE2    int2
+#else
+#define QANGLE_TYPE     uchar
+#define QANGLE_TYPE2    uchar2
+#endif
+
+//----------------------------------------------------------------------------
+// Histogram computation
+// 12 threads for a cell, 12x4 threads per block
+// Use pre-computed gaussian and interp_weight lookup tables
+__kernel void compute_hists_lut_kernel(
+    const int cblock_stride_x, const int cblock_stride_y,
+    const int cnbins, const int cblock_hist_size, const int img_block_width,
+    const int blocks_in_group, const int blocks_total,
+    const int grad_quadstep, const int qangle_step,
+    __global const float* grad, __global const QANGLE_TYPE* qangle,
+    __global const float* gauss_w_lut,
+    __global float* block_hists, __local float* smem)
+{
+    const int lx = get_local_id(0);
+    const int lp = lx / 24; /* local group id */
+    const int gid = get_group_id(0) * blocks_in_group + lp;/* global group id */
+    const int gidY = gid / img_block_width;
+    const int gidX = gid - gidY * img_block_width;
+
+    const int lidX = lx - lp * 24;
+    const int lidY = get_local_id(1);
+
+    const int cell_x = lidX / 12;
+    const int cell_y = lidY;
+    const int cell_thread_x = lidX - cell_x * 12;
+
+    __local float* hists = smem + lp * cnbins * (CELLS_PER_BLOCK_X *
+        CELLS_PER_BLOCK_Y * 12 + CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y);
+    __local float* final_hist = hists + cnbins *
+        (CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * 12);
+
+    const int offset_x = gidX * cblock_stride_x + (cell_x << 2) + cell_thread_x;
+    const int offset_y = gidY * cblock_stride_y + (cell_y << 2);
+
+    __global const float* grad_ptr = (gid < blocks_total) ?
+        grad + offset_y * grad_quadstep + (offset_x << 1) : grad;
+    __global const QANGLE_TYPE* qangle_ptr = (gid < blocks_total) ?
+        qangle + offset_y * qangle_step + (offset_x << 1) : qangle;
+
+    __local float* hist = hists + 12 * (cell_y * CELLS_PER_BLOCK_Y + cell_x) +
+        cell_thread_x;
+    for (int bin_id = 0; bin_id < cnbins; ++bin_id)
+        hist[bin_id * 48] = 0.f;
+
+    const int dist_x = -4 + cell_thread_x - 4 * cell_x;
+    const int dist_center_x = dist_x - 4 * (1 - 2 * cell_x);
+
+    const int dist_y_begin = -4 - 4 * lidY;
+    for (int dist_y = dist_y_begin; dist_y < dist_y_begin + 12; ++dist_y)
+    {
+        float2 vote = (float2) (grad_ptr[0], grad_ptr[1]);
+        QANGLE_TYPE2 bin = (QANGLE_TYPE2) (qangle_ptr[0], qangle_ptr[1]);
+
+        grad_ptr += grad_quadstep;
+        qangle_ptr += qangle_step;
+
+        int dist_center_y = dist_y - 4 * (1 - 2 * cell_y);
+
+        int idx = (dist_center_y + 8) * 16 + (dist_center_x + 8);
+        float gaussian = gauss_w_lut[idx];
+        idx = (dist_y + 8) * 16 + (dist_x + 8);
+        float interp_weight = gauss_w_lut[256+idx];
+
+        hist[bin.x * 48] += gaussian * interp_weight * vote.x;
+        hist[bin.y * 48] += gaussian * interp_weight * vote.y;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    volatile __local float* hist_ = hist;
+    for (int bin_id = 0; bin_id < cnbins; ++bin_id, hist_ += 48)
+    {
+        if (cell_thread_x < 6)
+            hist_[0] += hist_[6];
+        barrier(CLK_LOCAL_MEM_FENCE);
+        if (cell_thread_x < 3)
+            hist_[0] += hist_[3];
+        barrier(CLK_LOCAL_MEM_FENCE);
+        if (cell_thread_x == 0)
+            final_hist[(cell_x * 2 + cell_y) * cnbins + bin_id] =
+                hist_[0] + hist_[1] + hist_[2];
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    int tid = (cell_y * CELLS_PER_BLOCK_Y + cell_x) * 12 + cell_thread_x;
+    if ((tid < cblock_hist_size) && (gid < blocks_total))
+    {
+        __global float* block_hist = block_hists +
+            (gidY * img_block_width + gidX) * cblock_hist_size;
+        block_hist[tid] = final_hist[tid];
+    }
+}
+
+//-------------------------------------------------------------
+//  Normalization of histograms via L2Hys_norm
+//  optimized for the case of 9 bins
+__kernel void normalize_hists_36_kernel(__global float* block_hists,
+                                        const float threshold, __local float *squares)
+{
+    const int tid = get_local_id(0);
+    const int gid = get_global_id(0);
+    const int bid = tid / 36;      /* block-hist id, (0 - 6) */
+    const int boffset = bid * 36;  /* block-hist offset in the work-group */
+    const int hid = tid - boffset; /* histogram bin id, (0 - 35) */
+
+    float elem = block_hists[gid];
+    squares[tid] = elem * elem;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    __local float* smem = squares + boffset;
+    float sum = smem[hid];
+    if (hid < 18)
+        smem[hid] = sum = sum + smem[hid + 18];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (hid < 9)
+        smem[hid] = sum = sum + smem[hid + 9];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (hid < 4)
+        smem[hid] = sum + smem[hid + 4];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    sum = smem[0] + smem[1] + smem[2] + smem[3] + smem[8];
+
+    elem = elem / (sqrt(sum) + 3.6f);
+    elem = min(elem, threshold);
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+    squares[tid] = elem * elem;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    sum = smem[hid];
+    if (hid < 18)
+      smem[hid] = sum = sum + smem[hid + 18];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (hid < 9)
+        smem[hid] = sum = sum + smem[hid + 9];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (hid < 4)
+        smem[hid] = sum + smem[hid + 4];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    sum = smem[0] + smem[1] + smem[2] + smem[3] + smem[8];
+
+    block_hists[gid] = elem / (sqrt(sum) + 1e-3f);
+}
+
+//-------------------------------------------------------------
+//  Normalization of histograms via L2Hys_norm
+//
+inline float reduce_smem(volatile __local float* smem, int size)
+{
+    unsigned int tid = get_local_id(0);
+    float sum = smem[tid];
+
+    if (size >= 512) { if (tid < 256) smem[tid] = sum = sum + smem[tid + 256];
+        barrier(CLK_LOCAL_MEM_FENCE); }
+    if (size >= 256) { if (tid < 128) smem[tid] = sum = sum + smem[tid + 128];
+        barrier(CLK_LOCAL_MEM_FENCE); }
+    if (size >= 128) { if (tid < 64) smem[tid] = sum = sum + smem[tid + 64];
+        barrier(CLK_LOCAL_MEM_FENCE); }
+    if (size >= 64) { if (tid < 32) smem[tid] = sum = sum + smem[tid + 32];
+        barrier(CLK_LOCAL_MEM_FENCE); }
+    if (size >= 32) { if (tid < 16) smem[tid] = sum = sum + smem[tid + 16];
+        barrier(CLK_LOCAL_MEM_FENCE); }
+    if (size >= 16) { if (tid < 8) smem[tid] = sum = sum + smem[tid + 8];
+        barrier(CLK_LOCAL_MEM_FENCE); }
+    if (size >= 8) { if (tid < 4) smem[tid] = sum = sum + smem[tid + 4];
+        barrier(CLK_LOCAL_MEM_FENCE); }
+    if (size >= 4) { if (tid < 2) smem[tid] = sum = sum + smem[tid + 2];
+        barrier(CLK_LOCAL_MEM_FENCE); }
+    if (size >= 2) { if (tid < 1) smem[tid] = sum = sum + smem[tid + 1];
+        barrier(CLK_LOCAL_MEM_FENCE); }
+
+    return sum;
+}
+
+__kernel void normalize_hists_kernel(
+    const int nthreads, const int block_hist_size, const int img_block_width,
+    __global float* block_hists, const float threshold, __local float *squares)
+{
+    const int tid = get_local_id(0);
+    const int gidX = get_group_id(0);
+    const int gidY = get_group_id(1);
+
+    __global float* hist = block_hists + (gidY * img_block_width + gidX) *
+        block_hist_size + tid;
+
+    float elem = 0.f;
+    if (tid < block_hist_size)
+        elem = hist[0];
+
+    squares[tid] = elem * elem;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+    float sum = reduce_smem(squares, nthreads);
+
+    float scale = 1.0f / (sqrt(sum) + 0.1f * block_hist_size);
+    elem = min(elem * scale, threshold);
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+    squares[tid] = elem * elem;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+    sum = reduce_smem(squares, nthreads);
+    scale = 1.0f / (sqrt(sum) + 1e-3f);
+
+    if (tid < block_hist_size)
+        hist[0] = elem * scale;
+}
+
+#define reduce_with_sync(target, sharedMemory, localMemory, tid, offset) \
+    if (tid < target) sharedMemory[tid] = localMemory = localMemory + sharedMemory[tid + offset]; \
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+//---------------------------------------------------------------------
+//  Linear SVM based classification
+//  48x96 window, 9 bins and default parameters
+//  180 threads, each thread corresponds to a bin in a row
+__kernel void classify_hists_180_kernel(
+    const int cdescr_width, const int cdescr_height, const int cblock_hist_size,
+    const int img_win_width, const int img_block_width,
+    const int win_block_stride_x, const int win_block_stride_y,
+    __global const float * block_hists, __global const float* coefs,
+    float free_coef, float threshold, __global uchar* labels)
+{
+    const int tid = get_local_id(0);
+    const int gidX = get_group_id(0);
+    const int gidY = get_group_id(1);
+
+    __global const float* hist = block_hists + (gidY * win_block_stride_y *
+        img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
+
+    float product = 0.f;
+
+    for (int i = 0; i < cdescr_height; i++)
+    {
+        product += coefs[i * cdescr_width + tid] *
+            hist[i * img_block_width * cblock_hist_size + tid];
+    }
+
+    __local float products[180];
+
+    products[tid] = product;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    reduce_with_sync(90, products, product, tid, 90);
+    reduce_with_sync(45, products, product, tid, 45);
+    reduce_with_sync(13, products, product, tid, 32); // 13 is not typo
+    reduce_with_sync(16, products, product, tid, 16);
+    reduce_with_sync(8, products, product, tid, 8);
+    reduce_with_sync(4, products, product, tid, 4);
+    reduce_with_sync(2, products, product, tid, 2);
+
+    if (tid == 0){
+        product = product + products[tid + 1];
+        labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold);
+    }
+}
+
+//---------------------------------------------------------------------
+//  Linear SVM based classification
+//  64x128 window, 9 bins and default parameters
+//  256 threads, 252 of them are used
+__kernel void classify_hists_252_kernel(
+    const int cdescr_width, const int cdescr_height, const int cblock_hist_size,
+    const int img_win_width, const int img_block_width,
+    const int win_block_stride_x, const int win_block_stride_y,
+    __global const float * block_hists, __global const float* coefs,
+    float free_coef, float threshold, __global uchar* labels)
+{
+    const int tid = get_local_id(0);
+    const int gidX = get_group_id(0);
+    const int gidY = get_group_id(1);
+
+    __global const float* hist = block_hists + (gidY * win_block_stride_y *
+        img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
+
+    float product = 0.f;
+    if (tid < cdescr_width)
+    {
+        for (int i = 0; i < cdescr_height; i++)
+            product += coefs[i * cdescr_width + tid] *
+                hist[i * img_block_width * cblock_hist_size + tid];
+    }
+
+    __local float products[NTHREADS];
+
+    products[tid] = product;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    reduce_with_sync(128, products, product, tid, 128);
+    reduce_with_sync(64, products, product, tid, 64);
+    reduce_with_sync(32, products, product, tid, 32);
+    reduce_with_sync(16, products, product, tid, 16);
+    reduce_with_sync(8, products, product, tid, 8);
+    reduce_with_sync(4, products, product, tid, 4);
+    reduce_with_sync(2, products, product, tid, 2);
+
+    if (tid == 0){
+        product = product + products[tid + 1];
+        labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold);
+    }
+}
+
+//---------------------------------------------------------------------
+//  Linear SVM based classification
+//  256 threads
+__kernel void classify_hists_kernel(
+    const int cdescr_size, const int cdescr_width, const int cblock_hist_size,
+    const int img_win_width, const int img_block_width,
+    const int win_block_stride_x, const int win_block_stride_y,
+    __global const float * block_hists, __global const float* coefs,
+    float free_coef, float threshold, __global uchar* labels)
+{
+    const int tid = get_local_id(0);
+    const int gidX = get_group_id(0);
+    const int gidY = get_group_id(1);
+
+    __global const float* hist = block_hists + (gidY * win_block_stride_y *
+        img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
+
+    float product = 0.f;
+    for (int i = tid; i < cdescr_size; i += NTHREADS)
+    {
+        int offset_y = i / cdescr_width;
+        int offset_x = i - offset_y * cdescr_width;
+        product += coefs[i] *
+            hist[offset_y * img_block_width * cblock_hist_size + offset_x];
+    }
+
+    __local float products[NTHREADS];
+
+    products[tid] = product;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    reduce_with_sync(128, products, product, tid, 128);
+    reduce_with_sync(64, products, product, tid, 64);
+    reduce_with_sync(32, products, product, tid, 32);
+    reduce_with_sync(16, products, product, tid, 16);
+    reduce_with_sync(8, products, product, tid, 8);
+    reduce_with_sync(4, products, product, tid, 4);
+    reduce_with_sync(2, products, product, tid, 2);
+
+    if (tid == 0){
+        products[tid] = product = product + products[tid + 1];
+        labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold);
+    }
+}
+
+//----------------------------------------------------------------------------
+// Extract descriptors
+
+__kernel void extract_descrs_by_rows_kernel(
+    const int cblock_hist_size, const int descriptors_quadstep,
+    const int cdescr_size, const int cdescr_width, const int img_block_width,
+    const int win_block_stride_x, const int win_block_stride_y,
+    __global const float* block_hists, __global float* descriptors)
+{
+    int tid = get_local_id(0);
+    int gidX = get_group_id(0);
+    int gidY = get_group_id(1);
+
+    // Get left top corner of the window in src
+    __global const float* hist = block_hists + (gidY * win_block_stride_y *
+        img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
+
+    // Get left top corner of the window in dst
+    __global float* descriptor = descriptors +
+        (gidY * get_num_groups(0) + gidX) * descriptors_quadstep;
+
+    // Copy elements from src to dst
+    for (int i = tid; i < cdescr_size; i += NTHREADS)
+    {
+        int offset_y = i / cdescr_width;
+        int offset_x = i - offset_y * cdescr_width;
+        descriptor[i] = hist[offset_y * img_block_width * cblock_hist_size + offset_x];
+    }
+}
+
+__kernel void extract_descrs_by_cols_kernel(
+    const int cblock_hist_size, const int descriptors_quadstep, const int cdescr_size,
+    const int cnblocks_win_x, const int cnblocks_win_y, const int img_block_width,
+    const int win_block_stride_x, const int win_block_stride_y,
+    __global const float* block_hists, __global float* descriptors)
+{
+    int tid = get_local_id(0);
+    int gidX = get_group_id(0);
+    int gidY = get_group_id(1);
+
+    // Get left top corner of the window in src
+    __global const float* hist = block_hists +  (gidY * win_block_stride_y *
+        img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
+
+    // Get left top corner of the window in dst
+    __global float* descriptor = descriptors +
+        (gidY * get_num_groups(0) + gidX) * descriptors_quadstep;
+
+    // Copy elements from src to dst
+    for (int i = tid; i < cdescr_size; i += NTHREADS)
+    {
+        int block_idx = i / cblock_hist_size;
+        int idx_in_block = i - block_idx * cblock_hist_size;
+
+        int y = block_idx / cnblocks_win_x;
+        int x = block_idx - y * cnblocks_win_x;
+
+        descriptor[(x * cnblocks_win_y + y) * cblock_hist_size + idx_in_block] =
+            hist[(y * img_block_width  + x) * cblock_hist_size + idx_in_block];
+    }
+}
+
+//----------------------------------------------------------------------------
+// Gradients computation
+
+__kernel void compute_gradients_8UC4_kernel(
+    const int height, const int width,
+    const int img_step, const int grad_quadstep, const int qangle_step,
+    const __global uchar4 * img, __global float * grad, __global QANGLE_TYPE * qangle,
+    const float angle_scale, const char correct_gamma, const int cnbins)
+{
+    const int x = get_global_id(0);
+    const int tid = get_local_id(0);
+    const int gSizeX = get_local_size(0);
+    const int gidY = get_group_id(1);
+
+    __global const uchar4* row = img + gidY * img_step;
+
+    __local float sh_row[(NTHREADS + 2) * 3];
+
+    uchar4 val;
+    if (x < width)
+        val = row[x];
+    else
+        val = row[width - 2];
+
+    sh_row[tid + 1] = val.x;
+    sh_row[tid + 1 + (NTHREADS + 2)] = val.y;
+    sh_row[tid + 1 + 2 * (NTHREADS + 2)] = val.z;
+
+    if (tid == 0)
+    {
+        val = row[max(x - 1, 1)];
+        sh_row[0] = val.x;
+        sh_row[(NTHREADS + 2)] = val.y;
+        sh_row[2 * (NTHREADS + 2)] = val.z;
+    }
+
+    if (tid == gSizeX - 1)
+    {
+        val = row[min(x + 1, width - 2)];
+        sh_row[gSizeX + 1] = val.x;
+        sh_row[gSizeX + 1 + (NTHREADS + 2)] = val.y;
+        sh_row[gSizeX + 1 + 2 * (NTHREADS + 2)] = val.z;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (x < width)
+    {
+        float4 a = (float4) (sh_row[tid], sh_row[tid + (NTHREADS + 2)],
+            sh_row[tid + 2 * (NTHREADS + 2)], 0);
+        float4 b = (float4) (sh_row[tid + 2], sh_row[tid + 2 + (NTHREADS + 2)],
+            sh_row[tid + 2 + 2 * (NTHREADS + 2)], 0);
+
+        float4 dx;
+        if (correct_gamma == 1)
+            dx = sqrt(b) - sqrt(a);
+        else
+            dx = b - a;
+
+        float4 dy = (float4) 0.f;
+
+        if (gidY > 0 && gidY < height - 1)
+        {
+            a = convert_float4(img[(gidY - 1) * img_step + x].xyzw);
+            b = convert_float4(img[(gidY + 1) * img_step + x].xyzw);
+
+            if (correct_gamma == 1)
+                dy = sqrt(b) - sqrt(a);
+            else
+                dy = b - a;
+        }
+
+        float4 mag = hypot(dx, dy);
+        float best_dx = dx.x;
+        float best_dy = dy.x;
+
+        float mag0 = mag.x;
+        if (mag0 < mag.y)
+        {
+            best_dx = dx.y;
+            best_dy = dy.y;
+            mag0 = mag.y;
+        }
+
+        if (mag0 < mag.z)
+        {
+            best_dx = dx.z;
+            best_dy = dy.z;
+            mag0 = mag.z;
+        }
+
+        float ang = (atan2(best_dy, best_dx) + CV_PI_F) * angle_scale - 0.5f;
+        int hidx = (int)floor(ang);
+        ang -= hidx;
+        hidx = (hidx + cnbins) % cnbins;
+
+        qangle[(gidY * qangle_step + x) << 1] = hidx;
+        qangle[((gidY * qangle_step + x) << 1) + 1] = (hidx + 1) % cnbins;
+        grad[(gidY * grad_quadstep + x) << 1] = mag0 * (1.f - ang);
+        grad[((gidY * grad_quadstep + x) << 1) + 1] = mag0 * ang;
+    }
+}
+
+__kernel void compute_gradients_8UC1_kernel(
+    const int height, const int width,
+    const int img_step, const int grad_quadstep, const int qangle_step,
+    __global const uchar * img, __global float * grad, __global QANGLE_TYPE * qangle,
+    const float angle_scale, const char correct_gamma, const int cnbins)
+{
+    const int x = get_global_id(0);
+    const int tid = get_local_id(0);
+    const int gSizeX = get_local_size(0);
+    const int gidY = get_group_id(1);
+
+    __global const uchar* row = img + gidY * img_step;
+
+    __local float sh_row[NTHREADS + 2];
+
+    if (x < width)
+        sh_row[tid + 1] = row[x];
+    else
+        sh_row[tid + 1] = row[width - 2];
+
+    if (tid == 0)
+        sh_row[0] = row[max(x - 1, 1)];
+
+    if (tid == gSizeX - 1)
+        sh_row[gSizeX + 1] = row[min(x + 1, width - 2)];
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (x < width)
+    {
+        float dx;
+
+        if (correct_gamma == 1)
+            dx = sqrt(sh_row[tid + 2]) - sqrt(sh_row[tid]);
+        else
+            dx = sh_row[tid + 2] - sh_row[tid];
+
+        float dy = 0.f;
+        if (gidY > 0 && gidY < height - 1)
+        {
+            float a = (float) img[ (gidY + 1) * img_step + x ];
+            float b = (float) img[ (gidY - 1) * img_step + x ];
+            if (correct_gamma == 1)
+                dy = sqrt(a) - sqrt(b);
+            else
+                dy = a - b;
+        }
+        float mag = hypot(dx, dy);
+
+        float ang = (atan2(dy, dx) + CV_PI_F) * angle_scale - 0.5f;
+        int hidx = (int)floor(ang);
+        ang -= hidx;
+        hidx = (hidx + cnbins) % cnbins;
+
+        qangle[ (gidY * qangle_step + x) << 1 ]     = hidx;
+        qangle[ ((gidY * qangle_step + x) << 1) + 1 ] = (hidx + 1) % cnbins;
+        grad[ (gidY * grad_quadstep + x) << 1 ]       = mag * (1.f - ang);
+        grad[ ((gidY * grad_quadstep + x) << 1) + 1 ]   = mag * ang;
+    }
+}

modules/objdetect/src/precomp.hpp

@@ -0,0 +1,53 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_PRECOMP_H__
+#define __OPENCV_PRECOMP_H__
+
+#include "opencv2/objdetect.hpp"
+#include "opencv2/imgproc.hpp"
+
+#include "opencv2/core/utility.hpp"
+#include "opencv2/core/ocl.hpp"
+#include "opencv2/core/private.hpp"
+
+#endif

modules/objdetect/test/opencl/test_hogdetector.cpp

@@ -0,0 +1,121 @@
+///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//    Niko Li, newlife20080214@gmail.com
+//    Jia Haipeng, jiahaipeng95@gmail.com
+//    Shengen Yan, yanshengen@gmail.com
+//    Jiang Liyuan,jlyuan001.good@163.com
+//    Rock Li, Rock.Li@amd.com
+//    Zailong Wu, bullet@yeah.net
+//    Yao Wang, bitwangyaoyao@gmail.com
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "../test_precomp.hpp"
+#include "opencv2/ts/ocl_test.hpp"
+
+#ifdef HAVE_OPENCL
+
+namespace opencv_test {
+namespace ocl {
+
+///////////////////// HOG /////////////////////////////
+PARAM_TEST_CASE(HOG, Size, MatType)
+{
+    Size winSize;
+    int type;
+    Mat img;
+    UMat uimg;
+    virtual void SetUp()
+    {
+        winSize = GET_PARAM(0);
+        type = GET_PARAM(1);
+        img = readImage("cascadeandhog/images/image_00000000_0.png", IMREAD_GRAYSCALE);
+        ASSERT_FALSE(img.empty());
+        img.copyTo(uimg);
+    }
+};
+
+OCL_TEST_P(HOG, GetDescriptors)
+{
+    HOGDescriptor hog;
+    hog.gammaCorrection = true;
+
+    hog.setSVMDetector(hog.getDefaultPeopleDetector());
+
+    std::vector<float> cpu_descriptors;
+    std::vector<float> gpu_descriptors;
+
+    OCL_OFF(hog.compute(img, cpu_descriptors, hog.winSize));
+    OCL_ON(hog.compute(uimg, gpu_descriptors, hog.winSize));
+
+    Mat cpu_desc(cpu_descriptors), gpu_desc(gpu_descriptors);
+
+    EXPECT_MAT_SIMILAR(cpu_desc, gpu_desc, 1e-1);
+}
+
+OCL_TEST_P(HOG, Detect)
+{
+    HOGDescriptor hog;
+    hog.winSize = winSize;
+    hog.gammaCorrection = true;
+
+    if (winSize.width == 48 && winSize.height == 96)
+        hog.setSVMDetector(hog.getDaimlerPeopleDetector());
+    else
+        hog.setSVMDetector(hog.getDefaultPeopleDetector());
+
+    std::vector<Rect> cpu_found;
+    std::vector<Rect> gpu_found;
+
+    OCL_OFF(hog.detectMultiScale(img, cpu_found, 0, Size(8, 8), Size(0, 0), 1.05, 6));
+    OCL_ON(hog.detectMultiScale(uimg, gpu_found, 0, Size(8, 8), Size(0, 0), 1.05, 6));
+
+    EXPECT_LT(checkRectSimilarity(img.size(), cpu_found, gpu_found), 0.05);
+}
+
+INSTANTIATE_TEST_CASE_P(OCL_ObjDetect, HOG, testing::Combine(
+                            testing::Values(Size(64, 128), Size(48, 96)),
+                            testing::Values( MatType(CV_8UC1) ) ) );
+
+}} // namespace
+#endif

modules/objdetect/test/test_main.cpp

@@ -0,0 +1,10 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+#include "test_precomp.hpp"
+
+#if defined(HAVE_HPX)
+    #include <hpx/hpx_main.hpp>
+#endif
+
+CV_TEST_MAIN("cv")

modules/objdetect/test/test_precomp.hpp

@@ -0,0 +1,10 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+#ifndef __OPENCV_TEST_PRECOMP_HPP__
+#define __OPENCV_TEST_PRECOMP_HPP__
+
+#include "opencv2/ts.hpp"
+#include "opencv2/objdetect.hpp"
+
+#endif

modules/objdetect/test/test_qrcode.cpp

@@ -0,0 +1,670 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "test_precomp.hpp"
+
+namespace opencv_test { namespace {
+
+std::string qrcode_images_name[] = {
+  "version_1_down.jpg", "version_1_left.jpg", "version_1_right.jpg", "version_1_up.jpg", "version_1_top.jpg",
+  "version_2_down.jpg", "version_2_left.jpg", "version_2_right.jpg", "version_2_up.jpg", "version_2_top.jpg",
+  "version_3_down.jpg", "version_3_left.jpg", "version_3_right.jpg", "version_3_up.jpg", "version_3_top.jpg",
+  "version_4_down.jpg", "version_4_left.jpg", "version_4_right.jpg", "version_4_up.jpg", "version_4_top.jpg",
+  "version_5_down.jpg", "version_5_left.jpg", "version_5_right.jpg", "version_5_up.jpg", "version_5_top.jpg",
+  "russian.jpg", "kanji.jpg", "link_github_ocv.jpg", "link_ocv.jpg", "link_wiki_cv.jpg"
+};
+
+std::string qrcode_images_close[] = {
+  "close_1.png", "close_2.png", "close_3.png", "close_4.png", "close_5.png"
+};
+std::string qrcode_images_monitor[] = {
+  "monitor_1.png", "monitor_2.png", "monitor_3.png", "monitor_4.png", "monitor_5.png"
+};
+std::string qrcode_images_curved[] = {
+  "curved_1.jpg", "curved_2.jpg", "curved_3.jpg", "curved_4.jpg", "curved_5.jpg", "curved_6.jpg", "curved_7.jpg", "curved_8.jpg"
+};
+std::string qrcode_images_multiple[] = {
+  "2_qrcodes.png", "3_close_qrcodes.png", "3_qrcodes.png", "4_qrcodes.png",
+  "5_qrcodes.png", "6_qrcodes.png", "7_qrcodes.png", "8_close_qrcodes.png"
+};
+//#define UPDATE_QRCODE_TEST_DATA
+#ifdef  UPDATE_QRCODE_TEST_DATA
+
+TEST(Objdetect_QRCode, generate_test_data)
+{
+    const std::string root = "qrcode/";
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::WRITE);
+
+    file_config << "test_images" << "[";
+    size_t images_count = sizeof(qrcode_images_name) / sizeof(qrcode_images_name[0]);
+    for (size_t i = 0; i < images_count; i++)
+    {
+        file_config << "{:" << "image_name" << qrcode_images_name[i];
+        std::string image_path = findDataFile(root + qrcode_images_name[i]);
+        std::vector<Point> corners;
+        Mat src = imread(image_path, IMREAD_GRAYSCALE), straight_barcode;
+        std::string decoded_info;
+        ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+        EXPECT_TRUE(detectQRCode(src, corners));
+#ifdef HAVE_QUIRC
+        EXPECT_TRUE(decodeQRCode(src, corners, decoded_info, straight_barcode));
+#endif
+        file_config << "x" << "[:";
+        for (size_t j = 0; j < corners.size(); j++) { file_config << corners[j].x; }
+        file_config << "]";
+        file_config << "y" << "[:";
+        for (size_t j = 0; j < corners.size(); j++) { file_config << corners[j].y; }
+        file_config << "]";
+        file_config << "info" << decoded_info;
+        file_config << "}";
+    }
+    file_config << "]";
+    file_config.release();
+}
+
+TEST(Objdetect_QRCode_Close, generate_test_data)
+{
+    const std::string root = "qrcode/close/";
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::WRITE);
+
+    file_config << "close_images" << "[";
+    size_t close_count = sizeof(qrcode_images_close) / sizeof(qrcode_images_close[0]);
+    for (size_t i = 0; i < close_count; i++)
+    {
+        file_config << "{:" << "image_name" << qrcode_images_close[i];
+        std::string image_path = findDataFile(root + qrcode_images_close[i]);
+        std::vector<Point> corners;
+        Mat src = imread(image_path, IMREAD_GRAYSCALE), barcode, straight_barcode;
+        std::string decoded_info;
+        ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+        const double min_side = std::min(src.size().width, src.size().height);
+        double coeff_expansion = 1024.0 / min_side;
+        const int width  = cvRound(src.size().width  * coeff_expansion);
+        const int height = cvRound(src.size().height  * coeff_expansion);
+        Size new_size(width, height);
+        resize(src, barcode, new_size, 0, 0, INTER_LINEAR);
+        EXPECT_TRUE(detectQRCode(barcode, corners));
+#ifdef HAVE_QUIRC
+        EXPECT_TRUE(decodeQRCode(barcode, corners, decoded_info, straight_barcode));
+#endif
+        file_config << "x" << "[:";
+        for (size_t j = 0; j < corners.size(); j++) { file_config << corners[j].x; }
+        file_config << "]";
+        file_config << "y" << "[:";
+        for (size_t j = 0; j < corners.size(); j++) { file_config << corners[j].y; }
+        file_config << "]";
+        file_config << "info" << decoded_info;
+        file_config << "}";
+    }
+    file_config << "]";
+    file_config.release();
+}
+TEST(Objdetect_QRCode_Monitor, generate_test_data)
+{
+    const std::string root = "qrcode/monitor/";
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::WRITE);
+
+    file_config << "monitor_images" << "[";
+    size_t monitor_count = sizeof(qrcode_images_monitor) / sizeof(qrcode_images_monitor[0]);
+    for (size_t i = 0; i < monitor_count; i++)
+    {
+        file_config << "{:" << "image_name" << qrcode_images_monitor[i];
+        std::string image_path = findDataFile(root + qrcode_images_monitor[i]);
+        std::vector<Point> corners;
+        Mat src = imread(image_path, IMREAD_GRAYSCALE), barcode, straight_barcode;
+        std::string decoded_info;
+        ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+        const double min_side = std::min(src.size().width, src.size().height);
+        double coeff_expansion = 1024.0 / min_side;
+        const int width  = cvRound(src.size().width  * coeff_expansion);
+        const int height = cvRound(src.size().height  * coeff_expansion);
+        Size new_size(width, height);
+        resize(src, barcode, new_size, 0, 0, INTER_LINEAR);
+        EXPECT_TRUE(detectQRCode(barcode, corners));
+#ifdef HAVE_QUIRC
+        EXPECT_TRUE(decodeQRCode(barcode, corners, decoded_info, straight_barcode));
+#endif
+        file_config << "x" << "[:";
+        for (size_t j = 0; j < corners.size(); j++) { file_config << corners[j].x; }
+        file_config << "]";
+        file_config << "y" << "[:";
+        for (size_t j = 0; j < corners.size(); j++) { file_config << corners[j].y; }
+        file_config << "]";
+        file_config << "info" << decoded_info;
+        file_config << "}";
+    }
+    file_config << "]";
+    file_config.release();
+}
+TEST(Objdetect_QRCode_Curved, generate_test_data)
+{
+    const std::string root = "qrcode/curved/";
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::WRITE);
+
+    file_config << "test_images" << "[";
+    size_t images_count = sizeof(qrcode_images_curved) / sizeof(qrcode_images_curved[0]);
+    for (size_t i = 0; i < images_count; i++)
+    {
+        file_config << "{:" << "image_name" << qrcode_images_curved[i];
+        std::string image_path = findDataFile(root + qrcode_images_curved[i]);
+        std::vector<Point> corners;
+        Mat src = imread(image_path, IMREAD_GRAYSCALE), straight_barcode;
+        std::string decoded_info;
+        ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+        EXPECT_TRUE(detectQRCode(src, corners));
+#ifdef HAVE_QUIRC
+        EXPECT_TRUE(decodeCurvedQRCode(src, corners, decoded_info, straight_barcode));
+#endif
+        file_config << "x" << "[:";
+        for (size_t j = 0; j < corners.size(); j++) { file_config << corners[j].x; }
+        file_config << "]";
+        file_config << "y" << "[:";
+        for (size_t j = 0; j < corners.size(); j++) { file_config << corners[j].y; }
+        file_config << "]";
+        file_config << "info" << decoded_info;
+        file_config << "}";
+    }
+    file_config << "]";
+    file_config.release();
+}
+TEST(Objdetect_QRCode_Multi, generate_test_data)
+{
+    const std::string root = "qrcode/multiple/";
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::WRITE);
+
+    file_config << "multiple_images" << "[:";
+    size_t multiple_count = sizeof(qrcode_images_multiple) / sizeof(qrcode_images_multiple[0]);
+    for (size_t i = 0; i < multiple_count; i++)
+    {
+        file_config << "{:" << "image_name" << qrcode_images_multiple[i];
+        std::string image_path = findDataFile(root + qrcode_images_multiple[i]);
+        Mat src = imread(image_path);
+
+        ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+        std::vector<Point> corners;
+        QRCodeDetector qrcode;
+        EXPECT_TRUE(qrcode.detectMulti(src, corners));
+#ifdef HAVE_QUIRC
+        std::vector<cv::String> decoded_info;
+        std::vector<Mat> straight_barcode;
+        EXPECT_TRUE(qrcode.decodeMulti(src, corners, decoded_info, straight_barcode));
+#endif
+        file_config << "x" << "[:";
+        for(size_t j = 0; j < corners.size(); j += 4)
+        {
+            file_config << "[:";
+            for (size_t k = 0; k < 4; k++)
+            {
+                file_config << corners[j + k].x;
+            }
+            file_config << "]";
+        }
+        file_config << "]";
+        file_config << "y" << "[:";
+        for(size_t j = 0; j < corners.size(); j += 4)
+        {
+            file_config << "[:";
+            for (size_t k = 0; k < 4; k++)
+            {
+                file_config << corners[j + k].y;
+            }
+            file_config << "]";
+        }
+        file_config << "]";
+        file_config << "info";
+        file_config << "[:";
+
+        for(size_t j = 0; j < decoded_info.size(); j++)
+        {
+            file_config << decoded_info[j];
+        }
+        file_config << "]";
+        file_config << "}";
+    }
+
+    file_config << "]";
+    file_config.release();
+}
+
+#else
+
+typedef testing::TestWithParam< std::string > Objdetect_QRCode;
+TEST_P(Objdetect_QRCode, regression)
+{
+    const std::string name_current_image = GetParam();
+    const std::string root = "qrcode/";
+    const int pixels_error = 3;
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path, IMREAD_GRAYSCALE), straight_barcode;
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+
+    std::vector<Point> corners;
+    std::string decoded_info;
+    QRCodeDetector qrcode;
+#ifdef HAVE_QUIRC
+    decoded_info = qrcode.detectAndDecode(src, corners, straight_barcode);
+    ASSERT_FALSE(corners.empty());
+    ASSERT_FALSE(decoded_info.empty());
+    int expected_barcode_type = CV_8UC1;
+    EXPECT_EQ(expected_barcode_type, straight_barcode.type());
+#else
+    ASSERT_TRUE(qrcode.detect(src, corners));
+#endif
+
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::READ);
+    ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
+    {
+        FileNode images_list = file_config["test_images"];
+        size_t images_count = static_cast<size_t>(images_list.size());
+        ASSERT_GT(images_count, 0u) << "Can't find validation data entries in 'test_images': " << dataset_config;
+
+        for (size_t index = 0; index < images_count; index++)
+        {
+            FileNode config = images_list[(int)index];
+            std::string name_test_image = config["image_name"];
+            if (name_test_image == name_current_image)
+            {
+                for (int i = 0; i < 4; i++)
+                {
+                    int x = config["x"][i];
+                    int y = config["y"][i];
+                    EXPECT_NEAR(x, corners[i].x, pixels_error);
+                    EXPECT_NEAR(y, corners[i].y, pixels_error);
+                }
+
+#ifdef HAVE_QUIRC
+                std::string original_info = config["info"];
+                EXPECT_EQ(decoded_info, original_info);
+#endif
+
+                return; // done
+            }
+        }
+        std::cerr
+            << "Not found results for '" << name_current_image
+            << "' image in config file:" << dataset_config << std::endl
+            << "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
+            << std::endl;
+    }
+}
+
+typedef testing::TestWithParam< std::string > Objdetect_QRCode_Close;
+TEST_P(Objdetect_QRCode_Close, regression)
+{
+    const std::string name_current_image = GetParam();
+    const std::string root = "qrcode/close/";
+    const int pixels_error = 3;
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path, IMREAD_GRAYSCALE), barcode, straight_barcode;
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+    const double min_side = std::min(src.size().width, src.size().height);
+    double coeff_expansion = 1024.0 / min_side;
+    const int width  = cvRound(src.size().width  * coeff_expansion);
+    const int height = cvRound(src.size().height  * coeff_expansion);
+    Size new_size(width, height);
+    resize(src, barcode, new_size, 0, 0, INTER_LINEAR);
+    std::vector<Point> corners;
+    std::string decoded_info;
+    QRCodeDetector qrcode;
+#ifdef HAVE_QUIRC
+    decoded_info = qrcode.detectAndDecode(barcode, corners, straight_barcode);
+    ASSERT_FALSE(corners.empty());
+    ASSERT_FALSE(decoded_info.empty());
+    int expected_barcode_type = CV_8UC1;
+    EXPECT_EQ(expected_barcode_type, straight_barcode.type());
+#else
+    ASSERT_TRUE(qrcode.detect(barcode, corners));
+#endif
+
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::READ);
+    ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
+    {
+        FileNode images_list = file_config["close_images"];
+        size_t images_count = static_cast<size_t>(images_list.size());
+        ASSERT_GT(images_count, 0u) << "Can't find validation data entries in 'test_images': " << dataset_config;
+
+        for (size_t index = 0; index < images_count; index++)
+        {
+            FileNode config = images_list[(int)index];
+            std::string name_test_image = config["image_name"];
+            if (name_test_image == name_current_image)
+            {
+                for (int i = 0; i < 4; i++)
+                {
+                    int x = config["x"][i];
+                    int y = config["y"][i];
+                    EXPECT_NEAR(x, corners[i].x, pixels_error);
+                    EXPECT_NEAR(y, corners[i].y, pixels_error);
+                }
+
+#ifdef HAVE_QUIRC
+                std::string original_info = config["info"];
+                EXPECT_EQ(decoded_info, original_info);
+#endif
+
+                return; // done
+            }
+        }
+        std::cerr
+            << "Not found results for '" << name_current_image
+            << "' image in config file:" << dataset_config << std::endl
+            << "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
+            << std::endl;
+    }
+}
+
+typedef testing::TestWithParam< std::string > Objdetect_QRCode_Monitor;
+TEST_P(Objdetect_QRCode_Monitor, regression)
+{
+    const std::string name_current_image = GetParam();
+    const std::string root = "qrcode/monitor/";
+    const int pixels_error = 3;
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path, IMREAD_GRAYSCALE), barcode, straight_barcode;
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+    const double min_side = std::min(src.size().width, src.size().height);
+    double coeff_expansion = 1024.0 / min_side;
+    const int width  = cvRound(src.size().width  * coeff_expansion);
+    const int height = cvRound(src.size().height  * coeff_expansion);
+    Size new_size(width, height);
+    resize(src, barcode, new_size, 0, 0, INTER_LINEAR);
+    std::vector<Point> corners;
+    std::string decoded_info;
+    QRCodeDetector qrcode;
+#ifdef HAVE_QUIRC
+    decoded_info = qrcode.detectAndDecode(barcode, corners, straight_barcode);
+    ASSERT_FALSE(corners.empty());
+    ASSERT_FALSE(decoded_info.empty());
+    int expected_barcode_type = CV_8UC1;
+    EXPECT_EQ(expected_barcode_type, straight_barcode.type());
+#else
+    ASSERT_TRUE(qrcode.detect(barcode, corners));
+#endif
+
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::READ);
+    ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
+    {
+        FileNode images_list = file_config["monitor_images"];
+        size_t images_count = static_cast<size_t>(images_list.size());
+        ASSERT_GT(images_count, 0u) << "Can't find validation data entries in 'test_images': " << dataset_config;
+
+        for (size_t index = 0; index < images_count; index++)
+        {
+            FileNode config = images_list[(int)index];
+            std::string name_test_image = config["image_name"];
+            if (name_test_image == name_current_image)
+            {
+                for (int i = 0; i < 4; i++)
+                {
+                    int x = config["x"][i];
+                    int y = config["y"][i];
+                    EXPECT_NEAR(x, corners[i].x, pixels_error);
+                    EXPECT_NEAR(y, corners[i].y, pixels_error);
+                }
+
+#ifdef HAVE_QUIRC
+                std::string original_info = config["info"];
+                EXPECT_EQ(decoded_info, original_info);
+#endif
+
+                return; // done
+            }
+        }
+        std::cerr
+            << "Not found results for '" << name_current_image
+            << "' image in config file:" << dataset_config << std::endl
+            << "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
+            << std::endl;
+    }
+}
+
+typedef testing::TestWithParam< std::string > Objdetect_QRCode_Curved;
+TEST_P(Objdetect_QRCode_Curved, regression)
+{
+    const std::string name_current_image = GetParam();
+    const std::string root = "qrcode/curved/";
+    const int pixels_error = 3;
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path, IMREAD_GRAYSCALE), straight_barcode;
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+
+    std::vector<Point> corners;
+    std::string decoded_info;
+    QRCodeDetector qrcode;
+#ifdef HAVE_QUIRC
+    decoded_info = qrcode.detectAndDecodeCurved(src, corners, straight_barcode);
+    ASSERT_FALSE(corners.empty());
+    ASSERT_FALSE(decoded_info.empty());
+    int expected_barcode_type = CV_8UC1;
+    EXPECT_EQ(expected_barcode_type, straight_barcode.type());
+#else
+    ASSERT_TRUE(qrcode.detect(src, corners));
+#endif
+
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::READ);
+    ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
+    {
+        FileNode images_list = file_config["test_images"];
+        size_t images_count = static_cast<size_t>(images_list.size());
+        ASSERT_GT(images_count, 0u) << "Can't find validation data entries in 'test_images': " << dataset_config;
+
+        for (size_t index = 0; index < images_count; index++)
+        {
+            FileNode config = images_list[(int)index];
+            std::string name_test_image = config["image_name"];
+            if (name_test_image == name_current_image)
+            {
+                for (int i = 0; i < 4; i++)
+                {
+                    int x = config["x"][i];
+                    int y = config["y"][i];
+                    EXPECT_NEAR(x, corners[i].x, pixels_error);
+                    EXPECT_NEAR(y, corners[i].y, pixels_error);
+                }
+
+#ifdef HAVE_QUIRC
+                std::string original_info = config["info"];
+                EXPECT_EQ(decoded_info, original_info);
+#endif
+
+                return; // done
+            }
+        }
+        std::cerr
+            << "Not found results for '" << name_current_image
+            << "' image in config file:" << dataset_config << std::endl
+            << "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
+            << std::endl;
+    }
+}
+
+typedef testing::TestWithParam < std::string > Objdetect_QRCode_Multi;
+TEST_P(Objdetect_QRCode_Multi, regression)
+{
+    const std::string name_current_image = GetParam();
+    const std::string root = "qrcode/multiple/";
+    const int pixels_error = 3;
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path);
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+    QRCodeDetector qrcode;
+    std::vector<Point> corners;
+#ifdef HAVE_QUIRC
+    std::vector<cv::String> decoded_info;
+    std::vector<Mat> straight_barcode;
+    EXPECT_TRUE(qrcode.detectAndDecodeMulti(src, decoded_info, corners, straight_barcode));
+    ASSERT_FALSE(corners.empty());
+    ASSERT_FALSE(decoded_info.empty());
+    int expected_barcode_type = CV_8UC1;
+    for(size_t i = 0; i < straight_barcode.size(); i++)
+        EXPECT_EQ(expected_barcode_type, straight_barcode[i].type());
+#else
+    ASSERT_TRUE(qrcode.detectMulti(src, corners));
+#endif
+
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
+    FileStorage file_config(dataset_config, FileStorage::READ);
+    ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
+    {
+        FileNode images_list = file_config["multiple_images"];
+        size_t images_count = static_cast<size_t>(images_list.size());
+        ASSERT_GT(images_count, 0u) << "Can't find validation data entries in 'test_images': " << dataset_config;
+        for (size_t index = 0; index < images_count; index++)
+        {
+            FileNode config = images_list[(int)index];
+            std::string name_test_image = config["image_name"];
+            if (name_test_image == name_current_image)
+            {
+                for(int j = 0; j < int(corners.size()); j += 4)
+                {
+                    bool ok = false;
+                    for (int k = 0; k < int(corners.size() / 4); k++)
+                    {
+                        int count_eq_points = 0;
+                        for (int i = 0; i < 4; i++)
+                        {
+                            int x = config["x"][k][i];
+                            int y = config["y"][k][i];
+                            if(((abs(corners[j + i].x - x)) <= pixels_error) && ((abs(corners[j + i].y - y)) <= pixels_error))
+                              count_eq_points++;
+                        }
+                        if (count_eq_points == 4)
+                        {
+                            ok = true;
+                            break;
+                        }
+                    }
+                    EXPECT_TRUE(ok);
+                }
+
+#ifdef HAVE_QUIRC
+                  size_t count_eq_info = 0;
+                  for(int i = 0; i < int(decoded_info.size()); i++)
+                  {
+                      for(int j = 0; j < int(decoded_info.size()); j++)
+                      {
+                          std::string original_info = config["info"][j];
+                          if(original_info == decoded_info[i])
+                          {
+                             count_eq_info++;
+                             break;
+                          }
+                      }
+                  }
+                  EXPECT_EQ(decoded_info.size(), count_eq_info);
+#endif
+
+                  return; // done
+            }
+        }
+        std::cerr
+            << "Not found results for '" << name_current_image
+            << "' image in config file:" << dataset_config << std::endl
+            << "Re-run tests with enabled UPDATE_QRCODE_TEST_DATA macro to update test data."
+            << std::endl;
+    }
+}
+
+INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode, testing::ValuesIn(qrcode_images_name));
+INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Close, testing::ValuesIn(qrcode_images_close));
+INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Monitor, testing::ValuesIn(qrcode_images_monitor));
+INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Curved, testing::ValuesIn(qrcode_images_curved));
+INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Multi, testing::ValuesIn(qrcode_images_multiple));
+
+TEST(Objdetect_QRCode_decodeMulti, decode_regression_16491)
+{
+#ifdef HAVE_QUIRC
+    Mat zero_image = Mat::zeros(256, 256, CV_8UC1);
+    Point corners_[] = {Point(16, 16), Point(128, 16), Point(128, 128), Point(16, 128),
+                        Point(16, 16), Point(128, 16), Point(128, 128), Point(16, 128)};
+    std::vector<Point> vec_corners;
+    int array_size = 8;
+    vec_corners.assign(corners_, corners_ + array_size);
+    std::vector<cv::String> decoded_info;
+    std::vector<Mat> straight_barcode;
+    QRCodeDetector vec_qrcode;
+    EXPECT_NO_THROW(vec_qrcode.decodeMulti(zero_image, vec_corners, decoded_info, straight_barcode));
+
+    Mat mat_corners(2, 4, CV_32SC2, (void*)&vec_corners[0]);
+    QRCodeDetector mat_qrcode;
+    EXPECT_NO_THROW(mat_qrcode.decodeMulti(zero_image, mat_corners, decoded_info, straight_barcode));
+#endif
+}
+
+TEST(Objdetect_QRCode_detectMulti, detect_regression_16961)
+{
+    const std::string name_current_image = "9_qrcodes.jpg";
+    const std::string root = "qrcode/multiple/";
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path);
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+
+    QRCodeDetector qrcode;
+    std::vector<Point> corners;
+    EXPECT_TRUE(qrcode.detectMulti(src, corners));
+    ASSERT_FALSE(corners.empty());
+    size_t expect_corners_size = 36;
+    EXPECT_EQ(corners.size(), expect_corners_size);
+}
+
+TEST(Objdetect_QRCode_decodeMulti, check_output_parameters_type_19363)
+{
+    const std::string name_current_image = "9_qrcodes.jpg";
+    const std::string root = "qrcode/multiple/";
+
+    std::string image_path = findDataFile(root + name_current_image);
+    Mat src = imread(image_path);
+    ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path;
+#ifdef HAVE_QUIRC
+    QRCodeDetector qrcode;
+    std::vector<Point> corners;
+    std::vector<cv::String> decoded_info;
+#if 0  // FIXIT: OutputArray::create() type check
+    std::vector<Mat2b> straight_barcode_nchannels;
+    EXPECT_ANY_THROW(qrcode.detectAndDecodeMulti(src, decoded_info, corners, straight_barcode_nchannels));
+#endif
+
+    int expected_barcode_type = CV_8UC1;
+    std::vector<Mat1b> straight_barcode;
+    EXPECT_TRUE(qrcode.detectAndDecodeMulti(src, decoded_info, corners, straight_barcode));
+    ASSERT_FALSE(corners.empty());
+    for(size_t i = 0; i < straight_barcode.size(); i++)
+        EXPECT_EQ(expected_barcode_type, straight_barcode[i].type());
+#endif
+}
+
+TEST(Objdetect_QRCode_basic, not_found_qrcode)
+{
+    std::vector<Point> corners;
+    Mat straight_barcode;
+    std::string decoded_info;
+    Mat zero_image = Mat::zeros(256, 256, CV_8UC1);
+    QRCodeDetector qrcode;
+    EXPECT_FALSE(qrcode.detect(zero_image, corners));
+#ifdef HAVE_QUIRC
+    corners = std::vector<Point>(4);
+    EXPECT_ANY_THROW(qrcode.decode(zero_image, corners, straight_barcode));
+#endif
+}
+
+
+
+#endif // UPDATE_QRCODE_TEST_DATA
+
+}} // namespace