Merge pull request #3 from Itseez/master

Update

modules/README.md

@@ -51,3 +51,5 @@ $ cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -D BUILD_opencv_re
 21. **opencv_xobjdetect**: Integral Channel Features Detector Framework.
 
 22. **opencv_xphoto**: Additional photo processing algorithms: Color balance / Denoising / Inpainting.
+
+23. **opencv_stereo**: Stereo Correspondence done with different descriptors: Census / CS-Census / MCT / BRIEF / MV / RT.

modules/datasets/include/opencv2/datasets/or_pascal.hpp

+/*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) 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 Itseez Inc 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_DATASETS_VOC_PASCAL_HPP
+#define OPENCV_DATASETS_VOC_PASCAL_HPP
+
+#include <string>
+#include <vector>
+
+#include "opencv2/datasets/dataset.hpp"
+
+#include <opencv2/core.hpp>
+
+namespace cv
+{
+namespace datasets
+{
+
+//! @addtogroup datasets_or
+//! @{
+struct PascalPart: public Object
+{
+    std::string name;
+    int xmin;
+    int ymin;
+    int xmax;
+    int ymax;
+};
+
+struct PascalObj: public PascalPart
+{
+    std::string pose;
+    bool truncated;
+    bool difficult;
+    bool occluded;
+
+    std::vector<PascalPart> parts;
+};
+
+struct OR_pascalObj : public Object
+{
+    std::string filename;
+
+    int width;
+    int height;
+    int depth;
+
+    std::vector<PascalObj> objects;
+};
+
+class CV_EXPORTS OR_pascal : public Dataset
+{
+public:
+    virtual void load(const std::string &path) = 0;
+
+    static Ptr<OR_pascal> create();
+};
+
+//! @}
+
+}// namespace dataset
+}// namespace cv
+
+#endif

modules/datasets/include/opencv2/datasets/pd_inria.hpp

+/*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) 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 Itseez Inc 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_DATASETS_PD_INRIA_HPP
+#define OPENCV_DATASETS_PD_INRIA_HPP
+
+#include <string>
+#include <vector>
+
+#include "opencv2/datasets/dataset.hpp"
+
+#include <opencv2/core.hpp>
+
+namespace cv
+{
+namespace datasets
+{
+
+//! @addtogroup datasets_pd
+//! @{
+
+enum sampleType 
+{
+    POS = 0,
+    NEG = 1
+};
+
+struct PD_inriaObj : public Object
+{
+    // image file name
+    std::string filename;
+    
+    // positive or negative
+    sampleType sType;
+
+    // image size
+    int width;
+    int height;
+    int depth;
+
+    // bounding boxes
+    std::vector< Rect > bndboxes;
+};
+
+class CV_EXPORTS PD_inria : public Dataset
+{
+public:
+    virtual void load(const std::string &path) = 0;
+
+    static Ptr<PD_inria> create();
+};
+
+//! @}
+
+}
+}
+
+#endif

modules/datasets/samples/or_pascal.cpp

+/*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) 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 Itseez Inc 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 "opencv2/datasets/or_pascal.hpp"
+
+#include <opencv2/core.hpp>
+
+#include <cstdio>
+#include <cstdlib> // atoi
+
+#include <string>
+#include <vector>
+#include <set>
+
+using namespace std;
+using namespace cv;
+using namespace cv::datasets;
+
+int main(int argc, char *argv[])
+{
+    const char *keys =
+            "{ help h usage ? |    | show this message }"
+            "{ path p         |true| path to folder with dataset }";
+    CommandLineParser parser(argc, argv, keys);
+    string path(parser.get<string>("path"));
+    if (parser.has("help") || path=="true")
+    {
+        parser.printMessage();
+        return -1;
+    }
+
+    Ptr<OR_pascal> dataset = OR_pascal::create();
+    dataset->load(path);
+
+    // Print train/test/validation size and first example
+    OR_pascalObj *example;
+    vector< Ptr<Object> > &train = dataset->getTrain();
+    printf("\ntrain:\nsize: %u", (unsigned int)train.size());
+    example = static_cast<OR_pascalObj *>(train[0].get());
+    printf("\nfirst image: \n%s", example->filename.c_str());
+
+    printf("\nsize:");
+    printf("\n - width: %d", example->width);
+    printf("\n - height: %d", example->height);
+    printf("\n - depth: %d", example->depth);
+
+    for (unsigned int i = 0; i < example->objects.size(); i++)
+    {
+        printf("\nobject %d", i);
+        printf("\nname: %s", example->objects[i].name.c_str());
+        printf("\npose: %s", example->objects[i].pose.c_str());
+        printf("\ntruncated: %d", example->objects[i].truncated);
+        printf("\ndifficult: %d", example->objects[i].difficult);
+        printf("\noccluded: %d", example->objects[i].occluded);
+
+        printf("\nbounding box:");
+        printf("\n - xmin: %d", example->objects[i].xmin);
+        printf("\n - ymin: %d", example->objects[i].ymin);
+        printf("\n - xmax: %d", example->objects[i].xmax);
+        printf("\n - ymax: %d", example->objects[i].ymax);
+    }
+
+    vector< Ptr<Object> > &test = dataset->getTest();
+    printf("\ntest:\nsize: %u", (unsigned int)test.size());
+    example = static_cast<OR_pascalObj *>(test[0].get());
+    printf("\nfirst image: \n%s", example->filename.c_str());
+
+    vector< Ptr<Object> > &validation = dataset->getValidation();
+    printf("\nvalidation:\nsize: %u", (unsigned int)validation.size());
+    example = static_cast<OR_pascalObj *>(validation[0].get());
+    printf("\nfirst image: \n%s\n", example->filename.c_str());
+
+    return 0;
+}

modules/datasets/samples/pd_inria.cpp

+/*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) 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 Itseez Inc 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 "opencv2/datasets/pd_inria.hpp"
+#include <opencv2/core.hpp>
+
+#include <iostream>
+#include <string>
+#include <vector>
+
+using namespace std;
+using namespace cv;
+using namespace cv::datasets;
+
+int main(int argc, char *argv[])
+{
+    const char *keys =
+            "{ help h usage ? |    | show this message }"
+            "{ path p         |true| path to dataset }";
+    CommandLineParser parser(argc, argv, keys);
+    string path(parser.get<string>("path"));
+    if (parser.has("help") || path=="true")
+    {
+        parser.printMessage();
+        return -1;
+    }
+
+    Ptr<PD_inria> dataset = PD_inria::create();
+    dataset->load(path);
+
+    cout << "train size: " << (unsigned int)dataset->getTrain().size() << endl;
+
+    PD_inriaObj *example = static_cast<PD_inriaObj *>(dataset->getTrain()[0].get());
+    cout << "first train object: " << endl;
+    cout << "name: " << example->filename << endl;
+    
+    // image size
+    cout << "image size: " << endl;
+    cout << "  - width: " << example->width << endl;
+    cout << "  - height: " << example->height << endl;
+    cout << "  - depth: " << example->depth << endl;
+
+    // bounding boxes
+    for (unsigned int i = 0; i < (example->bndboxes).size(); i++)
+    {
+        cout << "object " << i << endl;
+        int x = (example->bndboxes)[i].x;
+        int y = (example->bndboxes)[i].y;
+        cout << "  - xmin = " << x  << endl;
+        cout << "  - ymin = " << y << endl;
+        cout << "  - xmax = " << (example->bndboxes)[i].width + x << endl;
+        cout << "  - ymax = " << (example->bndboxes)[i].height + y<< endl;
+    }
+
+    return 0;
+}

modules/datasets/src/or_pascal.cpp

+/*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) 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 Itseez Inc 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 "opencv2/datasets/or_pascal.hpp"
+#include "opencv2/datasets/util.hpp"
+#include <opencv2/datasets/tinyxml2/tinyxml2.h>
+#include <fstream>
+
+namespace cv
+{
+namespace datasets
+{
+
+using namespace std;
+using namespace tinyxml2;
+
+class OR_pascalImp : public OR_pascal
+{
+public:
+    OR_pascalImp() {}
+
+    virtual void load(const string &path);
+
+private:
+    void loadDataset(const string &path, const string &nameImageSet, vector< Ptr<Object> > &imageSet);
+    Ptr<Object> parseAnnotation(const string path, const string id);
+    const char*  parseNodeText(XMLElement* node, const string nodeName, const string defaultValue);
+};
+
+
+void OR_pascalImp::load(const string &path)
+{
+    train.push_back(vector< Ptr<Object> >());
+    test.push_back(vector< Ptr<Object> >());
+    validation.push_back(vector< Ptr<Object> >());
+
+    loadDataset(path, "train", train.back());
+    loadDataset(path, "test", test.back());
+    loadDataset(path, "val", validation.back());
+}
+
+void OR_pascalImp::loadDataset(const string &path, const string &nameImageSet, vector< Ptr<Object> > &imageSet)
+{
+    string pathImageSets(path + "ImageSets/Main/");
+    string imageList = pathImageSets + nameImageSet + ".txt";
+
+    ifstream in(imageList.c_str());
+    string error_message = format("Image list not exists!\n%s", imageList.c_str());
+
+    if (!in.is_open())
+        CV_Error(Error::StsBadArg, error_message);
+
+    string id = "";
+
+    while( getline(in, id) )
+    {
+        if( strcmp(nameImageSet.c_str(), "test") == 0 ) // test set ground truth is not available
+        {
+            Ptr<OR_pascalObj> annotation(new OR_pascalObj);
+            annotation->filename = path + "JPEGImages/" + id + ".jpg";
+            imageSet.push_back(annotation);
+        }
+        else
+        {
+            imageSet.push_back(parseAnnotation(path, id));
+        }
+    }
+}
+
+const char* OR_pascalImp::parseNodeText(XMLElement* node, const string nodeName, const string defaultValue)
+{
+    const char* e = node->FirstChildElement(nodeName.c_str())->GetText();
+
+    if( e != 0 )
+        return e ;
+    else
+        return defaultValue.c_str();
+}
+
+Ptr<Object> OR_pascalImp::parseAnnotation(const string path, const string id)
+{
+    string pathAnnotations(path + "Annotations/");
+    string pathImages(path + "JPEGImages/");
+    Ptr<OR_pascalObj> annotation(new OR_pascalObj);
+
+    XMLDocument doc;
+    string xml_file = pathAnnotations + id + ".xml";
+
+    XMLError error_code = doc.LoadFile(xml_file.c_str());
+    string error_message = format("Parsing XML failed. Error code = %d. \nFile = %s", error_code, xml_file.c_str());
+    switch (error_code)
+    {
+        case XML_SUCCESS:
+            break;
+        case XML_ERROR_FILE_NOT_FOUND:
+            error_message = "XML file not found! " + error_message;
+            CV_Error(Error::StsParseError, error_message);
+            return annotation;
+        default:
+            CV_Error(Error::StsParseError, error_message);
+            break;
+    }
+
+    // <annotation/>
+    XMLElement *xml_ann = doc.RootElement();
+
+    // <filename/>
+    string img_name = xml_ann->FirstChildElement("filename")->GetText();
+    annotation->filename = pathImages + img_name;
+
+    // <size/>
+    XMLElement *sz = xml_ann->FirstChildElement("size");
+    int width = atoi(sz->FirstChildElement("width")->GetText());
+    int height = atoi(sz->FirstChildElement("height")->GetText());
+    int depth = atoi(sz->FirstChildElement("depth")->GetText());
+    annotation->width = width;
+    annotation->height = height;
+    annotation->depth = depth;
+
+    // <object/>
+    vector<PascalObj> objects;
+    XMLElement *xml_obj = xml_ann->FirstChildElement("object");
+
+    while (xml_obj)
+    {
+        PascalObj pascal_obj;
+        pascal_obj.name = xml_obj->FirstChildElement("name")->GetText();
+        pascal_obj.pose = parseNodeText(xml_obj, "pose", "Unspecified");
+        pascal_obj.truncated = atoi(parseNodeText(xml_obj, "truncated", "0")) > 0;
+        pascal_obj.difficult = atoi(parseNodeText(xml_obj, "difficult", "0")) > 0;
+        pascal_obj.occluded = atoi(parseNodeText(xml_obj, "occluded", "0")) > 0;
+
+        // <bndbox/>
+        XMLElement *xml_bndbox = xml_obj->FirstChildElement("bndbox");
+        pascal_obj.xmin = atoi(xml_bndbox->FirstChildElement("xmin")->GetText());
+        pascal_obj.ymin = atoi(xml_bndbox->FirstChildElement("ymin")->GetText());
+        pascal_obj.xmax = atoi(xml_bndbox->FirstChildElement("xmax")->GetText());
+        pascal_obj.ymax = atoi(xml_bndbox->FirstChildElement("ymax")->GetText());
+
+        // <part/>
+        vector<PascalPart> parts;
+        XMLElement *xml_part = xml_obj->FirstChildElement("part");
+
+        while (xml_part)
+        {
+            PascalPart pascal_part;
+            pascal_part.name = xml_part->FirstChildElement("name")->GetText();
+
+            xml_bndbox = xml_part->FirstChildElement("bndbox");
+            pascal_part.xmin = atoi(xml_bndbox->FirstChildElement("xmin")->GetText());
+            pascal_part.ymin = atoi(xml_bndbox->FirstChildElement("ymin")->GetText());
+            pascal_part.xmax = atoi(xml_bndbox->FirstChildElement("xmax")->GetText());
+            pascal_part.ymax = atoi(xml_bndbox->FirstChildElement("ymax")->GetText());
+            parts.push_back(pascal_part);
+
+            xml_part = xml_part->NextSiblingElement("part");
+        }
+
+        pascal_obj.parts = parts;
+        objects.push_back(pascal_obj);
+
+        xml_obj = xml_obj->NextSiblingElement("object");
+    }
+
+    annotation->objects = objects;
+
+    return annotation;
+}
+
+Ptr<OR_pascal> OR_pascal::create()
+{
+    return Ptr<OR_pascalImp>(new OR_pascalImp);
+}
+
+}
+}

modules/datasets/src/pd_inria.cpp

+/*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) 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 Itseez Inc 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 "opencv2/datasets/pd_inria.hpp"
+#include "opencv2/datasets/util.hpp"
+
+namespace cv
+{
+namespace datasets
+{
+
+using namespace std;
+
+class PD_inriaImp : public PD_inria
+{
+public:
+    PD_inriaImp() {}
+    
+    virtual ~PD_inriaImp() {}
+
+    virtual void load(const string &path);
+
+private:
+    void loadDataset(const string &path, const string nameImageSet, vector< Ptr<Object> > &imageSet);
+    void readTextLines(const string filename, vector< string > &lines);
+    void parseAnnotation(const string filename, Ptr< PD_inriaObj > &object);
+};
+
+void PD_inriaImp::load(const string &path)
+{
+    // Training set
+    train.push_back(vector< Ptr<Object> >());
+    loadDataset(path, "Train", train.back());
+    
+    // Testing set
+    test.push_back(vector< Ptr<Object> >());
+    loadDataset(path, "Test", test.back());
+
+    // There is no validation set
+    validation.push_back(vector< Ptr<Object> >());
+}
+
+void PD_inriaImp::readTextLines(const string filename, vector< string > &lines)
+{
+    ifstream in(filename.c_str());
+    string error_message = "";
+
+    if (!in.is_open())
+    {
+        error_message = format("Unable to open file: \n%s\n", filename.c_str());
+        CV_Error(Error::StsBadArg, error_message);
+    }
+
+    string currline = "";
+
+    while (getline(in, currline))
+        lines.push_back(currline);
+}
+
+void PD_inriaImp::parseAnnotation(const string filename, Ptr< PD_inriaObj > &object)
+{
+    string error_message = "";
+
+    ifstream in(filename.c_str());
+
+    if (!in.is_open())
+    {
+        error_message = format("Unable to open file: \n%s\n", filename.c_str());
+        CV_Error(Error::StsBadArg, error_message);
+    }
+    
+    string imageSizeHeader = "Image size (X x Y x C) : ";
+    string imageSizeFmt = imageSizeHeader + "%d x %d x %d";
+    string objWithGTHeader = "Objects with ground truth : ";
+    string objWithGTFmt = objWithGTHeader + "%d { \"PASperson\" }";
+    string boundBoxHeader = "Bounding box for object ";
+    string boundBoxFmt = boundBoxHeader + "%*d \"PASperson\" (Xmin, Ymin) - (Xmax, Ymax) : (%d, %d) - (%d, %d)";
+    
+    string line = "";
+    
+    int width = 0;
+    int height = 0;
+    int depth = 0;
+    int xmin, ymin, xmax, ymax;
+
+    int numObjects = 0;
+
+    while (getline(in, line))
+    {
+        if (line[0] == '#' || !line[0])
+            continue;
+
+        if (strstr(line.c_str(), imageSizeHeader.c_str()))
+        {
+            sscanf(line.c_str(), imageSizeFmt.c_str(), &width, &height, &depth);
+            object->width = width;
+            object->height = height;
+            object->depth = depth;
+        }
+        else if (strstr(line.c_str(), objWithGTHeader.c_str()))
+        {
+            sscanf(line.c_str(), objWithGTFmt.c_str(), &numObjects);
+            
+            if (numObjects <= 0)
+                break;
+        }
+        else if (strstr(line.c_str(), boundBoxHeader.c_str()))
+        {
+            sscanf(line.c_str(), boundBoxFmt.c_str(), &xmin, &ymin, &xmax, &ymax);
+            Rect bndbox;
+            bndbox.x = xmin;
+            bndbox.y = ymin;
+            bndbox.width = xmax - xmin;
+            bndbox.height = ymax - ymin;
+            (object->bndboxes).push_back(bndbox);
+        }
+    }
+
+    CV_Assert((object->bndboxes).size() == (unsigned int)numObjects);
+}
+
+void PD_inriaImp::loadDataset(const string &path, const string nameImageSet, vector< Ptr<Object> > &imageSet)
+{
+    string listAnn = path + nameImageSet + "/annotations.lst";
+    string listPos = path + nameImageSet + "/pos.lst";
+    string listNeg = path + nameImageSet + "/neg.lst";
+    
+    vector< string > fsAnn;
+    vector< string > fsPos;
+    vector< string > fsNeg;
+   
+    // read file names
+    readTextLines(listAnn, fsAnn);
+    readTextLines(listPos, fsPos);
+    readTextLines(listNeg, fsNeg);
+   
+    CV_Assert(fsAnn.size() == fsPos.size());
+
+    for (unsigned int i = 0; i < fsPos.size(); i++)
+    {
+        Ptr<PD_inriaObj> curr(new PD_inriaObj);
+        parseAnnotation(path + fsAnn[i], curr);
+        curr->filename = path + fsPos[i];
+        curr->sType = POS;
+        
+        imageSet.push_back(curr);
+    }
+    
+    for (unsigned int i = 0; i < fsNeg.size(); i++)
+    {
+        Ptr<PD_inriaObj> curr(new PD_inriaObj);
+        curr->filename = path + fsNeg[i]; 
+        curr->sType = NEG;
+        
+        imageSet.push_back(curr);
+    }
+}
+
+Ptr<PD_inria> PD_inria::create()
+{
+    return Ptr<PD_inriaImp>(new PD_inriaImp);
+}
+
+}
+}

modules/stereo/CMakeLists.txt

+set(the_description "Stereo Correspondence")
+ocv_define_module(stereo opencv_imgproc opencv_features2d opencv_core opencv_highgui opencv_calib3d)

modules/stereo/README.md

+Stereo Correspondence with different descriptors
+================================================

modules/stereo/include/opencv2/stereo/stereo.hpp

+/*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/stereo.hpp"
+

modules/stereo/src/precomp.hpp

+/*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_STEREO_PRECOMP_H__
+#define __OPENCV_STEREO_PRECOMP_H__
+
+#include "opencv2/stereo.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/features2d.hpp"
+#include "opencv2/core.hpp"
+#include "opencv2/core/utility.hpp"
+#include "opencv2/core/private.hpp"
+#include "opencv2/core/cvdef.h"
+#include "opencv2/highgui.hpp"
+#include "opencv2/calib3d.hpp"
+
+#include <algorithm>
+#include <cmath>
+
+#endif
+

modules/stereo/test/test_main.cpp

+/*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*/
+
+#include "test_precomp.hpp"
+
+CV_TEST_MAIN("cv")

modules/stereo/test/test_precomp.hpp

+#ifdef __GNUC__
+#  pragma GCC diagnostic ignored "-Wmissing-declarations"
+#  if defined __clang__ || defined __APPLE__
+#    pragma GCC diagnostic ignored "-Wmissing-prototypes"
+#    pragma GCC diagnostic ignored "-Wextra"
+#  endif
+#endif
+
+#ifndef __OPENCV_TEST_PRECOMP_HPP__
+#define __OPENCV_TEST_PRECOMP_HPP__
+
+#include <iostream>
+#include "opencv2/ts.hpp"
+#include "opencv2/imgcodecs.hpp"
+
+
+#include "opencv2/stereo.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/features2d.hpp"
+#include "opencv2/core/utility.hpp"
+#include "opencv2/core/private.hpp"
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core.hpp"
+#include "opencv2/highgui.hpp"
+
+#include <algorithm>
+#include <cmath>
+
+
+
+#endif

modules/surface_matching/samples/ppf_load_match.cpp

@@ -111,8 +111,21 @@ int main(int argc, char** argv)
     cout << endl << "PPF Elapsed Time " <<
          (tick2-tick1)/cv::getTickFrequency() << " sec" << endl;
 
-    // Get only first N results
-    int N = 2;
+    //check results size from match call above
+    size_t results_size = results.size();
+    cout << "Number of matching poses: " << results_size;
+    if (results_size == 0) {
+        cout << endl << "No matching poses found. Exiting." << endl;
+        exit(0);
+    }
+
+    // Get only first N results - but adjust to results size if num of results are less than that specified by N
+    size_t N = 2;
+    if (results_size < N) {
+        cout << endl << "Reducing matching poses to be reported (as specified in code): "
+             << N << " to the number of matches found: " << results_size << endl;
+        N = results_size;
+    }
     vector<Pose3DPtr> resultsSub(results.begin(),results.begin()+N);
     
     // Create an instance of ICP

modules/surface_matching/src/ppf_match_3d.cpp

@@ -375,7 +375,7 @@ void PPF3DDetector::clusterPoses(std::vector<Pose3DPtr> poseList, int numPoses,
 #pragma omp parallel for
 #endif
     // uses weighting by the number of votes
-    for (size_t i=0; i<poseClusters.size(); i++)
+    for (int i=0; i<static_cast<int>(poseClusters.size()); i++)
     {
       // We could only average the quaternions. So I will make use of them here
       double qAvg[4]={0}, tAvg[3]={0};
@@ -426,7 +426,7 @@ void PPF3DDetector::clusterPoses(std::vector<Pose3DPtr> poseList, int numPoses,
 #if defined _OPENMP
 #pragma omp parallel for
 #endif
-    for (size_t i=0; i<poseClusters.size(); i++)
+    for (int i=0; i<static_cast<int>(poseClusters.size()); i++)
     {
       // We could only average the quaternions. So I will make use of them here
       double qAvg[4]={0}, tAvg[3]={0};

modules/text/include/opencv2/text/ocr.hpp

@@ -240,6 +240,119 @@ types.
  */
 CV_EXPORTS Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifierNM(const std::string& filename);
 
+
+/* OCR BeamSearch Decoder */
+
+/** @brief OCRBeamSearchDecoder class provides an interface for OCR using Beam Search algorithm.
+
+@note
+   -   (C++) An example on using OCRBeamSearchDecoder recognition combined with scene text detection can
+        be found at the demo sample:
+        <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/word_recognition.cpp>
+ */
+class CV_EXPORTS OCRBeamSearchDecoder : public BaseOCR
+{
+public:
+
+    /** @brief Callback with the character classifier is made a class.
+
+    This way it hides the feature extractor and the classifier itself, so developers can write
+    their own OCR code.
+
+    The default character classifier and feature extractor can be loaded using the utility funtion
+    loadOCRBeamSearchClassifierCNN with all its parameters provided in
+    <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/OCRBeamSearch_CNN_model_data.xml.gz>.
+     */
+    class CV_EXPORTS ClassifierCallback
+    {
+    public:
+        virtual ~ClassifierCallback() { }
+        /** @brief The character classifier must return a (ranked list of) class(es) id('s)
+
+        @param image Input image CV_8UC1 or CV_8UC3 with a single letter.
+        @param recognition_probabilities For each of the N characters found the classifier returns a list with
+        class probabilities for each class.
+        @param oversegmentation The classifier returns a list of N+1 character locations' x-coordinates,
+        including 0 as start-sequence location.
+         */
+        virtual void eval( InputArray image, std::vector< std::vector<double> >& recognition_probabilities, std::vector<int>& oversegmentation );
+    };
+
+public:
+    /** @brief Recognize text using Beam Search.
+
+    Takes image on input and returns recognized text in the output_text parameter. Optionally
+    provides also the Rects for individual text elements found (e.g. words), and the list of those
+    text elements with their confidence values.
+
+    @param image Input image CV_8UC1 with a single text line (or word).
+
+    @param output_text Output text. Most likely character sequence found by the HMM decoder.
+
+    @param component_rects If provided the method will output a list of Rects for the individual
+    text elements found (e.g. words).
+
+    @param component_texts If provided the method will output a list of text strings for the
+    recognition of individual text elements found (e.g. words).
+
+    @param component_confidences If provided the method will output a list of confidence values
+    for the recognition of individual text elements found (e.g. words).
+
+    @param component_level Only OCR_LEVEL_WORD is supported.
+     */
+    virtual void run(Mat& image, std::string& output_text, std::vector<Rect>* component_rects=NULL,
+                     std::vector<std::string>* component_texts=NULL, std::vector<float>* component_confidences=NULL,
+                     int component_level=0);
+
+    /** @brief Creates an instance of the OCRBeamSearchDecoder class. Initializes HMMDecoder.
+
+    @param classifier The character classifier with built in feature extractor.
+
+    @param vocabulary The language vocabulary (chars when ascii english text). vocabulary.size()
+    must be equal to the number of classes of the classifier.
+
+    @param transition_probabilities_table Table with transition probabilities between character
+    pairs. cols == rows == vocabulary.size().
+
+    @param emission_probabilities_table Table with observation emission probabilities. cols ==
+    rows == vocabulary.size().
+
+    @param mode HMM Decoding algorithm. Only OCR_DECODER_VITERBI is available for the moment
+    (<http://en.wikipedia.org/wiki/Viterbi_algorithm>).
+
+    @param beam_size Size of the beam in Beam Search algorithm.
+     */
+    static Ptr<OCRBeamSearchDecoder> create(const Ptr<OCRBeamSearchDecoder::ClassifierCallback> classifier,// The character classifier with built in feature extractor
+                                     const std::string& vocabulary,                    // The language vocabulary (chars when ascii english text)
+                                                                                       //     size() must be equal to the number of classes
+                                     InputArray transition_probabilities_table,        // Table with transition probabilities between character pairs
+                                                                                       //     cols == rows == vocabulari.size()
+                                     InputArray emission_probabilities_table,          // Table with observation emission probabilities
+                                                                                       //     cols == rows == vocabulari.size()
+                                     decoder_mode mode = OCR_DECODER_VITERBI,          // HMM Decoding algorithm (only Viterbi for the moment)
+                                     int beam_size = 50);                              // Size of the beam in Beam Search algorithm
+
+protected:
+
+    Ptr<OCRBeamSearchDecoder::ClassifierCallback> classifier;
+    std::string vocabulary;
+    Mat transition_p;
+    Mat emission_p;
+    decoder_mode mode;
+    int beam_size;
+};
+
+/** @brief Allow to implicitly load the default character classifier when creating an OCRBeamSearchDecoder object.
+
+@param filename The XML or YAML file with the classifier model (e.g. OCRHMM_knn_model_data.xml)
+
+The default classifier is based in the scene text recognition method proposed by Adam Coates &
+Andrew NG in [Coates11a]. The character classifier sonsists in a Single Layer Convolutional Neural Network and
+a linear classifier. It is applied to the input image in a sliding window fashion, providing a set of recognitions
+at each window location.
+ */
+CV_EXPORTS Ptr<OCRBeamSearchDecoder::ClassifierCallback> loadOCRBeamSearchClassifierCNN(const std::string& filename);
+
 //! @}
 
 }

modules/text/samples/cropped_word_recognition.cpp

+/*
+ * textdetection.cpp
+ *
+ * A demo program of End-to-end Scene Text Detection and Recognition:
+ * Shows the use of the Tesseract OCR API with the Extremal Region Filter algorithm described in:
+ * Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+ *
+ * Created on: Jul 31, 2014
+ *     Author: Lluis Gomez i Bigorda <lgomez AT cvc.uab.es>
+ */
+
+#include "opencv2/text.hpp"
+#include "opencv2/core/utility.hpp"
+#include "opencv2/highgui.hpp"
+#include "opencv2/imgproc.hpp"
+
+#include <iostream>
+
+using namespace std;
+using namespace cv;
+using namespace cv::text;
+
+//Perform text recognition in a given cropped word
+int main(int argc, char* argv[])
+{
+    cout << endl << argv[0] << endl << endl;
+    cout << "A demo program of Scene Text cropped word Recognition: " << endl;
+    cout << "Shows the use of the OCRBeamSearchDecoder class using the Single Layer CNN character classifier described in:" << endl;
+    cout << "Coates, Adam, et al. \"Text detection and character recognition in scene images with unsupervised feature learning.\" ICDAR 2011." << endl << endl;
+
+    Mat image;
+    if(argc>1)
+        image  = imread(argv[1]);
+    else
+    {
+        cout << "    Usage: " << argv[0] << " <input_image>" << endl << endl;
+        return(0);
+    }
+
+    Mat transition_p;
+    string filename = "OCRHMM_transitions_table.xml"; // TODO this table was done with a different vocabulary order?
+                                                      // TODO add a new function in ocr.cpp to create transition tab
+                                                      // for a given lexicon
+    FileStorage fs(filename, FileStorage::READ);
+    fs["transition_probabilities"] >> transition_p;
+    fs.release();
+    Mat emission_p = Mat::eye(62,62,CV_64FC1);
+    string voc = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyx0123456789";
+
+    Ptr<OCRBeamSearchDecoder> ocr = OCRBeamSearchDecoder::create(
+                loadOCRBeamSearchClassifierCNN("OCRBeamSearch_CNN_model_data.xml.gz"),
+                voc, transition_p, emission_p);
+
+    double t_r = (double)getTickCount();
+    string output;
+
+    vector<Rect>   boxes;
+    vector<string> words;
+    vector<float>  confidences;
+    ocr->run(image, output, &boxes, &words, &confidences, OCR_LEVEL_WORD);
+
+    cout << "OCR output = \"" << output << "\". Decoded in "
+         << ((double)getTickCount() - t_r)*1000/getTickFrequency() << " ms." << endl << endl;
+
+    return 0;
+}

modules/text/src/erfilter.cpp

@@ -287,7 +287,11 @@ void ERFilterNM::er_tree_extract( InputArray image )
     quads[1][3] = (1<<3)|(1<<2)|(1<<1);
     quads[2][0] = (1<<2)|(1<<1);
     quads[2][1] = (1<<3)|(1);
-    quads[2][3] = 255;
+    // quads[2][2] and quads[2][3] are never used so no need to initialize them.
+    // The four lowest bits in each quads[i][j] correspond to the 2x2 binary patterns 
+    // Q_1, Q_2, Q_3 in the Neumann and Matas CVPR 2012 paper 
+    // (see in page 4 at the end of first column). 
+    // Q_1 and Q_2 have four patterns, while Q_3 has only two.
 
 
     // masks to know if a pixel is accessible and if it has been already added to some region

modules/text/src/ocr_tesseract.cpp

@@ -127,7 +127,7 @@ public:
              int component_level=0)
     {
 
-        CV_Assert( (image.type() == CV_8UC1) || (image.type() == CV_8UC1) );
+        CV_Assert( (image.type() == CV_8UC1) || (image.type() == CV_8UC3) );
 
 #ifdef HAVE_TESSERACT
 

modules/tracking/doc/[Tutorial] Adding new Tracker Method for dummies

+
+/*---------------STEP 1---------------------*/
+/* modify this file
+*  opencv2/tracking/tracker.hpp
+*  and put several lines of snippet similar to 
+*  the following:
+*/ 
+/*------------------------------------------*/
+
+class CV_EXPORTS_W TrackerKCF : public Tracker
+{
+ public:
+  struct CV_EXPORTS Params
+  {
+    Params();
+    void read( const FileNode& /*fn*/ );
+    void write( FileStorage& /*fs*/ ) const;
+  };
+
+  /** @brief Constructor
+    @param parameters KCF parameters TrackerKCF::Params
+     */
+  BOILERPLATE_CODE("KCF",TrackerKCF);
+};
+
+
+/*---------------STEP 2---------------------*/
+/* modify this file
+*  src/tracker.cpp
+*  add one line in function  
+*  Ptr<Tracker> Tracker::create( const String& trackerType )
+*/ 
+/*------------------------------------------*/
+
+Ptr<Tracker> Tracker::create( const String& trackerType )
+{
+  BOILERPLATE_CODE("MIL",TrackerMIL);
+  BOILERPLATE_CODE("BOOSTING",TrackerBoosting);
+  BOILERPLATE_CODE("MEDIANFLOW",TrackerMedianFlow);
+  BOILERPLATE_CODE("TLD",TrackerTLD);
+  BOILERPLATE_CODE("KCF",TrackerKCF); // add this line!
+  return Ptr<Tracker>();
+}
+
+
+/*---------------STEP 3---------------------*/
+/* make a new file and paste the snippet below
+*  and modify it according to your needs.
+*  also make sure to put the LICENSE part.
+*  src/trackerKCF.cpp
+*/
+/*------------------------------------------*/
+
+/*---------------------------
+|  TrackerKCFModel
+|---------------------------*/
+namespace cv{
+   /**
+  * \brief Implementation of TrackerModel for MIL algorithm
+  */
+  class TrackerKCFModel : public TrackerModel{
+  public:
+    TrackerKCFModel(TrackerKCF::Params /*params*/){}
+    ~TrackerKCFModel(){}
+  protected:
+    void modelEstimationImpl( const std::vector<Mat>& responses ){}
+    void modelUpdateImpl(){}
+  };
+} /* namespace cv */
+
+
+/*---------------------------
+|  TrackerKCF
+|---------------------------*/
+namespace cv{
+  
+  /*
+ * Prototype
+ */
+  class TrackerKCFImpl : public TrackerKCF{
+  public:
+    TrackerKCFImpl( const TrackerKCF::Params &parameters = TrackerKCF::Params() );
+    void read( const FileNode& fn );
+    void write( FileStorage& fs ) const;
+  
+  protected:
+    bool initImpl( const Mat& image, const Rect2d& boundingBox );
+    bool updateImpl( const Mat& image, Rect2d& boundingBox );
+    
+    TrackerKCF::Params params;
+  };
+    
+  /*
+ * Constructor
+ */
+  Ptr<TrackerKCF> TrackerKCF::createTracker(const TrackerKCF::Params &parameters){
+      return Ptr<TrackerKCFImpl>(new TrackerKCFImpl(parameters));
+  }
+  TrackerKCFImpl::TrackerKCFImpl( const TrackerKCF::Params &parameters ) :
+      params( parameters )
+  {
+    isInit = false;
+  }
+  
+  void TrackerKCFImpl::read( const cv::FileNode& fn ){
+    params.read( fn );
+  }
+
+  void TrackerKCFImpl::write( cv::FileStorage& fs ) const{
+    params.write( fs );
+  }
+  
+  
+  bool TrackerKCFImpl::initImpl( const Mat& image, const Rect2d& boundingBox ){
+    model=Ptr<TrackerKCFModel>(new TrackerKCFModel(params));
+    return true;
+  }
+  bool TrackerKCFImpl::updateImpl( const Mat& image, Rect2d& boundingBox ){return true;}
+  
+  /*
+ * Parameters
+ */
+  TrackerKCF::Params::Params(){
+      
+  }
+
+  void TrackerKCF::Params::read( const cv::FileNode& fn ){
+    
+  }
+
+  void TrackerKCF::Params::write( cv::FileStorage& fs ) const{
+    
+  }
+  
+} /* namespace cv */

modules/tracking/doc/tracking.bib

@@ -67,3 +67,29 @@
   year={2013},
   organization={IEEE}
 }
+
+@article{KCF,
+  title = {High-Speed Tracking with Kernelized Correlation Filters},
+  journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
+  author = {Henriques, J. F. and Caseiro, R. and Martins, P. and Batista, J.},
+  year = {2015},
+  doi = {10.1109/TPAMI.2014.2345390},
+}
+
+@inproceedings{KCF_ECCV,
+  title = {Exploiting the Circulant Structure of Tracking-by-detection with Kernels},
+  author = {Henriques, J. F. and Caseiro, R. and Martins, P. and Batista, J.},
+  booktitle = {proceedings of the European Conference on Computer Vision},
+  year = {2012},
+}
+
+@INPROCEEDINGS{KCF_CN,
+  author={Danelljan, M. and Khan, F.S. and Felsberg, M. and van de Weijer, J.},
+  booktitle={Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
+  title={Adaptive Color Attributes for Real-Time Visual Tracking},
+  year={2014},
+  month={June},
+  pages={1090-1097},
+  keywords={computer vision;feature extraction;image colour analysis;image representation;image sequences;adaptive color attributes;benchmark color sequences;color features;color representations;computer vision;image description;real-time visual tracking;tracking-by-detection framework;Color;Computational modeling;Covariance matrices;Image color analysis;Kernel;Target tracking;Visualization;Adaptive Dimensionality Reduction;Appearance Model;Color Features;Visual Tracking},
+  doi={10.1109/CVPR.2014.143},
+}

modules/tracking/include/opencv2/tracking/tracker.hpp

@@ -1189,6 +1189,60 @@ class CV_EXPORTS_W TrackerTLD : public Tracker
   BOILERPLATE_CODE("TLD",TrackerTLD);
 };
 
+/** @brief KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed.
+ * This tracking method is an implementation of @cite KCF_ECCV which is extended to KFC with color-names features (@cite KCF_CN).
+ * The original paper of KCF is available at <http://home.isr.uc.pt/~henriques/circulant/index.html>
+ * as well as the matlab implementation. For more information about KCF with color-names features, please refer to
+ * <http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html>.
+ */
+class CV_EXPORTS_W TrackerKCF : public Tracker
+{
+ public:
+  /**
+   * \brief Feature type to be used in the tracking grayscale, colornames, compressed color-names
+   * The modes available now:
+    -   "GRAY" -- Use grayscale values as the feature
+    -   "CN" -- Color-names feature
+   */
+  enum MODE {GRAY, CN, CN2};
+
+  struct CV_EXPORTS Params
+  {
+    /**
+     * \brief Constructor
+     */
+    Params();
+
+    /**
+     * \brief Read parameters from file, currently unused
+     */
+    void read( const FileNode& /*fn*/ );
+
+    /**
+     * \brief Read parameters from file, currently unused
+     */
+    void write( FileStorage& /*fs*/ ) const;
+
+    double sigma;                 //!<  gaussian kernel bandwidth
+    double lambda;                //!<  regularization
+    double interp_factor;         //!<  linear interpolation factor for adaptation
+    double output_sigma_factor;   //!<  spatial bandwidth (proportional to target)
+    double pca_learning_rate;     //!<  compression learning rate
+    bool resize;                  //!<  activate the resize feature to improve the processing speed
+    bool split_coeff;             //!<  split the training coefficients into two matrices
+    bool wrap_kernel;             //!<  wrap around the kernel values
+    bool compress_feature;        //!<  activate the pca method to compress the features
+    int max_patch_size;           //!<  threshold for the ROI size
+    int compressed_size;          //!<  feature size after compression
+    MODE descriptor;              //!<  descriptor type
+  };
+
+  /** @brief Constructor
+      @param parameters KCF parameters TrackerKCF::Params
+  */
+  BOILERPLATE_CODE("KCF",TrackerKCF);
+};
+
 //! @}
 
 } /* namespace cv */

modules/tracking/samples/kcf.cpp

+/*----------------------------------------------
+ * Usage:
+ * example_tracking_kcf <video_name>
+ *
+ * example:
+ * example_tracking_kcf Bolt/img/%04.jpg
+ * example_tracking_kcf faceocc2.webm
+ *--------------------------------------------------*/
+
+#include <opencv2/core/utility.hpp>
+#include <opencv2/tracking.hpp>
+#include <opencv2/videoio.hpp>
+#include <opencv2/highgui.hpp>
+#include <iostream>
+#include <cstring>
+
+using namespace std;
+using namespace cv;
+
+class BoxExtractor {
+public:
+  Rect2d extract(Mat img);
+  Rect2d extract(const std::string& windowName, Mat img, bool showCrossair = true);
+
+  struct handlerT{
+    bool isDrawing;
+    Rect2d box;
+    Mat image;
+
+    // initializer list
+    handlerT(): isDrawing(false) {};
+  }params;
+
+private:
+  static void mouseHandler(int event, int x, int y, int flags, void *param);
+  void opencv_mouse_callback( int event, int x, int y, int , void *param );
+};
+
+int main( int argc, char** argv ){
+  // show help
+  if(argc<2){
+    cout<<
+      " Usage: example_tracking_kcf <video_name>\n"
+      " examples:\n"
+      " example_tracking_kcf Bolt/img/%04.jpg\n"
+      " example_tracking_kcf faceocc2.webm\n"
+      << endl;
+    return 0;
+  }
+
+  // ROI selector
+  BoxExtractor box;
+
+  // create the tracker
+  Ptr<Tracker> tracker = Tracker::create( "KCF" );
+
+  // set input video
+  std::string video = argv[1];
+  VideoCapture cap(video);
+
+  Mat frame;
+
+  // get bounding box
+  cap >> frame;
+  Rect2d roi=box.extract("tracker",frame);
+
+  //quit if ROI was not selected
+  if(roi.width==0 || roi.height==0)
+    return 0;
+
+  // initialize the tracker
+  tracker->init(frame,roi);
+
+  // do the tracking
+  printf("Start the tracking process, press ESC to quit.\n");
+  for ( ;; ){
+    // get frame from the video
+    cap >> frame;
+
+    // stop the program if no more images
+    if(frame.rows==0 || frame.cols==0)
+      break;
+
+    // update the tracking result
+    tracker->update(frame,roi);
+
+    // draw the tracked object
+    rectangle( frame, roi, Scalar( 255, 0, 0 ), 2, 1 );
+
+    // show image with the tracked object
+    imshow("tracker",frame);
+
+    //quit on ESC button
+    if(waitKey(1)==27)break;
+  }
+
+}
+
+void BoxExtractor::mouseHandler(int event, int x, int y, int flags, void *param){
+    BoxExtractor *self =static_cast<BoxExtractor*>(param);
+    self->opencv_mouse_callback(event,x,y,flags,param);
+}
+
+void BoxExtractor::opencv_mouse_callback( int event, int x, int y, int , void *param ){
+    handlerT * data = (handlerT*)param;
+    switch( event ){
+      // update the selected bounding box
+      case EVENT_MOUSEMOVE:
+        if( data->isDrawing ){
+          data->box.width = x-data->box.x;
+          data->box.height = y-data->box.y;
+        }
+      break;
+
+      // start to select the bounding box
+      case EVENT_LBUTTONDOWN:
+        data->isDrawing = true;
+        data->box = cvRect( x, y, 0, 0 );
+      break;
+
+      // cleaning up the selected bounding box
+      case EVENT_LBUTTONUP:
+        data->isDrawing = false;
+        if( data->box.width < 0 ){
+          data->box.x += data->box.width;
+          data->box.width *= -1;
+        }
+        if( data->box.height < 0 ){
+          data->box.y += data->box.height;
+          data->box.height *= -1;
+        }
+      break;
+    }
+}
+
+Rect2d BoxExtractor::extract(Mat img){
+  return extract("Bounding Box Extractor", img);
+}
+
+Rect2d BoxExtractor::extract(const std::string& windowName, Mat img, bool showCrossair){
+
+  int key=0;
+
+  // show the image and give feedback to user
+  imshow(windowName,img);
+  printf("Select an object to track and then press SPACE/BACKSPACE/ENTER button!\n");
+
+  // copy the data, rectangle should be drawn in the fresh image
+  params.image=img.clone();
+
+  // select the object
+  setMouseCallback( windowName, mouseHandler, (void *)&params );
+
+  // end selection process on SPACE (32) BACKSPACE (27) or ENTER (13)
+  while(!(key==32 || key==27 || key==13)){
+    // draw the selected object
+    rectangle(
+      params.image,
+      params.box,
+      Scalar(255,0,0),2,1
+    );
+
+    // draw cross air in the middle of bounding box
+    if(showCrossair){
+      // horizontal line
+      line(
+        params.image,
+        Point((int)params.box.x,(int)(params.box.y+params.box.height/2)),
+        Point((int)(params.box.x+params.box.width),(int)(params.box.y+params.box.height/2)),
+        Scalar(255,0,0),2,1
+      );
+
+      // vertical line
+      line(
+        params.image,
+        Point((int)(params.box.x+params.box.width/2),(int)params.box.y),
+        Point((int)(params.box.x+params.box.width/2),(int)(params.box.y+params.box.height)),
+        Scalar(255,0,0),2,1
+      );
+    }
+
+    // show the image bouding box
+    imshow(windowName,params.image);
+
+    // reset the image
+    params.image=img.clone();
+
+    //get keyboard event
+    key=waitKey(1);
+  }
+
+
+  return params.box;
+}

modules/tracking/samples/tld_test.cpp

@@ -48,8 +48,8 @@
 using namespace std;
 using namespace cv;
 
-#define NUM_TEST_FRAMES 500
-#define TEST_VIDEO_INDEX 1		//TLD Dataset Video Index from 1-10
+#define NUM_TEST_FRAMES 100
+#define TEST_VIDEO_INDEX 7		//TLD Dataset Video Index from 1-10
 //#define RECORD_VIDEO_FLG
 
 static Mat image;

modules/tracking/src/opencl/tldDetector.cl

+// 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.
+
+// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+
+
+__kernel void NCC(__global const uchar *patch,
+				 __global const uchar *positiveSamples,
+				 __global const uchar *negativeSamples,
+				 __global float *ncc,
+				 int posNum,
+				 int negNum)
+{
+	int id = get_global_id(0);
+	if (id >= 1000) return;
+	bool posFlg;
+
+	if (id < 500)
+		posFlg = true;
+	if (id >= 500)
+	{
+		//Negative index
+		id = id - 500;
+		posFlg = false;
+	}
+
+	//Variables
+	int s1 = 0, s2 = 0, n1 = 0, n2 = 0, prod = 0;
+	float sq1 = 0, sq2 = 0, ares = 0;
+	int N = 225;
+	//NCC with positive sample
+	if (posFlg && id < posNum)
+	{
+		for (int i = 0; i < N; i++)
+		{
+			s1 += positiveSamples[id * N + i];
+			s2 += patch[i];
+			n1 += positiveSamples[id * N + i] * positiveSamples[id * N + i];
+			n2 += patch[i] * patch[i];
+			prod += positiveSamples[id * N + i] * patch[i];
+		}
+		sq1 = sqrt(max(0.0, n1 - 1.0 * s1 * s1 / N));
+		sq2 = sqrt(max(0.0, n2 - 1.0 * s2 * s2 / N));
+		ares = (sq2 == 0) ? sq1 / fabs(sq1) : (prod - s1 * s2 / N) / sq1 / sq2;
+		ncc[id] = ares;
+	}
+
+	//NCC with negative sample
+	if (!posFlg && id < negNum)
+	{
+		for (int i = 0; i < N; i++)
+		{
+
+			s1 += negativeSamples[id * N + i];
+			s2 += patch[i];
+			n1 += negativeSamples[id * N + i] * negativeSamples[id * N + i];
+			n2 += patch[i] * patch[i];
+			prod += negativeSamples[id * N + i] * patch[i];
+		}
+		sq1 = sqrt(max(0.0, n1 - 1.0 * s1 * s1 / N));
+		sq2 = sqrt(max(0.0, n2 - 1.0 * s2 * s2 / N));
+		ares = (sq2 == 0) ? sq1 / fabs(sq1) : (prod - s1 * s2 / N) / sq1 / sq2;
+		ncc[id+500] = ares;
+	}
+}
+
+__kernel void batchNCC(__global const uchar *patches,
+	__global const uchar *positiveSamples,
+	__global const uchar *negativeSamples,
+	__global float *posNcc,
+	__global float *negNcc,
+	int posNum,
+	int negNum,
+	int patchNum)
+{
+	int id = get_global_id(0);
+	bool posFlg;
+
+	if (id < 500*patchNum)
+		posFlg = true;
+	if (id >= 500*patchNum)
+	{
+		//Negative index
+		id = id - 500*patchNum;
+		posFlg = false;
+	}
+
+	int modelSampleID = id % 500;
+	int patchID = id / 500;
+
+	//Variables
+	int s1 = 0, s2 = 0, n1 = 0, n2 = 0, prod = 0;
+	float sq1 = 0, sq2 = 0, ares = 0;
+	int N = 225;
+
+	//NCC with positive sample
+	if (posFlg && modelSampleID < posNum)
+	{
+		for (int i = 0; i < N; i++)
+		{
+			s1 += positiveSamples[modelSampleID * N + i];
+			s2 += patches[patchID*N + i];
+			n1 += positiveSamples[modelSampleID * N + i] * positiveSamples[modelSampleID * N + i];
+			n2 += patches[patchID*N + i] * patches[patchID*N + i];
+			prod += positiveSamples[modelSampleID * N + i] * patches[patchID*N + i];
+		}
+		sq1 = sqrt(max(0.0, n1 - 1.0 * s1 * s1 / N));
+		sq2 = sqrt(max(0.0, n2 - 1.0 * s2 * s2 / N));
+		ares = (sq2 == 0) ? sq1 / fabs(sq1) : (prod - s1 * s2 / N) / sq1 / sq2;
+		posNcc[id] = ares;
+	}
+
+	//NCC with negative sample
+	if (!posFlg && modelSampleID < negNum)
+	{
+		for (int i = 0; i < N; i++)
+		{
+
+			s1 += negativeSamples[modelSampleID * N + i];
+			s2 += patches[patchID*N + i];
+			n1 += negativeSamples[modelSampleID * N + i] * negativeSamples[modelSampleID * N + i];
+			n2 += patches[patchID*N + i] * patches[patchID*N + i];
+			prod += negativeSamples[modelSampleID * N + i] * patches[patchID*N + i];
+		}
+		sq1 = sqrt(max(0.0, n1 - 1.0 * s1 * s1 / N));
+		sq2 = sqrt(max(0.0, n2 - 1.0 * s2 * s2 / N));
+		ares = (sq2 == 0) ? sq1 / fabs(sq1) : (prod - s1 * s2 / N) / sq1 / sq2;
+		negNcc[id] = ares;
+	}
+}

modules/tracking/src/precomp.hpp

@@ -44,5 +44,11 @@
 
 #include "opencv2/tracking.hpp"
 #include "opencv2/core/utility.hpp"
+#include "opencv2/core/ocl.hpp"
+
+namespace cv
+{
+	extern const double ColorNames[][10];
+}
 
 #endif

modules/tracking/src/tldDetector.hpp

@@ -43,6 +43,7 @@
 #define OPENCV_TLD_DETECTOR
 
 #include "precomp.hpp"
+#include "opencl_kernels_tracking.hpp"
 #include "tldEnsembleClassifier.hpp"
 #include "tldUtils.hpp"
 
@@ -73,9 +74,14 @@ namespace cv
 			inline double ensembleClassifierNum(const uchar* data);
 			inline void prepareClassifiers(int rowstep);
 			double Sr(const Mat_<uchar>& patch);
+			double ocl_Sr(const Mat_<uchar>& patch);
 			double Sc(const Mat_<uchar>& patch);
+			double ocl_Sc(const Mat_<uchar>& patch);
+			void ocl_batchSrSc(const Mat_<uchar>& patches, double *resultSr, double *resultSc, int numOfPatches);
 
 			std::vector<TLDEnsembleClassifier> classifiers;
+			Mat *posExp, *negExp;
+			int *posNum, *negNum;
 			std::vector<Mat_<uchar> > *positiveExamples, *negativeExamples;
 			std::vector<int> *timeStampsPositive, *timeStampsNegative;
 			double *originalVariancePtr;
@@ -87,6 +93,7 @@ namespace cv
 				bool isObject, shouldBeIntegrated;
 			};
 			bool detect(const Mat& img, const Mat& imgBlurred, Rect2d& res, std::vector<LabeledPatch>& patches, Size initSize);
+			bool ocl_detect(const Mat& img, const Mat& imgBlurred, Rect2d& res, std::vector<LabeledPatch>& patches, Size initSize);
 		protected:
 
 

modules/tracking/src/tldEnsembleClassifier.cpp

@@ -58,11 +58,11 @@ namespace cv
 		// Calculate measure locations from 15x15 grid on minSize patches
 		void TLDEnsembleClassifier::stepPrefSuff(std::vector<Vec4b>& arr, int pos, int len, int gridSize)
 		{
-#if 0
+		#if 0
 			int step = len / (gridSize - 1), pref = (len - step * (gridSize - 1)) / 2;
 			for (int i = 0; i < (int)(sizeof(x1) / sizeof(x1[0])); i++)
 				arr[i] = pref + arr[i] * step;
-#else
+		#else
 			int total = len - gridSize;
 			int quo = total / (gridSize - 1), rem = total % (gridSize - 1);
 			int smallStep = quo, bigStep = quo + 1;

modules/tracking/src/tldEnsembleClassifier.hpp

@@ -64,6 +64,5 @@ namespace cv
 			std::vector<Point2i> offset;
 			int lastStep_;
 		};
-
 	}
 }
\ No newline at end of file

modules/tracking/src/tldModel.cpp

@@ -56,6 +56,15 @@ namespace cv
 			detector = Ptr<TLDDetector>(new TLDDetector());
 
 			//Propagate data to Detector
+			posNum = 0;
+			negNum = 0;
+			posExp = Mat(Size(225, 500), CV_8UC1);
+			negExp = Mat(Size(225, 500), CV_8UC1);
+			detector->posNum = &posNum;
+			detector->negNum = &negNum;
+			detector->posExp = &posExp;
+			detector->negExp = &negExp;
+
 			detector->positiveExamples = &positiveExamples;
 			detector->negativeExamples = &negativeExamples;
 			detector->timeStampsPositive = &timeStampsPositive;
@@ -69,14 +78,13 @@ namespace cv
 				scaledImg, blurredImg, GaussBlurKernelSize, SCALE_STEP);
 			GaussianBlur(image, image_blurred, GaussBlurKernelSize, 0.0);
 			TLDDetector::generateScanGrid(image.rows, image.cols, minSize_, scanGrid);
-			getClosestN(scanGrid, Rect2d(boundingBox.x / scale, boundingBox.y / scale, boundingBox.width / scale,
-				boundingBox.height / scale), 10, closest);
-
+			getClosestN(scanGrid, Rect2d(boundingBox.x / scale, boundingBox.y / scale, boundingBox.width / scale, boundingBox.height / scale), 10, closest);
 			Mat_<uchar> blurredPatch(minSize);
 			TLDEnsembleClassifier::makeClassifiers(minSize, MEASURES_PER_CLASSIFIER, GRIDSIZE, detector->classifiers);
 
 			//Generate initial positive samples and put them to the model
 			positiveExamples.reserve(200);
+
 			for (int i = 0; i < (int)closest.size(); i++)
 			{
 				for (int j = 0; j < 20; j++)
@@ -188,6 +196,11 @@ namespace cv
 		void TrackerTLDModel::integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive)
 		{
 			int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
+			if ((int)eForModel.size() == 0) return;
+
+			//int64 e1, e2;
+			//double t;
+			//e1 = getTickCount();
 			for (int k = 0; k < (int)eForModel.size(); k++)
 			{
 				double sr = detector->Sr(eForModel[k]);
@@ -218,6 +231,79 @@ namespace cv
 						detector->classifiers[i].integrate(eForEnsemble[k], isPositive);
 				}
 			}
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency() * 1000;
+			//printf("Integrate Additional: %fms\n", t);
+			/*
+			if( negativeIntoModel > 0 )
+			dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
+			if( positiveIntoModel > 0 )
+			dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
+			if( negativeIntoEnsemble > 0 )
+			dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
+			if( positiveIntoEnsemble > 0 )
+			dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
+			dfprintf((stdout, "\n"));*/
+		}
+
+		void TrackerTLDModel::ocl_integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive)
+		{
+			int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
+			if ((int)eForModel.size() == 0) return;
+
+			//int64 e1, e2;
+			//double t;
+			//e1 = getTickCount();
+
+			//Prepare batch of patches
+			int numOfPatches = (int)eForModel.size();
+			Mat_<uchar> stdPatches(numOfPatches, 225);
+			double *resultSr = new double[numOfPatches];
+			double *resultSc = new double[numOfPatches];
+			uchar *patchesData = stdPatches.data;
+			for (int i = 0; i < numOfPatches; i++)
+			{
+				uchar *stdPatchData = eForModel[i].data;
+				for (int j = 0; j < 225; j++)
+					patchesData[225 * i + j] = stdPatchData[j];
+			}
+
+			//Calculate Sr and Sc batches
+			detector->ocl_batchSrSc(stdPatches, resultSr, resultSc, numOfPatches);
+
+			for (int k = 0; k < (int)eForModel.size(); k++)
+			{
+				double sr = resultSr[k];
+				if ((sr > THETA_NN) != isPositive)
+				{
+					if (isPositive)
+					{
+						positiveIntoModel++;
+						pushIntoModel(eForModel[k], true);
+					}
+					else
+					{
+						negativeIntoModel++;
+						pushIntoModel(eForModel[k], false);
+					}
+				}
+				double p = 0;
+				for (int i = 0; i < (int)detector->classifiers.size(); i++)
+					p += detector->classifiers[i].posteriorProbability(eForEnsemble[k].data, (int)eForEnsemble[k].step[0]);
+				p /= detector->classifiers.size();
+				if ((p > ENSEMBLE_THRESHOLD) != isPositive)
+				{
+					if (isPositive)
+						positiveIntoEnsemble++;
+					else
+						negativeIntoEnsemble++;
+					for (int i = 0; i < (int)detector->classifiers.size(); i++)
+						detector->classifiers[i].integrate(eForEnsemble[k], isPositive);
+				}
+			}
+			//e2 = getTickCount();
+			//t = (e2 - e1) / getTickFrequency() * 1000;
+			//printf("Integrate Additional OCL: %fms\n", t);
 			/*
 			if( negativeIntoModel > 0 )
 			dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
@@ -238,12 +324,30 @@ namespace cv
 			std::vector<int>* proxyT;
 			if (positive)
 			{
+				if (posNum < 500)
+				{
+					uchar *patchPtr = example.data;
+					uchar *modelPtr = posExp.data;
+					for (int i = 0; i < STANDARD_PATCH_SIZE*STANDARD_PATCH_SIZE; i++)
+						modelPtr[posNum*STANDARD_PATCH_SIZE*STANDARD_PATCH_SIZE + i] = patchPtr[i];
+					posNum++;
+				}
+
 				proxyV = &positiveExamples;
 				proxyN = &timeStampPositiveNext;
 				proxyT = &timeStampsPositive;
 			}
 			else
 			{
+				if (negNum < 500)
+				{
+					uchar *patchPtr = example.data;
+					uchar *modelPtr = negExp.data;
+					for (int i = 0; i < STANDARD_PATCH_SIZE*STANDARD_PATCH_SIZE; i++)
+						modelPtr[negNum*STANDARD_PATCH_SIZE*STANDARD_PATCH_SIZE + i] = patchPtr[i];
+					negNum++;
+				}
+
 				proxyV = &negativeExamples;
 				proxyN = &timeStampNegativeNext;
 				proxyT = &timeStampsNegative;
@@ -268,9 +372,5 @@ namespace cv
 			dfprintf((port, "\tpositiveExamples.size() = %d\n", (int)positiveExamples.size()));
 			dfprintf((port, "\tnegativeExamples.size() = %d\n", (int)negativeExamples.size()));
 		}
-
-
-
-
 	}
 }
\ No newline at end of file

modules/tracking/src/tldModel.hpp

@@ -50,9 +50,6 @@ namespace cv
 {
 	namespace tld
 	{
-
-
-
 		class TrackerTLDModel : public TrackerModel
 		{
 		public:
@@ -61,11 +58,14 @@ namespace cv
 			void setBoudingBox(Rect2d boundingBox){ boundingBox_ = boundingBox; }
 			void integrateRelabeled(Mat& img, Mat& imgBlurred, const std::vector<TLDDetector::LabeledPatch>& patches);
 			void integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive);
+			void ocl_integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive);
 			Size getMinSize(){ return minSize_; }
 			void printme(FILE* port = stdout);
 			Ptr<TLDDetector> detector;
 
 			std::vector<Mat_<uchar> > positiveExamples, negativeExamples;
+			Mat posExp, negExp;
+			int posNum, negNum;
 			std::vector<int> timeStampsPositive, timeStampsNegative;
 			int timeStampPositiveNext, timeStampNegativeNext;
 			double originalVariance_;
@@ -80,7 +80,6 @@ namespace cv
 			void modelUpdateImpl(){}
 			Rect2d boundingBox_;
 			RNG rng;
-
 		};
 
 	}

modules/tracking/src/tldTracker.cpp

@@ -116,11 +116,20 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
     std::vector<Rect2d> candidates;
     std::vector<double> candidatesRes;
     bool trackerNeedsReInit = false;
+	bool DETECT_FLG = false;
     for( int i = 0; i < 2; i++ )
     {
         Rect2d tmpCandid = boundingBox;
-        if( ( (i == 0) && !data->failedLastTime && trackerProxy->update(image, tmpCandid) ) ||
-			((i == 1) && (tldModel->detector->detect(imageForDetector, image_blurred, tmpCandid, detectorResults, tldModel->getMinSize()))))
+
+		if (i == 1)
+		{
+			if (ocl::haveOpenCL())
+				DETECT_FLG = tldModel->detector->ocl_detect(imageForDetector, image_blurred, tmpCandid, detectorResults, tldModel->getMinSize());
+			else
+				DETECT_FLG = tldModel->detector->detect(imageForDetector, image_blurred, tmpCandid, detectorResults, tldModel->getMinSize());
+		}
+
+        if( ( (i == 0) && !data->failedLastTime && trackerProxy->update(image, tmpCandid) ) || ( DETECT_FLG))
         {
             candidates.push_back(tmpCandid);
             if( i == 0 )
@@ -202,9 +211,16 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
         tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults);
         //dprintf(("%d relabeled by nExpert\n", negRelabeled));
         pExpert.additionalExamples(examplesForModel, examplesForEnsemble);
+		if (ocl::haveOpenCL())
+			tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, true);
+		else
 			tldModel->integrateAdditional(examplesForModel, examplesForEnsemble, true);
         examplesForModel.clear(); examplesForEnsemble.clear();
         nExpert.additionalExamples(examplesForModel, examplesForEnsemble);
+
+		if (ocl::haveOpenCL())
+			tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, false);
+		else
 			tldModel->integrateAdditional(examplesForModel, examplesForEnsemble, false);
     }
     else

modules/tracking/src/tldTracker.hpp

@@ -60,7 +60,6 @@ void TrackerTLD::Params::write(cv::FileStorage& /*fs*/) const {}
 
 namespace tld
 {
-
 class TrackerProxy
 {
 public:

modules/tracking/src/tracker.cpp

@@ -110,6 +110,7 @@ Ptr<Tracker> Tracker::create( const String& trackerType )
   BOILERPLATE_CODE("BOOSTING",TrackerBoosting);
   BOILERPLATE_CODE("MEDIANFLOW",TrackerMedianFlow);
   BOILERPLATE_CODE("TLD",TrackerTLD);
+  BOILERPLATE_CODE("KCF",TrackerKCF);
   return Ptr<Tracker>();
 }
 

modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

@@ -165,10 +165,10 @@ Note: the descriptor can be coupled with any keypoint extractor. The only demand
 Note: a complete example can be found under /samples/cpp/tutorial_code/xfeatures2D/latch_match.cpp
 
 */
-class CV_EXPORTS LATCH : public DescriptorExtractor
+class CV_EXPORTS_W LATCH : public Feature2D
 {
 public:
-	static Ptr<LATCH> create(int bytes = 32, bool rotationInvariance = true, int half_ssd_size=3);
+	CV_WRAP static Ptr<LATCH> create(int bytes = 32, bool rotationInvariance = true, int half_ssd_size=3);
 };
 
 /** @brief Class implementing DAISY descriptor, described in @cite Tola10
@@ -187,14 +187,14 @@ DAISY::NRM_SIFT mean that descriptors are normalized for L2 norm equal to 1.0 bu
 @param use_orientation sample patterns using keypoints orientation, disabled by default.
 
  */
-class CV_EXPORTS DAISY : public DescriptorExtractor
+class CV_EXPORTS_W DAISY : public Feature2D
 {
 public:
     enum
     {
         NRM_NONE = 100, NRM_PARTIAL = 101, NRM_FULL = 102, NRM_SIFT = 103,
     };
-    static Ptr<DAISY> create( float radius = 15, int q_radius = 3, int q_theta = 8,
+    CV_WRAP static Ptr<DAISY> create( float radius = 15, int q_radius = 3, int q_theta = 8,
                 int q_hist = 8, int norm = DAISY::NRM_NONE, InputArray H = noArray(),
                 bool interpolation = true, bool use_orientation = false );
 

modules/xfeatures2d/src/daisy.cpp

@@ -1267,7 +1267,7 @@ struct RoundingInvoker : ParallelLoopBody
     {
       for (int c = range.start; c < range.end; ++c)
       {
-        scale_map->at<float>(r,c) = (float) round( scale_map->at<float>(r,c) );
+        scale_map->at<float>(r,c) = (float) cvRound( scale_map->at<float>(r,c) );
       }
     }
     int r;

modules/ximgproc/doc/ximgproc.bib

@@ -55,3 +55,25 @@
   year={2013},
   organization={IEEE}
 }
+
+@article{Min2014,
+  title={Fast global image smoothing based on weighted least squares},
+  author={Min, Dongbo and Choi, Sunghwan and Lu, Jiangbo and Ham, Bumsub and Sohn, Kwanghoon and Do, Minh N},
+  journal={Image Processing, IEEE Transactions on},
+  volume={23},
+  number={12},
+  pages={5638--5653},
+  year={2014},
+  publisher={IEEE}
+}
+
+@inproceedings{Farbman2008,
+  title={Edge-preserving decompositions for multi-scale tone and detail manipulation},
+  author={Farbman, Zeev and Fattal, Raanan and Lischinski, Dani and Szeliski, Richard},
+  booktitle={ACM Transactions on Graphics (TOG)},
+  volume={27},
+  number={3},
+  pages={67},
+  year={2008},
+  organization={ACM}
+}

modules/ximgproc/include/opencv2/ximgproc.hpp

@@ -38,6 +38,7 @@
 #define __OPENCV_XIMGPROC_HPP__
 
 #include "ximgproc/edge_filter.hpp"
+#include "ximgproc/disparity_filter.hpp"
 #include "ximgproc/structured_edge_detection.hpp"
 #include "ximgproc/seeds.hpp"
 

modules/ximgproc/include/opencv2/ximgproc/disparity_filter.hpp

@@ -43,49 +43,147 @@
 namespace cv {
 namespace ximgproc {
 
+//! @addtogroup ximgproc_filters
+//! @{
+
+/** @brief Main interface for all disparity map filters.
+ */
 class CV_EXPORTS_W DisparityFilter : public Algorithm
 {
 public:
-    CV_WRAP virtual void filter(InputArray disparity_map, InputArray left_view, OutputArray filtered_disparity_map,Rect ROI) = 0;
-};
 
-/////////////////////////////////////////////////////////////////////////////////////////////////
+    /** @brief Apply filtering to the disparity map.
 
-class CV_EXPORTS_W DisparityDTFilter : public DisparityFilter
-{
-public:
-    CV_WRAP virtual double getSigmaSpatial() = 0;
-    CV_WRAP virtual void setSigmaSpatial(double _sigmaSpatial) = 0;
-    CV_WRAP virtual double getSigmaColor() = 0;
-    CV_WRAP virtual void setSigmaColor(double _sigmaColor) = 0;
-};
+    @param disparity_map_left disparity map of the left view, 1 channel, CV_16S type. Implicitly assumes that disparity
+    values are scaled by 16 (one-pixel disparity corresponds to the value of 16 in the disparity map). Disparity map
+    can have any resolution, it will be automatically resized to fit left_view resolution.
 
-CV_EXPORTS_W
-Ptr<DisparityDTFilter> createDisparityDTFilter();
+    @param left_view left view of the original stereo-pair to guide the filtering process, 8-bit single-channel
+    or three-channel image.
 
-class CV_EXPORTS_W DisparityGuidedFilter : public DisparityFilter
-{
-public:
-    CV_WRAP virtual double getEps() = 0;
-    CV_WRAP virtual void setEps(double _eps) = 0;
-    CV_WRAP virtual int getRadius() = 0;
-    CV_WRAP virtual void setRadius(int _radius) = 0;
-};
+    @param filtered_disparity_map output disparity map.
 
-CV_EXPORTS_W
-Ptr<DisparityGuidedFilter> createDisparityGuidedFilter();
+    @param ROI region of the disparity map to filter.
+
+    @param disparity_map_right optional argument, some implementations might also use the disparity map
+    of the right view to compute confidence maps, for instance.
 
+    @param right_view optional argument, some implementations might also use the right view of the original
+    stereo-pair.
+     */
+    CV_WRAP virtual void filter(InputArray disparity_map_left, InputArray left_view, OutputArray filtered_disparity_map, Rect ROI, InputArray disparity_map_right = Mat(), InputArray right_view = Mat()) = 0;
+};
+
+/** @brief Disparity map filter based on Weighted Least Squares filter (in form of Fast Global Smoother that
+is a lot faster than traditional Weighted Least Squares filter implementations) and optional use of
+left-right-consistency-based confidence to refine the results in half-occlusions and uniform areas.
+ */
 class CV_EXPORTS_W DisparityWLSFilter : public DisparityFilter
 {
+public:
+    /** filter parameters */
+
+    /** @brief Lambda is a parameter defining the amount of regularization during filtering. Larger values force
+    filtered disparity map edges to adhere more to source image edges. Typical value is 8000.
+     */
     CV_WRAP virtual double getLambda() = 0;
+    /** @see getLambda */
     CV_WRAP virtual void setLambda(double _lambda) = 0;
+    /** @brief SigmaColor is a parameter defining how sensitive the filtering process is to source image edges.
+    Large values can lead to disparity leakage through low-contrast edges. Small values can make the filter too
+    sensitive to noise and textures in the source image. Typical values range from 0.8 to 2.0.
+     */
     CV_WRAP virtual double getSigmaColor() = 0;
+    /** @see getSigmaColor */
     CV_WRAP virtual void setSigmaColor(double _sigma_color) = 0;
+
+    /** confidence-related parameters */
+
+    /** @brief LRCthresh is a threshold of disparity difference used in left-right-consistency check during
+    confidence map computation. The default value of 24 (1.5 pixels) is virtually always good enough.
+     */
+    CV_WRAP virtual int getLRCthresh() = 0;
+    /** @see getLRCthresh */
+    CV_WRAP virtual void setLRCthresh(int _LRC_thresh) = 0;
+    /** @brief DepthDiscontinuityRadius is a parameter used in confidence computation. It defines the size of
+    low-confidence regions around depth discontinuities. For typical window sizes used in stereo matching the
+    optimal value is around 5.
+     */
+    CV_WRAP virtual int getDepthDiscontinuityRadius() = 0;
+    /** @see getDepthDiscontinuityRadius */
+    CV_WRAP virtual void setDepthDiscontinuityRadius(int _disc_radius) = 0;
+    /** @brief Get the confidence map that was used in the last filter call. It is a CV_32F one-channel image
+    with values ranging from 0.0 (totally untrusted regions of the raw disparity map) to 255.0 (regions containing
+    correct disparity values with a high degree of confidence).
+     */
+    CV_WRAP virtual Mat getConfidenceMap() = 0;
+
 };
 
+/** @brief Factory method, create instance of DisparityWLSFilter and execute the initialization routines.
+
+@param use_confidence filtering with confidence requires two disparity maps (for the left and right views) and is
+approximately two times slower. However, quality is typically significantly better.
+*/
 CV_EXPORTS_W
-Ptr<DisparityWLSFilter> createDisparityWLSFilter();
+Ptr<DisparityWLSFilter> createDisparityWLSFilter(bool use_confidence);
+
+//////////////////////////////////////////////////////////////////////////
+//////////////////////////////////////////////////////////////////////////
+
+/** @brief Function for reading ground truth disparity maps. Supports basic Middlebury
+and MPI-Sintel formats. Note that the resulting disparity map is scaled by 16.
+
+@param src_path path to the image, containing ground-truth disparity map
+
+@param dst output disparity map, CV_16S depth
+
+@result returns zero if successfully read the ground truth
+ */
+CV_EXPORTS
+int readGT(String src_path,OutputArray dst);
+
+/** @brief Function for computing mean square error for disparity maps
+
+@param GT ground truth disparity map
+
+@param src disparity map to evaluate
+
+@param ROI region of interest
+
+@result returns mean square error between GT and src
+ */
+CV_EXPORTS
+double computeMSE(InputArray GT, InputArray src, Rect ROI);
+
+/** @brief Function for computing the percent of "bad" pixels in the disparity map
+(pixels where error is higher than a specified threshold)
+
+@param GT ground truth disparity map
+
+@param src disparity map to evaluate
+
+@param ROI region of interest
+
+@param thresh threshold used to determine "bad" pixels
+
+@result returns mean square error between GT and src
+ */
+CV_EXPORTS
+double computeBadPixelPercent(InputArray GT, InputArray src, Rect ROI, int thresh=24/*1.5 pixels*/);
+
+/** @brief Function for creating a disparity map visualization (clamped CV_8U image)
+
+@param src input disparity map (CV_16S depth)
+
+@param dst output visualization
+
+@param scale disparity map will be multiplied by this value for visualization
+ */
+CV_EXPORTS
+void getDisparityVis(InputArray src,OutputArray dst,double scale=1.0);
 
+//! @}
 }
 }
 #endif

modules/ximgproc/src/seeds.cpp

@@ -273,8 +273,8 @@ void SuperpixelSEEDSImpl::initialize(int num_superpixels, int num_levels)
     parent_pre_init.resize(seeds_nr_levels);
     nr_wh.resize(2 * seeds_nr_levels);
     int level = 0;
-    int nr_seeds_w = (int)floor(width / seeds_w);
-    int nr_seeds_h = (int)floor(height / seeds_h);
+    int nr_seeds_w = (width / seeds_w);
+    int nr_seeds_h = (height / seeds_h);
     nr_wh[2 * level] = nr_seeds_w;
     nr_wh[2 * level + 1] = nr_seeds_h;
     parent_mat.push_back(Mat(nr_seeds_h, nr_seeds_w, CV_32SC1));

modules/ximgproc/src/structured_edge_detection.cpp

@@ -188,8 +188,8 @@ static void gradientHist(const cv::Mat &src, cv::Mat &magnitude, cv::Mat &histog
 
     magnitude.create( src.size(), cv::DataType<float>::type );
     phase.create( src.size(), cv::DataType<float>::type );
-    histogram.create( cv::Size( cvRound(src.size().width/float(pSize)),
-                                cvRound(src.size().height/float(pSize)) ),
+    histogram.create( cv::Size( cvCeil(src.size().width/float(pSize)),
+                                cvCeil(src.size().height/float(pSize)) ),
         CV_MAKETYPE(cv::DataType<float>::type, nBins) );
 
     histogram.setTo(0);