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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) 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.
+//
+// Implementation authors:
+// Jiaolong Xu - jiaolongxu@gmail.com
+// Evgeniy Kozinov - evgeniy.kozinov@gmail.com
+// Valentina Kustikova - valentina.kustikova@gmail.com
+// Nikolai Zolotykh - Nikolai.Zolotykh@gmail.com
+// Iosif Meyerov - meerov@vmk.unn.ru
+// Alexey Polovinkin - polovinkin.alexey@gmail.com
+//
+//M*/
+
+#ifndef __OPENCV_LATENTSVM_HPP__
+#define __OPENCV_LATENTSVM_HPP__
+
+#include "opencv2/core.hpp"
+
+#include <map>
+#include <vector>
+#include <string>
+
+/** @defgroup dpm Deformable Part-based Models
+
+Discriminatively Trained Part Based Models for Object Detection
+---------------------------------------------------------------
+
+The object detector described below has been initially proposed by P.F. Felzenszwalb in
+@cite Felzenszwalb2010a . It is based on a Dalal-Triggs detector that uses a single filter on histogram
+of oriented gradients (HOG) features to represent an object category. This detector uses a sliding
+window approach, where a filter is applied at all positions and scales of an image. The first
+innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a
+"root" filter (analogous to the Dalal-Triggs filter) plus a set of parts filters and associated
+deformation models. The score of one of star models at a particular position and scale within an
+image is the score of the root filter at the given location plus the sum over parts of the maximum,
+over placements of that part, of the part filter score on its location minus a deformation cost
+easuring the deviation of the part from its ideal location relative to the root. Both root and part
+filter scores are defined by the dot product between a filter (a set of weights) and a subwindow of
+a feature pyramid computed from the input image. Another improvement is a representation of the
+class of models by a mixture of star models. The score of a mixture model at a particular position
+and scale is the maximum over components, of the score of that component model at the given
+location.
+
+The detector was dramatically speeded-up with cascade algorithm proposed by P.F. Felzenszwalb in
+@cite Felzenszwalb2010b . The algorithm prunes partial hypotheses using thresholds on their scores.The
+basic idea of the algorithm is to use a hierarchy of models defined by an ordering of the original
+model's parts. For a model with (n+1) parts, including the root, a sequence of (n+1) models is
+obtained. The i-th model in this sequence is defined by the first i parts from the original model.
+Using this hierarchy, low scoring hypotheses can be pruned after looking at the best configuration
+of a subset of the parts. Hypotheses that score high under a weak model are evaluated further using
+a richer model.
+
+In OpenCV there is an C++ implementation of DPM cascade detector.
+
+*/
+
+namespace cv
+{
+
+namespace dpm
+{
+
+/** @brief This is a C++ abstract class, it provides external user API to work with DPM.
+ */
+class CV_EXPORTS_W DPMDetector
+{
+public:
+
+    struct CV_EXPORTS_W ObjectDetection
+    {
+        ObjectDetection();
+        ObjectDetection( const Rect& rect, float score, int classID=-1 );
+        Rect rect;
+        float score;
+        int classID;
+    };
+
+    virtual bool isEmpty() const = 0;
+
+    /** @brief Find rectangular regions in the given image that are likely to contain objects of loaded classes
+    (models) and corresponding confidence levels.
+    @param image An image.
+    @param objects The detections: rectangulars, scores and class IDs.
+    */
+    virtual void detect(cv::Mat &image, CV_OUT std::vector<ObjectDetection> &objects) = 0;
+
+    /** @brief Return the class (model) names that were passed in constructor or method load or extracted from
+    models filenames in those methods.
+     */
+    virtual std::vector<std::string> const& getClassNames() const = 0;
+
+    /** @brief Return a count of loaded models (classes).
+     */
+    virtual size_t getClassCount() const = 0;
+
+    /** @brief Load the trained models from given .xml files and return cv::Ptr\<DPMDetector\>.
+    @param filenames A set of filenames storing the trained detectors (models). Each file contains one
+    model. See examples of such files here `/opencv_extra/testdata/cv/dpm/VOC2007_Cascade/`.
+    @param classNames A set of trained models names. If it's empty then the name of each model will be
+    constructed from the name of file containing the model. E.g. the model stored in
+    "/home/user/cat.xml" will get the name "cat".
+     */
+    static cv::Ptr<DPMDetector> create(std::vector<std::string> const &filenames,
+            std::vector<std::string> const &classNames = std::vector<std::string>());
+
+    virtual ~DPMDetector(){}
+};
+
+} // namespace dpm
+} // namespace cv
+
+#endif