154 lines
6.6 KiB
C++
154 lines
6.6 KiB
C++
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2015, Itseez Inc, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Itseez Inc or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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// Implementation authors:
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// Jiaolong Xu - jiaolongxu@gmail.com
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// Evgeniy Kozinov - evgeniy.kozinov@gmail.com
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// Valentina Kustikova - valentina.kustikova@gmail.com
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// Nikolai Zolotykh - Nikolai.Zolotykh@gmail.com
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// Iosif Meyerov - meerov@vmk.unn.ru
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// Alexey Polovinkin - polovinkin.alexey@gmail.com
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//
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//M*/
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#ifndef __OPENCV_LATENTSVM_HPP__
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#define __OPENCV_LATENTSVM_HPP__
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#include "opencv2/core.hpp"
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#include <map>
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#include <vector>
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#include <string>
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/** @defgroup dpm Deformable Part-based Models
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Discriminatively Trained Part Based Models for Object Detection
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---------------------------------------------------------------
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The object detector described below has been initially proposed by P.F. Felzenszwalb in
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@cite Felzenszwalb2010a . It is based on a Dalal-Triggs detector that uses a single filter on histogram
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of oriented gradients (HOG) features to represent an object category. This detector uses a sliding
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window approach, where a filter is applied at all positions and scales of an image. The first
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innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a
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"root" filter (analogous to the Dalal-Triggs filter) plus a set of parts filters and associated
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deformation models. The score of one of star models at a particular position and scale within an
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image is the score of the root filter at the given location plus the sum over parts of the maximum,
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over placements of that part, of the part filter score on its location minus a deformation cost
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easuring the deviation of the part from its ideal location relative to the root. Both root and part
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filter scores are defined by the dot product between a filter (a set of weights) and a subwindow of
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a feature pyramid computed from the input image. Another improvement is a representation of the
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class of models by a mixture of star models. The score of a mixture model at a particular position
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and scale is the maximum over components, of the score of that component model at the given
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location.
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The detector was dramatically speeded-up with cascade algorithm proposed by P.F. Felzenszwalb in
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@cite Felzenszwalb2010b . The algorithm prunes partial hypotheses using thresholds on their scores.The
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basic idea of the algorithm is to use a hierarchy of models defined by an ordering of the original
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model's parts. For a model with (n+1) parts, including the root, a sequence of (n+1) models is
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obtained. The i-th model in this sequence is defined by the first i parts from the original model.
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Using this hierarchy, low scoring hypotheses can be pruned after looking at the best configuration
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of a subset of the parts. Hypotheses that score high under a weak model are evaluated further using
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a richer model.
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In OpenCV there is an C++ implementation of DPM cascade detector.
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*/
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namespace cv
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{
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namespace dpm
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{
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//! @addtogroup dpm
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//! @{
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/** @brief This is a C++ abstract class, it provides external user API to work with DPM.
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*/
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class CV_EXPORTS_W DPMDetector
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{
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public:
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struct CV_EXPORTS_W ObjectDetection
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{
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ObjectDetection();
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ObjectDetection( const Rect& rect, float score, int classID=-1 );
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Rect rect;
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float score;
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int classID;
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};
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virtual bool isEmpty() const = 0;
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/** @brief Find rectangular regions in the given image that are likely to contain objects of loaded classes
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(models) and corresponding confidence levels.
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@param image An image.
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@param objects The detections: rectangulars, scores and class IDs.
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*/
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virtual void detect(cv::Mat &image, CV_OUT std::vector<ObjectDetection> &objects) = 0;
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/** @brief Return the class (model) names that were passed in constructor or method load or extracted from
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models filenames in those methods.
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*/
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virtual std::vector<std::string> const& getClassNames() const = 0;
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/** @brief Return a count of loaded models (classes).
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*/
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virtual size_t getClassCount() const = 0;
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/** @brief Load the trained models from given .xml files and return cv::Ptr\<DPMDetector\>.
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@param filenames A set of filenames storing the trained detectors (models). Each file contains one
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model. See examples of such files here `/opencv_extra/testdata/cv/dpm/VOC2007_Cascade/`.
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@param classNames A set of trained models names. If it's empty then the name of each model will be
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constructed from the name of file containing the model. E.g. the model stored in
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"/home/user/cat.xml" will get the name "cat".
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*/
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static cv::Ptr<DPMDetector> create(std::vector<std::string> const &filenames,
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std::vector<std::string> const &classNames = std::vector<std::string>());
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virtual ~DPMDetector(){}
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};
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//! @}
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} // namespace dpm
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} // namespace cv
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#endif
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