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Diffstat (limited to 'thirdparty1/linux/include/opencv2/dpm.hpp')
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diff --git a/thirdparty1/linux/include/opencv2/dpm.hpp b/thirdparty1/linux/include/opencv2/dpm.hpp new file mode 100644 index 0000000..387a311 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/dpm.hpp @@ -0,0 +1,148 @@ +/*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 |