684 lines
33 KiB
C++
684 lines
33 KiB
C++
/*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) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, 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 Intel Corporation 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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//M*/
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#ifndef OPENCV_OBJDETECT_HPP
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#define OPENCV_OBJDETECT_HPP
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#include "opencv2/core.hpp"
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/**
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@defgroup objdetect Object Detection
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Haar Feature-based Cascade Classifier for Object Detection
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----------------------------------------------------------
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The object detector described below has been initially proposed by Paul Viola @cite Viola01 and
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improved by Rainer Lienhart @cite Lienhart02 .
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First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is
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trained with a few hundred sample views of a particular object (i.e., a face or a car), called
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positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary
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images of the same size.
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After a classifier is trained, it can be applied to a region of interest (of the same size as used
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during the training) in an input image. The classifier outputs a "1" if the region is likely to show
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the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can
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move the search window across the image and check every location using the classifier. The
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classifier is designed so that it can be easily "resized" in order to be able to find the objects of
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interest at different sizes, which is more efficient than resizing the image itself. So, to find an
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object of an unknown size in the image the scan procedure should be done several times at different
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scales.
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The word "cascade" in the classifier name means that the resultant classifier consists of several
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simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some
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stage the candidate is rejected or all the stages are passed. The word "boosted" means that the
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classifiers at every stage of the cascade are complex themselves and they are built out of basic
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classifiers using one of four different boosting techniques (weighted voting). Currently Discrete
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Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are
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decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic
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classifiers, and are calculated as described below. The current algorithm uses the following
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Haar-like features:
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The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within
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the region of interest and the scale (this scale is not the same as the scale used at the detection
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stage, though these two scales are multiplied). For example, in the case of the third line feature
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(2c) the response is calculated as the difference between the sum of image pixels under the
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rectangle covering the whole feature (including the two white stripes and the black stripe in the
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middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to
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compensate for the differences in the size of areas. The sums of pixel values over a rectangular
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regions are calculated rapidly using integral images (see below and the integral description).
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To see the object detector at work, have a look at the facedetect demo:
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<https://github.com/opencv/opencv/tree/master/samples/cpp/dbt_face_detection.cpp>
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The following reference is for the detection part only. There is a separate application called
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opencv_traincascade that can train a cascade of boosted classifiers from a set of samples.
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@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in
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addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection
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using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at
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<http://research.microsoft.com/en-us/um/people/viola/Pubs/Detect/violaJones_CVPR2001.pdf>
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@{
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@defgroup objdetect_c C API
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@}
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*/
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typedef struct CvHaarClassifierCascade CvHaarClassifierCascade;
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namespace cv
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{
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//! @addtogroup objdetect
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//! @{
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///////////////////////////// Object Detection ////////////////////////////
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//! class for grouping object candidates, detected by Cascade Classifier, HOG etc.
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//! instance of the class is to be passed to cv::partition (see cxoperations.hpp)
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class CV_EXPORTS SimilarRects
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{
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public:
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SimilarRects(double _eps) : eps(_eps) {}
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inline bool operator()(const Rect& r1, const Rect& r2) const
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{
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double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5;
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return std::abs(r1.x - r2.x) <= delta &&
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std::abs(r1.y - r2.y) <= delta &&
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std::abs(r1.x + r1.width - r2.x - r2.width) <= delta &&
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std::abs(r1.y + r1.height - r2.y - r2.height) <= delta;
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}
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double eps;
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};
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/** @brief Groups the object candidate rectangles.
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@param rectList Input/output vector of rectangles. Output vector includes retained and grouped
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rectangles. (The Python list is not modified in place.)
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@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a
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group of rectangles to retain it.
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@param eps Relative difference between sides of the rectangles to merge them into a group.
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The function is a wrapper for the generic function partition . It clusters all the input rectangles
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using the rectangle equivalence criteria that combines rectangles with similar sizes and similar
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locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If
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\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small
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clusters containing less than or equal to groupThreshold rectangles are rejected. In each other
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cluster, the average rectangle is computed and put into the output rectangle list.
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*/
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CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps = 0.2);
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/** @overload */
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CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights,
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int groupThreshold, double eps = 0.2);
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/** @overload */
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CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold,
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double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
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/** @overload */
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CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
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std::vector<double>& levelWeights, int groupThreshold, double eps = 0.2);
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/** @overload */
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CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
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std::vector<double>& foundScales,
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double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
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template<> CV_EXPORTS void DefaultDeleter<CvHaarClassifierCascade>::operator ()(CvHaarClassifierCascade* obj) const;
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enum { CASCADE_DO_CANNY_PRUNING = 1,
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CASCADE_SCALE_IMAGE = 2,
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CASCADE_FIND_BIGGEST_OBJECT = 4,
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CASCADE_DO_ROUGH_SEARCH = 8
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};
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class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm
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{
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public:
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virtual ~BaseCascadeClassifier();
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virtual bool empty() const = 0;
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virtual bool load( const String& filename ) = 0;
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virtual void detectMultiScale( InputArray image,
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CV_OUT std::vector<Rect>& objects,
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double scaleFactor,
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int minNeighbors, int flags,
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Size minSize, Size maxSize ) = 0;
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virtual void detectMultiScale( InputArray image,
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CV_OUT std::vector<Rect>& objects,
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CV_OUT std::vector<int>& numDetections,
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double scaleFactor,
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int minNeighbors, int flags,
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Size minSize, Size maxSize ) = 0;
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virtual void detectMultiScale( InputArray image,
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CV_OUT std::vector<Rect>& objects,
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CV_OUT std::vector<int>& rejectLevels,
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CV_OUT std::vector<double>& levelWeights,
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double scaleFactor,
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int minNeighbors, int flags,
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Size minSize, Size maxSize,
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bool outputRejectLevels ) = 0;
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virtual bool isOldFormatCascade() const = 0;
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virtual Size getOriginalWindowSize() const = 0;
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virtual int getFeatureType() const = 0;
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virtual void* getOldCascade() = 0;
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class CV_EXPORTS MaskGenerator
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{
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public:
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virtual ~MaskGenerator() {}
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virtual Mat generateMask(const Mat& src)=0;
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virtual void initializeMask(const Mat& /*src*/) { }
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};
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virtual void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator) = 0;
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virtual Ptr<MaskGenerator> getMaskGenerator() = 0;
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};
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/** @example facedetect.cpp
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This program demonstrates usage of the Cascade classifier class
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\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254
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*/
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/** @brief Cascade classifier class for object detection.
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*/
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class CV_EXPORTS_W CascadeClassifier
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{
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public:
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CV_WRAP CascadeClassifier();
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/** @brief Loads a classifier from a file.
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@param filename Name of the file from which the classifier is loaded.
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*/
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CV_WRAP CascadeClassifier(const String& filename);
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~CascadeClassifier();
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/** @brief Checks whether the classifier has been loaded.
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*/
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CV_WRAP bool empty() const;
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/** @brief Loads a classifier from a file.
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@param filename Name of the file from which the classifier is loaded. The file may contain an old
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HAAR classifier trained by the haartraining application or a new cascade classifier trained by the
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traincascade application.
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*/
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CV_WRAP bool load( const String& filename );
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/** @brief Reads a classifier from a FileStorage node.
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@note The file may contain a new cascade classifier (trained traincascade application) only.
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*/
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CV_WRAP bool read( const FileNode& node );
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/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
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of rectangles.
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@param image Matrix of the type CV_8U containing an image where objects are detected.
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@param objects Vector of rectangles where each rectangle contains the detected object, the
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rectangles may be partially outside the original image.
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@param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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to retain it.
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@param flags Parameter with the same meaning for an old cascade as in the function
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cvHaarDetectObjects. It is not used for a new cascade.
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@param minSize Minimum possible object size. Objects smaller than that are ignored.
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@param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
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The function is parallelized with the TBB library.
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@note
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- (Python) A face detection example using cascade classifiers can be found at
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opencv_source_code/samples/python/facedetect.py
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*/
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CV_WRAP void detectMultiScale( InputArray image,
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CV_OUT std::vector<Rect>& objects,
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double scaleFactor = 1.1,
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int minNeighbors = 3, int flags = 0,
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Size minSize = Size(),
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Size maxSize = Size() );
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/** @overload
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@param image Matrix of the type CV_8U containing an image where objects are detected.
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@param objects Vector of rectangles where each rectangle contains the detected object, the
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rectangles may be partially outside the original image.
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@param numDetections Vector of detection numbers for the corresponding objects. An object's number
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of detections is the number of neighboring positively classified rectangles that were joined
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together to form the object.
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@param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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to retain it.
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@param flags Parameter with the same meaning for an old cascade as in the function
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cvHaarDetectObjects. It is not used for a new cascade.
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@param minSize Minimum possible object size. Objects smaller than that are ignored.
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@param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
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*/
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CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image,
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CV_OUT std::vector<Rect>& objects,
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CV_OUT std::vector<int>& numDetections,
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double scaleFactor=1.1,
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int minNeighbors=3, int flags=0,
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Size minSize=Size(),
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Size maxSize=Size() );
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/** @overload
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This function allows you to retrieve the final stage decision certainty of classification.
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For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
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For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
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This value can then be used to separate strong from weaker classifications.
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A code sample on how to use it efficiently can be found below:
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@code
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Mat img;
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vector<double> weights;
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vector<int> levels;
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vector<Rect> detections;
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CascadeClassifier model("/path/to/your/model.xml");
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model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
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cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
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@endcode
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*/
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CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image,
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CV_OUT std::vector<Rect>& objects,
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CV_OUT std::vector<int>& rejectLevels,
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CV_OUT std::vector<double>& levelWeights,
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double scaleFactor = 1.1,
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int minNeighbors = 3, int flags = 0,
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Size minSize = Size(),
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Size maxSize = Size(),
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bool outputRejectLevels = false );
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CV_WRAP bool isOldFormatCascade() const;
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CV_WRAP Size getOriginalWindowSize() const;
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CV_WRAP int getFeatureType() const;
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void* getOldCascade();
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CV_WRAP static bool convert(const String& oldcascade, const String& newcascade);
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void setMaskGenerator(const Ptr<BaseCascadeClassifier::MaskGenerator>& maskGenerator);
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Ptr<BaseCascadeClassifier::MaskGenerator> getMaskGenerator();
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Ptr<BaseCascadeClassifier> cc;
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};
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CV_EXPORTS Ptr<BaseCascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator();
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//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
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//! struct for detection region of interest (ROI)
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struct DetectionROI
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{
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//! scale(size) of the bounding box
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double scale;
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//! set of requested locations to be evaluated
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std::vector<cv::Point> locations;
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//! vector that will contain confidence values for each location
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std::vector<double> confidences;
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};
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/**@brief Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
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the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs @cite Dalal2005 .
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useful links:
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https://hal.inria.fr/inria-00548512/document/
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https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
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https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor
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http://www.learnopencv.com/histogram-of-oriented-gradients
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http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial
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*/
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struct CV_EXPORTS_W HOGDescriptor
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{
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public:
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enum { L2Hys = 0 //!< Default histogramNormType
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};
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enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value.
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};
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/**@brief Creates the HOG descriptor and detector with default params.
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aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9, 1 )
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*/
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CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
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cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
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histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
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free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false)
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{}
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/** @overload
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@param _winSize sets winSize with given value.
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@param _blockSize sets blockSize with given value.
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@param _blockStride sets blockStride with given value.
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@param _cellSize sets cellSize with given value.
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@param _nbins sets nbins with given value.
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@param _derivAperture sets derivAperture with given value.
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@param _winSigma sets winSigma with given value.
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@param _histogramNormType sets histogramNormType with given value.
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@param _L2HysThreshold sets L2HysThreshold with given value.
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@param _gammaCorrection sets gammaCorrection with given value.
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@param _nlevels sets nlevels with given value.
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@param _signedGradient sets signedGradient with given value.
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*/
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CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
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Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
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int _histogramNormType=HOGDescriptor::L2Hys,
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double _L2HysThreshold=0.2, bool _gammaCorrection=false,
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int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false)
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: winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
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nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),
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histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
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gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
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{}
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/** @overload
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@param filename the file name containing HOGDescriptor properties and coefficients of the trained classifier
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*/
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CV_WRAP HOGDescriptor(const String& filename)
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{
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load(filename);
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}
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/** @overload
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@param d the HOGDescriptor which cloned to create a new one.
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*/
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HOGDescriptor(const HOGDescriptor& d)
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{
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d.copyTo(*this);
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}
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/**@brief Default destructor.
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*/
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virtual ~HOGDescriptor() {}
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/**@brief Returns the number of coefficients required for the classification.
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*/
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CV_WRAP size_t getDescriptorSize() const;
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/** @brief Checks if detector size equal to descriptor size.
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*/
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CV_WRAP bool checkDetectorSize() const;
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/** @brief Returns winSigma value
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*/
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CV_WRAP double getWinSigma() const;
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/**@example peopledetect.cpp
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*/
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/**@brief Sets coefficients for the linear SVM classifier.
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@param _svmdetector coefficients for the linear SVM classifier.
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*/
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CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
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/** @brief Reads HOGDescriptor parameters from a file node.
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@param fn File node
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*/
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virtual bool read(FileNode& fn);
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/** @brief Stores HOGDescriptor parameters in a file storage.
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@param fs File storage
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@param objname Object name
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*/
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virtual void write(FileStorage& fs, const String& objname) const;
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/** @brief loads coefficients for the linear SVM classifier from a file
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@param filename Name of the file to read.
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@param objname The optional name of the node to read (if empty, the first top-level node will be used).
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*/
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CV_WRAP virtual bool load(const String& filename, const String& objname = String());
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/** @brief saves coefficients for the linear SVM classifier to a file
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@param filename File name
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@param objname Object name
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*/
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CV_WRAP virtual void save(const String& filename, const String& objname = String()) const;
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/** @brief clones the HOGDescriptor
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@param c cloned HOGDescriptor
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*/
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virtual void copyTo(HOGDescriptor& c) const;
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/**@example train_HOG.cpp
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*/
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/** @brief Computes HOG descriptors of given image.
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@param img Matrix of the type CV_8U containing an image where HOG features will be calculated.
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@param descriptors Matrix of the type CV_32F
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param locations Vector of Point
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*/
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CV_WRAP virtual void compute(InputArray img,
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CV_OUT std::vector<float>& descriptors,
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Size winStride = Size(), Size padding = Size(),
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const std::vector<Point>& locations = std::vector<Point>()) const;
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/** @brief Performs object detection without a multi-scale window.
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
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@param weights Vector that will contain confidence values for each detected object.
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@param hitThreshold Threshold for the distance between features and SVM classifying plane.
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Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
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But if the free coefficient is omitted (which is allowed), you can specify it manually here.
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param searchLocations Vector of Point includes set of requested locations to be evaluated.
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*/
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CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
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CV_OUT std::vector<double>& weights,
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double hitThreshold = 0, Size winStride = Size(),
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Size padding = Size(),
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const std::vector<Point>& searchLocations = std::vector<Point>()) const;
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/** @brief Performs object detection without a multi-scale window.
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
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@param hitThreshold Threshold for the distance between features and SVM classifying plane.
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Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
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But if the free coefficient is omitted (which is allowed), you can specify it manually here.
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param searchLocations Vector of Point includes locations to search.
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*/
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virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
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double hitThreshold = 0, Size winStride = Size(),
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Size padding = Size(),
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const std::vector<Point>& searchLocations=std::vector<Point>()) const;
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/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
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of rectangles.
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of rectangles where each rectangle contains the detected object.
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@param foundWeights Vector that will contain confidence values for each detected object.
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@param hitThreshold Threshold for the distance between features and SVM classifying plane.
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Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
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But if the free coefficient is omitted (which is allowed), you can specify it manually here.
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param scale Coefficient of the detection window increase.
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@param finalThreshold Final threshold
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@param useMeanshiftGrouping indicates grouping algorithm
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*/
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CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
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CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
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Size winStride = Size(), Size padding = Size(), double scale = 1.05,
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double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
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/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
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of rectangles.
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of rectangles where each rectangle contains the detected object.
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@param hitThreshold Threshold for the distance between features and SVM classifying plane.
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Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
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But if the free coefficient is omitted (which is allowed), you can specify it manually here.
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param scale Coefficient of the detection window increase.
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@param finalThreshold Final threshold
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@param useMeanshiftGrouping indicates grouping algorithm
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*/
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virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
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double hitThreshold = 0, Size winStride = Size(),
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Size padding = Size(), double scale = 1.05,
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double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
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/** @brief Computes gradients and quantized gradient orientations.
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@param img Matrix contains the image to be computed
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@param grad Matrix of type CV_32FC2 contains computed gradients
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@param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations
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@param paddingTL Padding from top-left
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@param paddingBR Padding from bottom-right
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*/
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CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
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Size paddingTL = Size(), Size paddingBR = Size()) const;
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/** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows).
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*/
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CV_WRAP static std::vector<float> getDefaultPeopleDetector();
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/**@example hog.cpp
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*/
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/** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows).
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*/
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CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
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//! Detection window size. Align to block size and block stride. Default value is Size(64,128).
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CV_PROP Size winSize;
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//! Block size in pixels. Align to cell size. Default value is Size(16,16).
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CV_PROP Size blockSize;
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//! Block stride. It must be a multiple of cell size. Default value is Size(8,8).
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CV_PROP Size blockStride;
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//! Cell size. Default value is Size(8,8).
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CV_PROP Size cellSize;
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//! Number of bins used in the calculation of histogram of gradients. Default value is 9.
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CV_PROP int nbins;
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//! not documented
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CV_PROP int derivAperture;
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//! Gaussian smoothing window parameter.
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CV_PROP double winSigma;
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//! histogramNormType
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CV_PROP int histogramNormType;
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//! L2-Hys normalization method shrinkage.
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CV_PROP double L2HysThreshold;
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//! Flag to specify whether the gamma correction preprocessing is required or not.
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CV_PROP bool gammaCorrection;
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//! coefficients for the linear SVM classifier.
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CV_PROP std::vector<float> svmDetector;
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//! coefficients for the linear SVM classifier used when OpenCL is enabled
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UMat oclSvmDetector;
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//! not documented
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float free_coef;
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//! Maximum number of detection window increases. Default value is 64
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CV_PROP int nlevels;
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//! Indicates signed gradient will be used or not
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CV_PROP bool signedGradient;
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/** @brief evaluate specified ROI and return confidence value for each location
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param locations Vector of Point
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@param foundLocations Vector of Point where each Point is detected object's top-left point.
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@param confidences confidences
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@param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually
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it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if
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the free coefficient is omitted (which is allowed), you can specify it manually here
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@param winStride winStride
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@param padding padding
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*/
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virtual void detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
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CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
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double hitThreshold = 0, cv::Size winStride = Size(),
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cv::Size padding = Size()) const;
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/** @brief evaluate specified ROI and return confidence value for each location in multiple scales
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of rectangles where each rectangle contains the detected object.
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@param locations Vector of DetectionROI
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@param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified
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in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.
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@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
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*/
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virtual void detectMultiScaleROI(const cv::Mat& img,
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CV_OUT std::vector<cv::Rect>& foundLocations,
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std::vector<DetectionROI>& locations,
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double hitThreshold = 0,
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int groupThreshold = 0) const;
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/** @brief read/parse Dalal's alt model file
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@param modelfile Path of Dalal's alt model file.
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*/
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void readALTModel(String modelfile);
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/** @brief Groups the object candidate rectangles.
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@param rectList Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.)
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@param weights Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.)
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@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
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@param eps Relative difference between sides of the rectangles to merge them into a group.
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*/
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void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
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};
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//! @} objdetect
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}
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#include "opencv2/objdetect/detection_based_tracker.hpp"
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#ifndef DISABLE_OPENCV_24_COMPATIBILITY
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#include "opencv2/objdetect/objdetect_c.h"
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#endif
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#endif
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