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743 lines
36 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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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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//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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 |
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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/3.4/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 CV_OVERRIDE = 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 samples/cpp/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 samples/cpp/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 samples/cpp/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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|
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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. |
|
Usually it is 0 and should be specified 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 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. |
|
@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. |
|
Usually it is 0 and should be specified 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. |
|
@param winStride Window stride. It must be a multiple of block stride. |
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@param padding Padding |
|
@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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|
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/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list |
|
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. |
|
Usually it is 0 and should be specified 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. |
|
@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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|
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/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list |
|
of rectangles. |
|
@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. |
|
@param foundLocations Vector of rectangles where each rectangle contains the detected object. |
|
@param hitThreshold Threshold for the distance between features and SVM classifying plane. |
|
Usually it is 0 and should be specified 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. |
|
@param winStride Window stride. It must be a multiple of block stride. |
|
@param padding Padding |
|
@param scale Coefficient of the detection window increase. |
|
@param finalThreshold Final threshold |
|
@param useMeanshiftGrouping indicates grouping algorithm |
|
*/ |
|
virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations, |
|
double hitThreshold = 0, Size winStride = Size(), |
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Size padding = Size(), double scale = 1.05, |
|
double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const; |
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|
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/** @brief Computes gradients and quantized gradient orientations. |
|
@param img Matrix contains the image to be computed |
|
@param grad Matrix of type CV_32FC2 contains computed gradients |
|
@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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*/ |
|
CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs, |
|
Size paddingTL = Size(), Size paddingBR = Size()) const; |
|
|
|
/** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows). |
|
*/ |
|
CV_WRAP static std::vector<float> getDefaultPeopleDetector(); |
|
|
|
/**@example samples/tapi/hog.cpp |
|
*/ |
|
/** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows). |
|
*/ |
|
CV_WRAP static std::vector<float> getDaimlerPeopleDetector(); |
|
|
|
//! Detection window size. Align to block size and block stride. Default value is Size(64,128). |
|
CV_PROP Size winSize; |
|
|
|
//! Block size in pixels. Align to cell size. Default value is Size(16,16). |
|
CV_PROP Size blockSize; |
|
|
|
//! Block stride. It must be a multiple of cell size. Default value is Size(8,8). |
|
CV_PROP Size blockStride; |
|
|
|
//! Cell size. Default value is Size(8,8). |
|
CV_PROP Size cellSize; |
|
|
|
//! Number of bins used in the calculation of histogram of gradients. Default value is 9. |
|
CV_PROP int nbins; |
|
|
|
//! not documented |
|
CV_PROP int derivAperture; |
|
|
|
//! Gaussian smoothing window parameter. |
|
CV_PROP double winSigma; |
|
|
|
//! histogramNormType |
|
CV_PROP int histogramNormType; |
|
|
|
//! L2-Hys normalization method shrinkage. |
|
CV_PROP double L2HysThreshold; |
|
|
|
//! Flag to specify whether the gamma correction preprocessing is required or not. |
|
CV_PROP bool gammaCorrection; |
|
|
|
//! coefficients for the linear SVM classifier. |
|
CV_PROP std::vector<float> svmDetector; |
|
|
|
//! coefficients for the linear SVM classifier used when OpenCL is enabled |
|
UMat oclSvmDetector; |
|
|
|
//! not documented |
|
float free_coef; |
|
|
|
//! Maximum number of detection window increases. Default value is 64 |
|
CV_PROP int nlevels; |
|
|
|
//! Indicates signed gradient will be used or not |
|
CV_PROP bool signedGradient; |
|
|
|
/** @brief evaluate specified ROI and return confidence value for each location |
|
@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. |
|
@param locations Vector of Point |
|
@param foundLocations Vector of Point where each Point is detected object's top-left point. |
|
@param confidences confidences |
|
@param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually |
|
it is 0 and should be specified 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 |
|
@param winStride winStride |
|
@param padding padding |
|
*/ |
|
virtual void detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations, |
|
CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences, |
|
double hitThreshold = 0, cv::Size winStride = Size(), |
|
cv::Size padding = Size()) const; |
|
|
|
/** @brief evaluate specified ROI and return confidence value for each location in multiple scales |
|
@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. |
|
@param foundLocations Vector of rectangles where each rectangle contains the detected object. |
|
@param locations Vector of DetectionROI |
|
@param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified |
|
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. |
|
@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. |
|
*/ |
|
virtual void detectMultiScaleROI(const cv::Mat& img, |
|
CV_OUT std::vector<cv::Rect>& foundLocations, |
|
std::vector<DetectionROI>& locations, |
|
double hitThreshold = 0, |
|
int groupThreshold = 0) const; |
|
|
|
/** @brief read/parse Dalal's alt model file |
|
@param modelfile Path of Dalal's alt model file. |
|
*/ |
|
void readALTModel(String modelfile); |
|
|
|
/** @brief Groups the object candidate rectangles. |
|
@param rectList Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.) |
|
@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.) |
|
@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. |
|
@param eps Relative difference between sides of the rectangles to merge them into a group. |
|
*/ |
|
void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const; |
|
}; |
|
|
|
class CV_EXPORTS_W QRCodeDetector |
|
{ |
|
public: |
|
CV_WRAP QRCodeDetector(); |
|
~QRCodeDetector(); |
|
|
|
/** @brief sets the epsilon used during the horizontal scan of QR code stop marker detection. |
|
@param epsX Epsilon neighborhood, which allows you to determine the horizontal pattern |
|
of the scheme 1:1:3:1:1 according to QR code standard. |
|
*/ |
|
CV_WRAP void setEpsX(double epsX); |
|
/** @brief sets the epsilon used during the vertical scan of QR code stop marker detection. |
|
@param epsY Epsilon neighborhood, which allows you to determine the vertical pattern |
|
of the scheme 1:1:3:1:1 according to QR code standard. |
|
*/ |
|
CV_WRAP void setEpsY(double epsY); |
|
|
|
/** @brief Detects QR code in image and returns the quadrangle containing the code. |
|
@param img grayscale or color (BGR) image containing (or not) QR code. |
|
@param points Output vector of vertices of the minimum-area quadrangle containing the code. |
|
*/ |
|
CV_WRAP bool detect(InputArray img, OutputArray points) const; |
|
|
|
/** @brief Decodes QR code in image once it's found by the detect() method. |
|
Returns UTF8-encoded output string or empty string if the code cannot be decoded. |
|
|
|
@param img grayscale or color (BGR) image containing QR code. |
|
@param points Quadrangle vertices found by detect() method (or some other algorithm). |
|
@param straight_qrcode The optional output image containing rectified and binarized QR code |
|
*/ |
|
CV_WRAP cv::String decode(InputArray img, InputArray points, OutputArray straight_qrcode = noArray()); |
|
|
|
/** @brief Both detects and decodes QR code |
|
|
|
@param img grayscale or color (BGR) image containing QR code. |
|
@param points opiotnal output array of vertices of the found QR code quadrangle. Will be empty if not found. |
|
@param straight_qrcode The optional output image containing rectified and binarized QR code |
|
*/ |
|
CV_WRAP cv::String detectAndDecode(InputArray img, OutputArray points=noArray(), |
|
OutputArray straight_qrcode = noArray()); |
|
protected: |
|
struct Impl; |
|
Ptr<Impl> p; |
|
}; |
|
|
|
/** @brief Detect QR code in image and return minimum area of quadrangle that describes QR code. |
|
@param in Matrix of the type CV_8UC1 containing an image where QR code are detected. |
|
@param points Output vector of vertices of a quadrangle of minimal area that describes QR code. |
|
@param eps_x Epsilon neighborhood, which allows you to determine the horizontal pattern of the scheme 1:1:3:1:1 according to QR code standard. |
|
@param eps_y Epsilon neighborhood, which allows you to determine the vertical pattern of the scheme 1:1:3:1:1 according to QR code standard. |
|
*/ |
|
CV_EXPORTS bool detectQRCode(InputArray in, std::vector<Point> &points, double eps_x = 0.2, double eps_y = 0.1); |
|
|
|
/** @brief Decode QR code in image and return text that is encrypted in QR code. |
|
@param in Matrix of the type CV_8UC1 containing an image where QR code are detected. |
|
@param points Input vector of vertices of a quadrangle of minimal area that describes QR code. |
|
@param decoded_info String information that is encrypted in QR code. |
|
@param straight_qrcode Matrix of the type CV_8UC1 containing an binary straight QR code. |
|
*/ |
|
CV_EXPORTS bool decodeQRCode(InputArray in, InputArray points, std::string &decoded_info, OutputArray straight_qrcode = noArray()); |
|
|
|
//! @} objdetect |
|
} |
|
|
|
#include "opencv2/objdetect/detection_based_tracker.hpp" |
|
|
|
#ifndef DISABLE_OPENCV_24_COMPATIBILITY |
|
#include "opencv2/objdetect/objdetect_c.h" |
|
#endif |
|
|
|
#endif
|
|
|