mirror of
https://github.com/opencv/opencv.git
synced 2024-12-15 18:09:11 +08:00
bf06e3d09f
Documentation transition to fresh Doxygen #25042 * current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584) * Doxyfile adapted to new version * MathJax updated to 3.x * `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings) * refactored matx.hpp - extracted matx.inl.hpp * opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
874 lines
42 KiB
C++
874 lines
42 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#ifndef OPENCV_OBJDETECT_HPP
|
|
#define OPENCV_OBJDETECT_HPP
|
|
|
|
#include "opencv2/core.hpp"
|
|
#include "opencv2/objdetect/aruco_detector.hpp"
|
|
#include "opencv2/objdetect/graphical_code_detector.hpp"
|
|
|
|
/**
|
|
@defgroup objdetect Object Detection
|
|
|
|
@{
|
|
@defgroup objdetect_cascade_classifier Cascade Classifier for Object Detection
|
|
|
|
The object detector described below has been initially proposed by Paul Viola @cite Viola01 and
|
|
improved by Rainer Lienhart @cite Lienhart02 .
|
|
|
|
First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is
|
|
trained with a few hundred sample views of a particular object (i.e., a face or a car), called
|
|
positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary
|
|
images of the same size.
|
|
|
|
After a classifier is trained, it can be applied to a region of interest (of the same size as used
|
|
during the training) in an input image. The classifier outputs a "1" if the region is likely to show
|
|
the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can
|
|
move the search window across the image and check every location using the classifier. The
|
|
classifier is designed so that it can be easily "resized" in order to be able to find the objects of
|
|
interest at different sizes, which is more efficient than resizing the image itself. So, to find an
|
|
object of an unknown size in the image the scan procedure should be done several times at different
|
|
scales.
|
|
|
|
The word "cascade" in the classifier name means that the resultant classifier consists of several
|
|
simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some
|
|
stage the candidate is rejected or all the stages are passed. The word "boosted" means that the
|
|
classifiers at every stage of the cascade are complex themselves and they are built out of basic
|
|
classifiers using one of four different boosting techniques (weighted voting). Currently Discrete
|
|
Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are
|
|
decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic
|
|
classifiers, and are calculated as described below. The current algorithm uses the following
|
|
Haar-like features:
|
|
|
|
|
|
|
|
The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within
|
|
the region of interest and the scale (this scale is not the same as the scale used at the detection
|
|
stage, though these two scales are multiplied). For example, in the case of the third line feature
|
|
(2c) the response is calculated as the difference between the sum of image pixels under the
|
|
rectangle covering the whole feature (including the two white stripes and the black stripe in the
|
|
middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to
|
|
compensate for the differences in the size of areas. The sums of pixel values over a rectangular
|
|
regions are calculated rapidly using integral images (see below and the integral description).
|
|
|
|
Check @ref tutorial_cascade_classifier "the corresponding tutorial" for more details.
|
|
|
|
The following reference is for the detection part only. There is a separate application called
|
|
opencv_traincascade that can train a cascade of boosted classifiers from a set of samples.
|
|
|
|
@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in
|
|
addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection
|
|
using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at
|
|
<https://github.com/SvHey/thesis/blob/master/Literature/ObjectDetection/violaJones_CVPR2001.pdf>
|
|
|
|
@defgroup objdetect_hog HOG (Histogram of Oriented Gradients) descriptor and object detector
|
|
@defgroup objdetect_barcode Barcode detection and decoding
|
|
@defgroup objdetect_qrcode QRCode detection and encoding
|
|
@defgroup objdetect_dnn_face DNN-based face detection and recognition
|
|
|
|
Check @ref tutorial_dnn_face "the corresponding tutorial" for more details.
|
|
|
|
@defgroup objdetect_common Common functions and classes
|
|
@defgroup objdetect_aruco ArUco markers and boards detection for robust camera pose estimation
|
|
@{
|
|
ArUco Marker Detection
|
|
Square fiducial markers (also known as Augmented Reality Markers) are useful for easy,
|
|
fast and robust camera pose estimation.
|
|
|
|
The main functionality of ArucoDetector class is detection of markers in an image. If the markers are grouped
|
|
as a board, then you can try to recover the missing markers with ArucoDetector::refineDetectedMarkers().
|
|
ArUco markers can also be used for advanced chessboard corner finding. To do this, group the markers in the
|
|
CharucoBoard and find the corners of the chessboard with the CharucoDetector::detectBoard().
|
|
|
|
The implementation is based on the ArUco Library by R. Muñoz-Salinas and S. Garrido-Jurado @cite Aruco2014.
|
|
|
|
Markers can also be detected based on the AprilTag 2 @cite wang2016iros fiducial detection method.
|
|
|
|
@sa @cite Aruco2014
|
|
This code has been originally developed by Sergio Garrido-Jurado as a project
|
|
for Google Summer of Code 2015 (GSoC 15).
|
|
@}
|
|
|
|
@}
|
|
*/
|
|
|
|
typedef struct CvHaarClassifierCascade CvHaarClassifierCascade;
|
|
|
|
namespace cv
|
|
{
|
|
|
|
//! @addtogroup objdetect_common
|
|
//! @{
|
|
|
|
///////////////////////////// Object Detection ////////////////////////////
|
|
|
|
/** @brief This class is used for grouping object candidates detected by Cascade Classifier, HOG etc.
|
|
|
|
instance of the class is to be passed to cv::partition
|
|
*/
|
|
class CV_EXPORTS SimilarRects
|
|
{
|
|
public:
|
|
SimilarRects(double _eps) : eps(_eps) {}
|
|
inline bool operator()(const Rect& r1, const Rect& r2) const
|
|
{
|
|
double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5;
|
|
return std::abs(r1.x - r2.x) <= delta &&
|
|
std::abs(r1.y - r2.y) <= delta &&
|
|
std::abs(r1.x + r1.width - r2.x - r2.width) <= delta &&
|
|
std::abs(r1.y + r1.height - r2.y - r2.height) <= delta;
|
|
}
|
|
double eps;
|
|
};
|
|
|
|
/** @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 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.
|
|
|
|
The function is a wrapper for the generic function partition . It clusters all the input rectangles
|
|
using the rectangle equivalence criteria that combines rectangles with similar sizes and similar
|
|
locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If
|
|
\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small
|
|
clusters containing less than or equal to groupThreshold rectangles are rejected. In each other
|
|
cluster, the average rectangle is computed and put into the output rectangle list.
|
|
*/
|
|
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps = 0.2);
|
|
/** @overload */
|
|
CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights,
|
|
int groupThreshold, double eps = 0.2);
|
|
/** @overload */
|
|
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold,
|
|
double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
|
|
/** @overload */
|
|
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
|
|
std::vector<double>& levelWeights, int groupThreshold, double eps = 0.2);
|
|
/** @overload */
|
|
CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
|
|
std::vector<double>& foundScales,
|
|
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
|
|
//! @}
|
|
|
|
//! @addtogroup objdetect_cascade_classifier
|
|
//! @{
|
|
|
|
template<> struct DefaultDeleter<CvHaarClassifierCascade>{ CV_EXPORTS void operator ()(CvHaarClassifierCascade* obj) const; };
|
|
|
|
enum { CASCADE_DO_CANNY_PRUNING = 1,
|
|
CASCADE_SCALE_IMAGE = 2,
|
|
CASCADE_FIND_BIGGEST_OBJECT = 4,
|
|
CASCADE_DO_ROUGH_SEARCH = 8
|
|
};
|
|
|
|
class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm
|
|
{
|
|
public:
|
|
virtual ~BaseCascadeClassifier();
|
|
virtual bool empty() const CV_OVERRIDE = 0;
|
|
virtual bool load( const String& filename ) = 0;
|
|
virtual void detectMultiScale( InputArray image,
|
|
CV_OUT std::vector<Rect>& objects,
|
|
double scaleFactor,
|
|
int minNeighbors, int flags,
|
|
Size minSize, Size maxSize ) = 0;
|
|
|
|
virtual void detectMultiScale( InputArray image,
|
|
CV_OUT std::vector<Rect>& objects,
|
|
CV_OUT std::vector<int>& numDetections,
|
|
double scaleFactor,
|
|
int minNeighbors, int flags,
|
|
Size minSize, Size maxSize ) = 0;
|
|
|
|
virtual void detectMultiScale( InputArray image,
|
|
CV_OUT std::vector<Rect>& objects,
|
|
CV_OUT std::vector<int>& rejectLevels,
|
|
CV_OUT std::vector<double>& levelWeights,
|
|
double scaleFactor,
|
|
int minNeighbors, int flags,
|
|
Size minSize, Size maxSize,
|
|
bool outputRejectLevels ) = 0;
|
|
|
|
virtual bool isOldFormatCascade() const = 0;
|
|
virtual Size getOriginalWindowSize() const = 0;
|
|
virtual int getFeatureType() const = 0;
|
|
virtual void* getOldCascade() = 0;
|
|
|
|
class CV_EXPORTS MaskGenerator
|
|
{
|
|
public:
|
|
virtual ~MaskGenerator() {}
|
|
virtual Mat generateMask(const Mat& src)=0;
|
|
virtual void initializeMask(const Mat& /*src*/) { }
|
|
};
|
|
virtual void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator) = 0;
|
|
virtual Ptr<MaskGenerator> getMaskGenerator() = 0;
|
|
};
|
|
|
|
/** @example samples/cpp/facedetect.cpp
|
|
This program demonstrates usage of the Cascade classifier class
|
|
\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254
|
|
*/
|
|
/** @brief Cascade classifier class for object detection.
|
|
*/
|
|
class CV_EXPORTS_W CascadeClassifier
|
|
{
|
|
public:
|
|
CV_WRAP CascadeClassifier();
|
|
/** @brief Loads a classifier from a file.
|
|
|
|
@param filename Name of the file from which the classifier is loaded.
|
|
*/
|
|
CV_WRAP CascadeClassifier(const String& filename);
|
|
~CascadeClassifier();
|
|
/** @brief Checks whether the classifier has been loaded.
|
|
*/
|
|
CV_WRAP bool empty() const;
|
|
/** @brief Loads a classifier from a file.
|
|
|
|
@param filename Name of the file from which the classifier is loaded. The file may contain an old
|
|
HAAR classifier trained by the haartraining application or a new cascade classifier trained by the
|
|
traincascade application.
|
|
*/
|
|
CV_WRAP bool load( const String& filename );
|
|
/** @brief Reads a classifier from a FileStorage node.
|
|
|
|
@note The file may contain a new cascade classifier (trained by the traincascade application) only.
|
|
*/
|
|
CV_WRAP bool read( const FileNode& node );
|
|
|
|
/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
|
|
of rectangles.
|
|
|
|
@param image Matrix of the type CV_8U containing an image where objects are detected.
|
|
@param objects Vector of rectangles where each rectangle contains the detected object, the
|
|
rectangles may be partially outside the original image.
|
|
@param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
|
|
@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
|
|
to retain it.
|
|
@param flags Parameter with the same meaning for an old cascade as in the function
|
|
cvHaarDetectObjects. It is not used for a new cascade.
|
|
@param minSize Minimum possible object size. Objects smaller than that are ignored.
|
|
@param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
|
|
*/
|
|
CV_WRAP void detectMultiScale( InputArray image,
|
|
CV_OUT std::vector<Rect>& objects,
|
|
double scaleFactor = 1.1,
|
|
int minNeighbors = 3, int flags = 0,
|
|
Size minSize = Size(),
|
|
Size maxSize = Size() );
|
|
|
|
/** @overload
|
|
@param image Matrix of the type CV_8U containing an image where objects are detected.
|
|
@param objects Vector of rectangles where each rectangle contains the detected object, the
|
|
rectangles may be partially outside the original image.
|
|
@param numDetections Vector of detection numbers for the corresponding objects. An object's number
|
|
of detections is the number of neighboring positively classified rectangles that were joined
|
|
together to form the object.
|
|
@param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
|
|
@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
|
|
to retain it.
|
|
@param flags Parameter with the same meaning for an old cascade as in the function
|
|
cvHaarDetectObjects. It is not used for a new cascade.
|
|
@param minSize Minimum possible object size. Objects smaller than that are ignored.
|
|
@param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
|
|
*/
|
|
CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image,
|
|
CV_OUT std::vector<Rect>& objects,
|
|
CV_OUT std::vector<int>& numDetections,
|
|
double scaleFactor=1.1,
|
|
int minNeighbors=3, int flags=0,
|
|
Size minSize=Size(),
|
|
Size maxSize=Size() );
|
|
|
|
/** @overload
|
|
This function allows you to retrieve the final stage decision certainty of classification.
|
|
For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
|
|
For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
|
|
This value can then be used to separate strong from weaker classifications.
|
|
|
|
A code sample on how to use it efficiently can be found below:
|
|
@code
|
|
Mat img;
|
|
vector<double> weights;
|
|
vector<int> levels;
|
|
vector<Rect> detections;
|
|
CascadeClassifier model("/path/to/your/model.xml");
|
|
model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
|
|
cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
|
|
@endcode
|
|
*/
|
|
CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image,
|
|
CV_OUT std::vector<Rect>& objects,
|
|
CV_OUT std::vector<int>& rejectLevels,
|
|
CV_OUT std::vector<double>& levelWeights,
|
|
double scaleFactor = 1.1,
|
|
int minNeighbors = 3, int flags = 0,
|
|
Size minSize = Size(),
|
|
Size maxSize = Size(),
|
|
bool outputRejectLevels = false );
|
|
|
|
CV_WRAP bool isOldFormatCascade() const;
|
|
CV_WRAP Size getOriginalWindowSize() const;
|
|
CV_WRAP int getFeatureType() const;
|
|
void* getOldCascade();
|
|
|
|
CV_WRAP static bool convert(const String& oldcascade, const String& newcascade);
|
|
|
|
void setMaskGenerator(const Ptr<BaseCascadeClassifier::MaskGenerator>& maskGenerator);
|
|
Ptr<BaseCascadeClassifier::MaskGenerator> getMaskGenerator();
|
|
|
|
Ptr<BaseCascadeClassifier> cc;
|
|
};
|
|
|
|
CV_EXPORTS Ptr<BaseCascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator();
|
|
//! @}
|
|
|
|
//! @addtogroup objdetect_hog
|
|
//! @{
|
|
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
|
|
|
|
//! struct for detection region of interest (ROI)
|
|
struct DetectionROI
|
|
{
|
|
//! scale(size) of the bounding box
|
|
double scale;
|
|
//! set of requested locations to be evaluated
|
|
std::vector<cv::Point> locations;
|
|
//! vector that will contain confidence values for each location
|
|
std::vector<double> confidences;
|
|
};
|
|
|
|
/**@brief Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
|
|
|
|
the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs @cite Dalal2005 .
|
|
|
|
useful links:
|
|
|
|
https://hal.inria.fr/inria-00548512/document/
|
|
|
|
https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
|
|
|
|
https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor
|
|
|
|
http://www.learnopencv.com/histogram-of-oriented-gradients
|
|
|
|
http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial
|
|
|
|
*/
|
|
struct CV_EXPORTS_W HOGDescriptor
|
|
{
|
|
public:
|
|
enum HistogramNormType { L2Hys = 0 //!< Default histogramNormType
|
|
};
|
|
enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value.
|
|
};
|
|
enum DescriptorStorageFormat { DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW };
|
|
|
|
/**@brief Creates the HOG descriptor and detector with default parameters.
|
|
|
|
aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
|
|
*/
|
|
CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
|
|
cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
|
|
histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
|
|
free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false)
|
|
{}
|
|
|
|
/** @overload
|
|
@param _winSize sets winSize with given value.
|
|
@param _blockSize sets blockSize with given value.
|
|
@param _blockStride sets blockStride with given value.
|
|
@param _cellSize sets cellSize with given value.
|
|
@param _nbins sets nbins with given value.
|
|
@param _derivAperture sets derivAperture with given value.
|
|
@param _winSigma sets winSigma with given value.
|
|
@param _histogramNormType sets histogramNormType with given value.
|
|
@param _L2HysThreshold sets L2HysThreshold with given value.
|
|
@param _gammaCorrection sets gammaCorrection with given value.
|
|
@param _nlevels sets nlevels with given value.
|
|
@param _signedGradient sets signedGradient with given value.
|
|
*/
|
|
CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
|
|
Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
|
|
HOGDescriptor::HistogramNormType _histogramNormType=HOGDescriptor::L2Hys,
|
|
double _L2HysThreshold=0.2, bool _gammaCorrection=false,
|
|
int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false)
|
|
: winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
|
|
nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),
|
|
histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
|
|
gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
|
|
{}
|
|
|
|
/** @overload
|
|
|
|
Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
|
|
@param filename The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier.
|
|
*/
|
|
CV_WRAP HOGDescriptor(const String& filename)
|
|
{
|
|
load(filename);
|
|
}
|
|
|
|
/** @overload
|
|
@param d the HOGDescriptor which cloned to create a new one.
|
|
*/
|
|
HOGDescriptor(const HOGDescriptor& d)
|
|
{
|
|
d.copyTo(*this);
|
|
}
|
|
|
|
/**@brief Default destructor.
|
|
*/
|
|
virtual ~HOGDescriptor() {}
|
|
|
|
/**@brief Returns the number of coefficients required for the classification.
|
|
*/
|
|
CV_WRAP size_t getDescriptorSize() const;
|
|
|
|
/** @brief Checks if detector size equal to descriptor size.
|
|
*/
|
|
CV_WRAP bool checkDetectorSize() const;
|
|
|
|
/** @brief Returns winSigma value
|
|
*/
|
|
CV_WRAP double getWinSigma() const;
|
|
|
|
/**@example samples/cpp/peopledetect.cpp
|
|
*/
|
|
/**@brief Sets coefficients for the linear SVM classifier.
|
|
@param svmdetector coefficients for the linear SVM classifier.
|
|
*/
|
|
CV_WRAP virtual void setSVMDetector(InputArray svmdetector);
|
|
|
|
/** @brief Reads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file node.
|
|
@param fn File node
|
|
*/
|
|
virtual bool read(FileNode& fn);
|
|
|
|
/** @brief Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage.
|
|
@param fs File storage
|
|
@param objname Object name
|
|
*/
|
|
virtual void write(FileStorage& fs, const String& objname) const;
|
|
|
|
/** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
|
|
@param filename Name of the file to read.
|
|
@param objname The optional name of the node to read (if empty, the first top-level node will be used).
|
|
*/
|
|
CV_WRAP virtual bool load(const String& filename, const String& objname = String());
|
|
|
|
/** @brief saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
|
|
@param filename File name
|
|
@param objname Object name
|
|
*/
|
|
CV_WRAP virtual void save(const String& filename, const String& objname = String()) const;
|
|
|
|
/** @brief clones the HOGDescriptor
|
|
@param c cloned HOGDescriptor
|
|
*/
|
|
virtual void copyTo(HOGDescriptor& c) const;
|
|
|
|
/**@example samples/cpp/train_HOG.cpp
|
|
*/
|
|
/** @brief Computes HOG descriptors of given image.
|
|
@param img Matrix of the type CV_8U containing an image where HOG features will be calculated.
|
|
@param descriptors Matrix of the type CV_32F
|
|
@param winStride Window stride. It must be a multiple of block stride.
|
|
@param padding Padding
|
|
@param locations Vector of Point
|
|
*/
|
|
CV_WRAP virtual void compute(InputArray img,
|
|
CV_OUT std::vector<float>& descriptors,
|
|
Size winStride = Size(), Size padding = Size(),
|
|
const std::vector<Point>& locations = std::vector<Point>()) const;
|
|
|
|
/** @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.
|
|
@param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
|
|
@param weights Vector that will contain confidence values for each 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 searchLocations Vector of Point includes set of requested locations to be evaluated.
|
|
*/
|
|
CV_WRAP virtual void detect(InputArray img, CV_OUT std::vector<Point>& foundLocations,
|
|
CV_OUT std::vector<double>& weights,
|
|
double hitThreshold = 0, Size winStride = Size(),
|
|
Size padding = Size(),
|
|
const std::vector<Point>& searchLocations = std::vector<Point>()) const;
|
|
|
|
/** @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.
|
|
@param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
|
|
@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 searchLocations Vector of Point includes locations to search.
|
|
*/
|
|
virtual void detect(InputArray img, CV_OUT std::vector<Point>& foundLocations,
|
|
double hitThreshold = 0, Size winStride = Size(),
|
|
Size padding = Size(),
|
|
const std::vector<Point>& searchLocations=std::vector<Point>()) const;
|
|
|
|
/** @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 foundWeights Vector that will contain confidence values for each 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 groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered
|
|
by many rectangles. 0 means not to perform grouping.
|
|
@param useMeanshiftGrouping indicates grouping algorithm
|
|
*/
|
|
CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
|
|
CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
|
|
Size winStride = Size(), Size padding = Size(), double scale = 1.05,
|
|
double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
|
|
|
|
/** @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 groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered
|
|
by many rectangles. 0 means not to perform grouping.
|
|
@param useMeanshiftGrouping indicates grouping algorithm
|
|
*/
|
|
virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
|
|
double hitThreshold = 0, Size winStride = Size(),
|
|
Size padding = Size(), double scale = 1.05,
|
|
double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
|
|
|
|
/** @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
|
|
@param paddingTL Padding from top-left
|
|
@param paddingBR Padding from bottom-right
|
|
*/
|
|
CV_WRAP virtual void computeGradient(InputArray img, InputOutputArray grad, InputOutputArray 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 HOGDescriptor::HistogramNormType 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(InputArray 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(InputArray img,
|
|
CV_OUT std::vector<cv::Rect>& foundLocations,
|
|
std::vector<DetectionROI>& locations,
|
|
double hitThreshold = 0,
|
|
int groupThreshold = 0) const;
|
|
|
|
/** @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;
|
|
};
|
|
//! @}
|
|
|
|
//! @addtogroup objdetect_qrcode
|
|
//! @{
|
|
|
|
class CV_EXPORTS_W QRCodeEncoder {
|
|
protected:
|
|
QRCodeEncoder(); // use ::create()
|
|
public:
|
|
virtual ~QRCodeEncoder();
|
|
|
|
enum EncodeMode {
|
|
MODE_AUTO = -1,
|
|
MODE_NUMERIC = 1, // 0b0001
|
|
MODE_ALPHANUMERIC = 2, // 0b0010
|
|
MODE_BYTE = 4, // 0b0100
|
|
MODE_ECI = 7, // 0b0111
|
|
MODE_KANJI = 8, // 0b1000
|
|
MODE_STRUCTURED_APPEND = 3 // 0b0011
|
|
};
|
|
|
|
enum CorrectionLevel {
|
|
CORRECT_LEVEL_L = 0,
|
|
CORRECT_LEVEL_M = 1,
|
|
CORRECT_LEVEL_Q = 2,
|
|
CORRECT_LEVEL_H = 3
|
|
};
|
|
|
|
enum ECIEncodings {
|
|
ECI_UTF8 = 26
|
|
};
|
|
|
|
/** @brief QR code encoder parameters. */
|
|
struct CV_EXPORTS_W_SIMPLE Params
|
|
{
|
|
CV_WRAP Params();
|
|
|
|
//! The optional version of QR code (by default - maximum possible depending on the length of the string).
|
|
CV_PROP_RW int version;
|
|
|
|
//! The optional level of error correction (by default - the lowest).
|
|
CV_PROP_RW CorrectionLevel correction_level;
|
|
|
|
//! The optional encoding mode - Numeric, Alphanumeric, Byte, Kanji, ECI or Structured Append.
|
|
CV_PROP_RW EncodeMode mode;
|
|
|
|
//! The optional number of QR codes to generate in Structured Append mode.
|
|
CV_PROP_RW int structure_number;
|
|
};
|
|
|
|
/** @brief Constructor
|
|
@param parameters QR code encoder parameters QRCodeEncoder::Params
|
|
*/
|
|
static CV_WRAP
|
|
Ptr<QRCodeEncoder> create(const QRCodeEncoder::Params& parameters = QRCodeEncoder::Params());
|
|
|
|
/** @brief Generates QR code from input string.
|
|
@param encoded_info Input string to encode.
|
|
@param qrcode Generated QR code.
|
|
*/
|
|
CV_WRAP virtual void encode(const String& encoded_info, OutputArray qrcode) = 0;
|
|
|
|
/** @brief Generates QR code from input string in Structured Append mode. The encoded message is splitting over a number of QR codes.
|
|
@param encoded_info Input string to encode.
|
|
@param qrcodes Vector of generated QR codes.
|
|
*/
|
|
CV_WRAP virtual void encodeStructuredAppend(const String& encoded_info, OutputArrayOfArrays qrcodes) = 0;
|
|
|
|
};
|
|
class CV_EXPORTS_W_SIMPLE QRCodeDetector : public GraphicalCodeDetector
|
|
{
|
|
public:
|
|
CV_WRAP 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 QRCodeDetector& 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 QRCodeDetector& setEpsY(double epsY);
|
|
|
|
/** @brief use markers to improve the position of the corners of the QR code
|
|
*
|
|
* alignmentMarkers using by default
|
|
*/
|
|
CV_WRAP QRCodeDetector& setUseAlignmentMarkers(bool useAlignmentMarkers);
|
|
|
|
/** @brief Decodes QR code on a curved surface 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 decodeCurved(InputArray img, InputArray points, OutputArray straight_qrcode = noArray());
|
|
|
|
/** @brief Both detects and decodes QR code on a curved surface
|
|
|
|
@param img grayscale or color (BGR) image containing QR code.
|
|
@param points optional 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 std::string detectAndDecodeCurved(InputArray img, OutputArray points=noArray(),
|
|
OutputArray straight_qrcode = noArray());
|
|
};
|
|
|
|
class CV_EXPORTS_W_SIMPLE QRCodeDetectorAruco : public GraphicalCodeDetector {
|
|
public:
|
|
CV_WRAP QRCodeDetectorAruco();
|
|
|
|
struct CV_EXPORTS_W_SIMPLE Params {
|
|
CV_WRAP Params();
|
|
|
|
/** @brief The minimum allowed pixel size of a QR module in the smallest image in the image pyramid, default 4.f */
|
|
CV_PROP_RW float minModuleSizeInPyramid;
|
|
|
|
/** @brief The maximum allowed relative rotation for finder patterns in the same QR code, default pi/12 */
|
|
CV_PROP_RW float maxRotation;
|
|
|
|
/** @brief The maximum allowed relative mismatch in module sizes for finder patterns in the same QR code, default 1.75f */
|
|
CV_PROP_RW float maxModuleSizeMismatch;
|
|
|
|
/** @brief The maximum allowed module relative mismatch for timing pattern module, default 2.f
|
|
*
|
|
* If relative mismatch of timing pattern module more this value, penalty points will be added.
|
|
* If a lot of penalty points are added, QR code will be rejected. */
|
|
CV_PROP_RW float maxTimingPatternMismatch;
|
|
|
|
/** @brief The maximum allowed percentage of penalty points out of total pins in timing pattern, default 0.4f */
|
|
CV_PROP_RW float maxPenalties;
|
|
|
|
/** @brief The maximum allowed relative color mismatch in the timing pattern, default 0.2f*/
|
|
CV_PROP_RW float maxColorsMismatch;
|
|
|
|
/** @brief The algorithm find QR codes with almost minimum timing pattern score and minimum size, default 0.9f
|
|
*
|
|
* The QR code with the minimum "timing pattern score" and minimum "size" is selected as the best QR code.
|
|
* If for the current QR code "timing pattern score" * scaleTimingPatternScore < "previous timing pattern score" and "size" < "previous size", then
|
|
* current QR code set as the best QR code. */
|
|
CV_PROP_RW float scaleTimingPatternScore;
|
|
};
|
|
|
|
/** @brief QR code detector constructor for Aruco-based algorithm. See cv::QRCodeDetectorAruco::Params */
|
|
CV_WRAP explicit QRCodeDetectorAruco(const QRCodeDetectorAruco::Params& params);
|
|
|
|
/** @brief Detector parameters getter. See cv::QRCodeDetectorAruco::Params */
|
|
CV_WRAP const QRCodeDetectorAruco::Params& getDetectorParameters() const;
|
|
|
|
/** @brief Detector parameters setter. See cv::QRCodeDetectorAruco::Params */
|
|
CV_WRAP QRCodeDetectorAruco& setDetectorParameters(const QRCodeDetectorAruco::Params& params);
|
|
|
|
/** @brief Aruco detector parameters are used to search for the finder patterns. */
|
|
CV_WRAP const aruco::DetectorParameters& getArucoParameters() const;
|
|
|
|
/** @brief Aruco detector parameters are used to search for the finder patterns. */
|
|
CV_WRAP void setArucoParameters(const aruco::DetectorParameters& params);
|
|
};
|
|
|
|
//! @}
|
|
}
|
|
|
|
#include "opencv2/objdetect/detection_based_tracker.hpp"
|
|
#include "opencv2/objdetect/face.hpp"
|
|
#include "opencv2/objdetect/charuco_detector.hpp"
|
|
#include "opencv2/objdetect/barcode.hpp"
|
|
|
|
#endif
|