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opencv/modules/objdetect/include/opencv2/objdetect.hpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// By downloading, copying, installing or using the software you agree to this license.
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// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// and/or other materials provided with the distribution.
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#ifndef __OPENCV_OBJDETECT_HPP__
#define __OPENCV_OBJDETECT_HPP__
#include "opencv2/core.hpp"
/**
@defgroup objdetect Object Detection
Haar Feature-based 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:
![image](pics/haarfeatures.png)
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).
To see the object detector at work, have a look at the facedetect demo:
<https://github.com/opencv/opencv/tree/master/samples/cpp/dbt_face_detection.cpp>
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
<http://research.microsoft.com/en-us/um/people/viola/Pubs/Detect/violaJones_CVPR2001.pdf>
@{
@defgroup objdetect_c C API
@}
*/
typedef struct CvHaarClassifierCascade CvHaarClassifierCascade;
namespace cv
{
//! @addtogroup objdetect
//! @{
///////////////////////////// Object Detection ////////////////////////////
//! class for grouping object candidates, detected by Cascade Classifier, HOG etc.
//! instance of the class is to be passed to cv::partition (see cxoperations.hpp)
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));
template<> CV_EXPORTS void DefaultDeleter<CvHaarClassifierCascade>::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 = 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;
};
/** @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 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.
The function is parallelized with the TBB library.
@note
- (Python) A face detection example using cascade classifiers can be found at
opencv_source_code/samples/python/facedetect.py
*/
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.
*/
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
if `outputRejectLevels` is `true` returns `rejectLevels` and `levelWeights`
*/
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();
//////////////// 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 requrested locations to be evaluated
std::vector<cv::Point> locations;
//! vector that will contain confidence values for each location
std::vector<double> confidences;
};
struct CV_EXPORTS_W HOGDescriptor
{
public:
enum { L2Hys = 0
};
enum { DEFAULT_NLEVELS = 64
};
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)
{}
CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
int _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)
{}
CV_WRAP HOGDescriptor(const String& filename)
{
load(filename);
}
HOGDescriptor(const HOGDescriptor& d)
{
d.copyTo(*this);
}
virtual ~HOGDescriptor() {}
CV_WRAP size_t getDescriptorSize() const;
CV_WRAP bool checkDetectorSize() const;
CV_WRAP double getWinSigma() const;
CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
virtual bool read(FileNode& fn);
virtual void write(FileStorage& fs, const String& objname) const;
CV_WRAP virtual bool load(const String& filename, const String& objname = String());
CV_WRAP virtual void save(const String& filename, const String& objname = String()) const;
virtual void copyTo(HOGDescriptor& c) const;
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;
//! with found weights output
CV_WRAP virtual void detect(const Mat& 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;
//! without found weights output
virtual void detect(const Mat& 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;
//! with result weights output
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 finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
//! without found weights output
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 finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
Size paddingTL = Size(), Size paddingBR = Size()) const;
CV_WRAP static std::vector<float> getDefaultPeopleDetector();
CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
CV_PROP Size winSize;
CV_PROP Size blockSize;
CV_PROP Size blockStride;
CV_PROP Size cellSize;
CV_PROP int nbins;
CV_PROP int derivAperture;
CV_PROP double winSigma;
CV_PROP int histogramNormType;
CV_PROP double L2HysThreshold;
CV_PROP bool gammaCorrection;
CV_PROP std::vector<float> svmDetector;
UMat oclSvmDetector;
float free_coef;
CV_PROP int nlevels;
CV_PROP bool signedGradient;
//! evaluate specified ROI and return confidence value for each location
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;
//! evaluate specified ROI and return confidence value for each location in multiple scales
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;
//! read/parse Dalal's alt model file
void readALTModel(String modelfile);
void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
};
//! @} objdetect
}
#include "opencv2/objdetect/detection_based_tracker.hpp"
#ifndef DISABLE_OPENCV_24_COMPATIBILITY
#include "opencv2/objdetect/objdetect_c.h"
#endif
#endif