You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
467 lines
22 KiB
467 lines
22 KiB
/*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"
|
|
|
|
/**
|
|
@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:
|
|
|
|
|
|
|
|
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/Itseez/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/python2/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
|