cv::CascadeClassifier Class Reference

Cascade classifier class for object detection. More...

#include <opencv2/objdetect.hpp>

Collaboration diagram for cv::CascadeClassifier:

Public Member Functions
	CascadeClassifier ()
	CascadeClassifier (const String &filename)
	Loads a classifier from a file.
	~CascadeClassifier ()
void	detectMultiScale (InputArray image, std::vector< Rect > &objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size())
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (InputArray image, std::vector< Rect > &objects, std::vector< int > &numDetections, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size())
void	detectMultiScale (InputArray image, std::vector< Rect > &objects, std::vector< int > &rejectLevels, std::vector< double > &levelWeights, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size(), bool outputRejectLevels=false)
bool	empty () const
	Checks whether the classifier has been loaded.
int	getFeatureType () const
Ptr< BaseCascadeClassifier::MaskGenerator >	getMaskGenerator ()
void *	getOldCascade ()
Size	getOriginalWindowSize () const
bool	isOldFormatCascade () const
bool	load (const String &filename)
	Loads a classifier from a file.
bool	read (const FileNode &node)
	Reads a classifier from a FileStorage node.
void	setMaskGenerator (const Ptr< BaseCascadeClassifier::MaskGenerator > &maskGenerator)

Static Public Member Functions
static bool	convert (const String &oldcascade, const String &newcascade)

Public Attributes
Ptr< BaseCascadeClassifier >	cc

Detailed Description

Cascade classifier class for object detection.

Examples

samples/cpp/facedetect.cpp.

Constructor & Destructor Documentation

CascadeClassifier() [1/2]

cv::CascadeClassifier::CascadeClassifier

(

)

Python:
	cv.CascadeClassifier(		) ->	<CascadeClassifier object>
	cv.CascadeClassifier(	filename	) ->	<CascadeClassifier object>

CascadeClassifier() [2/2]

cv::CascadeClassifier::CascadeClassifier

(

const String &

filename

)

Python:
	cv.CascadeClassifier(		) ->	<CascadeClassifier object>
	cv.CascadeClassifier(	filename	) ->	<CascadeClassifier object>

Loads a classifier from a file.

Parameters

filename

Name of the file from which the classifier is loaded.

~CascadeClassifier()

cv::CascadeClassifier::~CascadeClassifier

(

)

Member Function Documentation

convert()

static bool cv::CascadeClassifier::convert ( const String &  oldcascade, const String &  newcascade  )

static

Python:
	cv.CascadeClassifier.convert(	oldcascade, newcascade	) ->	retval
	cv.CascadeClassifier_convert(	oldcascade, newcascade	) ->	retval

detectMultiScale() [1/3]

void cv::CascadeClassifier::detectMultiScale	(	InputArray	image,
		std::vector< Rect > &	objects,
		double	scaleFactor = 1.1,
		int	minNeighbors = 3,
		int	flags = 0,
		Size	minSize = Size(),
		Size	maxSize = Size()
	)

Python:
	cv.CascadeClassifier.detectMultiScale(	image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]	) ->	objects
	cv.CascadeClassifier.detectMultiScale2(	image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]	) ->	objects, numDetections
	cv.CascadeClassifier.detectMultiScale3(	image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]	) ->	objects, rejectLevels, levelWeights

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters

image	Matrix of the type CV_8U containing an image where objects are detected.
objects	Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
scaleFactor	Parameter specifying how much the image size is reduced at each image scale.
minNeighbors	Parameter specifying how many neighbors each candidate rectangle should have to retain it.
flags	Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
minSize	Minimum possible object size. Objects smaller than that are ignored.
maxSize	Maximum possible object size. Objects larger than that are ignored. If maxSize == minSize model is evaluated on single scale.

Examples

samples/cpp/facedetect.cpp.

detectMultiScale() [2/3]

void cv::CascadeClassifier::detectMultiScale	(	InputArray	image,
		std::vector< Rect > &	objects,
		std::vector< int > &	numDetections,
		double	scaleFactor = 1.1,
		int	minNeighbors = 3,
		int	flags = 0,
		Size	minSize = Size(),
		Size	maxSize = Size()
	)

Python:
	cv.CascadeClassifier.detectMultiScale(	image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]	) ->	objects
	cv.CascadeClassifier.detectMultiScale2(	image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]	) ->	objects, numDetections
	cv.CascadeClassifier.detectMultiScale3(	image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]	) ->	objects, rejectLevels, levelWeights

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

image	Matrix of the type CV_8U containing an image where objects are detected.
objects	Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
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.
scaleFactor	Parameter specifying how much the image size is reduced at each image scale.
minNeighbors	Parameter specifying how many neighbors each candidate rectangle should have to retain it.
flags	Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
minSize	Minimum possible object size. Objects smaller than that are ignored.
maxSize	Maximum possible object size. Objects larger than that are ignored. If maxSize == minSize model is evaluated on single scale.

detectMultiScale() [3/3]

void cv::CascadeClassifier::detectMultiScale	(	InputArray	image,
		std::vector< Rect > &	objects,
		std::vector< int > &	rejectLevels,
		std::vector< double > &	levelWeights,
		double	scaleFactor = 1.1,
		int	minNeighbors = 3,
		int	flags = 0,
		Size	minSize = Size(),
		Size	maxSize = Size(),
		bool	outputRejectLevels = false
	)

Python:
	cv.CascadeClassifier.detectMultiScale(	image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]	) ->	objects
	cv.CascadeClassifier.detectMultiScale2(	image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]	) ->	objects, numDetections
	cv.CascadeClassifier.detectMultiScale3(	image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]	) ->	objects, rejectLevels, levelWeights

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. 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:

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;

empty()

bool cv::CascadeClassifier::empty

(

)

const

Python:
	cv.CascadeClassifier.empty(		) ->	retval

Checks whether the classifier has been loaded.

Examples

samples/cpp/facedetect.cpp.

getFeatureType()

int cv::CascadeClassifier::getFeatureType

(

)

const

Python:
	cv.CascadeClassifier.getFeatureType(		) ->	retval

getMaskGenerator()

Ptr< BaseCascadeClassifier::MaskGenerator > cv::CascadeClassifier::getMaskGenerator

(

)

getOldCascade()

void * cv::CascadeClassifier::getOldCascade

(

)

getOriginalWindowSize()

Size cv::CascadeClassifier::getOriginalWindowSize

(

)

const

Python:
	cv.CascadeClassifier.getOriginalWindowSize(		) ->	retval

isOldFormatCascade()

bool cv::CascadeClassifier::isOldFormatCascade

(

)

const

Python:
	cv.CascadeClassifier.isOldFormatCascade(		) ->	retval

load()

bool cv::CascadeClassifier::load

(

const String &

filename

)

Python:
	cv.CascadeClassifier.load(	filename	) ->	retval

Loads a classifier from a file.

Parameters

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.

Examples

samples/cpp/facedetect.cpp.

read()

bool cv::CascadeClassifier::read

(

const FileNode &

node

)

Python:
	cv.CascadeClassifier.read(	node	) ->	retval

Reads a classifier from a FileStorage node.

Note

The file may contain a new cascade classifier (trained by the traincascade application) only.

setMaskGenerator()

void cv::CascadeClassifier::setMaskGenerator

(

const Ptr< BaseCascadeClassifier::MaskGenerator > &

maskGenerator

)

Member Data Documentation

cc

Ptr<BaseCascadeClassifier> cv::CascadeClassifier::cc

---

The documentation for this class was generated from the following file:

• opencv2/objdetect.hpp