OpenCVForUnity.ObjdetectModule.CascadeClassifier Class Reference

Cascade classifier class for object detection. More...

Public Member Functions
	CascadeClassifier ()
	CascadeClassifier (string filename)
	Loads a classifier from a file.
void	detectMultiScale (Mat image, MatOfRect objects)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (Mat image, MatOfRect objects, double scaleFactor)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (Mat image, MatOfRect objects, double scaleFactor, int minNeighbors)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags, in Vec2d minSize)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags, in Vec2d minSize, in Vec2d maxSize)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags, in(double width, double height) minSize)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags, in(double width, double height) minSize, in(double width, double height) maxSize)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags, Size minSize)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale (Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize)
	Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections)
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor)
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors)
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags)
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, in Vec2d minSize)
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, in Vec2d minSize, in Vec2d maxSize)
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, in(double width, double height) minSize)
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, in(double width, double height) minSize, in(double width, double height) maxSize)
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, Size minSize)
void	detectMultiScale2 (Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, in Vec2d minSize)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, in Vec2d minSize, in Vec2d maxSize)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, in Vec2d minSize, in Vec2d maxSize, bool outputRejectLevels)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, in(double width, double height) minSize)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, in(double width, double height) minSize, in(double width, double height) maxSize)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, in(double width, double height) minSize, in(double width, double height) maxSize, bool outputRejectLevels)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, Size minSize)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize)
void	detectMultiScale3 (Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize, bool outputRejectLevels)
bool	empty ()
	Checks whether the classifier has been loaded.
int	getFeatureType ()
IntPtr	getNativeObjAddr ()
Size	getOriginalWindowSize ()
double double height	getOriginalWindowSizeAsValueTuple ()
Vec2d	getOriginalWindowSizeAsVec2d ()
bool	isOldFormatCascade ()
bool	load (string filename)
	Loads a classifier from a file.
Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()
void	ThrowIfDisposed ()

Static Public Member Functions
static CascadeClassifier	__fromPtr__ (IntPtr addr)
static bool	convert (string oldcascade, string newcascade)
Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Public Attributes
double	width

Protected Member Functions
override void	Dispose (bool disposing)
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()
	DisposableOpenCVObject (bool isEnabledDispose)
	DisposableOpenCVObject (IntPtr ptr)
	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)
override void	Dispose (bool disposing)
Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()
	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed [get, protected set]
bool	IsEnabledDispose [get, set]

Detailed Description

Cascade classifier class for object detection.

Constructor & Destructor Documentation

CascadeClassifier() [1/2]

OpenCVForUnity.ObjdetectModule.CascadeClassifier.CascadeClassifier

(

)

CascadeClassifier() [2/2]

OpenCVForUnity.ObjdetectModule.CascadeClassifier.CascadeClassifier

(

string

filename

)

Loads a classifier from a file.

Parameters

filename

Name of the file from which the classifier is loaded.

Member Function Documentation

__fromPtr__()

static CascadeClassifier OpenCVForUnity.ObjdetectModule.CascadeClassifier.__fromPtr__ ( IntPtr addr )

static

convert()

static bool OpenCVForUnity.ObjdetectModule.CascadeClassifier.convert ( string oldcascade, string newcascade )

static

detectMultiScale() [1/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects )

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.

detectMultiScale() [2/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects,
		double	scaleFactor )

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.

detectMultiScale() [3/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects,
		double	scaleFactor,
		int	minNeighbors )

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.

detectMultiScale() [4/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects,
		double	scaleFactor,
		int	minNeighbors,
		int	flags )

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.

detectMultiScale() [5/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in Vec2d	minSize )

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.

detectMultiScale() [6/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in Vec2d	minSize,
		in Vec2d	maxSize )

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.

detectMultiScale() [7/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in(double width, double height)	minSize )

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.

detectMultiScale() [8/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in(double width, double height)	minSize,
		in(double width, double height)	maxSize )

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.

detectMultiScale() [9/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		Size	minSize )

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.

detectMultiScale() [10/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale	(	Mat	image,
		MatOfRect	objects,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		Size	minSize,
		Size	maxSize )

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.

detectMultiScale2() [1/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections )

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.

detectMultiScale2() [2/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections,
		double	scaleFactor )

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.

detectMultiScale2() [3/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections,
		double	scaleFactor,
		int	minNeighbors )

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.

detectMultiScale2() [4/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections,
		double	scaleFactor,
		int	minNeighbors,
		int	flags )

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.

detectMultiScale2() [5/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in Vec2d	minSize )

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.

detectMultiScale2() [6/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in Vec2d	minSize,
		in Vec2d	maxSize )

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.

detectMultiScale2() [7/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in(double width, double height)	minSize )

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.

detectMultiScale2() [8/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in(double width, double height)	minSize,
		in(double width, double height)	maxSize )

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.

detectMultiScale2() [9/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		Size	minSize )

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.

detectMultiScale2() [10/10]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale2	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	numDetections,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		Size	minSize,
		Size	maxSize )

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.

detectMultiScale3() [1/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	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;

detectMultiScale3() [2/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor )

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;

detectMultiScale3() [3/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors )

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;

detectMultiScale3() [4/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags )

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;

detectMultiScale3() [5/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in Vec2d	minSize )

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;

detectMultiScale3() [6/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in Vec2d	minSize,
		in Vec2d	maxSize )

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;

detectMultiScale3() [7/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in Vec2d	minSize,
		in Vec2d	maxSize,
		bool	outputRejectLevels )

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;

detectMultiScale3() [8/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in(double width, double height)	minSize )

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;

detectMultiScale3() [9/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in(double width, double height)	minSize,
		in(double width, double height)	maxSize )

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;

detectMultiScale3() [10/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		in(double width, double height)	minSize,
		in(double width, double height)	maxSize,
		bool	outputRejectLevels )

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;

detectMultiScale3() [11/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		Size	minSize )

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;

detectMultiScale3() [12/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		Size	minSize,
		Size	maxSize )

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;

detectMultiScale3() [13/13]

void OpenCVForUnity.ObjdetectModule.CascadeClassifier.detectMultiScale3	(	Mat	image,
		MatOfRect	objects,
		MatOfInt	rejectLevels,
		MatOfDouble	levelWeights,
		double	scaleFactor,
		int	minNeighbors,
		int	flags,
		Size	minSize,
		Size	maxSize,
		bool	outputRejectLevels )

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;

Dispose()

override void OpenCVForUnity.ObjdetectModule.CascadeClassifier.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.DisposableObject.

empty()

bool OpenCVForUnity.ObjdetectModule.CascadeClassifier.empty

(

)

Checks whether the classifier has been loaded.

getFeatureType()

int OpenCVForUnity.ObjdetectModule.CascadeClassifier.getFeatureType

(

)

getNativeObjAddr()

IntPtr OpenCVForUnity.ObjdetectModule.CascadeClassifier.getNativeObjAddr

(

)

getOriginalWindowSize()

Size OpenCVForUnity.ObjdetectModule.CascadeClassifier.getOriginalWindowSize

(

)

getOriginalWindowSizeAsValueTuple()

double double height OpenCVForUnity.ObjdetectModule.CascadeClassifier.getOriginalWindowSizeAsValueTuple

(

)

getOriginalWindowSizeAsVec2d()

Vec2d OpenCVForUnity.ObjdetectModule.CascadeClassifier.getOriginalWindowSizeAsVec2d

(

)

isOldFormatCascade()

bool OpenCVForUnity.ObjdetectModule.CascadeClassifier.isOldFormatCascade

(

)

load()

bool OpenCVForUnity.ObjdetectModule.CascadeClassifier.load

(

string

filename

)

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.

Member Data Documentation

width

double OpenCVForUnity.ObjdetectModule.CascadeClassifier.width

---

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

• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/objdetect/CascadeClassifier.cs
• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/objdetect/CascadeClassifier_Struct.cs
• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/objdetect/CascadeClassifier_ValueTuple.cs