cv::CascadeClassifier Class Reference
对象检测

Cascade classifier class for object detection. 更多...

#include <opencv2/objdetect.hpp>

Public Member Functions

CascadeClassifier ()
CascadeClassifier (const 字符串 &filename)
Loads a classifier from a file. 更多...
~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. 更多...
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. 更多...
int getFeatureType () const
Ptr < BaseCascadeClassifier::MaskGenerator > getMaskGenerator ()
void * getOldCascade ()
Size getOriginalWindowSize () const
bool isOldFormatCascade () const
bool load (const 字符串 &filename)
Loads a classifier from a file. 更多...
bool read (const FileNode &node)
Reads a classifier from a FileStorage node. 更多...
void setMaskGenerator (const Ptr < BaseCascadeClassifier::MaskGenerator > &maskGenerator)

Static Public Member Functions

static bool convert (const 字符串 &oldcascade, const 字符串 &newcascade)

Public Attributes

Ptr < BaseCascadeClassifier > cc

详细描述

Cascade classifier class for object detection.

范例:
samples/cpp/facedetect.cpp .

Constructor & Destructor Documentation

CascadeClassifier() [1/2]

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

CascadeClassifier() [2/2]

cv::CascadeClassifier::CascadeClassifier ( const 字符串 & filename )
Python:
<CascadeClassifier object> = cv.CascadeClassifier( )
<CascadeClassifier object> = cv.CascadeClassifier( filename )

Loads a classifier from a file.

参数
filename Name of the file from which the classifier is loaded.

~CascadeClassifier()

cv::CascadeClassifier::~CascadeClassifier ( )

成员函数文档编制

convert()

static bool cv::CascadeClassifier::convert ( const 字符串 & oldcascade ,
const 字符串 & newcascade
)
static
Python:
retval = cv.CascadeClassifier_convert( oldcascade, newcascade )

detectMultiScale() [1/3]

void cv::CascadeClassifier::detectMultiScale ( InputArray image ,
std::vector< Rect > & 对象 ,
double scaleFactor = 1.1 ,
int minNeighbors = 3 ,
int flags = 0 ,
Size minSize = Size () ,
Size maxSize = Size ()
)
Python:
对象 = cv.CascadeClassifier.detectMultiScale( image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]] )
objects, numDetections = cv.CascadeClassifier.detectMultiScale2( image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]] )
objects, rejectLevels, levelWeights = cv.CascadeClassifier.detectMultiScale3( image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]] )

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

参数
image Matrix of the type CV_8U containing an image where objects are detected.
对象 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.

The function is parallelized with the TBB library.

注意
  • (Python) A face detection example using cascade classifiers can be found at opencv_source_code/samples/python/facedetect.py
范例:
samples/cpp/facedetect.cpp .

detectMultiScale() [2/3]

void cv::CascadeClassifier::detectMultiScale ( InputArray image ,
std::vector< Rect > & 对象 ,
std::vector< int > & numDetections ,
double scaleFactor = 1.1 ,
int minNeighbors = 3 ,
int flags = 0 ,
Size minSize = Size () ,
Size maxSize = Size ()
)
Python:
对象 = cv.CascadeClassifier.detectMultiScale( image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]] )
objects, numDetections = cv.CascadeClassifier.detectMultiScale2( image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]] )
objects, rejectLevels, levelWeights = cv.CascadeClassifier.detectMultiScale3( image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]] )

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

参数
image Matrix of the type CV_8U containing an image where objects are detected.
对象 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 > & 对象 ,
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:
对象 = cv.CascadeClassifier.detectMultiScale( image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]] )
objects, numDetections = cv.CascadeClassifier.detectMultiScale2( image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]] )
objects, rejectLevels, levelWeights = cv.CascadeClassifier.detectMultiScale3( image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, 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;

empty()

bool cv::CascadeClassifier::empty ( ) const
Python:
retval = cv.CascadeClassifier.empty( )

Checks whether the classifier has been loaded.

范例:
samples/cpp/facedetect.cpp .

getFeatureType()

int cv::CascadeClassifier::getFeatureType ( ) const
Python:
retval = cv.CascadeClassifier.getFeatureType( )

getMaskGenerator()

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

getOldCascade()

void* cv::CascadeClassifier::getOldCascade ( )

getOriginalWindowSize()

Size cv::CascadeClassifier::getOriginalWindowSize ( ) const
Python:
retval = cv.CascadeClassifier.getOriginalWindowSize( )

isOldFormatCascade()

bool cv::CascadeClassifier::isOldFormatCascade ( ) const
Python:
retval = cv.CascadeClassifier.isOldFormatCascade( )

load()

bool cv::CascadeClassifier::load ( const 字符串 & filename )
Python:
retval = cv.CascadeClassifier.load( filename )

Loads a classifier from a file.

参数
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.
范例:
samples/cpp/facedetect.cpp .

read()

bool cv::CascadeClassifier::read ( const FileNode & node )
Python:
retval = cv.CascadeClassifier.read( node )

Reads a classifier from a FileStorage node.

注意
The file may contain a new cascade classifier (trained 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: