Class cv::CascadeClassifier#

Cascade classifier class for object detection.

#include <opencv2/xobjdetect.hpp>

Collaboration diagram for cv::CascadeClassifier:

Detailed Description#

Cascade classifier class for object detection.

Examples: samples/facedetect.cpp.

Constructor & Destructor Documentation#

CascadeClassifier()#

cv::CascadeClassifier::CascadeClassifier()

Python:

cv.CascadeClassifier() -> <CascadeClassifier object>

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

CascadeClassifier()#

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#

detectMultiScale()#

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.

detectMultiScale()#

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()#

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()

Python:

cv.CascadeClassifier.empty() -> retval

Checks whether the classifier has been loaded.

getFeatureType()#

int cv::CascadeClassifier::getFeatureType()

Python:

cv.CascadeClassifier.getFeatureType() -> retval

getMaskGenerator()#

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

getOldCascade()#

void * cv::CascadeClassifier::getOldCascade()

getOriginalWindowSize()#

Size cv::CascadeClassifier::getOriginalWindowSize()

Python:

cv.CascadeClassifier.getOriginalWindowSize() -> retval

isOldFormatCascade()#

bool cv::CascadeClassifier::isOldFormatCascade()

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.

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)

convert()#

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

Python:

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

cv.CascadeClassifier_convert(oldcascade, newcascade) -> retval

Member Data Documentation#

cc#

Ptr< BaseCascadeClassifier > cv::CascadeClassifier::cc

Source file#

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

opencv2/xobjdetect.hpp