Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
.. ocv:function:: void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size())
-.. ocv:function:: void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects, vector<int>& weights, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size())
.. ocv:pyfunction:: cv2.CascadeClassifier.detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) -> objects
.. ocv:pyfunction:: cv2.CascadeClassifier.detectMultiScale(image, rejectLevels, levelWeights[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]) -> objects
:param objects: Vector of rectangles where each rectangle contains the detected object.
- :param weights: Vector of weights of the corresponding objects. Weight is the number of neighboring positively classified rectangles that were joined into one 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.
CV_WRAP virtual void detectMultiScale( const Mat& image,
CV_OUT vector<Rect>& objects,
- vector<int>& weights,
- double scaleFactor=1.1,
- int minNeighbors=3, int flags=0,
- Size minSize=Size(),
- Size maxSize=Size() );
-
- CV_WRAP virtual void detectMultiScale( const Mat& image,
- CV_OUT vector<Rect>& objects,
vector<int>& rejectLevels,
vector<double>& levelWeights,
double scaleFactor=1.1,
int minNeighbors=3, int flags=0,
Size minSize=Size(),
Size maxSize=Size(),
- bool outputRejectLevels=false,
- bool outputWeights=false );
+ bool outputRejectLevels=false );
bool isOldFormatCascade() const;
};
struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } };
-struct getNeighbors { int operator ()(const CvAvgComp& e) const { return e.neighbors; } };
bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
vector<double>& levelWeights,
double scaleFactor, int minNeighbors,
int flags, Size minObjectSize, Size maxObjectSize,
- bool outputRejectLevels, bool outputWeights )
+ bool outputRejectLevels )
{
const double GROUP_EPS = 0.2;
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U );
- CV_Assert( !( outputRejectLevels && outputWeights ) );
if( empty() )
return;
Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
objects.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), objects.begin(), getRect());
- if( outputWeights )
- {
- rejectLevels.resize(vecAvgComp.size());
- std::transform(vecAvgComp.begin(), vecAvgComp.end(), rejectLevels.begin(),
- getNeighbors());
- }
return;
}
{
groupRectangles( objects, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
}
- else if( outputWeights )
- {
- groupRectangles( objects, rejectLevels, minNeighbors, GROUP_EPS );
- }
else
{
groupRectangles( objects, minNeighbors, GROUP_EPS );
minNeighbors, flags, minObjectSize, maxObjectSize, false );
}
-void CascadeClassifier::detectMultiScale( const Mat& image, CV_OUT vector<Rect>& objects,
- vector<int>& weights, double scaleFactor,
- int minNeighbors, int flags, Size minObjectSize,
- Size maxObjectSize )
-{
- vector<double> fakeLevelWeights;
- detectMultiScale( image, objects, weights, fakeLevelWeights, scaleFactor,
- minNeighbors, flags, minObjectSize, maxObjectSize, false, true );
-}
-
bool CascadeClassifier::Data::read(const FileNode &root)
{
static const float THRESHOLD_EPS = 1e-5f;