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Merged the trunk r8467:8507 (inclusive) (big bunch of documentation fixes)

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This commit is contained in:
Andrey Kamaev
2012-05-30 11:13:07 +00:00
parent 052d2dc23a
commit 81a5988015
120 changed files with 5407 additions and 4695 deletions
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14
modules/objdetect/doc/cascade_classification.rst
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@@ -131,7 +131,7 @@ FeatureEvaluator::create
----------------------------
Constructs the feature evaluator.
.. ocv:function:: static Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
.. ocv:function:: Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
:param type: Type of features evaluated by cascade (``HAAR`` or ``LBP`` for now).
@@ -148,7 +148,7 @@ Loads a classifier from a file.
.. ocv:function:: CascadeClassifier::CascadeClassifier(const string& filename)
.. ocv:pyfunction:: cv2.CascadeClassifier(filename) -> <CascadeClassifier object>
.. ocv:pyfunction:: cv2.CascadeClassifier([filename]) -> <CascadeClassifier object>
:param filename: Name of the file from which the classifier is loaded.
@@ -193,9 +193,9 @@ Detects objects of different sizes in the input image. The detected objects are
.. 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
.. ocv:cfunction:: CvSeq* cvHaarDetectObjects( const CvArr* image, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scaleFactor=1.1, int minNeighbors=3, int flags=0, CvSize minSize=cvSize(0, 0), CvSize maxSize=cvSize(0, 0) )
.. ocv:cfunction:: CvSeq* cvHaarDetectObjects( const CvArr* image, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scale_factor=1.1, int min_neighbors=3, int flags=0, CvSize min_size=cvSize(0,0), CvSize max_size=cvSize(0,0) )
.. ocv:pyoldfunction:: cv.HaarDetectObjects(image, cascade, storage, scaleFactor=1.1, minNeighbors=3, flags=0, minSize=(0, 0))-> detectedObjects
.. ocv:pyoldfunction:: cv.HaarDetectObjects(image, cascade, storage, scale_factor=1.1, min_neighbors=3, flags=0, min_size=(0, 0)) -> detectedObjects
:param cascade: Haar classifier cascade (OpenCV 1.x API only). It can be loaded from XML or YAML file using :ocv:cfunc:`Load`. When the cascade is not needed anymore, release it using ``cvReleaseHaarClassifierCascade(&cascade)``.
@@ -222,7 +222,7 @@ Sets an image for detection.
.. ocv:function:: bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>& feval, const Mat& image )
.. ocv:cfunction:: void cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, const CvArr* sum, const CvArr* sqsum, const CvArr* tiltedSum, double scale )
.. ocv:cfunction:: void cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, const CvArr* sum, const CvArr* sqsum, const CvArr* tilted_sum, double scale )
:param cascade: Haar classifier cascade (OpenCV 1.x API only). See :ocv:func:`CascadeClassifier::detectMultiScale` for more information.
@@ -239,9 +239,9 @@ CascadeClassifier::runAt
----------------------------
Runs the detector at the specified point.
.. ocv:function:: int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& feval, Point pt )
.. ocv:function:: int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& feval, Point pt, double& weight )
.. ocv:cfunction:: int cvRunHaarClassifierCascade( CvHaarClassifierCascade* cascade, CvPoint pt, int startStage=0 )
.. ocv:cfunction:: int cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade, CvPoint pt, int start_stage=0 )
:param cascade: Haar classifier cascade (OpenCV 1.x API only). See :ocv:func:`CascadeClassifier::detectMultiScale` for more information.

234
modules/objdetect/doc/latent_svm.rst
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@@ -27,13 +27,13 @@ model at a particular position and scale is the maximum over
components, of the score of that component model at the given
location.
In OpenCV there are C implementation of Latent SVM and C++ wrapper of it.
C version is the structure :ocv:struct:`CvObjectDetection` and a set of functions
working with this structure (see :ocv:func:`cvLoadLatentSvmDetector`,
In OpenCV there are C implementation of Latent SVM and C++ wrapper of it.
C version is the structure :ocv:struct:`CvObjectDetection` and a set of functions
working with this structure (see :ocv:func:`cvLoadLatentSvmDetector`,
:ocv:func:`cvReleaseLatentSvmDetector`, :ocv:func:`cvLatentSvmDetectObjects`).
C++ version is the class :ocv:class:`LatentSvmDetector` and has slightly different
functionality in contrast with C version - it supports loading and detection
of several models.
C++ version is the class :ocv:class:`LatentSvmDetector` and has slightly different
functionality in contrast with C version - it supports loading and detection
of several models.
There are two examples of Latent SVM usage: ``samples/c/latentsvmdetect.cpp``
and ``samples/cpp/latentsvm_multidetect.cpp``.
@@ -45,97 +45,97 @@ CvLSVMFilterPosition
--------------------
.. ocv:struct:: CvLSVMFilterPosition
Structure describes the position of the filter in the feature pyramid.
Structure describes the position of the filter in the feature pyramid.
.. ocv:member:: unsigned int l
level in the feature pyramid
.. ocv:member:: unsigned int x
x-coordinate in level l
.. ocv:member:: unsigned int y
y-coordinate in level l
.. ocv:member:: unsigned int l
level in the feature pyramid
.. ocv:member:: unsigned int x
x-coordinate in level l
.. ocv:member:: unsigned int y
y-coordinate in level l
CvLSVMFilterObject
------------------
.. ocv:struct:: CvLSVMFilterObject
Description of the filter, which corresponds to the part of the object.
Description of the filter, which corresponds to the part of the object.
.. ocv:member:: CvLSVMFilterPosition V
ideal (penalty = 0) position of the partial filter
from the root filter position (V_i in the paper)
.. ocv:member:: float fineFunction[4]
vector describes penalty function (d_i in the paper)
pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
.. ocv:member:: int sizeX
.. ocv:member:: int sizeY
Rectangular map (sizeX x sizeY),
every cell stores feature vector (dimension = p)
.. ocv:member:: int numFeatures
number of features
.. ocv:member:: float *H
matrix of feature vectors to set and get
feature vectors (i,j) used formula H[(j * sizeX + i) * p + k],
where k - component of feature vector in cell (i, j)
.. ocv:member:: CvLSVMFilterPosition V
ideal (penalty = 0) position of the partial filter
from the root filter position (V_i in the paper)
.. ocv:member:: float fineFunction[4]
vector describes penalty function (d_i in the paper)
pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
.. ocv:member:: int sizeX
.. ocv:member:: int sizeY
Rectangular map (sizeX x sizeY),
every cell stores feature vector (dimension = p)
.. ocv:member:: int numFeatures
number of features
.. ocv:member:: float *H
matrix of feature vectors to set and get
feature vectors (i,j) used formula H[(j * sizeX + i) * p + k],
where k - component of feature vector in cell (i, j)
CvLatentSvmDetector
-------------------
.. ocv:struct:: CvLatentSvmDetector
Structure contains internal representation of trained Latent SVM detector.
Structure contains internal representation of trained Latent SVM detector.
.. ocv:member:: int num_filters
total number of filters (root plus part) in model
.. ocv:member:: int num_components
number of components in model
.. ocv:member:: int* num_part_filters
array containing number of part filters for each component
.. ocv:member:: CvLSVMFilterObject** filters
root and part filters for all model components
.. ocv:member:: float* b
biases for all model components
.. ocv:member:: float score_threshold
confidence level threshold
.. ocv:member:: int num_filters
total number of filters (root plus part) in model
.. ocv:member:: int num_components
number of components in model
.. ocv:member:: int* num_part_filters
array containing number of part filters for each component
.. ocv:member:: CvLSVMFilterObject** filters
root and part filters for all model components
.. ocv:member:: float* b
biases for all model components
.. ocv:member:: float score_threshold
confidence level threshold
CvObjectDetection
-----------------
.. ocv:struct:: CvObjectDetection
Structure contains the bounding box and confidence level for detected object.
Structure contains the bounding box and confidence level for detected object.
.. ocv:member:: CvRect rect
bounding box for a detected object
.. ocv:member:: float score
confidence level
.. ocv:member:: CvRect rect
bounding box for a detected object
.. ocv:member:: float score
confidence level
cvLoadLatentSvmDetector
@@ -145,7 +145,7 @@ Loads trained detector from a file.
.. ocv:function:: CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename)
:param filename: Name of the file containing the description of a trained detector
cvReleaseLatentSvmDetector
--------------------------
@@ -158,46 +158,46 @@ Release memory allocated for CvLatentSvmDetector structure.
cvLatentSvmDetectObjects
------------------------
Find rectangular regions in the given image that are likely to contain objects
Find rectangular regions in the given image that are likely to contain objects
and corresponding confidence levels.
.. ocv:function:: CvSeq* cvLatentSvmDetectObjects(IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold, int numThreads)
:param image: image
.. ocv:function:: CvSeq* cvLatentSvmDetectObjects( IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold=0.5f, int numThreads=-1 )
:param image: image
:param detector: LatentSVM detector in internal representation
:param storage: Memory storage to store the resultant sequence of the object candidate rectangles
:param overlap_threshold: Threshold for the non-maximum suppression algorithm
:param numThreads: Number of threads used in parallel version of the algorithm
.. highlight:: cpp
LatentSvmDetector
-----------------
.. ocv:class:: LatentSvmDetector
This is a C++ wrapping class of Latent SVM. It contains internal representation of several
trained Latent SVM detectors (models) and a set of methods to load the detectors and detect objects
This is a C++ wrapping class of Latent SVM. It contains internal representation of several
trained Latent SVM detectors (models) and a set of methods to load the detectors and detect objects
using them.
LatentSvmDetector::ObjectDetection
----------------------------------
.. ocv:class:: LatentSvmDetector::ObjectDetection
.. ocv:struct:: LatentSvmDetector::ObjectDetection
Structure contains the detection information.
.. ocv:member:: Rect rect
bounding box for a detected object
.. ocv:member:: float score
confidence level
.. ocv:member:: int classID
class (model or detector) ID that detect an object
Structure contains the detection information.
.. ocv:member:: Rect rect
bounding box for a detected object
.. ocv:member:: float score
confidence level
.. ocv:member:: int classID
class (model or detector) ID that detect an object
LatentSvmDetector::LatentSvmDetector
------------------------------------
Two types of constructors.
@@ -208,8 +208,8 @@ Two types of constructors.
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
:param classNames: A set of trained models names. If it's empty then the name of each model will be constructed from the name of file containing the model. E.g. the model stored in "/home/user/cat.xml" will get the name "cat".
LatentSvmDetector::~LatentSvmDetector
@@ -228,10 +228,10 @@ LatentSvmDetector::load
-----------------------
Load the trained models from given ``.xml`` files and return ``true`` if at least one model was loaded.
.. ocv:function:: bool LatentSvmDetector::load(const vector<string>& filenames, const vector<string>& classNames)
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
.. ocv:function:: bool LatentSvmDetector::load( const vector<string>& filenames, const vector<string>& classNames=vector<string>() )
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
:param classNames: A set of trained models names. If it's empty then the name of each model will be constructed from the name of file containing the model. E.g. the model stored in "/home/user/cat.xml" will get the name "cat".
LatentSvmDetector::detect
@@ -239,13 +239,13 @@ LatentSvmDetector::detect
Find rectangular regions in the given image that are likely to contain objects of loaded classes (models)
and corresponding confidence levels.
.. ocv:function:: void LatentSvmDetector::detect( const Mat& image, vector<ObjectDetection>& objectDetections, float overlapThreshold=0.5, int numThreads=-1 )
.. ocv:function:: void LatentSvmDetector::detect( const Mat& image, vector<ObjectDetection>& objectDetections, float overlapThreshold=0.5f, int numThreads=-1 )
:param image: An image.
:param objectDetections: The detections: rectangulars, scores and class IDs.
:param overlapThreshold: Threshold for the non-maximum suppression algorithm.
:param numThreads: Number of threads used in parallel version of the algorithm.
LatentSvmDetector::getClassNames
--------------------------------
Return the class (model) names that were passed in constructor or method ``load`` or extracted from models filenames in those methods.
@@ -256,9 +256,9 @@ LatentSvmDetector::getClassCount
--------------------------------
Return a count of loaded models (classes).
.. ocv:function:: size_t getClassCount() const
.. [Felzenszwalb2010] Felzenszwalb, P. F. and Girshick, R. B. and McAllester, D. and Ramanan, D. *Object Detection with Discriminatively Trained Part Based Models*. PAMI, vol. 32, no. 9, pp. 1627-1645, September 2010
.. ocv:function:: size_t LatentSvmDetector::getClassCount() const
.. [Felzenszwalb2010] Felzenszwalb, P. F. and Girshick, R. B. and McAllester, D. and Ramanan, D. *Object Detection with Discriminatively Trained Part Based Models*. PAMI, vol. 32, no. 9, pp. 1627-1645, September 2010

96
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@@ -137,7 +137,7 @@ CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scale_factor CV_DEFAULT(1.1),
int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)));
@@ -160,7 +160,7 @@ CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade,
// Structure describes the position of the filter in the feature pyramid
// l - level in the feature pyramid
// (x, y) - coordinate in level l
typedef struct
typedef struct CvLSVMFilterPosition
{
int x;
int y;
@@ -174,14 +174,14 @@ typedef struct
// penaltyFunction - vector describes penalty function (d_i in the paper)
// pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
// FILTER DESCRIPTION
// Rectangular map (sizeX x sizeY),
// Rectangular map (sizeX x sizeY),
// every cell stores feature vector (dimension = p)
// H - matrix of feature vectors
// to set and get feature vectors (i,j)
// to set and get feature vectors (i,j)
// used formula H[(j * sizeX + i) * p + k], where
// k - component of feature vector in cell (i, j)
// END OF FILTER DESCRIPTION
typedef struct{
typedef struct CvLSVMFilterObject{
CvLSVMFilterPosition V;
float fineFunction[4];
int sizeX;
@@ -192,7 +192,7 @@ typedef struct{
// data type: STRUCT CvLatentSvmDetector
// structure contains internal representation of trained Latent SVM detector
// num_filters - total number of filters (root plus part) in model
// num_filters - total number of filters (root plus part) in model
// num_components - number of components in model
// num_part_filters - array containing number of part filters for each component
// filters - root and part filters for all model components
@@ -210,9 +210,9 @@ typedef struct CvLatentSvmDetector
CvLatentSvmDetector;
// data type: STRUCT CvObjectDetection
// structure contains the bounding box and confidence level for detected object
// structure contains the bounding box and confidence level for detected object
// rect - bounding box for a detected object
// score - confidence level
// score - confidence level
typedef struct CvObjectDetection
{
CvRect rect;
@@ -247,28 +247,28 @@ CVAPI(CvLatentSvmDetector*) cvLoadLatentSvmDetector(const char* filename);
CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
/*
// find rectangular regions in the given image that are likely
// find rectangular regions in the given image that are likely
// to contain objects and corresponding confidence levels
//
// API
// CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
// CvLatentSvmDetector* detector,
// CvMemStorage* storage,
// CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
// CvLatentSvmDetector* detector,
// CvMemStorage* storage,
// float overlap_threshold = 0.5f,
// int numThreads = -1);
// INPUT
// image - image to detect objects in
// detector - Latent SVM detector in internal representation
// storage - memory storage to store the resultant sequence
// storage - memory storage to store the resultant sequence
// of the object candidate rectangles
// overlap_threshold - threshold for the non-maximum suppression algorithm
// overlap_threshold - threshold for the non-maximum suppression algorithm
= 0.5f [here will be the reference to original paper]
// OUTPUT
// sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures)
*/
CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
CvLatentSvmDetector* detector,
CvMemStorage* storage,
CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
CvLatentSvmDetector* detector,
CvMemStorage* storage,
float overlap_threshold CV_DEFAULT(0.5f),
int numThreads CV_DEFAULT(-1));
@@ -285,7 +285,7 @@ CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image,
namespace cv
{
///////////////////////////// Object Detection ////////////////////////////
/*
@@ -330,23 +330,23 @@ private:
CV_EXPORTS void groupRectangles(CV_OUT CV_IN_OUT vector<Rect>& rectList, int groupThreshold, double eps=0.2);
CV_EXPORTS_W void groupRectangles(CV_OUT CV_IN_OUT vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles( vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights );
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels,
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels,
vector<double>& levelWeights, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights, vector<double>& foundScales,
CV_EXPORTS void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights, vector<double>& foundScales,
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
class CV_EXPORTS FeatureEvaluator
{
public:
public:
enum { HAAR = 0, LBP = 1, HOG = 2 };
virtual ~FeatureEvaluator();
virtual bool read(const FileNode& node);
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const;
virtual bool setImage(const Mat&, Size origWinSize);
virtual bool setImage(const Mat& img, Size origWinSize);
virtual bool setWindow(Point p);
virtual double calcOrd(int featureIdx) const;
@@ -371,7 +371,7 @@ public:
CV_WRAP CascadeClassifier();
CV_WRAP CascadeClassifier( const string& filename );
virtual ~CascadeClassifier();
CV_WRAP virtual bool empty() const;
CV_WRAP bool load( const string& filename );
virtual bool read( const FileNode& node );
@@ -425,8 +425,8 @@ protected:
template<class FEval>
friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
bool setImage( Ptr<FeatureEvaluator>&, const Mat& );
virtual int runAt( Ptr<FeatureEvaluator>&, Point, double& weight );
bool setImage( Ptr<FeatureEvaluator>& feval, const Mat& image);
virtual int runAt( Ptr<FeatureEvaluator>& feval, Point pt, double& weight );
class Data
{
@@ -475,7 +475,7 @@ public:
class CV_EXPORTS MaskGenerator
{
public:
virtual ~MaskGenerator() {}
virtual ~MaskGenerator() {}
virtual cv::Mat generateMask(const cv::Mat& src)=0;
virtual void initializeMask(const cv::Mat& /*src*/) {};
};
@@ -488,7 +488,7 @@ protected:
Ptr<MaskGenerator> maskGenerator;
};
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
struct CV_EXPORTS_W HOGDescriptor
@@ -496,13 +496,13 @@ struct CV_EXPORTS_W HOGDescriptor
public:
enum { L2Hys=0 };
enum { DEFAULT_NLEVELS=64 };
CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
nlevels(HOGDescriptor::DEFAULT_NLEVELS)
{}
CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
int _histogramNormType=HOGDescriptor::L2Hys,
@@ -513,28 +513,28 @@ public:
histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
gammaCorrection(_gammaCorrection), nlevels(_nlevels)
{}
CV_WRAP HOGDescriptor(const String& filename)
{
load(filename);
}
HOGDescriptor(const HOGDescriptor& d)
{
d.copyTo(*this);
}
virtual ~HOGDescriptor() {}
CV_WRAP size_t getDescriptorSize() const;
CV_WRAP bool checkDetectorSize() const;
CV_WRAP double getWinSigma() const;
CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
virtual bool read(FileNode& fn);
virtual void write(FileStorage& fs, const String& objname) const;
CV_WRAP virtual bool load(const String& filename, const String& objname=String());
CV_WRAP virtual void save(const String& filename, const String& objname=String()) const;
virtual void copyTo(HOGDescriptor& c) const;
@@ -544,9 +544,9 @@ public:
Size winStride=Size(), Size padding=Size(),
const vector<Point>& locations=vector<Point>()) const;
//with found weights output
CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
CV_OUT vector<double>& weights,
double hitThreshold=0, Size winStride=Size(),
double hitThreshold=0, Size winStride=Size(),
Size padding=Size(),
const vector<Point>& searchLocations=vector<Point>()) const;
//without found weights output
@@ -555,22 +555,22 @@ public:
Size padding=Size(),
const vector<Point>& searchLocations=vector<Point>()) const;
//with result weights output
CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
CV_OUT vector<double>& foundWeights, double hitThreshold=0,
Size winStride=Size(), Size padding=Size(), double scale=1.05,
CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
CV_OUT vector<double>& foundWeights, double hitThreshold=0,
Size winStride=Size(), Size padding=Size(), double scale=1.05,
double finalThreshold=2.0,bool useMeanshiftGrouping = false) const;
//without found weights output
virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
double hitThreshold=0, Size winStride=Size(),
Size padding=Size(), double scale=1.05,
Size padding=Size(), double scale=1.05,
double finalThreshold=2.0, bool useMeanshiftGrouping = false) const;
CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
Size paddingTL=Size(), Size paddingBR=Size()) const;
CV_WRAP static vector<float> getDefaultPeopleDetector();
CV_WRAP static vector<float> getDaimlerPeopleDetector();
CV_PROP Size winSize;
CV_PROP Size blockSize;
CV_PROP Size blockStride;