mirror of
https://github.com/opencv/opencv.git
synced 2024-12-02 07:39:57 +08:00
294 lines
11 KiB
ReStructuredText
294 lines
11 KiB
ReStructuredText
Cascade Classification
|
|
======================
|
|
|
|
.. highlight:: cpp
|
|
|
|
.. index:: FeatureEvaluator
|
|
|
|
FeatureEvaluator
|
|
----------------
|
|
.. c:type:: FeatureEvaluator
|
|
|
|
Base class for computing feature values in cascade classifiers. ::
|
|
|
|
class CV_EXPORTS FeatureEvaluator
|
|
{
|
|
public:
|
|
enum { HAAR = 0, LBP = 1 }; // supported feature types
|
|
virtual ~FeatureEvaluator(); // destructor
|
|
virtual bool read(const FileNode& node);
|
|
virtual Ptr<FeatureEvaluator> clone() const;
|
|
virtual int getFeatureType() const;
|
|
|
|
virtual bool setImage(const Mat& img, Size origWinSize);
|
|
virtual bool setWindow(Point p);
|
|
|
|
virtual double calcOrd(int featureIdx) const;
|
|
virtual int calcCat(int featureIdx) const;
|
|
|
|
static Ptr<FeatureEvaluator> create(int type);
|
|
};
|
|
|
|
|
|
.. index:: FeatureEvaluator::read
|
|
|
|
FeatureEvaluator::read
|
|
--------------------------
|
|
.. c:function:: bool FeatureEvaluator::read(const FileNode\& node)
|
|
|
|
Reads parameters of the features from a FileStorage node.
|
|
|
|
:param node: File node from which the feature parameters are read.
|
|
|
|
.. index:: FeatureEvaluator::clone
|
|
|
|
FeatureEvaluator::clone
|
|
---------------------------
|
|
.. c:function:: Ptr<FeatureEvaluator> FeatureEvaluator::clone() const
|
|
|
|
Returns a full copy of the feature evaluator.
|
|
|
|
.. index:: FeatureEvaluator::getFeatureType
|
|
|
|
FeatureEvaluator::getFeatureType
|
|
------------------------------------
|
|
.. c:function:: int FeatureEvaluator::getFeatureType() const
|
|
|
|
Returns the feature type (HAAR or LBP for now).
|
|
|
|
.. index:: FeatureEvaluator::setImage
|
|
|
|
FeatureEvaluator::setImage
|
|
------------------------------
|
|
.. c:function:: bool FeatureEvaluator::setImage(const Mat\& img, Size origWinSize)
|
|
|
|
Sets the image in which to compute the features.
|
|
|
|
:param img: Matrix of type ``CV_8UC1`` containing the image in which to compute the features.
|
|
|
|
:param origWinSize: Size of training images.
|
|
|
|
.. index:: FeatureEvaluator::setWindow
|
|
|
|
FeatureEvaluator::setWindow
|
|
-------------------------------
|
|
:func:`CascadeClassifier::runAt`
|
|
.. c:function:: bool FeatureEvaluator::setWindow(Point p)
|
|
|
|
Sets window in the current image in which the features will be computed (called by ).
|
|
|
|
:param p: The upper left point of window in which the features will be computed. Size of the window is equal to size of training images.
|
|
|
|
.. index:: FeatureEvaluator::calcOrd
|
|
|
|
FeatureEvaluator::calcOrd
|
|
-----------------------------
|
|
.. c:function:: double FeatureEvaluator::calcOrd(int featureIdx) const
|
|
|
|
Computes value of an ordered (numerical) feature.
|
|
|
|
:param featureIdx: Index of feature whose value will be computed.
|
|
|
|
Returns computed value of ordered feature.
|
|
|
|
.. index:: FeatureEvaluator::calcCat
|
|
|
|
FeatureEvaluator::calcCat
|
|
-----------------------------
|
|
.. c:function:: int FeatureEvaluator::calcCat(int featureIdx) const
|
|
|
|
Computes value of a categorical feature.
|
|
|
|
:param featureIdx: Index of feature whose value will be computed.
|
|
|
|
Returns computed label of categorical feature, i.e. value from [0,... (number of categories - 1)].
|
|
|
|
.. index:: FeatureEvaluator::create
|
|
|
|
FeatureEvaluator::create
|
|
----------------------------
|
|
.. c:function:: static Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
|
|
|
|
Constructs feature evaluator.
|
|
|
|
:param type: Type of features evaluated by cascade (HAAR or LBP for now).
|
|
|
|
.. index:: CascadeClassifier
|
|
|
|
.. _CascadeClassifier:
|
|
|
|
CascadeClassifier
|
|
-----------------
|
|
.. c:type:: CascadeClassifier
|
|
|
|
The cascade classifier class for object detection. ::
|
|
|
|
class CascadeClassifier
|
|
{
|
|
public:
|
|
// structure for storing tree node
|
|
struct CV_EXPORTS DTreeNode
|
|
{
|
|
int featureIdx; // feature index on which is a split
|
|
float threshold; // split threshold of ordered features only
|
|
int left; // left child index in the tree nodes array
|
|
int right; // right child index in the tree nodes array
|
|
};
|
|
|
|
// structure for storing desision tree
|
|
struct CV_EXPORTS DTree
|
|
{
|
|
int nodeCount; // nodes count
|
|
};
|
|
|
|
// structure for storing cascade stage (BOOST only for now)
|
|
struct CV_EXPORTS Stage
|
|
{
|
|
int first; // first tree index in tree array
|
|
int ntrees; // number of trees
|
|
float threshold; // treshold of stage sum
|
|
};
|
|
|
|
enum { BOOST = 0 }; // supported stage types
|
|
|
|
// mode of detection (see parameter flags in function HaarDetectObjects)
|
|
enum { DO_CANNY_PRUNING = CV_HAAR_DO_CANNY_PRUNING,
|
|
SCALE_IMAGE = CV_HAAR_SCALE_IMAGE,
|
|
FIND_BIGGEST_OBJECT = CV_HAAR_FIND_BIGGEST_OBJECT,
|
|
DO_ROUGH_SEARCH = CV_HAAR_DO_ROUGH_SEARCH };
|
|
|
|
CascadeClassifier(); // default constructor
|
|
CascadeClassifier(const string& filename);
|
|
~CascadeClassifier(); // destructor
|
|
|
|
bool empty() const;
|
|
bool load(const string& filename);
|
|
bool read(const FileNode& node);
|
|
|
|
void detectMultiScale( const Mat& image, vector<Rect>& objects,
|
|
double scaleFactor=1.1, int minNeighbors=3,
|
|
int flags=0, Size minSize=Size());
|
|
|
|
bool setImage( Ptr<FeatureEvaluator>&, const Mat& );
|
|
int runAt( Ptr<FeatureEvaluator>&, Point );
|
|
|
|
bool is_stump_based; // true, if the trees are stumps
|
|
|
|
int stageType; // stage type (BOOST only for now)
|
|
int featureType; // feature type (HAAR or LBP for now)
|
|
int ncategories; // number of categories (for categorical features only)
|
|
Size origWinSize; // size of training images
|
|
|
|
vector<Stage> stages; // vector of stages (BOOST for now)
|
|
vector<DTree> classifiers; // vector of decision trees
|
|
vector<DTreeNode> nodes; // vector of tree nodes
|
|
vector<float> leaves; // vector of leaf values
|
|
vector<int> subsets; // subsets of split by categorical feature
|
|
|
|
Ptr<FeatureEvaluator> feval; // pointer to feature evaluator
|
|
Ptr<CvHaarClassifierCascade> oldCascade; // pointer to old cascade
|
|
};
|
|
|
|
|
|
.. index:: CascadeClassifier::CascadeClassifier
|
|
|
|
CascadeClassifier::CascadeClassifier
|
|
----------------------------------------
|
|
.. c:function:: CascadeClassifier::CascadeClassifier(const string\& filename)
|
|
|
|
Loads the classifier from file.
|
|
|
|
:param filename: Name of file from which classifier will be load.
|
|
|
|
.. index:: CascadeClassifier::empty
|
|
|
|
CascadeClassifier::empty
|
|
----------------------------
|
|
.. c:function:: bool CascadeClassifier::empty() const
|
|
|
|
Checks if the classifier has been loaded or not.
|
|
|
|
.. index:: CascadeClassifier::load
|
|
|
|
CascadeClassifier::load
|
|
---------------------------
|
|
.. c:function:: bool CascadeClassifier::load(const string\& filename)
|
|
|
|
Loads the classifier from file. The previous content is destroyed.
|
|
|
|
:param filename: Name of file from which classifier will be load. File may contain as old haar classifier (trained by haartraining application) or new cascade classifier (trained traincascade application).
|
|
|
|
.. index:: CascadeClassifier::read
|
|
|
|
CascadeClassifier::read
|
|
---------------------------
|
|
.. c:function:: bool CascadeClassifier::read(const FileNode\& node)
|
|
|
|
Reads the classifier from a FileStorage node. File may contain a new cascade classifier (trained traincascade application) only.
|
|
|
|
.. index:: CascadeClassifier::detectMultiScale
|
|
|
|
CascadeClassifier::detectMultiScale
|
|
---------------------------------------
|
|
.. c:function:: void CascadeClassifier::detectMultiScale( const Mat\& image, vector<Rect>\& objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size())
|
|
|
|
Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
|
|
|
|
:param image: Matrix of type ``CV_8U`` containing the image in which to detect objects.
|
|
|
|
:param objects: Vector of rectangles such that each rectangle contains the detected object.
|
|
|
|
:param scaleFactor: Specifies how much the image size is reduced at each image scale.
|
|
|
|
:param minNeighbors: Speficifes how many neighbors should each candiate rectangle have to retain it.
|
|
|
|
:param flags: This parameter is not used for new cascade and have the same meaning for old cascade as in function cvHaarDetectObjects.
|
|
|
|
:param minSize: The minimum possible object size. Objects smaller than that are ignored.
|
|
|
|
.. index:: CascadeClassifier::setImage
|
|
|
|
CascadeClassifier::setImage
|
|
-------------------------------
|
|
.. c:function:: bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>\& feval, const Mat\& image )
|
|
|
|
Sets the image for detection (called by detectMultiScale at each image level).
|
|
|
|
:param feval: Pointer to feature evaluator which is used for computing features.
|
|
|
|
:param image: Matrix of type ``CV_8UC1`` containing the image in which to compute the features.
|
|
|
|
.. index:: CascadeClassifier::runAt
|
|
|
|
CascadeClassifier::runAt
|
|
----------------------------
|
|
.. c:function:: int CascadeClassifier::runAt( Ptr<FeatureEvaluator>\& feval, Point pt )
|
|
|
|
Runs the detector at the specified point (the image that the detector is working with should be set by setImage).
|
|
|
|
:param feval: Feature evaluator which is used for computing features.
|
|
|
|
:param pt: The upper left point of window in which the features will be computed. Size of the window is equal to size of training images.
|
|
|
|
Returns:
|
|
1 - if cascade classifier detects object in the given location.
|
|
-si - otherwise. si is an index of stage which first predicted that given window is a background image.
|
|
|
|
.. index:: groupRectangles
|
|
|
|
groupRectangles
|
|
-------------------
|
|
.. c:function:: void groupRectangles(vector<Rect>\& rectList, int groupThreshold, double eps=0.2)
|
|
|
|
Groups the object candidate rectangles
|
|
|
|
:param rectList: The input/output vector of rectangles. On output there will be retained and grouped rectangles
|
|
|
|
:param groupThreshold: The minimum possible number of rectangles, minus 1, in a group of rectangles to retain it.
|
|
|
|
:param eps: The relative difference between sides of the rectangles to merge them into a group
|
|
|
|
The function is a wrapper for a generic function
|
|
:func:`partition` . It clusters all the input rectangles using the rectangle equivalence criteria, that combines rectangles that have similar sizes and similar locations (the similarity is defined by ``eps`` ). When ``eps=0`` , no clustering is done at all. If
|
|
:math:`\texttt{eps}\rightarrow +\inf` , all the rectangles will be put in one cluster. Then, the small clusters, containing less than or equal to ``groupThreshold`` rectangles, will be rejected. In each other cluster the average rectangle will be computed and put into the output rectangle list.
|