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CascadeClassifier refactored. Most of the members and methods are private now.

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This commit is contained in:
Kirill Kornyakov
2010-12-14 10:17:45 +00:00
parent e7cf541f5f
commit e7f491ae1a
3 changed files with 236 additions and 178 deletions
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120
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@@ -278,6 +278,7 @@ class CV_EXPORTS FeatureEvaluator
public:
enum { HAAR = 0, LBP = 1 };
virtual ~FeatureEvaluator();
virtual bool read(const FileNode& node);
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const;
@@ -296,65 +297,96 @@ template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj();
class CV_EXPORTS_W CascadeClassifier
{
public:
struct CV_EXPORTS DTreeNode
{
int featureIdx;
float threshold; // for ordered features only
int left;
int right;
};
struct CV_EXPORTS DTree
{
int nodeCount;
};
struct CV_EXPORTS Stage
{
int first;
int ntrees;
float threshold;
};
enum { BOOST = 0 };
enum { DO_CANNY_PRUNING = 1, SCALE_IMAGE = 2,
FIND_BIGGEST_OBJECT = 4, DO_ROUGH_SEARCH = 8 };
CV_WRAP CascadeClassifier();
CV_WRAP CascadeClassifier(const string& filename);
~CascadeClassifier();
CV_WRAP CascadeClassifier( const string& filename );
virtual ~CascadeClassifier();
CV_WRAP bool empty() const;
CV_WRAP bool load(const string& filename);
bool read(const FileNode& node);
CV_WRAP virtual bool empty() const;
CV_WRAP bool load( const string& filename );
bool read( const FileNode& node );
CV_WRAP void detectMultiScale( const Mat& image,
CV_OUT vector<Rect>& objects,
double scaleFactor=1.1,
int minNeighbors=3, int flags=0,
Size minSize=Size(),
Size maxSize=Size());
Size maxSize=Size() );
bool isOldFormatCascade() const;
virtual Size getOriginalWindowSize() const;
int getFeatureType() const;
bool setImage(const Mat&);
protected:
virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
int stripSize, int yStep, double factor, vector<Rect>& candidates );
private:
enum { BOOST = 0 };
enum { DO_CANNY_PRUNING = 1, SCALE_IMAGE = 2,
FIND_BIGGEST_OBJECT = 4, DO_ROUGH_SEARCH = 8 };
friend class CascadeClassifierInvoker;
template<class FEval>
friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
template<class FEval>
friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
template<class FEval>
friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
template<class FEval>
friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
bool setImage( Ptr<FeatureEvaluator>&, const Mat& );
int runAt( Ptr<FeatureEvaluator>&, Point );
bool isStumpBased;
class Data
{
public:
struct CV_EXPORTS DTreeNode
{
int featureIdx;
float threshold; // for ordered features only
int left;
int right;
};
int stageType;
int featureType;
int ncategories;
Size origWinSize;
vector<Stage> stages;
vector<DTree> classifiers;
vector<DTreeNode> nodes;
vector<float> leaves;
vector<int> subsets;
struct CV_EXPORTS DTree
{
int nodeCount;
};
Ptr<FeatureEvaluator> feval;
struct CV_EXPORTS Stage
{
int first;
int ntrees;
float threshold;
};
bool read(const FileNode &node);
bool isStumpBased;
int stageType;
int featureType;
int ncategories;
Size origWinSize;
vector<Stage> stages;
vector<DTree> classifiers;
vector<DTreeNode> nodes;
vector<float> leaves;
vector<int> subsets;
};
Data data;
Ptr<FeatureEvaluator> featureEvaluator;
Ptr<CvHaarClassifierCascade> oldCascade;
};
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
struct CV_EXPORTS_W HOGDescriptor