generic-library/opencv
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added more helper macros to the function declarations, to assist the Python wrapper generator. Fixed memleak in Mat::operator()(Range,Range) and the related functions (Mat::row, Mat::col etc.)

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This commit is contained in:
Vadim Pisarevsky
2010-10-27 18:26:39 +00:00
parent 4c29ffecc0
commit 83f6085773
18 changed files with 685 additions and 987 deletions
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66
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@@ -270,8 +270,8 @@ namespace cv
///////////////////////////// Object Detection ////////////////////////////
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2);
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
class CV_EXPORTS FeatureEvaluator
{
@@ -293,7 +293,7 @@ public:
template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj();
class CV_EXPORTS CascadeClassifier
class CV_EXPORTS_W CascadeClassifier
{
public:
struct CV_EXPORTS DTreeNode
@@ -320,14 +320,14 @@ public:
enum { DO_CANNY_PRUNING = 1, SCALE_IMAGE = 2,
FIND_BIGGEST_OBJECT = 4, DO_ROUGH_SEARCH = 8 };
CascadeClassifier();
CascadeClassifier(const string& filename);
CV_WRAP CascadeClassifier();
CV_WRAP CascadeClassifier(const string& filename);
~CascadeClassifier();
bool empty() const;
bool load(const string& filename);
CV_WRAP bool empty() const;
CV_WRAP bool load(const string& filename);
bool read(const FileNode& node);
void detectMultiScale( const Mat& image,
CV_WRAP void detectMultiScale( const Mat& image,
CV_OUT vector<Rect>& objects,
double scaleFactor=1.1,
int minNeighbors=3, int flags=0,
@@ -356,17 +356,17 @@ public:
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
struct CV_EXPORTS HOGDescriptor
struct CV_EXPORTS_W HOGDescriptor
{
public:
enum { L2Hys=0 };
HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
histogramNormType(L2Hys), L2HysThreshold(0.2), gammaCorrection(true)
{}
HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
int _histogramNormType=L2Hys, double _L2HysThreshold=0.2, bool _gammaCorrection=false)
: winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
@@ -375,7 +375,7 @@ public:
gammaCorrection(_gammaCorrection)
{}
HOGDescriptor(const String& filename)
CV_WRAP HOGDescriptor(const String& filename)
{
load(filename);
}
@@ -387,47 +387,47 @@ public:
virtual ~HOGDescriptor() {}
size_t getDescriptorSize() const;
bool checkDetectorSize() const;
double getWinSigma() const;
CV_WRAP size_t getDescriptorSize() const;
CV_WRAP bool checkDetectorSize() const;
CV_WRAP double getWinSigma() const;
virtual void setSVMDetector(const vector<float>& _svmdetector);
CV_WRAP virtual void setSVMDetector(const vector<float>& _svmdetector);
virtual bool read(FileNode& fn);
virtual void write(FileStorage& fs, const String& objname) const;
virtual bool load(const String& filename, const String& objname=String());
virtual void save(const String& filename, const String& objname=String()) 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;
virtual void compute(const Mat& img,
CV_WRAP virtual void compute(const Mat& img,
CV_OUT vector<float>& descriptors,
Size winStride=Size(), Size padding=Size(),
const vector<Point>& locations=vector<Point>()) const;
virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
double hitThreshold=0, Size winStride=Size(),
Size padding=Size(),
const vector<Point>& searchLocations=vector<Point>()) const;
virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
double hitThreshold=0, Size winStride=Size(),
Size padding=Size(), double scale=1.05,
int groupThreshold=2) const;
virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
Size paddingTL=Size(), Size paddingBR=Size()) const;
static vector<float> getDefaultPeopleDetector();
Size winSize;
Size blockSize;
Size blockStride;
Size cellSize;
int nbins;
int derivAperture;
double winSigma;
int histogramNormType;
double L2HysThreshold;
bool gammaCorrection;
vector<float> svmDetector;
CV_PROP Size winSize;
CV_PROP Size blockSize;
CV_PROP Size blockStride;
CV_PROP Size cellSize;
CV_PROP int nbins;
CV_PROP int derivAperture;
CV_PROP double winSigma;
CV_PROP int histogramNormType;
CV_PROP double L2HysThreshold;
CV_PROP bool gammaCorrection;
CV_PROP vector<float> svmDetector;
};