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Commit 1b1eab8e authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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added helper macros to the function declarations

parent b59b0fd7
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Showing with 177 additions and 151 deletions
modules/core/include/opencv2/core/core.hpp
View file @ 1b1eab8e
......@@ -212,7 +212,7 @@ CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback,
#define CV_DbgAssert(expr)
#endif
CV_EXPORTS void setNumThreads(int);
CV_EXPORTS void setNumThreads(int nthreads);
CV_EXPORTS int getNumThreads();
CV_EXPORTS int getThreadNum();
......@@ -330,7 +330,7 @@ static inline size_t alignSize(size_t sz, int n)
\note{Since optimization may imply using special data structures, it may be unsafe
to call this function anywhere in the code. Instead, call it somewhere at the top level.}
*/
CV_EXPORTS void setUseOptimized(bool);
CV_EXPORTS void setUseOptimized(bool onoff);
/*!
Returns the current optimization status
......
modules/core/include/opencv2/core/types_c.h
View file @ 1b1eab8e
......@@ -158,9 +158,12 @@ typedef unsigned short ushort;
typedef signed char schar;
/* special informative macros for wrapper generators */
#define CV_OUT
#define CV_CARRAY(counter)
#define CV_CUSTOM_CARRAY(args)
#define CV_METHOD
#define CV_NO_WRAP
#define CV_OUT
#define CV_WRAP_AS(synonym)
/* CvArr* is used to pass arbitrary
* array-like data structures
......
modules/features2d/include/opencv2/features2d/features2d.hpp
View file @ 1b1eab8e
......@@ -232,10 +232,12 @@ public:
: pt(x, y), size(_size), angle(_angle),
response(_response), octave(_octave), class_id(_class_id) {}
//! converts vector of keypoints to vector of points
static void convert(const std::vector<KeyPoint>& keypoints, std::vector<Point2f>& points2f,
static void convert(const std::vector<KeyPoint>& keypoints,
CV_OUT std::vector<Point2f>& points2f,
const std::vector<int>& keypointIndexes=std::vector<int>());
//! converts vector of points to the vector of keypoints, where each keypoint is assigned the same size and the same orientation
static void convert(const std::vector<Point2f>& points2f, std::vector<KeyPoint>& keypoints,
static void convert(const std::vector<Point2f>& points2f,
CV_OUT std::vector<KeyPoint>& keypoints,
float size=1, float response=1, int octave=0, int class_id=-1);
//! computes overlap for pair of keypoints;
......@@ -254,7 +256,7 @@ public:
//! writes vector of keypoints to the file storage
CV_EXPORTS void write(FileStorage& fs, const string& name, const vector<KeyPoint>& keypoints);
//! reads vector of keypoints from the specified file storage node
CV_EXPORTS void read(const FileNode& node, vector<KeyPoint>& keypoints);
CV_EXPORTS void read(const FileNode& node, CV_OUT vector<KeyPoint>& keypoints);
/*!
SIFT implementation.
......@@ -357,12 +359,12 @@ public:
//! returns the descriptor size in float's (64 or 128)
int descriptorSize() const;
//! finds the keypoints using fast hessian detector used in SURF
void operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints) const;
CV_WRAP_AS(detect) void operator()(const Mat& img, const Mat& mask,
CV_OUT vector<KeyPoint>& keypoints) const;
//! finds the keypoints and computes their descriptors. Optionally it can compute descriptors for the user-provided keypoints
void operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints,
vector<float>& descriptors,
CV_WRAP_AS(detect) void operator()(const Mat& img, const Mat& mask,
CV_OUT vector<KeyPoint>& keypoints,
CV_OUT vector<float>& descriptors,
bool useProvidedKeypoints=false) const;
};
......@@ -386,7 +388,8 @@ public:
int _max_evolution, double _area_threshold,
double _min_margin, int _edge_blur_size );
//! the operator that extracts the MSERs from the image or the specific part of it
void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
CV_WRAP_AS(detect) void operator()( const Mat& image,
CV_OUT vector<vector<Point> >& msers, const Mat& mask ) const;
};
/*!
......@@ -405,11 +408,13 @@ public:
int _lineThresholdBinarized,
int _suppressNonmaxSize);
//! finds the keypoints in the image
void operator()(const Mat& image, vector<KeyPoint>& keypoints) const;
CV_WRAP_AS(detect) void operator()(const Mat& image,
CV_OUT vector<KeyPoint>& keypoints) const;
};
//! detects corners using FAST algorithm by E. Rosten
CV_EXPORTS void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true );
CV_EXPORTS void FAST( const Mat& image, CV_OUT vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSupression=true );
/*!
The Patch Generator class
......@@ -423,13 +428,14 @@ public:
double _lambdaMin=0.6, double _lambdaMax=1.5,
double _thetaMin=-CV_PI, double _thetaMax=CV_PI,
double _phiMin=-CV_PI, double _phiMax=CV_PI );
void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
void operator()(const Mat& image, const Mat& transform, Mat& patch,
CV_WRAP_AS(generate) void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
CV_WRAP_AS(generate) void operator()(const Mat& image, const Mat& transform, Mat& patch,
Size patchSize, RNG& rng) const;
void warpWholeImage(const Mat& image, Mat& matT, Mat& buf,
Mat& warped, int border, RNG& rng) const;
CV_OUT Mat& warped, int border, RNG& rng) const;
void generateRandomTransform(Point2f srcCenter, Point2f dstCenter,
Mat& transform, RNG& rng, bool inverse=false) const;
CV_OUT Mat& transform, RNG& rng,
bool inverse=false) const;
void setAffineParam(double lambda, double theta, double phi);
double backgroundMin, backgroundMax;
......@@ -447,9 +453,13 @@ public:
LDetector();
LDetector(int _radius, int _threshold, int _nOctaves,
int _nViews, double _baseFeatureSize, double _clusteringDistance);
void operator()(const Mat& image, vector<KeyPoint>& keypoints, int maxCount=0, bool scaleCoords=true) const;
void operator()(const vector<Mat>& pyr, vector<KeyPoint>& keypoints, int maxCount=0, bool scaleCoords=true) const;
void getMostStable2D(const Mat& image, vector<KeyPoint>& keypoints,
CV_WRAP_AS(detect) void operator()(const Mat& image,
CV_OUT vector<KeyPoint>& keypoints,
int maxCount=0, bool scaleCoords=true) const;
CV_WRAP_AS(detect) void operator()(const vector<Mat>& pyr,
CV_OUT vector<KeyPoint>& keypoints,
int maxCount=0, bool scaleCoords=true) const;
void getMostStable2D(const Mat& image, CV_OUT vector<KeyPoint>& keypoints,
int maxCount, const PatchGenerator& patchGenerator) const;
void setVerbose(bool verbose);
......@@ -561,6 +571,7 @@ protected:
vector<float> posteriors;
};
class CV_EXPORTS PlanarObjectDetector
{
public:
......@@ -596,9 +607,10 @@ public:
void read(const FileNode& node);
void write(FileStorage& fs, const String& name=String()) const;
bool operator()(const Mat& image, Mat& H, vector<Point2f>& corners) const;
bool operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
Mat& H, vector<Point2f>& corners, vector<int>* pairs=0) const;
CV_WRAP_AS(detect) bool operator()(const Mat& image, CV_OUT Mat& H, CV_OUT vector<Point2f>& corners) const;
CV_WRAP_AS(detect) bool operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
CV_OUT Mat& H, CV_OUT vector<Point2f>& corners,
CV_OUT vector<int>* pairs=0) const;
protected:
bool verbose;
......@@ -735,7 +747,6 @@ struct CV_EXPORTS RTreeNode
short offset1, offset2;
RTreeNode() {}
RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
: offset1(y1*RandomizedTree::PATCH_SIZE + x1),
offset2(y2*RandomizedTree::PATCH_SIZE + x2)
......@@ -755,7 +766,6 @@ public:
static const size_t DEFAULT_NUM_QUANT_BITS = 4;
RTreeClassifier();
void train(std::vector<BaseKeypoint> const& base_set,
RNG &rng,
int num_trees = RTreeClassifier::DEFAULT_TREES,
......
modules/highgui/include/opencv2/highgui/highgui.hpp
View file @ 1b1eab8e
......@@ -106,7 +106,7 @@ CV_EXPORTS bool imwrite( const string& filename, const Mat& img,
const vector<int>& params=vector<int>());
CV_EXPORTS Mat imdecode( const Mat& buf, int flags );
CV_EXPORTS bool imencode( const string& ext, const Mat& img,
vector<uchar>& buf,
CV_OUT vector<uchar>& buf,
const vector<int>& params=vector<int>());
CV_EXPORTS int waitKey(int delay=0);
......@@ -130,8 +130,8 @@ public:
virtual void release();
virtual bool grab();
virtual bool retrieve(Mat& image, int channel=0);
virtual VideoCapture& operator >> (Mat& image);
virtual bool retrieve(CV_OUT Mat& image, int channel=0);
virtual CV_WRAP_AS(query) VideoCapture& operator >> (Mat& image);
virtual bool set(int propId, double value);
virtual double get(int propId);
......@@ -145,12 +145,14 @@ class CV_EXPORTS VideoWriter
{
public:
VideoWriter();
VideoWriter(const string& filename, int fourcc, double fps, Size frameSize, bool isColor=true);
VideoWriter(const string& filename, int fourcc, double fps,
Size frameSize, bool isColor=true);
virtual ~VideoWriter();
virtual bool open(const string& filename, int fourcc, double fps, Size frameSize, bool isColor=true);
virtual bool open(const string& filename, int fourcc, double fps,
Size frameSize, bool isColor=true);
virtual bool isOpened() const;
virtual VideoWriter& operator << (const Mat& image);
virtual CV_WRAP_AS(write) VideoWriter& operator << (const Mat& image);
protected:
Ptr<CvVideoWriter> writer;
......
modules/imgproc/include/opencv2/imgproc/imgproc.hpp
View file @ 1b1eab8e
......@@ -327,7 +327,8 @@ CV_EXPORTS Ptr<FilterEngine> createGaussianFilter( int type, Size ksize,
double sigma1, double sigma2=0,
int borderType=BORDER_DEFAULT);
//! initializes kernels of the generalized Sobel operator
CV_EXPORTS void getDerivKernels( Mat& kx, Mat& ky, int dx, int dy, int ksize,
CV_EXPORTS void getDerivKernels( CV_OUT Mat& kx, CV_OUT Mat& ky,
int dx, int dy, int ksize,
bool normalize=false, int ktype=CV_32F );
//! returns filter engine for the generalized Sobel operator
CV_EXPORTS Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
......@@ -335,16 +336,16 @@ CV_EXPORTS Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
int borderType=BORDER_DEFAULT );
//! returns horizontal 1D box filter
CV_EXPORTS Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType,
int ksize, int anchor=-1);
int ksize, int anchor=-1);
//! returns vertical 1D box filter
CV_EXPORTS Ptr<BaseColumnFilter> getColumnSumFilter(int sumType, int dstType,
int ksize, int anchor=-1,
double scale=1);
CV_EXPORTS Ptr<BaseColumnFilter> getColumnSumFilter( int sumType, int dstType,
int ksize, int anchor=-1,
double scale=1);
//! returns box filter engine
CV_EXPORTS Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize,
Point anchor=Point(-1,-1),
bool normalize=true,
int borderType=BORDER_DEFAULT);
Point anchor=Point(-1,-1),
bool normalize=true,
int borderType=BORDER_DEFAULT);
//! type of morphological operation
enum { MORPH_ERODE=0, MORPH_DILATE=1, MORPH_OPEN=2, MORPH_CLOSE=3,
MORPH_GRADIENT=4, MORPH_TOPHAT=5, MORPH_BLACKHAT=6 };
......@@ -374,27 +375,27 @@ CV_EXPORTS Mat getStructuringElement(int shape, Size ksize, Point anchor=Point(-
template<> CV_EXPORTS void Ptr<IplConvKernel>::delete_obj();
//! copies 2D array to a larger destination array with extrapolation of the outer part of src using the specified border mode
CV_EXPORTS void copyMakeBorder( const Mat& src, Mat& dst,
CV_EXPORTS void copyMakeBorder( const Mat& src, CV_OUT Mat& dst,
int top, int bottom, int left, int right,
int borderType, const Scalar& value=Scalar() );
//! smooths the image using median filter.
CV_EXPORTS void medianBlur( const Mat& src, Mat& dst, int ksize );
CV_EXPORTS void medianBlur( const Mat& src, CV_OUT Mat& dst, int ksize );
//! smooths the image using Gaussian filter.
CV_EXPORTS void GaussianBlur( const Mat& src, Mat& dst, Size ksize,
CV_EXPORTS void GaussianBlur( const Mat& src, CV_OUT Mat& dst, Size ksize,
double sigma1, double sigma2=0,
int borderType=BORDER_DEFAULT );
//! smooths the image using bilateral filter
CV_EXPORTS void bilateralFilter( const Mat& src, Mat& dst, int d,
CV_EXPORTS void bilateralFilter( const Mat& src, CV_OUT Mat& dst, int d,
double sigmaColor, double sigmaSpace,
int borderType=BORDER_DEFAULT );
//! smooths the image using the box filter. Each pixel is processed in O(1) time
CV_EXPORTS void boxFilter( const Mat& src, Mat& dst, int ddepth,
CV_EXPORTS void boxFilter( const Mat& src, CV_OUT Mat& dst, int ddepth,
Size ksize, Point anchor=Point(-1,-1),
bool normalize=true,
int borderType=BORDER_DEFAULT );
//! a synonym for normalized box filter
static inline void blur( const Mat& src, Mat& dst,
static inline void blur( const Mat& src, CV_OUT Mat& dst,
Size ksize, Point anchor=Point(-1,-1),
int borderType=BORDER_DEFAULT )
{
......@@ -402,54 +403,54 @@ static inline void blur( const Mat& src, Mat& dst,
}
//! applies non-separable 2D linear filter to the image
CV_EXPORTS void filter2D( const Mat& src, Mat& dst, int ddepth,
CV_EXPORTS void filter2D( const Mat& src, CV_OUT Mat& dst, int ddepth,
const Mat& kernel, Point anchor=Point(-1,-1),
double delta=0, int borderType=BORDER_DEFAULT );
//! applies separable 2D linear filter to the image
CV_EXPORTS void sepFilter2D( const Mat& src, Mat& dst, int ddepth,
CV_EXPORTS void sepFilter2D( const Mat& src, CV_OUT Mat& dst, int ddepth,
const Mat& kernelX, const Mat& kernelY,
Point anchor=Point(-1,-1),
double delta=0, int borderType=BORDER_DEFAULT );
//! applies generalized Sobel operator to the image
CV_EXPORTS void Sobel( const Mat& src, Mat& dst, int ddepth,
CV_EXPORTS void Sobel( const Mat& src, CV_OUT Mat& dst, int ddepth,
int dx, int dy, int ksize=3,
double scale=1, double delta=0,
int borderType=BORDER_DEFAULT );
//! applies the vertical or horizontal Scharr operator to the image
CV_EXPORTS void Scharr( const Mat& src, Mat& dst, int ddepth,
CV_EXPORTS void Scharr( const Mat& src, CV_OUT Mat& dst, int ddepth,
int dx, int dy, double scale=1, double delta=0,
int borderType=BORDER_DEFAULT );
//! applies Laplacian operator to the image
CV_EXPORTS void Laplacian( const Mat& src, Mat& dst, int ddepth,
CV_EXPORTS void Laplacian( const Mat& src, CV_OUT Mat& dst, int ddepth,
int ksize=1, double scale=1, double delta=0,
int borderType=BORDER_DEFAULT );
//! applies Canny edge detector and produces the edge map.
CV_EXPORTS void Canny( const Mat& image, Mat& edges,
CV_EXPORTS void Canny( const Mat& image, CV_OUT Mat& edges,
double threshold1, double threshold2,
int apertureSize=3, bool L2gradient=false );
//! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
CV_EXPORTS void cornerMinEigenVal( const Mat& src, Mat& dst,
CV_EXPORTS void cornerMinEigenVal( const Mat& src, CV_OUT Mat& dst,
int blockSize, int ksize=3,
int borderType=BORDER_DEFAULT );
//! computes Harris cornerness criteria at each image pixel
CV_EXPORTS void cornerHarris( const Mat& src, Mat& dst, int blockSize,
CV_EXPORTS void cornerHarris( const Mat& src, CV_OUT Mat& dst, int blockSize,
int ksize, double k,
int borderType=BORDER_DEFAULT );
//! computes both eigenvalues and the eigenvectors of 2x2 derivative covariation matrix at each pixel. The output is stored as 6-channel matrix.
CV_EXPORTS void cornerEigenValsAndVecs( const Mat& src, Mat& dst,
CV_EXPORTS void cornerEigenValsAndVecs( const Mat& src, CV_OUT Mat& dst,
int blockSize, int ksize,
int borderType=BORDER_DEFAULT );
//! computes another complex cornerness criteria at each pixel
CV_EXPORTS void preCornerDetect( const Mat& src, Mat& dst, int ksize,
CV_EXPORTS void preCornerDetect( const Mat& src, CV_OUT Mat& dst, int ksize,
int borderType=BORDER_DEFAULT );
//! adjusts the corner locations with sub-pixel accuracy to maximize the certain cornerness criteria
......@@ -458,41 +459,42 @@ CV_EXPORTS void cornerSubPix( const Mat& image, vector<Point2f>& corners,
TermCriteria criteria );
//! finds the strong enough corners where the cornerMinEigenVal() or cornerHarris() report the local maxima
CV_EXPORTS void goodFeaturesToTrack( const Mat& image, vector<Point2f>& corners,
CV_EXPORTS void goodFeaturesToTrack( const Mat& image, CV_OUT vector<Point2f>& corners,
int maxCorners, double qualityLevel, double minDistance,
const Mat& mask=Mat(), int blockSize=3,
bool useHarrisDetector=false, double k=0.04 );
//! finds lines in the black-n-white image using the standard or pyramid Hough transform
CV_EXPORTS void HoughLines( const Mat& image, vector<Vec2f>& lines,
CV_EXPORTS void HoughLines( const Mat& image, CV_OUT vector<Vec2f>& lines,
double rho, double theta, int threshold,
double srn=0, double stn=0 );
//! finds line segments in the black-n-white image using probabalistic Hough transform
CV_EXPORTS void HoughLinesP( Mat& image, vector<Vec4i>& lines,
CV_EXPORTS void HoughLinesP( Mat& image, CV_OUT vector<Vec4i>& lines,
double rho, double theta, int threshold,
double minLineLength=0, double maxLineGap=0 );
//! finds circles in the grayscale image using 2+1 gradient Hough transform
CV_EXPORTS void HoughCircles( const Mat& image, vector<Vec3f>& circles,
CV_EXPORTS void HoughCircles( const Mat& image, CV_OUT vector<Vec3f>& circles,
int method, double dp, double minDist,
double param1=100, double param2=100,
int minRadius=0, int maxRadius=0 );
//! erodes the image (applies the local minimum operator)
CV_EXPORTS void erode( const Mat& src, Mat& dst, const Mat& kernel,
CV_EXPORTS void erode( const Mat& src, CV_OUT Mat& dst, const Mat& kernel,
Point anchor=Point(-1,-1), int iterations=1,
int borderType=BORDER_CONSTANT,
const Scalar& borderValue=morphologyDefaultBorderValue() );
//! dilates the image (applies the local maximum operator)
CV_EXPORTS void dilate( const Mat& src, Mat& dst, const Mat& kernel,
CV_EXPORTS void dilate( const Mat& src, CV_OUT Mat& dst, const Mat& kernel,
Point anchor=Point(-1,-1), int iterations=1,
int borderType=BORDER_CONSTANT,
const Scalar& borderValue=morphologyDefaultBorderValue() );
//! applies an advanced morphological operation to the image
CV_EXPORTS void morphologyEx( const Mat& src, Mat& dst, int op, const Mat& kernel,
CV_EXPORTS void morphologyEx( const Mat& src, CV_OUT Mat& dst,
int op, const Mat& kernel,
Point anchor=Point(-1,-1), int iterations=1,
int borderType=BORDER_CONSTANT,
const Scalar& borderValue=morphologyDefaultBorderValue() );
......@@ -510,19 +512,19 @@ enum
};
//! resizes the image
CV_EXPORTS void resize( const Mat& src, Mat& dst,
CV_EXPORTS void resize( const Mat& src, CV_OUT Mat& dst,
Size dsize, double fx=0, double fy=0,
int interpolation=INTER_LINEAR );
//! warps the image using affine transformation
CV_EXPORTS void warpAffine( const Mat& src, Mat& dst,
CV_EXPORTS void warpAffine( const Mat& src, CV_OUT Mat& dst,
const Mat& M, Size dsize,
int flags=INTER_LINEAR,
int borderMode=BORDER_CONSTANT,
const Scalar& borderValue=Scalar());
//! warps the image using perspective transformation
CV_EXPORTS void warpPerspective( const Mat& src, Mat& dst,
CV_EXPORTS void warpPerspective( const Mat& src, CV_OUT Mat& dst,
const Mat& M, Size dsize,
int flags=INTER_LINEAR,
int borderMode=BORDER_CONSTANT,
......@@ -533,12 +535,13 @@ enum { INTER_BITS=5, INTER_BITS2=INTER_BITS*2,
INTER_TAB_SIZE2=INTER_TAB_SIZE*INTER_TAB_SIZE };
//! warps the image using the precomputed maps. The maps are stored in either floating-point or integer fixed-point format
CV_EXPORTS void remap( const Mat& src, Mat& dst, const Mat& map1, const Mat& map2,
CV_EXPORTS void remap( const Mat& src, CV_OUT Mat& dst, const Mat& map1, const Mat& map2,
int interpolation, int borderMode=BORDER_CONSTANT,
const Scalar& borderValue=Scalar());
//! converts maps for remap from floating-point to fixed-point format or backwards
CV_EXPORTS void convertMaps( const Mat& map1, const Mat& map2, Mat& dstmap1, Mat& dstmap2,
CV_EXPORTS void convertMaps( const Mat& map1, const Mat& map2,
CV_OUT Mat& dstmap1, CV_OUT Mat& dstmap2,
int dstmap1type, bool nninterpolation=false );
//! returns 2x3 affine transformation matrix for the planar rotation.
......@@ -548,28 +551,28 @@ CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[]
//! returns 2x3 affine transformation for the corresponding 3 point pairs.
CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );
//! computes 2x3 affine transformation matrix that is inverse to the specified 2x3 affine transformation.
CV_EXPORTS void invertAffineTransform(const Mat& M, Mat& iM);
CV_EXPORTS void invertAffineTransform( const Mat& M, CV_OUT Mat& iM );
//! extracts rectangle from the image at sub-pixel location
CV_EXPORTS void getRectSubPix( const Mat& image, Size patchSize,
Point2f center, Mat& patch, int patchType=-1 );
Point2f center, CV_OUT Mat& patch, int patchType=-1 );
//! computes the integral image
CV_EXPORTS void integral( const Mat& src, Mat& sum, int sdepth=-1 );
CV_EXPORTS void integral( const Mat& src, CV_OUT Mat& sum, int sdepth=-1 );
//! computes the integral image and integral for the squared image
CV_EXPORTS void integral( const Mat& src, Mat& sum, Mat& sqsum, int sdepth=-1 );
CV_EXPORTS void integral( const Mat& src, CV_OUT Mat& sum, CV_OUT Mat& sqsum, int sdepth=-1 );
//! computes the integral image, integral for the squared image and the tilted integral image
CV_EXPORTS void integral( const Mat& src, Mat& sum, Mat& sqsum, Mat& tilted, int sdepth=-1 );
CV_EXPORTS void integral( const Mat& src, CV_OUT Mat& sum, CV_OUT Mat& sqsum, CV_OUT Mat& tilted, int sdepth=-1 );
//! adds image to the accumulator (dst += src). Unlike cv::add, dst and src can have different types.
CV_EXPORTS void accumulate( const Mat& src, Mat& dst, const Mat& mask=Mat() );
CV_EXPORTS void accumulate( const Mat& src, CV_OUT Mat& dst, const Mat& mask=Mat() );
//! adds squared src image to the accumulator (dst += src*src).
CV_EXPORTS void accumulateSquare( const Mat& src, Mat& dst, const Mat& mask=Mat() );
CV_EXPORTS void accumulateSquare( const Mat& src, CV_OUT Mat& dst, const Mat& mask=Mat() );
//! adds product of the 2 images to the accumulator (dst += src1*src2).
CV_EXPORTS void accumulateProduct( const Mat& src1, const Mat& src2,
Mat& dst, const Mat& mask=Mat() );
CV_OUT Mat& dst, const Mat& mask=Mat() );
//! updates the running average (dst = dst*(1-alpha) + src*alpha)
CV_EXPORTS void accumulateWeighted( const Mat& src, Mat& dst,
CV_EXPORTS void accumulateWeighted( const Mat& src, CV_OUT Mat& dst,
double alpha, const Mat& mask=Mat() );
//! type of the threshold operation
......@@ -577,30 +580,30 @@ enum { THRESH_BINARY=0, THRESH_BINARY_INV=1, THRESH_TRUNC=2, THRESH_TOZERO=3,
THRESH_TOZERO_INV=4, THRESH_MASK=7, THRESH_OTSU=8 };
//! applies fixed threshold to the image
CV_EXPORTS double threshold( const Mat& src, Mat& dst, double thresh, double maxval, int type );
CV_EXPORTS double threshold( const Mat& src, CV_OUT Mat& dst, double thresh, double maxval, int type );
//! adaptive threshold algorithm
enum { ADAPTIVE_THRESH_MEAN_C=0, ADAPTIVE_THRESH_GAUSSIAN_C=1 };
//! applies variable (adaptive) threshold to the image
CV_EXPORTS void adaptiveThreshold( const Mat& src, Mat& dst, double maxValue,
CV_EXPORTS void adaptiveThreshold( const Mat& src, CV_OUT Mat& dst, double maxValue,
int adaptiveMethod, int thresholdType,
int blockSize, double C );
//! smooths and downsamples the image
CV_EXPORTS void pyrDown( const Mat& src, Mat& dst, const Size& dstsize=Size());
CV_EXPORTS void pyrDown( const Mat& src, CV_OUT Mat& dst, const Size& dstsize=Size());
//! upsamples and smoothes the image
CV_EXPORTS void pyrUp( const Mat& src, Mat& dst, const Size& dstsize=Size());
CV_EXPORTS void pyrUp( const Mat& src, CV_OUT Mat& dst, const Size& dstsize=Size());
//! builds the gaussian pyramid using pyrDown() as a basic operation
CV_EXPORTS void buildPyramid( const Mat& src, vector<Mat>& dst, int maxlevel );
CV_EXPORTS void buildPyramid( const Mat& src, CV_OUT vector<Mat>& dst, int maxlevel );
//! corrects lens distortion for the given camera matrix and distortion coefficients
CV_EXPORTS void undistort( const Mat& src, Mat& dst, const Mat& cameraMatrix,
CV_EXPORTS void undistort( const Mat& src, CV_OUT Mat& dst, const Mat& cameraMatrix,
const Mat& distCoeffs, const Mat& newCameraMatrix=Mat() );
//! initializes maps for cv::remap() to correct lens distortion and optionally rectify the image
CV_EXPORTS void initUndistortRectifyMap( const Mat& cameraMatrix, const Mat& distCoeffs,
const Mat& R, const Mat& newCameraMatrix,
Size size, int m1type, Mat& map1, Mat& map2 );
Size size, int m1type, CV_OUT Mat& map1, CV_OUT Mat& map2 );
enum
{
......@@ -611,63 +614,65 @@ enum
//! initializes maps for cv::remap() for wide-angle
CV_EXPORTS float initWideAngleProjMap( const Mat& cameraMatrix, const Mat& distCoeffs,
Size imageSize, int destImageWidth,
int m1type, Mat& map1, Mat& map2,
int m1type, CV_OUT Mat& map1, CV_OUT Mat& map2,
int projType=PROJ_SPHERICAL_EQRECT, double alpha=0);
//! returns the default new camera matrix (by default it is the same as cameraMatrix unless centerPricipalPoint=true)
CV_EXPORTS Mat getDefaultNewCameraMatrix( const Mat& cameraMatrix, Size imgsize=Size(),
bool centerPrincipalPoint=false );
//! returns points' coordinates after lens distortion correction
CV_EXPORTS void undistortPoints( const Mat& src, vector<Point2f>& dst,
CV_EXPORTS void undistortPoints( const Mat& src, CV_OUT vector<Point2f>& dst,
const Mat& cameraMatrix, const Mat& distCoeffs,
const Mat& R=Mat(), const Mat& P=Mat());
//! returns points' coordinates after lens distortion correction
CV_EXPORTS void undistortPoints( const Mat& src, Mat& dst,
CV_EXPORTS void undistortPoints( const Mat& src, CV_OUT Mat& dst,
const Mat& cameraMatrix, const Mat& distCoeffs,
const Mat& R=Mat(), const Mat& P=Mat());
template<> CV_EXPORTS void Ptr<CvHistogram>::delete_obj();
//! computes the joint dense histogram for a set of images.
CV_EXPORTS void calcHist( const Mat* images, int nimages,
const int* channels, const Mat& mask,
MatND& hist, int dims, const int* histSize,
const float** ranges, bool uniform=true,
bool accumulate=false );
CV_EXPORTS void calcHist( CV_CARRAY(nimages) const Mat* images, int nimages,
CV_CARRAY(dims) const int* channels, const Mat& mask,
CV_OUT Mat& hist, int dims, CV_CARRAY(dims) const int* histSize,
CV_CUSTOM_CARRAY((dims,histSize,uniform)) const float** ranges,
bool uniform=true, bool accumulate=false );
//! computes the joint sparse histogram for a set of images.
CV_EXPORTS void calcHist( const Mat* images, int nimages,
const int* channels, const Mat& mask,
SparseMat& hist, int dims, const int* histSize,
const float** ranges, bool uniform=true,
bool accumulate=false );
CV_EXPORTS void calcHist( CV_CARRAY(nimages) const Mat* images, int nimages,
CV_CARRAY(dims) const int* channels, const Mat& mask,
CV_OUT SparseMat& hist, int dims, CV_CARRAY(dims) const int* histSize,
CV_CUSTOM_CARRAY((dims,histSize,uniform)) const float** ranges,
bool uniform=true, bool accumulate=false );
//! computes back projection for the set of images
CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
const int* channels, const MatND& hist,
Mat& backProject, const float** ranges,
CV_EXPORTS void calcBackProject( CV_CARRAY(nimages) const Mat* images, int nimages,
CV_CARRAY(hist.dims) const int* channels, const Mat& hist,
CV_OUT Mat& backProject,
CV_CUSTOM_CARRAY(hist) const float** ranges,
double scale=1, bool uniform=true );
//! computes back projection for the set of images
CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
const int* channels, const SparseMat& hist,
Mat& backProject, const float** ranges,
CV_EXPORTS void calcBackProject( CV_CARRAY(nimages) const Mat* images, int nimages,
CV_CARRAY(hist.dims()) const int* channels,
const SparseMat& hist, CV_OUT Mat& backProject,
CV_CUSTOM_CARRAY(hist) const float** ranges,
double scale=1, bool uniform=true );
//! compares two histograms stored in dense arrays
CV_EXPORTS double compareHist( const MatND& H1, const MatND& H2, int method );
CV_EXPORTS double compareHist( const Mat& H1, const Mat& H2, int method );
//! compares two histograms stored in sparse arrays
CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method );
//! normalizes the grayscale image brightness and contrast by normalizing its histogram
CV_EXPORTS void equalizeHist( const Mat& src, Mat& dst );
CV_EXPORTS void equalizeHist( const Mat& src, CV_OUT Mat& dst );
//! segments the image using watershed algorithm
CV_EXPORTS void watershed( const Mat& image, Mat& markers );
//! filters image using meanshift algorithm
CV_EXPORTS void pyrMeanShiftFiltering( const Mat& src, Mat& dst,
CV_EXPORTS void pyrMeanShiftFiltering( const Mat& src, CV_OUT Mat& dst,
double sp, double sr, int maxLevel=1,
TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) );
......@@ -698,14 +703,14 @@ enum
//! restores the damaged image areas using one of the available intpainting algorithms
CV_EXPORTS void inpaint( const Mat& src, const Mat& inpaintMask,
Mat& dst, double inpaintRange, int flags );
CV_OUT Mat& dst, double inpaintRange, int flags );
//! builds the discrete Voronoi diagram
CV_EXPORTS void distanceTransform( const Mat& src, Mat& dst, Mat& labels,
CV_EXPORTS void distanceTransform( const Mat& src, CV_OUT Mat& dst, Mat& labels,
int distanceType, int maskSize );
//! computes the distance transform map
CV_EXPORTS void distanceTransform( const Mat& src, Mat& dst,
CV_EXPORTS void distanceTransform( const Mat& src, CV_OUT Mat& dst,
int distanceType, int maskSize );
enum { FLOODFILL_FIXED_RANGE = 1 << 16,
......@@ -724,7 +729,7 @@ CV_EXPORTS int floodFill( Mat& image, Mat& mask,
int flags=4 );
//! converts image from one color space to another
CV_EXPORTS void cvtColor( const Mat& src, Mat& dst, int code, int dstCn=0 );
CV_EXPORTS void cvtColor( const Mat& src, CV_OUT Mat& dst, int code, int dstCn=0 );
//! raster image moments
class CV_EXPORTS Moments
......@@ -758,7 +763,7 @@ CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] );
enum { TM_SQDIFF=0, TM_SQDIFF_NORMED=1, TM_CCORR=2, TM_CCORR_NORMED=3, TM_CCOEFF=4, TM_CCOEFF_NORMED=5 };
//! computes the proximity map for the raster template and the image where the template is searched for
CV_EXPORTS void matchTemplate( const Mat& image, const Mat& templ, Mat& result, int method );
CV_EXPORTS void matchTemplate( const Mat& image, const Mat& templ, CV_OUT Mat& result, int method );
//! mode of the contour retrieval algorithm
enum
......@@ -779,12 +784,12 @@ enum
};
//! retrieves contours and the hierarchical information from black-n-white image.
CV_EXPORTS void findContours( Mat& image, vector<vector<Point> >& contours,
CV_EXPORTS void findContours( Mat& image, CV_OUT vector<vector<Point> >& contours,
vector<Vec4i>& hierarchy, int mode,
int method, Point offset=Point());
//! retrieves contours from black-n-white image.
CV_EXPORTS void findContours( Mat& image, vector<vector<Point> >& contours,
CV_EXPORTS void findContours( Mat& image, CV_OUT vector<vector<Point> >& contours,
int mode, int method, Point offset=Point());
//! draws contours in the image
......@@ -796,11 +801,11 @@ CV_EXPORTS void drawContours( Mat& image, const vector<vector<Point> >& contours
//! approximates contour or a curve using Douglas-Peucker algorithm
CV_EXPORTS void approxPolyDP( const Mat& curve,
vector<Point>& approxCurve,
CV_OUT vector<Point>& approxCurve,
double epsilon, bool closed );
//! approximates contour or a curve using Douglas-Peucker algorithm
CV_EXPORTS void approxPolyDP( const Mat& curve,
vector<Point2f>& approxCurve,
CV_OUT vector<Point2f>& approxCurve,
double epsilon, bool closed );
//! computes the contour perimeter (closed=true) or a curve length
CV_EXPORTS double arcLength( const Mat& curve, bool closed );
......@@ -818,11 +823,11 @@ CV_EXPORTS double matchShapes( const Mat& contour1,
const Mat& contour2,
int method, double parameter );
//! computes convex hull for a set of 2D points.
CV_EXPORTS void convexHull( const Mat& points, vector<int>& hull, bool clockwise=false );
CV_EXPORTS void convexHull( const Mat& points, CV_OUT vector<int>& hull, bool clockwise=false );
//! computes convex hull for a set of 2D points.
CV_EXPORTS void convexHull( const Mat& points, vector<Point>& hull, bool clockwise=false );
CV_EXPORTS void convexHull( const Mat& points, CV_OUT vector<Point>& hull, bool clockwise=false );
//! computes convex hull for a set of 2D points.
CV_EXPORTS void convexHull( const Mat& points, vector<Point2f>& hull, bool clockwise=false );
CV_EXPORTS void convexHull( const Mat& points, CV_OUT vector<Point2f>& hull, bool clockwise=false );
//! returns true iff the contour is convex. Does not support contours with self-intersection
CV_EXPORTS bool isContourConvex( const Mat& contour );
......@@ -831,10 +836,10 @@ CV_EXPORTS bool isContourConvex( const Mat& contour );
CV_EXPORTS RotatedRect fitEllipse( const Mat& points );
//! fits line to the set of 2D points using M-estimator algorithm
CV_EXPORTS void fitLine( const Mat& points, Vec4f& line, int distType,
CV_EXPORTS void fitLine( const Mat& points, CV_OUT Vec4f& line, int distType,
double param, double reps, double aeps );
//! fits line to the set of 3D points using M-estimator algorithm
CV_EXPORTS void fitLine( const Mat& points, Vec6f& line, int distType,
CV_EXPORTS void fitLine( const Mat& points, CV_OUT Vec6f& line, int distType,
double param, double reps, double aeps );
//! checks if the point is inside the contour. Optionally computes the signed distance from the point to the contour boundary
CV_EXPORTS double pointPolygonTest( const Mat& contour,
......@@ -845,7 +850,7 @@ CV_EXPORTS Mat estimateRigidTransform( const Mat& A, const Mat& B,
bool fullAffine );
//! computes the best-fit affine transformation that maps one 3D point set to another (RANSAC algorithm is used)
CV_EXPORTS int estimateAffine3D(const Mat& from, const Mat& to, Mat& out,
CV_EXPORTS int estimateAffine3D(const Mat& from, const Mat& to, CV_OUT Mat& dst,
vector<uchar>& outliers,
double param1 = 3.0, double param2 = 0.99);
......
modules/ml/include/opencv2/ml/ml.hpp
View file @ 1b1eab8e
......@@ -247,23 +247,21 @@ public:
CvNormalBayesClassifier();
virtual ~CvNormalBayesClassifier();
CvNormalBayesClassifier( const CvMat* _train_data, const CvMat* _responses,
CV_NO_WRAP CvNormalBayesClassifier( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _var_idx=0, const CvMat* _sample_idx=0 );
virtual bool train( const CvMat* _train_data, const CvMat* _responses,
CV_NO_WRAP virtual bool train( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _var_idx = 0, const CvMat* _sample_idx=0, bool update=false );
virtual float predict( const CvMat* _samples, CvMat* results=0 ) const;
CV_NO_WRAP virtual float predict( const CvMat* _samples, CvMat* results=0 ) const;
virtual void clear();
#ifndef SWIG
CvNormalBayesClassifier( const cv::Mat& _train_data, const cv::Mat& _responses,
const cv::Mat& _var_idx=cv::Mat(), const cv::Mat& _sample_idx=cv::Mat() );
virtual bool train( const cv::Mat& _train_data, const cv::Mat& _responses,
const cv::Mat& _var_idx = cv::Mat(), const cv::Mat& _sample_idx=cv::Mat(),
bool update=false );
virtual float predict( const cv::Mat& _samples, cv::Mat* results=0 ) const;
#endif
virtual void write( CvFileStorage* storage, const char* name ) const;
virtual void read( CvFileStorage* storage, CvFileNode* node );
......
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
View file @ 1b1eab8e
......@@ -271,7 +271,7 @@ namespace cv
///////////////////////////// Object Detection ////////////////////////////
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& weights, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
class CV_EXPORTS FeatureEvaluator
{
......@@ -328,7 +328,7 @@ public:
bool load(const string& filename);
bool read(const FileNode& node);
void detectMultiScale( const Mat& image,
vector<Rect>& objects,
CV_OUT vector<Rect>& objects,
double scaleFactor=1.1,
int minNeighbors=3, int flags=0,
Size minSize=Size());
......@@ -401,18 +401,18 @@ public:
virtual void copyTo(HOGDescriptor& c) const;
virtual void compute(const Mat& img,
vector<float>& descriptors,
CV_OUT vector<float>& descriptors,
Size winStride=Size(), Size padding=Size(),
const vector<Point>& locations=vector<Point>()) const;
virtual void detect(const Mat& img, vector<Point>& foundLocations,
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, 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,
int groupThreshold=2) const;
virtual void computeGradient(const Mat& img, Mat& grad, Mat& angleOfs,
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();
......
modules/video/include/opencv2/video/background_segm.hpp
View file @ 1b1eab8e
......@@ -112,17 +112,11 @@ typedef struct CvBGStatModel
//
// Releases memory used by BGStatModel
CV_INLINE void cvReleaseBGStatModel( CvBGStatModel** bg_model )
{
if( bg_model && *bg_model && (*bg_model)->release )
(*bg_model)->release( bg_model );
}
CVAPI(void) cvReleaseBGStatModel( CvBGStatModel** bg_model );
// Updates statistical model and returns number of found foreground regions
CV_INLINE int cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel* bg_model, double learningRate CV_DEFAULT(-1))
{
return bg_model && bg_model->update ? bg_model->update( current_frame, bg_model, learningRate ) : 0;
}
CVAPI(int) cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel* bg_model,
double learningRate CV_DEFAULT(-1));
// Performs FG post-processing using segmentation
// (all pixels of a region will be classified as foreground if majority of pixels of the region are FG).
......@@ -365,7 +359,8 @@ public:
//! the virtual destructor
virtual ~BackgroundSubtractor();
//! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
virtual void operator()(const Mat& image, Mat& fgmask, double learningRate=0);
virtual CV_WRAP_AS(apply) void operator()(const Mat& image, CV_OUT Mat& fgmask,
double learningRate=0);
};
......
modules/video/include/opencv2/video/tracking.hpp
View file @ 1b1eab8e
......@@ -248,8 +248,8 @@ CV_EXPORTS void updateMotionHistory( const Mat& silhouette, Mat& mhi,
double timestamp, double duration );
//! computes the motion gradient orientation image from the motion history image
CV_EXPORTS void calcMotionGradient( const Mat& mhi, Mat& mask,
Mat& orientation,
CV_EXPORTS void calcMotionGradient( const Mat& mhi, CV_OUT Mat& mask,
CV_OUT Mat& orientation,
double delta1, double delta2,
int apertureSize=3 );
......@@ -260,11 +260,11 @@ CV_EXPORTS double calcGlobalOrientation( const Mat& orientation, const Mat& mask
// TODO: need good API for cvSegmentMotion
//! updates the object tracking window using CAMSHIFT algorithm
CV_EXPORTS RotatedRect CamShift( const Mat& probImage, Rect& window,
CV_EXPORTS RotatedRect CamShift( const Mat& probImage, CV_OUT Rect& window,
TermCriteria criteria );
//! updates the object tracking window using meanshift algorithm
CV_EXPORTS int meanShift( const Mat& probImage, Rect& window,
CV_EXPORTS int meanShift( const Mat& probImage, CV_OUT Rect& window,
TermCriteria criteria );
/*!
......@@ -313,8 +313,8 @@ enum { OPTFLOW_USE_INITIAL_FLOW=4, OPTFLOW_FARNEBACK_GAUSSIAN=256 };
//! computes sparse optical flow using multi-scale Lucas-Kanade algorithm
CV_EXPORTS void calcOpticalFlowPyrLK( const Mat& prevImg, const Mat& nextImg,
const vector<Point2f>& prevPts, vector<Point2f>& nextPts,
vector<uchar>& status, vector<float>& err,
const vector<Point2f>& prevPts, CV_OUT vector<Point2f>& nextPts,
CV_OUT vector<uchar>& status, CV_OUT vector<float>& err,
Size winSize=Size(15,15), int maxLevel=3,
TermCriteria criteria=TermCriteria(
TermCriteria::COUNT+TermCriteria::EPS,
......@@ -323,8 +323,8 @@ CV_EXPORTS void calcOpticalFlowPyrLK( const Mat& prevImg, const Mat& nextImg,
int flags=0 );
//! computes dense optical flow using Farneback algorithm
CV_EXPORTS void calcOpticalFlowFarneback( const Mat& prev0, const Mat& next0,
Mat& flow0, double pyr_scale, int levels, int winsize,
CV_EXPORTS void calcOpticalFlowFarneback( const Mat& prev, const Mat& next,
CV_OUT Mat& flow, double pyr_scale, int levels, int winsize,
int iterations, int poly_n, double poly_sigma, int flags );
}
......
modules/video/src/bgfg_estimation.cpp
View file @ 1b1eab8e
......@@ -56,9 +56,22 @@ CvBGStatModel* cvCreateBGStatModel( IplImage* first_frame, int model_type, void*
return bg_model;
}
void cvReleaseBGStatModel( CvBGStatModel** bg_model )
{
if( bg_model && *bg_model && (*bg_model)->release )
(*bg_model)->release( bg_model );
}
int cvUpdateBGStatModel( IplImage* current_frame,
CvBGStatModel* bg_model,
double learningRate )
{
return bg_model && bg_model->update ? bg_model->update( current_frame, bg_model, learningRate ) : 0;
}
/* FOREGROUND DETECTOR INTERFACE */
class CvFGDetectorBase:public CvFGDetector
class CvFGDetectorBase : public CvFGDetector
{
protected:
CvBGStatModel* m_pFG;
......
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