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*/
#ifndef __OPENCV_IMGPROC_FILTERENGINE_HPP__
#define __OPENCV_IMGPROC_FILTERENGINE_HPP__
namespace cv
{
//! type of the kernel
enum
{
KERNEL_GENERAL = 0, // the kernel is generic. No any type of symmetry or other properties.
KERNEL_SYMMETRICAL = 1, // kernel[i] == kernel[ksize-i-1] , and the anchor is at the center
KERNEL_ASYMMETRICAL = 2, // kernel[i] == -kernel[ksize-i-1] , and the anchor is at the center
KERNEL_SMOOTH = 4, // all the kernel elements are non-negative and summed to 1
KERNEL_INTEGER = 8 // all the kernel coefficients are integer numbers
};
/*!
The Base Class for 1D or Row-wise Filters
This is the base class for linear or non-linear filters that process 1D data.
In particular, such filters are used for the "horizontal" filtering parts in separable filters.
Several functions in OpenCV return Ptr<BaseRowFilter> for the specific types of filters,
and those pointers can be used directly or within cv::FilterEngine.
*/
class BaseRowFilter
{
public:
//! the default constructor
BaseRowFilter();
//! the destructor
virtual ~BaseRowFilter();
//! the filtering operator. Must be overridden in the derived classes. The horizontal border interpolation is done outside of the class.
virtual void operator()(const uchar* src, uchar* dst, int width, int cn) = 0;
int ksize;
int anchor;
};
/*!
The Base Class for Column-wise Filters
This is the base class for linear or non-linear filters that process columns of 2D arrays.
Such filters are used for the "vertical" filtering parts in separable filters.
Several functions in OpenCV return Ptr<BaseColumnFilter> for the specific types of filters,
and those pointers can be used directly or within cv::FilterEngine.
Unlike cv::BaseRowFilter, cv::BaseColumnFilter may have some context information,
i.e. box filter keeps the sliding sum of elements. To reset the state BaseColumnFilter::reset()
must be called (e.g. the method is called by cv::FilterEngine)
*/
class BaseColumnFilter
{
public:
//! the default constructor
BaseColumnFilter();
//! the destructor
virtual ~BaseColumnFilter();
//! the filtering operator. Must be overridden in the derived classes. The vertical border interpolation is done outside of the class.
virtual void operator()(const uchar** src, uchar* dst, int dststep, int dstcount, int width) = 0;
//! resets the internal buffers, if any
virtual void reset();
int ksize;
int anchor;
};
/*!
The Base Class for Non-Separable 2D Filters.
This is the base class for linear or non-linear 2D filters.
Several functions in OpenCV return Ptr<BaseFilter> for the specific types of filters,
and those pointers can be used directly or within cv::FilterEngine.
Similar to cv::BaseColumnFilter, the class may have some context information,
that should be reset using BaseFilter::reset() method before processing the new array.
*/
class BaseFilter
{
public:
//! the default constructor
BaseFilter();
//! the destructor
virtual ~BaseFilter();
//! the filtering operator. The horizontal and the vertical border interpolation is done outside of the class.
virtual void operator()(const uchar** src, uchar* dst, int dststep, int dstcount, int width, int cn) = 0;
//! resets the internal buffers, if any
virtual void reset();
Size ksize;
Point anchor;
};
/*!
The Main Class for Image Filtering.
The class can be used to apply an arbitrary filtering operation to an image.
It contains all the necessary intermediate buffers, it computes extrapolated values
of the "virtual" pixels outside of the image etc.
Pointers to the initialized cv::FilterEngine instances
are returned by various OpenCV functions, such as cv::createSeparableLinearFilter(),
cv::createLinearFilter(), cv::createGaussianFilter(), cv::createDerivFilter(),
cv::createBoxFilter() and cv::createMorphologyFilter().
Using the class you can process large images by parts and build complex pipelines
that include filtering as some of the stages. If all you need is to apply some pre-defined
filtering operation, you may use cv::filter2D(), cv::erode(), cv::dilate() etc.
functions that create FilterEngine internally.
Here is the example on how to use the class to implement Laplacian operator, which is the sum of
second-order derivatives. More complex variant for different types is implemented in cv::Laplacian().
\code
void laplace_f(const Mat& src, Mat& dst)
{
CV_Assert( src.type() == CV_32F );
// make sure the destination array has the proper size and type
dst.create(src.size(), src.type());
// get the derivative and smooth kernels for d2I/dx2.
// for d2I/dy2 we could use the same kernels, just swapped
Mat kd, ks;
getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );
// let's process 10 source rows at once
int DELTA = std::min(10, src.rows);
Ptr<FilterEngine> Fxx = createSeparableLinearFilter(src.type(),
dst.type(), kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );
Ptr<FilterEngine> Fyy = createSeparableLinearFilter(src.type(),
dst.type(), ks, kd, Point(-1,-1), 0, borderType, borderType, Scalar() );
int y = Fxx->start(src), dsty = 0, dy = 0;
Fyy->start(src);
const uchar* sptr = src.data + y*src.step;
// allocate the buffers for the spatial image derivatives;
// the buffers need to have more than DELTA rows, because at the
// last iteration the output may take max(kd.rows-1,ks.rows-1)
// rows more than the input.
Mat Ixx( DELTA + kd.rows - 1, src.cols, dst.type() );
Mat Iyy( DELTA + kd.rows - 1, src.cols, dst.type() );
// inside the loop we always pass DELTA rows to the filter
// (note that the "proceed" method takes care of possibe overflow, since
// it was given the actual image height in the "start" method)
// on output we can get:
// * < DELTA rows (the initial buffer accumulation stage)
// * = DELTA rows (settled state in the middle)
// * > DELTA rows (then the input image is over, but we generate
// "virtual" rows using the border mode and filter them)
// this variable number of output rows is dy.
// dsty is the current output row.
// sptr is the pointer to the first input row in the portion to process
for( ; dsty < dst.rows; sptr += DELTA*src.step, dsty += dy )
{
Fxx->proceed( sptr, (int)src.step, DELTA, Ixx.data, (int)Ixx.step );
dy = Fyy->proceed( sptr, (int)src.step, DELTA, d2y.data, (int)Iyy.step );
if( dy > 0 )
{
Mat dstripe = dst.rowRange(dsty, dsty + dy);
add(Ixx.rowRange(0, dy), Iyy.rowRange(0, dy), dstripe);
}
}
}
\endcode
*/
class FilterEngine
{
public:
//! the default constructor
FilterEngine();
//! the full constructor. Either _filter2D or both _rowFilter and _columnFilter must be non-empty.
FilterEngine(const Ptr<BaseFilter>& _filter2D,
const Ptr<BaseRowFilter>& _rowFilter,
const Ptr<BaseColumnFilter>& _columnFilter,
int srcType, int dstType, int bufType,
int _rowBorderType = BORDER_REPLICATE,
int _columnBorderType = -1,
const Scalar& _borderValue = Scalar());
//! the destructor
virtual ~FilterEngine();
//! reinitializes the engine. The previously assigned filters are released.
void init(const Ptr<BaseFilter>& _filter2D,
const Ptr<BaseRowFilter>& _rowFilter,
const Ptr<BaseColumnFilter>& _columnFilter,
int srcType, int dstType, int bufType,
int _rowBorderType = BORDER_REPLICATE,
int _columnBorderType = -1,
const Scalar& _borderValue = Scalar());
//! starts filtering of the specified ROI of an image of size wholeSize.
virtual int start(Size wholeSize, Rect roi, int maxBufRows = -1);
//! starts filtering of the specified ROI of the specified image.
virtual int start(const Mat& src, const Rect& srcRoi = Rect(0,0,-1,-1),
bool isolated = false, int maxBufRows = -1);
//! processes the next srcCount rows of the image.
virtual int proceed(const uchar* src, int srcStep, int srcCount,
uchar* dst, int dstStep);
//! applies filter to the specified ROI of the image. if srcRoi=(0,0,-1,-1), the whole image is filtered.
virtual void apply( const Mat& src, Mat& dst,
const Rect& srcRoi = Rect(0,0,-1,-1),
Point dstOfs = Point(0,0),
bool isolated = false);
//! returns true if the filter is separable
bool isSeparable() const { return !filter2D; }
//! returns the number
int remainingInputRows() const;
int remainingOutputRows() const;
int srcType;
int dstType;
int bufType;
Size ksize;
Point anchor;
int maxWidth;
Size wholeSize;
Rect roi;
int dx1;
int dx2;
int rowBorderType;
int columnBorderType;
std::vector<int> borderTab;
int borderElemSize;
std::vector<uchar> ringBuf;
std::vector<uchar> srcRow;
std::vector<uchar> constBorderValue;
std::vector<uchar> constBorderRow;
int bufStep;
int startY;
int startY0;
int endY;
int rowCount;
int dstY;
std::vector<uchar*> rows;
Ptr<BaseFilter> filter2D;
Ptr<BaseRowFilter> rowFilter;
Ptr<BaseColumnFilter> columnFilter;
};
//! returns type (one of KERNEL_*) of 1D or 2D kernel specified by its coefficients.
int getKernelType(InputArray kernel, Point anchor);
//! returns the primitive row filter with the specified kernel
Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType,
InputArray kernel, int anchor,
int symmetryType);
//! returns the primitive column filter with the specified kernel
Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType,
InputArray kernel, int anchor,
int symmetryType, double delta = 0,
int bits = 0);
//! returns 2D filter with the specified kernel
Ptr<BaseFilter> getLinearFilter(int srcType, int dstType,
InputArray kernel,
Point anchor = Point(-1,-1),
double delta = 0, int bits = 0);
//! returns the separable linear filter engine
Ptr<FilterEngine> createSeparableLinearFilter(int srcType, int dstType,
InputArray rowKernel, InputArray columnKernel,
Point anchor = Point(-1,-1), double delta = 0,
int rowBorderType = BORDER_DEFAULT,
int columnBorderType = -1,
const Scalar& borderValue = Scalar());
//! returns the non-separable linear filter engine
Ptr<FilterEngine> createLinearFilter(int srcType, int dstType,
InputArray kernel, Point _anchor = Point(-1,-1),
double delta = 0, int rowBorderType = BORDER_DEFAULT,
int columnBorderType = -1, const Scalar& borderValue = Scalar());
//! returns the Gaussian filter engine
Ptr<FilterEngine> createGaussianFilter( int type, Size ksize,
double sigma1, double sigma2 = 0,
int borderType = BORDER_DEFAULT);
//! returns filter engine for the generalized Sobel operator
Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
int dx, int dy, int ksize,
int borderType = BORDER_DEFAULT );
//! returns horizontal 1D box filter
Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType,
int ksize, int anchor = -1);
//! returns vertical 1D box filter
Ptr<BaseColumnFilter> getColumnSumFilter( int sumType, int dstType,
int ksize, int anchor = -1,
double scale = 1);
//! returns box filter engine
Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize,
Point anchor = Point(-1,-1),
bool normalize = true,
int borderType = BORDER_DEFAULT);
//! returns horizontal 1D morphological filter
Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int ksize, int anchor = -1);
//! returns vertical 1D morphological filter
Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int ksize, int anchor = -1);
//! returns 2D morphological filter
Ptr<BaseFilter> getMorphologyFilter(int op, int type, InputArray kernel,
Point anchor = Point(-1,-1));
//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
CV_EXPORTS Ptr<FilterEngine> createMorphologyFilter(int op, int type, InputArray kernel,
Point anchor = Point(-1,-1), int rowBorderType = BORDER_CONSTANT,
int columnBorderType = -1,
const Scalar& borderValue = morphologyDefaultBorderValue());
static inline Point normalizeAnchor( Point anchor, Size ksize )
{
if( anchor.x == -1 )
anchor.x = ksize.width/2;
if( anchor.y == -1 )
anchor.y = ksize.height/2;
CV_Assert( anchor.inside(Rect(0, 0, ksize.width, ksize.height)) );
return anchor;
}
void preprocess2DKernel( const Mat& kernel, std::vector<Point>& coords, std::vector<uchar>& coeffs );
void crossCorr( const Mat& src, const Mat& templ, Mat& dst,
Size corrsize, int ctype,
Point anchor=Point(0,0), double delta=0,
int borderType=BORDER_REFLECT_101 );
}
#endif