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tenmon/3rdparty/include/pcl/SeparableConvolution.h
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2022-04-12 08:17:18 +02:00

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// ____ ______ __
// / __ \ / ____// /
// / /_/ // / / /
// / ____// /___ / /___ PixInsight Class Library
// /_/ \____//_____/ PCL 2.4.23
// ----------------------------------------------------------------------------
// pcl/SeparableConvolution.h - Released 2022-03-12T18:59:29Z
// ----------------------------------------------------------------------------
// This file is part of the PixInsight Class Library (PCL).
// PCL is a multiplatform C++ framework for development of PixInsight modules.
//
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// ----------------------------------------------------------------------------
#ifndef __PCL_SeparableConvolution_h
#define __PCL_SeparableConvolution_h
/// \file pcl/SeparableConvolution.h
#include <pcl/Defs.h>
#include <pcl/Diagnostics.h>
#include <pcl/AutoPointer.h>
#include <pcl/InterlacedTransformation.h>
#include <pcl/ParallelProcess.h>
#include <pcl/SeparableFilter.h>
#define __PCL_SEPARABLE_CONVOLUTION_TINY_WEIGHT 1.0e-20
namespace pcl
{
// ----------------------------------------------------------------------------
/*!
* \class SeparableConvolution
* \brief Discrete two-dimensional separable convolution in the spatial domain
*
* %SeparableConvolution implements a fully multithreaded, two-dimensional
* discrete separable convolution algorithm. It performs automatic fixing of
* border artifacts by applying Neumann boundary conditions (mirroring).
*
* \sa Convolution, FFTConvolution, SeparableFilter
*/
class PCL_CLASS SeparableConvolution : public InterlacedTransformation,
public ParallelProcess
{
public:
/*!
* Represents a filter coefficient.
*/
typedef SeparableFilter::coefficient coefficient;
/*!
* Represents a one-dimensional filter.
*/
typedef SeparableFilter::coefficient_vector coefficient_vector;
/*!
* Default constructor.
*
* \note This constructor yields an uninitialized instance that cannot be
* used before explicit association with a SeparableFilter instance.
*/
SeparableConvolution() = default;
/*!
* Constructs a %SeparableConvolution object with the specified filter.
*
* \param filter Response function, or <em>convolution filter</em>. The
* specified object does not have to remain valid while this
* instance is actively used, since %SeparableConvolution
* owns a private copy of the separable filter (note that
* SeparableFilter is a reference-counted class).
*/
SeparableConvolution( const SeparableFilter& filter )
{
SetFilter( filter );
}
/*!
* Copy constructor.
*/
SeparableConvolution( const SeparableConvolution& x )
: InterlacedTransformation( x )
, ParallelProcess( x )
, m_weight( x.m_weight )
, m_highPass( x.m_highPass )
, m_rawHighPass( x.m_rawHighPass )
, m_rescaleHighPass( x.m_rescaleHighPass )
, m_convolveRows( x.m_convolveRows )
, m_convolveCols( x.m_convolveCols )
{
if ( !x.m_filter.IsNull() )
m_filter = x.m_filter->Clone();
}
/*!
* Move constructor.
*/
SeparableConvolution( SeparableConvolution&& ) = default;
/*!
* Destroys this %SeparableConvolution object.
*/
virtual ~SeparableConvolution()
{
}
/*!
* Copy assignment operator. Returns a reference to this object.
*/
SeparableConvolution& operator =( const SeparableConvolution& x )
{
if ( &x != this )
{
(void)InterlacedTransformation::operator =( x );
(void)ParallelProcess::operator =( x );
if ( x.m_filter.IsNull() )
m_filter.Destroy();
else
m_filter = x.m_filter->Clone();
m_weight = x.m_weight;
m_highPass = x.m_highPass;
m_rawHighPass = x.m_rawHighPass;
m_rescaleHighPass = x.m_rescaleHighPass;
m_convolveRows = x.m_convolveRows;
m_convolveCols = x.m_convolveCols;
}
return *this;
}
/*!
* Move assignment operator. Returns a reference to this object.
*/
SeparableConvolution& operator =( SeparableConvolution&& ) = default;
/*!
* Returns a constant reference to the separable filter currently associated
* with this %SeparableConvolution object.
*
* \note If this object has not been initialized, this member function
* throws an Error exception.
*/
const SeparableFilter& Filter() const
{
PCL_PRECONDITION( !m_filter.IsNull() )
return *m_filter;
}
/*!
* Returns a copy of the internal vector of filter coefficients
* corresponding to the specified \a phase. If \a phase is zero, the
* one-dimensional row filter is returned; otherwise the one-dimensional
* column filter is returned.
*
* \note If this object has not been initialized, this member function
* throws an Error exception.
*/
coefficient_vector Filter( int phase ) const
{
PCL_PRECONDITION( !m_filter.IsNull() )
return m_filter->Filter( phase );
}
/*!
* Sets a new filter to be applied by this %SeparableConvolution object.
*/
void SetFilter( const SeparableFilter& filter )
{
m_filter = filter.Clone();
CacheFilterProperties();
}
/*!
* Returns the current filter weight. The filter weight is computed each
* time a separable filter is associated with this object. It is only
* applied for low-pass filters as a normalization factor. For more
* information, see the documentation for SeparableFilter::Weight().
*
* The filter weight and other filter properties are cached in private data
* members for quick reference.
*/
double FilterWeight() const
{
return m_weight;
}
/*!
* Returns true if the separable filter currently associated with this
* %SeparableConvolution object is a high-pass filter; false if it is a
* low-pass filter. For more information, see the documentation for
* SeparableFilter::IsHighPassFilter().
*
* Each time a separable filter is associated with this object, its
* high-pass nature is checked and stored, along with other filter
* properties, in private data members. This allows for quick lookup of
* critical filter characteristics without degrading performance.
*/
bool IsHighPassFilter() const
{
return m_highPass;
}
/*!
* Returns true iff out-of-range values will be rescaled for normalization of
* images after convolution with a high-pass filter.
*
* A high-pass filter has negative coefficients. As a result, some pixels in
* the convolved image may have negative values. Saturated pixels (values
* above one) can also result, depending on the filter coefficients. The
* standard behavior is to truncate out-of-range pixel values to the [0,1]
* range, which preserves the dynamics of the convolved image, so high-pass
* rescaling is disabled by default. When high-pass rescaling is enabled,
* the resulting image is \e normalized (that is, rescaled to [0,1] only if
* there are out-of-range values) and hence all the data after convolution
* are preserved at the cost of reducing the overall contrast of the image.
* Finally, if <em>raw high-pass convolution</em> has been enabled,
* out-of-range values are neither truncated nor rescaled irrespective of
* the value returned by this function. See the documentation for
* IsRawHighPassEnabled() for more information.
*/
bool IsHighPassRescalingEnabled() const
{
return m_rescaleHighPass;
}
/*!
* Enables (or disables) high-pass rescaling of out-of-range convolved pixel
* values. See the documentation for IsHighPassRescalingEnabled() for more
* information.
*/
void EnableHighPassRescaling( bool enable = true )
{
m_rescaleHighPass = enable;
}
/*!
* Disables (or enables) high-pass rescaling of out-of-range convolved pixel
* values. See the documentation for IsHighPassRescalingEnabled() for more
* information.
*/
void DisableHighPassRescaling( bool disable = true )
{
EnableHighPassRescaling( !disable );
}
/*!
* Returns true iff <em>raw high-pass convolution</em> is enabled. When raw
* high-pass convolution is enabled, out-of-range values after convolution
* with a high-pass filter are neither truncated nor normalized. Note that
* this is only relevant to convolution of floating point data.
*
* Raw high-pass convolution is disabled by default. For more information on
* out-of-range convolution results, refer to the documentation for
* IsHighPassRescalingEnabled().
*/
bool IsRawHighPassEnabled() const
{
return m_rawHighPass;
}
/*!
* Enables (or disables) raw high-pass convolution. See the documentation
* for IsRawHighPassEnabled() for more information.
*/
void EnableRawHighPass( bool enable = true )
{
m_rawHighPass = enable;
}
/*!
* Disables (or enables) raw high-pass convolution. See the documentation
* for IsRawHighPassEnabled() for more information.
*/
void DisableRawHighPass( bool disable = true )
{
EnableRawHighPass( !disable );
}
/*!
* Returns true iff this object will convolve pixel rows on target images.
*/
bool IsRowConvolutionEnabled() const
{
return m_convolveRows;
}
/*!
* Enables (or disables) convolution of pixel rows.
*/
void EnableRowConvolution( bool enable = true )
{
m_convolveRows = enable;
}
/*!
* Disables (or enables) convolution of pixel rows.
*
* This is a convenience function, equivalent to:
* EnableRowConvolution( !disable )
*/
void DisableRowConvolution( bool disable = true )
{
EnableRowConvolution( !disable );
}
/*!
* Returns true iff this object will convolve pixel columns on target images.
*/
bool IsColumnConvolutionEnabled() const
{
return m_convolveCols;
}
/*!
* Enables (or disables) convolution of pixel columns.
*/
void EnableColumnConvolution( bool enable = true )
{
m_convolveCols = enable;
}
/*!
* Disables (or enables) convolution of pixel columns.
*
* This is a convenience function, equivalent to:
* EnableColumnConvolution( !disable )
*/
void DisableColumnConvolution( bool disable = true )
{
EnableColumnConvolution( !disable );
}
/*!
* Returns the length in pixels of the overlapping regions between adjacent
* areas processed by parallel execution threads. The overlapping distance
* is a function of the filter size and the interlacing distance.
*/
int OverlappingDistance() const
{
PCL_PRECONDITION( !m_filter.IsNull() )
return m_filter->Size() + (m_filter->Size() - 1)*(InterlacingDistance() - 1);
}
/*!
* Returns the minimum filter size in pixels for which separable convolution
* is consistently faster than nonseparable convolution on the current
* PixInsight/PCL platform, for the specified number of parallel execution
* threads.
*
* The values returned by this function have been determined experimentally
* on reference hardware for optimized execution on machines and builds with
* and without AVX2/FMA3 processor instruction support.
*
* \ingroup convolution_speed_limits
*/
static constexpr int FasterThanNonseparableFilterSize( int numThreads )
{
#ifdef __PCL_COMPATIBILITY
// No vectorization
if ( numThreads >= 32 )
return 15;
if ( numThreads >= 16 )
return 11;
if ( numThreads >= 8 )
return 9;
if ( numThreads >= 4 )
return 7;
return 5;
#else
// Vectorization with SSE4.2 / AVX2 and FMA instructions
if ( numThreads >= 32 )
return 29;
if ( numThreads >= 28 )
return 25;
if ( numThreads >= 24 )
return 21;
if ( numThreads >= 16 )
return 19;
if ( numThreads >= 8 )
return 13;
if ( numThreads >= 4 )
return 11;
if ( numThreads >= 2 )
return 9;
return 7;
#endif
}
protected:
/*
* The response function for convolution is a separable filter.
*/
AutoPointer<SeparableFilter> m_filter;
/*
* Cached filter properties.
*/
double m_weight = 0; // filter weight for low-pass normalization
bool m_highPass = false; // true iff this is a high-pass filter
/*
* User-selectable options.
*/
bool m_rawHighPass = false; // neither truncate nor normalize out-of-range values
bool m_rescaleHighPass = false; // truncate out-of-range values instead of normalize
bool m_convolveRows = true; // perform one-dimensional convolution of pixel rows
bool m_convolveCols = true; // perform one-dimensional convolution of pixel columns
/*
* In-place 2-D separable convolution algorithm.
*/
void Apply( pcl::Image& ) const override;
void Apply( pcl::DImage& ) const override;
void Apply( pcl::UInt8Image& ) const override;
void Apply( pcl::UInt16Image& ) const override;
void Apply( pcl::UInt32Image& ) const override;
private:
void CacheFilterProperties()
{
PCL_PRECONDITION( !m_filter.IsNull() )
PCL_PRECONDITION( !m_filter->IsEmpty() )
ValidateFilter();
m_highPass = m_filter->IsHighPassFilter();
m_weight = m_filter->Weight();
if ( pcl::Abs( m_weight ) < __PCL_SEPARABLE_CONVOLUTION_TINY_WEIGHT )
m_weight = 1;
}
void ValidateFilter() const;
};
// ----------------------------------------------------------------------------
} // pcl
#endif // __PCL_SeparableConvolution_h
// ----------------------------------------------------------------------------
// EOF pcl/SeparableConvolution.h - Released 2022-03-12T18:59:29Z