Namespace lsst::afw::math::detail

namespace detail

Functions

template<typename OutImageT, typename InImageT>
void basicConvolve(OutImageT &convolvedImage, InImageT const &inImage, lsst::afw::math::Kernel const &kernel, lsst::afw::math::ConvolutionControl const &convolutionControl)

Low-level convolution function that does not set edge pixels.

convolvedImage must be the same size as inImage. convolvedImage has a border in which the output pixels are not set. This border has size:

  • kernel.getCtrX() along the left edge

  • kernel.getCtrY() along the bottom edge

  • kernel.getWidth() - 1 - kernel.getCtrX() along the right edge

  • kernel.getHeight() - 1 - kernel.getCtrY() along the top edge

Parameters

  • [out] convolvedImage: convolved image

  • [in] inImage: image to convolve

  • [in] kernel: convolution kernel

  • [in] convolutionControl: convolution control parameters

Exceptions

  • lsst::pex::exceptions::InvalidParameterError: if convolvedImage dimensions != inImage dimensions

  • lsst::pex::exceptions::InvalidParameterError: if inImage smaller than kernel in width or height

  • lsst::pex::exceptions::InvalidParameterError: if kernel width or height < 1

  • std::bad_alloc: when allocation of CPU memory fails

template<typename OutImageT, typename InImageT>
void basicConvolve(OutImageT &convolvedImage, InImageT const &inImage, lsst::afw::math::DeltaFunctionKernel const &kernel, lsst::afw::math::ConvolutionControl const &convolutionControl)

A version of basicConvolve that should be used when convolving delta function kernels

Parameters

  • [out] convolvedImage: convolved image

  • [in] inImage: image to convolve

  • [in] kernel: convolution kernel

  • [in] convolutionControl: convolution control parameters

template<typename OutImageT, typename InImageT>
void basicConvolve(OutImageT &convolvedImage, InImageT const &inImage, lsst::afw::math::LinearCombinationKernel const &kernel, lsst::afw::math::ConvolutionControl const &convolutionControl)

A version of basicConvolve that should be used when convolving a LinearCombinationKernel

The Algorithm:

  • If the kernel is spatially varying and contains only DeltaFunctionKernels then convolves the input Image by each basis kernel in turn, solves the spatial model for that component and adds in the appropriate amount of the convolved image.

  • In all other cases uses normal convolution

Parameters

  • [out] convolvedImage: convolved image

  • [in] inImage: image to convolve

  • [in] kernel: convolution kernel

  • [in] convolutionControl: convolution control parameters

Exceptions

  • lsst::pex::exceptions::InvalidParameterError: if convolvedImage dimensions != inImage dimensions

  • lsst::pex::exceptions::InvalidParameterError: if inImage smaller than kernel in width or height

  • lsst::pex::exceptions::InvalidParameterError: if kernel width or height < 1

  • std::bad_alloc: when allocation of CPU memory fails

template<typename OutImageT, typename InImageT>
void basicConvolve(OutImageT &convolvedImage, InImageT const &inImage, lsst::afw::math::SeparableKernel const &kernel, lsst::afw::math::ConvolutionControl const &convolutionControl)

A version of basicConvolve that should be used when convolving separable kernels

Parameters

  • [out] convolvedImage: convolved image

  • [in] inImage: image to convolve

  • [in] kernel: convolution kernel

  • [in] convolutionControl: convolution control parameters

template<typename OutImageT, typename InImageT>
void convolveWithBruteForce(OutImageT &convolvedImage, InImageT const &inImage, lsst::afw::math::Kernel const &kernel, lsst::afw::math::ConvolutionControl const &convolutionControl)

Convolve an Image or MaskedImage with a Kernel by computing the kernel image at every point. (If the kernel is not spatially varying then only compute it once).

convolvedImage must be the same size as inImage. convolvedImage has a border in which the output pixels are not set. This border has size:

  • kernel.getCtrX() along the left edge

  • kernel.getCtrY() along the bottom edge

  • kernel.getWidth() - 1 - kernel.getCtrX() along the right edge

  • kernel.getHeight() - 1 - kernel.getCtrY() along the top edge

Warning

Low-level convolution function that does not set edge pixels.

Parameters

  • [out] convolvedImage: convolved image

  • [in] inImage: image to convolve

  • [in] kernel: convolution kernel

  • [in] convolutionControl: convolution control parameters

Exceptions

  • lsst::pex::exceptions::InvalidParameterError: if convolvedImage dimensions != inImage dimensions

  • lsst::pex::exceptions::InvalidParameterError: if inImage smaller than kernel in width or height

  • lsst::pex::exceptions::InvalidParameterError: if kernel width or height < 1

  • std::bad_alloc: when allocation of CPU memory fails

template<typename OutImageT, typename InImageT>
void convolveWithInterpolation(OutImageT &outImage, InImageT const &inImage, lsst::afw::math::Kernel const &kernel, ConvolutionControl const &convolutionControl)

Convolve an Image or MaskedImage with a spatially varying Kernel using linear interpolation.

This is a low-level convolution function that does not set edge pixels.

The algorithm is as follows:

  • divide the image into regions whose size is no larger than maxInterpolationDistance

  • for each region:

    • convolve it using convolveRegionWithInterpolation (which see)

Note that this routine will also work with spatially invariant kernels, but not efficiently.

Parameters

  • [out] outImage: convolved image = inImage convolved with kernel

  • [in] inImage: input image

  • [in] kernel: convolution kernel

  • [in] convolutionControl: convolution control parameters

Exceptions

  • lsst::pex::exceptions::InvalidParameterError: if outImage is not the same size as inImage

template<typename OutImageT, typename InImageT>
void convolveRegionWithInterpolation(OutImageT &outImage, InImageT const &inImage, KernelImagesForRegion const &region, ConvolveWithInterpolationWorkingImages &workingImages)

Convolve a region of an Image or MaskedImage with a spatially varying Kernel using interpolation.

This is a low-level convolution function that does not set edge pixels.

Warning

: this is a low-level routine that performs no bounds checking.

Parameters

  • [out] outImage: convolved image = inImage convolved with kernel

  • [in] inImage: input image

  • [in] region: kernel image region over which to convolve

  • [in] workingImages: working kernel images

struct ConvolveWithInterpolationWorkingImages
#include <Convolve.h>

kernel images used by convolveRegionWithInterpolation

class KernelImagesForRegion
#include <Convolve.h>

A collection of Kernel images for special locations on a rectangular region of an image

See the Location enum for a list of those special locations.

This is a low-level helper class for recursive convolving with interpolation. Many of these objects may be created during a convolution, and many will share kernel images. It uses shared pointers to kernels and kernel images for increased speed and decreased memory usage (at the expense of safety). Note that null pointers are NOT acceptable for the constructors!

Warning

The kernel images along the top and right edges are computed one row or column past the bounding box. This allows abutting KernelImagesForRegion to share corner and edge kernel images, which is useful when dividing a KernelImagesForRegion into subregions.

Warning

The bounding box for the region applies to the parent image.

Also note that it uses lazy evaluation: images are computed when they are wanted.

class RowOfKernelImagesForRegion
#include <Convolve.h>

A row of KernelImagesForRegion

Intended for iterating over subregions of a KernelImagesForRegion using computeNextRow.

class Spline

Subclassed by lsst::afw::math::detail::SmoothedSpline, lsst::afw::math::detail::TautSpline

struct TrapezoidalPacker
#include <TrapezoidalPacker.h>

A helper class ChebyshevBoundedField, for mapping trapezoidal matrices to 1-d arrays.

This class is not Swigged, and should not be included by any other .h files (including lsst/afw/math/detail.h); it’s for internal use by ChebyshevBoundedField only, and it’s only in a header file instead of that .cc file only so it can be unit tested.

We characterize the matrices by their number of columns (nx) and rows (ny), and the number of complete rows minus one (m).

This splits up the matrix into a rectangular part, in which the number of columns is the same for each row, and a wide trapezoidal or triangular part, in which the number of columns decreases by one for each row.

Here are some examples of how this class handles different kinds of matrices:

A wide trapezoidal matrix with orderX=4, orderY=3: nx=5, ny=4, m=0

0 1 2 3 4 5 6 7 8 9 10 11 12 13

A tall trapezoidal matrix with orderX=2, orderY=4 nx=3, ny=5, m=2

0 1 2 3 4 5 6 7 8 9 10 11

A triangular matrix with orderX=3, orderY=3 nx=4, ny=5, m=0

0 1 2 3 4 5 6 7 8 9

A wide rectangular matrix with orderX=3, orderY=2 nx=4, ny=3, m=3

0 1 2 3 4 5 6 7 8 9 10 11

A tall rectangular matrix with orderX=2, orderY=3 nx=3, ny=4, m=4

0 1 2 3 4 5 6 7 8 9 10 11

template<typename DestImageT, typename SrcImageT>
class WarpAtOnePoint
#include <WarpAtOnePoint.h>

A functor that computes one warped pixel