Filters
Filters transform data and have at least one input and one output.
Point-based transformation
Binarization
Clipping
Masking
- class mask
Mask the circular outer region by setting values to zero.
Arithmetic expressions
- class calculate
Calculate an arithmetic expression. If you choose one dimention you have access to the value stored in the input buffer via the v letter in
expression
, to the index of v via letter x and to the size of the buffer via size. If you choose two dimentions, instead of size you can access width and height and on top of x you also have the y for the vertical coordinate and linearized index (computed as y * width + x). Please be aware that v is a floating point number while the rest of the variables are integers. One dimension is useful if you have multidimensional data and want to address only one dimension. Let’s say the input is two dimensional, 256 pixels wide and you want to fill the x-coordinate with x for all respective y-coordinates (a gradient in x-direction). Then you can write expression=”x % 256”. Another example is the sinc function which you would calculate as expression=”sin(v) / x” for 1D input. For more complex math or other operations please consider using OpenCL.- "expression": string
Arithmetic expression with math functions supported by OpenCL.
- "dimensions": uint
Number of dimensions in [1, 2].
Statistics
Generic OpenCL
- class opencl
Load an arbitrary OpenCL
kernel
fromfilename
orsource
and execute it on each input. The kernel must accept as many global float array parameters as connected to the filter and one additional as an output. For example, to compute the difference between two images, the kernel would look like:kernel void difference (global float *a, global float *b, global float *c) { size_t idx = get_global_id (1) * get_global_size (0) + get_global_id (0); c[idx] = a[idx] - b[idx]; }
and could be used like so if defined in a file named
diff.cl
:$ ufo-launch [read, read] ! opencl kernel=difference filename=diff.cl ! null
If
filename
is not set, a default kernel file (opencl.cl
) is loaded. See OpenCL default kernels for a list of kernel names defined in that file.- "filename": string
Filename with kernel sources to load.
- "source": string
String with OpenCL kernel code.
- "kernel": string
Name of the kernel that this filter is associated with.
- "options": string
OpenCL build options.
- "dimensions": uint
Number of dimensions the kernel works on. Must be in [1, 3].
- "halve-width": boolean
Use half the width of the input size for calculation if complex, i.e. account for x[0] = Re(z[0]), x[1] = Im(z[0]), …
Spatial transformation
Transposition
- class transpose
Transpose images from (x, y) to (y, x).
Rotation
- class rotate
Rotates images clockwise by an
angle
around acenter
(x, y). Whenreshape
isTrue
, the rotated image is not cropped, i.e. the output image size can be larger that the input size. Moreover, this mode makes sure that the original coordinates of the input are all contained in the output so that it is easier to see the rotation in the output. Try e.g. rotation withcenter
equal to \((0, 0)\) and angle \(\pi / 2\).- "angle": float
Rotation angle in radians.
- "reshape": boolean
Reshape the result to encompass the complete input image and input indices.
- "center": GValueArray
Center of rotation (x, y)
- "addressing-mode": enum
Addressing mode specifies the behavior for pixels falling outside the original image. See OpenCL
sampler_t
documentation for more information.
- "interpolation": enum
Specifies interpolation when a computed pixel coordinate falls between pixels, can be nearest or linear.
Flipping
Binning
Rescaling
- class rescale
Rescale input data by a fixed
factor
.- "factor": float
Fixed factor for scaling the input in both directions.
- "x-factor": float
Fixed factor for scaling the input width.
- "y-factor": float
Fixed factor for scaling the input height.
- "width": uint
Fixed width, disabling scalar rescaling.
- "height": uint
Fixed height, disabling scalar rescaling.
- "interpolation": enum
Interpolation method used for rescaling which can be either
nearest
orlinear
.
Padding
- class pad
Pad an image to some extent with specific behavior for pixels falling outside the original image.
- "x": int
Horizontal coordinate in the output image which will contain the first input column.
- "y": int
Vertical coordinate in the output image which will contain the first input row.
- "width": uint
Width of the padded image.
- "height": uint
Height of the padded image.
- "addressing-mode": enum
Addressing mode specifies the behavior for pixels falling outside the original image. See OpenCL
sampler_t
documentation for more information.
Cropping
- class crop
Crop a region of interest from two-dimensional input. If the region is (partially) outside the input, only accessible data will be copied.
- "x": uint
Horizontal coordinate from where to start the ROI.
- "y": uint
Vertical coordinate from where to start the ROI.
- "width": uint
Width of the region of interest.
- "height": uint
Height of the region of interest.
- "from-center": boolean
Start cropping from the center outwards.
Cutting
Tiling
- class tile
Cuts input into multiple tiles. The stream contains tiles in a zig-zag pattern, i.e. the first tile starts at the top left corner of the input goes on the same row until the end and continues on the first tile of the next row until the final tile in the lower right corner.
- "width": uint
Width of a tile which must be a divisor of the input width. If this is not changed, the full width will be used.
- "height": uint
Width of a tile which must be a divisor of the input height. If this is not changed, the full height will be used.
Swapping quadrants
- class swap-quadrants
Cuts the input into four quadrants and swaps the lower right with the upper left and the lower left with the upper right quadrant.
Polar transformation
- class polar-coordinates
Transformation between polar and cartesian coordinate systems.
When transforming from cartesian to polar coordinates the origin is in the image center (
width
/ 2,height
/ 2). When transforming from polar to cartesian coordinates the origin is in the image corner (0, 0).- "width": uint
Final width after transformation.
- "height": uint
Final height after transformation.
- "direction": string
Conversion direction from
polar_to_cartesian
.
Stitching
- class stitch
Stitches two images horizontally based on their relative given
shift
, which indicates how much is the second image shifted with respect to the first one, i.e. there is an overlapping region given by \(first\_width - shift\). First image is inserted to the stitched image from its left edge and the second image is inserted after the overlapping region. If shift is negative, the two images are swapped and stitched as described above with shift made positive.If you are stitching a 360-degree off-centered tomographic data set and know the axis of rotation, shift can be computed as \(2axis - second\_width\) for the case the axis of rotation is greater than half of the first image. If it is less, then the shift is \(first\_width - 2 axis\). Moreover, you need to horizontally flip one of the images because this task expects images which can be stitched directly, without additional needed transformations.
Stitching requires two inputs. If you want to stitch a 360-degree off-centered tomographic data set you can use:
ufo-launch [read path=projections_left/, read path=projections_right/ ! flip direction=horizontal] ! stitch shift=N ! write filename=foo.tif
- "shift": int
How much is second image shifted with respect to the first one. For example, shift 0 means that both images overlap perfectly and the stitching doesn’t actually broaden the image. Shift corresponding to image width makes for a stitched image with twice the width of the respective images (if they have equal width).
- "adjust-mean": boolean
Compute the mean of the overlapping region in the two images and adjust the second image to match the mean of the first one.
- "blend": boolean
Linearly interpolate between the two images in the overlapping region.
Multi-stream
Interpolation
- class interpolate
Interpolates incoming data from two compatible streams, i.e. the task computes \((1 - \alpha) s_1 + \alpha s_2\) where \(s_1\) and \(s_2\) are the two input streams and \(\alpha\) a blend factor. \(\alpha\) is \(i / (n - 1)\) for \(n > 1\), \(n\) being
number
and \(i\) the current iteration.- "number": uint
Number of total output stream length.
Subtract
- class subtract
Subtract data items of the second from the first stream.
Correlate
- class correlate-stacks
Reads two datastreams, the first must provide a 3D stack of images that is used to correlate individal 2D images from the second datastream. The
number
property must contain the expected number of items in the second stream.- "number": uint
Number of data items in the second data stream.
Filters
Median
Edge detection
- class detect-edge
Detect edges by computing the power gradient image using different edge filters.
- "filter": enum
Edge filter (or operator) which is one of
sobel
,laplace
andprewitt
. By default, thesobel
operator is used.
- "addressing-mode": enum
Addressing mode specifies the behavior for pixels falling outside the original image. See OpenCL
sampler_t
documentation for more information.
Gaussian blur
Gradient
- class gradient
Compute gradient.
- "direction": enum
Direction of the gradient, can be either
horizontal
,vertical
,both
orboth_abs
.
- "finite-difference-type": enum
Direction of the gradient, can be either
forward
,backward
, orcentral
.
- "addressing-mode": enum
Addressing mode specifies the behavior for pixels falling outside the original image. See OpenCL
sampler_t
documentation for more information.
Non-local-means denoising
- class non-local-means
Reduce noise using Buades’ non-local means algorithm.
- "search-radius": uint
Radius for similarity search.
- "patch-radius": uint
Radius of patches.
- "h": float
Smoothing control parameter, should be around noise standard deviation or slightly less. Higher h results in a smoother image but with blurred features. If it is 0, estimate noise standard deviation and use it as the parameter value.
- "sigma": float
Noise standard deviation, improves weights computation. If it is zero, it is not automatically estimated as opposed to
h
.estimate-sigma
has to be specified in order to overridesigma
value.
- "window": boolean
Apply Gaussian profile with \(\sigma = \frac{P}{2}\), where \(P\) is the
patch-radius
parameter to the weight computation which decreases the influence of pixels towards the corners of the patches.
- "fast": boolean
Use a fast version of the algorithm described in 1. The only difference in the result from the classical algorithm is that there is no Gaussian profile used and from the nature of the fast algorithm, floating point precision errors might occur for large images.
- "estimate-sigma": boolean
Estimate sigma based on 2, which overrides
sigma
parameter value. Only the first image in a sequence is used for estimation and the estimated sigma is re-used for every consequent image.
- "addressing-mode": enum
Addressing mode specifies the behavior for pixels falling outside the original image. See OpenCL
sampler_t
documentation for more information.
- 1
J. Darbon, A. Cunha, T.F. Chan, S. Osher, and G.J. Jensen, Fast nonlocal filtering applied to electron cryomicroscopy in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, pp. 1331-1334. DOI:10.1109/ISBI.2008.4541250
- 2
J. Immerkaer, Fast noise variance estimation in Computer vision and image understanding 64.2 (1996): 300-302. DOI:10.1006/cviu.1996.0060
Finding large spots
- class find-large-spots
Find large spots with extreme values in an image. First, pixels with values greater than
spot_threshold
are added to the mask, then connected pixels with absolute difference between them and the originally detected greater thangrow-threshold
are added to the mask. In the end, holes are also removed from the mask.- "spot-threshold": float
Pixels with values greater than this threshold are added to the mask.
- "grow-threshold": float
Pixels connected to the ones found by
spot-threshold
with absolute difference greater than this threshold are added to the mask. If the value is 0, it is automatically set to full width at tenth maximum of the estimated noise standard deviation.
- "addressing-mode": enum
Addressing mode specifies the behavior for pixels falling outside the original image. See OpenCL
sampler_t
documentation for more information. This parameter is used only for automatic noise standard deviation estimation.
Horizontal interpolation
- class horizontal-interpolate
Interpolate masked values in rows of an image. For all pixels equal to one in the mask, find the closest pixel where mask is zero to the left and right and linearly interpolate the value in the current pixel based on the found left and right values. If
use-one-sided-gradient
is TRUE and the mask goes to the left or right border of the image and on the other side there are at least two non-masked pixels \(x_1\) and \(x_2\), compute the value in the current pixel \(x\) by (in case the mask goes to the right border, left is analogous) \(f(x) = f(x_2) + (x - x_2) * (f(x_2) - f(x_1))\). In caseuse-one-sided-gradient
is FALSE or there is only one valid pixel on one of the borders and all the others are masked, use that pixel’s value in all the remaining ones.- "use-one-sided-gradient": boolean
If TRUE, use two good pixels on one side to compute gradient and fill the masked values accordingly (for the case the mask spans to the border). If FALSE, just copy the last “good” pixel value to the masked values.
Stream transformations
Averaging
Reducing with OpenCL
- class opencl-reduce
Reduces or folds the input stream using a generic OpenCL kernel by loading an arbitrary
kernel
fromfilename
orsource
. The kernel must accept exactly two global float arrays, one for the input and one for the output. Additionally a secondfinish
kernel can be specified which is called once when the processing finished. This kernel must have two arguments as well, the global float array and an unsigned integer count. Folding (i.e. setting the initial data to a known value) is enabled by setting thefold-value
.Here is an OpenCL example how to compute the average:
kernel void sum (global float *in, global float *out) { size_t idx = get_global_id (1) * get_global_size (0) + get_global_id (0); out[idx] += in[idx]; } kernel void divide (global float *out, uint count) { size_t idx = get_global_id (1) * get_global_size (0) + get_global_id (0); out[idx] /= count; }
And this is how you would use it with
ufo-launch
:ufo-launch ... ! opencl-reduce kernel=sum finish=divide ! ...
If
filename
is not set, a default kernel file is loaded. See OpenCL reduction default kernels for a list of possible kernels.- "filename": string
Filename with kernel sources to load.
- "source": string
String with OpenCL kernel code.
- "kernel": string
Name of the kernel that is called on each iteration. Must have two global float array arguments, the first being the input, the second the output.
- "finish": string
Name of the kernel that is called at the end after all iterations. Must have a global float array and an unsigned integer arguments, the first being the data, the second the iteration counter.
- "fold-value": float
If given, the initial data is filled with this value, otherwise the first input element is used.
- "dimensions": uint
Number of dimensions the kernel works on. Must be in [1, 3].
Statistics
Slicing
- class slice
Slices a three-dimensional input buffer to two-dimensional slices.
Stacking
- class stack
Symmetrical to the slice filter, the stack filter stacks two-dimensional input. If
number
is not a divisor of the number of input images, the last produced stack at index which starts to exceed the number of input images will contain arbitrary images from the previous iterations.- "number": uint
Number of items, i.e. the length of the third dimension.
Stacking with sliding window
- class sliding-stack
Stacks input images up to the specified
number
and then replaces old images with incoming new ones as they come. The first image is copied to all positions in the beginning. By default, images in the window are not ordered, i.e. if e.g.number
= 3, then the window will contain the following input images: (0, 0, 0), (0, 1, 0), (0, 1, 2), (3, 1, 2), (3, 4, 2), (3, 4, 5) and so on. If you want them to appear ordered with respect to their arrival time, useordered
.- "number": uint
Number of items, i.e. the length of the third dimension.
- "ordered": boolean
Order items in the sliding window.
Merging
Slice mapping
- class map-slice
Lays out input images on a quadratic grid. If the
number
of input elements is not the square of some integer value, the next higher number is chosen and the remaining data is blackened.- "number": uint
Number of expected input elements. If more elements are sent to the mapper, warnings are issued.
Color mapping
- class map-color
Receives a two-dimensional image and maps its gray values to three red, green and blue color channels using the Viridis color map.
Splitting channels
- class unsplit
Turns a three-dimensional image into two-dimensional image by interleaving the third dimension, i.e. [[[XXX],[YYY],[ZZZ]]] is turned into [[XYZ],[XYZ],[XYZ]]. This is useful to merge a separate multi-channel RGB image into a “regular” RGB image that can be shown with
cv-show
.This task adds the
channels
key to the output buffer containing the original depth of the input buffer.
Fourier domain
Fast Fourier transform
- class fft
Compute the Fourier spectrum of input data.
dimensions
specifies the dimensionality of the transform, it is independent from the input dimensions. E.g. if you have 3D input, you can compute a 3D FT, a batch of 2D FTs of every plane, or a batch of 1D FTs of every row. If you have 2D input, you may compute a 2D FT or a batch of 1D FTs of every row. For every dimension, if size is not specified andauto-zeropadding
is True, the input is padded to the next power of two. If it is False, the output has the same size as the input (via the Chirp-z transform from [Rabiner et al., 1969]).Please note that Chirp-z needs to perform 2 padded-size FFTs and pads the input to the next power of two of double the input size, so it can be considerably slower than using
auto-zeropadding
. E.g. if the input size is1023 x 1023
pixels, auto-zeropadding=True pads the input to1024 x 1024
pixels. In the case of auto-zeropadding=False and no user size specification (see parameters below), Chirp-z pads the input to2048 x 2048
. On the top of that, it requires two FFTs with the padded size, so in this case it is eight times slower than using auto-zeropadding=True (factor of four for the padding in the two dimensions and the additional factor of two for the two FFTs).Example usage:
# Suppose input.tif is 3D and has the following size: width=17, height=15, depth=9 # 3D transform, input size = output size ufo-launch read path=input.tif ! stack number=9 ! fft dimensions=3 ! ifft dimensions=3 ! slice ! write filename=inverse.tif # 3D transform, auto zeropadding ufo-launch read path=input.tif ! stack number=9 ! fft dimensions=3 auto-zeropadding=True ! ifft dimensions=3 ! slice ! write filename=inverse.tif # 3D transform, custom size ufo-launch read path=input.tif ! stack number=9 ! fft dimensions=3 size-x=20 size-y=64 size-z=10 ! ifft dimensions=3 ! slice ! write filename=inverse.tif # Batch of nine 2D transforms ufo-launch read path=input.tif ! stack number=9 ! fft dimensions=2 ! ifft dimensions=2 ! slice ! write filename=inverse.tif # 2D transform ufo-launch read path=input.tif number=1 ! fft dimensions=2 ! ifft dimensions=2 ! write filename=inverse.tif # 2D transform, auto zeropadding ufo-launch read path=input.tif number=1 ! fft dimensions=2 auto-zeropadding=True ! ifft dimensions=2 ! write filename=inverse.tif # 2D transform, custom size ufo-launch read path=input.tif number=1 ! fft dimensions=2 size-x=20 size-y=19 ! ifft dimensions=2 ! write filename=inverse.tif # Batch of fifteen 1D transforms ufo-launch read path=input.tif number=1 ! fft dimensions=1 ! ifft dimensions=1 ! write filename=inverse.tif
- "auto-zeropadding": boolean
Automatically zeropad input data to a size to the next power of 2.
- "dimensions": uint
Number of dimensions in [1, 3].
- "size-x": uint
Size of FFT transform in x-direction, 0=automatic selection.
- "size-y": uint
Size of FFT transform in y-direction, 0=automatic selection.
- "size-z": uint
Size of FFT transform in z-direction, 0=automatic selection.
- class ifft
Compute the inverse Fourier of spectral input data (see fft) for details on how the transform works. You may crop the output by setting the
crop-width
andcrop-height
parameters, otherwise the output has the same size as the input.- "dimensions": uint
Number of dimensions in [1, 3].
- "crop-width": int
Width to crop output, 0=automatic selection.
- "crop-height": int
Height to crop output, 0=automatic selection.
- class power-spectrum
Compute power spectrum from fourier coefficients.
Frequency filtering
- class filter
Computes a frequency filter function and multiplies it with its input, effectively attenuating certain frequencies.
- "filter ": enum
Any of
ramp
,ramp-fromreal
,butterworth
,faris-byer
,hamming
andbh3
(Blackman-Harris-3). The default filter isramp-fromreal
which computes a correct ramp filter avoiding offset issues encountered with naive implementations.
- "scale": float
Arbitrary scale that is multiplied to each frequency component.
- "cutoff": float
Cutoff frequency of the Butterworth filter.
- "order": float
Order of the Butterworth filter.
- "tau": float
Tau parameter of Faris-Byer filter.
- "theta": float
Theta parameter of Faris-Byer filter.
Stripe filtering
- class filter-stripes
Filter vertical stripes. The input and output are in 2D frequency domain. The filter multiplies horizontal frequencies (for frequency ky=0) with a Gaussian profile centered at 0 frequency if
vertical-sigma
is 0. Otherwise it applies also a vertical Gaussian profile (1 - Gaussian), which enables filtering of not perfectly vertical stripes, which is useful for broader stripes and stripes which are not perfectly straight. Ifhorizontal-sigma
is 0, only the vertical Gaussian profile is applied (i.e. a horizontal stripe is cut out around ky=0). This is useful e.g. for filtering DMM stripes.Example usage:
$ ufo-launch read path=sino.tif ! fft dimensions=2 ! filter-stripes sigma=1 ! ifft dimensions=2 ! write filename=sino-filtered.tif
- "horizontal-sigma": float
Horizontal filter strength, which is the sigma of the Gaussian. Small values, e.g. 1e-7 cause only the zero frequency to remain in the signal, i.e. stronger filtering. Values around 1 are a good starting point.
- "vertical-sigma": float
Vertical filter strength, which is the sigma of the Gaussian. The larger the value, the more non-vertical frequencies are removed. Value around 4 is a good starting point.
1D stripe filtering
- class filter-stripes1d
Filter stripes in 1D along the x-axis. The input and output are in frequency domain. The filter multiplies the frequencies with an inverse Gaussian profile centered at 0 frequency. The inversed profile means that the filter is f(k) = 1 - gauss(k) in order to suppress the low frequencies.
- "strength": float
Filter strength, which is the full width at half maximum of the gaussian.
Zeropadding
Reconstruction
Flat-field correction
- class flat-field-correct
Computes the flat field correction using three data streams:
Projection data on input 0
Dark field data on input 1
Flat field data on input 2
- "absorption-correct": boolean
If TRUE, compute the negative natural logarithm of the flat-corrected data.
- "fix-nan-and-inf": boolean
If TRUE, replace all resulting NANs and INFs with zeros.
- "sinogram-input": boolean
If TRUE, correct only one line (the sinogram), thus darks are flats are 1D.
- "dark-scale": float
Scale the dark field prior to the flat field correct.
- "flat-scale": float
Scale the flat field prior to the flat field correct.
Sinogram transposition
- class transpose-projections
Read a stream of two-dimensional projections and output a stream of transposed sinograms.
number
must be set to the number of incoming projections to allocate enough memory.- "number": uint
Number of projections.
Warning
This is a memory intensive task and can easily exhaust your system memory. Make sure you have enough memory, otherwise the process will be killed.
Tomographic backprojection
- class backproject
Computes the backprojection for a single sinogram.
- "num-projections": uint
Number of projections between 0 and 180 degrees.
- "offset": uint
Offset to the first projection.
- "axis-pos": double
Position of the rotation axis in horizontal pixel dimension of a sinogram or projection. If not given, the center of the sinogram is assumed.
- "angle-step": double
Angle step increment in radians. If not given, pi divided by height of input sinogram is assumed.
- "angle-offset": double
Constant angle offset in radians. This determines effectively the starting angle.
- "mode": enum
Reconstruction mode which can be either
nearest
ortexture
.
- "roi-x": uint
Horizontal coordinate of the start of the ROI. By default 0.
- "roi-y": uint
Vertical coordinate of the start of the ROI. By default 0.
- "roi-width": uint
Width of the region of interest. The default value of 0 denotes full width.
- "roi-height": uint
Height of the region of interest. The default value of 0 denotes full height.
Tomographic Stacked backprojection
- class stacked-backproject
Computes the backprojection of multiple sinograms in parallel. Stream multiple sinograms by introducing a stack filter of certain size before this filter. A suitable minimum stack size must be specified based on precision mode
single - 2
half - 4
int8 - 4
- "num-projections": uint
Number of projections between 0 and 180 degrees
- "offset": uint
Offset to the first projection.
- "axis-pos": double
Position of the rotation axis in horizontal pixel dimension of a sinogram or projection. If not given, the center of the sinogram is assumed.
- "angle-step": double
Angle step increment in radians. If not given, pi divided by height of input sinogram is assumed.
- "angle-offset": double
Constant angle offset in radians. This determines effectively the starting angle.
- "roi-x": uint
Horizontal coordinate of the start of the ROI. By default 0.
- "roi-y": uint
Vertical coordinate of the start of the ROI. By default 0.
- "roi-width": uint
Width of the region of interest. The default value of 0 denotes full width.
- "roi-height": uint
Height of the region of interest. The default value of 0 denotes full height.
- "precision-mode": enum
Precision mode or storage format which can be
single
orhalf
orint8
Correspondingly it represents storage in 32, 16 and 8-bits.
Forward projection
Laminographic backprojection
- class lamino-backproject
Backprojects parallel beam computed laminography projection-by-projection into a 3D volume.
- "region-values": int
Elements in regions.
- "float-region-values": float
Elements in float regions.
- "x-region": GValueArray
X region for reconstruction as (from, to, step).
- "y-region": GValueArray
Y region for reconstruction as (from, to, step).
- "z": float
Z coordinate of the reconstructed slice.
- "region": GValueArray
Region for the parameter along z-axis as (from, to, step).
- "projection-offset": GValueArray
Offset to projection data as (x, y) for the case input data is cropped to the necessary range of interest.
- "center": GValueArray
Center of the volume with respect to projections (x, y), (rotation axes).
- "overall-angle": float
Angle covered by all projections (can be negative for negative steps in case only num-projections is specified)
- "num-projections": uint
Number of projections.
- "tomo-angle": float
Tomographic rotation angle in radians (used for acquiring projections).
- "lamino-angle": float
Absolute laminogrpahic angle in radians determining the sample tilt.
- "roll-angle": float
Sample angular misalignment to the side (roll) in radians (CW is positive).
- "parameter": enum
Which paramter will be varied along the z-axis, from
z
,x-center
,lamino-angle
,roll-angle
.
Fourier interpolation
- class dfi-sinc
Computes the 2D Fourier spectrum of reconstructed image using 1D Fourier projection of sinogram (fft filter must be applied before). There are no default values for properties, therefore they should be assigned manually.
- "kernel-size": uint
The length of kernel which will be used in interpolation.
- "number-presampled-values": uint
Number of presampled values which will be used to calculate
kernel-size
kernel coefficients.
- "roi-size": int
The length of one side of region of Interest.
- "angle-step": double
Increment of angle in radians.
Center of rotation
Sinogram offset shift
Phase retrieval
- class retrieve-phase
Computes and applies a fourier filter to correct phase-shifted data. Expects frequencies as an input and produces frequencies as an output. Propagation distance can be specified for both x and y directions together by the
distance
parameter or separately bydistance-x
anddistance-y
, which is useful e.g. when pixel size is not symmetrical.distance
may be a list in which case a multi-distance CTF phase retrieval is performed. In this casemethod
must be set toctf_multidistance
.- "method": enum
Retrieval method which is one of
tie
,ctf
,ctf_multidistance
,qp
orqp2
.
- "energy": float
Energy in keV.
- "distance": float
Distance in meters.
- "distance-x": float
Distance in x-direction in meters.
- "distance-y": float
Distance in y-direction in meters.
- "pixel-size": float
Pixel size in meters.
- "regularization-rate": float
Regularization parameter is log10 of the constant to be added to the denominator to regularize the singularity at zero frequency: 1/sin(x) -> 1/(sin(x)+10^-RegPar). It is also log10(delta / beta) where the complex refractive index is delta + beta * 1j.
Typical values [2, 3].
- "thresholding-rate": float
Parameter for Quasiparticle phase retrieval which defines the width of the rings to be cropped around the zero crossing of the CTF denominator in Fourier space.
Typical values in [0.01, 0.1],
qp
retrieval is rather independent of cropping width.
- "frequency-cutoff": float
Cutoff frequency after which the filter is set to 0 in radians.
- "output-filter": boolean
Output filter values instead of the filtered frequencies.
General matrix-matrix multiplication
- class gemm
Computes \(\alpha A \cdot B + \beta C\) where \(A\), \(B\) and \(C\) are input streams 0, 1 and 2 respectively. \(A\) must be of size \(m\times k\), \(B\) \(k\times n\) and \(C\) \(m\times n\).
Note
This filter is only available if CLBlast support is available.
- "alpha": float
Scalar multiplied with \(AB\).
- "beta": float
Scalar multiplied with \(C\).
Segmentation
- class segment
Segments a stack of images given a field of labels using the random walk algorithm described in 3. The first input stream must contain three-dimensional image stacks, the second input stream a label image with the same width and height as the images. Any pixel value other than zero is treated as a label and used to determine segments in all directions.
- 3
Lösel and Heuveline, Enhancing a Diffusion Algorithm for 4D Image Segmentation Using Local Information in Proc. SPIE 9784, Medical Imaging 2016, http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2506235
Auxiliary
Buffering
- class buffer
Buffers items internally until data stream has finished. After that all buffered elements are forwarded to the next task.
- "number": uint
Number of pre-allocated buffers.
- "dup-count": uint
Number of times each image should be duplicated.
- "loop": boolean
Duplicates the data in a loop manner
dup-count
times.
Stamp
Loops
Monitoring
Sleep
- class sleep
Wait
time
seconds before continuing. Useful for debugging throughput issues.- "time": double
Time to sleep in seconds.