img.alg  Image Processing Algorithms
Applying algorithms
While image properties are usually manipulated using method of the
ImageHandle class, their data content is manipulated using
image algorithms. Image algorithms are objects. Each of them is a class, and its
methods are used to handle the algorithm parameters. Applying an algorithm to an
image is then conceptually a twostep process. First, an instance of an
algorithm class is created, yielding an algorithm object. In a second step, the
algorithm object is applied to an image. An algorithm can be applied inplace
(using the Apply() method), modifying the image, or
outofplace, (using ApplyIP() ), leaving the original image
untouched, and returning the result as a new image.
Here is an example. All the algorithms used in the following are described in the Selected Algorithms section.
# creates an algorithm object
rand_alg = img.alg.Randomize()
# applies algorithm object in place, overwriting the image
im.ApplyIP( rand_alg )
Sometimes, there is no need to create a permanent instance of an algorithm
object. A temporary object enough:
# applies temporary algorithm object inplace
im.ApplyIP( img.alg.GaussianFilter(4.0) )
When used this way, the algorithm class will cease to exist as soon as the
algorithm is applied. However, some algorithm are stateful and store
information. One good example is the Stat algorithm, which does not
modify the image when applied, but change its internal state to store
information extracted from the image, which can be recovered later. For example:
# creates and applies an algorithm object
stat=img.alg.Stat()
im.ApplyIP(stat)
# extracts information from the algorithm
mean=stat.GetMean()
It is important to remember that when the algorithms ceases to exist, all information it stores is lost.
Filters
OpenStructure makes several image filters available. Most of them are Fourier
space filters, others are real space ones. However, since the
ImagerHandle class is aware of its own The data domain,
the user does not need to convert the image to Fourier space or to real space.
Irrespective of which domain the filter applies to, OpenStructure will
internally convert the image to the appropriate domain, apply the filter, and
then return the image to its original conditions.
The following filters are available (their are described in the Selected Algorithms section below)
Fourier space filters:
Real space filters:
Selected Algorithms
Many algorithms are available for image manipulation. What follows is a description of the
most important ones.

class DFT
This algorithm performs a Fourier Transform of the image, honoring its
The spatial origin, thus applying the corresponding phase shift in Fourier
space.

class DiscreteShrink(block_size)
The algorithm performs a scaling of the original image by merging adjacent
blocks of pixels. The block size is passed in the constructor in the form of
a Size but can be changed later using the relevant method.
The Size and the Extent of the image are
changed when the algorithm is applied. The Pixel sampling of the image
is also adjusted according to the scaling, so that the size of the image in
the absolute reference system used by OpenStructure stays constant.
Parameters:  block_size (Size) – Size of the blocks to be merged 

GetBlocksize()
Returns the current size of the blocks to be merged

SetBlocksize(block size)
Sets the size of the blocks to be shrunk to the specified value
Parameters:  block_size (Size) – 

class FFT
This algorithm performs a Fourier Transform of the image, without honoring
its The spatial origin (See DFT)

class LowPassFilter(cutoff=1.0)
This algorithm applies a Fourier low pass filter to the image. The filter cutoff frequency needs
to be provided in sampling units (for example 8 Angstrom). Please notice that this filter features a sharp dropoff.
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

GetLimit()
Returns the current value of the filter cutoff frequency (in sampling units).

SetLimit(cutoff)
Sets the value of the filter cutoff frequency to the specified value (in sampling units).
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

class HighPassFilter(cutoff=1.0)
This algorithm applies a Fourier high pass filter to the image. The filter cutoff frequency needs
to be provided in sampling units (for example 8 Angstrom). Please notice that this filter features a sharp dropoff.
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

GetLimit()
Returns the current value of the filter cutoff frequency (in sampling units).

SetLimit(cutoff)
Sets the value of the filter cutoff frequency to the specified value (in sampling units).
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

class GaussianLowPassFilter(cutoff=1.0)
This algorithm applies a Fourier Gaussian low pass filter to the
image. The filter cutoff frequency needs to be provided in sampling units (for example 8 Angstrom).
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

GetLimit()
Returns the current value of the filter cutoff frequency (in sampling units).

SetLimit(cutoff)
Sets the value of the filter cutoff frequency to the specified value (in sampling units).
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

class GaussianHighPassFilter(cutoff=1.0)
This algorithm applies a Fourier Gaussian High pass filter to the
image. The filter cutoff frequency needs to be provided in sampling units (for example 8 Angstrom).
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

GetLimit()
Returns the current value of the filter cutoff frequency (in sampling units).

SetLimit(cutoff)
Sets the value of the filter cutoff frequency to the specified value (in sampling units).
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

class FermiLowPassFilter(cutoff=1.0, t=1.0)
This algorithm applies a Fourier Fermi low pass filter to the
image. The filter cutoff frequency and the temperature parameter T need to be provided in sampling units
(for example 8 Angstrom).
Parameters: 
 cutoff (float) – Frequency cutoff in sampling units
 t (float) – Temperature factor in sampling units


GetLimit()
Returns the current value of the filter cutoff frequency in sampling units.

SetLimit(cutoff)
Sets the value of the filter cutoff frequency to the specified value (in sampling units).
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

GetT()
Returns the current value of the filter’s T factor (in sampling units).

SetT(t_factor)
Sets the value of the filter’s T factor to the specified value (in sampling units).
Parameters:  t_factor (float) – Frequency cutoff in sampling units 

class FermiHighPassFilter(cutoff=1.0, t=1.0)
This algorithm applies a Fourier Fermi high pass filter to the
image. The filter cutoff frequency and the temperature parameter T need to be provided in sampling units
(for example 8 Angstrom).
Parameters: 
 cutoff (float) – Frequency cutoff in sampling units
 t (float) – Temperature factor in sampling units


GetLimit()
Returns the current value of the filter cutoff frequency in sampling units.

SetLimit(cutoff)
Sets the value of the filter cutoff frequency to the specified value (in sampling units).
Parameters:  cutoff (float) – Frequency cutoff in sampling units 

GetT()
Returns the current value of the filter’s T factor (in sampling units).

SetT(t_factor)
Sets the value of the filter’s T factor to the specified value (in sampling units).
Parameters:  t_factor (float) – Frequency cutoff in sampling units 

class ButterworthLowPassFilter(passband=1.0, stopband=1.0)
This algorithm applies a Fourier Butterworth low pass filter to
the image. The filter passband and stopband frequencies need to be provided in sampling units (for example 8 Angstrom).
The default values of the Epsilon and Maximum Passband Gain parameters are set to 0.882 and 10.624 respectively.
Parameters: 
 passband (float) – Passband frequency in sampling units
 stopband (float) – Stopband frequency in sampling units


GetLimit()
Returns the current value of the filter passband frequency in sampling units.

SetLimit(passband)
Sets the value of the filter passband frequency to the specified value (in sampling units).
Parameters:  passband (float) – Frequency cutoff in sampling units 

GetStop()
Returns the current value of the filter’s stopband frequency (in sampling units).

SetStop(stopband)
Sets the value of the filter’s stopband frequency to the specified value (in sampling units).
Parameters:  stopband (float) – Frequency cutoff in sampling units 

GetEps()
Returns the current value of the filter’s Epsilon parameter.

SetEps(epsilon)
Sets the value of the filter’s epsilon parameter to the specified value.
Parameters:  eps (float) – Epsilon parameter 

GetA()
Returns the current value of the filter’s Maximum Passband Gain parameter.

SetA(gain)
Sets the value of the filter’s Maximum Passband Gain parameter to the specified value.
Parameters:  gain (float) – Maximum Passband Gain parameter 

class ButterworthHighPassFilter(passband=1.0, stopband=1.0)
This algorithm applies a Fourier Butterworth high pass filter
to the image. The filter passband and stopband frequencies need to be provided in sampling units (for example 8
Angstrom). The default values of the Epsilon and Maximum Passband Gain parameters are set to 0.882 and 10.624
respectively.
Parameters: 
 passband (float) – Passband frequency in sampling units
 stopband (float) – Stopband frequency in sampling units


GetLimit()
Returns the current value of the filter passband frequency in sampling units.

SetLimit(passband)
Sets the value of the filter passband frequency to the specified value (in sampling units).
Parameters:  passband (float) – Frequency cutoff in sampling units 

GetStop()
Returns the current value of the filter’s stopband frequency (in sampling units).

SetStop(stopband)
Sets the value of the filter’s stopband frequency to the specified value (in sampling units).
Parameters:  stopband (float) – Frequency cutoff in sampling units 

GetEps()
Returns the current value of the filter’s Epsilon parameter.

SetEps(epsilon)
Sets the value of the filter’s epsilon parameter to the specified value.
Parameters:  eps (float) – Epsilon parameter 

GetA()
Returns the current value of the filter’s Maximum Passband Gain parameter.

SetA(gain)
Sets the value of the filter’s Maximum Passband Gain parameter to the specified value.
Parameters:  gain (float) – Maximum Passband Gain parameter 

class GaussianFilter(sigma=1.0)
This algorithm applies a real space Gaussian filter to the image, as defined in the following publication:
I.T.Young, L.J. van Vliet,”Recursive implementation of the Gaussian filter”,Signal Processing, 44(1995), 139151
Parameters:  sigma (float) – Width of the Gaussian filter 

GetSigma()
Returns the current value of the filter’s width.

SetSigma(width)
Sets the value of the filter’s width to the specified value.
Parameters:  sigma (float) – Width of the Gaussian filter 

SetQ(q_param)
Sets the value of the filter’s Q parameter (see publication) to the specified value.
Parameters:  q_param (float) – Filter’s Q parameter 

class Histogram(bins, minimum, maximum)
This algorithm performs an histogram analysis of the image. The minimum and
maximum pixel values of the histogram representation must be provided when
the algorithm object is created, as well as the number of bins in the
histogram. Bins are equally spaced and minimum and maximum values for each
bin are automatically computed.
When the algorithm is applied to an image, the analysis is carried out. A
python ‘list’ object containing in sequence the pixel counts for all the bins
can the be recovered from the algorithm object.
Parameters: 
 bins (int) – Number of bins in the histogram
 minimum (float) – Minimum value in the histogram
 maximum – Maximum value in the histogram


GetBins()
Returns the bins of the histogram representation

GetBins()

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