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cpu_toolbox
background_mask(image, threshold=10)
: Creates a background mask by setting all image pixels with low scattering
signals to zero. As all background pixels are near zero for all images in
the SLI image stack, this method should remove most of the background
allowing for better approximations using the available features.
It is advised to use this function.
Parameters
----------
image: Complete SLI measurement image stack as a 2D/3D Numpy array
threshold: Threshhold for mask creation (default: 10)
Returns
-------
numpy.array: 1D/2D-image which masks the background as True and foreground
as False
centroid_correction(image, peak_image, low_prominence=0.08, high_prominence=None)
: Correct peak positions from a line profile by looking at only the peak with
a given threshold using a centroid calculation. If a minimum is found in
the considered interval, this minimum will be used as the limit instead.
The range for the peak correction is limited by
MAX_DISTANCE_FOR_CENTROID_ESTIMATION.
Parameters
----------
image: Original line profile used to detect all peaks. This array will be
further analyzed to better determine the peak positions.
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack.
low_prominence: Lower prominence bound for detecting a peak.
high_prominence: Higher prominence bound for detecting a peak.
Returns
-------
NumPy array with the positions of all detected peak positions corrected
with the centroid calculation.
direction(peak_image, centroids, number_of_directions=3)
: Calculate up to number_of_directions direction angles based on the given
peak positions.
If more than number_of_directions*2 peaks are present, no
direction angle will be calculated to avoid errors. This will result in a
direction angle of BACKGROUND_COLOR. The peak positions are determined by
the position of the corresponding peak pairs (i.e. 6 peaks: 1+4, 2+5, 3+6).
If two peaks are too far away or too near (outside of 180°±35°),
the direction angle will be considered as invalid, resulting in a
direction angle of BACKGROUND_COLOR.
Parameters
----------
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack.
centroids: Centroids resulting from `centroid_correction` for
more accurate results.
number_of_directions: Number of directions which shall be generated.
Returns
-------
NumPy array with the shape (x, y, `number_of_directions`) containing up to
`number_of_directions` direction angles.
x equals the number of pixels of the SLI image series. If a direction angle
is invalid or missing, the array entry will be BACKGROUND_COLOR instead.
mean_peak_distance(peak_image, centroids)
: Calculate the mean peak distance in degrees between two corresponding peaks
for each line profile in an SLI image series.
Parameters
----------
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack.
centroids: Use centroid calculation to better determine the peak position
regardless of the number of measurements / illumination angles used.
Returns
-------
NumPy array of floating point values containing the mean peak distance of
the line profiles in degrees.
mean_peak_prominence(image, peak_image=None, kind_of_normalization=0)
: Calculate the mean peak prominence of all given peak positions within a
line profile. The line profile will be normalized by dividing the line
profile through its mean value. Therefore, values above 1 are possible.
Parameters
----------
image: Original line profile used to detect all peaks. This array will be
further analyzed to better determine the peak positions.
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack.
kind_of_normalization: Normalize given line profile by using a
normalization technique based on the kind_of_normalization parameter.
0 : Scale line profile to be between 0 and 1
1 : Divide line profile through its mean value
Returns
-------
Floating point value containing the mean peak prominence of the line
profile in degrees.
mean_peak_width(image, peak_image=None, target_height=0.5)
: Calculate the mean peak width of all given peak positions within a line
profile.
Parameters
----------
image: Original line profile used to detect all peaks. This array will be
further analyzed to better determine the peak positions.
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack.
target_height: Relative peak height in relation to the prominence of
the peak.
Returns
-------
NumPy array where each entry corresponds to the mean peak width of the
line profile. The values are in degree.
normalize(image, kind_of_normalization=0)
: Normalize given line profile by using a normalization technique based on
the kind_of_normalization parameter.
0 : Scale line profile to be between 0 and 1
1 : Divide line profile through its mean value
Arguments:
image: Full SLI measurement (series of images) which is
prepared for the pipeline using the SLIX toolbox methods.
kind_of_normalization: Normalization technique which will be used for
the calculation
Returns:
numpy.array -- Image where each pixel is normalized by the last axis
of the image
num_peaks(image=None, peak_image=None)
: Calculate the number of peaks from each line profile in an SLI image series
by detecting all peaks and applying thresholds to remove unwanted peaks.
Parameters
----------
image: Full SLI measurement (series of images) which is prepared for the
pipeline using the SLIX toolbox methods.
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack
Returns
-------
Array where each entry corresponds to the number of detected peaks within
the first dimension of the SLI image series.
peak_distance(peak_image, centroids)
: Calculate the mean peak distance in degrees between two corresponding
peaks for each line profile in an SLI image series.
Parameters
----------
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack.
centroids: Use centroid calculation to better determine the peak position
regardless of the number of measurements / illumination angles used.
Returns
-------
NumPy array of floating point values containing the peak distance of the
line profiles in degrees in their respective peak position. The first peak
of each peak pair will show the distance between peak_1 and peak_2 while
the second peak will show 360 - (peak_2 - peak_1).
peak_prominence(image, peak_image=None, kind_of_normalization=0)
: Calculate the peak prominence of all given peak positions within a line
profile. The line profile will be normalized by dividing the line profile
through its mean value. Therefore, values above 1 are possible.
Parameters
----------
image: Original line profile used to detect all peaks. This array will be
further analyzed to better determine the peak positions.
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack.
kind_of_normalization: Normalize given line profile by using a
normalization technique based on the kind_of_normalization parameter.
0 : Scale line profile to be between 0 and 1
1 : Divide line profile through its mean value
Returns
-------
Floating point value containing the mean peak prominence of the line
profile in degrees.
peak_width(image, peak_image=None, target_height=0.5)
: Calculate the peak width of all given peak positions within a line profile.
Parameters
----------
image: Original line profile used to detect all peaks. This array will be
further analyzed to better determine the peak positions.
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack.
target_height: Relative peak height in relation to the prominence of
the peak.
Returns
-------
NumPy array where each entry corresponds to the peak width of the line
profile. The values are in degree.
peaks(image)
: Detect all peaks from a full SLI measurement. Peaks will not be filtered
in any way. To detect only significant peaks, filter the peaks by using
the prominence as a threshold.
Parameters
----------
image: Complete SLI measurement image stack as a 2D/3D Numpy array
Returns
-------
2D/3D boolean image containing masking the peaks with `True`
unit_vectors(direction)
: Calculate the unit vectors (UnitX, UnitY) from a given direction angle.
Parameters
----------
direction: 3D NumPy array - direction angles in degrees
Returns
-------
UnitX, UnitY: 3D NumPy array, 3D NumPy array
x- and y-vector component in arrays