def jaccard_index(source1 : Image, source2 : Image):
"""Determines the overlap of two binary images using the Jaccard index.
A value of 0 suggests no overlap, 1 means perfect overlap.
The resulting Jaccard index is saved to the results table in the
'Jaccard_Index' column.
Note that the Sorensen-Dice coefficient can be calculated from the Jaccard
index j using this formula:
<pre>s = f(j) = 2 j / (j + 1)</pre>
Parameters
----------
source1 : Image
source2 : Image
References
----------
.. [1] https://clij.github.io/clij2-docs/reference_jaccardIndex
"""
intersection = create_like(source1)
binary_and(source1, source2, intersection)
union = create_like(source1)
binary_or(source1, source2, union)
return sum_of_all_pixels(intersection) / sum_of_all_pixels(union)
def difference_of_gaussian(input: Image,
destination: Image = None,
sigma1_x: float = 2,
sigma1_y: float = 2,
sigma1_z: float = 2,
sigma2_x: float = 2,
sigma2_y: float = 2,
sigma2_z: float = 2) -> Image:
"""Applies Gaussian blur to the input image twice with different sigma
values resulting in two images which are then subtracted from each other.
It is recommended to apply this operation to images of type Float (32 bit)
as results might be negative.
Parameters
----------
input : Image
The input image to be processed.
destination : Image
The output image where results are written into.
sigma1_x : float
Sigma of the first Gaussian filter in x
sigma1_y : float
Sigma of the first Gaussian filter in y
sigma1_z : float
Sigma of the first Gaussian filter in z
sigma2_x : float
Sigma of the second Gaussian filter in x
sigma2_y : float
Sigma of the second Gaussian filter in y
sigma2_z : float
Sigma of the second Gaussian filter in z
Returns
-------
destination
Examples
--------
>>> import pyclesperanto_prototype as cle
>>> cle.difference_of_gaussian(input, destination, sigma1x, sigma1y, sigma1z, sigma2x, sigma2y, sigma2z)
References
----------
.. [1] https://clij.github.io/clij2-docs/reference_differenceOfGaussian3D
"""
temp1 = create_like(destination)
temp2 = create_like(destination)
gaussian_blur(input, temp1, sigma1_x, sigma1_y, sigma1_z)
gaussian_blur(input, temp2, sigma2_x, sigma2_y, sigma2_z)
return subtract_images(temp1, temp2, destination)
def squared_difference(source1: Image,
source2: Image,
destination: Image = None):
"""Determines the squared difference pixel by pixel between two images.
Parameters
----------
source1 : Image
source2 : Image
destination : Image
Returns
-------
destination
References
----------
.. [1] https://clij.github.io/clij2-docs/reference_squaredDifference
"""
temp = create_like(destination)
subtract_images(source1, source2, temp)
return power(temp, destination, 2)
def subtract_gaussian_background(input: Image,
destination: Image = None,
sigma_x: float = 2,
sigma_y: float = 2,
sigma_z: float = 2) -> Image:
"""Applies Gaussian blur to the input image and subtracts the result from the original.
Parameters
----------
input : Image
destination : Image
sigmaX : Number
sigmaY : Number
sigmaZ : Number
Returns
-------
destination
References
----------
..[1] https://clij.github.io/clij2-docs/reference_subtractGaussianBackground
"""
temp1 = create_like(destination)
gaussian_blur(input, temp1, sigma_x, sigma_y, sigma_z)
return subtract_images(input, temp1, destination)
def absolute_difference(source1 : Image, source2 : Image, destination : Image = None):
"""Determines the absolute difference pixel by pixel between two images.
<pre>f(x, y) = |x - y| </pre>
Parameters
----------
source1 : Image
The input image to be subtracted from.
source2 : Image
The input image which is subtracted.
destination : Image
The output image where results are written into.
Returns
-------
destination
References
----------
.. [1] https://clij.github.io/clij2-docs/reference_absoluteDifference
"""
temp = create_like(destination)
subtract_images(source1, source2, temp)
return absolute(temp, destination)
def squared_difference(source1 : Image, source2 : Image, destination : Image = None) -> Image:
"""Determines the squared difference pixel by pixel between two images.
Parameters
----------
source1 : Image
source2 : Image
destination : Image
Returns
-------
destination
Examples
--------
>>> import pyclesperanto_prototype as cle
>>> cle.squared_difference(source1, source2, destination)
References
----------
.. [1] https://clij.github.io/clij2-docs/reference_squaredDifference
"""
temp = create_like(destination)
subtract_images(source1, source2, temp)
return power(temp, destination, 2)
