Bases: Subfunction_Base
A Subfunction class which which can be used for clipping intensity pixel
values on a certain range.
Typical use case is clipping Hounsfield Units (HU) in CT scans for focusing
on tissue types of interest.
Source code in aucmedi/data_processing/subfunctions/clip.py
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81 | class Clip(Subfunction_Base):
""" A Subfunction class which which can be used for clipping intensity pixel
values on a certain range.
Typical use case is clipping Hounsfield Units (HU) in CT scans for focusing
on tissue types of interest.
"""
#---------------------------------------------#
# Initialization #
#---------------------------------------------#
def __init__(self, min=None, max=None, per_channel=False):
""" Initialization function for creating a Clip Subfunction which can be passed to a
[DataGenerator][aucmedi.data_processing.data_generator.DataGenerator].
Args:
min (float or int or list): Desired minimum value for clipping (if `None`, no lower limit is applied).
Also possible to pass a list of minimum values if `per_channel=True`.
max (float or int or list): Desired maximum value for clipping (if `None`, no upper limit is applied).
Also possible to pass a list of maximum values if `per_channel=True`.
per_channel (bool): Option if clipping should be applied per channel with different clipping ranges.
"""
self.min = min
self.max = max
self.per_channel = per_channel
#---------------------------------------------#
# Transformation #
#---------------------------------------------#
def transform(self, image):
# Perform clipping on all channels
if not self.per_channel:
image_clipped = np.clip(image, a_min=self.min, a_max=self.max)
# Perform clipping on each channel
else:
image_clipped = image.copy()
for c in range(0, image.shape[-1]):
# Identify minimum to clip
if self.min is not None and \
type(self.min) in [list, tuple, np.ndarray]:
min = self.min[c]
else : min = self.min
# Identify maximum to clip
if self.max is not None and \
type(self.max) in [list, tuple, np.ndarray]:
max = self.max[c]
else : max = self.max
# Perform clipping
image_clipped[..., c] = np.clip(image[...,c],
a_min=min,
a_max=max)
# Return clipped image
return image_clipped
|
__init__(min=None, max=None, per_channel=False)
Initialization function for creating a Clip Subfunction which can be passed to a
DataGenerator.
Parameters:
| Name |
Type |
Description |
Default |
min |
float or int or list
|
Desired minimum value for clipping (if None, no lower limit is applied).
Also possible to pass a list of minimum values if per_channel=True. |
None
|
max |
float or int or list
|
Desired maximum value for clipping (if None, no upper limit is applied).
Also possible to pass a list of maximum values if per_channel=True. |
None
|
per_channel |
bool
|
Option if clipping should be applied per channel with different clipping ranges. |
False
|
Source code in aucmedi/data_processing/subfunctions/clip.py
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53 | def __init__(self, min=None, max=None, per_channel=False):
""" Initialization function for creating a Clip Subfunction which can be passed to a
[DataGenerator][aucmedi.data_processing.data_generator.DataGenerator].
Args:
min (float or int or list): Desired minimum value for clipping (if `None`, no lower limit is applied).
Also possible to pass a list of minimum values if `per_channel=True`.
max (float or int or list): Desired maximum value for clipping (if `None`, no upper limit is applied).
Also possible to pass a list of maximum values if `per_channel=True`.
per_channel (bool): Option if clipping should be applied per channel with different clipping ranges.
"""
self.min = min
self.max = max
self.per_channel = per_channel
|