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Clean up LUV background detect calculation

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This commit is contained in:
Julian Stirling 2025-07-27 10:32:33 +01:00
parent 238d022342
commit 8e133a978d
1 changed files with 18 additions and 7 deletions
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25
src/openflexure_microscope_server/background_detect.py
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@ -83,6 +83,12 @@ class BackgroundDetectAlgorithm:
@background_data.setter @background_data.setter
def background_data(self, value: Optional[BaseModel | dict]) -> None: def background_data(self, value: Optional[BaseModel | dict]) -> None:
"""Set the statistics for the background image.
This should be None, of no data is available. It can be set from either
a dictionary or a base model of the type specified in
``self.background_data_model``.
"""
try: try:
if value is None: if value is None:
self._background_data = None self._background_data = None
@ -179,17 +185,22 @@ class ColourChannelDetectLUV(BackgroundDetectAlgorithm):
The image should be in LUV format, the output will be binary with the The image should be in LUV format, the output will be binary with the
same shape in the first two dimensions. same shape in the first two dimensions.
""" """
d = self.background_data if not self.background_data:
if not d:
raise MissingBackgroundDataError( raise MissingBackgroundDataError(
"Background is not set: you need to calibrate background detection." "Background is not set: you need to calibrate background detection."
) )
# Only use the U and V channels of as brightness (L) often changes as the
# height of the sample changes. # The ``[1:]`` is to only use the U and V channels of as brightness (L) often
# changes as the height of the sample changes.
# Wrapping in ``[[ ]]`` forces the colour channels to the numpy axis 2
# (3rd axis) so they are compared to the colour channel of each pixel.
means = np.array([[self.background_data.means[1:]]])
stds = np.array([[self.background_data.standard_deviations[1:]]])
# Compare each image in the pixel with the mean and standard deviation along
# axis to (the colour channels).
return np.all( return np.all(
np.abs(image[:, :, 1:] - np.array(d.means[1:])[np.newaxis, np.newaxis, :]) np.abs(image[:, :, 1:] - means) < stds * self.settings.channel_tolerance,
< np.array(d.standard_deviations[1:])[np.newaxis, np.newaxis, :]
* self.settings.channel_tolerance,
axis=2, axis=2,
) )