openflexure_microscope_server.things.background_detect.ColourChannelDetectLUV

class documentation

class ColourChannelDetectLUV(BackgroundDetectAlgorithm): (source)

Constructor: ColourChannelDetectLUV(thing_server_interface)

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Compare images with a known background in LUV colourspace.

This uses an LUV colour space checking only the mean and standard deviation of the U and V channels. The LUV colourspace as it collect colours together in a human- intuitive way.

Method background_mask Calculate a binary image, showing whether each pixel is background.
Method get_sample_coverage Return the percentage of the input image that is background.
Method image_is_sample Label the current image as either background or sample.
Method set_background Use the input image to update the background distributions.
Class Variable channel_tolerance Channel Tolerance
Class Variable display_name Undocumented
Class Variable min_sample_coverage Sample Coverage Required (%)
Class Variable min_stds Undocumented
Instance Variable background_means The mean of each channel in the colourspace.
Instance Variable background_stds The standard deviation of each channel in the colourspace.
Property ready Whether the background detector is ready.
Property settings_ui A list of PropertyControl objects to create the settings in the UI.

Inherited from BackgroundDetectAlgorithm:

Method __init__ Initialise and create the lock.
def background_mask(self, image: np.ndarray) -> np.ndarray: (source)

Calculate a binary image, showing whether each pixel is background.

True is background.

The image should be in LUV format, the output will be binary with the same shape in the first two dimensions.

def get_sample_coverage(self, image: np.ndarray) -> float: (source)

Return the percentage of the input image that is background.

Evaluate whether it is foreground or background by comparing it to the saved statistics for a background image on a per-pixel basis

Returns
floatA value (between 0 and 100) is the percentage of the image that is sample.
def image_is_sample(self, image: np.ndarray) -> tuple[bool, str]: (source)
overrides openflexure_microscope_server.things.background_detect.BackgroundDetectAlgorithm.image_is_sample

Label the current image as either background or sample.

Returns
tuple[bool, str]A tuple of the result (boolean), and explanation string. The explanation string is formatted so it can be added into a sentence such as An action was taken because the image is {message}.
def set_background(self, image: np.ndarray): (source)
overrides openflexure_microscope_server.things.background_detect.BackgroundDetectAlgorithm.set_background

Use the input image to update the background distributions.

channel_tolerance: float = (source)

Channel Tolerance

The number of standard deviations a pixel value must be from the background mean to be considered sample.

display_name: str = (source)
overrides openflexure_microscope_server.things.background_detect.BackgroundDetectAlgorithm.display_name

Undocumented

min_sample_coverage: float = (source)

Sample Coverage Required (%)

The minimum percentage of the image that needs to be identified as sample for the image to be labeled as containing sample.

min_stds: list[float] = (source)

Undocumented

background_means: list[float] | None = (source)

The mean of each channel in the colourspace.

background_stds: list[float] | None = (source)

The standard deviation of each channel in the colourspace.

@lt.property
ready: bool = (source)
overrides openflexure_microscope_server.things.background_detect.BackgroundDetectAlgorithm.ready

Whether the background detector is ready.

@lt.property
settings_ui: list[PropertyControl] = (source)
overrides openflexure_microscope_server.things.background_detect.BackgroundDetectAlgorithm.settings_ui

A list of PropertyControl objects to create the settings in the UI.