eda_plugin.analysers.keras.KerasAnalyser

class eda_plugin.analysers.keras.KerasAnalyser(*args: Any, **kwargs: Any)

ImageAnalyser that can use a neural network for analysis of the acquired images.

Add signals to be sent out with information about the output of the network. These will be used by the specific GUI to be displayed.

__init__(event_bus: EventBus): Load and connect the GUI. Initialise settings from the GUI.

Methods

`__init__`(event_bus)	Load and connect the GUI.
`connect_incoming_events`(event_bus)	Connect the events here, so subclasses can choose to not do so.
`connect_worker_signals`(worker)	Connect the additional worker signals.
`gather_images`(py_image)	Limit the gathering to only the channels in channel_choosers and rearrange
`new_mda_settings`(new_settings)	Skip settting the number of slices or channels from the MDA settings.
`new_settings`(new_settings)	Load and initialize model so first predict is fast(er).
`start_analysis`

Attributes

new_network_image

connect_incoming_events(event_bus: EventBus) → None: Connect the events here, so subclasses can choose to not do so.

connect_worker_signals(worker: qtpy.QtCore.QRunnable): Connect the additional worker signals.

gather_images(py_image: pymm_eventserver.data_structures.PyImage) → bool: Limit the gathering to only the channels in channel_choosers and rearrange

new_mda_settings(new_settings: pymm_eventserver.data_structures.MMSettings): Skip settting the number of slices or channels from the MDA settings.

new_settings(new_settings): Load and initialize model so first predict is fast(er).

start_analysis(evt: pymm_eventserver.data_structures.PyImage): Image arrived, see if all images were gathered and if so, start analysis.