pysilcam.silcam_classify module

pysilcam.silcam_classify.check_model(model_path)

Raises errors if classification model is not found, or if it is not a valid file.

Parameters

model_path (str) – path to particle-classifier e.g. ‘/mnt/ARRAY/classifier/model/particle_classifier.h5’ usually obtained from settings.NNClassify.model_path

pysilcam.silcam_classify.get_class_labels(model_path)

Read the header file that defines the catagories of particles in the model

Parameters

model_path (str) – path to particle-classifier e.g. ‘/testdata/model_name/particle_classifier.h5’ usually obtained from settings.NNClassify.model_path

Returns

labelled catagories which can be predicted

Return type

class_labels (str)

pysilcam.silcam_classify.load_model(model_path)

Load the trained tensorflow keras model

Parameters

model_path (str) – path to particle-classifier e.g. ‘/testdata/model_name/particle_classifier.h5’

Returns

loaded tf.keras model from load_model()

Return type

model (tf model object)

pysilcam.silcam_classify.predict(img, model)

Use tensorflow model to classify particles

Parameters

• img (uint8) – a particle ROI, corrected and treated with the silcam explode_contrast function

• model (tf model object) – loaded tfl model from load_model()

Returns

the probability of the roi belonging to each class

Return type

prediction (array)