cytounet 0.2.2

[Nelson-Gon]

Changes to cytounet

Version 0.2.2

• Fixed a bug in setting test paths when using script mode.

• Versioning is now automated, as is linking to the GitHub release. Please ensure you release in the
  form v#versionnumberhere.

• Fixed issues with script mode

• Using tensorflow.Keras instead of Keras.

• Added sanity checks to ensure paths actually exist.

Version 0.2.1

• Extended script to handle fine-tuning and from scratch-training

• Added a script only mode.

• Added support for docs.

• Added original a549 sample data, notebook, and pre-trained weights.

• Added experimental results to the README.

• Fixed issues with original images being overwritten. It is now possible to return a copy of non
  overwritten images.

• Made draw_contours more flexible. Specifically, it is now possible to turn off text display as
  this makes the image crowded.

• Added find_contours and draw_contours, useful methods for area determination.

• Added read_image_spec for use only for post modeling processing. This fixes issues with incorrect
  shapes when using read_images

Version 0.2.0

• Kernel regularization can now be turned off via a boolean argument(use_regularizer)

• Added a new data set from BBBC.

• finetune is a new function dedicated to the finetuning workflow.

• Regularization is now supported. It is currently limited to L1 and L2.

• pretrained_weights was dropped as an argument to unet. Use a callback instead. A future
  version wil include a fine tuning function.

• save_as was removed from train. Use ModelCheckpoint instead and provide it as a callback.

• show_images now shows titles. These functions will be removed later and imported from pyautocv
  instead.

• Fixed issues with reading mixed jpg and png images.

• Added reshape_images and resize_images. These are helper functions that may be useful when plotting
  or restoring original image size.

• show_images and read_images are now imported from pyautocv >= 0.2.2

• Fixed issues with inconsistent image order in show_images when reading from a directory.

• Added filename printing to data generators to make it easier to show what order the files are
  being read in. This can be disabled by setting show_names to False.

• Changes to prediction generation were made. We now use ImageDataGenerator for
  test time data generation.

• Fixed a bug related to load_augmentations that led to image flipping.

• Changed outputs to sigmoid instead of ReLU

• Updated to latest API ie predict vs predict_generator

• Added train to simplify model fitting.

• Added predict to reduce code repetition and make predicting easier.

• unet was rewritten to increase complexity and solve issues with blank predictions. It now also uses Conv2DTranspose instead of UpSampling2D.

• Initial support for a simpler model to optimise the bias-variance trade off for small(er) datasets.

• Removed Dropout since this is known to have no improvement over Batch Normalisation.

• Initial support for SGD as the default optimiser

• Moved from camelCase to snake_case, now using more descriptive function names.

• Fixed issues with list input to show_images

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• Release 0.1.0

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• Renamed repository to cytounet to reflect the heavy focus on biological images.

• Initiated support for validation via validGenerator.

• Fixed issues with show_images failing to load numpy ndarray images.

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• Initiated ability to install with pip and setup.py.

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• show_augmented was renamed to show_images and refactored as a more general method not limited

to just augmented images. A cmap argument was also added for more flexibility. This replaces labelVisualize
which has now been dropped.

• Introduced a separate save method for images and predictions. Use saveImages and savePredictions
  respectively.

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• Fixed issues with information loss following saving of predictions.

• geneTrainNpY was refactored and renamed LoadAugmented

• Added thresholdImages to threshold masks(mostly). Please see pyautocv
  for a more general and flexible way to manipulate images.

• Added saveImages, a helper to save images as(by default) .tif. This is because biological
  images are normally tiff in nature.

• Removed savePredictions. Use saveImages instead.

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• Updated module documentation

• adjustData was removed since it had known issues. It may be restored in the future.

• Fixed issues that resulted in blank predictions

• Added show_augmented to show results of data augmentation

• Added BatchNormarmalisation steps

• Training made more flexible by allowing usage of different metrics and loss functions without editing source code(i.e change on the fly)

• Saving and image reading functions made more flexible to read/save any image file format.

• Made most functions compatible with Keras >= 2.0

• Added dice loss and dice coefficient.

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This discussion was created from the release cytounet 0.2.2.