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``

----


* Release 0.1.0

----


* 
  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.   

----


* Initiated ability to install with ``pip`` and ``setup.py``.

----


* ``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. 

----


* 
  Fixed issues with information loss following saving of predictions. 

* 
  ``geneTrainNpY`` was refactored and renamed ``LoadAugmented``

* 
  Added ``thresholdImages`` to threshold masks(mostly). Please see `pyautocv <https://github.com/Nelson-Gon/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. 

----


* 
  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.