Changes to cytounetΒΆ

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 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.