# dltk.io package¶

## dltk.io.augmentation module¶

dltk.io.augmentation.add_gaussian_noise(image, sigma=0.05)[source]

Add Gaussian noise to an image

Parameters: image (np.ndarray) – image to add noise to sigma (float) – stddev of the Gaussian distribution to generate noise from same as image but with added offset to each channel np.ndarray
dltk.io.augmentation.add_gaussian_offset(image, sigma=0.1)[source]

Add Gaussian offset to an image. Adds the offset to each channel independently.

Parameters: image (np.ndarray) – image to add noise to sigma (float) – stddev of the Gaussian distribution to generate noise from same as image but with added offset to each channel np.ndarray
dltk.io.augmentation.elastic_transform(image, alpha, sigma)[source]

Elastic deformation of images as described in [1].

[1] Simard, Steinkraus and Platt, “Best Practices for Convolutional
Neural Networks applied to Visual Document Analysis”, in Proc. of the International Conference on Document Analysis and Recognition, 2003.
Parameters: image (np.ndarray) – image to be deformed alpha (list) – scale of transformation for each dimension, where larger values have more deformation sigma (list) – Gaussian window of deformation for each dimension, where smaller values have more localised deformation deformed image np.ndarray
dltk.io.augmentation.extract_class_balanced_example_array(image, label, example_size=[1, 64, 64], n_examples=1, classes=2, class_weights=None)[source]
Extract training examples from an image (and corresponding label) subject
to class balancing. Returns an image example array and the corresponding label array.
Parameters: image (np.ndarray) – image to extract class-balanced patches from label (np.ndarray) – labels to use for balancing the classes example_size (list or tuple) – shape of the patches to extract n_examples (int) – number of patches to extract in total classes (int or list or tuple) – number of classes or list of classes to extract class-balanced patches extracted from full images with the shape [batch, example_size..., image_channels] np.ndarray, np.ndarray
dltk.io.augmentation.extract_random_example_array(image_list, example_size=[1, 64, 64], n_examples=1)[source]
Randomly extract training examples from image (and a corresponding label).
Returns an image example array and the corresponding label array.
Parameters: image_list (np.ndarray or list or tuple) – image(s) to extract random patches from example_size (list or tuple) – shape of the patches to extract n_examples (int) – number of patches to extract in total class-balanced patches extracted from full images with the shape [batch, example_size..., image_channels] np.ndarray, np.ndarray
dltk.io.augmentation.flip(imagelist, axis=1)[source]

Randomly flip spatial dimensions

Parameters: imagelist (np.ndarray or list or tuple) – image(s) to be flipped axis (int) – axis along which to flip the images same as imagelist but randomly flipped along axis np.ndarray or list or tuple

## dltk.io.preprocessing module¶

dltk.io.preprocessing.normalise_one_one(image)[source]

Image normalisation. Normalises image to fit [-1, 1] range.

dltk.io.preprocessing.normalise_zero_one(image)[source]

Image normalisation. Normalises image to fit [0, 1] range.

dltk.io.preprocessing.resize_image_with_crop_or_pad(image, img_size=(64, 64, 64), **kwargs)[source]
Image resizing. Resizes image by cropping or padding dimension
to fit specified size.
Parameters: image (np.ndarray) – image to be resized img_size (list or tuple) – new image size kwargs () – additional arguments to be passed to np.pad resized image np.ndarray
dltk.io.preprocessing.whitening(image)[source]

Whitening. Normalises image to zero mean and unit variance.

class dltk.io.abstract_reader.IteratorInitializerHook[source]

Bases: tensorflow.python.training.session_run_hook.SessionRunHook

Hook to initialise data iterator after Session is created.

after_create_session(session, coord)[source]

Initialise the iterator after the session has been created.

class dltk.io.abstract_reader.Reader(read_fn, dtypes)[source]

Bases: object

Wrapper for dataset generation given a read function

Constructs a Reader instance

Parameters: read_fn – Input function returning features which is a dictionary of string feature name to Tensor or SparseTensor. If it returns a tuple, first item is extracted as features. Prediction continues until input_fn raises an end-of-input exception (OutOfRangeError or StopIteration). dtypes – A nested structure of tf.DType objects corresponding to each component of an element yielded by generator.
get_inputs(file_references, mode, example_shapes=None, shuffle_cache_size=100, batch_size=4, params=None)[source]

Function to provide the input_fn for a tf.Estimator.

Parameters: file_references – An array like structure that holds the reference to the file to read. It can also be None if not needed. mode – A tf.estimator.ModeKeys. It is passed on to read_fn to trigger specific functions there. example_shapes (optional) – A nested structure of lists or tuples corresponding to the shape of each component of an element yielded by generator. shuffle_cache_size (int, optional) – An int determining the number of examples that are held in the shuffle queue. batch_size (int, optional) – An int specifying the number of examples returned in a batch. params (dict, optional) – A dict passed on to the read_fn. a handle to the input_fn to be passed the relevant tf estimator functions. tf.train.SessionRunHook: A hook to initialize the queue within the dataset. function
serving_input_receiver_fn(placeholder_shapes)[source]

Build the serving inputs.

Parameters: placeholder_shapes – A nested structure of lists or tuples corresponding to the shape of each component of the feature elements yieled by the read_fn. A function to be passed to the tf.estimator.Estimator instance when exporting a saved model with estimator.export_savedmodel. function