utilities.segmentation_utils.flowreader module

FlowGenerator is a wrapper around the keras ImageDataGenerator class.

class FlowGenerator(image_path: str, mask_path: str, image_size: tuple[int, int], output_size: tuple[int, int], num_classes: int, shuffle: bool = True, batch_size: int = 2, preprocessing_enabled: bool = True, seed: int = 909, preprocessing_seed: int | None = None)

Bases: object

Initializes the flow generator object, which can be used to read in images for semantic segmentation. Additionally, the reader can apply augmentation on the images, and one-hot encode them on the fly.

Note: in case the output is a column vector it has to be in the shape (x, 1)

Parameters

string image:

path to the image directory

string mask:

path to the mask directory

int batch_size:

batch size

tuple image_size:

image size

tuple output_size:

output size

int num_classes:

number of classes

Keyword Arguments

bool shuffle:

whether to shuffle the dataset or not

int batch_size:

batch size

bool preprocessing_enabled:

whether to apply preprocessing or not

int seed:

seed for flow from directory

int preprocessing_seed:

seed for preprocessing, defaults to None

Raises

ValueError:

if the output size is not a tuple of length 2

ValueError:

if the output size is not a square matrix or a column vector

get_dataset_size() → int

Returns the length of the dataset

Returns

returns int:

length of the dataset

get_generator()

Returns the generator object

Returns

return ImageDataGenerator:

generator object

preprocess(generator_zip)

Preprocessor function encapsulates both the image, and mask generator objects. Augments the images and masks and onehot encodes the masks

Parameters

tuple generator_zip:

tuple of image and mask generator

int, optional state:

random state for reproducibility, defaults to None

Returns

return tuple(tf.Tensor,tf.Tensor):

generator batch of image and mask

preprocessing_queue_image = None

preprocessing_queue_mask = None

preprocessing_seed = None

set_preprocessing_pipeline(preprocessing_queue_image: PreprocessorInterface, preprocessing_queue_mask: PreprocessorInterface) → None

Sets the preprocessing pipeline

Parameters

PreprocessingQueue preprocessing_queue_image:

preprocessing queue for images

PreprocessingQueue preprocessing_queue_mask:

preprocessing queue for masks

class FlowGeneratorExperimental(image_path: str, mask_path: str, image_size: tuple[int, int], output_size: tuple[int, int], channel_mask: list[bool], num_classes: int, shuffle: bool = True, batch_size: int = 2, preprocessing_enabled: bool = True, seed: int = 909, preprocessing_seed: int | None = None, read_weights: bool = False, weights_path: str | None = None, preprocessing_queue_image=None, preprocessing_queue_mask=None, shuffle_counter=0)

Bases: Sequence

Initializes the flow generator object, which can be used to read in images for semantic segmentation. Additionally, the reader can apply augmentation on the images, and one-hot encode them on the fly.

Note: in case the output is a column vector it has to be in the shape (x, 1) Note: this is an experimental version of the flow generator, which uses a custom implemented dataloader instead of the keras ImageDataGenerator #TODO: Instead of using direct paths, and arguments, reading heads should be used #TODO: as it reduces the number of arguments, and makes the code more readable and reduces #TODO: cupling

Parameters

string image:

path to the image directory

string mask:

path to the mask directory

int batch_size:

tuple image_size:

specifies the size of the input image

tuple output_size:

specifies the size of the output mask

list[bool] channel_mask:

specifies which channels of the input image to use

int num_classes:

number of classes in the output mask

Keyword Arguments

bool, optional shuffle:

whether to shuffle the dataset or not, defaults to True

int batch_size:

specifies the number of images read in one batch, defaults to 2

bool preprocessing_enabled:

whether to apply preprocessing or not, defaults to True

int seed:

seed for flow from directory

int preprocessing_seed:

seed for preprocessing, defaults to None

bool read_weights:

whether to read the weights from the mask directory, defaults to False

Raises

ValueError:

if the names of the images and masks do not match

ValueError:

if the output size is not a tuple of length 2

ValueError:

if the output size is not a square matrix or a column vector

on_epoch_end() → None

Method called at the end of every epoch.

set_mini_batch_size(batch_size: int) → None

Function to set the appropriate minibatch size. Required to allign batch size in the reader with the model. Does not change the batch size of the reader.

Parameters

int batch_size:

the mini batch size

Raises

raises ValueError:

if the mini batch size is larger than the batch size

raises ValueError:

if the batch size is not divisible by the mini batch size

set_preprocessing_pipeline(preprocessing_queue_image: PreprocessorInterface, preprocessing_queue_mask: PreprocessorInterface) → None

Sets the preprocessing pipeline

Parameters

PreprocessingQueue preprocessing_queue_image:

preprocessing queue for images

PreprocessingQueue preprocessing_queue_mask:

preprocessing queue for masks