aigarmic.file_handlers

Functions to facilitate working with image data files

Functions

convert_cv2_to_keras(→ numpy.ndarray)	Convert a cv2 image to a keras image
create_dataset_from_directory(...)	Create a training and validation dataset from a directory containing subdirectories for each class of image data.
predict_colony_images_from_directory(→ list[dict])	Predict the class of images in a directory using a trained model, and compare prediction to
save_training_log(→ None)	Save training log to CSV file
get_concentration_from_path(→ float)	get concentration from plate image path, e.g.
get_paths_from_directory(→ dict[str, list[str]])	Returns a dict of abx_names: [image1_path, image2_path, etc.]

Module Contents

aigarmic.file_handlers.convert_cv2_to_keras(image, size_x=160, size_y=160) → numpy.ndarray

Convert a cv2 image to a keras image

Parameters:

• image – Image loaded using cv2.imread

• size_x – Width to resize image to (pixels)

• size_y – Height to resize image to (pixels)

Returns:

Image as a numpy array

aigarmic.file_handlers.create_dataset_from_directory(directory: str, label_mode: str, image_width: int, image_height: int, seed: int = 12345, val_split: float = 0.2, batch_size: int = 32) → tuple[tensorflow.data.Dataset, tensorflow.data.Dataset]

Create a training and validation dataset from a directory containing subdirectories for each class of image data.

Parameters:

• directory – path containing images, each in subdirectory corresponding to class

• label_mode – Labelling depending on model type (“binary” for binary or “int” for softmax)

• image_width – Image width in pixels

• image_height – Image height in pixels

• seed – Random seed for dataset splitting

• val_split – Proportion of data to use for validation

• batch_size – Batch size for datasets

Returns:

Tuple containing training and validation datasets

aigarmic.file_handlers.predict_colony_images_from_directory(directory: str | pathlib.Path | None, model: tensorflow.keras.models.Model, class_names: list[str], image_width: int, image_height: int, model_type: str, save_path: str | pathlib.Path | None = None, binary_threshold: float = 0.5) → list[dict]

Predict the class of images in a directory using a trained model, and compare prediction to true class (based on subdirectory in which image is located, which should correspond to class name).

Parameters:

• directory – Directory containing images to predict

• model – Model to use for prediction

• class_names – List of class names

• image_width – Image width in pixels

• image_height – Image height in pixels

• model_type – “binary” or “softmax”

• save_path – Path to save prediction log

• binary_threshold – For binary models, threshold for classifying as positive

Returns:

List of dictionaries containing image, path, prediction, predicted class, and true class (for each image)

aigarmic.file_handlers.save_training_log(model_history: keras.callbacks.History, save_path: str | pathlib.Path) → None

Save training log to CSV file

Parameters:

• model_history – Training history object

• save_path – Directory to save training log

aigarmic.file_handlers.get_concentration_from_path(path: str | pathlib.Path) → float

get concentration from plate image path, e.g. antibiotic1/0.125.jpg -> 0.125

Parameters:

path – Path to plate image

Returns:

Concentration

aigarmic.file_handlers.get_paths_from_directory(path: str | pathlib.Path) → dict[str, list[str]]

Returns a dict of abx_names: [image1_path, image2_path, etc.] If there are no antibiotic subdirectories, “unnamed” is used for abx_names (length = 1)

Parameters:

path – Path to directory containing antibiotic subdirectories

Returns:

dict of abx_names: [image1_path, image2_path, etc.]