unetseg package
Subpackages
Submodules
unetseg.evaluate module
- unetseg.evaluate.plot_data_generator(num_samples: int = 3, fig_size=(20, 10), *, train_config: unetseg.train.TrainConfig, img_ch: int = 3)[source]
Plots some samples from a data generator.
- Parameters
num_samples (int) – Number of samples to plot.
fig_size (tuple) – Figure size.
img_ch (int) – Number of channels.
train_config (TrainConfig) – Training configuration object.
- unetseg.evaluate.plot_data_results(num_samples: int = 3, fig_size=(20, 10), *, predict_config: unetseg.predict.PredictConfig, img_ch: int = 3, n_bands: int = 3)[source]
Plots some samples from the results directory. :param num_samples: Number of samples to plot. :type num_samples: int :param fig_size: Figure size. :type fig_size: tuple :param img_ch: Number of channels. :type img_ch: int :param predict_config: Prediction onfiguration object. :type predict_config: PredictConfig
unetseg.postprocess module
- unetseg.postprocess.crop_image(img: numpy.ndarray, margin_ratio: float) numpy.ndarray [source]
Center crop an image, with a margin of
margin_ratio
unetseg.predict module
- class unetseg.predict.PredictConfig(images_path='', results_path='', batch_size=32, model_architecture='unet', model_path='unet.h5', height=320, width=320, n_channels=3, n_classes=1, class_weights=0)[source]
Bases:
object
- unetseg.predict.predict(cfg: unetseg.predict.PredictConfig)[source]
Performs inference based on a configuration object
- Parameters
cfg (PredictConfig) – Configuration object
unetseg.train module
- class unetseg.train.TrainConfig(images_path, masks_path=None, width=200, height=200, n_channels=3, n_classes=1, apply_image_augmentation=True, model_path='unet.h5', model_architecture='unet', validation_split=0.1, test_split=0.1, epochs=15, steps_per_epoch=2000, early_stopping_patience=3, batch_size=32, seed=None, evaluate=True, class_weights=0)[source]
Bases:
object
- unetseg.train.build_data_generator(image_files: List[str], *, config: unetseg.train.TrainConfig, mask_dir: str)[source]
Build data generator based on a list of images and directory of binary masks.
- Parameters
image_files (List[str]) – List of paths to images.
config (TrainConfig) – Configuration object.
mask_dir (str) – Path to directory with binary masks.
- Yields
tuple – Tuple of image and mask batch.
- unetseg.train.build_model_unet(cfg: unetseg.train.TrainConfig) keras.engine.training.Model [source]
Build U-Net model class.
- Parameters
cfg (TrainConfig) – Configuration for training.
- Returns
U-Net model class.
- Return type
Model
- unetseg.train.build_model_unetplusplus(cfg: unetseg.train.TrainConfig) keras.engine.training.Model [source]
Builds a U-Net++ model.
- Parameters
cfg (TrainConfig) – Training configuration.
- Returns
The U-Net++ model.
- Return type
Model
- unetseg.train.get_mask_raster(image_path: str, n_channels: Optional[int] = None, *, mask_dir: str) numpy.ndarray [source]
Get respective mask raster from image path.
- unetseg.train.get_raster(image_path: str, n_channels: Optional[int] = None) numpy.ndarray [source]
Loads a raster image from a file.
- unetseg.train.preprocess_input(image: numpy.ndarray, mask: numpy.ndarray, *, config: unetseg.train.TrainConfig) Tuple[numpy.ndarray, numpy.ndarray] [source]
Preprocess input image and masks.
- Parameters
image (np.ndarray) – Input image.
mask (np.ndarray) – Input mask.
config (TrainConfig) – Training configuration.
- Returns
Preprocessed image and mask.
- Return type
Tuple[np.ndarray, np.ndarray]
- unetseg.train.train(cfg: unetseg.train.TrainConfig)[source]
Performs training and evaluation of the model based on a configuration object.
- Parameters
cfg (TrainConfig) – Configuration object containing all the necessary parameters for training.
unetseg.utils module
- unetseg.utils.grouper(iterable, n, fillvalue=None)[source]
Collect data into fixed-length chunks or blocks