topostats.unet_masking#

Segment grains using a U-Net model.

Attributes#

LOGGER

Functions#

dice_loss(→ tensorflow.Tensor)

DICE loss function.

iou_loss(→ tensorflow.Tensor)

Intersection over Union loss function.

mean_iou(y_true, y_pred)

Mean Intersection Over Union metric, ignoring the background class.

predict_unet(→ numpy.typing.NDArray[numpy.bool_])

Predict cats segmentation from a flattened image.

make_bounding_box_square(→ tuple[int, int, int, int])

Make a bounding box square.

pad_bounding_box(→ tuple[int, int, int, int])

Pad a bounding box.

Module Contents#

topostats.unet_masking.LOGGER#
topostats.unet_masking.dice_loss(y_true: numpy.typing.NDArray[numpy.float32], y_pred: numpy.typing.NDArray[numpy.float32], smooth: float = 1e-05) → tensorflow.Tensor[source]#

DICE loss function.

Expects y_true and y_pred to be of shape (batch_size, height, width, 1).

Parameters:

  • y_true (npt.NDArray[np.float32]) – True values.

  • y_pred (npt.NDArray[np.float32]) – Predicted values.

  • smooth (float) – Smoothing factor to prevent division by zero.

Returns:

The DICE loss.

Return type:

tf.Tensor

topostats.unet_masking.iou_loss(y_true: numpy.typing.NDArray[numpy.float32], y_pred: numpy.typing.NDArray[numpy.float32], smooth: float = 1e-05) → tensorflow.Tensor[source]#

Intersection over Union loss function.

Expects y_true and y_pred to be of shape (batch_size, height, width, 1).

Parameters:

  • y_true (npt.NDArray[np.float32]) – True values.

  • y_pred (npt.NDArray[np.float32]) – Predicted values.

  • smooth (float) – Smoothing factor to prevent division by zero.

Returns:

The IoU loss.

Return type:

tf.Tensor

topostats.unet_masking.mean_iou(y_true: numpy.typing.NDArray[numpy.float32], y_pred: numpy.typing.NDArray[numpy.float32])[source]#

Mean Intersection Over Union metric, ignoring the background class.

Parameters:

  • y_true (npt.NDArray[np.float32]) – True values.

  • y_pred (npt.NDArray[np.float32]) – Predicted values.

Returns:

The mean IoU.

Return type:

tf.Tensor

topostats.unet_masking.predict_unet(image: numpy.typing.NDArray[numpy.float32], model: keras.Model, confidence: float, model_input_shape: tuple[int | None, int, int, int], upper_norm_bound: float, lower_norm_bound: float) → numpy.typing.NDArray[numpy.bool_][source]#

Predict cats segmentation from a flattened image.

Parameters:

  • image (npt.NDArray[np.float32]) – The image to predict the mask for.

  • model (keras.Model) – The U-Net model.

  • confidence (float) – The confidence threshold for the mask.

  • model_input_shape (tuple[int | None, int, int, int]) – The shape of the model input, including the batch and channel dimensions.

  • upper_norm_bound (float) – The upper bound for normalising the image.

  • lower_norm_bound (float) – The lower bound for normalising the image.

Returns:

The predicted mask.

Return type:

npt.NDArray[np.bool_]

topostats.unet_masking.make_bounding_box_square(crop_min_row: int, crop_min_col: int, crop_max_row: int, crop_max_col: int, image_shape: tuple[int, int]) → tuple[int, int, int, int][source]#

Make a bounding box square.

Parameters:

  • crop_min_row (int) – The minimum row index of the crop.

  • crop_min_col (int) – The minimum column index of the crop.

  • crop_max_row (int) – The maximum row index of the crop.

  • crop_max_col (int) – The maximum column index of the crop.

  • image_shape (tuple[int, int]) – The shape of the image.

Returns:

The new crop indices.

Return type:

tuple[int, int, int, int]

topostats.unet_masking.pad_bounding_box(crop_min_row: int, crop_min_col: int, crop_max_row: int, crop_max_col: int, image_shape: tuple[int, int], padding: int) → tuple[int, int, int, int][source]#

Pad a bounding box.

Parameters:

  • crop_min_row (int) – The minimum row index of the crop.

  • crop_min_col (int) – The minimum column index of the crop.

  • crop_max_row (int) – The maximum row index of the crop.

  • crop_max_col (int) – The maximum column index of the crop.

  • image_shape (tuple[int, int]) – The shape of the image.

  • padding (int) – The padding to apply to the bounding box.

Returns:

The new crop indices.

Return type:

tuple[int, int, int, int]