gingerbread.analytics package#
Submodules#
gingerbread.analytics.analytics_module module#
- class gingerbread.analytics.analytics_module.Analytics#
Bases:
object- concat_results(results: List[Dict[str, float]]) Dict[str, float] | None#
Concatenates the results of the analytics :param results: List of results :type results: List[Dict[str, float]]
- Returns:
Concatenated results
- Return type:
Dict[str, float]
- convert_class_to_binary(postproc: ndarray, convert_class: int = 1) ndarray | None#
Converts a multiclass postprocessed image to a binary postprocessed image :param postproc: Multiclass postprocessed image :type postproc: np.ndarray
- Returns:
Binary postprocessed image
- Return type:
np.ndarray
- get_units(resolution: ndarray)#
Returns the units of the resolution :param resolution: Resolution of the postprocessed image :type resolution: np.ndarray
- Returns:
Units of the resolution
- Return type:
str
- lesion_count(postproc: ndarray) Dict[str, int] | None#
Returns the number of lesions in the postprocessed image :param postproc: Postprocessed image :type postproc: np.ndarray
- Returns:
Number of lesions
- Return type:
Dict[str, float]
- lesion_volume_ml(postproc: ndarray, resolution: ndarray) Dict[str, List[float]] | None#
Returns the volume of each lesion in the postprocessed image :param postproc: Postprocessed image :type postproc: np.ndarray :param resolution: Resolution of the postprocessed image :type resolution: np.ndarray
- Returns:
Volume of each lesion
- Return type:
Dict[str, float]
- multiclass_tester(postproc: ndarray)#
Tests if the postprocessed image is multiclass :param postproc: Postprocessed image :type postproc: np.ndarray
- Returns:
True if multiclass, False if not
- Return type:
Tuple[bool, List[int]] | Tuple[None, None]
- run_classification_analytics(postproc: ndarray, resolution: ndarray) Dict[str, str | int | List[float]] | Dict[str, str | Dict[int, List[float]]] | None#
Runs all the classification analytics :param postproc: Postprocessed image :type postproc: np.ndarray :param resolution: Resolution of the postprocessed image :type resolution: np.ndarray
- Returns:
Dictionary of all the results
- Return type:
Dict[str, Union[str, int, List[float]]] | Dict[str, Union[str, Dict[int, List[float]]]]
- run_general_analytics(postproc: ndarray, resolution: ndarray) Dict[str, str | int | List[float]] | Dict[str, str | Dict[int, List[float]]] | None#
Runs all the general analytics :param postproc: Postprocessed image :type postproc: np.ndarray :param resolution: Resolution of the postprocessed image :type resolution: np.ndarray
- Returns:
Dictionary of all the results
- Return type:
Dict[str, Union[str, int, List[float]]] | Dict[str, Union[str, Dict[int, List[float]]]]
- segmentation_analysis(postproc: ndarray, resolution: ndarray | None) Dict[str, str | int | List[float]] | Dict[str, str | Dict[int, List[float]]] | None#
Runs the segmentation analytics :param postproc: Postprocessed image :type postproc: np.ndarray :param resolution: Resolution of the postprocessed image :type resolution: np.ndarray
- Returns:
Segmentation analytics
- Return type:
Dict[str, Union[str, int, List[float]]] | Dict[str, Union[str, Dict[int, List[float]]]] | None
- total_lesion_ml(postproc: ndarray, resolution: ndarray) Dict[str, float] | None#
Returns the total volume of lesions in the postprocessed image :param postproc: Postprocessed image :type postproc: np.ndarray :param resolution: Resolution of the postprocessed image :type resolution: np.ndarray
- Returns:
Total volume of lesions
- Return type:
Dict[str, float]