leaf.inference
==============

.. py:module:: leaf.inference




Module Contents
---------------

.. py:class:: Predictor(config_path: str = 'config', config_name: str = 'canopy_portrait', symptoms_det_params: Union[dict, None] = None, symptoms_seg_params: Union[dict, None] = None, organs_params: Union[dict, None] = None, focus_params: Union[dict, None] = None, module_params: Union[dict, None] = None)

   This class is used to unify the complete pipeline into a single object with a simple predict method.
   It provides capability to use a yaml file configuration for repeatability. Furthermore, it allows for
   passing all the arguments for cofigring the individual building blocks of the pipeline. If a new block

   Constructor of the predictor object. For possible parameters to configure see either the individual
   models directly or see the configuration yaml files in the config folder.

   :param config_path: relative path from the location of this file to a config directory.
                       Defaults to "config".
   :type config_path: str, optional
   :param config_name: name of a config within the config_path directory.
                       New configurations can be added. Defaults to "canopy_portrait".
   :type config_name: str, optional
   :param symptoms_det_params: An optional dictionary which directly passes
                               the contents as **kwargs to symptoms detection model. It overrides the parameters from the
                               configuration file. Defaults to None.
   :type symptoms_det_params: Union[dict, None], optional
   :param symptoms_seg_params: An optional dictionary which directly passes
                               the contents as **kwargs to symptoms segmentation model. It overrides the parameters from the
                               configuration file. Defaults to None.
   :type symptoms_seg_params: Union[dict, None], optional
   :param organs_params: An optional dictionary which directly passes
                         the contents as **kwargs to organs segmentation model. It overrides the parameters from the
                         configuration file. Defaults to None.
   :type organs_params: Union[dict, None], optional
   :param focus_params: An optional dictionary which directly passes
                        the contents as **kwargs to focus estimation model. It overrides the parameters from the
                        configuration file. Defaults to None.
   :type focus_params: Union[dict, None], optional
   :param module_params: An optional dictionary which controls which parts
                         of the pipeline are executed. It overrides the parameters from the configuration file.
                         Defaults to None.
   :type module_params: Union[dict, None], optional


   .. py:attribute:: module_params


   .. py:attribute:: symptoms_det_params


   .. py:attribute:: symptoms_seg_params


   .. py:attribute:: organs_params


   .. py:attribute:: focus_params


   .. py:method:: predict(images_src: str, export_dst: str) -> None

      This method provides a simple interface to predict on images from a specified folder and
      save the results to a specified location.

      :param images_src: Path to location of images.
      :type images_src: str
      :param export_dst: Path where the results should be saved.
      :type export_dst: str