:py:mod:`macrobot.segmentation`
===============================

.. py:module:: macrobot.segmentation


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


Functions
~~~~~~~~~

.. autoapisummary::

   macrobot.segmentation.segment_lanes_rgb
   macrobot.segmentation.segment_lanes_binary
   macrobot.segmentation.segment_leaf_binary



.. py:function:: segment_lanes_rgb(rgb_image, image_backlight, image_tresholded)

   Extraction the lanes between the white frames.
   First we find and filter the contours of the threshold image to find the frames.
   Then we extract a rectangle inside the white frames and oder the position from left to right.

   :param rgb_image: 3-channel RGB image to extract the lanes.
   :type rgb_image: numpy array.
   :param image_backlight: The backlight image.
   :type image_backlight: numpy array.
   :param image_tresholded: The threshold image we use as source to find the frames.
   :type image_tresholded: numpy array.
   :return: Two list with contains the RGB and backlight lanes and their positions as tuple(image, position).
   :rtype: list
    


.. py:function:: segment_lanes_binary(lanes_roi_backlight)

   Threshold the lanes by Otsu method to get a binary image for leaf segmentation.
   The backlight image is used for this step.

   :param lanes_roi_backlight: The lanes of the backlight image as list of tuple(image, position).
   :type lanes_roi_backlight: list
   :return: Two list with contains the backlight lanes as binary image and it's positions as tuple(image, position).
   :rtype: list
    


.. py:function:: segment_leaf_binary(lanes_roi_binary, lanes_roi_rgb, plate_id, leaves_per_lane, predicted_lanes, destination_path, y_position, experiment, dai, file_results, store_leaf_path, report_path, report=True)

   Threshold the leaves by finding and filtering the contours of the binary lane image.

   :param lanes_roi_binary: The lanes of the binary image as list of tuple(image, position).
   :type lanes_roi_backlight: list
   :param lanes_roi_rgb: The lanes of the RGB image as list of tuple(image, position).
   :type lanes_roi_rgb: list
   :param plate_id: The plate ID.
   :type plate_id: str
   :param leaves_per_lane: maximum leaves per lane.
   :type leaves_per_lane: int
   :param predicted_lanes: The lanes of the predicted image as list of tuple(image, position).
   :type predicted_lanes: list
   :param destination_path: The path to store the final result images and csv file.
   :type destination_path: str
   :param y_position: Y Position for the leaves.
   :type y_position: int
   :param experiment: The experiment name.
   :type experiment: str
   :param dai: Days after inoculation.
   :type dai: str
   :param file_results: The CSV file for each experiments which contains the pathogen prediction per leaf.
   :type file_results: file object
   :return: Two list with contains the backlight lanes as binary image and it's positions as tuple(image, position).
   :rtype: list