"""

This script is to segment the lung out of background as a prepocessing for lobes segementation.

"""

import SimpleITK as sitk

import gui

class LungSegment:

    """

    This class is designed for 3D segmentation of lung, including the methods:

    ...

    """

    def __init__(self, img):

        self.img = img

        self.temp_img = None

        self.img_uint8 = None

    def conv_2_uint8(self, WINDOW_LEVEL=(1050,500)):

        """

        Convert original image to 8-bit image

        :param WINDOW_LEVEL: Using an external viewer (ITK-SNAP or 3DSlicer)

                             we identified a visually appealing window-level setting

        :return: None

        """

        # self.img_uint8 = sitk.Cast(self.img,

        #                           sitk.sitkUInt8)

        self.img_uint8 = sitk.Cast(sitk.IntensityWindowing(self.img,

                                  windowMinimum=WINDOW_LEVEL[1] - WINDOW_LEVEL[0] / 2.0,

                                  windowMaximum=WINDOW_LEVEL[1] + WINDOW_LEVEL[0] / 2.0),

                                  sitk.sitkUInt8)

    def regiongrowing(self, seed_pts):

        """

        Implement ConfidenceConnected by SimpleITK tools with given seed points

        :param seed_pts: seed points for region growing [(z,y,x), ...]

        :return: None

        """

        self.temp_img = sitk.ConfidenceConnected(self.img, seedList=seed_pts,

                                                           numberOfIterations=0,

                                                           multiplier=2,

                                                           initialNeighborhoodRadius=1,

                                                           replaceValue=1)

    def image_showing(self, title=''):

        """

        Showing image.

        :return: None

        """

        gui.MultiImageDisplay(image_list=[sitk.LabelOverlay(self.img_uint8, self.temp_img)],

                              title_list=[title])

    def image_closing(self, size=7):

        """

        Implement morphological closing to fix the "holes" inside the image.

        :param size: the size the closing kernel

        :return: None

        """

        closing = sitk.BinaryMorphologicalClosingImageFilter()

        closing.SetForegroundValue(1)

        closing.SetKernelRadius(size)

        self.temp_img = closing.Execute(self.temp_img)