B6_dapi_template/automatic_segmentation_program/tools.py

import os
import nibabel as nib
from PIL import Image
import numpy as np
from sklearn.metrics import mutual_info_score
import shutil

from preprocess import mask_foreground
def save_slice(template, filename, index, affine=np.eye(4), header=None):
    _slice = nib.load(str(template))
    _slice_data = _slice.get_fdata()

    newimg = nib.Nifti1Image(_slice_data[:, index, :], affine, header)
    newimg.header['pixdim'] = header['pixdim']

    if header is None:
        newimg.header['pixdim'][1:3] = _slice.header['pixdim'][1], _slice.header['pixdim'][3]
    newimg.to_filename(str(filename))
    return filename

def create_dir(name):
    if not os.path.exists(name):
        os.mkdir(name)
def remove_dir(loc):
    shutil.rmtree(loc)
    
def mutual_info_mask(slice1,slice2, bins=32):
    slice1_masked = mask_foreground(slice1)
    slice2_masked = mask_foreground(slice2)
    common = np.logical_and(slice1_masked, slice2_masked)
    slice1, slice2 = np.where(common, slice1, 0), np.where(common, slice2, 0)
    hist = np.histogram2d(slice1.ravel(), slice2.ravel(), bins=bins)[0]
    return mutual_info_score(None, None, contingency=hist)


def dice_coef(slice1, slice2):
    mask_1 = mask_foreground(slice1).astype(np.bool)
    mask_2 = mask_foreground(slice2).astype(np.bool)
    intersection = np.logical_and(mask_1, mask_2)
    return 2. * intersection.sum() / (mask_1.sum() + mask_2.sum())


def resize_im(image_data, image_with_dimensions):
    dimensions = (image_with_dimensions.shape[1], image_with_dimensions.shape[0])
    resized = np.array(Image.fromarray(image_data).resize(dimensions))
    return resized

def bregma_to_slice_index(bregma):
   return round(27.908*bregma + 116.831)