"""
===================================
Brain segmentation with median_otsu
===================================

We show how to extract brain information and mask from a b0 image using DIPY_'s
``segment.mask`` module.

First import the necessary modules:
"""

import numpy as np
import nibabel as nib

"""
Download and read the data for this tutorial.

The ``scil_b0`` dataset contains different data from different companies and
models. For this example, the data comes from a 1.5 Tesla Siemens MRI.
"""

from dipy.data.fetcher import fetch_scil_b0, read_siemens_scil_b0
fetch_scil_b0()
img = read_siemens_scil_b0()
data = np.squeeze(img.get_data())

"""
``img`` contains a nibabel Nifti1Image object. Data is the actual brain data as
a numpy ndarray.

Segment the brain using DIPY's ``mask`` module.

``median_otsu`` returns the segmented brain data and a binary mask of the brain.
It is possible to fine tune the parameters of ``median_otsu`` (``median_radius``
and ``num_pass``) if extraction yields incorrect results but the default
parameters work well on most volumes. For this example, we used 2 as
``median_radius`` and 1 as ``num_pass``
"""

from dipy.segment.mask import median_otsu
b0_mask, mask = median_otsu(data, 2, 1)

"""
Saving the segmentation results is very easy using nibabel. We need the
``b0_mask``, and the binary mask volumes. The affine matrix which transform the
image's coordinates to the world coordinates is also needed. Here, we choose to
save both images in ``float32``.
"""

mask_img = nib.Nifti1Image(mask.astype(np.float32), img.affine)
b0_img = nib.Nifti1Image(b0_mask.astype(np.float32), img.affine)

fname = 'se_1.5t'
nib.save(mask_img, fname + '_binary_mask.nii.gz')
nib.save(b0_img, fname + '_mask.nii.gz')

"""
Quick view of the results middle slice using ``matplotlib``.
"""

import matplotlib.pyplot as plt
from dipy.core.histeq import histeq

sli = data.shape[2] // 2
plt.figure('Brain segmentation')
plt.subplot(1, 2, 1).set_axis_off()
plt.imshow(histeq(data[:, :, sli].astype('float')).T,
           cmap='gray', origin='lower')

plt.subplot(1, 2, 2).set_axis_off()
plt.imshow(histeq(b0_mask[:, :, sli].astype('float')).T,
           cmap='gray', origin='lower')

plt.savefig('median_otsu.png')

"""
.. figure:: median_otsu.png
   :align: center

   An application of median_otsu for brain segmentation.

``median_otsu`` can also automatically crop the outputs to remove the largest
possible number of background voxels. This makes outputted data significantly
smaller. Auto-cropping in ``median_otsu`` is activated by setting the
``autocrop`` parameter to ``True``.
"""

b0_mask_crop, mask_crop = median_otsu(data, 4, 4, autocrop=True)

"""
Saving cropped data using nibabel as demonstrated previously.

.. include:: ../links_names.inc
"""

mask_img_crop = nib.Nifti1Image(mask_crop.astype(np.float32), img.affine)
b0_img_crop = nib.Nifti1Image(
    b0_mask_crop.astype(np.float32), img.affine)
nib.save(mask_img_crop, fname + '_binary_mask_crop.nii.gz')
nib.save(b0_img_crop, fname + '_mask_crop.nii.gz')