Generating animated snapshots for Quality Control of coregistration

qc_reg.py

#! /usr/bin/env python
# Usage: python qc_reg.py path_to_T1 path_to_T2 path_to_GIF

import sys
t1_fp, t2_fp, gif_fp = sys.argv[1:]


def ants_snapshot(t1w_fp, coreg_fp):
    """ Create 2 sets of snapshots, one with the background (T1) image,
    one with the overlay. Both along the 3 axes (axial, coronal and sagittal).
    Returns the paths to the created images. """

    from nilearn import plotting
    from nilearn import image
    import tempfile

    # compute a threshold for the overlay based on range of intensity values
    data = image.load_img(coreg_fp).get_fdata()
    val_range = abs(data.min()) + abs(data.max())
    thresh = val_range / 6

    paths = []
    paths_orig = []
    for each in 'xyz':
        _, path = tempfile.mkstemp(suffix='.jpg')
        paths_orig.append(path)
        im = plotting.plot_anat(t1w_fp,
                                black_bg=True,
                                bg_img=None,
                                display_mode=each,
                                draw_cross=False)
        im.savefig(path)
        _, path = tempfile.mkstemp(suffix='.jpg')
        paths.append(path)
        im.add_overlay(coreg_fp,
                       threshold=thresh,
                       cmap=plotting.cm.black_red)
        im.savefig(path)
    return paths, paths_orig


print('Processing: %s over %s' % (t2_fp, t1_fp))
p1, p2 = ants_snapshot(t1_fp, t2_fp)

# Preparing the previous outputs for __montage__
paths = {'x': [p1[0]], 'y':[p1[1]], 'z': [p1[2]]}
paths_orig = {'x': [p2[0]], 'y': [p2[1]], 'z': [p2[2]]}

from nisnap.utils.montage import __montage__
__montage__(paths, paths_orig, 'xyz', 50, True, True, savefig=gif_fp)
print('Saving %s' % gif_fp)