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voxel_slices
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voxel_slices
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#!/usr/bin/env python
# Voxel slices
# Copyright (C) 2019 Tristram Lett
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import os
import numpy as np
import argparse as ap
import nibabel as nib
from ants_tbss.functions import autothreshold, draw_outline, nonempty_coordinate_range, correct_image, write_padded_png, linear_cm, outlay_png, mask_png, cm_hide_lower, write_colorbar
DESCRIPTION = "Creates images slices with optional masking images"
def getArgumentParser(ap = ap.ArgumentParser(description = DESCRIPTION)):
ap.add_argument("-i", "--input",
help = "[Required] Input images(s) in nifti, minc, or mgh.",
nargs = '+',
type = str,
required = True,
metavar = 'image')
group = ap.add_mutually_exclusive_group(required=True)
group.add_argument("-mo", "--addmaskoutline",
help = "Add masking image for creating a red outline",
nargs = 1,
type = str,
metavar = 'image')
group.add_argument("-mi", "--addoutlineimage",
help = "Add an image for creating a red outline set by the threshold algorithm based on two thresholds",
nargs = 1,
type = str,
metavar = 'image')
group.add_argument("-si", "--addstatisticimage",
help = "Overlay a statistic image",
nargs = 1,
type = str,
metavar = 'image')
group.add_argument("-nm", "--nomask",
help = "Binarizes each input image to create a mask.",
action = 'store_true')
ap.add_argument("-no", "--nooutline",
help = "Do not create a mask outline.",
action = 'store_true')
ap.add_argument("-neg", "--statsneg",
help = "Must be used with -si. Overlay the negative statistic image too.",
action = 'store_true')
ap.add_argument("-ta", "--thesholdalgorithm",
help = "Thresholding method (Default is otsu).",
type = str,
choices = ['otsu', 'otsu_p', 'li', 'li_p', 'yen', 'yen_p', 'zscore', 'zscore_p'])
ap.add_argument("-st", "--setthreshold",
help = "Manually set the values for thresholding. -st {lower} {higher}.",
nargs = 2,
type = str,
metavar = ['lowthreshold','highthreshold'])
ap.add_argument("-od", "--outputdir",
help = "[Optional] Output directory. -od {/path/to/ouputdirectory}",
nargs = 1,
type = str,
metavar = 'str')
ap.add_argument("-cm", "--colourmap",
help = "The output colour map for input image. -cm {colourmapname}, Default: %(default)s) ",
nargs = 1,
default = ['viridis'],
metavar = 'str')
ap.add_argument("-ns", "--numberofslices",
help = "The number of slices in each direction. -ns {# slices}. Default: %(default)s)",
nargs = 1,
default = [3],
metavar = 'int')
return ap
def run(opts):
numslices = int(opts.numberofslices[0]) + 2
colourmapname = opts.colourmap[0]
thr_alg = 'otsu'
if opts.thesholdalgorithm:
thr_alg = opts.thesholdalgorithm
if opts.addmaskoutline:
mask = nib.load(opts.addmaskoutline[0])
mask_data = mask.get_fdata()
affine = mask.affine
elif opts.addstatisticimage:
aimg = nib.load(opts.addstatisticimage[0])
aimg_data = aimg.get_fdata()
mask_data = np.zeros_like(aimg_data)
mask_data[aimg_data!=0]=1
affine = aimg.affine
elif opts.addoutlineimage:
aimg = nib.load(opts.addoutlineimage[0])
aimg_data = aimg.get_fdata()
mask_data = np.zeros_like(aimg_data)
mask_data[aimg_data!=0]=1
affine = aimg.affine
else:
pass
if not opts.nomask:
x_rng, y_rng, z_rng = nonempty_coordinate_range(mask_data)
x_space = np.round(np.linspace(x_rng[0]+10, x_rng[1]-10, numslices))
y_space = np.round(np.linspace(y_rng[0]+10, y_rng[1]-10, numslices))
z_space = np.round(np.linspace(z_rng[0]+10, z_rng[1]-10, numslices))
native_space = []
for i in range(len(x_space)):
native_space.append(nib.affines.apply_affine(affine,[x_space[i],y_space[i],z_space[i]]))
native_space = np.array(native_space)
x_space = x_space[1:-1]
y_space = y_space[1:-1]
z_space = z_space[1:-1]
# for parallelization
rand_value = str(np.random.randint(1,99999999)).zfill(8)
mask_name = rand_value + '_mask.png'
if opts.addoutlineimage:
lowmask_name = rand_value + '_lowmask.png'
write_padded_png(mask_data, x_space, y_space, z_space, mask_name, cmap = 'binary_r')
correct_image(mask_name)
if opts.addoutlineimage:
thrs1 = autothreshold(aimg_data, thr_alg)[0]
thrs2 = autothreshold(aimg_data[aimg_data>thrs1], thr_alg)[0]
low = np.zeros_like(aimg_data)
low[aimg_data>thrs2]=1
write_padded_png(low, x_space, y_space, z_space, lowmask_name, cmap = "binary_r")
correct_image(lowmask_name)
temp_masks = []
for input_image in opts.input:
img = nib.load(input_image)
img_data = img.get_fdata()
# This creates a non-zero mask for each input image
if opts.nomask:
mask_data = np.zeros_like(img_data)
mask_data[img_data!=0] = 1
affine = img.affine
x_rng, y_rng, z_rng = nonempty_coordinate_range(mask_data)
x_space = np.round(np.linspace(x_rng[0]+10, x_rng[1]-10, numslices))
y_space = np.round(np.linspace(y_rng[0]+10, y_rng[1]-10, numslices))
z_space = np.round(np.linspace(z_rng[0]+10, z_rng[1]-10, numslices))
native_space = []
for i in range(len(x_space)):
native_space.append(nib.affines.apply_affine(affine,[x_space[i],y_space[i],z_space[i]]))
native_space = np.array(native_space)
x_space = x_space[1:-1]
y_space = y_space[1:-1]
z_space = z_space[1:-1]
# for parallelization
rand_value = str(np.random.randint(1,99999999)).zfill(8)
mask_name = rand_value + '_mask.png'
temp_masks.append(mask_name)
write_padded_png(mask_data, x_space, y_space, z_space, mask_name, cmap = 'binary_r')
correct_image(mask_name)
outname = input_image.replace('.nii','')
outname = outname.replace('.gz','')
outname = outname.replace('.','_')
outname = outname.replace('/','_') + ".png"
write_padded_png(img_data, x_space, y_space, z_space, outname,
cmap = cm_hide_lower(colourmapname))
mask_png(img_png = outname, mask_png = mask_name, remove_mask = False)
if opts.addstatisticimage:
statmask_name = rand_value + '_stat.png'
if opts.setthreshold:
thrs1 = float(opts.setthreshold[0])
thrs2 = float(opts.setthreshold[1])
else:
thrs1 = autothreshold(aimg_data, thr_alg)[0]
thrs2 = autothreshold(aimg_data[aimg_data>thrs1], thr_alg)[0]
cmap_ry = linear_cm([1,0,0],[1,1,0])
write_padded_png(aimg_data, x_space, y_space, z_space, statmask_name, vmin = thrs1, vmax = thrs2, cmap = cmap_ry)
overlayname = os.path.basename(opts.addstatisticimage[0]).replace('/','_') + outname
overlayname = overlayname.replace('.nii','')
overlayname = overlayname.replace('.gz','')
outlay_png(img_png = outname, outlay_png = statmask_name, outname = overlayname, cleanup = True)
write_colorbar(threshold = [thrs1, thrs2], input_cmap = cmap_ry, name_cmap = overlayname[:-4], outtype = 'png', transparent = True)
outname = overlayname
if opts.statsneg:
cmap_blb = linear_cm([0,0,1],[0,1,1])
write_padded_png(aimg_data*-1, x_space, y_space, z_space, statmask_name, vmin = thrs1, vmax = thrs2, cmap = cmap_blb)
overlayname = os.path.basename(opts.addstatisticimage[0]).replace('/','_') + outname
overlayname = overlayname.replace('.nii','')
overlayname = overlayname.replace('.gz','')
outlay_png(img_png = outname, outlay_png = statmask_name, outname = overlayname, cleanup = True)
write_colorbar(threshold = [thrs1, thrs2], input_cmap = cmap_blb, name_cmap = "neg_" + overlayname[:-4], outtype = 'png', transparent = True)
elif opts.addoutlineimage:
draw_outline(outname, mask_name)
draw_outline(outname, lowmask_name)
print("Input image : %s" % outname)
print("Mean white matter = %1.5e" % np.mean(img_data*low))
print("Mean grey matter = %1.5e" % np.mean(img_data*(mask_data-low)))
else:
if not opts.nooutline:
draw_outline(outname, mask_name)
if opts.outputdir:
outdir = opts.outputdir[0]
if outdir[:-1] != '/':
outdir += '/'
os.system("mkdir -p %s; mv %s %s" % (outdir, outname, outdir))
if opts.addstatisticimage:
os.system("mv %s_colorbar.png %s" % (overlayname[:-4], outdir))
# delete mask(s)
if opts.nomask:
for mask_name in temp_masks:
os.remove(mask_name)
else:
os.remove(mask_name)
if opts.addoutlineimage:
if os.path.exists(lowmask_name):
os.remove(lowmask_name)
if opts.addstatisticimage:
if os.path.exists(statmask_name):
os.remove(statmask_name)
if __name__ == "__main__":
parser = getArgumentParser()
opts = parser.parse_args()
run(opts)