nii_img = nii_obj.get_data()
header = nii_obj.header
affine = nii_obj.affine
# reshape to account for implicit "1" channel
nii_img = np.reshape(nii_img, nii_img.shape + (1, ))
nii_img = pad_image(nii_img)
# segment
segmented_img = apply_model_single_input(nii_img, model)
# save resultant image
segmented_filename = os.path.join(SEG_DIR, filename)
segmented_nii_obj = nib.Nifti1Image(segmented_img,
affine=affine,
header=header)
nib.save(segmented_nii_obj, segmented_filename)
# Reorient back to original before comparisons
print("Reorienting...")
utils.reorient(filename, DATA_DIR, SEG_DIR)
# get probability volumes and threshold image
print("Thresholding...")
utils.threshold(filename, REORIENT_DIR, REORIENT_DIR, 0.5)
if os.path.exists(TMPDIR):
shutil.rmtree(TMPDIR)
K.clear_session()
# reshape to account for implicit "1" channel
nii_img = np.reshape(nii_img, nii_img.shape + (1, ))
# segment
segmented_img = apply_model_single_input(nii_img, model)
# save resultant image
segmented_filename = os.path.join(SEG_DIR, filename)
segmented_nii_obj = nib.Nifti1Image(segmented_img,
affine=affine,
header=header)
nib.save(segmented_nii_obj, segmented_filename)
# Reorient back to original before comparisons
print("Reorienting...")
utils.reorient(filename, src_dir, SEG_DIR)
# get probability volumes and threshold image
print("Thresholding...")
utils.threshold(filename, REORIENT_DIR, REORIENT_DIR, 0.5)
if results.INMASK:
mask_src_dir, mask = os.path.split(results.INMASK)
preprocess.preprocess(mask,
src_dir=mask_src_dir,
dst_dir=PREPROCESSING_DIR,
tmp_dir=TMPDIR,
verbose=0,
skullstrip_script_path=SKULLSTRIP_SCRIPT_PATH,
remove_tmp_files=True)