def main():
# Parse input arguments and get sanitised version
sargs = mm_parse_inputs.SanitisedArgs(mm_parse_inputs.parse_input_arguments())
# Enable calling NiftyReg and NiftySeg
nk = mm_niftk.MM_Niftk()
mmfn.check_create_directories([sargs.output_directory])
# Set default parameters
if sargs.number is not None:
iterations = sargs.number
else:
iterations = 200
fwhm = 0.15 # default 0.15
subsampleFactor = 4 # default 4; recommend 2 for in vivo mouse brains with resolution >100 micrometres.
nlevels = 4 # default 4
conv = 0.001 # default 0.001
nhistbins = 256 # default 200
# Get list of files (use file_name_filter)
nifti_files_list = sorted(glob.glob(os.path.join(sargs.input_directory, sargs.file_name_filter + '.nii*')))
print(" Processing {0} files: \n - - -\n \t{1}\n - - -\n ...".format(len(nifti_files_list), '\n\t'.join([ str(item) for item in nifti_files_list ])))
TASKS = [(nk, sargs, nf_path, iterations, fwhm, subsampleFactor, nlevels, conv, nhistbins) for nf_path in nifti_files_list]
# Set the number of parallel processes to use
pool = multiprocessing.Pool(np.int(multiprocessing.cpu_count() / 2))
# The _star function will unpack TASKS to use in the actual function
# Using pool.map because we do care about the order of the results.
all_output_paths = pool.map(nuc_n4_debias_star, TASKS)
pool.close()
pool.join()
all_output_paths = []
for counter, nf_path in enumerate(nifti_files_list):
outpath = nuc_n4_debias(nk, sargs, nf_path, iterations, fwhm, subsampleFactor, nlevels, conv, nhistbins)
all_output_paths.append(outpath)
print(" all_output_paths: {0}".format(all_output_paths))
print(" Copying headers from original images to bias-corrected images ...")
for counter, nifti_file in enumerate(nifti_files_list):
print " Processing {0} / {1}: {2} ...".format((counter + 1), len(nifti_files_list), nifti_file)
original_nf_name = os.path.basename(nifti_file).split(os.extsep)[0]
original_nifti = nib.load(nifti_file)
bias_corrected_path = mmfn.get_corresponding_file(sargs.output_directory, original_nf_name, path_only=True)
print("Bias corrected result path: '{0}'".format(bias_corrected_path))
updated_nii = mmfn.copy_header(nib.load(bias_corrected_path), original_nifti)
# This will overwrite the bias corrected files
nib.save(updated_nii, bias_corrected_path)
print(" Bias field correction completed; files saved to: \n{0}".format('\n\t'.join([ str(item) for item in all_output_paths ])))
def main():
# Parse input arguments and get sanitised version
sargs = mm_parse_inputs.SanitisedArgs(
mm_parse_inputs.parse_input_arguments())
# Enable calling NiftyReg and NiftySeg
nk = mm_niftk.MM_Niftk()
mmfn.check_create_directories([sargs.output_directory])
# Set default parameters
if sargs.number is not None:
iterations = sargs.number
else:
iterations = 200
fwhm = 0.15 # default 0.15
subsampleFactor = 4 # default 4; recommend 2 for in vivo mouse brains with resolution >100 micrometres.
nlevels = 4 # default 4
conv = 0.001 # default 0.001
nhistbins = 256 # default 200
# Get list of files (use file_name_filter)
nifti_files_list = sorted(
glob.glob(
os.path.join(sargs.input_directory,
sargs.file_name_filter + '.nii*')))
print(
" Processing {0} files: \n - - -\n \t{1}\n - - -\n ...".format(
len(nifti_files_list),
'\n\t'.join([str(item) for item in nifti_files_list])))
TASKS = [(nk, sargs, nf_path, iterations, fwhm, subsampleFactor, nlevels,
conv, nhistbins) for nf_path in nifti_files_list]
# Set the number of parallel processes to use
pool = multiprocessing.Pool(np.int(multiprocessing.cpu_count() / 2))
# The _star function will unpack TASKS to use in the actual function
# Using pool.map because we do care about the order of the results.
all_output_paths = pool.map(nuc_n4_debias_star, TASKS)
pool.close()
pool.join()
all_output_paths = []
for counter, nf_path in enumerate(nifti_files_list):
outpath = nuc_n4_debias(nk, sargs, nf_path, iterations, fwhm,
subsampleFactor, nlevels, conv, nhistbins)
all_output_paths.append(outpath)
print(" all_output_paths: {0}".format(all_output_paths))
print(
" Copying headers from original images to bias-corrected images ...")
for counter, nifti_file in enumerate(nifti_files_list):
print " Processing {0} / {1}: {2} ...".format((counter + 1),
len(nifti_files_list),
nifti_file)
original_nf_name = os.path.basename(nifti_file).split(os.extsep)[0]
original_nifti = nib.load(nifti_file)
bias_corrected_path = mmfn.get_corresponding_file(
sargs.output_directory, original_nf_name, path_only=True)
print("Bias corrected result path: '{0}'".format(bias_corrected_path))
updated_nii = mmfn.copy_header(nib.load(bias_corrected_path),
original_nifti)
# This will overwrite the bias corrected files
nib.save(updated_nii, bias_corrected_path)
print(" Bias field correction completed; files saved to: \n{0}".format(
'\n\t'.join([str(item) for item in all_output_paths])))
def sanitise_arguments(self, args):
""" """
args.in_ext_filter = '.nii*'
if not args.unzip:
args.ext = '.nii.gz'
else:
args.ext = '.nii'
if args.input:
if os.path.isdir(args.input):
# print (" Input {0} is a directory ...".format(args.input))
args.input_name_filter = '*' + args.in_filter + '*' # use wildcards if provided a directory alone
args.input_name_filter_exact = args.in_filter
args.input_directory = os.path.normpath(os.path.join(args.input))
elif os.path.isfile(args.input):
# print (" Input {0} is a file ...".format(args.input))
# Get the filename, removing path and 1+ extensions
args.input_name_filter = os.path.basename(args.input).split(os.extsep)[0]
args.input_directory = os.path.dirname(args.input)
else:
raise Exception("Input not recognised or does not exist: {0}".format(args.input))
else:
args.input_directory = os.getcwd()
if not args.no_output:
if args.output:
if os.path.isdir(args.output):
# print (" Output {0} is a directory ...".format(args.input))
args.output_directory = os.path.normpath(os.path.join(args.output))
else:
print "Specified output ({0}) is not a directory; creating it ...".format(args.output)
args.output_directory = os.path.normpath(os.path.join(args.output))
mmfn.check_create_directories([args.output_directory])
else:
print "No output directory specified. Setting to input directory ({0}) in case it is required.".format(args.input_directory)
args.output_directory = args.input_directory
if hasattr(args, 'input_2'):
if args.input_2:
if os.path.isdir(args.input_2):
# print (" Input 2 {0} is a directory ...".format(args.input))
args.input_name_filter_2 = '*' + args.in_filter + '*' # use wildcards if provided a directory alone
args.input_name_filter_exact_2 = args.in_filter
args.input_directory_2 = os.path.normpath(os.path.join(args.input_2))
elif os.path.isfile(args.input_2):
# print (" Input 2 {0} is a file ...".format(args.input))
# Get the filename, removing path and 1+ extensions
args.input_name_filter_2 = os.path.basename(args.input_2).split(os.extsep)[0]
args.input_directory_2 = os.path.dirname(args.input_2)
else:
raise Exception("Input 2 not recognised or does not exist: {0}".format(args.input_2))
else:
args.input_directory_2 = os.getcwd()
if hasattr(args, 'mask'):
if args.mask:
args.mask = os.path.normpath(args.mask)
if os.path.isdir(args.mask):
args.mask_name_filter = '*' + args.mn_filter + '*' # use wildcards if provided a directory alone
args.mask_name_filter_exact = args.mn_filter
args.mask_directory = os.path.normpath(os.path.join(args.mask))
elif os.path.isfile(args.mask):
# Get the filename, removing path and 1+ extensions
args.mask_name_filter = os.path.basename(args.mask).split(os.extsep)[0]
args.mask_directory = os.path.dirname(args.mask)
else:
args.mask_directory = None
if hasattr(args, 'tpm'):
if args.tpm:
args.tpm = os.path.normpath(args.tpm)
if os.path.isdir(args.tpm):
args.tpm_name_filter = '*' + args.tn_filter + '*' # use wildcards if provided a directory alone
args.tpm_name_filter_exact = args.tn_filter
args.tpm_directory = os.path.normpath(os.path.join(args.tpm))
elif os.path.isfile(args.tpm):
# Get the filename, removing path and 1+ extensions
args.tpm_name_filter = os.path.basename(args.tpm).split(os.extsep)[0]
args.tpm_directory = os.path.dirname(args.tpm)
else:
args.tpm_directory = None
# Either get a list of strings as file names from a directory, or from a given column of a .CSV file
if hasattr(args, 'list'):
if args.list:
args.list = os.path.normpath(args.list)
if os.path.isdir(os.path.normpath(args.list)):
args.list_names = mmfn.get_names_list(args.list, args.list_filter, extension=args.in_ext_filter)
# elif os.path.isfile(os.path.normpath(args.list)):
# args.column
return args
def sanitise_arguments(self, args):
""" """
args.in_ext_filter = '.nii*'
if not args.unzip:
args.ext = '.nii.gz'
else:
args.ext = '.nii'
if args.input:
if os.path.isdir(args.input):
# print (" Input {0} is a directory ...".format(args.input))
args.input_name_filter = '*' + args.in_filter + '*' # use wildcards if provided a directory alone
args.input_name_filter_exact = args.in_filter
args.input_directory = os.path.normpath(
os.path.join(args.input))
elif os.path.isfile(args.input):
# print (" Input {0} is a file ...".format(args.input))
# Get the filename, removing path and 1+ extensions
args.input_name_filter = os.path.basename(args.input).split(
os.extsep)[0]
args.input_directory = os.path.dirname(args.input)
else:
raise Exception(
"Input not recognised or does not exist: {0}".format(
args.input))
else:
args.input_directory = os.getcwd()
if not args.no_output:
if args.output:
if os.path.isdir(args.output):
# print (" Output {0} is a directory ...".format(args.input))
args.output_directory = os.path.normpath(
os.path.join(args.output))
else:
print "Specified output ({0}) is not a directory; creating it ...".format(
args.output)
args.output_directory = os.path.normpath(
os.path.join(args.output))
mmfn.check_create_directories([args.output_directory])
else:
print "No output directory specified. Setting to input directory ({0}) in case it is required.".format(
args.input_directory)
args.output_directory = args.input_directory
if hasattr(args, 'input_2'):
if args.input_2:
if os.path.isdir(args.input_2):
# print (" Input 2 {0} is a directory ...".format(args.input))
args.input_name_filter_2 = '*' + args.in_filter + '*' # use wildcards if provided a directory alone
args.input_name_filter_exact_2 = args.in_filter
args.input_directory_2 = os.path.normpath(
os.path.join(args.input_2))
elif os.path.isfile(args.input_2):
# print (" Input 2 {0} is a file ...".format(args.input))
# Get the filename, removing path and 1+ extensions
args.input_name_filter_2 = os.path.basename(
args.input_2).split(os.extsep)[0]
args.input_directory_2 = os.path.dirname(args.input_2)
else:
raise Exception(
"Input 2 not recognised or does not exist: {0}".format(
args.input_2))
else:
args.input_directory_2 = os.getcwd()
if hasattr(args, 'mask'):
if args.mask:
args.mask = os.path.normpath(args.mask)
if os.path.isdir(args.mask):
args.mask_name_filter = '*' + args.mn_filter + '*' # use wildcards if provided a directory alone
args.mask_name_filter_exact = args.mn_filter
args.mask_directory = os.path.normpath(
os.path.join(args.mask))
elif os.path.isfile(args.mask):
# Get the filename, removing path and 1+ extensions
args.mask_name_filter = os.path.basename(args.mask).split(
os.extsep)[0]
args.mask_directory = os.path.dirname(args.mask)
else:
args.mask_directory = None
if hasattr(args, 'tpm'):
if args.tpm:
args.tpm = os.path.normpath(args.tpm)
if os.path.isdir(args.tpm):
args.tpm_name_filter = '*' + args.tn_filter + '*' # use wildcards if provided a directory alone
args.tpm_name_filter_exact = args.tn_filter
args.tpm_directory = os.path.normpath(
os.path.join(args.tpm))
elif os.path.isfile(args.tpm):
# Get the filename, removing path and 1+ extensions
args.tpm_name_filter = os.path.basename(args.tpm).split(
os.extsep)[0]
args.tpm_directory = os.path.dirname(args.tpm)
else:
args.tpm_directory = None
# Either get a list of strings as file names from a directory, or from a given column of a .CSV file
if hasattr(args, 'list'):
if args.list:
args.list = os.path.normpath(args.list)
if os.path.isdir(os.path.normpath(args.list)):
args.list_names = mmfn.get_names_list(
args.list,
args.list_filter,
extension=args.in_ext_filter)
# elif os.path.isfile(os.path.normpath(args.list)):
# args.column
return args
