def prepare_image(file):
from .extract_utils import extract_images
logger.debug(f" Processing of {file}.")
container = container_from_filename(file)
subfolder = "image_based"
output_mode = extract_images(file)
logger.debug(f" Image extracted.")
write_output_imgs(output_mode, container, subfolder)
def prepare_patch(file):
from .extract_utils import extract_patches
logger.debug(f" Processing of {file}.")
container = container_from_filename(file)
subfolder = "patch_based"
output_mode = extract_patches(
file,
patch_size=parameters["patch_size"],
stride_size=parameters["stride_size"],
)
logger.debug(f" {len(output_mode)} patches extracted.")
write_output_imgs(output_mode, container, subfolder)
def prepare_slice(file):
from .extract_utils import extract_slices
logger.debug(f" Processing of {file}.")
container = container_from_filename(file)
subfolder = "slice_based"
output_mode = extract_slices(
file,
slice_direction=parameters["slice_direction"],
slice_mode=parameters["slice_mode"],
discarded_slices=parameters["discarded_slices"],
)
logger.debug(f" {len(output_mode)} slices extracted.")
write_output_imgs(output_mode, container, subfolder)
def prepare_roi(file):
from .extract_utils import extract_roi
logger.debug(f" Processing of {file}.")
container = container_from_filename(file)
subfolder = "roi_based"
if parameters["preprocessing"] == "custom":
if not parameters["roi_custom_template"]:
raise ClinicaDLArgumentError(
"A custom template must be defined when the modality is set to custom."
)
parameters["roi_template"] = parameters["roi_custom_template"]
parameters["roi_mask_pattern"] = parameters[
"roi_custom_mask_pattern"]
else:
from .extract_utils import PATTERN_DICT, TEMPLATE_DICT
parameters["roi_template"] = TEMPLATE_DICT[
parameters["preprocessing"]]
parameters["roi_mask_pattern"] = PATTERN_DICT[
parameters["preprocessing"]]
parameters["masks_location"] = path.join(
caps_directory, "masks", f"tpl-{parameters['roi_template']}")
if len(parameters["roi_list"]) == 0:
raise ClinicaDLArgumentError(
"A list of regions of interest must be given.")
else:
check_mask_list(
parameters["masks_location"],
parameters["roi_list"],
parameters["roi_mask_pattern"],
None if parameters["use_uncropped_image"] is None else
not parameters["use_uncropped_image"],
)
output_mode = extract_roi(
file,
masks_location=parameters["masks_location"],
mask_pattern=parameters["roi_mask_pattern"],
cropped_input=None if parameters["use_uncropped_image"] is None
else not parameters["use_uncropped_image"],
roi_names=parameters["roi_list"],
uncrop_output=parameters["uncropped_roi"],
)
logger.debug(f" ROI extracted.")
write_output_imgs(output_mode, container, subfolder)
def DeepLearningPrepareData(caps_directory, tsv_file, parameters):
import os
from os import path
from clinica.utils.exceptions import (
ClinicaBIDSError,
ClinicaCAPSError,
ClinicaException,
)
from clinica.utils.input_files import (
T1W_EXTENSIVE,
T1W_LINEAR,
T1W_LINEAR_CROPPED,
pet_linear_nii,
)
from clinica.utils.inputs import check_caps_folder, clinica_file_reader
from clinica.utils.nipype import container_from_filename
from clinica.utils.participant import get_subject_session_list
from torch import save as save_tensor
from clinicadl.utils.preprocessing import write_preprocessing
from .extract_utils import (
check_mask_list,
extract_images,
extract_patches,
extract_roi,
extract_slices,
)
logger = getLogger("clinicadl")
# Get subject and session list
check_caps_folder(caps_directory)
input_dir = caps_directory
logger.debug(f"CAPS directory : {input_dir}.")
is_bids_dir = False
sessions, subjects = get_subject_session_list(input_dir, tsv_file,
is_bids_dir, False, None)
logger.info(
f"{parameters['mode']}s will be extracted in Pytorch tensor from {len(sessions)} images."
)
logger.debug(f"List of subjects: \n{subjects}.")
logger.debug(f"List of sessions: \n{sessions}.")
# Select the correct filetype corresponding to modality
# and select the right folder output name corresponding to modality
logger.debug(
f"Selected images are preprocessed with {parameters['preprocessing']} pipeline`."
)
if parameters["preprocessing"] == "t1-linear":
mod_subfolder = "t1_linear"
if parameters["use_uncropped_image"]:
FILE_TYPE = T1W_LINEAR
else:
FILE_TYPE = T1W_LINEAR_CROPPED
if parameters["preprocessing"] == "t1-extensive":
mod_subfolder = "t1_extensive"
FILE_TYPE = T1W_EXTENSIVE
parameters["uncropped_image"] = None
if parameters["preprocessing"] == "pet-linear":
mod_subfolder = "pet_linear"
FILE_TYPE = pet_linear_nii(
parameters["acq_label"],
parameters["suvr_reference_region"],
parameters["use_uncropped_image"],
)
if parameters["preprocessing"] == "custom":
mod_subfolder = "custom"
FILE_TYPE = {
"pattern": f"*{parameters['custom_suffix']}",
"description": "Custom suffix",
}
parameters["use_uncropped_image"] = None
parameters["file_type"] = FILE_TYPE
# Input file:
input_files = clinica_file_reader(subjects, sessions, caps_directory,
FILE_TYPE)
# Loop on the images
for file in input_files:
logger.debug(f" Processing of {file}.")
container = container_from_filename(file)
# Extract the wanted tensor
if parameters["mode"] == "image":
subfolder = "image_based"
output_mode = extract_images(file)
logger.debug(f" Image extracted.")
elif parameters["mode"] == "slice":
subfolder = "slice_based"
output_mode = extract_slices(
file,
slice_direction=parameters["slice_direction"],
slice_mode=parameters["slice_mode"],
discarded_slices=parameters["discarded_slices"],
)
logger.debug(f" {len(output_mode)} slices extracted.")
elif parameters["mode"] == "patch":
subfolder = "patch_based"
output_mode = extract_patches(
file,
patch_size=parameters["patch_size"],
stride_size=parameters["stride_size"],
)
logger.debug(f" {len(output_mode)} patches extracted.")
elif parameters["mode"] == "roi":
subfolder = "roi_based"
if parameters["preprocessing"] == "custom":
parameters["roi_template"] = parameters["roi_custom_template"]
if parameters["roi_custom_template"] is None:
raise ValueError(
"A custom template must be defined when the modality is set to custom."
)
else:
from .extract_utils import TEMPLATE_DICT
parameters["roi_template"] = TEMPLATE_DICT[
parameters["preprocessing"]]
parameters["masks_location"] = path.join(
caps_directory, "masks", f"tpl-{parameters['roi_template']}")
if len(parameters["roi_list"]) == 0:
raise ValueError("A list of regions must be given.")
else:
check_mask_list(
parameters["masks_location"],
parameters["roi_list"],
parameters["roi_custom_mask_pattern"],
None if parameters["use_uncropped_image"] is None else
not parameters["use_uncropped_image"],
)
output_mode = extract_roi(
file,
masks_location=parameters["masks_location"],
mask_pattern=parameters["roi_custom_mask_pattern"],
cropped_input=None if parameters["use_uncropped_image"] is None
else not parameters["use_uncropped_image"],
roi_list=parameters["roi_list"],
uncrop_output=parameters["uncropped_roi"],
)
logger.debug(f" ROI extracted.")
# Write the extracted tensor on a .pt file
for tensor in output_mode:
output_file_dir = path.join(
caps_directory,
container,
"deeplearning_prepare_data",
subfolder,
mod_subfolder,
)
if not path.exists(output_file_dir):
os.makedirs(output_file_dir)
output_file = path.join(output_file_dir, tensor[0])
save_tensor(tensor[1], output_file)
logger.debug(f" Output tensor saved at {output_file}")
# Save parameters dictionary
preprocessing_json_path = write_preprocessing(parameters, caps_directory)
logger.info(f"Preprocessing JSON saved at {preprocessing_json_path}.")
def DeepLearningPrepareData(caps_directory, tsv_file, parameters):
import os
from os import path
from clinica.utils.inputs import check_caps_folder, clinica_file_reader
from clinica.utils.nipype import container_from_filename
from clinica.utils.participant import get_subject_session_list
from torch import save as save_tensor
from clinicadl.utils.preprocessing import write_preprocessing
from .extract_utils import (
check_mask_list,
compute_folder_and_file_type,
extract_images,
extract_patches,
extract_roi,
extract_slices,
)
logger = getLogger("clinicadl")
# Get subject and session list
check_caps_folder(caps_directory)
logger.debug(f"CAPS directory : {caps_directory}.")
is_bids_dir = False
sessions, subjects = get_subject_session_list(caps_directory, tsv_file,
is_bids_dir, False, None)
if parameters["prepare_dl"]:
logger.info(
f"{parameters['mode']}s will be extracted in Pytorch tensor from {len(sessions)} images."
)
else:
logger.info(
f"Images will be extracted in Pytorch tensor from {len(sessions)} images."
)
logger.info(
f"Information for {parameters['mode']} will be saved in output JSON file and will be used "
f"during training for on-the-fly extraction.")
logger.debug(f"List of subjects: \n{subjects}.")
logger.debug(f"List of sessions: \n{sessions}.")
# Select the correct filetype corresponding to modality
# and select the right folder output name corresponding to modality
logger.debug(
f"Selected images are preprocessed with {parameters['preprocessing']} pipeline`."
)
mod_subfolder, file_type = compute_folder_and_file_type(parameters)
parameters["file_type"] = file_type
# Input file:
input_files = clinica_file_reader(subjects, sessions, caps_directory,
file_type)
# Loop on the images
for file in input_files:
logger.debug(f" Processing of {file}.")
container = container_from_filename(file)
# Extract the wanted tensor
if parameters["mode"] == "image" or not parameters["prepare_dl"]:
subfolder = "image_based"
output_mode = extract_images(file)
logger.debug(f" Image extracted.")
elif parameters["prepare_dl"] and parameters["mode"] == "slice":
subfolder = "slice_based"
output_mode = extract_slices(
file,
slice_direction=parameters["slice_direction"],
slice_mode=parameters["slice_mode"],
discarded_slices=parameters["discarded_slices"],
)
logger.debug(f" {len(output_mode)} slices extracted.")
elif parameters["prepare_dl"] and parameters["mode"] == "patch":
subfolder = "patch_based"
output_mode = extract_patches(
file,
patch_size=parameters["patch_size"],
stride_size=parameters["stride_size"],
)
logger.debug(f" {len(output_mode)} patches extracted.")
elif parameters["prepare_dl"] and parameters["mode"] == "roi":
subfolder = "roi_based"
if parameters["preprocessing"] == "custom":
if not parameters["roi_custom_template"]:
raise ValueError(
"A custom template must be defined when the modality is set to custom."
)
parameters["roi_template"] = parameters["roi_custom_template"]
parameters["roi_mask_pattern"] = parameters[
"roi_custom_mask_pattern"]
else:
from .extract_utils import PATTERN_DICT, TEMPLATE_DICT
parameters["roi_template"] = TEMPLATE_DICT[
parameters["preprocessing"]]
parameters["roi_mask_pattern"] = PATTERN_DICT[
parameters["preprocessing"]]
parameters["masks_location"] = path.join(
caps_directory, "masks", f"tpl-{parameters['roi_template']}")
if len(parameters["roi_list"]) == 0:
raise ValueError("A list of regions must be given.")
else:
check_mask_list(
parameters["masks_location"],
parameters["roi_list"],
parameters["roi_mask_pattern"],
None if parameters["use_uncropped_image"] is None else
not parameters["use_uncropped_image"],
)
output_mode = extract_roi(
file,
masks_location=parameters["masks_location"],
mask_pattern=parameters["roi_mask_pattern"],
cropped_input=None if parameters["use_uncropped_image"] is None
else not parameters["use_uncropped_image"],
roi_names=parameters["roi_list"],
uncrop_output=parameters["uncropped_roi"],
)
logger.debug(f" ROI extracted.")
else:
raise NotImplementedError(
f"Extraction is not implemented for mode {parameters['mode']}."
)
# Write the extracted tensor on a .pt file
for filename, tensor in output_mode:
output_file_dir = path.join(
caps_directory,
container,
"deeplearning_prepare_data",
subfolder,
mod_subfolder,
)
if not path.exists(output_file_dir):
os.makedirs(output_file_dir)
output_file = path.join(output_file_dir, filename)
save_tensor(tensor, output_file)
logger.debug(f" Output tensor saved at {output_file}")
# Save parameters dictionary
preprocessing_json_path = write_preprocessing(parameters, caps_directory)
logger.info(f"Preprocessing JSON saved at {preprocessing_json_path}.")