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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import copy
import os
import numpy as np
import monai
from monai.bundle import ConfigParser
from monai.utils import StrEnum, ensure_tuple, optional_import
tqdm, has_tqdm = optional_import("tqdm", name="tqdm")
nib, _ = optional_import("nibabel")
logger = monai.apps.utils.get_logger(__name__)
__all__ = ["analyze_data", "create_new_data_copy", "create_new_dataset_json", "NNUNETMode"]
class NNUNETMode(StrEnum):
N_2D = "2d"
N_3D_FULLRES = "3d_fullres"
N_3D_LOWRES = "3d_lowres"
N_3D_CASCADE_FULLRES = "3d_cascade_fullres"
def analyze_data(datalist_json: dict, data_dir: str) -> tuple[int, int]:
"""
Analyze (training) data
Args:
datalist_json: original data list .json (required by most monai tutorials).
data_dir: raw data directory.
"""
img = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True, simple_keys=True)(
os.path.join(data_dir, datalist_json["training"][0]["image"])
)
num_input_channels = img.size()[0] if img.dim() == 4 else 1
logger.info(f"num_input_channels: {num_input_channels}")
num_foreground_classes = 0
for _i in range(len(datalist_json["training"])):
seg = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True, simple_keys=True)(
os.path.join(data_dir, datalist_json["training"][_i]["label"])
)
num_foreground_classes = max(num_foreground_classes, int(seg.max()))
logger.info(f"num_foreground_classes: {num_foreground_classes}")
return num_input_channels, num_foreground_classes
def create_new_data_copy(
test_key: str, datalist_json: dict, data_dir: str, num_input_channels: int, output_datafolder: str
) -> None:
"""
Create and organize a new copy of data to meet the requirements of nnU-Net V2
Args:
test_key: key for test data in the data list .json.
datalist_json: original data list .json (required by most monai tutorials).
data_dir: raw data directory.
num_input_channels: number of input (image) channels.
output_datafolder: output folder.
"""
_index = 0
new_datalist_json: dict = {"training": [], test_key: []}
for _key, _folder, _label_folder in list(
zip(["training", test_key], ["imagesTr", "imagesTs"], ["labelsTr", "labelsTs"])
):
if _key is None:
continue
logger.info(f"converting data section: {_key}...")
for _k in tqdm(range(len(datalist_json[_key]))) if has_tqdm else range(len(datalist_json[_key])):
orig_img_name = (
datalist_json[_key][_k]["image"]
if isinstance(datalist_json[_key][_k], dict)
else datalist_json[_key][_k]
)
img_name = f"case_{_index}"
_index += 1
# copy image
nda = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True, simple_keys=True)(
os.path.join(data_dir, orig_img_name)
)
affine = nda.meta["original_affine"]
nda = nda.numpy()
for _l in range(num_input_channels):
outimg = nib.Nifti1Image(nda[_l, ...], affine)
index = "_" + str(_l + 10000)[-4:]
nib.save(outimg, os.path.join(output_datafolder, _folder, img_name + index + ".nii.gz"))
# copy label
if isinstance(datalist_json[_key][_k], dict) and "label" in datalist_json[_key][_k]:
nda = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True, simple_keys=True)(
os.path.join(data_dir, datalist_json[_key][_k]["label"])
)
affine = nda.meta["original_affine"]
nda = nda.numpy().astype(np.uint8)
nda = nda[0, ...] if nda.ndim == 4 and nda.shape[0] == 1 else nda
nib.save(
nib.Nifti1Image(nda, affine), os.path.join(output_datafolder, _label_folder, img_name + ".nii.gz")
)
if isinstance(datalist_json[_key][_k], dict):
_val = copy.deepcopy(datalist_json[_key][_k])
_val["new_name"] = img_name
new_datalist_json[_key].append(_val)
else:
new_datalist_json[_key].append({"image": datalist_json[_key][_k], "new_name": img_name})
ConfigParser.export_config_file(
config=new_datalist_json,
filepath=os.path.join(output_datafolder, "datalist.json"),
fmt="json",
sort_keys=True,
indent=4,
ensure_ascii=False,
)
return
def create_new_dataset_json(
modality: str, num_foreground_classes: int, num_input_channels: int, num_training_data: int, output_filepath: str
) -> None:
"""
Create a new copy of dataset .json to meet the requirements of nnU-Net V2
Args:
modality: image modality, could a string or a list of strings.
num_foreground_classes: number of foreground classes.
num_input_channels: number of input (image) channels.
num_training_data: number of training data.
output_filepath: output file path/name.
"""
new_json_data: dict = {}
# modality = self.input_info.pop("modality")
modality = ensure_tuple(modality) # type: ignore
new_json_data["channel_names"] = {}
for _j in range(num_input_channels):
new_json_data["channel_names"][str(_j)] = modality[_j]
new_json_data["labels"] = {}
new_json_data["labels"]["background"] = 0
for _j in range(num_foreground_classes):
new_json_data["labels"][f"class{_j + 1}"] = _j + 1
# new_json_data["numTraining"] = len(datalist_json["training"])
new_json_data["numTraining"] = num_training_data
new_json_data["file_ending"] = ".nii.gz"
ConfigParser.export_config_file(
config=new_json_data,
# filepath=os.path.join(raw_data_foldername, "dataset.json"),
filepath=output_filepath,
fmt="json",
sort_keys=True,
indent=4,
ensure_ascii=False,
)
return
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