# Copyright 2020 - 2022 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. import argparse import os from functools import partial import nibabel as nib import numpy as np import torch import torch.nn.functional as F from torch.cuda.amp import GradScaler, autocast import SimpleITK as sitk from monai.inferers import sliding_window_inference # from monai.data import decollate_batch from monai.losses import DiceCELoss from monai.metrics import DiceMetric from monai.networks.nets import SwinUNETR from monai.transforms import * from monai.utils.enums import MetricReduction from monai.handlers import StatsHandler, from_engine import matplotlib.pyplot as plt from PIL import Image from monai import data, transforms from monai.data import * import resource rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) resource.setrlimit(resource.RLIMIT_NOFILE, (8192, rlimit[1])) print('Setting resource limit:', str(resource.getrlimit(resource.RLIMIT_NOFILE))) os.environ['MASTER_ADDR'] = 'localhost' os.environ['MASTER_PORT'] = '28890' parser = argparse.ArgumentParser(description="process 3d to 2d") parser.add_argument( "--test_data_path", default="/data/imagesTr/", type=str, help="The path to 3d image") parser.add_argument( "--save_path", default="/data/YOUR_DATASET_NAME/process_image/", type=str, help="The path to save 2d image") roi = 96 parser.add_argument("--use_normal_dataset", default=True, help="use monai Dataset class") parser.add_argument("--feature_size", default=48, type=int, help="feature size") parser.add_argument("--batch_size", default=1, type=int, help="number of batch size") parser.add_argument("--sw_batch_size", default=1, type=int, help="number of sliding window batch size") parser.add_argument("--infer_overlap", default=0.75, type=float, help="sliding window inference overlap") parser.add_argument("--in_channels", default=1, type=int, help="number of input channels") parser.add_argument("--out_channels", default=7, type=int, help="number of output channels") parser.add_argument("--a_min", default=-175.0, type=float, help="a_min in ScaleIntensityRanged") parser.add_argument("--a_max", default=250.0, type=float, help="a_max in ScaleIntensityRanged") parser.add_argument("--b_min", default=0.0, type=float, help="b_min in ScaleIntensityRanged") parser.add_argument("--b_max", default=1.0, type=float, help="b_max in ScaleIntensityRanged") parser.add_argument("--space_x", default=1.5, type=float, help="spacing in x direction") parser.add_argument("--space_y", default=1.5, type=float, help="spacing in y direction") parser.add_argument("--space_z", default=1.5, type=float, help="spacing in z direction") parser.add_argument("--roi_x", default=roi, type=int, help="roi size in x direction") parser.add_argument("--roi_y", default=roi, type=int, help="roi size in y direction") parser.add_argument("--roi_z", default=roi, type=int, help="roi size in z direction") parser.add_argument("--dropout_rate", default=0.0, type=float, help="dropout rate") parser.add_argument("--distributed", action="store_true", help="start distributed training") parser.add_argument("--workers", default=4, type=int, help="number of workers") parser.add_argument("--spatial_dims", default=3, type=int, help="spatial dimension of input data") parser.add_argument("--use_checkpoint", default=True, help="use gradient checkpointing to save memory") parser.add_argument("--rank", default=0, type=int, help="node rank for distributed training") def check_dir(dir): if not os.path.exists(dir): os.makedirs(dir) def get_test_loader(args): """ Creates training transforms, constructs a dataset, and returns a dataloader. Args: args: Command line arguments containing dataset paths and hyperparameters. """ test_transforms = transforms.Compose([ LoadImaged(keys=["image"]), EnsureChannelFirstd(keys=["image"]), Orientationd(keys=["image"], axcodes="RAS"), Spacingd(keys=["image"], pixdim=(args.space_x, args.space_y, args.space_z), mode=("bilinear")), ScaleIntensityRanged( keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image"], source_key="image"), SpatialPadd(keys=["image"], spatial_size=(args.roi_x, args.roi_y, args.roi_z), mode='constant'), ]) # constructing training dataset test_img = [] test_name = [] dataset_list = os.listdir(args.test_data_path) check_dir(args.save_path) already_exist_list = os.listdir(args.save_path) new_list = [] for item in dataset_list: if item not in already_exist_list: new_list.append(item) for item in new_list: name = item print(name) test_img_path = os.path.join(args.test_data_path, name) test_img.append(test_img_path) test_name.append(name) data_dicts_test = [{'image': image, 'name': name} for image, name in zip(test_img, test_name)] print('test len {}'.format(len(data_dicts_test))) test_ds = Dataset(data=data_dicts_test, transform=test_transforms) test_loader = DataLoader( test_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=None, pin_memory=True ) return test_loader, test_transforms def main(): args = parser.parse_args() test_loader, test_transforms = get_test_loader(args) post_ori_transforms = Compose([EnsureTyped(keys=["image"]), Invertd(keys=["image"], transform=test_transforms, orig_keys="image", meta_keys="image_meta_dict", orig_meta_keys="image_meta_dict", meta_key_postfix="meta_dict", nearest_interp=True, to_tensor=True), SaveImaged(keys="image", meta_keys="img_meta_dict", output_dir=args.save_path, separate_folder=False, folder_layout=None, resample=False), ]) num = 0 with torch.no_grad(): for idx, batch_data in enumerate(test_loader): img = batch_data["image"] name = batch_data['name'][0] with autocast(enabled=True): for i in decollate_batch(batch_data): post_ori_transforms(i) os.rename(os.path.join(args.save_path, name.split('/')[-1][:-7] + '_trans.nii.gz'), os.path.join(args.save_path, name.split('/')[-1][:-7] + '.nii.gz')) if __name__ == "__main__": main()