import inspect import multiprocessing import os import shutil import sys import warnings from copy import deepcopy from datetime import datetime from time import time, sleep from typing import Union, Tuple, List import numpy as np import torch from batchgenerators.dataloading.single_threaded_augmenter import SingleThreadedAugmenter from batchgenerators.transforms.abstract_transforms import AbstractTransform, Compose from batchgenerators.transforms.color_transforms import BrightnessMultiplicativeTransform, \ ContrastAugmentationTransform, GammaTransform from batchgenerators.transforms.noise_transforms import GaussianNoiseTransform, GaussianBlurTransform from batchgenerators.transforms.resample_transforms import SimulateLowResolutionTransform from batchgenerators.transforms.spatial_transforms import SpatialTransform, MirrorTransform from batchgenerators.transforms.utility_transforms import RemoveLabelTransform, RenameTransform, NumpyToTensor def get_train_transforms(patch_size, mirror_axes=None): tr_transforms = [] patch_size_spatial = patch_size ignore_axes = None angle = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi) tr_transforms.append(SpatialTransform( patch_size_spatial, patch_center_dist_from_border=None, do_elastic_deform=False, alpha=(0, 0), sigma=(0, 0), do_rotation=True, angle_x=angle, angle_y=angle, angle_z=angle, p_rot_per_axis=1, # todo experiment with this do_scale=True, scale=(0.7, 1.4), border_mode_data="constant", border_cval_data=0, order_data=3, border_mode_seg="constant", border_cval_seg=-1, order_seg=1, random_crop=False, # random cropping is part of our dataloaders p_el_per_sample=0, p_scale_per_sample=0.2, p_rot_per_sample=0.2, independent_scale_for_each_axis=False # todo experiment with this )) tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1)) tr_transforms.append(GaussianBlurTransform((0.5, 1.), different_sigma_per_channel=True, p_per_sample=0.2, p_per_channel=0.5)) tr_transforms.append(BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25), p_per_sample=0.15)) tr_transforms.append(ContrastAugmentationTransform(p_per_sample=0.15)) tr_transforms.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), per_channel=True, p_per_channel=0.5, order_downsample=0, order_upsample=3, p_per_sample=0.25, ignore_axes=ignore_axes)) tr_transforms.append(GammaTransform((0.7, 1.5), True, True, retain_stats=True, p_per_sample=0.1)) tr_transforms.append(GammaTransform((0.7, 1.5), False, True, retain_stats=True, p_per_sample=0.3)) if mirror_axes is not None and len(mirror_axes) > 0: tr_transforms.append(MirrorTransform(mirror_axes)) tr_transforms.append(RemoveLabelTransform(-1, 0)) tr_transforms.append(NumpyToTensor(['data', 'seg'], 'float')) tr_transforms = Compose(tr_transforms) return tr_transforms def get_train_transforms_nomirror(patch_size, mirror_axes=None): tr_transforms = [] patch_size_spatial = patch_size ignore_axes = None angle = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi) tr_transforms.append(SpatialTransform( patch_size_spatial, patch_center_dist_from_border=None, do_elastic_deform=False, alpha=(0, 0), sigma=(0, 0), do_rotation=True, angle_x=angle, angle_y=angle, angle_z=angle, p_rot_per_axis=1, # todo experiment with this do_scale=True, scale=(0.7, 1.4), border_mode_data="constant", border_cval_data=0, order_data=3, border_mode_seg="constant", border_cval_seg=-1, order_seg=1, random_crop=False, # random cropping is part of our dataloaders p_el_per_sample=0, p_scale_per_sample=0.2, p_rot_per_sample=0.2, independent_scale_for_each_axis=False # todo experiment with this )) tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1)) tr_transforms.append(GaussianBlurTransform((0.5, 1.), different_sigma_per_channel=True, p_per_sample=0.2, p_per_channel=0.5)) tr_transforms.append(BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25), p_per_sample=0.15)) tr_transforms.append(ContrastAugmentationTransform(p_per_sample=0.15)) tr_transforms.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), per_channel=True, p_per_channel=0.5, order_downsample=0, order_upsample=3, p_per_sample=0.25, ignore_axes=ignore_axes)) tr_transforms.append(GammaTransform((0.7, 1.5), True, True, retain_stats=True, p_per_sample=0.1)) tr_transforms.append(GammaTransform((0.7, 1.5), False, True, retain_stats=True, p_per_sample=0.3)) # if mirror_axes is not None and len(mirror_axes) > 0: # tr_transforms.append(MirrorTransform(mirror_axes)) tr_transforms.append(RemoveLabelTransform(-1, 0)) tr_transforms.append(NumpyToTensor(['data', 'seg'], 'float')) tr_transforms = Compose(tr_transforms) return tr_transforms def get_train_transforms_onlymirror(patch_size, mirror_axes=None): tr_transforms = [] patch_size_spatial = patch_size ignore_axes = None angle = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi) # tr_transforms.append(SpatialTransform( # patch_size_spatial, patch_center_dist_from_border=None, # do_elastic_deform=False, alpha=(0, 0), sigma=(0, 0), # do_rotation=True, angle_x=angle, angle_y=angle, angle_z=angle, # p_rot_per_axis=1, # todo experiment with this # do_scale=True, scale=(0.7, 1.4), # border_mode_data="constant", border_cval_data=0, order_data=3, # border_mode_seg="constant", border_cval_seg=-1, order_seg=1, # random_crop=False, # random cropping is part of our dataloaders # p_el_per_sample=0, p_scale_per_sample=0.2, p_rot_per_sample=0.2, # independent_scale_for_each_axis=False # todo experiment with this # )) tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1)) tr_transforms.append(GaussianBlurTransform((0.5, 1.), different_sigma_per_channel=True, p_per_sample=0.2, p_per_channel=0.5)) tr_transforms.append(BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25), p_per_sample=0.15)) tr_transforms.append(ContrastAugmentationTransform(p_per_sample=0.15)) tr_transforms.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), per_channel=True, p_per_channel=0.5, order_downsample=0, order_upsample=3, p_per_sample=0.25, ignore_axes=ignore_axes)) tr_transforms.append(GammaTransform((0.7, 1.5), True, True, retain_stats=True, p_per_sample=0.1)) tr_transforms.append(GammaTransform((0.7, 1.5), False, True, retain_stats=True, p_per_sample=0.3)) if mirror_axes is not None and len(mirror_axes) > 0: tr_transforms.append(MirrorTransform(mirror_axes)) tr_transforms.append(RemoveLabelTransform(-1, 0)) tr_transforms.append(NumpyToTensor(['data', 'seg'], 'float')) tr_transforms = Compose(tr_transforms) return tr_transforms def get_train_transforms_onlyspatial(patch_size, mirror_axes=None): tr_transforms = [] patch_size_spatial = patch_size ignore_axes = None angle = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi) tr_transforms.append(SpatialTransform( patch_size_spatial, patch_center_dist_from_border=None, do_elastic_deform=False, alpha=(0, 0), sigma=(0, 0), do_rotation=True, angle_x=angle, angle_y=angle, angle_z=angle, p_rot_per_axis=1, # todo experiment with this do_scale=True, scale=(0.7, 1.4), border_mode_data="constant", border_cval_data=0, order_data=3, border_mode_seg="constant", border_cval_seg=-1, order_seg=1, random_crop=False, # random cropping is part of our dataloaders p_el_per_sample=0, p_scale_per_sample=0.2, p_rot_per_sample=0.2, independent_scale_for_each_axis=False # todo experiment with this )) # tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1)) # tr_transforms.append(GaussianBlurTransform((0.5, 1.), different_sigma_per_channel=True, p_per_sample=0.2, # p_per_channel=0.5)) # tr_transforms.append(BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25), p_per_sample=0.15)) # tr_transforms.append(ContrastAugmentationTransform(p_per_sample=0.15)) # tr_transforms.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), per_channel=True, # p_per_channel=0.5, # order_downsample=0, order_upsample=3, p_per_sample=0.25, # ignore_axes=ignore_axes)) # tr_transforms.append(GammaTransform((0.7, 1.5), True, True, retain_stats=True, p_per_sample=0.1)) # tr_transforms.append(GammaTransform((0.7, 1.5), False, True, retain_stats=True, p_per_sample=0.3)) if mirror_axes is not None and len(mirror_axes) > 0: tr_transforms.append(MirrorTransform(mirror_axes)) tr_transforms.append(RemoveLabelTransform(-1, 0)) tr_transforms.append(NumpyToTensor(['data', 'seg'], 'float')) tr_transforms = Compose(tr_transforms) return tr_transforms def get_train_transforms_noaug(patch_size, mirror_axes=None): tr_transforms = [] # patch_size_spatial = patch_size # ignore_axes = None # angle = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi) # tr_transforms.append(SpatialTransform( # patch_size_spatial, patch_center_dist_from_border=None, # do_elastic_deform=False, alpha=(0, 0), sigma=(0, 0), # do_rotation=True, angle_x=angle, angle_y=angle, angle_z=angle, # p_rot_per_axis=1, # todo experiment with this # do_scale=True, scale=(0.7, 1.4), # border_mode_data="constant", border_cval_data=0, order_data=3, # border_mode_seg="constant", border_cval_seg=-1, order_seg=1, # random_crop=False, # random cropping is part of our dataloaders # p_el_per_sample=0, p_scale_per_sample=0.2, p_rot_per_sample=0.2, # independent_scale_for_each_axis=False # todo experiment with this # )) # tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1)) # tr_transforms.append(GaussianBlurTransform((0.5, 1.), different_sigma_per_channel=True, p_per_sample=0.2, # p_per_channel=0.5)) # tr_transforms.append(BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25), p_per_sample=0.15)) # tr_transforms.append(ContrastAugmentationTransform(p_per_sample=0.15)) # tr_transforms.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), per_channel=True, # p_per_channel=0.5, # order_downsample=0, order_upsample=3, p_per_sample=0.25, # ignore_axes=ignore_axes)) # tr_transforms.append(GammaTransform((0.7, 1.5), True, True, retain_stats=True, p_per_sample=0.1)) # tr_transforms.append(GammaTransform((0.7, 1.5), False, True, retain_stats=True, p_per_sample=0.3)) # if mirror_axes is not None and len(mirror_axes) > 0: # tr_transforms.append(MirrorTransform(mirror_axes)) tr_transforms.append(RemoveLabelTransform(-1, 0)) tr_transforms.append(NumpyToTensor(['data', 'seg'], 'float')) tr_transforms = Compose(tr_transforms) return tr_transforms def get_validation_transforms() -> AbstractTransform: val_transforms = [] val_transforms.append(RemoveLabelTransform(-1, 0)) # val_transforms.append(RenameTransform('seg', 'target', True)) val_transforms.append(NumpyToTensor(['data', 'seg'], 'float')) val_transforms = Compose(val_transforms) return val_transforms # import SimpleITK as sitk # import matplotlib.pyplot as plt # image = sitk.ReadImage("/Users/xingzhaohu/Documents/工作/code/medical_image_processing/SSL/BraTS20_Training_365/BraTS20_Training_365_flair.nii.gz") # label = sitk.ReadImage("/Users/xingzhaohu/Documents/工作/code/medical_image_processing/SSL/BraTS20_Training_365/BraTS20_Training_365_seg.nii.gz") # # image = sitk.ReadImage("./AIIB/image/AIIB23_171.nii.gz") # # label = sitk.ReadImage("./AIIB/gt/AIIB23_171.nii.gz") # image_arr = sitk.GetArrayFromImage(image) # label_arr = sitk.GetArrayFromImage(label) # intensityproperties = {} # norm = RescaleTo01Normalization(intensityproperties=intensityproperties) # image_arr = image_arr[0:128, 0:128, 0:128][None, None] # label_arr = label_arr[0:128, 0:128, 0:128][None, None] # image_arr = norm.run(image_arr, label_arr) # print(image_arr.shape, label_arr.shape) # tr_transforms = Compose(tr_transforms) # trans_out = tr_transforms(data=image_arr, seg=label_arr) # image_arr_aug = trans_out["data"] # label_arr_aug = trans_out["seg"] # print(image_arr_aug.shape, label_arr_aug.shape) # for i in range(40, 128): # plt.subplot(1, 4, 1) # plt.imshow(image_arr[0, 0, i], cmap="gray") # plt.subplot(1, 4, 2) # plt.imshow(label_arr[0, 0, i], cmap="gray") # plt.subplot(1, 4, 3) # plt.imshow(image_arr_aug[0, 0, i], cmap="gray") # plt.subplot(1, 4, 4) # plt.imshow(label_arr_aug[0, 0, i], cmap="gray") # plt.show()