File size: 13,728 Bytes
b4d7ac8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 |
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() |