data/TriD-master/PROSTATE/dataloaders/transform.py

import numpy as np
from batchgenerators.transforms.abstract_transforms import Compose
from batchgenerators.transforms.spatial_transforms import SpatialTransform, MirrorTransform
from batchgenerators.transforms.color_transforms import BrightnessMultiplicativeTransform, GammaTransform, \
    BrightnessTransform, ContrastAugmentationTransform
from batchgenerators.transforms.noise_transforms import GaussianNoiseTransform, GaussianBlurTransform
from batchgenerators.transforms.resample_transforms import SimulateLowResolutionTransform
from batchgenerators.transforms.crop_and_pad_transforms import RandomCropTransform


def get_train_transform(patch_size=(384, 384)):
    tr_transforms = []
    # tr_transforms.append(RandomCropTransform(crop_size=256, margins=(0, 0, 0), data_key="image", label_key="mask"))
    tr_transforms.append(
        SpatialTransform(
            patch_size, patch_center_dist_from_border=[i // 2 for i in patch_size],
            do_elastic_deform=True, alpha=(0., 900.), sigma=(9., 13.),
            do_rotation=True,
            angle_x=(-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
            angle_y=(-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
            do_scale=True, scale=(0.85, 1.25),
            border_mode_data='constant', border_cval_data=0,
            order_data=3, border_mode_seg="constant", border_cval_seg=-1,
            order_seg=1,
            random_crop=True,
            p_el_per_sample=0.2, p_scale_per_sample=0.2, p_rot_per_sample=0.2,
            independent_scale_for_each_axis=False,
            data_key="data", label_key="mask")
    )
    tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1, data_key="data"))
    tr_transforms.append(
        GaussianBlurTransform(blur_sigma=(0.5, 1.), different_sigma_per_channel=True, p_per_channel=0.5,
                              p_per_sample=0.2, data_key="data"))
    tr_transforms.append(BrightnessMultiplicativeTransform((0.75, 1.25), p_per_sample=0.15, data_key="data"))
    tr_transforms.append(BrightnessTransform(0.0, 0.1, True, p_per_sample=0.15, p_per_channel=0.5, data_key="data"))
    tr_transforms.append(ContrastAugmentationTransform(p_per_sample=0.15, data_key="data"))
    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=None, data_key="data"))
    tr_transforms.append(GammaTransform(gamma_range=(0.7, 1.5), invert_image=False, per_channel=True, retain_stats=True,
                                        p_per_sample=0.15, data_key="data"))

    tr_transforms.append(MirrorTransform(axes=(0, 1), data_key="data", label_key="mask"))

    # now we compose these transforms together
    tr_transforms = Compose(tr_transforms)
    return tr_transforms


def collate_fn_w_transform(batch):
    image, label, name = zip(*batch)
    image = np.stack(image, 0)
    label = np.stack(label, 0)
    name = np.stack(name, 0)
    data_dict = {'data': image, 'mask': label, 'name': name}
    tr_transforms = get_train_transform()
    data_dict = tr_transforms(**data_dict)
    data_dict['data'] = np.repeat(data_dict['data'], 3, axis=1)
    return data_dict


def collate_fn_wo_transform(batch):
    image, label, name = zip(*batch)
    image = np.stack(image, 0)
    label = np.stack(label, 0)
    name = np.stack(name, 0)
    data_dict = {'data': image, 'mask': label, 'name': name}
    data_dict['data'] = np.repeat(data_dict['data'], 3, axis=1)
    return data_dict