def custom_augment(img): if len(img.shape) == 3: imgs = img.expand_dims(img, axis=0) else: imgs = img out_imgs = [] for b in range(img.shape[0]): img1 = imgs[b, :, :, 0:1].astype(np.uint8) img2 = imgs[b, :, :, 1:2].astype(np.uint8) tr = Compose([ wrap2solt, slc.Stream([ slt.ImageAdditiveGaussianNoise(p=0.5, gain_range=0.3), slt.RandomRotate(p=1, rotation_range=(-10, 10)), slt.PadTransform(pad_to=int(STD_SZ[0] * 1.05)), slt.CropTransform(crop_size=STD_SZ[0], crop_mode='r'), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), ]), unpack_solt, ApplyTransform(Normalize((0.5, ), (0.5, ))) ]) img1, _ = tr((img1, 0)) img2, _ = tr((img2, 0)) out_img = torch.cat((img1, img2), dim=0) out_imgs.append(out_img) out_imgs = torch.stack(out_imgs, dim=0) return out_imgs
def get_wrist_fracture_transformation(crop_size): return transforms.Compose([ SplitDataToFunction(wrap_img_target_solt), slc.Stream([ slt.RandomFlip(p=1, axis=1), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.8, 1.2), p=1), slt.RandomShear(range_x=(-0.1, 0.1), p=0.5), slt.RandomShear(range_y=(-0.1, 0.1), p=0.5), slt.RandomRotate(rotation_range=(-10, 10), p=1), ]), v_range=(1e-5, 5e-4), p=0.8), slt.PadTransform(pad_to=(256, 256), padding='z'), slt.CropTransform(crop_size, crop_mode='r'), slc.SelectiveStream([ slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=0.5, blur_type='m', k_size=(11, )), ]), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), ]), slt.ImageGammaCorrection(p=1, gamma_range=(0.5, 1.5)), ]), DataToFunction(solt_to_img_target), ApplyByIndex(transforms.ToTensor(), 0) ])
def get_landmark_transform(config): return transforms.Compose([ # WrapImageLandmarksSOLT(), slc.Stream([ slt.RandomFlip(p=0.5, axis=1), slt.RandomScale(range_x=(0.8, 1.2), p=1), slt.RandomRotate(rotation_range=(-180, 180), p=0.2), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.8, 1.3), p=1), slt.RandomRotate(rotation_range=(-180, 180), p=1), slt.RandomShear(range_x=(-0.1, 0.1), range_y=(0, 0), p=0.5), slt.RandomShear(range_y=(-0.1, 0.1), range_x=(0, 0), p=0.5), ]), v_range=(1e-5, 2e-3), p=0.8), slt.PadTransform(int(config.dataset.crop_size * 1.4), padding='z'), slt.CropTransform(config.dataset.crop_size, crop_mode='r'), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=1, blur_type='g', k_size=(3, 5)), slt.ImageBlur(p=1, blur_type='m', k_size=(3, 5)), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), slc.Stream([ slt.ImageSaltAndPepper(p=1, gain_range=0.05), slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), ]), slc.Stream([ slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), slt.ImageSaltAndPepper(p=1, gain_range=0.01), ]), slc.Stream() ]), slt.ImageGammaCorrection(p=1, gamma_range=(0.5, 1.5)) ]), SOLTtoHourGlassGSinput(downsample=4, sigma=3), ApplyTransformByIndex(transform=dwutils.npg2tens, ids=[0, 1]), ])
def get_landmark_transform_kneel(config): cutout = slt.ImageCutOut( cutout_size=(int(config.dataset.cutout * config.dataset.augs.crop.crop_x), int(config.dataset.cutout * config.dataset.augs.crop.crop_y)), p=0.5) ppl = transforms.Compose([ slc.Stream(), slc.SelectiveStream( [ slc.Stream([ slt.RandomFlip(p=0.5, axis=1), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.9, 1.1), p=1), slt.RandomRotate(rotation_range=(-90, 90), p=1), slt.RandomShear( range_x=(-0.1, 0.1), range_y=(-0.1, 0.1), p=0.5), slt.RandomShear( range_x=(-0.1, 0.1), range_y=(-0.1, 0.1), p=0.5), ]), v_range=(1e-5, 2e-3), p=0.5), # slt.RandomScale(range_x=(0.5, 2.5), p=0.5), ]), slc.Stream() ], probs=[0.7, 0.3]), slc.Stream([ slt.PadTransform( (config.dataset.augs.pad.pad_x, config.dataset.augs.pad.pad_y), padding='z'), slt.CropTransform((config.dataset.augs.crop.crop_x, config.dataset.augs.crop.crop_y), crop_mode='r'), ]), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=1, blur_type='g', k_size=(3, 5)), slt.ImageBlur(p=1, blur_type='m', k_size=(3, 5)), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), slc.Stream([ slt.ImageSaltAndPepper(p=1, gain_range=0.05), slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), ]), slc.Stream([ slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), slt.ImageSaltAndPepper(p=1, gain_range=0.01), ]), slc.Stream() ], n=1), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), cutout if config.dataset.use_cutout else slc.Stream(), DataToFunction(solt_to_img_target), ApplyByIndex(transforms.ToTensor(), 0) ]) return ppl
def init_augs(): kvs = GlobalKVS() args = kvs['args'] cutout = slt.ImageCutOut(cutout_size=(int(args.cutout * args.crop_x), int(args.cutout * args.crop_y)), p=0.5) # plus-minus 1.3 pixels jitter = slt.KeypointsJitter(dx_range=(-0.003, 0.003), dy_range=(-0.003, 0.003)) ppl = tvt.Compose([ jitter if args.use_target_jitter else slc.Stream(), slc.SelectiveStream([ slc.Stream([ slt.RandomFlip(p=0.5, axis=1), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.8, 1.3), p=1), slt.RandomRotate(rotation_range=(-90, 90), p=1), slt.RandomShear( range_x=(-0.1, 0.1), range_y=(-0.1, 0.1), p=0.5), ]), v_range=(1e-5, 2e-3), p=0.5), slt.RandomScale(range_x=(0.5, 2.5), p=0.5), ]), slc.Stream() ], probs=[0.7, 0.3]), slc.Stream([ slt.PadTransform((args.pad_x, args.pad_y), padding='z'), slt.CropTransform((args.crop_x, args.crop_y), crop_mode='r'), ]), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=1, blur_type='g', k_size=(3, 5)), slt.ImageBlur(p=1, blur_type='m', k_size=(3, 5)), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), slc.Stream([ slt.ImageSaltAndPepper(p=1, gain_range=0.05), slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), ]), slc.Stream([ slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), slt.ImageSaltAndPepper(p=1, gain_range=0.01), ]), slc.Stream() ], n=1), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), cutout if args.use_cutout else slc.Stream(), partial(solt2torchhm, downsample=None, sigma=None), ]) kvs.update('train_trf', ppl)
def get_landmark_transform_kneel(config): cutout = slt.ImageCutOut(cutout_size=(int(config.dataset.cutout * config.dataset.augs.crop.crop_x), int(config.dataset.cutout * config.dataset.augs.crop.crop_y)), p=0.5) # plus-minus 1.3 pixels jitter = slt.KeypointsJitter(dx_range=(-0.003, 0.003), dy_range=(-0.003, 0.003)) ppl = transforms.Compose([ ColorPaddingWithSide(p=0.05, pad_size=10, side=SIDES.RANDOM, color=(50,100)), TriangularMask(p=0.025, arm_lengths=(100, 50), side=SIDES.RANDOM, color=(50,100)), TriangularMask(p=0.025, arm_lengths=(50, 100), side=SIDES.RANDOM, color=(50,100)), LowVisibilityTransform(p=0.05, alpha=0.15, bgcolor=(50,100)), SubSampleUpScale(p=0.01), jitter if config.dataset.augs.use_target_jitter else slc.Stream(), slc.SelectiveStream([ slc.Stream([ slt.RandomFlip(p=0.5, axis=1), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.9, 1.1), p=1), slt.RandomRotate(rotation_range=(-90, 90), p=1), slt.RandomShear(range_x=(-0.1, 0.1), range_y=(-0.1, 0.1), p=0.5), ]), v_range=(1e-5, 2e-3), p=0.5), # slt.RandomScale(range_x=(0.5, 2.5), p=0.5), ]), slc.Stream() ], probs=[0.7, 0.3]), slc.Stream([ slt.PadTransform((config.dataset.augs.pad.pad_x, config.dataset.augs.pad.pad_y), padding='z'), slt.CropTransform((config.dataset.augs.crop.crop_x, config.dataset.augs.crop.crop_y), crop_mode='r'), ]), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=1, blur_type='g', k_size=(3, 5)), slt.ImageBlur(p=1, blur_type='m', k_size=(3, 5)), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), slc.Stream([ slt.ImageSaltAndPepper(p=1, gain_range=0.05), slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), ]), slc.Stream([ slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), slt.ImageSaltAndPepper(p=1, gain_range=0.01), ]), slc.Stream() ], n=1), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), cutout if config.dataset.use_cutout else slc.Stream(), partial(solt2torchhm, downsample=None, sigma=None), ]) return ppl
def init_train_augs(): trf = transforms.Compose([ img_labels2solt, slc.Stream([ slt.PadTransform(pad_to=(700, 700)), slt.CropTransform(crop_size=(700, 700), crop_mode='c'), slt.ResizeTransform((310, 310)), slt.ImageAdditiveGaussianNoise(p=0.5, gain_range=0.3), slt.RandomRotate(p=1, rotation_range=(-10, 10)), slt.CropTransform(crop_size=(300, 300), crop_mode='r'), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), slt.ImageColorTransform(mode='gs2rgb') ], interpolation='bicubic', padding='z'), unpack_solt_data, partial(apply_by_index, transform=transforms.ToTensor(), idx=0), ]) return trf
def init_train_augmentation_pipeline(): kvs = GlobalKVS() ppl = transforms.Compose([ img_mask2solt, slc.Stream([ slt.RandomFlip(axis=1, p=0.5), slt.ImageGammaCorrection(gamma_range=(0.5, 2), p=0.5), slt.PadTransform(pad_to=(kvs['args'].crop_x + 1, kvs['args'].crop_y + 1)), slt.CropTransform(crop_size=(kvs['args'].crop_x, kvs['args'].crop_y), crop_mode='r') ]), solt2img_mask, partial(apply_by_index, transform=gs2tens, idx=[0, 1]), ]) return ppl
def custom_augment(img): img1 = img[:, :, 0:1].astype(np.uint8) img2 = img[:, :, 1:2].astype(np.uint8) tr = Compose([ wrap2solt, slc.Stream([ slt.ImageAdditiveGaussianNoise(p=0.5, gain_range=0.3), slt.RandomRotate(p=1, rotation_range=(-10, 10)), slt.PadTransform(pad_to=int(STD_SZ[0] * 1.05)), slt.CropTransform(crop_size=STD_SZ[0], crop_mode='r'), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), ]), unpack_solt, ApplyTransform(Normalize((0.5, ), (0.5, ))) ]) img1, _ = tr((img1, 0)) img2, _ = tr((img2, 0)) out_img = torch.cat((img1, img2), dim=0) return out_img
def init_binary_segmentation_augs(): kvs = GlobalKVS() ppl = tvt.Compose([ img_binary_mask2solt, slc.Stream([ slt.PadTransform(pad_to=(kvs['args'].pad_x, kvs['args'].pad_y)), slt.RandomFlip(axis=1, p=0.5), slt.CropTransform(crop_size=(kvs['args'].crop_x, kvs['args'].crop_y), crop_mode='r'), slt.ImageGammaCorrection(gamma_range=(kvs['args'].gamma_min, kvs['args'].gamma_max), p=0.5), ]), solt2img_binary_mask, partial(apply_by_index, transform=numpy2tens, idx=[0, 1]), ]) kvs.update('train_trf', ppl) return ppl
def init_transforms(): train_trf = Compose([ wrap2solt, slc.Stream([ slt.ImageAdditiveGaussianNoise(p=0.5, gain_range=0.3), slt.RandomRotate(p=1, rotation_range=(-10, 10)), slt.PadTransform(pad_to=int(STD_SZ[0] * 1.05)), slt.CropTransform(crop_size=STD_SZ[0], crop_mode='r'), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), ]), unpack_solt, ApplyTransform(Normalize((0.5,), (0.5,))) ]) test_trf = Compose([ wrap2solt, unpack_solt, ApplyTransform(Normalize((0.5,), (0.5,))) ]) return {"train": train_trf, "eval": test_trf}
def init_transforms(nc=1): if nc == 1: norm_mean_std = Normalize((0.5, ), (0.5, )) elif nc == 3: norm_mean_std = Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) else: raise ValueError("Not support channels of {}".format(nc)) train_trf = Compose([ wrap2solt, slc.Stream([ slt.ImageAdditiveGaussianNoise(p=0.5, gain_range=0.3), slt.RandomRotate(p=1, rotation_range=(-10, 10)), slt.PadTransform(pad_to=int(STD_SZ[0] * 1.05)), slt.CropTransform(crop_size=STD_SZ[0], crop_mode='r'), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), ]), unpack_solt, ApplyTransform(norm_mean_std) ]) test_trf = Compose([wrap2solt, unpack_solt, ApplyTransform(norm_mean_std)]) return train_trf, test_trf, custom_augment
def test_gamma_correction_raises_errors(gamma_range, to_catch): with pytest.raises(to_catch): slt.ImageGammaCorrection(gamma_range=gamma_range)
def test_lut_transforms_raise_errors(value_range, to_catch): with pytest.raises(to_catch): slt.ImageGammaCorrection(gamma_range=value_range) with pytest.raises(to_catch): slt.ImageRandomContrast(contrast_range=value_range)
def __init__(self): self.solt_stream = slc.Stream( [slt.ImageGammaCorrection(p=1, gamma_range=(0.5, 0.5))])
stream = slc.Stream([ slt.RandomProjection(slc.Stream([ slt.RandomScale(range_x=(0.8, 1.1), p=1), slt.RandomRotate(rotation_range=(-90, 90), p=1), slt.RandomShear(range_x=(-0.2, 0.2), range_y=None, p=0.7), ]), v_range=(1e-6, 3e-4), p=1), # Various cropping and padding tricks slt.PadTransform(1000, 'z'), slt.CropTransform(1000, crop_mode='c'), slt.CropTransform(950, crop_mode='r'), slt.PadTransform(1000, 'z'), # Intensity augmentations slt.ImageGammaCorrection(p=1, gamma_range=(0.5, 3)), slc.SelectiveStream([ slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=0.5, blur_type='m', k_size=(11, )), ]), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), ]), ]) for i in range(10): res = stream(dc) img_res, kp_c, lbl_c = res.data fig = plt.figure(figsize=(10, 10)) ax = fig.add_subplot(1, 1, 1)
def train_test_transforms(conf, mean=None, std=None): trf = conf['training'] prob = trf['transform_probability'] crop_size = tuple(trf['crop_size']) # Training transforms if trf['uCT']: train_transforms = [ slt.RandomProjection(slc.Stream([ slt.RandomRotate(rotation_range=tuple(trf['rotation_range']), p=prob), slt.RandomScale(range_x=tuple(trf['scale_range']), range_y=tuple(trf['scale_range']), same=False, p=prob), slt.RandomShear(range_x=tuple(trf['shear_range']), range_y=tuple(trf['shear_range']), p=prob), slt.RandomTranslate(range_x=trf['translation_range'], range_y=trf['translation_range'], p=prob) ]), v_range=tuple(trf['v_range'])), # Spatial slt.RandomFlip(p=prob), slt.PadTransform(pad_to=crop_size), slt.CropTransform(crop_mode='r', crop_size=crop_size), # Intensity #slt.ImageGammaCorrection(gamma_range=tuple(trf['gamma_range']), p=prob), #slt.ImageRandomHSV(h_range=tuple(trf['hsv_range']), # s_range=tuple(trf['hsv_range']), # v_range=tuple(trf['hsv_range']), p=prob), # Brightness/contrast slc.SelectiveStream([ slt.ImageRandomBrightness(brightness_range=tuple( trf['brightness_range']), p=prob), slt.ImageRandomContrast(contrast_range=trf['contrast_range'], p=prob) ]), # Noise slc.SelectiveStream([ slt.ImageSaltAndPepper(p=prob, gain_range=trf['gain_range_sp']), slt.ImageAdditiveGaussianNoise( p=prob, gain_range=trf['gain_range_gn']), slc.SelectiveStream([ slt.ImageBlur(p=prob, blur_type='g', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])), slt.ImageBlur(p=prob, blur_type='m', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])) ]) ]) ] else: train_transforms = [ # Projection slt.RandomProjection( slc.Stream([ slt.RandomRotate(rotation_range=tuple( trf['rotation_range']), p=prob), slt.RandomScale(range_x=tuple(trf['scale_range']), range_y=tuple(trf['scale_range']), same=False, p=prob), #slt.RandomShear(range_x=tuple(trf['shear_range']), # range_y=tuple(trf['shear_range']), p=prob), #slt.RandomTranslate(range_x=trf['translation_range'], range_y=trf['translation_range'], p=prob) ]), v_range=tuple(trf['v_range'])), # Spatial slt.RandomFlip(p=prob), slt.PadTransform(pad_to=crop_size[1]), slt.CropTransform(crop_mode='r', crop_size=crop_size), # Intensity # Add an empty stream #slc.SelectiveStream([]), slc.SelectiveStream([ slt.ImageGammaCorrection(gamma_range=tuple(trf['gamma_range']), p=prob), slt.ImageRandomHSV(h_range=tuple(trf['hsv_range']), s_range=tuple(trf['hsv_range']), v_range=tuple(trf['hsv_range']), p=prob) ]), slc.SelectiveStream([ slt.ImageRandomBrightness(brightness_range=tuple( trf['brightness_range']), p=prob), slt.ImageRandomContrast(contrast_range=trf['contrast_range'], p=prob) ]), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=prob, gain_range=trf['gain_range_sp']), slt.ImageAdditiveGaussianNoise( p=prob, gain_range=trf['gain_range_gn']), slc.SelectiveStream([ slt.ImageBlur(p=prob, blur_type='g', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])), slt.ImageBlur(p=prob, blur_type='m', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])) ]) ]) ] train_trf = [ wrap_solt, #slc.Stream(train_transforms), slc.Stream([ slt.PadTransform(pad_to=crop_size[1]), slt.CropTransform(crop_mode='r', crop_size=crop_size) ]), unwrap_solt, ApplyTransform(numpy2tens, (0, 1, 2)) ] # Validation transforms val_trf = [ wrap_solt, slc.Stream([ slt.PadTransform(pad_to=crop_size[1]), slt.CropTransform(crop_mode='r', crop_size=crop_size) ]), unwrap_solt, ApplyTransform(numpy2tens, idx=(0, 1, 2)) ] # Test transforms test_trf = [unwrap_solt, ApplyTransform(numpy2tens, idx=(0, 1, 2))] # Use normalize_channel_wise if mean and std not calculated if mean is not None and std is not None: train_trf.append( ApplyTransform(partial(normalize_channel_wise, mean=mean, std=std))) if mean is not None and std is not None: val_trf.append( ApplyTransform(partial(normalize_channel_wise, mean=mean, std=std))) # Compose transforms train_trf_cmp = Compose(train_trf) val_trf_cmp = Compose(val_trf) test_trf_cmp = Compose(test_trf) return { 'train': train_trf_cmp, 'val': val_trf_cmp, 'test': test_trf_cmp, 'train_list': train_trf, 'val_list': val_trf, 'test_list': test_trf }
def train_test_transforms(conf, mean=None, std=None, crop_size=(512, 1024)): """ Compiles the different image augmentations that are used for input images. :param conf: Transformation parameters :param mean: Dataset image mean :param std: Dataset image std :param crop_size: Image size for the segmentation model :return: Compiled transformation objects, and lists of the used transforms """ trf = conf['training'] prob = trf['transform_probability'] # Training transforms # 3D transforms if trf['experiment'] == '3D': train_transforms = [ slc.SelectiveStream([ slc.Stream([ slt.RandomProjection( slc.Stream([ slt.RandomRotate(rotation_range=tuple( trf['rotation_range']), p=prob), slt.RandomScale(range_x=tuple(trf['scale_range']), range_y=tuple(trf['scale_range']), same=False, p=prob), #slt.RandomShear(range_x=tuple(trf['shear_range']), # range_y=tuple(trf['shear_range']), p=prob), slt.RandomTranslate( range_x=trf['translation_range'], range_y=trf['translation_range'], p=prob) ]), v_range=None #tuple(trf['v_range']) ), # Spatial slt.RandomFlip(p=prob), slt.PadTransform(pad_to=crop_size), slt.CropTransform(crop_mode='r', crop_size=crop_size), # Intensity # Brightness/contrast slc.SelectiveStream([ slt.ImageRandomBrightness(brightness_range=tuple( trf['brightness_range']), p=prob), slt.ImageRandomContrast( contrast_range=trf['contrast_range'], p=prob) ]), # Noise slc.SelectiveStream([ slt.ImageSaltAndPepper( p=prob, gain_range=trf['gain_range_sp']), slt.ImageAdditiveGaussianNoise( p=prob, gain_range=trf['gain_range_gn']), slc.SelectiveStream([ slt.ImageBlur(p=prob, blur_type='g', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])), slt.ImageBlur(p=prob, blur_type='m', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])) ]) ]) ]), # Empty stream slc.Stream([ slt.PadTransform(pad_to=crop_size), slt.CropTransform(crop_mode='r', crop_size=crop_size) ]) ]) ] # 2D transforms else: train_transforms = [ slc.SelectiveStream([ slc.Stream([ # Projection slt.RandomProjection( slc.Stream([ slt.RandomRotate(rotation_range=tuple( trf['rotation_range']), p=prob), slt.RandomScale(range_x=tuple(trf['scale_range']), range_y=tuple(trf['scale_range']), same=False, p=prob), # slt.RandomShear(range_x=tuple(trf['shear_range']), # range_y=tuple(trf['shear_range']), p=prob), slt.RandomTranslate( range_x=trf['translation_range'], range_y=trf['translation_range'], p=prob) ]), v_range=tuple(trf['v_range'])), # Spatial slt.RandomFlip(p=prob), slt.PadTransform(pad_to=crop_size), slt.CropTransform(crop_mode='r', crop_size=crop_size), # Intensity slc.SelectiveStream([ slt.ImageGammaCorrection(gamma_range=tuple( trf['gamma_range']), p=prob), slt.ImageRandomHSV(h_range=tuple(trf['hsv_range']), s_range=tuple(trf['hsv_range']), v_range=tuple(trf['hsv_range']), p=prob) ]), slc.SelectiveStream([ slt.ImageRandomBrightness(brightness_range=tuple( trf['brightness_range']), p=prob), slt.ImageRandomContrast( contrast_range=trf['contrast_range'], p=prob) ]), slc.SelectiveStream([ slt.ImageSaltAndPepper( p=prob, gain_range=trf['gain_range_sp']), slt.ImageAdditiveGaussianNoise( p=prob, gain_range=trf['gain_range_gn']), slc.SelectiveStream([ slt.ImageBlur(p=prob, blur_type='g', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])), slt.ImageBlur(p=prob, blur_type='m', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])) ]) ]) ]), # Empty stream slc.Stream([ slt.PadTransform(pad_to=crop_size), slt.CropTransform(crop_mode='r', crop_size=crop_size) ]) ]) ] # Compile training transforms train_trf = [ # Move to SOLT format wrap_solt, # Transforms slc.Stream(train_transforms), # Extract image unwrap_solt, # Move to tensor ApplyTransform(numpy2tens, (0, 1, 2)) ] # Validation transforms val_trf = [ wrap_solt, slc.Stream([ slt.PadTransform(pad_to=crop_size[1]), slt.CropTransform(crop_mode='r', crop_size=crop_size) ]), unwrap_solt, ApplyTransform(numpy2tens, idx=(0, 1, 2)) ] # Test transforms test_trf = [unwrap_solt, ApplyTransform(numpy2tens, idx=(0, 1, 2))] # Normalize train and val images if mean and std are given if mean is not None and std is not None: train_trf.append( ApplyTransform(partial(normalize_channel_wise, mean=mean, std=std))) if mean is not None and std is not None: val_trf.append( ApplyTransform(partial(normalize_channel_wise, mean=mean, std=std))) # Compose transforms train_trf_cmp = Compose(train_trf) val_trf_cmp = Compose(val_trf) test_trf_cmp = Compose(test_trf) return { 'train': train_trf_cmp, 'val': val_trf_cmp, 'test': test_trf_cmp, 'train_list': train_trf, 'val_list': val_trf, 'test_list': test_trf }
def init_transforms(mean_vector, std_vector): kvs = GlobalKVS() if mean_vector is not None: mean_vector = torch.from_numpy(mean_vector).float() std_vector = torch.from_numpy(std_vector).float() norm_trf = partial(normalize_channel_wise, mean=mean_vector, std=std_vector) norm_trf = partial(apply_by_index, transform=norm_trf, idx=[0, 1, 2]) else: norm_trf = None if kvs['args'].siamese: resize_train = slc.Stream() crop_train = slt.CropTransform(crop_size=(kvs['args'].imsize, kvs['args'].imsize), crop_mode='c') else: resize_train = slt.ResizeTransform( (kvs['args'].inp_size, kvs['args'].inp_size)) crop_train = slt.CropTransform(crop_size=(kvs['args'].crop_size, kvs['args'].crop_size), crop_mode='r') train_trf = [ wrap2solt, slc.Stream([ slt.PadTransform(pad_to=(kvs['args'].imsize, kvs['args'].imsize)), slt.CropTransform(crop_size=(kvs['args'].imsize, kvs['args'].imsize), crop_mode='c'), resize_train, slt.ImageAdditiveGaussianNoise(p=0.5, gain_range=0.3), slt.RandomRotate(p=1, rotation_range=(-10, 10)), crop_train, slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), ]), unpack_solt_data, partial(pack_tensors, no_kl=kvs['args'].no_kl), ] if not kvs['args'].siamese: resize_val = slc.Stream([ slt.ResizeTransform((kvs['args'].inp_size, kvs['args'].inp_size)), slt.CropTransform(crop_size=(kvs['args'].crop_size, kvs['args'].crop_size), crop_mode='c'), ]) else: resize_val = slc.Stream() val_trf = [ wrap2solt, slc.Stream([ slt.PadTransform(pad_to=(kvs['args'].imsize, kvs['args'].imsize)), slt.CropTransform(crop_size=(kvs['args'].imsize, kvs['args'].imsize), crop_mode='c'), resize_val, ]), unpack_solt_data, partial(pack_tensors, no_kl=kvs['args'].no_kl), ] if norm_trf is not None: train_trf.append(norm_trf) val_trf.append(norm_trf) train_trf = transforms.Compose(train_trf) val_trf = transforms.Compose(val_trf) return train_trf, val_trf