def train_transforms(image, masks): seq = augmentation.get_train_augmenters_seq1() hooks_masks = augmentation.get_train_masks_augmenters_deactivator() # Convert the stochastic sequence of augmenters to a deterministic one. # The deterministic sequence will always apply the exactly same effects to the images. seq_det = seq.to_deterministic( ) # call this for each batch again, NOT only once at the start image_aug = seq_det.augment_images([image])[0] masks_aug = seq_det.augment_images([masks], hooks=hooks_masks)[0] image_aug_tensor = transforms.ToTensor()(image_aug.copy()) masks_aug = (masks_aug >= MASK_THRESHOLD).astype(np.uint8) return image_aug_tensor, masks_aug
def train_transforms(image, masks, labels=None): seq = augmentation.get_train_augmenters_seq2(mode='constant') hooks_masks = augmentation.get_train_masks_augmenters_deactivator() # Convert the stochastic sequence of augmenters to a deterministic one. # The deterministic sequence will always apply the exactly same effects to the images. seq_det = seq.to_deterministic() # call this for each batch again, NOT only once at the start image = seq_det.augment_images([image])[0] image = transforms.ToTensor()(image.copy()) image = transforms.Normalize(IMAGES_MEAN, IMAGES_STD)(image) masks = seq_det.augment_images([masks], hooks=hooks_masks)[0] masks = (masks >= MASK_THRESHOLD).astype(np.uint8) if labels is not None: labels = seq_det.augment_images([labels], hooks=hooks_masks)[0] labels = (labels > 0).astype(np.uint8) return image, masks, labels else: return image, masks
def train_transforms(image, mask, labels): seq = augmentation.get_train_augmenters_seq2(mode='constant') hooks_masks = augmentation.get_train_masks_augmenters_deactivator() # Convert the stochastic sequence of augmenters to a deterministic one. # The deterministic sequence will always apply the exactly same effects to the images. seq_det = seq.to_deterministic( ) # call this for each batch again, NOT only once at the start image_aug = seq_det.augment_images([image])[0] image_aug_tensor = transforms.ToTensor()(image_aug.copy()) image_aug_tensor = transforms.Normalize(IMAGES_MEAN, IMAGES_STD)(image_aug_tensor) mask_aug = seq_det.augment_images([mask], hooks=hooks_masks)[0] mask_aug = (mask_aug >= MASK_THRESHOLD).astype(np.uint8) labels_aug = seq_det.augment_images([labels], hooks=hooks_masks)[0] for index in range(labels_aug.shape[-1]): labels_aug[..., index] = (labels_aug[..., index] > 0).astype(np.uint8) return image_aug_tensor, mask_aug, labels_aug