Source code for bio_volumentations.core.transforms_interface

# ============================================================================================= #
#  Author:       Pavel Iakubovskii, ZFTurbo, ashawkey, Dominik Müller,                          #
#                Samuel Šuľan, Lucia Hradecká, Filip Lux                                        #
#  Copyright:    albumentations:    : https://github.com/albumentations-team                    #
#                Pavel Iakubovskii  : https://github.com/qubvel                                 #
#                ZFTurbo            : https://github.com/ZFTurbo                                #
#                ashawkey           : https://github.com/ashawkey                               #
#                Dominik Müller     : https://github.com/muellerdo                              #
#                Lucia Hradecká     : lucia.d.hradecka@gmail.com                                #
#                Filip Lux          : lux.filip@gmail.com                                       #
#                                                                                               #
#  Volumentations History:                                                                      #
#       - Original:                 https://github.com/albumentations-team/albumentations       #
#       - 3D Conversion:            https://github.com/ashawkey/volumentations                  #
#       - Continued Development:    https://github.com/ZFTurbo/volumentations                   #
#       - Enhancements:             https://github.com/qubvel/volumentations                    #
#       - Further Enhancements:     https://github.com/muellerdo/volumentations                 #
#       - Biomedical Enhancements:  https://gitlab.fi.muni.cz/cbia/bio-volumentations           #
#                                                                                               #
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# ============================================================================================= #


import random
import numpy as np
from typing import Sequence, Tuple

# DEBUG only flag
VERBOSE = False


[docs] class Transform: """The base class. """ def __init__(self, always_apply=False, p=0.5): assert 0 <= p <= 1 self.p = p self.always_apply = always_apply def __call__(self, force_apply, targets, **data): if force_apply or self.always_apply or random.random() < self.p: params = self.get_params(**data) if VERBOSE: print('RUN', self.__class__.__name__, params) for k, v in data.items(): if k in targets[0]: data[k] = self.apply(v, **params) else: data[k] = v return data
[docs] def get_params(self, **data): # Shared parameters for one apply (usually random values). return {}
[docs] def apply(self, volume, **params): raise NotImplementedError
[docs] class DualTransform(Transform): """The base class of transforms applied to all target types. """ def __call__(self, force_apply, targets, **data): if force_apply or self.always_apply or random.random() < self.p: params = self.get_params(**data) if VERBOSE: print('RUN', self.__class__.__name__, params) for k, v in data.items(): if k in targets[0]: data[k] = self.apply(v, **params) elif k in targets[1]: data[k] = self.apply_to_mask(v, **params) elif k in targets[2]: data[k] = self.apply_to_float_mask(v, **params) else: data[k] = v return data
[docs] def apply_to_mask(self, mask, **params): return self.apply(mask, **params)
[docs] def apply_to_float_mask(self, float_mask, **params): return self.apply_to_mask(float_mask, **params)
[docs] class ImageOnlyTransform(Transform): """The base class of transforms applied to the `image` target only. """ @property def targets(self): return {"image": self.apply}