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from typing import Dict, Optional |
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import torch |
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from ...data.image import LabelMap |
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from ...data.subject import Subject |
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from ...typing import TypePatchSize |
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from ...constants import TYPE, LABEL |
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from .weighted import WeightedSampler |
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class LabelSampler(WeightedSampler): |
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r"""Extract random patches with labeled voxels at their center. |
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This sampler yields patches whose center value is greater than 0 |
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in the :attr:`label_name`. |
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Args: |
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patch_size: See :class:`~torchio.data.PatchSampler`. |
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label_name: Name of the label image in the subject that will be used to |
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generate the sampling probability map. If ``None``, the first image |
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of type :attr:`torchio.LABEL` found in the subject subject will be |
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used. |
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label_probabilities: Dictionary containing the probability that each |
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class will be sampled. Probabilities do not need to be normalized. |
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For example, a value of ``{0: 0, 1: 2, 2: 1, 3: 1}`` will create a |
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sampler whose patches centers will have 50% probability of being |
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labeled as ``1``, 25% of being ``2`` and 25% of being ``3``. |
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If ``None``, the label map is binarized and the value is set to |
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``{0: 0, 1: 1}``. |
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If the input has multiple channels, a value of |
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``{0: 0, 1: 2, 2: 1, 3: 1}`` will create a |
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sampler whose patches centers will have 50% probability of being |
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taken from a non zero value of channel ``1``, 25% from channel |
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``2`` and 25% from channel ``3``. |
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Example: |
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>>> import torchio as tio |
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>>> subject = tio.datasets.Colin27() |
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>>> subject |
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Colin27(Keys: ('t1', 'head', 'brain'); images: 3) |
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>>> subject = tio.SubjectsDataset([subject])[0] |
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>>> sampler = tio.data.LabelSampler(64, 'brain') |
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>>> generator = sampler(subject) |
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>>> for patch in generator: |
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... print(patch.shape) |
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If you want a specific number of patches from a volume, e.g. 10: |
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>>> generator = sampler(subject, num_patches=10) |
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>>> for patch in iterator: |
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... print(patch.shape) |
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""" |
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def __init__( |
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self, |
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patch_size: TypePatchSize, |
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label_name: Optional[str] = None, |
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label_probabilities: Optional[Dict[int, float]] = None, |
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): |
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super().__init__(patch_size, probability_map=label_name) |
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self.label_probabilities_dict = label_probabilities |
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def get_probability_map_image(self, subject: Subject) -> LabelMap: |
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if self.probability_map_name is None: |
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for image in subject.get_images(intensity_only=False): |
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if image[TYPE] == LABEL: |
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label_map = image |
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break |
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else: |
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images = subject.get_images(intensity_only=False) |
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message = ( |
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f'No label maps found in subject {subject} with image paths' |
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f' {[image.path for image in images]}' |
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) |
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raise RuntimeError(message) |
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elif self.probability_map_name in subject: |
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label_map = subject[self.probability_map_name] |
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else: |
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message = ( |
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f'Image "{self.probability_map_name}"' |
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f' not found in subject subject: {subject}' |
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) |
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raise KeyError(message) |
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return label_map |
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def get_probability_map(self, subject: Subject) -> torch.Tensor: |
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label_map_tensor = self.get_probability_map_image(subject).data |
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label_map_tensor = label_map_tensor.float() |
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if self.label_probabilities_dict is None: |
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return label_map_tensor > 0 |
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probability_map = self.get_probabilities_from_label_map( |
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label_map_tensor, |
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self.label_probabilities_dict, |
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) |
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return probability_map |
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@staticmethod |
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def get_probabilities_from_label_map( |
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label_map: torch.Tensor, |
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label_probabilities_dict: Dict[int, float], |
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) -> torch.Tensor: |
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"""Create probability map according to label map probabilities.""" |
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multichannel = label_map.shape[0] > 1 |
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probability_map = torch.zeros_like(label_map) |
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label_probs = torch.Tensor(list(label_probabilities_dict.values())) |
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normalized_probs = label_probs / label_probs.sum() |
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iterable = zip(label_probabilities_dict, normalized_probs) |
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for label, label_probability in iterable: |
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if multichannel: |
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mask = label_map[label] |
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else: |
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mask = label_map == label |
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label_size = mask.sum() |
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if not label_size: |
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continue |
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prob_voxels = label_probability / label_size |
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if multichannel: |
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probability_map[label] = prob_voxels * mask |
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else: |
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probability_map[mask] = prob_voxels |
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if multichannel: |
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probability_map = probability_map.sum(dim=0, keepdim=True) |
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return probability_map |
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fepegar /
torchio
