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May 17, 2020 01:08
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| from collections import defaultdict | |
| import numpy as np | |
| from typing import Dict, Any, List, Union, Callable, Set, Iterable, Iterator, Optional, Tuple | |
| from dataclasses import dataclass | |
| @dataclass | |
| class Image: | |
| """ Representation of an image, along with metadata. """ | |
| data: np.ndarray | |
| header: Dict[str, Any] | |
| @property | |
| def shape(self) -> Tuple[int]: | |
| return self.data.shape | |
| def metadata(self, *keys: str) -> Tuple[Any]: | |
| return tuple(self.header.get(key, None) for key in keys) | |
| class ImageSet: | |
| """ A queryable set of images. """ | |
| def __init__(self, *imagesets: Union[Image, Iterable[Image]]): | |
| self.images = [] | |
| for ims in imagesets: | |
| if isinstance(ims, Image): | |
| self.images.append(ims) | |
| else: | |
| for im in ims: | |
| self.images.append(im) | |
| def __iter__(self) -> Iterator[Image]: | |
| return iter(self.images) | |
| def __len__(self) -> int: | |
| return len(self.images) | |
| def query(self, **kwargs) -> 'ImageSet': | |
| """ Get a subset of images with metadata satisfying certain conditions. """ | |
| def matches(image: Image) -> bool: | |
| return image.metadata(*kwargs.keys()) == tuple(kwargs.values()) | |
| return ImageSet([img for img in self if matches(img)]) | |
| def __repr__(self): | |
| return f"<ImageSet of size {len(self)}>" | |
| @dataclass | |
| class Transform: | |
| """ A transformation mapping an ImageSet to an ImageSet. """ | |
| transform_op: Callable[..., Union[Image, Iterable[Image]]] | |
| partition_keys: Optional[List[str]] = None | |
| def __call__(self, images: ImageSet) -> ImageSet: | |
| def partition_value(image: Image): | |
| if self.partition_keys is not None: | |
| return image.metadata(*self.partition_keys) | |
| else: | |
| return id(image) | |
| partition = defaultdict(list) | |
| for image in images: | |
| partition[partition_value(image)].append(image) | |
| result = ImageSet([self.transform_op(*images) for images in partition.values()]) | |
| return result | |
| # Examples | |
| a = Image(data=np.random.rand(10), header={'type': 'raw', 'segment': 10}) | |
| b = Image(data=np.random.rand(10), header={'type': 'hr', 'segment': 10}) | |
| c = Image(data=np.random.rand(10), header={'type': 'raw', 'segment': 12}) | |
| d = Image(data=np.random.rand(10), header={'type': 'hr', 'segment': 12}) | |
| images = ImageSet(a,b,c,d) | |
| raw = images.query(type='raw') | |
| doubled = ImageSet(images, images) | |
| def increment_data(img: Image) -> Image: | |
| return Image(data=img.data + 1.0, header=img.header) | |
| def subtract_raw(*imgs: Image) -> List[Image]: | |
| raws = [img for img in imgs if img.metadata('type') == ('raw',)] | |
| assert len(raws) == 1 | |
| raw, = raws | |
| return [Image(data=img.data-raw.data, header=img.header) | |
| for img in imgs if img.metadata('type') == ('hr',)] | |
| IncrementTransform = Transform(transform_op=increment_data) | |
| SubtractRaw = Transform(transform_op=subtract_raw, partition_keys=['segment']) | |
| reduced = SubtractRaw(images) |
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