41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
from itertools import starmap, product, repeat
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from typing import Iterable
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import numpy as np
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from PIL.Image import Image
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from frozendict import frozendict
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from funcy import ilen
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from image_prediction.estimator.preprocessor.utils import image_to_normalized_tensor
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def transform_equal(a, b):
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return (list(a) if isinstance(a, map) else a) == b
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def images_equal(im1: Image, im2: Image, **kwargs):
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return np.allclose(image_to_normalized_tensor(im1), image_to_normalized_tensor(im2), **kwargs)
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def metadata_equal(mdat1: Iterable[dict], mdat2: Iterable[dict]):
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return set(map(frozendict, mdat1)) == set(map(frozendict, mdat2))
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def image_sets_equal(ims1: Iterable[Image], ims2: Iterable[Image]):
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ims1, ims2 = map(lambda x: sorted(map(image_to_normalized_tensor, x), key=np.mean), (ims1, ims2))
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n = len(ims1)
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assert isinstance(ims1, list)
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assert len(ims2) == n
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used = set()
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covered = set()
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for im1i, im2i in product(*repeat(range(n), 2)):
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if im1i not in covered and im2i not in used and images_equal(ims1[im1i], ims2[im2i]):
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covered.add(im1i)
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used.add(im2i)
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return len(covered) == len(used) == n
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