57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
from itertools import groupby, chain
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from typing import Iterable, List
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from funcy import compose, second
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from image_prediction.image_extractor.extractor import ImageMetadataPair
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from image_prediction.stitcher.utils import make_coord_getter, make_group_merger
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from image_prediction.utils.generic import until
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class Stitcher:
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@staticmethod
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def groupby(pairs, coord_getter):
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pairs = sorted(pairs, key=coord_getter)
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return map(compose(list, second), groupby(pairs, coord_getter))
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@staticmethod
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def other_axis(axis):
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return "y" if axis == "x" else "x"
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def merge_along_axis(self, pairs, axis):
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def group_pairs_by_c1(pairs):
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return self.groupby(pairs, c1_getter)
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def group_by_c2(pairs):
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return self.groupby(pairs, c2_getter)
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def group_pairs_within_groups_by_c2(groups):
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return map(group_by_c2, groups)
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def merge_groups_along_orthogonal_axis(groups):
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return map(group_merger, groups)
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c1_getter = make_coord_getter(f"{self.other_axis(axis)}1")
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c2_getter = make_coord_getter(f"{self.other_axis(axis)}2")
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group_merger = make_group_merger(axis)
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groups_of_pairs_with_same_c1 = group_pairs_by_c1(pairs)
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groups_of_groups_of_pairs_with_same_c1_and_c2 = group_pairs_within_groups_by_c2(groups_of_pairs_with_same_c1)
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groups_of_pairs_with_matching_c1_and_c2 = chain(*groups_of_groups_of_pairs_with_same_c1_and_c2)
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groups_of_merged_pairs = merge_groups_along_orthogonal_axis(groups_of_pairs_with_matching_c1_and_c2)
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pairs = chain(*groups_of_merged_pairs)
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return pairs
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def merge_along_both_axes(self, pairs):
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pairs = self.merge_along_axis(pairs, "x")
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pairs = list(self.merge_along_axis(pairs, "y"))
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return pairs
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def stitch(self, pairs: Iterable[ImageMetadataPair]) -> List[ImageMetadataPair]:
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def break_condition(pairs1, pairs2):
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return len(pairs1) == len(pairs2)
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return until(self.merge_along_both_axes, break_condition, pairs)
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