64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
from functools import lru_cache
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from itertools import groupby
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import numpy as np
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from funcy import compose, second
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from image_prediction.stitching.utils import make_coord_getter
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class CoordGrouper:
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def __init__(self, axis, tolerance=0):
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self.c1_getter = make_coord_getter(f"{other_axis(axis)}1")
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self.c2_getter = make_coord_getter(f"{other_axis(axis)}2")
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self.tolerance = tolerance
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def group_pairs_by_lesser_coordinate(self, pairs):
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return group_by_coordinate(pairs, self.c1_getter, self.tolerance)
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def group_pairs_by_greater_coordinate(self, pairs):
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return group_by_coordinate(pairs, self.c2_getter, self.tolerance)
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def other_axis(axis):
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return "y" if axis == "x" else "x"
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def fuzzify(func, tolerance):
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def inner(item):
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nonlocal mid_points
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nonlocal lower_bounds
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nonlocal upper_bounds
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value = func(item)
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fits = (array(lower_bounds_array()) <= value) & (value <= array(upper_bounds_array()))
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if any(fits):
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return mid_points[np.argmax(fits)]
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else:
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mid_points = [*mid_points, value]
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lower_bounds = [*lower_bounds, value - tolerance]
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upper_bounds = [*upper_bounds, value + tolerance]
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return value
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def lower_bounds_array():
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return tuple(lower_bounds)
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def upper_bounds_array():
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return tuple(upper_bounds)
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@lru_cache(maxsize=None)
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def array(tpl):
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return np.array(tpl)
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lower_bounds = []
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upper_bounds = []
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mid_points = []
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return inner
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def group_by_coordinate(pairs, coord_getter, tolerance=0):
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coord_getter = fuzzify(coord_getter, tolerance)
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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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