80 lines
1.4 KiB
Python
80 lines
1.4 KiB
Python
from __future__ import annotations
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import cv2
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import numpy as np
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from PIL import Image
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from funcy import first, iterate, keep
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from numpy import generic
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def copy_and_normalize_channels(image):
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if isinstance(image, Image.Image):
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image = np.array(image)
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image = image.copy()
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try:
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image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
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except cv2.error:
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pass
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return image
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def npconvert(ob):
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if isinstance(ob, generic):
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return ob.item()
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raise TypeError
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def lift(fn):
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def lifted(coll):
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yield from map(fn, coll)
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return lifted
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def star(fn):
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def starred(args):
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return fn(*args)
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return starred
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def lstarkeep(fn, coll):
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return list(starkeep(fn, coll))
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def starkeep(fn, coll):
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yield from keep(star(fn), coll)
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def until(cond, func, *args, **kwargs):
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return first(filter(cond, iterate(func, *args, **kwargs)))
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def conj(x, xs):
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return [x, *xs]
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def rconj(xs, x):
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return [*xs, x]
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def make_merger_sentinel():
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def no_new_mergers(records):
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nonlocal number_of_records_so_far
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number_of_records_now = len(records)
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if number_of_records_now == number_of_records_so_far:
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return True
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else:
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number_of_records_so_far = number_of_records_now
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return False
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number_of_records_so_far = -1
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return no_new_mergers
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