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import torch
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import numpy as np
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import scipy
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import os
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from pathlib import Path
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import folder_paths
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FONTS_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "fonts")
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SCRIPT_DIR = Path(__file__).parent
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folder_paths.add_model_folder_path("luts", (SCRIPT_DIR / "luts").as_posix())
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folder_paths.add_model_folder_path(
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"luts", (Path(folder_paths.models_dir) / "luts").as_posix()
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)
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class AnyType(str):
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def __ne__(self, __value: object) -> bool:
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return False
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def min_(tensor_list):
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x = torch.stack(tensor_list)
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mn = x.min(axis=0)[0]
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return torch.clamp(mn, min=0)
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def max_(tensor_list):
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x = torch.stack(tensor_list)
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mx = x.max(axis=0)[0]
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return torch.clamp(mx, max=1)
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def expand_mask(mask, expand, tapered_corners):
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c = 0 if tapered_corners else 1
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kernel = np.array([[c, 1, c],
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[1, 1, 1],
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[c, 1, c]])
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mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
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out = []
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for m in mask:
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output = m.numpy()
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for _ in range(abs(expand)):
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if expand < 0:
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output = scipy.ndimage.grey_erosion(output, footprint=kernel)
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else:
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output = scipy.ndimage.grey_dilation(output, footprint=kernel)
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output = torch.from_numpy(output)
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out.append(output)
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return torch.stack(out, dim=0)
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def parse_string_to_list(s):
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elements = s.split(',')
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result = []
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def parse_number(s):
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try:
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if '.' in s:
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return float(s)
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else:
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return int(s)
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except ValueError:
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return 0
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def decimal_places(s):
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if '.' in s:
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return len(s.split('.')[1])
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return 0
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for element in elements:
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element = element.strip()
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if '...' in element:
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start, rest = element.split('...')
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end, step = rest.split('+')
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decimals = decimal_places(step)
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start = parse_number(start)
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end = parse_number(end)
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step = parse_number(step)
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current = start
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if (start > end and step > 0) or (start < end and step < 0):
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step = -step
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while current <= end:
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result.append(round(current, decimals))
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current += step
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else:
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result.append(round(parse_number(element), decimal_places(element)))
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return result |
