Master
943acbc573
Durcit la convention de nommage des projets (dérive constatée : 'Sliding Automation', 'code_versioning'... au lieu des formes canoniques). - trilium_api.py : projets_canoniques() lit le référentiel = valeurs du label projet sur les notes de type=projet (source unique, pas de constante en dur). Note-projet CodeVersioning créée (manquait). - mcp_server.py : _valider_projet() branché dans les 6 tools de création (add_decision/history/backlog, new_conversation, create_entite, add_skill). Refuse un projet non canonique (suggestion si faute) ou inconnu (renvoi au processus de création de projet). Ne verrouille pas si référentiel illisible. - lint_audit.py : VAL-nommage aligné sur le référentiel (attrape casse, espace ET snake_case ; l'ancien 'contient un espace' ratait code_versioning). - Données : 79 notes ré-étiquetées vers les 3 formes canoniques. Quality by design : l'erreur de nommage devient impossible à l'écriture, le Lint n'est plus que le filet de sécurité.
312 lines
7.8 KiB
Python
312 lines
7.8 KiB
Python
#
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# The Python Imaging Library.
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# $Id$
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#
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# standard channel operations
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#
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# History:
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# 1996-03-24 fl Created
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# 1996-08-13 fl Added logical operations (for "1" images)
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# 2000-10-12 fl Added offset method (from Image.py)
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#
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# Copyright (c) 1997-2000 by Secret Labs AB
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# Copyright (c) 1996-2000 by Fredrik Lundh
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#
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# See the README file for information on usage and redistribution.
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#
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from __future__ import annotations
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from . import Image
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def constant(image: Image.Image, value: int) -> Image.Image:
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"""Fill a channel with a given gray level.
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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return Image.new("L", image.size, value)
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def duplicate(image: Image.Image) -> Image.Image:
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"""Copy a channel. Alias for :py:meth:`PIL.Image.Image.copy`.
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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return image.copy()
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def invert(image: Image.Image) -> Image.Image:
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"""
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Invert an image (channel). ::
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out = MAX - image
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image.load()
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return image._new(image.im.chop_invert())
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def lighter(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""
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Compares the two images, pixel by pixel, and returns a new image containing
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the lighter values. ::
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out = max(image1, image2)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_lighter(image2.im))
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def darker(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""
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Compares the two images, pixel by pixel, and returns a new image containing
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the darker values. ::
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out = min(image1, image2)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_darker(image2.im))
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def difference(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""
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Returns the absolute value of the pixel-by-pixel difference between the two
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images. ::
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out = abs(image1 - image2)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_difference(image2.im))
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def multiply(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""
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Superimposes two images on top of each other.
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If you multiply an image with a solid black image, the result is black. If
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you multiply with a solid white image, the image is unaffected. ::
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out = image1 * image2 / MAX
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_multiply(image2.im))
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def screen(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""
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Superimposes two inverted images on top of each other. ::
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out = MAX - ((MAX - image1) * (MAX - image2) / MAX)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_screen(image2.im))
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def soft_light(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""
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Superimposes two images on top of each other using the Soft Light algorithm
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_soft_light(image2.im))
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def hard_light(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""
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Superimposes two images on top of each other using the Hard Light algorithm
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_hard_light(image2.im))
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def overlay(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""
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Superimposes two images on top of each other using the Overlay algorithm
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_overlay(image2.im))
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def add(
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image1: Image.Image, image2: Image.Image, scale: float = 1.0, offset: float = 0
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) -> Image.Image:
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"""
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Adds two images, dividing the result by scale and adding the
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offset. If omitted, scale defaults to 1.0, and offset to 0.0. ::
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out = ((image1 + image2) / scale + offset)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_add(image2.im, scale, offset))
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def subtract(
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image1: Image.Image, image2: Image.Image, scale: float = 1.0, offset: float = 0
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) -> Image.Image:
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"""
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Subtracts two images, dividing the result by scale and adding the offset.
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If omitted, scale defaults to 1.0, and offset to 0.0. ::
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out = ((image1 - image2) / scale + offset)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_subtract(image2.im, scale, offset))
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def add_modulo(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""Add two images, without clipping the result. ::
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out = ((image1 + image2) % MAX)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_add_modulo(image2.im))
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def subtract_modulo(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""Subtract two images, without clipping the result. ::
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out = ((image1 - image2) % MAX)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_subtract_modulo(image2.im))
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def logical_and(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""Logical AND between two images.
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Both of the images must have mode "1". If you would like to perform a
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logical AND on an image with a mode other than "1", try
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:py:meth:`~PIL.ImageChops.multiply` instead, using a black-and-white mask
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as the second image. ::
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out = ((image1 and image2) % MAX)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_and(image2.im))
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def logical_or(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""Logical OR between two images.
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Both of the images must have mode "1". ::
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out = ((image1 or image2) % MAX)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_or(image2.im))
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def logical_xor(image1: Image.Image, image2: Image.Image) -> Image.Image:
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"""Logical XOR between two images.
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Both of the images must have mode "1". ::
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out = ((bool(image1) != bool(image2)) % MAX)
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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image1.load()
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image2.load()
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return image1._new(image1.im.chop_xor(image2.im))
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def blend(image1: Image.Image, image2: Image.Image, alpha: float) -> Image.Image:
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"""Blend images using constant transparency weight. Alias for
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:py:func:`PIL.Image.blend`.
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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return Image.blend(image1, image2, alpha)
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def composite(
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image1: Image.Image, image2: Image.Image, mask: Image.Image
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) -> Image.Image:
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"""Create composite using transparency mask. Alias for
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:py:func:`PIL.Image.composite`.
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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return Image.composite(image1, image2, mask)
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def offset(image: Image.Image, xoffset: int, yoffset: int | None = None) -> Image.Image:
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"""Returns a copy of the image where data has been offset by the given
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distances. Data wraps around the edges. If ``yoffset`` is omitted, it
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is assumed to be equal to ``xoffset``.
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:param image: Input image.
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:param xoffset: The horizontal distance.
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:param yoffset: The vertical distance. If omitted, both
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distances are set to the same value.
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:rtype: :py:class:`~PIL.Image.Image`
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"""
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if yoffset is None:
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yoffset = xoffset
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image.load()
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return image._new(image.im.offset(xoffset, yoffset))
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