358 lines
11 KiB
Python
358 lines
11 KiB
Python
#
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# The Python Imaging Library
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# $Id$
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#
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# a simple math add-on for the Python Imaging Library
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#
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# History:
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# 1999-02-15 fl Original PIL Plus release
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# 2005-05-05 fl Simplified and cleaned up for PIL 1.1.6
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# 2005-09-12 fl Fixed int() and float() for Python 2.4.1
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#
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# Copyright (c) 1999-2005 by Secret Labs AB
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# Copyright (c) 2005 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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import builtins
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from types import CodeType
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from typing import Any, Callable
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from . import Image, _imagingmath
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from ._deprecate import deprecate
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class _Operand:
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"""Wraps an image operand, providing standard operators"""
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def __init__(self, im: Image.Image):
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self.im = im
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def __fixup(self, im1: _Operand | float) -> Image.Image:
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# convert image to suitable mode
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if isinstance(im1, _Operand):
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# argument was an image.
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if im1.im.mode in ("1", "L"):
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return im1.im.convert("I")
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elif im1.im.mode in ("I", "F"):
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return im1.im
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else:
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msg = f"unsupported mode: {im1.im.mode}"
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raise ValueError(msg)
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else:
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# argument was a constant
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if isinstance(im1, (int, float)) and self.im.mode in ("1", "L", "I"):
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return Image.new("I", self.im.size, im1)
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else:
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return Image.new("F", self.im.size, im1)
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def apply(
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self,
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op: str,
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im1: _Operand | float,
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im2: _Operand | float | None = None,
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mode: str | None = None,
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) -> _Operand:
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im_1 = self.__fixup(im1)
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if im2 is None:
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# unary operation
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out = Image.new(mode or im_1.mode, im_1.size, None)
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im_1.load()
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try:
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op = getattr(_imagingmath, f"{op}_{im_1.mode}")
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except AttributeError as e:
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msg = f"bad operand type for '{op}'"
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raise TypeError(msg) from e
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_imagingmath.unop(op, out.im.id, im_1.im.id)
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else:
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# binary operation
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im_2 = self.__fixup(im2)
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if im_1.mode != im_2.mode:
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# convert both arguments to floating point
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if im_1.mode != "F":
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im_1 = im_1.convert("F")
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if im_2.mode != "F":
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im_2 = im_2.convert("F")
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if im_1.size != im_2.size:
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# crop both arguments to a common size
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size = (
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min(im_1.size[0], im_2.size[0]),
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min(im_1.size[1], im_2.size[1]),
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)
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if im_1.size != size:
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im_1 = im_1.crop((0, 0) + size)
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if im_2.size != size:
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im_2 = im_2.crop((0, 0) + size)
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out = Image.new(mode or im_1.mode, im_1.size, None)
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im_1.load()
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im_2.load()
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try:
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op = getattr(_imagingmath, f"{op}_{im_1.mode}")
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except AttributeError as e:
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msg = f"bad operand type for '{op}'"
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raise TypeError(msg) from e
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_imagingmath.binop(op, out.im.id, im_1.im.id, im_2.im.id)
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return _Operand(out)
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# unary operators
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def __bool__(self) -> bool:
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# an image is "true" if it contains at least one non-zero pixel
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return self.im.getbbox() is not None
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def __abs__(self) -> _Operand:
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return self.apply("abs", self)
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def __pos__(self) -> _Operand:
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return self
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def __neg__(self) -> _Operand:
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return self.apply("neg", self)
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# binary operators
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def __add__(self, other: _Operand | float) -> _Operand:
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return self.apply("add", self, other)
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def __radd__(self, other: _Operand | float) -> _Operand:
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return self.apply("add", other, self)
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def __sub__(self, other: _Operand | float) -> _Operand:
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return self.apply("sub", self, other)
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def __rsub__(self, other: _Operand | float) -> _Operand:
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return self.apply("sub", other, self)
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def __mul__(self, other: _Operand | float) -> _Operand:
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return self.apply("mul", self, other)
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def __rmul__(self, other: _Operand | float) -> _Operand:
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return self.apply("mul", other, self)
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def __truediv__(self, other: _Operand | float) -> _Operand:
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return self.apply("div", self, other)
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def __rtruediv__(self, other: _Operand | float) -> _Operand:
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return self.apply("div", other, self)
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def __mod__(self, other: _Operand | float) -> _Operand:
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return self.apply("mod", self, other)
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def __rmod__(self, other: _Operand | float) -> _Operand:
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return self.apply("mod", other, self)
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def __pow__(self, other: _Operand | float) -> _Operand:
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return self.apply("pow", self, other)
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def __rpow__(self, other: _Operand | float) -> _Operand:
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return self.apply("pow", other, self)
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# bitwise
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def __invert__(self) -> _Operand:
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return self.apply("invert", self)
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def __and__(self, other: _Operand | float) -> _Operand:
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return self.apply("and", self, other)
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def __rand__(self, other: _Operand | float) -> _Operand:
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return self.apply("and", other, self)
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def __or__(self, other: _Operand | float) -> _Operand:
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return self.apply("or", self, other)
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def __ror__(self, other: _Operand | float) -> _Operand:
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return self.apply("or", other, self)
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def __xor__(self, other: _Operand | float) -> _Operand:
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return self.apply("xor", self, other)
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def __rxor__(self, other: _Operand | float) -> _Operand:
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return self.apply("xor", other, self)
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def __lshift__(self, other: _Operand | float) -> _Operand:
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return self.apply("lshift", self, other)
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def __rshift__(self, other: _Operand | float) -> _Operand:
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return self.apply("rshift", self, other)
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# logical
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def __eq__(self, other):
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return self.apply("eq", self, other)
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def __ne__(self, other):
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return self.apply("ne", self, other)
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def __lt__(self, other: _Operand | float) -> _Operand:
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return self.apply("lt", self, other)
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def __le__(self, other: _Operand | float) -> _Operand:
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return self.apply("le", self, other)
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def __gt__(self, other: _Operand | float) -> _Operand:
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return self.apply("gt", self, other)
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def __ge__(self, other: _Operand | float) -> _Operand:
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return self.apply("ge", self, other)
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# conversions
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def imagemath_int(self: _Operand) -> _Operand:
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return _Operand(self.im.convert("I"))
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def imagemath_float(self: _Operand) -> _Operand:
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return _Operand(self.im.convert("F"))
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# logical
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def imagemath_equal(self: _Operand, other: _Operand | float | None) -> _Operand:
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return self.apply("eq", self, other, mode="I")
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def imagemath_notequal(self: _Operand, other: _Operand | float | None) -> _Operand:
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return self.apply("ne", self, other, mode="I")
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def imagemath_min(self: _Operand, other: _Operand | float | None) -> _Operand:
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return self.apply("min", self, other)
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def imagemath_max(self: _Operand, other: _Operand | float | None) -> _Operand:
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return self.apply("max", self, other)
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def imagemath_convert(self: _Operand, mode: str) -> _Operand:
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return _Operand(self.im.convert(mode))
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ops = {
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"int": imagemath_int,
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"float": imagemath_float,
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"equal": imagemath_equal,
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"notequal": imagemath_notequal,
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"min": imagemath_min,
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"max": imagemath_max,
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"convert": imagemath_convert,
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}
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def lambda_eval(
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expression: Callable[[dict[str, Any]], Any],
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options: dict[str, Any] = {},
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**kw: Any,
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) -> Any:
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"""
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Returns the result of an image function.
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:py:mod:`~PIL.ImageMath` only supports single-layer images. To process multi-band
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images, use the :py:meth:`~PIL.Image.Image.split` method or
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:py:func:`~PIL.Image.merge` function.
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:param expression: A function that receives a dictionary.
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:param options: Values to add to the function's dictionary. You
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can either use a dictionary, or one or more keyword
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arguments.
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:return: The expression result. This is usually an image object, but can
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also be an integer, a floating point value, or a pixel tuple,
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depending on the expression.
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"""
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args: dict[str, Any] = ops.copy()
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args.update(options)
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args.update(kw)
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for k, v in args.items():
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if hasattr(v, "im"):
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args[k] = _Operand(v)
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out = expression(args)
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try:
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return out.im
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except AttributeError:
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return out
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def unsafe_eval(
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expression: str,
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options: dict[str, Any] = {},
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**kw: Any,
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) -> Any:
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"""
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Evaluates an image expression. This uses Python's ``eval()`` function to process
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the expression string, and carries the security risks of doing so. It is not
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recommended to process expressions without considering this.
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:py:meth:`~lambda_eval` is a more secure alternative.
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:py:mod:`~PIL.ImageMath` only supports single-layer images. To process multi-band
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images, use the :py:meth:`~PIL.Image.Image.split` method or
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:py:func:`~PIL.Image.merge` function.
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:param expression: A string containing a Python-style expression.
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:param options: Values to add to the evaluation context. You
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can either use a dictionary, or one or more keyword
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arguments.
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:return: The evaluated expression. This is usually an image object, but can
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also be an integer, a floating point value, or a pixel tuple,
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depending on the expression.
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"""
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# build execution namespace
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args: dict[str, Any] = ops.copy()
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for k in list(options.keys()) + list(kw.keys()):
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if "__" in k or hasattr(builtins, k):
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msg = f"'{k}' not allowed"
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raise ValueError(msg)
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args.update(options)
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args.update(kw)
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for k, v in args.items():
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if hasattr(v, "im"):
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args[k] = _Operand(v)
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compiled_code = compile(expression, "<string>", "eval")
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def scan(code: CodeType) -> None:
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for const in code.co_consts:
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if type(const) is type(compiled_code):
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scan(const)
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for name in code.co_names:
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if name not in args and name != "abs":
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msg = f"'{name}' not allowed"
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raise ValueError(msg)
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scan(compiled_code)
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out = builtins.eval(expression, {"__builtins": {"abs": abs}}, args)
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try:
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return out.im
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except AttributeError:
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return out
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def eval(
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expression: str,
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_dict: dict[str, Any] = {},
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**kw: Any,
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) -> Any:
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"""
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Evaluates an image expression.
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Deprecated. Use lambda_eval() or unsafe_eval() instead.
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:param expression: A string containing a Python-style expression.
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:param _dict: Values to add to the evaluation context. You
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can either use a dictionary, or one or more keyword
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arguments.
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:return: The evaluated expression. This is usually an image object, but can
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also be an integer, a floating point value, or a pixel tuple,
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depending on the expression.
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.. deprecated:: 10.3.0
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"""
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deprecate(
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"ImageMath.eval",
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12,
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"ImageMath.lambda_eval or ImageMath.unsafe_eval",
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)
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return unsafe_eval(expression, _dict, **kw)
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