mirror of
https://github.com/python-pillow/Pillow.git
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383 lines
12 KiB
Python
383 lines
12 KiB
Python
from __future__ import annotations
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import math
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from typing import Callable
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import pytest
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from PIL import Image, ImageTransform
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from .helper import assert_image_equal, assert_image_similar, hopper
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class TestImageTransform:
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def test_sanity(self) -> None:
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im = hopper()
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for transform in (
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ImageTransform.AffineTransform((1, 0, 0, 0, 1, 0)),
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ImageTransform.PerspectiveTransform((1, 0, 0, 0, 1, 0, 0, 0)),
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ImageTransform.ExtentTransform((0, 0) + im.size),
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ImageTransform.QuadTransform(
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(0, 0, 0, im.height, im.width, im.height, im.width, 0)
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),
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ImageTransform.MeshTransform(
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[
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(
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(0, 0) + im.size,
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(0, 0, 0, im.height, im.width, im.height, im.width, 0),
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)
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]
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),
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):
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assert_image_equal(im, im.transform(im.size, transform))
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def test_info(self) -> None:
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comment = b"File written by Adobe Photoshop\xa8 4.0"
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with Image.open("Tests/images/hopper.gif") as im:
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assert im.info["comment"] == comment
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transform = ImageTransform.ExtentTransform((0, 0, 0, 0))
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new_im = im.transform((100, 100), transform)
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assert new_im.info["comment"] == comment
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def test_palette(self) -> None:
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with Image.open("Tests/images/hopper.gif") as im:
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transformed = im.transform(
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im.size, Image.Transform.AFFINE, [1, 0, 0, 0, 1, 0]
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)
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assert im.palette.palette == transformed.palette.palette
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def test_extent(self) -> None:
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im = hopper("RGB")
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(w, h) = im.size
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transformed = im.transform(
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im.size,
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Image.Transform.EXTENT,
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(0, 0, w // 2, h // 2), # ul -> lr
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Image.Resampling.BILINEAR,
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)
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scaled = im.resize((w * 2, h * 2), Image.Resampling.BILINEAR).crop((0, 0, w, h))
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# undone -- precision?
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assert_image_similar(transformed, scaled, 23)
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def test_quad(self) -> None:
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# one simple quad transform, equivalent to scale & crop upper left quad
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im = hopper("RGB")
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(w, h) = im.size
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transformed = im.transform(
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im.size,
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Image.Transform.QUAD,
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(0, 0, 0, h // 2, w // 2, h // 2, w // 2, 0), # ul -> ccw around quad
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Image.Resampling.BILINEAR,
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)
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scaled = im.transform(
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(w, h),
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Image.Transform.AFFINE,
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(0.5, 0, 0, 0, 0.5, 0),
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Image.Resampling.BILINEAR,
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)
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assert_image_equal(transformed, scaled)
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@pytest.mark.parametrize(
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"mode, expected_pixel",
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(
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("RGB", (255, 0, 0)),
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("RGBA", (255, 0, 0, 255)),
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("LA", (76, 0)),
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),
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)
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def test_fill(self, mode: str, expected_pixel: tuple[int, ...]) -> None:
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im = hopper(mode)
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(w, h) = im.size
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transformed = im.transform(
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im.size,
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Image.Transform.EXTENT,
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(0, 0, w * 2, h * 2),
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Image.Resampling.BILINEAR,
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fillcolor="red",
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)
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assert transformed.getpixel((w - 1, h - 1)) == expected_pixel
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def test_mesh(self) -> None:
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# this should be a checkerboard of halfsized hoppers in ul, lr
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im = hopper("RGBA")
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(w, h) = im.size
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transformed = im.transform(
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im.size,
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Image.Transform.MESH,
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(
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(
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(0, 0, w // 2, h // 2), # box
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(0, 0, 0, h, w, h, w, 0), # ul -> ccw around quad
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),
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(
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(w // 2, h // 2, w, h), # box
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(0, 0, 0, h, w, h, w, 0), # ul -> ccw around quad
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),
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),
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Image.Resampling.BILINEAR,
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)
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scaled = im.transform(
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(w // 2, h // 2),
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Image.Transform.AFFINE,
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(2, 0, 0, 0, 2, 0),
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Image.Resampling.BILINEAR,
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)
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checker = Image.new("RGBA", im.size)
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checker.paste(scaled, (0, 0))
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checker.paste(scaled, (w // 2, h // 2))
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assert_image_equal(transformed, checker)
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# now, check to see that the extra area is (0, 0, 0, 0)
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blank = Image.new("RGBA", (w // 2, h // 2), (0, 0, 0, 0))
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assert_image_equal(blank, transformed.crop((w // 2, 0, w, h // 2)))
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assert_image_equal(blank, transformed.crop((0, h // 2, w // 2, h)))
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def _test_alpha_premult(
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self, op: Callable[[Image.Image, tuple[int, int]], Image.Image]
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) -> None:
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# create image with half white, half black,
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# with the black half transparent.
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# do op,
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# there should be no darkness in the white section.
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im = Image.new("RGBA", (10, 10), (0, 0, 0, 0))
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im2 = Image.new("RGBA", (5, 10), (255, 255, 255, 255))
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im.paste(im2, (0, 0))
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im = op(im, (40, 10))
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im_background = Image.new("RGB", (40, 10), (255, 255, 255))
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im_background.paste(im, (0, 0), im)
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hist = im_background.histogram()
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assert 40 * 10 == hist[-1]
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def test_alpha_premult_resize(self) -> None:
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def op(im: Image.Image, sz: tuple[int, int]) -> Image.Image:
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return im.resize(sz, Image.Resampling.BILINEAR)
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self._test_alpha_premult(op)
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def test_alpha_premult_transform(self) -> None:
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def op(im: Image.Image, sz: tuple[int, int]) -> Image.Image:
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(w, h) = im.size
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return im.transform(
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sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.BILINEAR
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)
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self._test_alpha_premult(op)
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def _test_nearest(
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self, op: Callable[[Image.Image, tuple[int, int]], Image.Image], mode: str
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) -> None:
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# create white image with half transparent,
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# do op,
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# the image should remain white with half transparent
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transparent, opaque = {
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"RGBA": ((255, 255, 255, 0), (255, 255, 255, 255)),
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"LA": ((255, 0), (255, 255)),
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}[mode]
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im = Image.new(mode, (10, 10), transparent)
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im2 = Image.new(mode, (5, 10), opaque)
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im.paste(im2, (0, 0))
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im = op(im, (40, 10))
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colors = im.getcolors()
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assert colors is not None
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assert sorted(colors) == sorted(
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(
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(20 * 10, opaque),
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(20 * 10, transparent),
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)
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)
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@pytest.mark.parametrize("mode", ("RGBA", "LA"))
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def test_nearest_resize(self, mode: str) -> None:
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def op(im: Image.Image, sz: tuple[int, int]) -> Image.Image:
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return im.resize(sz, Image.Resampling.NEAREST)
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self._test_nearest(op, mode)
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@pytest.mark.parametrize("mode", ("RGBA", "LA"))
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def test_nearest_transform(self, mode: str) -> None:
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def op(im: Image.Image, sz: tuple[int, int]) -> Image.Image:
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(w, h) = im.size
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return im.transform(
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sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.NEAREST
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)
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self._test_nearest(op, mode)
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def test_blank_fill(self) -> None:
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# attempting to hit
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# https://github.com/python-pillow/Pillow/issues/254 reported
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#
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# issue is that transforms with transparent overflow area
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# contained junk from previous images, especially on systems with
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# constrained memory. So, attempt to fill up memory with a
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# pattern, free it, and then run the mesh test again. Using a 1Mp
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# image with 4 bands, for 4 megs of data allocated, x 64. OMM (64
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# bit 12.04 VM with 512 megs available, this fails with Pillow <
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# a0eaf06cc5f62a6fb6de556989ac1014ff3348ea
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#
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# Running by default, but I'd totally understand not doing it in
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# the future
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pattern: list[Image.Image] | None = [
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Image.new("RGBA", (1024, 1024), (a, a, a, a)) for a in range(1, 65)
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]
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# Yeah. Watch some JIT optimize this out.
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pattern = None # noqa: F841
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self.test_mesh()
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def test_missing_method_data(self) -> None:
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with hopper() as im:
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with pytest.raises(ValueError):
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im.transform((100, 100), None)
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@pytest.mark.parametrize("resample", (Image.Resampling.BOX, "unknown"))
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def test_unknown_resampling_filter(self, resample: Image.Resampling | str) -> None:
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with hopper() as im:
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(w, h) = im.size
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with pytest.raises(ValueError):
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im.transform((100, 100), Image.Transform.EXTENT, (0, 0, w, h), resample)
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class TestImageTransformAffine:
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transform = Image.Transform.AFFINE
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def _test_image(self) -> Image.Image:
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im = hopper("RGB")
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return im.crop((10, 20, im.width - 10, im.height - 20))
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@pytest.mark.parametrize(
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"deg, transpose",
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(
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(0, None),
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(90, Image.Transpose.ROTATE_90),
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(180, Image.Transpose.ROTATE_180),
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(270, Image.Transpose.ROTATE_270),
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),
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)
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def test_rotate(self, deg: int, transpose: Image.Transpose | None) -> None:
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im = self._test_image()
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angle = -math.radians(deg)
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matrix = [
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round(math.cos(angle), 15),
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round(math.sin(angle), 15),
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0.0,
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round(-math.sin(angle), 15),
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round(math.cos(angle), 15),
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0.0,
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0,
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0,
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]
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matrix[2] = (1 - matrix[0] - matrix[1]) * im.width / 2
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matrix[5] = (1 - matrix[3] - matrix[4]) * im.height / 2
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if transpose is not None:
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transposed = im.transpose(transpose)
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else:
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transposed = im
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for resample in [
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Image.Resampling.NEAREST,
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Image.Resampling.BILINEAR,
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Image.Resampling.BICUBIC,
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]:
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transformed = im.transform(
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transposed.size, self.transform, matrix, resample
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)
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assert_image_equal(transposed, transformed)
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@pytest.mark.parametrize(
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"scale, epsilon_scale",
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(
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(1.1, 6.9),
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(1.5, 5.5),
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(2.0, 5.5),
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(2.3, 3.7),
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(2.5, 3.7),
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),
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)
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@pytest.mark.parametrize(
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"resample,epsilon",
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(
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(Image.Resampling.NEAREST, 0),
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(Image.Resampling.BILINEAR, 2),
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(Image.Resampling.BICUBIC, 1),
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),
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)
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def test_resize(
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self,
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scale: float,
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epsilon_scale: float,
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resample: Image.Resampling,
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epsilon: int,
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) -> None:
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im = self._test_image()
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size_up = int(round(im.width * scale)), int(round(im.height * scale))
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matrix_up = [1 / scale, 0, 0, 0, 1 / scale, 0, 0, 0]
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matrix_down = [scale, 0, 0, 0, scale, 0, 0, 0]
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transformed = im.transform(size_up, self.transform, matrix_up, resample)
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transformed = transformed.transform(
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im.size, self.transform, matrix_down, resample
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)
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assert_image_similar(transformed, im, epsilon * epsilon_scale)
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@pytest.mark.parametrize(
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"x, y, epsilon_scale",
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(
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(0.1, 0, 3.7),
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(0.6, 0, 9.1),
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(50, 50, 0),
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),
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)
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@pytest.mark.parametrize(
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"resample, epsilon",
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(
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(Image.Resampling.NEAREST, 0),
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(Image.Resampling.BILINEAR, 1.5),
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(Image.Resampling.BICUBIC, 1),
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),
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)
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def test_translate(
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self,
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x: float,
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y: float,
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epsilon_scale: float,
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resample: Image.Resampling,
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epsilon: float,
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) -> None:
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im = self._test_image()
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size_up = int(round(im.width + x)), int(round(im.height + y))
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matrix_up = [1, 0, -x, 0, 1, -y, 0, 0]
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matrix_down = [1, 0, x, 0, 1, y, 0, 0]
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transformed = im.transform(size_up, self.transform, matrix_up, resample)
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transformed = transformed.transform(
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im.size, self.transform, matrix_down, resample
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)
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assert_image_similar(transformed, im, epsilon * epsilon_scale)
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class TestImageTransformPerspective(TestImageTransformAffine):
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# Repeat all tests for AFFINE transformations with PERSPECTIVE
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transform = Image.Transform.PERSPECTIVE
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