Pillow/Tests/test_image_transform.py
2024-02-12 09:28:53 +11:00

382 lines
12 KiB
Python

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