mirror of
https://github.com/python-pillow/Pillow.git
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331 lines
11 KiB
Python
331 lines
11 KiB
Python
"""
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Tests for resize functionality.
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"""
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from __future__ import annotations
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from collections.abc import Generator
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from itertools import permutations
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from pathlib import Path
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import pytest
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from PIL import Image, ImageFile
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from .helper import (
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assert_image_equal,
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assert_image_equal_tofile,
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assert_image_similar,
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hopper,
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skip_unless_feature,
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)
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class TestImagingCoreResize:
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def resize(
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self, im: Image.Image, size: tuple[int, int], f: Image.Resampling
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) -> Image.Image:
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# Image class independent version of resize.
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im.load()
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return im._new(im.im.resize(size, f))
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@pytest.mark.parametrize(
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"mode", ("1", "P", "L", "I", "F", "RGB", "RGBA", "CMYK", "YCbCr", "I;16")
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)
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def test_nearest_mode(self, mode: str) -> None:
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im = hopper(mode)
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r = self.resize(im, (15, 12), Image.Resampling.NEAREST)
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assert r.mode == mode
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assert r.size == (15, 12)
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assert r.im.bands == im.im.bands
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def test_convolution_modes(self) -> None:
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with pytest.raises(ValueError):
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self.resize(hopper("1"), (15, 12), Image.Resampling.BILINEAR)
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with pytest.raises(ValueError):
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self.resize(hopper("P"), (15, 12), Image.Resampling.BILINEAR)
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for mode in [
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"L",
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"I",
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"I;16",
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"I;16L",
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"I;16B",
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"I;16N",
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"F",
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"RGB",
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"RGBA",
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"CMYK",
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"YCbCr",
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]:
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im = hopper(mode)
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r = self.resize(im, (15, 12), Image.Resampling.BILINEAR)
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assert r.mode == mode
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assert r.size == (15, 12)
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assert r.im.bands == im.im.bands
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@pytest.mark.parametrize(
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"resample",
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(
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Image.Resampling.NEAREST,
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Image.Resampling.BOX,
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Image.Resampling.BILINEAR,
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Image.Resampling.HAMMING,
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Image.Resampling.BICUBIC,
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Image.Resampling.LANCZOS,
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),
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)
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def test_reduce_filters(self, resample: Image.Resampling) -> None:
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r = self.resize(hopper("RGB"), (15, 12), resample)
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assert r.mode == "RGB"
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assert r.size == (15, 12)
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@pytest.mark.parametrize(
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"resample",
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(
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Image.Resampling.NEAREST,
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Image.Resampling.BOX,
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Image.Resampling.BILINEAR,
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Image.Resampling.HAMMING,
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Image.Resampling.BICUBIC,
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Image.Resampling.LANCZOS,
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),
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)
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def test_enlarge_filters(self, resample: Image.Resampling) -> None:
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r = self.resize(hopper("RGB"), (212, 195), resample)
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assert r.mode == "RGB"
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assert r.size == (212, 195)
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@pytest.mark.parametrize(
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"resample",
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(
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Image.Resampling.NEAREST,
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Image.Resampling.BOX,
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Image.Resampling.BILINEAR,
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Image.Resampling.HAMMING,
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Image.Resampling.BICUBIC,
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Image.Resampling.LANCZOS,
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),
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)
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@pytest.mark.parametrize(
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"mode, channels_set",
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(
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("RGB", ("blank", "filled", "dirty")),
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("RGBA", ("blank", "blank", "filled", "dirty")),
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("LA", ("filled", "dirty")),
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),
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)
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def test_endianness(
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self, resample: Image.Resampling, mode: str, channels_set: tuple[str, ...]
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) -> None:
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# Make an image with one colored pixel, in one channel.
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# When resized, that channel should be the same as a GS image.
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# Other channels should be unaffected.
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# The R and A channels should not swap, which is indicative of
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# an endianness issues.
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samples = {
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"blank": Image.new("L", (2, 2), 0),
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"filled": Image.new("L", (2, 2), 255),
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"dirty": Image.new("L", (2, 2), 0),
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}
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samples["dirty"].putpixel((1, 1), 128)
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# samples resized with current filter
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references = {
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name: self.resize(ch, (4, 4), resample) for name, ch in samples.items()
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}
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for channels in set(permutations(channels_set)):
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# compile image from different channels permutations
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im = Image.merge(mode, [samples[ch] for ch in channels])
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resized = self.resize(im, (4, 4), resample)
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for i, ch in enumerate(resized.split()):
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# check what resized channel in image is the same
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# as separately resized channel
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assert_image_equal(ch, references[channels[i]])
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@pytest.mark.parametrize(
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"resample",
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(
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Image.Resampling.NEAREST,
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Image.Resampling.BOX,
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Image.Resampling.BILINEAR,
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Image.Resampling.HAMMING,
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Image.Resampling.BICUBIC,
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Image.Resampling.LANCZOS,
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),
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)
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def test_enlarge_zero(self, resample: Image.Resampling) -> None:
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r = self.resize(Image.new("RGB", (0, 0), "white"), (212, 195), resample)
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assert r.mode == "RGB"
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assert r.size == (212, 195)
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assert r.getdata()[0] == (0, 0, 0)
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def test_unknown_filter(self) -> None:
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with pytest.raises(ValueError):
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self.resize(hopper(), (10, 10), 9) # type: ignore[arg-type]
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def test_cross_platform(self, tmp_path: Path) -> None:
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# This test is intended for only check for consistent behaviour across
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# platforms. So if a future Pillow change requires that the test file
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# be updated, that is okay.
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im = hopper().resize((64, 64))
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temp_file = tmp_path / "temp.gif"
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im.save(temp_file)
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with Image.open(temp_file) as reloaded:
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assert_image_equal_tofile(reloaded, "Tests/images/hopper_resized.gif")
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@pytest.fixture
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def gradients_image() -> Generator[ImageFile.ImageFile, None, None]:
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with Image.open("Tests/images/radial_gradients.png") as im:
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im.load()
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try:
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yield im
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finally:
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im.close()
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class TestReducingGapResize:
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def test_reducing_gap_values(self, gradients_image: ImageFile.ImageFile) -> None:
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ref = gradients_image.resize(
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(52, 34), Image.Resampling.BICUBIC, reducing_gap=None
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)
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im = gradients_image.resize((52, 34), Image.Resampling.BICUBIC)
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assert_image_equal(ref, im)
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with pytest.raises(ValueError):
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gradients_image.resize((52, 34), Image.Resampling.BICUBIC, reducing_gap=0)
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with pytest.raises(ValueError):
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gradients_image.resize(
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(52, 34), Image.Resampling.BICUBIC, reducing_gap=0.99
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)
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@pytest.mark.parametrize(
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"box, epsilon",
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((None, 4), ((1.1, 2.2, 510.8, 510.9), 4), ((3, 10, 410, 256), 10)),
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)
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def test_reducing_gap_1(
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self,
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gradients_image: ImageFile.ImageFile,
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box: tuple[float, float, float, float],
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epsilon: float,
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) -> None:
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ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
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im = gradients_image.resize(
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(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=1.0
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)
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with pytest.raises(pytest.fail.Exception):
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assert_image_equal(ref, im)
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assert_image_similar(ref, im, epsilon)
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@pytest.mark.parametrize(
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"box, epsilon",
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((None, 1.5), ((1.1, 2.2, 510.8, 510.9), 1.5), ((3, 10, 410, 256), 1)),
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)
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def test_reducing_gap_2(
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self,
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gradients_image: ImageFile.ImageFile,
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box: tuple[float, float, float, float],
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epsilon: float,
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) -> None:
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ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
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im = gradients_image.resize(
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(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=2.0
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)
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with pytest.raises(pytest.fail.Exception):
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assert_image_equal(ref, im)
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assert_image_similar(ref, im, epsilon)
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@pytest.mark.parametrize(
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"box, epsilon",
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((None, 1), ((1.1, 2.2, 510.8, 510.9), 1), ((3, 10, 410, 256), 0.5)),
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)
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def test_reducing_gap_3(
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self,
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gradients_image: ImageFile.ImageFile,
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box: tuple[float, float, float, float],
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epsilon: float,
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) -> None:
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ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
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im = gradients_image.resize(
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(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=3.0
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)
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with pytest.raises(pytest.fail.Exception):
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assert_image_equal(ref, im)
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assert_image_similar(ref, im, epsilon)
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@pytest.mark.parametrize("box", (None, (1.1, 2.2, 510.8, 510.9), (3, 10, 410, 256)))
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def test_reducing_gap_8(
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self,
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gradients_image: ImageFile.ImageFile,
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box: tuple[float, float, float, float],
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) -> None:
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ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
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im = gradients_image.resize(
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(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=8.0
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)
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assert_image_equal(ref, im)
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@pytest.mark.parametrize(
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"box, epsilon",
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(((0, 0, 512, 512), 5.5), ((0.9, 1.7, 128, 128), 9.5)),
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)
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def test_box_filter(
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self,
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gradients_image: ImageFile.ImageFile,
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box: tuple[float, float, float, float],
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epsilon: float,
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) -> None:
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ref = gradients_image.resize((52, 34), Image.Resampling.BOX, box=box)
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im = gradients_image.resize(
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(52, 34), Image.Resampling.BOX, box=box, reducing_gap=1.0
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)
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assert_image_similar(ref, im, epsilon)
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class TestImageResize:
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def test_resize(self) -> None:
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def resize(mode: str, size: tuple[int, int] | list[int]) -> None:
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out = hopper(mode).resize(size)
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assert out.mode == mode
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assert out.size == tuple(size)
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for mode in "1", "P", "L", "RGB", "I", "F":
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resize(mode, (112, 103))
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resize(mode, [188, 214])
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# Test unknown resampling filter
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with hopper() as im:
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with pytest.raises(ValueError):
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im.resize((10, 10), -1)
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@skip_unless_feature("libtiff")
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def test_transposed(self) -> None:
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with Image.open("Tests/images/g4_orientation_5.tif") as im:
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im = im.resize((64, 64))
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assert im.size == (64, 64)
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@pytest.mark.parametrize(
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"mode", ("L", "RGB", "I", "I;16", "I;16L", "I;16B", "I;16N", "F")
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)
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def test_default_filter_bicubic(self, mode: str) -> None:
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im = hopper(mode)
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assert im.resize((20, 20), Image.Resampling.BICUBIC) == im.resize((20, 20))
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@pytest.mark.parametrize("mode", ("1", "P"))
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def test_default_filter_nearest(self, mode: str) -> None:
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im = hopper(mode)
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assert im.resize((20, 20), Image.Resampling.NEAREST) == im.resize((20, 20))
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