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https://github.com/python-pillow/Pillow.git
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284 lines
8.6 KiB
Python
284 lines
8.6 KiB
Python
from __future__ import annotations
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import pytest
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from PIL import Image, ImageMath, ImageMode
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from .helper import convert_to_comparable, skip_unless_feature
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codecs = dir(Image.core)
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# There are several internal implementations
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remarkable_factors = [
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# special implementations
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1,
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2,
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3,
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4,
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5,
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6,
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# 1xN implementation
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(1, 2),
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(1, 3),
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(1, 4),
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(1, 7),
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# Nx1 implementation
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(2, 1),
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(3, 1),
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(4, 1),
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(7, 1),
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# general implementation with different paths
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(4, 6),
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(5, 6),
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(4, 7),
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(5, 7),
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(19, 17),
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]
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gradients_image = Image.open("Tests/images/radial_gradients.png")
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gradients_image.load()
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@pytest.mark.parametrize(
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"size, expected",
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(
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(3, (4, 4)),
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((3, 1), (4, 10)),
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((1, 3), (10, 4)),
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),
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)
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def test_args_factor(size: int | tuple[int, int], expected: tuple[int, int]) -> None:
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im = Image.new("L", (10, 10))
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assert expected == im.reduce(size).size
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@pytest.mark.parametrize(
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"size, expected_error", ((0, ValueError), (2.0, TypeError), ((0, 10), ValueError))
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)
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def test_args_factor_error(size: float | tuple[int, int], expected_error) -> None:
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im = Image.new("L", (10, 10))
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with pytest.raises(expected_error):
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im.reduce(size)
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@pytest.mark.parametrize(
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"size, expected",
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(
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((0, 0, 10, 10), (5, 5)),
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((5, 5, 6, 6), (1, 1)),
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),
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)
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def test_args_box(size: tuple[int, int, int, int], expected: tuple[int, int]) -> None:
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im = Image.new("L", (10, 10))
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assert expected == im.reduce(2, size).size
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@pytest.mark.parametrize(
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"size, expected_error",
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(
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("stri", TypeError),
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((0, 0, 11, 10), ValueError),
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((0, 0, 10, 11), ValueError),
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((-1, 0, 10, 10), ValueError),
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((0, -1, 10, 10), ValueError),
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((0, 5, 10, 5), ValueError),
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((5, 0, 5, 10), ValueError),
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),
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)
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def test_args_box_error(size: str | tuple[int, int, int, int], expected_error) -> None:
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im = Image.new("L", (10, 10))
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with pytest.raises(expected_error):
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im.reduce(2, size).size
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@pytest.mark.parametrize("mode", ("P", "1", "I;16"))
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def test_unsupported_modes(mode: str) -> None:
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im = Image.new("P", (10, 10))
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with pytest.raises(ValueError):
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im.reduce(3)
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def get_image(mode: str) -> Image.Image:
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mode_info = ImageMode.getmode(mode)
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if mode_info.basetype == "L":
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bands = [gradients_image]
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for _ in mode_info.bands[1:]:
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# rotate previous image
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band = bands[-1].transpose(Image.Transpose.ROTATE_90)
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bands.append(band)
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# Correct alpha channel by transforming completely transparent pixels.
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# Low alpha values also emphasize error after alpha multiplication.
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if mode.endswith("A"):
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bands[-1] = bands[-1].point(lambda x: int(85 + x / 1.5))
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im = Image.merge(mode, bands)
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else:
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assert len(mode_info.bands) == 1
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im = gradients_image.convert(mode)
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# change the height to make a not-square image
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return im.crop((0, 0, im.width, im.height - 5))
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def compare_reduce_with_box(im: Image.Image, factor: int | tuple[int, int]) -> None:
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box = (11, 13, 146, 164)
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reduced = im.reduce(factor, box=box)
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reference = im.crop(box).reduce(factor)
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assert reduced == reference
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def compare_reduce_with_reference(
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im: Image.Image,
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factor: int | tuple[int, int],
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average_diff: float = 0.4,
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max_diff: int = 1,
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) -> None:
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"""Image.reduce() should look very similar to Image.resize(BOX).
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A reference image is compiled from a large source area
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and possible last column and last row.
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+-----------+
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|..........c|
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|..........c|
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|..........c|
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|rrrrrrrrrrp|
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+-----------+
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"""
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reduced = im.reduce(factor)
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if not isinstance(factor, (list, tuple)):
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factor = (factor, factor)
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reference = Image.new(im.mode, reduced.size)
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area_size = (im.size[0] // factor[0], im.size[1] // factor[1])
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area_box = (0, 0, area_size[0] * factor[0], area_size[1] * factor[1])
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area = im.resize(area_size, Image.Resampling.BOX, area_box)
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reference.paste(area, (0, 0))
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if area_size[0] < reduced.size[0]:
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assert reduced.size[0] - area_size[0] == 1
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last_column_box = (area_box[2], 0, im.size[0], area_box[3])
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last_column = im.resize(
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(1, area_size[1]), Image.Resampling.BOX, last_column_box
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)
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reference.paste(last_column, (area_size[0], 0))
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if area_size[1] < reduced.size[1]:
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assert reduced.size[1] - area_size[1] == 1
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last_row_box = (0, area_box[3], area_box[2], im.size[1])
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last_row = im.resize((area_size[0], 1), Image.Resampling.BOX, last_row_box)
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reference.paste(last_row, (0, area_size[1]))
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if area_size[0] < reduced.size[0] and area_size[1] < reduced.size[1]:
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last_pixel_box = (area_box[2], area_box[3], im.size[0], im.size[1])
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last_pixel = im.resize((1, 1), Image.Resampling.BOX, last_pixel_box)
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reference.paste(last_pixel, area_size)
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assert_compare_images(reduced, reference, average_diff, max_diff)
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def assert_compare_images(
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a: Image.Image, b: Image.Image, max_average_diff: float, max_diff: int = 255
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) -> None:
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assert a.mode == b.mode, f"got mode {repr(a.mode)}, expected {repr(b.mode)}"
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assert a.size == b.size, f"got size {repr(a.size)}, expected {repr(b.size)}"
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a, b = convert_to_comparable(a, b)
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bands = ImageMode.getmode(a.mode).bands
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for band, ach, bch in zip(bands, a.split(), b.split()):
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ch_diff = ImageMath.eval("convert(abs(a - b), 'L')", a=ach, b=bch)
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ch_hist = ch_diff.histogram()
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average_diff = sum(i * num for i, num in enumerate(ch_hist)) / (
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a.size[0] * a.size[1]
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)
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msg = (
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f"average pixel value difference {average_diff:.4f} > "
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f"expected {max_average_diff:.4f} for '{band}' band"
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)
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assert max_average_diff >= average_diff, msg
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last_diff = [i for i, num in enumerate(ch_hist) if num > 0][-1]
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assert max_diff >= last_diff, (
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f"max pixel value difference {last_diff} > expected {max_diff} "
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f"for '{band}' band"
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)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_L(factor: int | tuple[int, int]) -> None:
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im = get_image("L")
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compare_reduce_with_reference(im, factor)
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compare_reduce_with_box(im, factor)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_LA(factor: int | tuple[int, int]) -> None:
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im = get_image("LA")
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compare_reduce_with_reference(im, factor, 0.8, 5)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_LA_opaque(factor: int | tuple[int, int]) -> None:
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im = get_image("LA")
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# With opaque alpha, an error should be way smaller.
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im.putalpha(Image.new("L", im.size, 255))
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compare_reduce_with_reference(im, factor)
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compare_reduce_with_box(im, factor)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_La(factor: int | tuple[int, int]) -> None:
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im = get_image("La")
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compare_reduce_with_reference(im, factor)
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compare_reduce_with_box(im, factor)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_RGB(factor: int | tuple[int, int]) -> None:
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im = get_image("RGB")
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compare_reduce_with_reference(im, factor)
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compare_reduce_with_box(im, factor)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_RGBA(factor: int | tuple[int, int]) -> None:
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im = get_image("RGBA")
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compare_reduce_with_reference(im, factor, 0.8, 5)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_RGBA_opaque(factor: int | tuple[int, int]) -> None:
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im = get_image("RGBA")
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# With opaque alpha, an error should be way smaller.
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im.putalpha(Image.new("L", im.size, 255))
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compare_reduce_with_reference(im, factor)
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compare_reduce_with_box(im, factor)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_RGBa(factor: int | tuple[int, int]) -> None:
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im = get_image("RGBa")
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compare_reduce_with_reference(im, factor)
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compare_reduce_with_box(im, factor)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_I(factor: int | tuple[int, int]) -> None:
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im = get_image("I")
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compare_reduce_with_reference(im, factor)
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compare_reduce_with_box(im, factor)
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@pytest.mark.parametrize("factor", remarkable_factors)
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def test_mode_F(factor: int | tuple[int, int]) -> None:
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im = get_image("F")
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compare_reduce_with_reference(im, factor, 0, 0)
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compare_reduce_with_box(im, factor)
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@skip_unless_feature("jpg_2000")
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def test_jpeg2k() -> None:
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with Image.open("Tests/images/test-card-lossless.jp2") as im:
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assert im.reduce(2).size == (320, 240)
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