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279 lines
9.3 KiB
Python
279 lines
9.3 KiB
Python
"""
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Tests for resize functionality.
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"""
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from itertools import permutations
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import pytest
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from PIL import Image
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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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)
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class TestImagingCoreResize:
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def resize(self, im, size, f):
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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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def test_nearest_mode(self):
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for mode in [
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"1",
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"P",
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"L",
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"I",
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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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"I;16",
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]: # exotic mode
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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):
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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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with pytest.raises(ValueError):
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self.resize(hopper("I;16"), (15, 12), Image.Resampling.BILINEAR)
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for mode in ["L", "I", "F", "RGB", "RGBA", "CMYK", "YCbCr"]:
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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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def test_reduce_filters(self):
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for f in [
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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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r = self.resize(hopper("RGB"), (15, 12), f)
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assert r.mode == "RGB"
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assert r.size == (15, 12)
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def test_enlarge_filters(self):
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for f in [
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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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r = self.resize(hopper("RGB"), (212, 195), f)
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assert r.mode == "RGB"
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assert r.size == (212, 195)
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def test_endianness(self):
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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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for f in [
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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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# samples resized with current filter
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references = {
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name: self.resize(ch, (4, 4), f) for name, ch in samples.items()
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}
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for mode, channels_set in [
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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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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), f)
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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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def test_enlarge_zero(self):
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for f in [
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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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r = self.resize(Image.new("RGB", (0, 0), "white"), (212, 195), f)
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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):
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with pytest.raises(ValueError):
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self.resize(hopper(), (10, 10), 9)
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def test_cross_platform(self, tmp_path):
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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 = str(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():
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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):
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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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def test_reducing_gap_1(self, gradients_image):
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for box, epsilon in [
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(None, 4),
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((1.1, 2.2, 510.8, 510.9), 4),
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((3, 10, 410, 256), 10),
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]:
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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(AssertionError):
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assert_image_equal(ref, im)
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assert_image_similar(ref, im, epsilon)
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def test_reducing_gap_2(self, gradients_image):
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for box, epsilon in [
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(None, 1.5),
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((1.1, 2.2, 510.8, 510.9), 1.5),
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((3, 10, 410, 256), 1),
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]:
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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(AssertionError):
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assert_image_equal(ref, im)
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assert_image_similar(ref, im, epsilon)
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def test_reducing_gap_3(self, gradients_image):
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for box, epsilon in [
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(None, 1),
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((1.1, 2.2, 510.8, 510.9), 1),
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((3, 10, 410, 256), 0.5),
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]:
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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(AssertionError):
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assert_image_equal(ref, im)
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assert_image_similar(ref, im, epsilon)
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def test_reducing_gap_8(self, gradients_image):
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for box in [None, (1.1, 2.2, 510.8, 510.9), (3, 10, 410, 256)]:
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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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def test_box_filter(self, gradients_image):
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for box, epsilon in [
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((0, 0, 512, 512), 5.5),
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((0.9, 1.7, 128, 128), 9.5),
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]:
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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):
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def resize(mode, size):
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out = hopper(mode).resize(size)
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assert out.mode == mode
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assert out.size == 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), "unknown")
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def test_default_filter(self):
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for mode in "L", "RGB", "I", "F":
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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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for mode in "1", "P":
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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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for mode in "I;16", "I;16L", "I;16B", "BGR;15", "BGR;16":
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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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