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https://github.com/python-pillow/Pillow.git
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d50445ff30
Similar to the recent adoption of Black. isort is a Python utility to sort imports alphabetically and automatically separate into sections. By using isort, contributors can quickly and automatically conform to the projects style without thinking. Just let the tool do it. Uses the configuration recommended by the Black to avoid conflicts of style. Rewrite TestImageQt.test_deprecated to no rely on import order.
153 lines
4.9 KiB
Python
153 lines
4.9 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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from PIL import Image
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from .helper import PillowTestCase, hopper
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class TestImagingCoreResize(PillowTestCase):
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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.NEAREST)
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self.assertEqual(r.mode, mode)
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self.assertEqual(r.size, (15, 12))
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self.assertEqual(r.im.bands, im.im.bands)
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def test_convolution_modes(self):
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self.assertRaises(
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ValueError, self.resize, hopper("1"), (15, 12), Image.BILINEAR
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)
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self.assertRaises(
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ValueError, self.resize, hopper("P"), (15, 12), Image.BILINEAR
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)
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self.assertRaises(
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ValueError, self.resize, hopper("I;16"), (15, 12), Image.BILINEAR
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)
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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.BILINEAR)
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self.assertEqual(r.mode, mode)
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self.assertEqual(r.size, (15, 12))
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self.assertEqual(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.NEAREST,
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Image.BOX,
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Image.BILINEAR,
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Image.HAMMING,
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Image.BICUBIC,
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Image.LANCZOS,
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]:
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r = self.resize(hopper("RGB"), (15, 12), f)
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self.assertEqual(r.mode, "RGB")
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self.assertEqual(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.NEAREST,
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Image.BOX,
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Image.BILINEAR,
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Image.HAMMING,
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Image.BICUBIC,
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Image.LANCZOS,
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]:
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r = self.resize(hopper("RGB"), (212, 195), f)
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self.assertEqual(r.mode, "RGB")
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self.assertEqual(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.NEAREST,
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Image.BOX,
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Image.BILINEAR,
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Image.HAMMING,
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Image.BICUBIC,
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Image.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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self.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.NEAREST,
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Image.BOX,
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Image.BILINEAR,
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Image.HAMMING,
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Image.BICUBIC,
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Image.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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self.assertEqual(r.mode, "RGB")
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self.assertEqual(r.size, (212, 195))
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self.assertEqual(r.getdata()[0], (0, 0, 0))
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def test_unknown_filter(self):
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self.assertRaises(ValueError, self.resize, hopper(), (10, 10), 9)
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class TestImageResize(PillowTestCase):
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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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self.assertEqual(out.mode, mode)
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self.assertEqual(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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im = hopper()
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self.assertRaises(ValueError, im.resize, (10, 10), "unknown")
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