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Follow Python's file object semantics. User code is responsible for closing resources (usually through a context manager) in a deterministic way. To achieve this, remove __del__ functions. These functions used to closed open file handlers in an attempt to silence Python ResourceWarnings. However, using __del__ has the following drawbacks: - __del__ isn't called until the object's reference count reaches 0. Therefore, resource handlers remain open or in use longer than necessary. - The __del__ method isn't guaranteed to execute on system exit. See the Python documentation: https://docs.python.org/3/reference/datamodel.html#object.__del__ > It is not guaranteed that __del__() methods are called for objects > that still exist when the interpreter exits. - Exceptions that occur inside __del__ are ignored instead of raised. This has the potential of hiding bugs. This is also in the Python documentation: > Warning: Due to the precarious circumstances under which __del__() > methods are invoked, exceptions that occur during their execution > are ignored, and a warning is printed to sys.stderr instead. Instead, always close resource handlers when they are no longer in use. This will close the file handler at a specified point in the user's code and not wait until the interpreter chooses to. It is always guaranteed to run. And, if an exception occurs while closing the file handler, the bug will not be ignored. Now, when code receives a ResourceWarning, it will highlight an area that is mishandling resources. It should not simply be silenced, but fixed by closing resources with a context manager. All warnings that were emitted during tests have been cleaned up. To enable warnings, I passed the `-Wa` CLI option to Python. This exposed some mishandling of resources in ImageFile.__init__() and SpiderImagePlugin.loadImageSeries(), they too were fixed.
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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with hopper() as im:
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self.assertRaises(ValueError, im.resize, (10, 10), "unknown")
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