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Further parametrizations
This commit is contained in:
Andrew Murray
parent
8f25ea46eb
commit
3353ea80e1
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@ -514,13 +514,15 @@ class TestCoreResampleBox:
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assert_image_similar(reference, without_box, 5)
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@pytest.mark.parametrize("mode", ("RGB", "L", "RGBA", "LA", "I", ""))
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def test_formats(self, mode):
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for resample in [Image.Resampling.NEAREST, Image.Resampling.BILINEAR]:
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im = hopper(mode)
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box = (20, 20, im.size[0] - 20, im.size[1] - 20)
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with_box = im.resize((32, 32), resample, box)
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cropped = im.crop(box).resize((32, 32), resample)
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assert_image_similar(cropped, with_box, 0.4)
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@pytest.mark.parametrize(
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"resample", (Image.Resampling.NEAREST, Image.Resampling.BILINEAR)
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)
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def test_formats(self, mode, resample):
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im = hopper(mode)
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box = (20, 20, im.size[0] - 20, im.size[1] - 20)
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with_box = im.resize((32, 32), resample, box)
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cropped = im.crop(box).resize((32, 32), resample)
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assert_image_similar(cropped, with_box, 0.4)
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def test_passthrough(self):
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# When no resize is required
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@ -46,33 +46,58 @@ class TestImagingCoreResize:
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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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@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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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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),
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)
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def test_reduce_filters(self, resample):
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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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def test_enlarge_filters(self):
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for f in [
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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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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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),
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)
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def test_enlarge_filters(self, resample):
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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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def test_endianness(self):
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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(self, resample, mode, channels_set):
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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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@ -86,47 +111,37 @@ class TestImagingCoreResize:
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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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# 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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# 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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),
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)
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def test_enlarge_zero(self, resample):
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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):
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with pytest.raises(ValueError):
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@ -170,74 +185,71 @@ class TestReducingGapResize:
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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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@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(self, gradients_image, box, epsilon):
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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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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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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(self, gradients_image, box, epsilon):
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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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@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(self, gradients_image, box, epsilon):
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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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@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(self, gradients_image, box):
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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(self, gradients_image, box, epsilon):
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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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@ -264,15 +276,14 @@ class TestImageResize:
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im = im.resize((64, 64))
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assert im.size == (64, 64)
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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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@pytest.mark.parametrize("mode", ("L", "RGB", "I", "F"))
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def test_default_filter_bicubic(self, mode):
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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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@pytest.mark.parametrize(
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"mode", ("1", "P", "I;16", "I;16L", "I;16B", "BGR;15", "BGR;16")
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)
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def test_default_filter_nearest(self, mode):
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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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