mirror of
https://github.com/python-pillow/Pillow.git
synced 2025-10-31 16:07:30 +03:00
Further parametrizations
This commit is contained in:
Andrew Murray
parent
8f25ea46eb
commit
3353ea80e1
|
|
@ -514,13 +514,15 @@ class TestCoreResampleBox:
|
|||
assert_image_similar(reference, without_box, 5)
|
||||
|
||||
@pytest.mark.parametrize("mode", ("RGB", "L", "RGBA", "LA", "I", ""))
|
||||
def test_formats(self, mode):
|
||||
for resample in [Image.Resampling.NEAREST, Image.Resampling.BILINEAR]:
|
||||
im = hopper(mode)
|
||||
box = (20, 20, im.size[0] - 20, im.size[1] - 20)
|
||||
with_box = im.resize((32, 32), resample, box)
|
||||
cropped = im.crop(box).resize((32, 32), resample)
|
||||
assert_image_similar(cropped, with_box, 0.4)
|
||||
@pytest.mark.parametrize(
|
||||
"resample", (Image.Resampling.NEAREST, Image.Resampling.BILINEAR)
|
||||
)
|
||||
def test_formats(self, mode, resample):
|
||||
im = hopper(mode)
|
||||
box = (20, 20, im.size[0] - 20, im.size[1] - 20)
|
||||
with_box = im.resize((32, 32), resample, box)
|
||||
cropped = im.crop(box).resize((32, 32), resample)
|
||||
assert_image_similar(cropped, with_box, 0.4)
|
||||
|
||||
def test_passthrough(self):
|
||||
# When no resize is required
|
||||
|
|
|
|||
|
|
@ -46,33 +46,58 @@ class TestImagingCoreResize:
|
|||
assert r.size == (15, 12)
|
||||
assert r.im.bands == im.im.bands
|
||||
|
||||
def test_reduce_filters(self):
|
||||
for f in [
|
||||
@pytest.mark.parametrize(
|
||||
"resample",
|
||||
(
|
||||
Image.Resampling.NEAREST,
|
||||
Image.Resampling.BOX,
|
||||
Image.Resampling.BILINEAR,
|
||||
Image.Resampling.HAMMING,
|
||||
Image.Resampling.BICUBIC,
|
||||
Image.Resampling.LANCZOS,
|
||||
]:
|
||||
r = self.resize(hopper("RGB"), (15, 12), f)
|
||||
assert r.mode == "RGB"
|
||||
assert r.size == (15, 12)
|
||||
),
|
||||
)
|
||||
def test_reduce_filters(self, resample):
|
||||
r = self.resize(hopper("RGB"), (15, 12), resample)
|
||||
assert r.mode == "RGB"
|
||||
assert r.size == (15, 12)
|
||||
|
||||
def test_enlarge_filters(self):
|
||||
for f in [
|
||||
@pytest.mark.parametrize(
|
||||
"resample",
|
||||
(
|
||||
Image.Resampling.NEAREST,
|
||||
Image.Resampling.BOX,
|
||||
Image.Resampling.BILINEAR,
|
||||
Image.Resampling.HAMMING,
|
||||
Image.Resampling.BICUBIC,
|
||||
Image.Resampling.LANCZOS,
|
||||
]:
|
||||
r = self.resize(hopper("RGB"), (212, 195), f)
|
||||
assert r.mode == "RGB"
|
||||
assert r.size == (212, 195)
|
||||
),
|
||||
)
|
||||
def test_enlarge_filters(self, resample):
|
||||
r = self.resize(hopper("RGB"), (212, 195), resample)
|
||||
assert r.mode == "RGB"
|
||||
assert r.size == (212, 195)
|
||||
|
||||
def test_endianness(self):
|
||||
@pytest.mark.parametrize(
|
||||
"resample",
|
||||
(
|
||||
Image.Resampling.NEAREST,
|
||||
Image.Resampling.BOX,
|
||||
Image.Resampling.BILINEAR,
|
||||
Image.Resampling.HAMMING,
|
||||
Image.Resampling.BICUBIC,
|
||||
Image.Resampling.LANCZOS,
|
||||
),
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"mode, channels_set",
|
||||
(
|
||||
("RGB", ("blank", "filled", "dirty")),
|
||||
("RGBA", ("blank", "blank", "filled", "dirty")),
|
||||
("LA", ("filled", "dirty")),
|
||||
),
|
||||
)
|
||||
def test_endianness(self, resample, mode, channels_set):
|
||||
# Make an image with one colored pixel, in one channel.
|
||||
# When resized, that channel should be the same as a GS image.
|
||||
# Other channels should be unaffected.
|
||||
|
|
@ -86,47 +111,37 @@ class TestImagingCoreResize:
|
|||
}
|
||||
samples["dirty"].putpixel((1, 1), 128)
|
||||
|
||||
for f in [
|
||||
# samples resized with current filter
|
||||
references = {
|
||||
name: self.resize(ch, (4, 4), resample) for name, ch in samples.items()
|
||||
}
|
||||
|
||||
for channels in set(permutations(channels_set)):
|
||||
# compile image from different channels permutations
|
||||
im = Image.merge(mode, [samples[ch] for ch in channels])
|
||||
resized = self.resize(im, (4, 4), resample)
|
||||
|
||||
for i, ch in enumerate(resized.split()):
|
||||
# check what resized channel in image is the same
|
||||
# as separately resized channel
|
||||
assert_image_equal(ch, references[channels[i]])
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"resample",
|
||||
(
|
||||
Image.Resampling.NEAREST,
|
||||
Image.Resampling.BOX,
|
||||
Image.Resampling.BILINEAR,
|
||||
Image.Resampling.HAMMING,
|
||||
Image.Resampling.BICUBIC,
|
||||
Image.Resampling.LANCZOS,
|
||||
]:
|
||||
# samples resized with current filter
|
||||
references = {
|
||||
name: self.resize(ch, (4, 4), f) for name, ch in samples.items()
|
||||
}
|
||||
|
||||
for mode, channels_set in [
|
||||
("RGB", ("blank", "filled", "dirty")),
|
||||
("RGBA", ("blank", "blank", "filled", "dirty")),
|
||||
("LA", ("filled", "dirty")),
|
||||
]:
|
||||
for channels in set(permutations(channels_set)):
|
||||
# compile image from different channels permutations
|
||||
im = Image.merge(mode, [samples[ch] for ch in channels])
|
||||
resized = self.resize(im, (4, 4), f)
|
||||
|
||||
for i, ch in enumerate(resized.split()):
|
||||
# check what resized channel in image is the same
|
||||
# as separately resized channel
|
||||
assert_image_equal(ch, references[channels[i]])
|
||||
|
||||
def test_enlarge_zero(self):
|
||||
for f in [
|
||||
Image.Resampling.NEAREST,
|
||||
Image.Resampling.BOX,
|
||||
Image.Resampling.BILINEAR,
|
||||
Image.Resampling.HAMMING,
|
||||
Image.Resampling.BICUBIC,
|
||||
Image.Resampling.LANCZOS,
|
||||
]:
|
||||
r = self.resize(Image.new("RGB", (0, 0), "white"), (212, 195), f)
|
||||
assert r.mode == "RGB"
|
||||
assert r.size == (212, 195)
|
||||
assert r.getdata()[0] == (0, 0, 0)
|
||||
),
|
||||
)
|
||||
def test_enlarge_zero(self, resample):
|
||||
r = self.resize(Image.new("RGB", (0, 0), "white"), (212, 195), resample)
|
||||
assert r.mode == "RGB"
|
||||
assert r.size == (212, 195)
|
||||
assert r.getdata()[0] == (0, 0, 0)
|
||||
|
||||
def test_unknown_filter(self):
|
||||
with pytest.raises(ValueError):
|
||||
|
|
@ -170,74 +185,71 @@ class TestReducingGapResize:
|
|||
(52, 34), Image.Resampling.BICUBIC, reducing_gap=0.99
|
||||
)
|
||||
|
||||
def test_reducing_gap_1(self, gradients_image):
|
||||
for box, epsilon in [
|
||||
(None, 4),
|
||||
((1.1, 2.2, 510.8, 510.9), 4),
|
||||
((3, 10, 410, 256), 10),
|
||||
]:
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=1.0
|
||||
)
|
||||
|
||||
with pytest.raises(AssertionError):
|
||||
assert_image_equal(ref, im)
|
||||
|
||||
assert_image_similar(ref, im, epsilon)
|
||||
|
||||
def test_reducing_gap_2(self, gradients_image):
|
||||
for box, epsilon in [
|
||||
(None, 1.5),
|
||||
((1.1, 2.2, 510.8, 510.9), 1.5),
|
||||
((3, 10, 410, 256), 1),
|
||||
]:
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=2.0
|
||||
)
|
||||
|
||||
with pytest.raises(AssertionError):
|
||||
assert_image_equal(ref, im)
|
||||
|
||||
assert_image_similar(ref, im, epsilon)
|
||||
|
||||
def test_reducing_gap_3(self, gradients_image):
|
||||
for box, epsilon in [
|
||||
(None, 1),
|
||||
((1.1, 2.2, 510.8, 510.9), 1),
|
||||
((3, 10, 410, 256), 0.5),
|
||||
]:
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=3.0
|
||||
)
|
||||
|
||||
with pytest.raises(AssertionError):
|
||||
assert_image_equal(ref, im)
|
||||
|
||||
assert_image_similar(ref, im, epsilon)
|
||||
|
||||
def test_reducing_gap_8(self, gradients_image):
|
||||
for box in [None, (1.1, 2.2, 510.8, 510.9), (3, 10, 410, 256)]:
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=8.0
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"box, epsilon",
|
||||
((None, 4), ((1.1, 2.2, 510.8, 510.9), 4), ((3, 10, 410, 256), 10)),
|
||||
)
|
||||
def test_reducing_gap_1(self, gradients_image, box, epsilon):
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=1.0
|
||||
)
|
||||
|
||||
with pytest.raises(AssertionError):
|
||||
assert_image_equal(ref, im)
|
||||
|
||||
def test_box_filter(self, gradients_image):
|
||||
for box, epsilon in [
|
||||
((0, 0, 512, 512), 5.5),
|
||||
((0.9, 1.7, 128, 128), 9.5),
|
||||
]:
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BOX, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BOX, box=box, reducing_gap=1.0
|
||||
)
|
||||
assert_image_similar(ref, im, epsilon)
|
||||
|
||||
assert_image_similar(ref, im, epsilon)
|
||||
@pytest.mark.parametrize(
|
||||
"box, epsilon",
|
||||
((None, 1.5), ((1.1, 2.2, 510.8, 510.9), 1.5), ((3, 10, 410, 256), 1)),
|
||||
)
|
||||
def test_reducing_gap_2(self, gradients_image, box, epsilon):
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=2.0
|
||||
)
|
||||
|
||||
with pytest.raises(AssertionError):
|
||||
assert_image_equal(ref, im)
|
||||
|
||||
assert_image_similar(ref, im, epsilon)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"box, epsilon",
|
||||
((None, 1), ((1.1, 2.2, 510.8, 510.9), 1), ((3, 10, 410, 256), 0.5)),
|
||||
)
|
||||
def test_reducing_gap_3(self, gradients_image, box, epsilon):
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=3.0
|
||||
)
|
||||
|
||||
with pytest.raises(AssertionError):
|
||||
assert_image_equal(ref, im)
|
||||
|
||||
assert_image_similar(ref, im, epsilon)
|
||||
|
||||
@pytest.mark.parametrize("box", (None, (1.1, 2.2, 510.8, 510.9), (3, 10, 410, 256)))
|
||||
def test_reducing_gap_8(self, gradients_image, box):
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BICUBIC, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BICUBIC, box=box, reducing_gap=8.0
|
||||
)
|
||||
|
||||
assert_image_equal(ref, im)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"box, epsilon",
|
||||
(((0, 0, 512, 512), 5.5), ((0.9, 1.7, 128, 128), 9.5)),
|
||||
)
|
||||
def test_box_filter(self, gradients_image, box, epsilon):
|
||||
ref = gradients_image.resize((52, 34), Image.Resampling.BOX, box=box)
|
||||
im = gradients_image.resize(
|
||||
(52, 34), Image.Resampling.BOX, box=box, reducing_gap=1.0
|
||||
)
|
||||
|
||||
assert_image_similar(ref, im, epsilon)
|
||||
|
||||
|
||||
class TestImageResize:
|
||||
|
|
@ -264,15 +276,14 @@ class TestImageResize:
|
|||
im = im.resize((64, 64))
|
||||
assert im.size == (64, 64)
|
||||
|
||||
def test_default_filter(self):
|
||||
for mode in "L", "RGB", "I", "F":
|
||||
im = hopper(mode)
|
||||
assert im.resize((20, 20), Image.Resampling.BICUBIC) == im.resize((20, 20))
|
||||
@pytest.mark.parametrize("mode", ("L", "RGB", "I", "F"))
|
||||
def test_default_filter_bicubic(self, mode):
|
||||
im = hopper(mode)
|
||||
assert im.resize((20, 20), Image.Resampling.BICUBIC) == im.resize((20, 20))
|
||||
|
||||
for mode in "1", "P":
|
||||
im = hopper(mode)
|
||||
assert im.resize((20, 20), Image.Resampling.NEAREST) == im.resize((20, 20))
|
||||
|
||||
for mode in "I;16", "I;16L", "I;16B", "BGR;15", "BGR;16":
|
||||
im = hopper(mode)
|
||||
assert im.resize((20, 20), Image.Resampling.NEAREST) == im.resize((20, 20))
|
||||
@pytest.mark.parametrize(
|
||||
"mode", ("1", "P", "I;16", "I;16L", "I;16B", "BGR;15", "BGR;16")
|
||||
)
|
||||
def test_default_filter_nearest(self, mode):
|
||||
im = hopper(mode)
|
||||
assert im.resize((20, 20), Image.Resampling.NEAREST) == im.resize((20, 20))
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user