python-pillow/Pillow
1
1
Fork 0
mirror of https://github.com/python-pillow/Pillow.git synced 2024-12-25 17:36:18 +03:00
Code Issues Packages Projects Releases Wiki Activity

Further parametrizations

Browse Source
This commit is contained in:
Andrew Murray 2022-08-24 10:37:40 +10:00
parent 8f25ea46eb
commit 3353ea80e1
2 changed files with 143 additions and 130 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
Tests/test_image_resample.py
View File

@ -514,8 +514,10 @@ 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]:
@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)

131
Tests/test_image_resize.py
View File

@ -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)
),
)
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)
),
)
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.
@ -86,44 +111,34 @@ class TestImagingCoreResize:
}
samples["dirty"].putpixel((1, 1), 128)
for f in [
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()
name: self.resize(ch, (4, 4), resample) 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)
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]])
def test_enlarge_zero(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(Image.new("RGB", (0, 0), "white"), (212, 195), f)
),
)
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)
@ -170,12 +185,11 @@ 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),
]:
@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
@ -186,12 +200,11 @@ class TestReducingGapResize:
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),
]:
@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
@ -202,12 +215,11 @@ class TestReducingGapResize:
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),
]:
@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
@ -218,8 +230,8 @@ class TestReducingGapResize:
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)]:
@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
@ -227,11 +239,11 @@ class TestReducingGapResize:
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),
]:
@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
@ -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":
@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":
@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))