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Further parametrizations
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@radarhere
radarhere committed Aug 23, 2022
1 parent 8f25ea4 commit f82bd91
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14 changes: 7 additions & 7 deletions Tests/test_image_resample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -514,13 +514,13 @@ def test_subsample(self):
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
Expand Down
241 changes: 126 additions & 115 deletions Tests/test_image_resize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,33 +46,58 @@ def test_convolution_modes(self):
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.
Expand All @@ -86,47 +111,37 @@ def test_endianness(self):
}
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()
}

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 [
# 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,
]:
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):
Expand Down Expand Up @@ -170,74 +185,71 @@ def test_reducing_gap_values(self, gradients_image):
(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
)
@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)
with pytest.raises(AssertionError):
assert_image_equal(ref, im)

assert_image_similar(ref, im, epsilon)
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
)
@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)
with pytest.raises(AssertionError):
assert_image_equal(ref, im)

assert_image_similar(ref, im, epsilon)
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
)
@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)
with pytest.raises(AssertionError):
assert_image_equal(ref, im)

assert_image_similar(ref, im, epsilon)
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", (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)
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
)
@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)
assert_image_similar(ref, im, epsilon)


class TestImageResize:
Expand All @@ -264,15 +276,14 @@ def test_load_first(self):
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))

for mode in "1", "P":
im = hopper(mode)
assert im.resize((20, 20), Image.Resampling.NEAREST) == 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 "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))

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