mirror of
https://github.com/python-pillow/Pillow.git
synced 2024-12-27 10:26:19 +03:00
249 lines
8.2 KiB
Python
249 lines
8.2 KiB
Python
"""
|
|
Tests for resize functionality.
|
|
"""
|
|
from itertools import permutations
|
|
|
|
from PIL import Image
|
|
|
|
from .helper import PillowTestCase, assert_image_equal, assert_image_similar, hopper
|
|
|
|
|
|
class TestImagingCoreResize(PillowTestCase):
|
|
def resize(self, im, size, f):
|
|
# Image class independent version of resize.
|
|
im.load()
|
|
return im._new(im.im.resize(size, f))
|
|
|
|
def test_nearest_mode(self):
|
|
for mode in [
|
|
"1",
|
|
"P",
|
|
"L",
|
|
"I",
|
|
"F",
|
|
"RGB",
|
|
"RGBA",
|
|
"CMYK",
|
|
"YCbCr",
|
|
"I;16",
|
|
]: # exotic mode
|
|
im = hopper(mode)
|
|
r = self.resize(im, (15, 12), Image.NEAREST)
|
|
self.assertEqual(r.mode, mode)
|
|
self.assertEqual(r.size, (15, 12))
|
|
self.assertEqual(r.im.bands, im.im.bands)
|
|
|
|
def test_convolution_modes(self):
|
|
self.assertRaises(
|
|
ValueError, self.resize, hopper("1"), (15, 12), Image.BILINEAR
|
|
)
|
|
self.assertRaises(
|
|
ValueError, self.resize, hopper("P"), (15, 12), Image.BILINEAR
|
|
)
|
|
self.assertRaises(
|
|
ValueError, self.resize, hopper("I;16"), (15, 12), Image.BILINEAR
|
|
)
|
|
for mode in ["L", "I", "F", "RGB", "RGBA", "CMYK", "YCbCr"]:
|
|
im = hopper(mode)
|
|
r = self.resize(im, (15, 12), Image.BILINEAR)
|
|
self.assertEqual(r.mode, mode)
|
|
self.assertEqual(r.size, (15, 12))
|
|
self.assertEqual(r.im.bands, im.im.bands)
|
|
|
|
def test_reduce_filters(self):
|
|
for f in [
|
|
Image.NEAREST,
|
|
Image.BOX,
|
|
Image.BILINEAR,
|
|
Image.HAMMING,
|
|
Image.BICUBIC,
|
|
Image.LANCZOS,
|
|
]:
|
|
r = self.resize(hopper("RGB"), (15, 12), f)
|
|
self.assertEqual(r.mode, "RGB")
|
|
self.assertEqual(r.size, (15, 12))
|
|
|
|
def test_enlarge_filters(self):
|
|
for f in [
|
|
Image.NEAREST,
|
|
Image.BOX,
|
|
Image.BILINEAR,
|
|
Image.HAMMING,
|
|
Image.BICUBIC,
|
|
Image.LANCZOS,
|
|
]:
|
|
r = self.resize(hopper("RGB"), (212, 195), f)
|
|
self.assertEqual(r.mode, "RGB")
|
|
self.assertEqual(r.size, (212, 195))
|
|
|
|
def test_endianness(self):
|
|
# 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.
|
|
# The R and A channels should not swap, which is indicative of
|
|
# an endianness issues.
|
|
|
|
samples = {
|
|
"blank": Image.new("L", (2, 2), 0),
|
|
"filled": Image.new("L", (2, 2), 255),
|
|
"dirty": Image.new("L", (2, 2), 0),
|
|
}
|
|
samples["dirty"].putpixel((1, 1), 128)
|
|
|
|
for f in [
|
|
Image.NEAREST,
|
|
Image.BOX,
|
|
Image.BILINEAR,
|
|
Image.HAMMING,
|
|
Image.BICUBIC,
|
|
Image.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.NEAREST,
|
|
Image.BOX,
|
|
Image.BILINEAR,
|
|
Image.HAMMING,
|
|
Image.BICUBIC,
|
|
Image.LANCZOS,
|
|
]:
|
|
r = self.resize(Image.new("RGB", (0, 0), "white"), (212, 195), f)
|
|
self.assertEqual(r.mode, "RGB")
|
|
self.assertEqual(r.size, (212, 195))
|
|
self.assertEqual(r.getdata()[0], (0, 0, 0))
|
|
|
|
def test_unknown_filter(self):
|
|
self.assertRaises(ValueError, self.resize, hopper(), (10, 10), 9)
|
|
|
|
|
|
class TestReducingGapResize(PillowTestCase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.gradients_image = Image.open("Tests/images/radial_gradients.png")
|
|
cls.gradients_image.load()
|
|
|
|
def test_reducing_gap_values(self):
|
|
ref = self.gradients_image.resize((52, 34), Image.BICUBIC, reducing_gap=None)
|
|
im = self.gradients_image.resize((52, 34), Image.BICUBIC)
|
|
assert_image_equal(ref, im)
|
|
|
|
with self.assertRaises(ValueError):
|
|
self.gradients_image.resize((52, 34), Image.BICUBIC, reducing_gap=0)
|
|
|
|
with self.assertRaises(ValueError):
|
|
self.gradients_image.resize((52, 34), Image.BICUBIC, reducing_gap=0.99)
|
|
|
|
def test_reducing_gap_1(self):
|
|
for box, epsilon in [
|
|
(None, 4),
|
|
((1.1, 2.2, 510.8, 510.9), 4),
|
|
((3, 10, 410, 256), 10),
|
|
]:
|
|
ref = self.gradients_image.resize((52, 34), Image.BICUBIC, box=box)
|
|
im = self.gradients_image.resize(
|
|
(52, 34), Image.BICUBIC, box=box, reducing_gap=1.0
|
|
)
|
|
|
|
with self.assertRaises(AssertionError):
|
|
assert_image_equal(ref, im)
|
|
|
|
assert_image_similar(ref, im, epsilon)
|
|
|
|
def test_reducing_gap_2(self):
|
|
for box, epsilon in [
|
|
(None, 1.5),
|
|
((1.1, 2.2, 510.8, 510.9), 1.5),
|
|
((3, 10, 410, 256), 1),
|
|
]:
|
|
ref = self.gradients_image.resize((52, 34), Image.BICUBIC, box=box)
|
|
im = self.gradients_image.resize(
|
|
(52, 34), Image.BICUBIC, box=box, reducing_gap=2.0
|
|
)
|
|
|
|
with self.assertRaises(AssertionError):
|
|
assert_image_equal(ref, im)
|
|
|
|
assert_image_similar(ref, im, epsilon)
|
|
|
|
def test_reducing_gap_3(self):
|
|
for box, epsilon in [
|
|
(None, 1),
|
|
((1.1, 2.2, 510.8, 510.9), 1),
|
|
((3, 10, 410, 256), 0.5),
|
|
]:
|
|
ref = self.gradients_image.resize((52, 34), Image.BICUBIC, box=box)
|
|
im = self.gradients_image.resize(
|
|
(52, 34), Image.BICUBIC, box=box, reducing_gap=3.0
|
|
)
|
|
|
|
with self.assertRaises(AssertionError):
|
|
assert_image_equal(ref, im)
|
|
|
|
assert_image_similar(ref, im, epsilon)
|
|
|
|
def test_reducing_gap_8(self):
|
|
for box in [None, (1.1, 2.2, 510.8, 510.9), (3, 10, 410, 256)]:
|
|
ref = self.gradients_image.resize((52, 34), Image.BICUBIC, box=box)
|
|
im = self.gradients_image.resize(
|
|
(52, 34), Image.BICUBIC, box=box, reducing_gap=8.0
|
|
)
|
|
|
|
assert_image_equal(ref, im)
|
|
|
|
def test_box_filter(self):
|
|
for box, epsilon in [
|
|
((0, 0, 512, 512), 5.5),
|
|
((0.9, 1.7, 128, 128), 9.5),
|
|
]:
|
|
ref = self.gradients_image.resize((52, 34), Image.BOX, box=box)
|
|
im = self.gradients_image.resize(
|
|
(52, 34), Image.BOX, box=box, reducing_gap=1.0
|
|
)
|
|
|
|
assert_image_similar(ref, im, epsilon)
|
|
|
|
|
|
class TestImageResize(PillowTestCase):
|
|
def test_resize(self):
|
|
def resize(mode, size):
|
|
out = hopper(mode).resize(size)
|
|
self.assertEqual(out.mode, mode)
|
|
self.assertEqual(out.size, size)
|
|
|
|
for mode in "1", "P", "L", "RGB", "I", "F":
|
|
resize(mode, (112, 103))
|
|
resize(mode, (188, 214))
|
|
|
|
# Test unknown resampling filter
|
|
with hopper() as im:
|
|
self.assertRaises(ValueError, im.resize, (10, 10), "unknown")
|
|
|
|
def test_default_filter(self):
|
|
for mode in "L", "RGB", "I", "F":
|
|
im = hopper(mode)
|
|
self.assertEqual(im.resize((20, 20), Image.BICUBIC), im.resize((20, 20)))
|
|
|
|
for mode in "1", "P":
|
|
im = hopper(mode)
|
|
self.assertEqual(im.resize((20, 20), Image.NEAREST), im.resize((20, 20)))
|