""" Tests for resize functionality. """ from itertools import permutations import pytest 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) assert r.mode == mode assert r.size == (15, 12) assert r.im.bands == im.im.bands def test_convolution_modes(self): with pytest.raises(ValueError): self.resize(hopper("1"), (15, 12), Image.BILINEAR) with pytest.raises(ValueError): self.resize(hopper("P"), (15, 12), Image.BILINEAR) with pytest.raises(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) assert r.mode == mode assert r.size == (15, 12) assert 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) assert r.mode == "RGB" assert 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) assert r.mode == "RGB" assert 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) 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): 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 pytest.raises(ValueError): self.gradients_image.resize((52, 34), Image.BICUBIC, reducing_gap=0) with pytest.raises(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 pytest.raises(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 pytest.raises(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 pytest.raises(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) assert out.mode == mode assert 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: with pytest.raises(ValueError): im.resize((10, 10), "unknown") def test_default_filter(self): for mode in "L", "RGB", "I", "F": im = hopper(mode) assert im.resize((20, 20), Image.BICUBIC) == im.resize((20, 20)) for mode in "1", "P": im = hopper(mode) assert im.resize((20, 20), Image.NEAREST) == im.resize((20, 20))