from __future__ import annotations import math import pytest from PIL import Image, ImageTransform from .helper import assert_image_equal, assert_image_similar, hopper class TestImageTransform: def test_sanity(self): im = Image.new("L", (100, 100)) seq = tuple(range(10)) transform = ImageTransform.AffineTransform(seq[:6]) im.transform((100, 100), transform) transform = ImageTransform.ExtentTransform(seq[:4]) im.transform((100, 100), transform) transform = ImageTransform.QuadTransform(seq[:8]) im.transform((100, 100), transform) transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])]) im.transform((100, 100), transform) def test_info(self): comment = b"File written by Adobe Photoshop\xa8 4.0" with Image.open("Tests/images/hopper.gif") as im: assert im.info["comment"] == comment transform = ImageTransform.ExtentTransform((0, 0, 0, 0)) new_im = im.transform((100, 100), transform) assert new_im.info["comment"] == comment def test_palette(self): with Image.open("Tests/images/hopper.gif") as im: transformed = im.transform( im.size, Image.Transform.AFFINE, [1, 0, 0, 0, 1, 0] ) assert im.palette.palette == transformed.palette.palette def test_extent(self): im = hopper("RGB") (w, h) = im.size transformed = im.transform( im.size, Image.Transform.EXTENT, (0, 0, w // 2, h // 2), # ul -> lr Image.Resampling.BILINEAR, ) scaled = im.resize((w * 2, h * 2), Image.Resampling.BILINEAR).crop((0, 0, w, h)) # undone -- precision? assert_image_similar(transformed, scaled, 23) def test_quad(self): # one simple quad transform, equivalent to scale & crop upper left quad im = hopper("RGB") (w, h) = im.size transformed = im.transform( im.size, Image.Transform.QUAD, (0, 0, 0, h // 2, w // 2, h // 2, w // 2, 0), # ul -> ccw around quad Image.Resampling.BILINEAR, ) scaled = im.transform( (w, h), Image.Transform.AFFINE, (0.5, 0, 0, 0, 0.5, 0), Image.Resampling.BILINEAR, ) assert_image_equal(transformed, scaled) @pytest.mark.parametrize( "mode, expected_pixel", ( ("RGB", (255, 0, 0)), ("RGBA", (255, 0, 0, 255)), ("LA", (76, 0)), ), ) def test_fill(self, mode, expected_pixel): im = hopper(mode) (w, h) = im.size transformed = im.transform( im.size, Image.Transform.EXTENT, (0, 0, w * 2, h * 2), Image.Resampling.BILINEAR, fillcolor="red", ) assert transformed.getpixel((w - 1, h - 1)) == expected_pixel def test_mesh(self): # this should be a checkerboard of halfsized hoppers in ul, lr im = hopper("RGBA") (w, h) = im.size transformed = im.transform( im.size, Image.Transform.MESH, ( ( (0, 0, w // 2, h // 2), # box (0, 0, 0, h, w, h, w, 0), # ul -> ccw around quad ), ( (w // 2, h // 2, w, h), # box (0, 0, 0, h, w, h, w, 0), # ul -> ccw around quad ), ), Image.Resampling.BILINEAR, ) scaled = im.transform( (w // 2, h // 2), Image.Transform.AFFINE, (2, 0, 0, 0, 2, 0), Image.Resampling.BILINEAR, ) checker = Image.new("RGBA", im.size) checker.paste(scaled, (0, 0)) checker.paste(scaled, (w // 2, h // 2)) assert_image_equal(transformed, checker) # now, check to see that the extra area is (0, 0, 0, 0) blank = Image.new("RGBA", (w // 2, h // 2), (0, 0, 0, 0)) assert_image_equal(blank, transformed.crop((w // 2, 0, w, h // 2))) assert_image_equal(blank, transformed.crop((0, h // 2, w // 2, h))) def _test_alpha_premult(self, op): # create image with half white, half black, # with the black half transparent. # do op, # there should be no darkness in the white section. im = Image.new("RGBA", (10, 10), (0, 0, 0, 0)) im2 = Image.new("RGBA", (5, 10), (255, 255, 255, 255)) im.paste(im2, (0, 0)) im = op(im, (40, 10)) im_background = Image.new("RGB", (40, 10), (255, 255, 255)) im_background.paste(im, (0, 0), im) hist = im_background.histogram() assert 40 * 10 == hist[-1] def test_alpha_premult_resize(self): def op(im, sz): return im.resize(sz, Image.Resampling.BILINEAR) self._test_alpha_premult(op) def test_alpha_premult_transform(self): def op(im, sz): (w, h) = im.size return im.transform( sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.BILINEAR ) self._test_alpha_premult(op) def _test_nearest(self, op, mode): # create white image with half transparent, # do op, # the image should remain white with half transparent transparent, opaque = { "RGBA": ((255, 255, 255, 0), (255, 255, 255, 255)), "LA": ((255, 0), (255, 255)), }[mode] im = Image.new(mode, (10, 10), transparent) im2 = Image.new(mode, (5, 10), opaque) im.paste(im2, (0, 0)) im = op(im, (40, 10)) colors = sorted(im.getcolors()) assert colors == sorted( ( (20 * 10, opaque), (20 * 10, transparent), ) ) @pytest.mark.parametrize("mode", ("RGBA", "LA")) def test_nearest_resize(self, mode): def op(im, sz): return im.resize(sz, Image.Resampling.NEAREST) self._test_nearest(op, mode) @pytest.mark.parametrize("mode", ("RGBA", "LA")) def test_nearest_transform(self, mode): def op(im, sz): (w, h) = im.size return im.transform( sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.NEAREST ) self._test_nearest(op, mode) def test_blank_fill(self): # attempting to hit # https://github.com/python-pillow/Pillow/issues/254 reported # # issue is that transforms with transparent overflow area # contained junk from previous images, especially on systems with # constrained memory. So, attempt to fill up memory with a # pattern, free it, and then run the mesh test again. Using a 1Mp # image with 4 bands, for 4 megs of data allocated, x 64. OMM (64 # bit 12.04 VM with 512 megs available, this fails with Pillow < # a0eaf06cc5f62a6fb6de556989ac1014ff3348ea # # Running by default, but I'd totally understand not doing it in # the future pattern = [Image.new("RGBA", (1024, 1024), (a, a, a, a)) for a in range(1, 65)] # Yeah. Watch some JIT optimize this out. pattern = None # noqa: F841 self.test_mesh() def test_missing_method_data(self): with hopper() as im: with pytest.raises(ValueError): im.transform((100, 100), None) @pytest.mark.parametrize("resample", (Image.Resampling.BOX, "unknown")) def test_unknown_resampling_filter(self, resample): with hopper() as im: (w, h) = im.size with pytest.raises(ValueError): im.transform((100, 100), Image.Transform.EXTENT, (0, 0, w, h), resample) class TestImageTransformAffine: transform = Image.Transform.AFFINE def _test_image(self): im = hopper("RGB") return im.crop((10, 20, im.width - 10, im.height - 20)) @pytest.mark.parametrize( "deg, transpose", ( (0, None), (90, Image.Transpose.ROTATE_90), (180, Image.Transpose.ROTATE_180), (270, Image.Transpose.ROTATE_270), ), ) def test_rotate(self, deg, transpose): im = self._test_image() angle = -math.radians(deg) matrix = [ round(math.cos(angle), 15), round(math.sin(angle), 15), 0.0, round(-math.sin(angle), 15), round(math.cos(angle), 15), 0.0, 0, 0, ] matrix[2] = (1 - matrix[0] - matrix[1]) * im.width / 2 matrix[5] = (1 - matrix[3] - matrix[4]) * im.height / 2 if transpose is not None: transposed = im.transpose(transpose) else: transposed = im for resample in [ Image.Resampling.NEAREST, Image.Resampling.BILINEAR, Image.Resampling.BICUBIC, ]: transformed = im.transform( transposed.size, self.transform, matrix, resample ) assert_image_equal(transposed, transformed) @pytest.mark.parametrize( "scale, epsilon_scale", ( (1.1, 6.9), (1.5, 5.5), (2.0, 5.5), (2.3, 3.7), (2.5, 3.7), ), ) @pytest.mark.parametrize( "resample,epsilon", ( (Image.Resampling.NEAREST, 0), (Image.Resampling.BILINEAR, 2), (Image.Resampling.BICUBIC, 1), ), ) def test_resize(self, scale, epsilon_scale, resample, epsilon): im = self._test_image() size_up = int(round(im.width * scale)), int(round(im.height * scale)) matrix_up = [1 / scale, 0, 0, 0, 1 / scale, 0, 0, 0] matrix_down = [scale, 0, 0, 0, scale, 0, 0, 0] transformed = im.transform(size_up, self.transform, matrix_up, resample) transformed = transformed.transform( im.size, self.transform, matrix_down, resample ) assert_image_similar(transformed, im, epsilon * epsilon_scale) @pytest.mark.parametrize( "x, y, epsilon_scale", ( (0.1, 0, 3.7), (0.6, 0, 9.1), (50, 50, 0), ), ) @pytest.mark.parametrize( "resample, epsilon", ( (Image.Resampling.NEAREST, 0), (Image.Resampling.BILINEAR, 1.5), (Image.Resampling.BICUBIC, 1), ), ) def test_translate(self, x, y, epsilon_scale, resample, epsilon): im = self._test_image() size_up = int(round(im.width + x)), int(round(im.height + y)) matrix_up = [1, 0, -x, 0, 1, -y, 0, 0] matrix_down = [1, 0, x, 0, 1, y, 0, 0] transformed = im.transform(size_up, self.transform, matrix_up, resample) transformed = transformed.transform( im.size, self.transform, matrix_down, resample ) assert_image_similar(transformed, im, epsilon * epsilon_scale) class TestImageTransformPerspective(TestImageTransformAffine): # Repeat all tests for AFFINE transformations with PERSPECTIVE transform = Image.Transform.PERSPECTIVE