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https://github.com/python-pillow/Pillow.git
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7da17ad41e
The previous test configuration made it difficult to run a single test with the pytest CLI. There were two major issues: - The Tests directory was not a package. It now includes a __init__.py file and imports from other tests modules are done with relative imports. - setup.cfg always specified the Tests directory. So even if a specific test were specified as a CLI arg, this configuration would also always include all tests. This configuration has been removed to allow specifying a single test on the command line. Contributors can now run specific tests with a single command such as: $ tox -e py37 -- Tests/test_file_pdf.py::TestFilePdf.test_rgb This makes it easy and faster to iterate on a single test failure and is very familiar to those that have previously used tox and pytest. When running tox or pytest with no arguments, they still discover and runs all tests in the Tests directory.
278 lines
9.2 KiB
Python
278 lines
9.2 KiB
Python
import math
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from .helper import unittest, PillowTestCase, hopper
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from PIL import Image
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class TestImageTransform(PillowTestCase):
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def test_sanity(self):
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from PIL import ImageTransform
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im = Image.new("L", (100, 100))
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seq = tuple(range(10))
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transform = ImageTransform.AffineTransform(seq[:6])
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im.transform((100, 100), transform)
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transform = ImageTransform.ExtentTransform(seq[:4])
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im.transform((100, 100), transform)
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transform = ImageTransform.QuadTransform(seq[:8])
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im.transform((100, 100), transform)
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transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])])
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im.transform((100, 100), transform)
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def test_extent(self):
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im = hopper('RGB')
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(w, h) = im.size
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transformed = im.transform(im.size, Image.EXTENT,
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(0, 0,
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w//2, h//2), # ul -> lr
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Image.BILINEAR)
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scaled = im.resize((w*2, h*2), Image.BILINEAR).crop((0, 0, w, h))
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# undone -- precision?
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self.assert_image_similar(transformed, scaled, 23)
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def test_quad(self):
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# one simple quad transform, equivalent to scale & crop upper left quad
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im = hopper('RGB')
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(w, h) = im.size
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transformed = im.transform(im.size, Image.QUAD,
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(0, 0, 0, h//2,
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# ul -> ccw around quad:
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w//2, h//2, w//2, 0),
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Image.BILINEAR)
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scaled = im.transform((w, h), Image.AFFINE,
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(.5, 0, 0, 0, .5, 0),
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Image.BILINEAR)
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self.assert_image_equal(transformed, scaled)
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def test_fill(self):
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for mode, pixel in [
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['RGB', (255, 0, 0)],
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['RGBA', (255, 0, 0, 255)],
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['LA', (76, 0)]
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]:
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im = hopper(mode)
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(w, h) = im.size
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transformed = im.transform(im.size, Image.EXTENT,
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(0, 0,
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w*2, h*2),
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Image.BILINEAR,
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fillcolor='red')
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self.assertEqual(transformed.getpixel((w-1, h-1)), pixel)
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def test_mesh(self):
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# this should be a checkerboard of halfsized hoppers in ul, lr
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im = hopper('RGBA')
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(w, h) = im.size
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transformed = im.transform(im.size, Image.MESH,
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[((0, 0, w//2, h//2), # box
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(0, 0, 0, h,
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w, h, w, 0)), # ul -> ccw around quad
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((w//2, h//2, w, h), # box
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(0, 0, 0, h,
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w, h, w, 0))], # ul -> ccw around quad
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Image.BILINEAR)
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scaled = im.transform((w//2, h//2), Image.AFFINE,
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(2, 0, 0, 0, 2, 0),
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Image.BILINEAR)
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checker = Image.new('RGBA', im.size)
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checker.paste(scaled, (0, 0))
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checker.paste(scaled, (w//2, h//2))
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self.assert_image_equal(transformed, checker)
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# now, check to see that the extra area is (0, 0, 0, 0)
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blank = Image.new('RGBA', (w//2, h//2), (0, 0, 0, 0))
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self.assert_image_equal(blank, transformed.crop((w//2, 0, w, h//2)))
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self.assert_image_equal(blank, transformed.crop((0, h//2, w//2, h)))
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def _test_alpha_premult(self, op):
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# create image with half white, half black,
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# with the black half transparent.
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# do op,
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# there should be no darkness in the white section.
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im = Image.new('RGBA', (10, 10), (0, 0, 0, 0))
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im2 = Image.new('RGBA', (5, 10), (255, 255, 255, 255))
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im.paste(im2, (0, 0))
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im = op(im, (40, 10))
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im_background = Image.new('RGB', (40, 10), (255, 255, 255))
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im_background.paste(im, (0, 0), im)
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hist = im_background.histogram()
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self.assertEqual(40*10, hist[-1])
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def test_alpha_premult_resize(self):
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def op(im, sz):
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return im.resize(sz, Image.BILINEAR)
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self._test_alpha_premult(op)
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def test_alpha_premult_transform(self):
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def op(im, sz):
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(w, h) = im.size
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return im.transform(sz, Image.EXTENT,
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(0, 0,
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w, h),
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Image.BILINEAR)
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self._test_alpha_premult(op)
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def test_blank_fill(self):
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# attempting to hit
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# https://github.com/python-pillow/Pillow/issues/254 reported
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#
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# issue is that transforms with transparent overflow area
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# contained junk from previous images, especially on systems with
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# constrained memory. So, attempt to fill up memory with a
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# pattern, free it, and then run the mesh test again. Using a 1Mp
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# image with 4 bands, for 4 megs of data allocated, x 64. OMM (64
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# bit 12.04 VM with 512 megs available, this fails with Pillow <
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# a0eaf06cc5f62a6fb6de556989ac1014ff3348ea
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#
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# Running by default, but I'd totally understand not doing it in
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# the future
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pattern = [
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Image.new('RGBA', (1024, 1024), (a, a, a, a))
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for a in range(1, 65)
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]
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# Yeah. Watch some JIT optimize this out.
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pattern = None # noqa: F841
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self.test_mesh()
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def test_missing_method_data(self):
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im = hopper()
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self.assertRaises(ValueError, im.transform, (100, 100), None)
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class TestImageTransformAffine(PillowTestCase):
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transform = Image.AFFINE
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def _test_image(self):
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im = hopper('RGB')
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return im.crop((10, 20, im.width - 10, im.height - 20))
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def _test_rotate(self, deg, transpose):
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im = self._test_image()
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angle = - math.radians(deg)
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matrix = [
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round(math.cos(angle), 15), round(math.sin(angle), 15), 0.0,
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round(-math.sin(angle), 15), round(math.cos(angle), 15), 0.0,
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0, 0]
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matrix[2] = (1 - matrix[0] - matrix[1]) * im.width / 2
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matrix[5] = (1 - matrix[3] - matrix[4]) * im.height / 2
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if transpose is not None:
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transposed = im.transpose(transpose)
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else:
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transposed = im
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for resample in [Image.NEAREST, Image.BILINEAR, Image.BICUBIC]:
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transformed = im.transform(transposed.size, self.transform,
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matrix, resample)
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self.assert_image_equal(transposed, transformed)
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def test_rotate_0_deg(self):
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self._test_rotate(0, None)
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def test_rotate_90_deg(self):
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self._test_rotate(90, Image.ROTATE_90)
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def test_rotate_180_deg(self):
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self._test_rotate(180, Image.ROTATE_180)
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def test_rotate_270_deg(self):
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self._test_rotate(270, Image.ROTATE_270)
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def _test_resize(self, scale, epsilonscale):
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im = self._test_image()
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size_up = int(round(im.width * scale)), int(round(im.height * scale))
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matrix_up = [
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1 / scale, 0, 0,
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0, 1 / scale, 0,
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0, 0]
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matrix_down = [
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scale, 0, 0,
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0, scale, 0,
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0, 0]
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for resample, epsilon in [(Image.NEAREST, 0),
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(Image.BILINEAR, 2), (Image.BICUBIC, 1)]:
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transformed = im.transform(
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size_up, self.transform, matrix_up, resample)
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transformed = transformed.transform(
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im.size, self.transform, matrix_down, resample)
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self.assert_image_similar(transformed, im, epsilon * epsilonscale)
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def test_resize_1_1x(self):
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self._test_resize(1.1, 6.9)
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def test_resize_1_5x(self):
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self._test_resize(1.5, 5.5)
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def test_resize_2_0x(self):
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self._test_resize(2.0, 5.5)
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def test_resize_2_3x(self):
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self._test_resize(2.3, 3.7)
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def test_resize_2_5x(self):
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self._test_resize(2.5, 3.7)
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def _test_translate(self, x, y, epsilonscale):
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im = self._test_image()
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size_up = int(round(im.width + x)), int(round(im.height + y))
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matrix_up = [
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1, 0, -x,
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0, 1, -y,
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0, 0]
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matrix_down = [
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1, 0, x,
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0, 1, y,
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0, 0]
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for resample, epsilon in [(Image.NEAREST, 0),
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(Image.BILINEAR, 1.5), (Image.BICUBIC, 1)]:
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transformed = im.transform(
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size_up, self.transform, matrix_up, resample)
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transformed = transformed.transform(
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im.size, self.transform, matrix_down, resample)
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self.assert_image_similar(transformed, im, epsilon * epsilonscale)
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def test_translate_0_1(self):
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self._test_translate(.1, 0, 3.7)
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def test_translate_0_6(self):
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self._test_translate(.6, 0, 9.1)
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def test_translate_50(self):
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self._test_translate(50, 50, 0)
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class TestImageTransformPerspective(TestImageTransformAffine):
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# Repeat all tests for AFFINE transformations with PERSPECTIVE
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transform = Image.PERSPECTIVE
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if __name__ == '__main__':
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unittest.main()
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