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Pillow/Tests/test_image_transform.py
Jon Dufresne 4de5477b61 Remove unnecessary unittest.main() boilerplate from test files 
With the introduction and use of pytest, it is simple and easy to
execute specific tests in isolation through documented command line
arguments. Either by specifying the module path or through the `-k
EXPRESSION` argument. There is no longer any need to provide the
boilerplate:

    if __name__ == '__main__':
        unittest.main()

To every test file. It is simply noise.

The pattern remains in test files that aren't named with `test_*` as
those files are not discovered and executed by pytest by default.
2019-02-03 10:10:16 -08:00

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import math
from .helper import PillowTestCase, hopper
from PIL import Image
class TestImageTransform(PillowTestCase):
def test_sanity(self):
from PIL import ImageTransform
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_extent(self):
im = hopper('RGB')
(w, h) = im.size
transformed = im.transform(im.size, Image.EXTENT,
(0, 0,
w//2, h//2), # ul -> lr
Image.BILINEAR)
scaled = im.resize((w*2, h*2), Image.BILINEAR).crop((0, 0, w, h))
# undone -- precision?
self.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.QUAD,
(0, 0, 0, h//2,
# ul -> ccw around quad:
w//2, h//2, w//2, 0),
Image.BILINEAR)
scaled = im.transform((w, h), Image.AFFINE,
(.5, 0, 0, 0, .5, 0),
Image.BILINEAR)
self.assert_image_equal(transformed, scaled)
def test_fill(self):
for mode, pixel in [
['RGB', (255, 0, 0)],
['RGBA', (255, 0, 0, 255)],
['LA', (76, 0)]
]:
im = hopper(mode)
(w, h) = im.size
transformed = im.transform(im.size, Image.EXTENT,
(0, 0,
w*2, h*2),
Image.BILINEAR,
fillcolor='red')
self.assertEqual(transformed.getpixel((w-1, h-1)), 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.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.BILINEAR)
scaled = im.transform((w//2, h//2), Image.AFFINE,
(2, 0, 0, 0, 2, 0),
Image.BILINEAR)
checker = Image.new('RGBA', im.size)
checker.paste(scaled, (0, 0))
checker.paste(scaled, (w//2, h//2))
self.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))
self.assert_image_equal(blank, transformed.crop((w//2, 0, w, h//2)))
self.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()
self.assertEqual(40*10, hist[-1])
def test_alpha_premult_resize(self):
def op(im, sz):
return im.resize(sz, Image.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.EXTENT,
(0, 0,
w, h),
Image.BILINEAR)
self._test_alpha_premult(op)
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):
im = hopper()
self.assertRaises(ValueError, im.transform, (100, 100), None)
class TestImageTransformAffine(PillowTestCase):
transform = Image.AFFINE
def _test_image(self):
im = hopper('RGB')
return im.crop((10, 20, im.width - 10, im.height - 20))
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.NEAREST, Image.BILINEAR, Image.BICUBIC]:
transformed = im.transform(transposed.size, self.transform,
matrix, resample)
self.assert_image_equal(transposed, transformed)
def test_rotate_0_deg(self):
self._test_rotate(0, None)
def test_rotate_90_deg(self):
self._test_rotate(90, Image.ROTATE_90)
def test_rotate_180_deg(self):
self._test_rotate(180, Image.ROTATE_180)
def test_rotate_270_deg(self):
self._test_rotate(270, Image.ROTATE_270)
def _test_resize(self, scale, epsilonscale):
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]
for resample, epsilon in [(Image.NEAREST, 0),
(Image.BILINEAR, 2), (Image.BICUBIC, 1)]:
transformed = im.transform(
size_up, self.transform, matrix_up, resample)
transformed = transformed.transform(
im.size, self.transform, matrix_down, resample)
self.assert_image_similar(transformed, im, epsilon * epsilonscale)
def test_resize_1_1x(self):
self._test_resize(1.1, 6.9)
def test_resize_1_5x(self):
self._test_resize(1.5, 5.5)
def test_resize_2_0x(self):
self._test_resize(2.0, 5.5)
def test_resize_2_3x(self):
self._test_resize(2.3, 3.7)
def test_resize_2_5x(self):
self._test_resize(2.5, 3.7)
def _test_translate(self, x, y, epsilonscale):
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]
for resample, epsilon in [(Image.NEAREST, 0),
(Image.BILINEAR, 1.5), (Image.BICUBIC, 1)]:
transformed = im.transform(
size_up, self.transform, matrix_up, resample)
transformed = transformed.transform(
im.size, self.transform, matrix_down, resample)
self.assert_image_similar(transformed, im, epsilon * epsilonscale)
def test_translate_0_1(self):
self._test_translate(.1, 0, 3.7)
def test_translate_0_6(self):
self._test_translate(.6, 0, 9.1)
def test_translate_50(self):
self._test_translate(50, 50, 0)
class TestImageTransformPerspective(TestImageTransformAffine):
# Repeat all tests for AFFINE transformations with PERSPECTIVE
transform = Image.PERSPECTIVE
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