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Pillow/Tests/test_image_transform.py
Jon Dufresne d50445ff30 Introduce isort to automate import ordering and formatting 
Similar to the recent adoption of Black. isort is a Python utility to
sort imports alphabetically and automatically separate into sections. By
using isort, contributors can quickly and automatically conform to the
projects style without thinking. Just let the tool do it.

Uses the configuration recommended by the Black to avoid conflicts of
style.

Rewrite TestImageQt.test_deprecated to no rely on import order.
2019-07-06 16:11:35 -07:00

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import math
from PIL import Image
from .helper import PillowTestCase, hopper
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
# fmt: off
transformed = im.transform(im.size, Image.EXTENT,
(0, 0,
w//2, h//2), # ul -> lr
Image.BILINEAR)
# fmt: on
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
# fmt: off
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)
# fmt: on
scaled = im.transform(
(w, h), Image.AFFINE, (0.5, 0, 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
# fmt: off
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)
# fmt: on
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)
def test_unknown_resampling_filter(self):
im = hopper()
(w, h) = im.size
for resample in (Image.BOX, "unknown"):
self.assertRaises(
ValueError,
im.transform,
(100, 100),
Image.EXTENT,
(0, 0, w, h),
resample,
)
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(0.1, 0, 3.7)
def test_translate_0_6(self):
self._test_translate(0.6, 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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