def test_pyramid_expand_rgb_deprecated_multichannel():
rows, cols, dim = image.shape
with expected_warnings(["`multichannel` is a deprecated argument"]):
out = pyramids.pyramid_expand(image, upscale=2, multichannel=True)
assert_array_equal(out.shape, (rows * 2, cols * 2, dim))
# repeat prior test, but check for positional multichannel warning
with expected_warnings(["Providing the `multichannel` argument"]):
out = pyramids.pyramid_expand(image, 2, None, 1, 'reflect', 0, True)
assert_array_equal(out.shape, (rows * 2, cols * 2, dim))
def test_pyramid_expand_nd():
for ndim in [1, 2, 3, 4]:
img = np.random.randn(*((4, ) * ndim))
out = pyramids.pyramid_expand(img, upscale=2,
multichannel=False)
expected_shape = np.asarray(img.shape) * 2
assert_array_equal(out.shape, expected_shape)
def resize_by_factor(im, factor):
"""
Resizes the image according to a factor. The image is pre-filtered
with a Gaussian and then resampled with bilinear interpolation.
This function uses scikit-image and essentially combines its
`pyramid_reduce` with `pyramid_expand` into one function.
Returns the same object if factor is 1, not a copy.
Parameters
----------
im : ndarray, ndim=2 or 3
Image. Either 2D or 3D with 3 or 4 channels.
factor : float
Resize factor, e.g. a factor of 0.5 will halve both sides.
"""
_import_skimage()
from skimage.transform.pyramids import pyramid_reduce, pyramid_expand
if factor < 1:
return pyramid_reduce(im, downscale=1/factor)
elif factor > 1:
return pyramid_expand(im, upscale=factor)
else:
return im
def test_pyramid_expand_nd():
for ndim in [1, 2, 3, 4]:
img = np.random.randn(*((4, ) * ndim))
out = pyramids.pyramid_expand(img, upscale=2,
multichannel=False)
expected_shape = np.asarray(img.shape) * 2
assert_array_equal(out.shape, expected_shape)
def resize_by_factor(im, factor):
"""
Resizes the image according to a factor. The image is pre-filtered
with a Gaussian and then resampled with bilinear interpolation.
This function uses scikit-image and essentially combines its
`pyramid_reduce` with `pyramid_expand` into one function.
Returns the same object if factor is 1, not a copy.
Parameters
----------
im : ndarray, ndim=2 or 3
Image. Either 2D or 3D with 3 or 4 channels.
factor : float
Resize factor, e.g. a factor of 0.5 will halve both sides.
"""
_import_skimage()
from skimage.transform.pyramids import pyramid_reduce, pyramid_expand
if factor < 1:
return pyramid_reduce(im, downscale=1 / factor)
elif factor > 1:
return pyramid_expand(im, upscale=factor)
else:
return im
def resize_with_factor_new(im, factor):
if factor < 1 - 1e-8:
im2 = pyramid_reduce(im, downscale=1/factor)
elif factor > 1 + 1e-8:
im2 = pyramid_expand(im, upscale=factor)
else:
im2 = im.copy()
return im2
def test_pyramid_expand_rgb(channel_axis):
image = data.astronaut()
rows, cols, dim = image.shape
image = np.moveaxis(image, source=-1, destination=channel_axis)
out = pyramids.pyramid_expand(image, upscale=2, channel_axis=channel_axis)
expected_shape = [rows * 2, cols * 2]
expected_shape.insert(channel_axis % image.ndim, dim)
assert_array_equal(out.shape, expected_shape)
def resize_with_factor_new(im, factor):
if factor < 1 - 1e-8:
im2 = pyramid_reduce(im, downscale=1 / factor)
elif factor > 1 + 1e-8:
im2 = pyramid_expand(im, upscale=factor)
else:
im2 = im.copy()
return im2
def resize(im, f):
if f < 1:
im2 = pyramid_reduce(im, downscale=1/f)
elif f > 1:
im2 = pyramid_expand(im, upscale=f)
else:
im2 = im
return im2
def resize(im, new_size, preserve_aspect_ratio=True, prefilter=True):
"""
This is a not-very-rigorous function to do image resizing, with
pre-filtering.
"""
factors = [new_size[i] / im.shape[i] for i in range(2)]
#assert factors[0] == factors[1], "Must have same factor for now"
f = factors[0]
if f < 1:
im2 = pyramid_reduce(im, downscale=1/f)
elif f > 1:
im2 = pyramid_expand(im, upscale=f)
else:
im2 = im
assert im2.shape[:2] == tuple(new_size), "{0} != {1} (original size: {2})".format(im2.shape, new_size, im.shape)
return im2
def resize(im, new_size, preserve_aspect_ratio=True, prefilter=True):
"""
This is a not-very-rigorous function to do image resizing, with
pre-filtering.
"""
factors = [new_size[i] / im.shape[i] for i in range(2)]
#assert factors[0] == factors[1], "Must have same factor for now"
f = factors[0]
if f < 1:
im2 = pyramid_reduce(im, downscale=1 / f)
elif f > 1:
im2 = pyramid_expand(im, upscale=f)
else:
im2 = im
assert im2.shape[:2] == tuple(
new_size), "{0} != {1} (original size: {2})".format(
im2.shape, new_size, im.shape)
return im2
def test_pyramid_expand_rgb():
rows, cols, dim = image.shape
out = pyramids.pyramid_expand(image, upscale=2, channel_axis=-1)
assert_array_equal(out.shape, (rows * 2, cols * 2, dim))
def test_pyramid_expand_gray():
rows, cols = image_gray.shape
out = pyramids.pyramid_expand(image_gray, upscale=2)
assert_array_equal(out.shape, (rows * 2, cols * 2))
def test_pyramid_expand_gray():
rows, cols = image_gray.shape
with expected_warnings(['The default multichannel']):
out = pyramids.pyramid_expand(image_gray, upscale=2)
assert_array_equal(out.shape, (rows * 2, cols * 2))
def test_pyramid_expand_rgb():
rows, cols, dim = image.shape
out = pyramids.pyramid_expand(image, upscale=2)
assert_array_equal(out.shape, (rows * 2, cols * 2, dim))
def test_pyramid_expand_gray():
rows, cols = image_gray.shape
out = pyramids.pyramid_expand(image_gray, upscale=2, channel_axis=None)
assert_array_equal(out.shape, (rows * 2, cols * 2))
def test_pyramid_expand_gray():
rows, cols = image_gray.shape
with expected_warnings(['The default multichannel']):
out = pyramids.pyramid_expand(image_gray, upscale=2)
assert_array_equal(out.shape, (rows * 2, cols * 2))
