def _logpolar(image, shape, log_base, bgval=None):
"""
Return log-polar transformed image
Takes into account anisotropicity of the freq spectrum
of rectangular images
Args:
image: The image to be transformed
shape: Shape of the transformed image
log_base: Parameter of the transformation, get it via
:func:`_get_log_base`
bgval: The backround value. If None, use minimum of the image.
Returns:
The transformed image
"""
if bgval is None:
bgval = np.percentile(image, 1)
imshape = np.array(image.shape)
center = imshape[0] / 2.0, imshape[1] / 2.0
# 0 .. pi = only half of the spectrum is used
theta = utils._get_angles(shape)
radius_x = utils._get_lograd(shape, log_base)
radius_y = radius_x.copy()
ellipse_coef = imshape[0] / float(imshape[1])
# We have to acknowledge that the frequency spectrum can be deformed
# if the image aspect ratio is not 1.0
# The image is x-thin, so we acknowledge that the frequency spectra
# scale in x is shrunk.
# radius_x /= ellipse_coef
y = radius_y * np.sin(theta) + center[0]
x = radius_x * np.cos(theta) + center[1]
output = np.empty_like(y)
ndii.map_coordinates(image, [y, x],
output=output,
order=3,
mode="constant",
cval=bgval)
# print(radius_x)
# imshow(image, image, image)
# imshow(output, output, output)
# print(output)
return output
def _logpolar(image, shape, log_base, bgval=None):
"""
Return log-polar transformed image
Takes into account anisotropicity of the freq spectrum
of rectangular images
Args:
image: The image to be transformed
shape: Shape of the transformed image
log_base: Parameter of the transformation, get it via
:func:`_get_log_base`
bgval: The backround value. If None, use minimum of the image.
Returns:
The transformed image
"""
if bgval is None:
bgval = np.percentile(image, 1)
imshape = np.array(image.shape)
center = imshape[0] / 2.0, imshape[1] / 2.0
# 0 .. pi = only half of the spectrum is used
theta = utils._get_angles(shape)
radius_x = utils._get_lograd(shape, log_base)
radius_y = radius_x.copy()
ellipse_coef = imshape[0] / float(imshape[1])
# We have to acknowledge that the frequency spectrum can be deformed
# if the image aspect ratio is not 1.0
# The image is x-thin, so we acknowledge that the frequency spectra
# scale in x is shrunk.
radius_x /= ellipse_coef
y = radius_y * np.sin(theta) + center[0]
x = radius_x * np.cos(theta) + center[1]
output = np.empty_like(y)
ndii.map_coordinates(image, [y, x], output=output, order=3,
mode="constant", cval=bgval)
return output