Source code for polarTransform.imageTransform
[docs]class ImageTransform:
"""Class to store settings when converting between cartesian and polar domain"""
def __init__(self, center, initialRadius, finalRadius, initialAngle, finalAngle, cartesianImageSize,
polarImageSize):
"""Polar and Cartesian Transform Metadata
ImageTransform contains polar and cartesian transform metadata for the conversion between the two domains.
This metadata is stored in a class to allow for easy conversion between the domains.
Parameters
----------
center : (2,) :class:`numpy.ndarray` of :class:`int`
Specifies the center in the cartesian image to use as the origin in polar domain. The center in the
cartesian domain will be (0, 0) in the polar domain.
The center is structured as (x, y) where the first item is the x-coordinate and second item is the
y-coordinate.
initialRadius : :class:`int`
Starting radius in pixels from the center of the cartesian image in the polar image
The polar image begins at this radius, i.e. the first row of the polar image corresponds to this
starting radius.
finalRadius : :class:`int`, optional
Final radius in pixels from the center of the cartesian image in the polar image
The polar image ends at this radius, i.e. the last row of the polar image corresponds to this ending
radius.
initialAngle : :class:`float`, optional
Starting angle in radians in the polar image
The polar image begins at this angle, i.e. the first column of the polar image corresponds to this
starting angle.
Radian angle is with respect to the x-axis and rotates counter-clockwise. The angle should be in the range
of 0 to :math:`2\\pi`.
finalAngle : :class:`float`, optional
Final angle in radians in the polar image
The polar image ends at this angle, i.e. the last column of the polar image corresponds to this
ending angle.
Radian angle is with respect to the x-axis and rotates counter-clockwise. The angle should be in the range
of 0 to :math:`2\\pi`.
cartesianImageSize : (2,) :class:`tuple` of :class:`int`
Size of cartesian image
polarImageSize : (2,) :class:`tuple` of :class:`int`
Size of polar image
"""
self.center = center
self.initialRadius = initialRadius
self.finalRadius = finalRadius
self.initialAngle = initialAngle
self.finalAngle = finalAngle
self.cartesianImageSize = cartesianImageSize
self.polarImageSize = polarImageSize
[docs] def convertToPolarImage(self, image, order=3, border='constant', borderVal=0.0, useMultiThreading=False):
"""Convert cartesian image to polar image.
Using a cartesian image, this function creates a polar domain image where the first dimension is radius and
second dimension is the angle. This function is versatile because it allows different starting and stopping
radii and angles to extract the polar region you are interested in.
.. note::
Traditionally images are loaded such that the origin is in the upper-left hand corner. In these cases the
:obj:`initialAngle` and :obj:`finalAngle` will rotate clockwise from the x-axis. For simplicitly, it is
recommended to flip the image along first dimension before passing to this function.
Parameters
----------
image : (N, M) or (N, M, P) :class:`numpy.ndarray`
Cartesian image to convert to polar domain
.. note::
For a 3D array, polar transformation is applied separately across each 2D slice
.. note::
If an alpha band (4th channel of image is present), then it will be converted. Typically, this is
unwanted, so the recommended solution is to transform the first 3 channels and set the 4th channel to
fully on.
order : :class:`int` (0-5), optional
The order of the spline interpolation, default is 3. The order has to be in the range 0-5.
The following orders have special names:
* 0 - nearest neighbor
* 1 - bilinear
* 3 - bicubic
border : {'constant', 'nearest', 'wrap', 'reflect'}, optional
Polar points outside the cartesian image boundaries are filled according to the given mode.
Default is 'constant'
The following table describes the mode and expected output when seeking past the boundaries. The input
column is the 1D input array whilst the extended columns on either side of the input array correspond to
the expected values for the given mode if one extends past the boundaries.
.. table:: Valid border modes and expected output
:widths: auto
========== ====== ================= ======
Mode Ext. Input Ext.
========== ====== ================= ======
mirror 4 3 2 1 2 3 4 5 6 7 8 7 6 5
reflect 3 2 1 1 2 3 4 5 6 7 8 8 7 6
nearest 1 1 1 1 2 3 4 5 6 7 8 8 8 8
constant 0 0 0 1 2 3 4 5 6 7 8 0 0 0
wrap 6 7 8 1 2 3 4 5 6 7 8 1 2 3
========== ====== ================= ======
Refer to :func:`scipy.ndimage.map_coordinates` for more details on this argument.
borderVal : same datatype as :obj:`image`, optional
Value used for polar points outside the cartesian image boundaries if :obj:`border` = 'constant'.
Default is 0.0
Returns
-------
polarImage : (N, M) or (N, M, P) :class:`numpy.ndarray`
Polar image where first dimension is radii and second dimension is angle (3D polar image if 3D input image
is given)
"""
image, ptSettings = convertToPolarImage(image, order=order, border=border, borderVal=borderVal,
useMultiThreading=useMultiThreading, settings=self)
return image
[docs] def convertToCartesianImage(self, image, order=3, border='constant', borderVal=0.0, useMultiThreading=False):
"""Convert polar image to cartesian image.
Using a polar image, this function creates a cartesian image. This function is versatile because it can
automatically calculate an appropiate cartesian image size and center given the polar image. In addition,
parameters for converting to the polar domain are necessary for the conversion back to the cartesian domain.
Parameters
----------
image : (N, M) or (N, M, P) :class:`numpy.ndarray`
Polar image to convert to cartesian domain
.. note::
For a 3D array, polar transformation is applied separately across each 2D slice
.. note::
If an alpha band (4th channel of image is present), then it will be converted. Typically, this is
unwanted, so the recommended solution is to transform the first 3 channels and set the 4th channel to
fully on.
order : :class:`int` (0-5), optional
The order of the spline interpolation, default is 3. The order has to be in the range 0-5.
The following orders have special names:
* 0 - nearest neighbor
* 1 - bilinear
* 3 - bicubic
border : {'constant', 'nearest', 'wrap', 'reflect'}, optional
Polar points outside the cartesian image boundaries are filled according to the given mode.
Default is 'constant'
The following table describes the mode and expected output when seeking past the boundaries. The input
column is the 1D input array whilst the extended columns on either side of the input array correspond to
the expected values for the given mode if one extends past the boundaries.
.. table:: Valid border modes and expected output
:widths: auto
========== ====== ================= ======
Mode Ext. Input Ext.
========== ====== ================= ======
mirror 4 3 2 1 2 3 4 5 6 7 8 7 6 5
reflect 3 2 1 1 2 3 4 5 6 7 8 8 7 6
nearest 1 1 1 1 2 3 4 5 6 7 8 8 8 8
constant 0 0 0 1 2 3 4 5 6 7 8 0 0 0
wrap 6 7 8 1 2 3 4 5 6 7 8 1 2 3
========== ====== ================= ======
Refer to :func:`scipy.ndimage.map_coordinates` for more details on this argument.
borderVal : same datatype as :obj:`image`, optional
Value used for polar points outside the cartesian image boundaries if :obj:`border` = 'constant'.
Default is 0.0
useMultiThreading : :class:`bool`, optional
Whether to use multithreading when applying transformation for 3D images. This considerably speeds up the
execution time for large images but adds overhead for smaller 3D images.
Default is :obj:`False`
Returns
-------
cartesianImage : (N, M) or (N, M, P) :class:`numpy.ndarray`
Cartesian image (3D cartesian image if 3D input image is given)
See Also
--------
:meth:`convertToCartesianImage`
"""
image, ptSettings = convertToCartesianImage(image, order=order, border=border, borderVal=borderVal,
useMultiThreading=useMultiThreading, settings=self)
return image
[docs] def getPolarPointsImage(self, points):
"""Convert list of cartesian points from image to polar image points based on transform metadata
.. note::
This does **not** convert from cartesian to polar points, but rather converts pixels from cartesian image to
pixels from polar image using :class:`ImageTransform`.
The returned points are not rounded to the nearest point. User must do that by hand if desired.
Parameters
----------
points : (N, 2) or (2,) :class:`numpy.ndarray`
List of cartesian points to convert to polar domain
First column is x and second column is y
Returns
-------
polarPoints : (N, 2) or (2,) :class:`numpy.ndarray`
Corresponding polar points from cartesian :obj:`points` using :class:`ImageTransform`
See Also
--------
:meth:`getPolarPointsImage`, :meth:`getPolarPoints`, :meth:`getPolarPoints2`
"""
return getPolarPointsImage(points, self)
[docs] def getCartesianPointsImage(self, points):
"""Convert list of polar points from image to cartesian image points based on transform metadata
.. note::
This does **not** convert from polar to cartesian points, but rather converts pixels from polar image to
pixels from cartesian image using :class:`ImageTransform`.
The returned points are not rounded to the nearest point. User must do that by hand if desired.
Parameters
----------
points : (N, 2) or (2,) :class:`numpy.ndarray`
List of polar points to convert to cartesian domain
First column is r and second column is theta
Returns
-------
cartesianPoints : (N, 2) or (2,) :class:`numpy.ndarray`
Corresponding cartesian points from polar :obj:`points` using :class:`ImageTransform`
See Also
--------
:meth:`getCartesianPointsImage`, :meth:`getCartesianPoints`, :meth:`getCartesianPoints2`
"""
return getCartesianPointsImage(points, self)
def __repr__(self):
return 'ImageTransform(center=%s, initialRadius=%i, finalRadius=%i, initialAngle=%f, finalAngle=%f, ' \
'cartesianImageSize=%s, polarImageSize=%s)' % (self.center, self.initialRadius, self.finalRadius,
self.initialAngle, self.finalAngle,
self.cartesianImageSize, self.polarImageSize)
def __str__(self):
return self.__repr__()
# Bypasses issue with ImageTransform not being defined for cyclic imports
# The answer is to include imports at the end so that everything is already defined before you import anything else
from polarTransform.convertToCartesianImage import convertToCartesianImage
from polarTransform.convertToPolarImage import convertToPolarImage
from polarTransform.pointsConversion import getCartesianPointsImage, getPolarPointsImage