def test_interp_bilinear(self):
x = np.mgrid[:10, :10].sum(axis=0)
out = ext.interp_bilinear(x, [[1, 2], [1.5, 1.5]],
[[2, 1], [1.5, 1.5]])
assert_equal(out, [[3, 3], [3, 3]])
def logpolar(image, angles=None, Rs=None, mode='M', cval=0, output=None,
_coords_r=None, _coords_c=None, extra_info=False):
"""Perform the log polar transform on an image.
Input:
------
image : MxNxC array
An MxN image with C colour bands.
Rs : int
Number of samples in the radial direction.
angles : 1D array of floats
Angles at which to evaluate. Defaults to 0..2*Pi in 4*Rs steps
([1] below suggests 8*Rs, but that causes too much oversampling).
mode : string
How values outside borders are handled. 'C' for constant, 'M'
for mirror and 'W' for wrap.
cval : int or float
Used in conjunction with mode 'C', the value outside the border.
extra_info : bool
Whether to return the angles and log base in addition
to the transform. False by default.
Returns
-------
lpt : ndarray of uint8
Log polar transform of the input image.
angles : ndarray of float
Angles used. Only returned if `extra_info` is set
to True.
log_base : int
Log base used. Only returned if `extra_info` is set
to True.
Optimisation parameters:
------------------------
_coords_r, _coords_c : 2D array
Pre-calculated coords, as given by _lpcoords.
References
----------
.. [1] Matungka, Zheng and Ewing, "Image Registration Using Adaptive
Polar Transform". IEEE Transactions on Image Processing, Vol. 18,
No. 10, October 2009.
"""
if image.ndim < 2 or image.ndim > 3:
raise ValueError("Input image must be 2 or 3 dimensional.")
image = np.atleast_3d(image)
if Rs is None:
Rs = max(image.shape[:2])
if _coords_r is None or _coords_c is None:
_coords_r, _coords_c, angles, log_base = \
_lpcoords(image.shape, Rs, angles)
bands = image.shape[2]
if output is None:
output = np.empty(_coords_r.shape + (bands,),dtype=np.uint8)
else:
output = np.atleast_3d(np.ascontiguousarray(output))
for band in range(bands):
output[...,band] = interp_bilinear(image[...,band],
_coords_r,_coords_c,mode=mode,
cval=cval,output=output[...,band])
output = output.squeeze()
if extra_info:
return output, angles, log_base
else:
return output
def test_interp_bilinear(self):
x = np.mgrid[:10, :10].sum(axis=0)
out = ext.interp_bilinear(x, [[1, 2], [1.5, 1.5]],
[[2, 1], [1.5, 1.5]])
assert_equal(out, [[3, 3], [3, 3]])