def estimate_shift2D(self,
reference='current',
correlation_threshold=None,
chunk_size=30,
roi=None,
normalize_corr=False,
sobel=True,
medfilter=True,
hanning=True,
plot=False,
dtype='float',
show_progressbar=None,
sub_pixel_factor=1):
"""Estimate the shifts in a image using phase correlation
This method can only estimate the shift by comparing
bidimensional features that should not change position
between frames. To decrease the memory usage, the time of
computation and the accuracy of the results it is convenient
to select a region of interest by setting the roi keyword.
Parameters
----------
reference : {'current', 'cascade' ,'stat'}
If 'current' (default) the image at the current
coordinates is taken as reference. If 'cascade' each image
is aligned with the previous one. If 'stat' the translation
of every image with all the rest is estimated and by
performing statistical analysis on the result the
translation is estimated.
correlation_threshold : {None, 'auto', float}
This parameter is only relevant when reference='stat'.
If float, the shift estimations with a maximum correlation
value lower than the given value are not used to compute
the estimated shifts. If 'auto' the threshold is calculated
automatically as the minimum maximum correlation value
of the automatically selected reference image.
chunk_size : {None, int}
If int and reference='stat' the number of images used
as reference are limited to the given value.
roi : tuple of ints or floats (left, right, top, bottom)
Define the region of interest. If int(float) the position
is given axis index(value). Note that ROIs can be used
in place of a tuple.
sobel : bool
apply a sobel filter for edge enhancement
medfilter : bool
apply a median filter for noise reduction
hanning : bool
Apply a 2d hanning filter
plot : bool or 'reuse'
If True plots the images after applying the filters and
the phase correlation. If 'reuse', it will also plot the images,
but it will only use one figure, and continuously update the images
in that figure as it progresses through the stack.
dtype : str or dtype
Typecode or data-type in which the calculations must be
performed.
%s
sub_pixel_factor : float
Estimate shifts with a sub-pixel accuracy of 1/sub_pixel_factor
parts of a pixel. Default is 1, i.e. no sub-pixel accuracy.
Returns
-------
shifts : list of array
List of estimated shifts
Notes
-----
The statistical analysis approach to the translation estimation
when using reference='stat' roughly follows [*]_ . If you use
it please cite their article.
References
----------
.. [*] Schaffer, Bernhard, Werner Grogger, and Gerald Kothleitner.
“Automated Spatial Drift Correction for EFTEM Image Series.”
Ultramicroscopy 102, no. 1 (December 2004): 27–36.
"""
if show_progressbar is None:
show_progressbar = preferences.General.show_progressbar
self._check_signal_dimension_equals_two()
if roi is not None:
# Get the indices of the roi
yaxis = self.axes_manager.signal_axes[1]
xaxis = self.axes_manager.signal_axes[0]
roi = tuple([xaxis._get_index(i) for i in roi[2:]] +
[yaxis._get_index(i) for i in roi[:2]])
ref = None if reference == 'cascade' else \
self.__call__().copy()
shifts = []
nrows = None
images_number = self.axes_manager._max_index + 1
if plot == 'reuse':
# Reuse figure for plots
plot = plt.figure()
if reference == 'stat':
nrows = images_number if chunk_size is None else \
min(images_number, chunk_size)
pcarray = ma.zeros((
nrows,
self.axes_manager._max_index + 1,
),
dtype=np.dtype([('max_value', np.float),
('shift', np.int32, (2, ))]))
nshift, max_value = estimate_image_shift(
self(),
self(),
roi=roi,
sobel=sobel,
medfilter=medfilter,
hanning=hanning,
normalize_corr=normalize_corr,
plot=plot,
dtype=dtype,
sub_pixel_factor=sub_pixel_factor)
np.fill_diagonal(pcarray['max_value'], max_value)
pbar_max = nrows * images_number
else:
pbar_max = images_number
# Main iteration loop. Fills the rows of pcarray when reference
# is stat
with progressbar(total=pbar_max,
disable=not show_progressbar,
leave=True) as pbar:
for i1, im in enumerate(self._iterate_signal()):
if reference in ['current', 'cascade']:
if ref is None:
ref = im.copy()
shift = np.array([0, 0])
nshift, max_val = estimate_image_shift(
ref,
im,
roi=roi,
sobel=sobel,
medfilter=medfilter,
hanning=hanning,
plot=plot,
normalize_corr=normalize_corr,
dtype=dtype,
sub_pixel_factor=sub_pixel_factor)
if reference == 'cascade':
shift += nshift
ref = im.copy()
else:
shift = nshift
shifts.append(shift.copy())
pbar.update(1)
elif reference == 'stat':
if i1 == nrows:
break
# Iterate to fill the columns of pcarray
for i2, im2 in enumerate(self._iterate_signal()):
if i2 > i1:
nshift, max_value = estimate_image_shift(
im,
im2,
roi=roi,
sobel=sobel,
medfilter=medfilter,
hanning=hanning,
normalize_corr=normalize_corr,
plot=plot,
dtype=dtype,
sub_pixel_factor=sub_pixel_factor)
pcarray[i1, i2] = max_value, nshift
del im2
pbar.update(1)
del im
if reference == 'stat':
# Select the reference image as the one that has the
# higher max_value in the row
sqpcarr = pcarray[:, :nrows]
sqpcarr['max_value'][:] = symmetrize(sqpcarr['max_value'])
sqpcarr['shift'][:] = antisymmetrize(sqpcarr['shift'])
ref_index = np.argmax(pcarray['max_value'].min(1))
self.ref_index = ref_index
shifts = (pcarray['shift'] +
pcarray['shift'][ref_index, :nrows][:, np.newaxis])
if correlation_threshold is not None:
if correlation_threshold == 'auto':
correlation_threshold = \
(pcarray['max_value'].min(0)).max()
_logger.info("Correlation threshold = %1.2f",
correlation_threshold)
shifts[
pcarray['max_value'] < correlation_threshold] = ma.masked
shifts.mask[ref_index, :] = False
shifts = shifts.mean(0)
else:
shifts = np.array(shifts)
del ref
return shifts
def estimate_shift2D(self,
reference='current',
correlation_threshold=None,
chunk_size=30,
roi=None,
normalize_corr=False,
sobel=True,
medfilter=True,
hanning=True,
plot=False,
dtype='float',
show_progressbar=None):
"""Estimate the shifts in a image using phase correlation
This method can only estimate the shift by comparing
bidimensional features that should not change position
between frames. To decrease the memory usage, the time of
computation and the accuracy of the results it is convenient
to select a region of interest by setting the roi keyword.
Parameters
----------
reference : {'current', 'cascade' ,'stat'}
If 'current' (default) the image at the current
coordinates is taken as reference. If 'cascade' each image
is aligned with the previous one. If 'stat' the translation
of every image with all the rest is estimated and by
performing statistical analysis on the result the
translation is estimated.
correlation_threshold : {None, 'auto', float}
This parameter is only relevant when `reference` is 'stat'.
If float, the shift estimations with a maximum correlation
value lower than the given value are not used to compute
the estimated shifts. If 'auto' the threshold is calculated
automatically as the minimum maximum correlation value
of the automatically selected reference image.
chunk_size: {None, int}
If int and `reference`=='stat' the number of images used
as reference are limited to the given value.
roi : tuple of ints or floats (left, right, top bottom)
Define the region of interest. If int(float) the position
is given axis index(value).
sobel : bool
apply a sobel filter for edge enhancement
medfilter : bool
apply a median filter for noise reduction
hanning : bool
Apply a 2d hanning filter
plot : bool
If True plots the images after applying the filters and
the phase correlation
dtype : str or dtype
Typecode or data-type in which the calculations must be
performed.
show_progressbar : None or bool
If True, display a progress bar. If None the default is set in
`preferences`.
Returns
-------
list of applied shifts
Notes
-----
The statistical analysis approach to the translation estimation
when using `reference`='stat' roughly follows [1]_ . If you use
it please cite their article.
References
----------
.. [1] Schaffer, Bernhard, Werner Grogger, and Gerald
Kothleitner. “Automated Spatial Drift Correction for EFTEM
Signal2D Series.”
Ultramicroscopy 102, no. 1 (December 2004): 27–36.
"""
if show_progressbar is None:
show_progressbar = preferences.General.show_progressbar
self._check_signal_dimension_equals_two()
if roi is not None:
# Get the indices of the roi
yaxis = self.axes_manager.signal_axes[1]
xaxis = self.axes_manager.signal_axes[0]
roi = tuple([xaxis._get_index(i) for i in roi[2:]] +
[yaxis._get_index(i) for i in roi[:2]])
ref = None if reference == 'cascade' else \
self.__call__().copy()
shifts = []
nrows = None
images_number = self.axes_manager._max_index + 1
if reference == 'stat':
nrows = images_number if chunk_size is None else \
min(images_number, chunk_size)
pcarray = ma.zeros((nrows, self.axes_manager._max_index + 1,
),
dtype=np.dtype([('max_value', np.float),
('shift', np.int32,
(2,))]))
nshift, max_value = estimate_image_shift(
self(),
self(),
roi=roi,
sobel=sobel,
medfilter=medfilter,
hanning=hanning,
normalize_corr=normalize_corr,
plot=plot,
dtype=dtype)
np.fill_diagonal(pcarray['max_value'], max_value)
pbar_max = nrows * images_number
else:
pbar_max = images_number
# Main iteration loop. Fills the rows of pcarray when reference
# is stat
with progressbar(total=pbar_max,
disable=not show_progressbar,
leave=True) as pbar:
for i1, im in enumerate(self._iterate_signal()):
if reference in ['current', 'cascade']:
if ref is None:
ref = im.copy()
shift = np.array([0, 0])
nshift, max_val = estimate_image_shift(
ref, im, roi=roi, sobel=sobel, medfilter=medfilter,
hanning=hanning, plot=plot,
normalize_corr=normalize_corr, dtype=dtype)
if reference == 'cascade':
shift += nshift
ref = im.copy()
else:
shift = nshift
shifts.append(shift.copy())
pbar.update(1)
elif reference == 'stat':
if i1 == nrows:
break
# Iterate to fill the columns of pcarray
for i2, im2 in enumerate(
self._iterate_signal()):
if i2 > i1:
nshift, max_value = estimate_image_shift(
im,
im2,
roi=roi,
sobel=sobel,
medfilter=medfilter,
hanning=hanning,
normalize_corr=normalize_corr,
plot=plot,
dtype=dtype)
pcarray[i1, i2] = max_value, nshift
del im2
pbar.update(1)
del im
if reference == 'stat':
# Select the reference image as the one that has the
# higher max_value in the row
sqpcarr = pcarray[:, :nrows]
sqpcarr['max_value'][:] = symmetrize(sqpcarr['max_value'])
sqpcarr['shift'][:] = antisymmetrize(sqpcarr['shift'])
ref_index = np.argmax(pcarray['max_value'].min(1))
self.ref_index = ref_index
shifts = (pcarray['shift'] +
pcarray['shift'][ref_index, :nrows][:, np.newaxis])
if correlation_threshold is not None:
if correlation_threshold == 'auto':
correlation_threshold = \
(pcarray['max_value'].min(0)).max()
_logger.info("Correlation threshold = %1.2f",
correlation_threshold)
shifts[pcarray['max_value'] <
correlation_threshold] = ma.masked
shifts.mask[ref_index, :] = False
shifts = shifts.mean(0)
else:
shifts = np.array(shifts)
del ref
return shifts