Ma Nan
ce397b35aa
fix: contrast formulation cause leak of high value fix: empty series_id cause fail to read chore: restructure folder chore!: change layer vars BREAKING CHANGE: not support 4.1 any more
314 lines
11 KiB
Python
314 lines
11 KiB
Python
import numpy as np
|
|
import warnings
|
|
|
|
from . import _ni_support
|
|
from . import _nd_image
|
|
from ._utils import normalize_axis_index
|
|
|
|
|
|
def _prepad_for_spline_filter(input, mode, cval):
|
|
if mode in ['nearest', 'grid-constant']:
|
|
npad = 12
|
|
if mode == 'grid-constant':
|
|
padded = np.pad(input, npad, mode='constant',
|
|
constant_values=cval)
|
|
elif mode == 'nearest':
|
|
padded = np.pad(input, npad, mode='edge')
|
|
else:
|
|
# other modes have exact boundary conditions implemented so
|
|
# no prepadding is needed
|
|
npad = 0
|
|
padded = input
|
|
return padded, npad
|
|
|
|
def spline_filter1d(input, order=3, axis=-1, output=np.float64,
|
|
mode='mirror'):
|
|
"""
|
|
Calculate a 1-D spline filter along the given axis.
|
|
|
|
The lines of the array along the given axis are filtered by a
|
|
spline filter. The order of the spline must be >= 2 and <= 5.
|
|
|
|
Parameters
|
|
----------
|
|
%(input)s
|
|
order : int, optional
|
|
The order of the spline, default is 3.
|
|
axis : int, optional
|
|
The axis along which the spline filter is applied. Default is the last
|
|
axis.
|
|
output : ndarray or dtype, optional
|
|
The array in which to place the output, or the dtype of the returned
|
|
array. Default is ``numpy.float64``.
|
|
%(mode_interp_mirror)s
|
|
|
|
Returns
|
|
-------
|
|
spline_filter1d : ndarray
|
|
The filtered input.
|
|
|
|
See Also
|
|
--------
|
|
spline_filter : Multidimensional spline filter.
|
|
|
|
Notes
|
|
-----
|
|
All of the interpolation functions in `ndimage` do spline interpolation of
|
|
the input image. If using B-splines of `order > 1`, the input image
|
|
values have to be converted to B-spline coefficients first, which is
|
|
done by applying this 1-D filter sequentially along all
|
|
axes of the input. All functions that require B-spline coefficients
|
|
will automatically filter their inputs, a behavior controllable with
|
|
the `prefilter` keyword argument. For functions that accept a `mode`
|
|
parameter, the result will only be correct if it matches the `mode`
|
|
used when filtering.
|
|
|
|
For complex-valued `input`, this function processes the real and imaginary
|
|
components independently.
|
|
|
|
.. versionadded:: 1.6.0
|
|
Complex-valued support added.
|
|
|
|
Examples
|
|
--------
|
|
We can filter an image using 1-D spline along the given axis:
|
|
|
|
>>> from scipy.ndimage import spline_filter1d
|
|
>>> import numpy as np
|
|
>>> import matplotlib.pyplot as plt
|
|
>>> orig_img = np.eye(20) # create an image
|
|
>>> orig_img[10, :] = 1.0
|
|
>>> sp_filter_axis_0 = spline_filter1d(orig_img, axis=0)
|
|
>>> sp_filter_axis_1 = spline_filter1d(orig_img, axis=1)
|
|
>>> f, ax = plt.subplots(1, 3, sharex=True)
|
|
>>> for ind, data in enumerate([[orig_img, "original image"],
|
|
... [sp_filter_axis_0, "spline filter (axis=0)"],
|
|
... [sp_filter_axis_1, "spline filter (axis=1)"]]):
|
|
... ax[ind].imshow(data[0], cmap='gray_r')
|
|
... ax[ind].set_title(data[1])
|
|
>>> plt.tight_layout()
|
|
>>> plt.show()
|
|
|
|
"""
|
|
if order < 0 or order > 5:
|
|
raise RuntimeError('spline order not supported')
|
|
input = np.asarray(input)
|
|
complex_output = np.iscomplexobj(input)
|
|
output = _ni_support._get_output(output, input,
|
|
complex_output=complex_output)
|
|
if complex_output:
|
|
spline_filter1d(input.real, order, axis, output.real, mode)
|
|
spline_filter1d(input.imag, order, axis, output.imag, mode)
|
|
return output
|
|
if order in [0, 1]:
|
|
output[...] = np.array(input)
|
|
else:
|
|
mode = _ni_support._extend_mode_to_code(mode)
|
|
axis = normalize_axis_index(axis, input.ndim)
|
|
_nd_image.spline_filter1d(input, order, axis, output, mode)
|
|
return output
|
|
|
|
|
|
def spline_filter(input, order=3, output=np.float64, mode='mirror'):
|
|
"""
|
|
Multidimensional spline filter.
|
|
|
|
Parameters
|
|
----------
|
|
%(input)s
|
|
order : int, optional
|
|
The order of the spline, default is 3.
|
|
output : ndarray or dtype, optional
|
|
The array in which to place the output, or the dtype of the returned
|
|
array. Default is ``numpy.float64``.
|
|
%(mode_interp_mirror)s
|
|
|
|
Returns
|
|
-------
|
|
spline_filter : ndarray
|
|
Filtered array. Has the same shape as `input`.
|
|
|
|
See Also
|
|
--------
|
|
spline_filter1d : Calculate a 1-D spline filter along the given axis.
|
|
|
|
Notes
|
|
-----
|
|
The multidimensional filter is implemented as a sequence of
|
|
1-D spline filters. The intermediate arrays are stored
|
|
in the same data type as the output. Therefore, for output types
|
|
with a limited precision, the results may be imprecise because
|
|
intermediate results may be stored with insufficient precision.
|
|
|
|
For complex-valued `input`, this function processes the real and imaginary
|
|
components independently.
|
|
|
|
.. versionadded:: 1.6.0
|
|
Complex-valued support added.
|
|
|
|
Examples
|
|
--------
|
|
We can filter an image using multidimentional splines:
|
|
|
|
>>> from scipy.ndimage import spline_filter
|
|
>>> import numpy as np
|
|
>>> import matplotlib.pyplot as plt
|
|
>>> orig_img = np.eye(20) # create an image
|
|
>>> orig_img[10, :] = 1.0
|
|
>>> sp_filter = spline_filter(orig_img, order=3)
|
|
>>> f, ax = plt.subplots(1, 2, sharex=True)
|
|
>>> for ind, data in enumerate([[orig_img, "original image"],
|
|
... [sp_filter, "spline filter"]]):
|
|
... ax[ind].imshow(data[0], cmap='gray_r')
|
|
... ax[ind].set_title(data[1])
|
|
>>> plt.tight_layout()
|
|
>>> plt.show()
|
|
|
|
"""
|
|
if order < 2 or order > 5:
|
|
raise RuntimeError('spline order not supported')
|
|
input = np.asarray(input)
|
|
complex_output = np.iscomplexobj(input)
|
|
output = _ni_support._get_output(output, input,
|
|
complex_output=complex_output)
|
|
if complex_output:
|
|
spline_filter(input.real, order, output.real, mode)
|
|
spline_filter(input.imag, order, output.imag, mode)
|
|
return output
|
|
if order not in [0, 1] and input.ndim > 0:
|
|
for axis in range(input.ndim):
|
|
spline_filter1d(input, order, axis, output=output, mode=mode)
|
|
input = output
|
|
else:
|
|
output[...] = input[...]
|
|
return output
|
|
|
|
|
|
def zoom(input, zoom, output=None, order=3, mode='constant', cval=0.0,
|
|
prefilter=True, *, grid_mode=False):
|
|
"""
|
|
Zoom an array.
|
|
|
|
The array is zoomed using spline interpolation of the requested order.
|
|
|
|
Parameters
|
|
----------
|
|
%(input)s
|
|
zoom : float or sequence
|
|
The zoom factor along the axes. If a float, `zoom` is the same for each
|
|
axis. If a sequence, `zoom` should contain one value for each axis.
|
|
%(output)s
|
|
order : int, optional
|
|
The order of the spline interpolation, default is 3.
|
|
The order has to be in the range 0-5.
|
|
%(mode_interp_constant)s
|
|
%(cval)s
|
|
%(prefilter)s
|
|
grid_mode : bool, optional
|
|
If False, the distance from the pixel centers is zoomed. Otherwise, the
|
|
distance including the full pixel extent is used. For example, a 1d
|
|
signal of length 5 is considered to have length 4 when `grid_mode` is
|
|
False, but length 5 when `grid_mode` is True. See the following
|
|
visual illustration:
|
|
|
|
.. code-block:: text
|
|
|
|
| pixel 1 | pixel 2 | pixel 3 | pixel 4 | pixel 5 |
|
|
|<-------------------------------------->|
|
|
vs.
|
|
|<----------------------------------------------->|
|
|
|
|
The starting point of the arrow in the diagram above corresponds to
|
|
coordinate location 0 in each mode.
|
|
|
|
Returns
|
|
-------
|
|
zoom : ndarray
|
|
The zoomed input.
|
|
|
|
Notes
|
|
-----
|
|
For complex-valued `input`, this function zooms the real and imaginary
|
|
components independently.
|
|
|
|
.. versionadded:: 1.6.0
|
|
Complex-valued support added.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import ndimage, datasets
|
|
>>> import matplotlib.pyplot as plt
|
|
|
|
>>> fig = plt.figure()
|
|
>>> ax1 = fig.add_subplot(121) # left side
|
|
>>> ax2 = fig.add_subplot(122) # right side
|
|
>>> ascent = datasets.ascent()
|
|
>>> result = ndimage.zoom(ascent, 3.0)
|
|
>>> ax1.imshow(ascent, vmin=0, vmax=255)
|
|
>>> ax2.imshow(result, vmin=0, vmax=255)
|
|
>>> plt.show()
|
|
|
|
>>> print(ascent.shape)
|
|
(512, 512)
|
|
|
|
>>> print(result.shape)
|
|
(1536, 1536)
|
|
"""
|
|
if order < 0 or order > 5:
|
|
raise RuntimeError('spline order not supported')
|
|
input = np.asarray(input)
|
|
if input.ndim < 1:
|
|
raise RuntimeError('input and output rank must be > 0')
|
|
zoom = _ni_support._normalize_sequence(zoom, input.ndim)
|
|
output_shape = tuple(
|
|
[int(round(ii * jj)) for ii, jj in zip(input.shape, zoom)])
|
|
complex_output = np.iscomplexobj(input)
|
|
output = _ni_support._get_output(output, input, shape=output_shape,
|
|
complex_output=complex_output)
|
|
if complex_output:
|
|
# import under different name to avoid confusion with zoom parameter
|
|
from scipy.ndimage._interpolation import zoom as _zoom
|
|
|
|
kwargs = dict(order=order, mode=mode, prefilter=prefilter)
|
|
_zoom(input.real, zoom, output=output.real,
|
|
cval=np.real(cval), **kwargs)
|
|
_zoom(input.imag, zoom, output=output.imag,
|
|
cval=np.imag(cval), **kwargs)
|
|
return output
|
|
if prefilter and order > 1:
|
|
padded, npad = _prepad_for_spline_filter(input, mode, cval)
|
|
filtered = spline_filter(padded, order, output=np.float64, mode=mode)
|
|
else:
|
|
npad = 0
|
|
filtered = input
|
|
if grid_mode:
|
|
# warn about modes that may have surprising behavior
|
|
suggest_mode = None
|
|
if mode == 'constant':
|
|
suggest_mode = 'grid-constant'
|
|
elif mode == 'wrap':
|
|
suggest_mode = 'grid-wrap'
|
|
if suggest_mode is not None:
|
|
warnings.warn(
|
|
(f"It is recommended to use mode = {suggest_mode} instead of {mode} "
|
|
f"when grid_mode is True."),
|
|
stacklevel=2
|
|
)
|
|
mode = _ni_support._extend_mode_to_code(mode)
|
|
|
|
zoom_div = np.array(output_shape)
|
|
zoom_nominator = np.array(input.shape)
|
|
if not grid_mode:
|
|
zoom_div -= 1
|
|
zoom_nominator -= 1
|
|
|
|
# Zooming to infinite values is unpredictable, so just choose
|
|
# zoom factor 1 instead
|
|
zoom = np.divide(zoom_nominator, zoom_div,
|
|
out=np.ones_like(input.shape, dtype=np.float64),
|
|
where=zoom_div != 0)
|
|
zoom = np.ascontiguousarray(zoom)
|
|
_nd_image.zoom_shift(filtered, zoom, None, output, order, mode, cval, npad,
|
|
grid_mode)
|
|
return output
|