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Remove deprecated modules and functions from bioxel package

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- Deleted _ni_support.py, _utils.py, and dtype.py as they contained unused or redundant functions.
- Removed the resize function and related utilities from _warps.py to streamline image processing.
- Cleaned up the skimage module by removing unnecessary imports and functions.
This commit is contained in:
马南
2026-07-07 13:07:54 +08:00
parent 6fe7e90bc5
commit 35faebb911
10 changed files with 10 additions and 3249 deletions
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25
src/bioxelnodes/bioxel/layer.py
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@@ -7,8 +7,7 @@ try:
except ImportError:
vdb = None
from . import scipy
from . import scipy as ndi
from scipy import ndimage as ndi
# 3rd-party
import transforms3d
@@ -83,21 +82,17 @@ class Layer():
for f in range(self.frame_count):
mask_frame = mask[f, :, :, :]
if smooth > 0:
mask_frame = scipy.median_filter(mask_frame.astype(np.float32),
mode="nearest",
size=smooth)
# mask_frame = scipy.median_filter(
# mask_frame.astype(np.float32), size=2)
mask_frame = ndi.median_filter(mask_frame.astype(np.float32),
mode="nearest",
size=smooth)
mask_frames += (mask_frame,)
elif mask.ndim == 3:
for f in range(self.frame_count):
mask_frame = mask[:, :, :]
if smooth > 0:
mask_frame = scipy.median_filter(mask_frame.astype(np.float32),
mode="nearest",
size=smooth)
# mask_frame = scipy.median_filter(
# mask_frame.astype(np.float32), size=2)
mask_frame = ndi.median_filter(mask_frame.astype(np.float32),
mode="nearest",
size=smooth)
mask_frames += (mask_frame,)
else:
raise Exception("Mask shape order should be TXYZ or XYZ")
@@ -127,9 +122,9 @@ class Layer():
frame = data[f, :, :, :, :]
if smooth > 0:
frame = scipy.median_filter(frame.astype(np.float32),
mode="nearest",
size=smooth)
frame = ndi.median_filter(frame.astype(np.float32),
mode="nearest",
size=smooth)
factors = np.divide(self.shape, shape)
zoom_factors = [1 / f for f in factors]

5
src/bioxelnodes/bioxel/scipy/__init__.py
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@@ -1,5 +0,0 @@
from ._interpolation import zoom
from ._filters import (gaussian_filter,
median_filter,
maximum_filter,
minimum_filter)

1823
src/bioxelnodes/bioxel/scipy/_filters.py
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File diff suppressed because it is too large Load Diff

314
src/bioxelnodes/bioxel/scipy/_interpolation.py
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@@ -1,314 +0,0 @@
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

120
src/bioxelnodes/bioxel/scipy/_ni_support.py
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@@ -1,120 +0,0 @@
# Copyright (C) 2003-2005 Peter J. Verveer
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of the author may not be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from collections.abc import Iterable
import operator
import warnings
import numpy as np
def _extend_mode_to_code(mode):
"""Convert an extension mode to the corresponding integer code.
"""
if mode == 'nearest':
return 0
elif mode == 'wrap':
return 1
elif mode in ['reflect', 'grid-mirror']:
return 2
elif mode == 'mirror':
return 3
elif mode == 'constant':
return 4
elif mode == 'grid-wrap':
return 5
elif mode == 'grid-constant':
return 6
else:
raise RuntimeError('boundary mode not supported')
def _normalize_sequence(input, rank):
"""If input is a scalar, create a sequence of length equal to the
rank by duplicating the input. If input is a sequence,
check if its length is equal to the length of array.
"""
is_str = isinstance(input, str)
if not is_str and isinstance(input, Iterable):
normalized = list(input)
if len(normalized) != rank:
err = "sequence argument must have length equal to input rank"
raise RuntimeError(err)
else:
normalized = [input] * rank
return normalized
def _get_output(output, input, shape=None, complex_output=False):
if shape is None:
shape = input.shape
if output is None:
if not complex_output:
output = np.zeros(shape, dtype=input.dtype.name)
else:
complex_type = np.promote_types(input.dtype, np.complex64)
output = np.zeros(shape, dtype=complex_type)
elif isinstance(output, (type, np.dtype)):
# Classes (like `np.float32`) and dtypes are interpreted as dtype
if complex_output and np.dtype(output).kind != 'c':
warnings.warn(
"promoting specified output dtype to complex", stacklevel=3)
output = np.promote_types(output, np.complex64)
output = np.zeros(shape, dtype=output)
elif isinstance(output, str):
output = np.dtype(output)
if complex_output and output.kind != 'c':
raise RuntimeError("output must have complex dtype")
elif not issubclass(output.type, np.number):
raise RuntimeError("output must have numeric dtype")
output = np.zeros(shape, dtype=output)
elif output.shape != shape:
raise RuntimeError("output shape not correct")
elif complex_output and output.dtype.kind != 'c':
raise RuntimeError("output must have complex dtype")
return output
def _check_axes(axes, ndim):
if axes is None:
return tuple(range(ndim))
elif np.isscalar(axes):
axes = (operator.index(axes),)
elif isinstance(axes, Iterable):
for ax in axes:
axes = tuple(operator.index(ax) for ax in axes)
if ax < -ndim or ax > ndim - 1:
raise ValueError(f"specified axis: {ax} is out of range")
axes = tuple(ax % ndim if ax < 0 else ax for ax in axes)
else:
message = "axes must be an integer, iterable of integers, or None"
raise ValueError(message)
if len(tuple(set(axes))) != len(axes):
raise ValueError("axes must be unique")
return axes

10
src/bioxelnodes/bioxel/scipy/_utils.py
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@@ -1,10 +0,0 @@
def normalize_axis_index(axis, ndim):
# Check if `axis` is in the correct range and normalize it
if axis < -ndim or axis >= ndim:
msg = f"axis {axis} is out of bounds for array of dimension {ndim}"
raise Exception(msg)
if axis < 0:
axis = axis + ndim
return axis

1
src/bioxelnodes/bioxel/skimage/__init__.py
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@@ -1 +0,0 @@
from ._warps import resize

37
src/bioxelnodes/bioxel/skimage/_utils.py
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@@ -1,37 +0,0 @@
import numpy as np
def convert_to_float(image, preserve_range):
"""Convert input image to float image with the appropriate range.
Parameters
----------
image : ndarray
Input image.
preserve_range : bool
Determines if the range of the image should be kept or transformed
using img_as_float. Also see
https://scikit-image.org/docs/dev/user_guide/data_types.html
Notes
-----
* Input images with `float32` data type are not upcast.
Returns
-------
image : ndarray
Transformed version of the input.
"""
if image.dtype == np.float16:
return image.astype(np.float32)
if preserve_range:
# Convert image to double only if it is not single or double
# precision float
if image.dtype.char not in 'df':
image = image.astype(float)
else:
from .dtype import img_as_float
image = img_as_float(image)
return image

319
src/bioxelnodes/bioxel/skimage/_warps.py
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@@ -1,319 +0,0 @@
import warnings
import numpy as np
from .. import scipy as ndi
from ._utils import convert_to_float
def _clip_warp_output(input_image, output_image, mode, cval, clip):
"""Clip output image to range of values of input image.
Note that this function modifies the values of `output_image` in-place
and it is only modified if ``clip=True``.
Parameters
----------
input_image : ndarray
Input image.
output_image : ndarray
Output image, which is modified in-place.
Other parameters
----------------
mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}
Points outside the boundaries of the input are filled according
to the given mode. Modes match the behaviour of `numpy.pad`.
cval : float
Used in conjunction with mode 'constant', the value outside
the image boundaries.
clip : bool
Whether to clip the output to the range of values of the input image.
This is enabled by default, since higher order interpolation may
produce values outside the given input range.
"""
if clip:
min_val = np.min(input_image)
if np.isnan(min_val):
# NaNs detected, use NaN-safe min/max
min_func = np.nanmin
max_func = np.nanmax
min_val = min_func(input_image)
else:
min_func = np.min
max_func = np.max
max_val = max_func(input_image)
# Check if cval has been used such that it expands the effective input
# range
preserve_cval = (
mode == 'constant'
and not min_val <= cval <= max_val
and min_func(output_image) <= cval <= max_func(output_image)
)
# expand min/max range to account for cval
if preserve_cval:
# cast cval to the same dtype as the input image
cval = input_image.dtype.type(cval)
min_val = min(min_val, cval)
max_val = max(max_val, cval)
# Convert array-like types to ndarrays (gh-7159)
min_val, max_val = np.asarray(min_val), np.asarray(max_val)
np.clip(output_image, min_val, max_val, out=output_image)
def _validate_interpolation_order(image_dtype, order):
"""Validate and return spline interpolation's order.
Parameters
----------
image_dtype : dtype
Image dtype.
order : int, optional
The order of the spline interpolation. The order has to be in
the range 0-5. See `skimage.transform.warp` for detail.
Returns
-------
order : int
if input order is None, returns 0 if image_dtype is bool and 1
otherwise. Otherwise, image_dtype is checked and input order
is validated accordingly (order > 0 is not supported for bool
image dtype)
"""
if order is None:
return 0 if image_dtype == bool else 1
if order < 0 or order > 5:
raise ValueError(
"Spline interpolation order has to be in the " "range 0-5.")
if image_dtype == bool and order != 0:
raise ValueError(
"Input image dtype is bool. Interpolation is not defined "
"with bool data type. Please set order to 0 or explicitly "
"cast input image to another data type."
)
return order
def _preprocess_resize_output_shape(image, output_shape):
"""Validate resize output shape according to input image.
Parameters
----------
image: ndarray
Image to be resized.
output_shape: iterable
Size of the generated output image `(rows, cols[, ...][, dim])`. If
`dim` is not provided, the number of channels is preserved.
Returns
-------
image: ndarray
The input image, but with additional singleton dimensions appended in
the case where ``len(output_shape) > input.ndim``.
output_shape: tuple
The output image converted to tuple.
Raises
------
ValueError:
If output_shape length is smaller than the image number of
dimensions
Notes
-----
The input image is reshaped if its number of dimensions is not
equal to output_shape_length.
"""
output_shape = tuple(output_shape)
output_ndim = len(output_shape)
input_shape = image.shape
if output_ndim > image.ndim:
# append dimensions to input_shape
input_shape += (1,) * (output_ndim - image.ndim)
image = np.reshape(image, input_shape)
elif output_ndim == image.ndim - 1:
# multichannel case: append shape of last axis
output_shape = output_shape + (image.shape[-1],)
elif output_ndim < image.ndim:
raise ValueError(
"output_shape length cannot be smaller than the "
"image number of dimensions"
)
return image, output_shape
def _to_ndimage_mode(mode):
"""Convert from `numpy.pad` mode name to the corresponding ndimage mode."""
mode_translation_dict = dict(
constant='constant',
edge='nearest',
symmetric='reflect',
reflect='mirror',
wrap='wrap',
)
if mode not in mode_translation_dict:
raise ValueError(
f"Unknown mode: '{mode}', or cannot translate mode. The "
f"mode should be one of 'constant', 'edge', 'symmetric', "
f"'reflect', or 'wrap'. See the documentation of numpy.pad for "
f"more info."
)
return _fix_ndimage_mode(mode_translation_dict[mode])
def _fix_ndimage_mode(mode):
# SciPy 1.6.0 introduced grid variants of constant and wrap which
# have less surprising behavior for images. Use these when available
grid_modes = {'constant': 'grid-constant', 'wrap': 'grid-wrap'}
return grid_modes.get(mode, mode)
def resize(
image,
output_shape,
order=None,
mode='reflect',
cval=0,
clip=True,
preserve_range=False,
anti_aliasing=None,
anti_aliasing_sigma=None,
):
"""Resize image to match a certain size.
Performs interpolation to up-size or down-size N-dimensional images. Note
that anti-aliasing should be enabled when down-sizing images to avoid
aliasing artifacts. For downsampling with an integer factor also see
`skimage.transform.downscale_local_mean`.
Parameters
----------
image : ndarray
Input image.
output_shape : iterable
Size of the generated output image `(rows, cols[, ...][, dim])`. If
`dim` is not provided, the number of channels is preserved. In case the
number of input channels does not equal the number of output channels a
n-dimensional interpolation is applied.
Returns
-------
resized : ndarray
Resized version of the input.
Other parameters
----------------
order : int, optional
The order of the spline interpolation, default is 0 if
image.dtype is bool and 1 otherwise. The order has to be in
the range 0-5. See `skimage.transform.warp` for detail.
mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
Points outside the boundaries of the input are filled according
to the given mode. Modes match the behaviour of `numpy.pad`.
cval : float, optional
Used in conjunction with mode 'constant', the value outside
the image boundaries.
clip : bool, optional
Whether to clip the output to the range of values of the input image.
This is enabled by default, since higher order interpolation may
produce values outside the given input range.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input
image is converted according to the conventions of `img_as_float`.
Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
anti_aliasing : bool, optional
Whether to apply a Gaussian filter to smooth the image prior
to downsampling. It is crucial to filter when downsampling
the image to avoid aliasing artifacts. If not specified, it is set to
True when downsampling an image whose data type is not bool.
It is also set to False when using nearest neighbor interpolation
(``order`` == 0) with integer input data type.
anti_aliasing_sigma : {float, tuple of floats}, optional
Standard deviation for Gaussian filtering used when anti-aliasing.
By default, this value is chosen as (s - 1) / 2 where s is the
downsampling factor, where s > 1. For the up-size case, s < 1, no
anti-aliasing is performed prior to rescaling.
Notes
-----
Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge
pixels are duplicated during the reflection. As an example, if an array
has values [0, 1, 2] and was padded to the right by four values using
symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it
would be [0, 1, 2, 1, 0, 1, 2].
Examples
--------
>>> from skimage import data
>>> from skimage.transform import resize
>>> image = data.camera()
>>> resize(image, (100, 100)).shape
(100, 100)
"""
image, output_shape = _preprocess_resize_output_shape(image, output_shape)
input_shape = image.shape
input_type = image.dtype
if input_type == np.float16:
image = image.astype(np.float32)
if anti_aliasing is None:
anti_aliasing = (
not input_type == bool
and not (np.issubdtype(input_type, np.integer) and order == 0)
and any(x < y for x, y in zip(output_shape, input_shape))
)
if input_type == bool and anti_aliasing:
raise ValueError("anti_aliasing must be False for boolean images")
factors = np.divide(input_shape, output_shape)
order = _validate_interpolation_order(input_type, order)
if order > 0:
image = convert_to_float(image, preserve_range)
# Translate modes used by np.pad to those used by scipy.ndimage
ndi_mode = _to_ndimage_mode(mode)
if anti_aliasing:
if anti_aliasing_sigma is None:
anti_aliasing_sigma = np.maximum(0, (factors - 1) / 2)
else:
anti_aliasing_sigma = np.atleast_1d(anti_aliasing_sigma) * np.ones_like(
factors
)
if np.any(anti_aliasing_sigma < 0):
raise ValueError(
"Anti-aliasing standard deviation must be "
"greater than or equal to zero"
)
elif np.any((anti_aliasing_sigma > 0) & (factors <= 1)):
warnings.warn(
"Anti-aliasing standard deviation greater than zero but "
"not down-sampling along all axes",
stacklevel=2
)
filtered = ndi.gaussian_filter(
image, anti_aliasing_sigma, cval=cval, mode=ndi_mode
)
else:
filtered = image
zoom_factors = [1 / f for f in factors]
out = ndi.zoom(
filtered, zoom_factors, order=order, mode=ndi_mode, cval=cval, grid_mode=True
)
_clip_warp_output(image, out, mode, cval, clip)
return out

605
src/bioxelnodes/bioxel/skimage/dtype.py
View File

@@ -1,605 +0,0 @@
import warnings
from warnings import warn
import numpy as np
__all__ = [
'img_as_float32',
'img_as_float64',
'img_as_float',
'img_as_int',
'img_as_uint',
'img_as_ubyte',
'img_as_bool',
'dtype_limits',
]
# Some of these may or may not be aliases depending on architecture & platform
_integer_types = (
np.int8,
np.byte,
np.int16,
np.short,
np.int32,
np.int64,
np.longlong,
np.int_,
np.intp,
np.intc,
int,
np.uint8,
np.ubyte,
np.uint16,
np.ushort,
np.uint32,
np.uint64,
np.ulonglong,
np.uint,
np.uintp,
np.uintc,
)
_integer_ranges = {t: (np.iinfo(t).min, np.iinfo(t).max)
for t in _integer_types}
dtype_range = {
bool: (False, True),
np.bool_: (False, True),
float: (-1, 1),
np.float16: (-1, 1),
np.float32: (-1, 1),
np.float64: (-1, 1),
}
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
# np.bool8 is a deprecated alias of np.bool_
if hasattr(np, 'bool8'):
dtype_range[np.bool8] = (False, True)
dtype_range.update(_integer_ranges)
_supported_types = list(dtype_range.keys())
def dtype_limits(image, clip_negative=False):
"""Return intensity limits, i.e. (min, max) tuple, of the image's dtype.
Parameters
----------
image : ndarray
Input image.
clip_negative : bool, optional
If True, clip the negative range (i.e. return 0 for min intensity)
even if the image dtype allows negative values.
Returns
-------
imin, imax : tuple
Lower and upper intensity limits.
"""
imin, imax = dtype_range[image.dtype.type]
if clip_negative:
imin = 0
return imin, imax
def _dtype_itemsize(itemsize, *dtypes):
"""Return first of `dtypes` with itemsize greater than `itemsize`
Parameters
----------
itemsize: int
The data type object element size.
Other Parameters
----------------
*dtypes:
Any Object accepted by `np.dtype` to be converted to a data
type object
Returns
-------
dtype: data type object
First of `dtypes` with itemsize greater than `itemsize`.
"""
return next(dt for dt in dtypes if np.dtype(dt).itemsize >= itemsize)
def _dtype_bits(kind, bits, itemsize=1):
"""Return dtype of `kind` that can store a `bits` wide unsigned int
Parameters:
kind: str
Data type kind.
bits: int
Desired number of bits.
itemsize: int
The data type object element size.
Returns
-------
dtype: data type object
Data type of `kind` that can store a `bits` wide unsigned int
"""
s = next(
i
for i in (itemsize,) + (2, 4, 8)
if bits < (i * 8) or (bits == (i * 8) and kind == 'u')
)
return np.dtype(kind + str(s))
def _scale(a, n, m, copy=True):
"""Scale an array of unsigned/positive integers from `n` to `m` bits.
Numbers can be represented exactly only if `m` is a multiple of `n`.
Parameters
----------
a : ndarray
Input image array.
n : int
Number of bits currently used to encode the values in `a`.
m : int
Desired number of bits to encode the values in `out`.
copy : bool, optional
If True, allocates and returns new array. Otherwise, modifies
`a` in place.
Returns
-------
out : array
Output image array. Has the same kind as `a`.
"""
kind = a.dtype.kind
if n > m and a.max() < 2**m:
mnew = int(np.ceil(m / 2) * 2)
if mnew > m:
dtype = f'int{mnew}'
else:
dtype = f'uint{mnew}'
n = int(np.ceil(n / 2) * 2)
warn(
f'Downcasting {a.dtype} to {dtype} without scaling because max '
f'value {a.max()} fits in {dtype}',
stacklevel=3,
)
return a.astype(_dtype_bits(kind, m))
elif n == m:
return a.copy() if copy else a
elif n > m:
# downscale with precision loss
if copy:
b = np.empty(a.shape, _dtype_bits(kind, m))
np.floor_divide(a, 2 ** (n - m), out=b,
dtype=a.dtype, casting='unsafe')
return b
else:
a //= 2 ** (n - m)
return a
elif m % n == 0:
# exact upscale to a multiple of `n` bits
if copy:
b = np.empty(a.shape, _dtype_bits(kind, m))
np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
return b
else:
a = a.astype(_dtype_bits(kind, m, a.dtype.itemsize), copy=False)
a *= (2**m - 1) // (2**n - 1)
return a
else:
# upscale to a multiple of `n` bits,
# then downscale with precision loss
o = (m // n + 1) * n
if copy:
b = np.empty(a.shape, _dtype_bits(kind, o))
np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
b //= 2 ** (o - m)
return b
else:
a = a.astype(_dtype_bits(kind, o, a.dtype.itemsize), copy=False)
a *= (2**o - 1) // (2**n - 1)
a //= 2 ** (o - m)
return a
def _convert(image, dtype, force_copy=False, uniform=False):
"""
Convert an image to the requested data-type.
Warnings are issued in case of precision loss, or when negative values
are clipped during conversion to unsigned integer types (sign loss).
Floating point values are expected to be normalized and will be clipped
to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
signed integers respectively.
Numbers are not shifted to the negative side when converting from
unsigned to signed integer types. Negative values will be clipped when
converting to unsigned integers.
Parameters
----------
image : ndarray
Input image.
dtype : dtype
Target data-type.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
uniform : bool, optional
Uniformly quantize the floating point range to the integer range.
By default (uniform=False) floating point values are scaled and
rounded to the nearest integers, which minimizes back and forth
conversion errors.
.. versionchanged:: 0.15
``_convert`` no longer warns about possible precision or sign
information loss. See discussions on these warnings at:
https://github.com/scikit-image/scikit-image/issues/2602
https://github.com/scikit-image/scikit-image/issues/543#issuecomment-208202228
https://github.com/scikit-image/scikit-image/pull/3575
References
----------
.. [1] DirectX data conversion rules.
https://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
.. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25",
pp 7-8. Khronos Group, 2010.
.. [3] Proper treatment of pixels as integers. A.W. Paeth.
In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
.. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels",
pp 47-57. Morgan Kaufmann, 1998.
"""
image = np.asarray(image)
dtypeobj_in = image.dtype
if dtype is np.floating:
dtypeobj_out = np.dtype('float64')
else:
dtypeobj_out = np.dtype(dtype)
dtype_in = dtypeobj_in.type
dtype_out = dtypeobj_out.type
kind_in = dtypeobj_in.kind
kind_out = dtypeobj_out.kind
itemsize_in = dtypeobj_in.itemsize
itemsize_out = dtypeobj_out.itemsize
# Below, we do an `issubdtype` check. Its purpose is to find out
# whether we can get away without doing any image conversion. This happens
# when:
#
# - the output and input dtypes are the same or
# - when the output is specified as a type, and the input dtype
# is a subclass of that type (e.g. `np.floating` will allow
# `float32` and `float64` arrays through)
if np.issubdtype(dtype_in, dtype):
if force_copy:
image = image.copy()
return image
if not (dtype_in in _supported_types and dtype_out in _supported_types):
raise ValueError(
f'Cannot convert from {dtypeobj_in} to ' f'{dtypeobj_out}.')
if kind_in in 'ui':
imin_in = np.iinfo(dtype_in).min
imax_in = np.iinfo(dtype_in).max
if kind_out in 'ui':
imin_out = np.iinfo(dtype_out).min
imax_out = np.iinfo(dtype_out).max
# any -> binary
if kind_out == 'b':
return image > dtype_in(dtype_range[dtype_in][1] / 2)
# binary -> any
if kind_in == 'b':
result = image.astype(dtype_out)
if kind_out != 'f':
result *= dtype_out(dtype_range[dtype_out][1])
return result
# float -> any
if kind_in == 'f':
if kind_out == 'f':
# float -> float
return image.astype(dtype_out)
if np.min(image) < -1.0 or np.max(image) > 1.0:
raise ValueError("Images of type float must be between -1 and 1.")
# floating point -> integer
# use float type that can represent output integer type
computation_type = _dtype_itemsize(
itemsize_out, dtype_in, np.float32, np.float64
)
if not uniform:
if kind_out == 'u':
image_out = np.multiply(
image, imax_out, dtype=computation_type)
else:
image_out = np.multiply(
image, (imax_out - imin_out) / 2, dtype=computation_type
)
image_out -= 1.0 / 2.0
np.rint(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
elif kind_out == 'u':
image_out = np.multiply(
image, imax_out + 1, dtype=computation_type)
np.clip(image_out, 0, imax_out, out=image_out)
else:
image_out = np.multiply(
image, (imax_out - imin_out + 1.0) / 2.0, dtype=computation_type
)
np.floor(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
return image_out.astype(dtype_out)
# signed/unsigned int -> float
if kind_out == 'f':
# use float type that can exactly represent input integers
computation_type = _dtype_itemsize(
itemsize_in, dtype_out, np.float32, np.float64
)
if kind_in == 'u':
# using np.divide or np.multiply doesn't copy the data
# until the computation time
image = np.multiply(image, 1.0 / imax_in, dtype=computation_type)
# DirectX uses this conversion also for signed ints
# if imin_in:
# np.maximum(image, -1.0, out=image)
elif kind_in == 'i':
# From DirectX conversions:
# The most negative value maps to -1.0f
# Every other value is converted to a float (call it c)
# and then result = c * (1.0f / (2⁽ⁿ⁻¹⁾-1)).
image = np.multiply(image, 1.0 / imax_in, dtype=computation_type)
np.maximum(image, -1.0, out=image)
else:
image = np.add(image, 0.5, dtype=computation_type)
image *= 2 / (imax_in - imin_in)
return np.asarray(image, dtype_out)
# unsigned int -> signed/unsigned int
if kind_in == 'u':
if kind_out == 'i':
# unsigned int -> signed int
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
return image.view(dtype_out)
else:
# unsigned int -> unsigned int
return _scale(image, 8 * itemsize_in, 8 * itemsize_out)
# signed int -> unsigned int
if kind_out == 'u':
image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
result = np.empty(image.shape, dtype_out)
np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
return result
# signed int -> signed int
if itemsize_in > itemsize_out:
return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)
image = image.astype(_dtype_bits('i', itemsize_out * 8))
image -= imin_in
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
image += imin_out
return image.astype(dtype_out)
def convert(image, dtype, force_copy=False, uniform=False):
warn(
"The use of this function is discouraged as its behavior may change "
"dramatically in scikit-image 1.0. This function will be removed "
"in scikit-image 1.0.",
FutureWarning,
stacklevel=2,
)
return _convert(image=image, dtype=dtype, force_copy=force_copy, uniform=uniform)
if _convert.__doc__ is not None:
convert.__doc__ = (
_convert.__doc__
+ """
Warns
-----
FutureWarning:
.. versionadded:: 0.17
The use of this function is discouraged as its behavior may change
dramatically in scikit-image 1.0. This function will be removed
in scikit-image 1.0.
"""
)
def img_as_float32(image, force_copy=False):
"""Convert an image to single-precision (32-bit) floating point format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of float32
Output image.
Notes
-----
The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
converting from unsigned or signed datatypes, respectively.
If the input image has a float type, intensity values are not modified
and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0].
"""
return _convert(image, np.float32, force_copy)
def img_as_float64(image, force_copy=False):
"""Convert an image to double-precision (64-bit) floating point format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of float64
Output image.
Notes
-----
The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
converting from unsigned or signed datatypes, respectively.
If the input image has a float type, intensity values are not modified
and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0].
"""
return _convert(image, np.float64, force_copy)
def img_as_float(image, force_copy=False):
"""Convert an image to floating point format.
This function is similar to `img_as_float64`, but will not convert
lower-precision floating point arrays to `float64`.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of float
Output image.
Notes
-----
The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
converting from unsigned or signed datatypes, respectively.
If the input image has a float type, intensity values are not modified
and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0].
"""
return _convert(image, np.floating, force_copy)
def img_as_uint(image, force_copy=False):
"""Convert an image to 16-bit unsigned integer format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of uint16
Output image.
Notes
-----
Negative input values will be clipped.
Positive values are scaled between 0 and 65535.
"""
return _convert(image, np.uint16, force_copy)
def img_as_int(image, force_copy=False):
"""Convert an image to 16-bit signed integer format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of int16
Output image.
Notes
-----
The values are scaled between -32768 and 32767.
If the input data-type is positive-only (e.g., uint8), then
the output image will still only have positive values.
"""
return _convert(image, np.int16, force_copy)
def img_as_ubyte(image, force_copy=False):
"""Convert an image to 8-bit unsigned integer format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of ubyte (uint8)
Output image.
Notes
-----
Negative input values will be clipped.
Positive values are scaled between 0 and 255.
"""
return _convert(image, np.uint8, force_copy)
def img_as_bool(image, force_copy=False):
"""Convert an image to boolean format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of bool (`bool_`)
Output image.
Notes
-----
The upper half of the input dtype's positive range is True, and the lower
half is False. All negative values (if present) are False.
"""
return _convert(image, bool, force_copy)