util
Module: util
skimage.util.apply_parallel (function, array) | Map a function in parallel across an array. |
skimage.util.crop (ar, crop_width[, copy, order]) | Crop array ar by crop_width along each dimension. |
skimage.util.dtype_limits (image[, clip_negative]) | Return intensity limits, i.e. |
skimage.util.img_as_bool (image[, force_copy]) | Convert an image to boolean format. |
skimage.util.img_as_float (image[, force_copy]) | Convert an image to double-precision floating point format. |
skimage.util.img_as_int (image[, force_copy]) | Convert an image to 16-bit signed integer format. |
skimage.util.img_as_ubyte (image[, force_copy]) | Convert an image to 8-bit unsigned integer format. |
skimage.util.img_as_uint (image[, force_copy]) | Convert an image to 16-bit unsigned integer format. |
skimage.util.pad (array, pad_width[, mode]) | Pads an array. |
skimage.util.random_noise (image[, mode, ...]) | Function to add random noise of various types to a floating-point image. |
skimage.util.regular_grid (ar_shape, n_points) | Find n_points regularly spaced along ar_shape . |
skimage.util.unique_rows (ar) | Remove repeated rows from a 2D array. |
skimage.util.view_as_blocks (arr_in, block_shape) | Block view of the input n-dimensional array (using re-striding). |
skimage.util.view_as_windows (arr_in, ...[, step]) | Rolling window view of the input n-dimensional array. |
apply_parallel
-
skimage.util.apply_parallel(function, array, chunks=None, depth=0, mode=None, extra_arguments=(), extra_keywords={})
[source] -
Map a function in parallel across an array.
Split an array into possibly overlapping chunks of a given depth and boundary type, call the given function in parallel on the chunks, combine the chunks and return the resulting array.
Parameters: function : function
Function to be mapped which takes an array as an argument.
array : numpy array
Array which the function will be applied to.
chunks : int, tuple, or tuple of tuples, optional
A single integer is interpreted as the length of one side of a square chunk that should be tiled across the array. One tuple of length
array.ndim
represents the shape of a chunk, and it is tiled across the array. A list of tuples of lengthndim
, where each sub-tuple is a sequence of chunk sizes along the corresponding dimension. If None, the array is broken up into chunks based on the number of available cpus. More information about chunks is in the documentation here.depth : int, optional
Integer equal to the depth of the added boundary cells. Defaults to zero.
mode : {‘reflect’, ‘symmetric’, ‘periodic’, ‘wrap’, ‘nearest’, ‘edge’}, optional
type of external boundary padding.
extra_arguments : tuple, optional
Tuple of arguments to be passed to the function.
extra_keywords : dictionary, optional
Dictionary of keyword arguments to be passed to the function.
Notes
Numpy edge modes ‘symmetric’, ‘wrap’, and ‘edge’ are converted to the equivalent
dask
boundary modes ‘reflect’, ‘periodic’ and ‘nearest’, respectively.
crop
-
skimage.util.crop(ar, crop_width, copy=False, order='K')
[source] -
Crop array
ar
bycrop_width
along each dimension.Parameters: ar : array-like of rank N
Input array.
crop_width : {sequence, int}
Number of values to remove from the edges of each axis.
((before_1, after_1),
...(before_N, after_N))
specifies unique crop widths at the start and end of each axis.((before, after),)
specifies a fixed start and end crop for every axis.(n,)
orn
for integern
is a shortcut for before = after =n
for all axes.copy : bool, optional
Ensure the returned array is a contiguous copy. Normally, a crop operation will return a discontiguous view of the underlying input array. Passing
copy=True
will result in a contiguous copy.order : {‘C’, ‘F’, ‘A’, ‘K’}, optional
If
copy==True
, control the memory layout of the copy. Seenp.copy
.Returns: cropped : array
The cropped array. If
copy=False
(default), this is a sliced view of the input array.
dtype_limits
-
skimage.util.dtype_limits(image, clip_negative=True)
[source] -
Return intensity limits, i.e. (min, max) tuple, of the image’s dtype.
Parameters: image : ndarray
Input image.
clip_negative : bool
If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values.
img_as_bool
-
skimage.util.img_as_bool(image, force_copy=False)
[source] -
Convert an image to boolean format.
Parameters: image : ndarray
Input image.
force_copy : bool
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.
img_as_float
-
skimage.util.img_as_float(image, force_copy=False)
[source] -
Convert an image to double-precision floating point format.
Parameters: image : ndarray
Input image.
force_copy : bool
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.
img_as_int
-
skimage.util.img_as_int(image, force_copy=False)
[source] -
Convert an image to 16-bit signed integer format.
Parameters: image : ndarray
Input image.
force_copy : bool
Force a copy of the data, irrespective of its current dtype.
Returns: out : ndarray of uint16
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.
img_as_ubyte
-
skimage.util.img_as_ubyte(image, force_copy=False)
[source] -
Convert an image to 8-bit unsigned integer format.
Parameters: image : ndarray
Input image.
force_copy : bool
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.
img_as_uint
-
skimage.util.img_as_uint(image, force_copy=False)
[source] -
Convert an image to 16-bit unsigned integer format.
Parameters: image : ndarray
Input image.
force_copy : bool
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.
pad
-
skimage.util.pad(array, pad_width, mode=None, **kwargs)
[source] -
Pads an array.
Parameters: array : array_like of rank N
Input array
pad_width : {sequence, array_like, int}
Number of values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes.
mode : str or function
One of the following string values or a user supplied function.
- ‘constant’
-
Pads with a constant value.
- ‘edge’
-
Pads with the edge values of array.
- ‘linear_ramp’
-
Pads with the linear ramp between end_value and the array edge value.
- ‘maximum’
-
Pads with the maximum value of all or part of the vector along each axis.
- ‘mean’
-
Pads with the mean value of all or part of the vector along each axis.
- ‘median’
-
Pads with the median value of all or part of the vector along each axis.
- ‘minimum’
-
Pads with the minimum value of all or part of the vector along each axis.
- ‘reflect’
-
Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis.
- ‘symmetric’
-
Pads with the reflection of the vector mirrored along the edge of the array.
- ‘wrap’
-
Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning.
- <function>
-
Padding function, see Notes.
stat_length : sequence or int, optional
Used in ‘maximum’, ‘mean’, ‘median’, and ‘minimum’. Number of values at edge of each axis used to calculate the statistic value.
((before_1, after_1), ... (before_N, after_N)) unique statistic lengths for each axis.
((before, after),) yields same before and after statistic lengths for each axis.
(stat_length,) or int is a shortcut for before = after = statistic length for all axes.
Default is
None
, to use the entire axis.constant_values : sequence or int, optional
Used in ‘constant’. The values to set the padded values for each axis.
((before_1, after_1), ... (before_N, after_N)) unique pad constants for each axis.
((before, after),) yields same before and after constants for each axis.
(constant,) or int is a shortcut for before = after = constant for all axes.
Default is 0.
end_values : sequence or int, optional
Used in ‘linear_ramp’. The values used for the ending value of the linear_ramp and that will form the edge of the padded array.
((before_1, after_1), ... (before_N, after_N)) unique end values for each axis.
((before, after),) yields same before and after end values for each axis.
(constant,) or int is a shortcut for before = after = end value for all axes.
Default is 0.
reflect_type : {‘even’, ‘odd’}, optional
Used in ‘reflect’, and ‘symmetric’. The ‘even’ style is the default with an unaltered reflection around the edge value. For the ‘odd’ style, the extented part of the array is created by subtracting the reflected values from two times the edge value.
Returns: pad : ndarray
Padded array of rank equal to
array
with shape increased according topad_width
.Notes
New in version 1.7.0.
For an array with rank greater than 1, some of the padding of later axes is calculated from padding of previous axes. This is easiest to think about with a rank 2 array where the corners of the padded array are calculated by using padded values from the first axis.
The padding function, if used, should return a rank 1 array equal in length to the vector argument with padded values replaced. It has the following signature:
padding_func(vector, iaxis_pad_width, iaxis, **kwargs)
where
-
vector : ndarray
- A rank 1 array already padded with zeros. Padded values are vector[:pad_tuple[0]] and vector[-pad_tuple[1]:].
-
iaxis_pad_width : tuple
- A 2-tuple of ints, iaxis_pad_width[0] represents the number of values padded at the beginning of vector where iaxis_pad_width[1] represents the number of values padded at the end of vector.
-
iaxis : int
- The axis currently being calculated.
-
kwargs : misc
- Any keyword arguments the function requires.
Examples
>>> from skimage.util import pad >>> a = [1, 2, 3, 4, 5] >>> pad(a, (2,3), 'constant', constant_values=(4, 6)) array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])
>>> pad(a, (2, 3), 'edge') array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])
>>> pad(a, (2, 3), 'linear_ramp', end_values=(5, -4)) array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])
>>> pad(a, (2,), 'maximum') array([5, 5, 1, 2, 3, 4, 5, 5, 5])
>>> pad(a, (2,), 'mean') array([3, 3, 1, 2, 3, 4, 5, 3, 3])
>>> pad(a, (2,), 'median') array([3, 3, 1, 2, 3, 4, 5, 3, 3])
>>> a = [[1, 2], [3, 4]] >>> pad(a, ((3, 2), (2, 3)), 'minimum') array([[1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [3, 3, 3, 4, 3, 3, 3], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1]])
>>> a = [1, 2, 3, 4, 5] >>> pad(a, (2, 3), 'reflect') array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
>>> pad(a, (2, 3), 'reflect', reflect_type='odd') array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8])
>>> pad(a, (2, 3), 'symmetric') array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
>>> pad(a, (2, 3), 'symmetric', reflect_type='odd') array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
>>> pad(a, (2, 3), 'wrap') array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
>>> def padwithtens(vector, pad_width, iaxis, kwargs): ... vector[:pad_width[0]] = 10 ... vector[-pad_width[1]:] = 10 ... return vector
>>> a = np.arange(6) >>> a = a.reshape((2, 3))
>>> pad(a, 2, padwithtens) array([[10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 0, 1, 2, 10, 10], [10, 10, 3, 4, 5, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10]])
random_noise
-
skimage.util.random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs)
[source] -
Function to add random noise of various types to a floating-point image.
Parameters: image : ndarray
Input image data. Will be converted to float.
mode : str
One of the following strings, selecting the type of noise to add:
- ‘gaussian’ Gaussian-distributed additive noise.
-
- ‘localvar’ Gaussian-distributed additive noise, with specified
-
local variance at each point of
image
- ‘poisson’ Poisson-distributed noise generated from the data.
- ‘salt’ Replaces random pixels with 1.
- ‘pepper’ Replaces random pixels with 0.
- ‘s&p’ Replaces random pixels with 0 or 1.
-
- ‘speckle’ Multiplicative noise using out = image + n*image, where
-
n is uniform noise with specified mean & variance.
seed : int
If provided, this will set the random seed before generating noise, for valid pseudo-random comparisons.
clip : bool
If True (default), the output will be clipped after noise applied for modes
‘speckle’
,‘poisson’
, and‘gaussian’
. This is needed to maintain the proper image data range. If False, clipping is not applied, and the output may extend beyond the range [-1, 1].mean : float
Mean of random distribution. Used in ‘gaussian’ and ‘speckle’. Default : 0.
var : float
Variance of random distribution. Used in ‘gaussian’ and ‘speckle’. Note: variance = (standard deviation) ** 2. Default : 0.01
local_vars : ndarray
Array of positive floats, same shape as
image
, defining the local variance at every image point. Used in ‘localvar’.amount : float
Proportion of image pixels to replace with noise on range [0, 1]. Used in ‘salt’, ‘pepper’, and ‘salt & pepper’. Default : 0.05
salt_vs_pepper : float
Proportion of salt vs. pepper noise for ‘s&p’ on range [0, 1]. Higher values represent more salt. Default : 0.5 (equal amounts)
Returns: out : ndarray
Output floating-point image data on range [0, 1] or [-1, 1] if the input
image
was unsigned or signed, respectively.Notes
Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside the valid image range. The default is to clip (not alias) these values, but they may be preserved by setting
clip=False
. Note that in this case the output may contain values outside the ranges [0, 1] or [-1, 1]. Use this option with care.Because of the prevalence of exclusively positive floating-point images in intermediate calculations, it is not possible to intuit if an input is signed based on dtype alone. Instead, negative values are explicity searched for. Only if found does this function assume signed input. Unexpected results only occur in rare, poorly exposes cases (e.g. if all values are above 50 percent gray in a signed
image
). In this event, manually scaling the input to the positive domain will solve the problem.The Poisson distribution is only defined for positive integers. To apply this noise type, the number of unique values in the image is found and the next round power of two is used to scale up the floating-point result, after which it is scaled back down to the floating-point image range.
To generate Poisson noise against a signed image, the signed image is temporarily converted to an unsigned image in the floating point domain, Poisson noise is generated, then it is returned to the original range.
regular_grid
-
skimage.util.regular_grid(ar_shape, n_points)
[source] -
Find
n_points
regularly spaced alongar_shape
.The returned points (as slices) should be as close to cubically-spaced as possible. Essentially, the points are spaced by the Nth root of the input array size, where N is the number of dimensions. However, if an array dimension cannot fit a full step size, it is “discarded”, and the computation is done for only the remaining dimensions.
Parameters: ar_shape : array-like of ints
The shape of the space embedding the grid.
len(ar_shape)
is the number of dimensions.n_points : int
The (approximate) number of points to embed in the space.
Returns: slices : list of slice objects
A slice along each dimension of
ar_shape
, such that the intersection of all the slices give the coordinates of regularly spaced points.Examples
>>> ar = np.zeros((20, 40)) >>> g = regular_grid(ar.shape, 8) >>> g [slice(5, None, 10), slice(5, None, 10)] >>> ar[g] = 1 >>> ar.sum() 8.0 >>> ar = np.zeros((20, 40)) >>> g = regular_grid(ar.shape, 32) >>> g [slice(2, None, 5), slice(2, None, 5)] >>> ar[g] = 1 >>> ar.sum() 32.0 >>> ar = np.zeros((3, 20, 40)) >>> g = regular_grid(ar.shape, 8) >>> g [slice(1, None, 3), slice(5, None, 10), slice(5, None, 10)] >>> ar[g] = 1 >>> ar.sum() 8.0
unique_rows
-
skimage.util.unique_rows(ar)
[source] -
Remove repeated rows from a 2D array.
In particular, if given an array of coordinates of shape (Npoints, Ndim), it will remove repeated points.
Parameters: ar : 2-D ndarray
The input array.
Returns: ar_out : 2-D ndarray
A copy of the input array with repeated rows removed.
Raises: ValueError : if
ar
is not two-dimensional.Notes
The function will generate a copy of
ar
if it is not C-contiguous, which will negatively affect performance for large input arrays.Examples
>>> ar = np.array([[1, 0, 1], ... [0, 1, 0], ... [1, 0, 1]], np.uint8) >>> unique_rows(ar) array([[0, 1, 0], [1, 0, 1]], dtype=uint8)
view_as_blocks
-
skimage.util.view_as_blocks(arr_in, block_shape)
[source] -
Block view of the input n-dimensional array (using re-striding).
Blocks are non-overlapping views of the input array.
Parameters: arr_in : ndarray
N-d input array.
block_shape : tuple
The shape of the block. Each dimension must divide evenly into the corresponding dimensions of
arr_in
.Returns: arr_out : ndarray
Block view of the input array. If
arr_in
is non-contiguous, a copy is made.Examples
>>> import numpy as np >>> from skimage.util.shape import view_as_blocks >>> A = np.arange(4*4).reshape(4,4) >>> A array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> B = view_as_blocks(A, block_shape=(2, 2)) >>> B[0, 0] array([[0, 1], [4, 5]]) >>> B[0, 1] array([[2, 3], [6, 7]]) >>> B[1, 0, 1, 1] 13
>>> A = np.arange(4*4*6).reshape(4,4,6) >>> A array([[[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23]], [[24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35], [36, 37, 38, 39, 40, 41], [42, 43, 44, 45, 46, 47]], [[48, 49, 50, 51, 52, 53], [54, 55, 56, 57, 58, 59], [60, 61, 62, 63, 64, 65], [66, 67, 68, 69, 70, 71]], [[72, 73, 74, 75, 76, 77], [78, 79, 80, 81, 82, 83], [84, 85, 86, 87, 88, 89], [90, 91, 92, 93, 94, 95]]]) >>> B = view_as_blocks(A, block_shape=(1, 2, 2)) >>> B.shape (4, 2, 3, 1, 2, 2) >>> B[2:, 0, 2] array([[[[52, 53], [58, 59]]], [[[76, 77], [82, 83]]]])
view_as_windows
-
skimage.util.view_as_windows(arr_in, window_shape, step=1)
[source] -
Rolling window view of the input n-dimensional array.
Windows are overlapping views of the input array, with adjacent windows shifted by a single row or column (or an index of a higher dimension).
Parameters: arr_in : ndarray
N-d input array.
window_shape : integer or tuple of length arr_in.ndim
Defines the shape of the elementary n-dimensional orthotope (better know as hyperrectangle [R383]) of the rolling window view. If an integer is given, the shape will be a hypercube of sidelength given by its value.
step : integer or tuple of length arr_in.ndim
Indicates step size at which extraction shall be performed. If integer is given, then the step is uniform in all dimensions.
Returns: arr_out : ndarray
(rolling) window view of the input array. If
arr_in
is non-contiguous, a copy is made.Notes
One should be very careful with rolling views when it comes to memory usage. Indeed, although a ‘view’ has the same memory footprint as its base array, the actual array that emerges when this ‘view’ is used in a computation is generally a (much) larger array than the original, especially for 2-dimensional arrays and above.
For example, let us consider a 3 dimensional array of size (100, 100, 100) of
float64
. This array takes about 8*100**3 Bytes for storage which is just 8 MB. If one decides to build a rolling view on this array with a window of (3, 3, 3) the hypothetical size of the rolling view (if one was to reshape the view for example) would be 8*(100-3+1)**3*3**3 which is about 203 MB! The scaling becomes even worse as the dimension of the input array becomes larger.References
[R383] (1, 2) http://en.wikipedia.org/wiki/Hyperrectangle Examples
>>> import numpy as np >>> from skimage.util.shape import view_as_windows >>> A = np.arange(4*4).reshape(4,4) >>> A array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> window_shape = (2, 2) >>> B = view_as_windows(A, window_shape) >>> B[0, 0] array([[0, 1], [4, 5]]) >>> B[0, 1] array([[1, 2], [5, 6]])
>>> A = np.arange(10) >>> A array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> window_shape = (3,) >>> B = view_as_windows(A, window_shape) >>> B.shape (8, 3) >>> B array([[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6], [5, 6, 7], [6, 7, 8], [7, 8, 9]])
>>> A = np.arange(5*4).reshape(5, 4) >>> A array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]]) >>> window_shape = (4, 3) >>> B = view_as_windows(A, window_shape) >>> B.shape (2, 2, 4, 3) >>> B array([[[[ 0, 1, 2], [ 4, 5, 6], [ 8, 9, 10], [12, 13, 14]], [[ 1, 2, 3], [ 5, 6, 7], [ 9, 10, 11], [13, 14, 15]]], [[[ 4, 5, 6], [ 8, 9, 10], [12, 13, 14], [16, 17, 18]], [[ 5, 6, 7], [ 9, 10, 11], [13, 14, 15], [17, 18, 19]]]])
© 2011 the scikit-image team
Licensed under the BSD 3-clause License.
http://scikit-image.org/docs/0.12.x/api/skimage.util.html