| 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. |
Return intensity limits, i.e. (min, max) tuple, of the image’s dtype.
| Parameters: | image : ndarray
clip_negative : bool
|
|---|
Convert an image to boolean format.
| Parameters: | image : ndarray
force_copy : bool
|
|---|---|
| Returns: | out : ndarray of bool (bool_)
|
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.
Convert an image to double-precision floating point format.
| Parameters: | image : ndarray
force_copy : bool
|
|---|---|
| Returns: | out : ndarray of float64
|
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.
Convert an image to 16-bit signed integer format.
| Parameters: | image : ndarray
force_copy : bool
|
|---|---|
| Returns: | out : ndarray of uint16
|
Notes
If the input data-type is positive-only (e.g., uint8), then the output image will still only have positive values.
Convert an image to 8-bit unsigned integer format.
| Parameters: | image : ndarray
force_copy : bool
|
|---|---|
| Returns: | out : ndarray of ubyte (uint8)
|
Notes
If the input data-type is positive-only (e.g., uint16), then the output image will still only have positive values.
Convert an image to 16-bit unsigned integer format.
| Parameters: | image : ndarray
force_copy : bool
|
|---|---|
| Returns: | out : ndarray of uint16
|
Notes
Negative input values will be shifted to the positive domain.
Pads an array.
| Parameters: | array : array_like of rank N
pad_width : {sequence, int}
mode : {str, function}
stat_length : {sequence, int}, optional
constant_values : {sequence, int}, optional
end_values : {sequence, int}, optional
reflect_type : str {‘even’, ‘odd’}, optional
|
|---|---|
| Returns: | pad : ndarray
|
Notes
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
>>> 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]])
Function to add random noise of various types to a floating-point image.
| Parameters: | image : ndarray
mode : str
seed : int
clip : bool
mean : float
var : float
local_vars : ndarray
amount : float
salt_vs_pepper : float
|
|---|---|
| Returns: | out : ndarray
|
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.
Find n_points regularly spaced along ar_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
n_points : int
|
|---|---|
| Returns: | slices : list of slice objects
|
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
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
|
|---|---|
| Returns: | ar_out : 2-D ndarray
|
| 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)
Block view of the input n-dimensional array (using re-striding).
Blocks are non-overlapping views of the input array.
| Parameters: | arr_in : ndarray
block_shape : tuple
|
|---|---|
| Returns: | arr_out : ndarray
|
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]]]])
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
window_shape : tuple
step : int, optional
|
|---|---|
| Returns: | arr_out : ndarray
|
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
| [R328] | (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]]]])