Module: util¶
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.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, ...) | Rolling window view of the input n-dimensional array. |
img_as_float¶
- skimage.util.img_as_float(image, force_copy=False)¶
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, 1]. Negative input values will be shifted to the positive domain.
img_as_int¶
- skimage.util.img_as_int(image, force_copy=False)¶
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
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)¶
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
If the input data-type is positive-only (e.g., uint16), then the output image will still only have positive values.
img_as_uint¶
- skimage.util.img_as_uint(image, force_copy=False)¶
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 shifted to the positive domain.
view_as_blocks¶
- skimage.util.view_as_blocks(arr_in, block_shape)¶
Block view of the input n-dimensional array (using re-striding).
Blocks are non-overlapping views of the input array.
Parameters : arr_in: ndarray :
The n-dimensional 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.
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)¶
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 :
The n-dimensional input array.
window_shape: tuple :
Defines the shape of the elementary n-dimensional orthotope (better know as hyperrectangle [R56]) of the rolling window view.
Returns : arr_out: ndarray :
(rolling) window view of the input array.
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
[R56] (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]]]])