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712b117 | 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | import numpy as np
def map_array(input_arr, input_vals, output_vals, out=None):
"""Map values from input array from input_vals to output_vals.
Parameters
----------
input_arr : array of int, shape (M[, ...])
The input label image.
input_vals : array of int, shape (K,)
The values to map from.
output_vals : array, shape (K,)
The values to map to.
out : array, same shape as `input_arr`
The output array. Will be created if not provided. It should
have the same dtype as `output_vals`.
Returns
-------
out : array, same shape as `input_arr`
The array of mapped values.
Notes
-----
If `input_arr` contains values that aren't covered by `input_vals`, they
are set to 0.
Examples
--------
>>> import numpy as np
>>> import skimage as ski
>>> ski.util.map_array(
... input_arr=np.array([[0, 2, 2, 0], [3, 4, 5, 0]]),
... input_vals=np.array([1, 2, 3, 4, 6]),
... output_vals=np.array([6, 7, 8, 9, 10]),
... )
array([[0, 7, 7, 0],
[8, 9, 0, 0]])
"""
from ._remap import _map_array
if not np.issubdtype(input_arr.dtype, np.integer):
raise TypeError('The dtype of an array to be remapped should be integer.')
# We ravel the input array for simplicity of iteration in Cython:
orig_shape = input_arr.shape
# NumPy docs for `np.ravel()` says:
# "When a view is desired in as many cases as possible,
# arr.reshape(-1) may be preferable."
input_arr = input_arr.reshape(-1)
if out is None:
out = np.empty(orig_shape, dtype=output_vals.dtype)
elif out.shape != orig_shape:
raise ValueError(
'If out array is provided, it should have the same shape as '
f'the input array. Input array has shape {orig_shape}, provided '
f'output array has shape {out.shape}.'
)
try:
out_view = out.view()
out_view.shape = (-1,) # no-copy reshape/ravel
except AttributeError: # if out strides are not compatible with 0-copy
raise ValueError(
'If out array is provided, it should be either contiguous '
f'or 1-dimensional. Got array with shape {out.shape} and '
f'strides {out.strides}.'
)
# ensure all arrays have matching types before sending to Cython
input_vals = input_vals.astype(input_arr.dtype, copy=False)
output_vals = output_vals.astype(out.dtype, copy=False)
_map_array(input_arr, out_view, input_vals, output_vals)
return out
class ArrayMap:
"""Class designed to mimic mapping by NumPy array indexing.
This class is designed to replicate the use of NumPy arrays for mapping
values with indexing:
>>> values = np.array([0.25, 0.5, 1.0])
>>> indices = np.array([[0, 0, 1], [2, 2, 1]])
>>> values[indices]
array([[0.25, 0.25, 0.5 ],
[1. , 1. , 0.5 ]])
The issue with this indexing is that you need a very large ``values``
array if the values in the ``indices`` array are large.
>>> values = np.array([0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.0])
>>> indices = np.array([[0, 0, 10], [0, 10, 10]])
>>> values[indices]
array([[0.25, 0.25, 1. ],
[0.25, 1. , 1. ]])
Using this class, the approach is similar, but there is no need to
create a large values array:
>>> in_indices = np.array([0, 10])
>>> out_values = np.array([0.25, 1.0])
>>> values = ArrayMap(in_indices, out_values)
>>> values
ArrayMap(array([ 0, 10]), array([0.25, 1. ]))
>>> print(values)
ArrayMap:
0 → 0.25
10 → 1.0
>>> indices = np.array([[0, 0, 10], [0, 10, 10]])
>>> values[indices]
array([[0.25, 0.25, 1. ],
[0.25, 1. , 1. ]])
Parameters
----------
in_values : array of int, shape (K,)
The source values from which to map.
out_values : array, shape (K,)
The destination values from which to map.
"""
def __init__(self, in_values, out_values):
self.in_values = in_values
self.out_values = out_values
self._max_str_lines = 4
self._array = None
def __len__(self):
"""Return one more than the maximum label value being remapped."""
return np.max(self.in_values) + 1
def __array__(self, dtype=None, copy=None):
"""Return an array that behaves like the arraymap when indexed.
This array can be very large: it is the size of the largest value
in the ``in_vals`` array, plus one.
"""
if dtype is None:
dtype = self.out_values.dtype
output = np.zeros(np.max(self.in_values) + 1, dtype=dtype)
output[self.in_values] = self.out_values
return output
@property
def dtype(self):
return self.out_values.dtype
def __repr__(self):
return f'ArrayMap({repr(self.in_values)}, {repr(self.out_values)})'
def __str__(self):
if len(self.in_values) <= self._max_str_lines + 1:
rows = range(len(self.in_values))
string = '\n'.join(
['ArrayMap:']
+ [f' {self.in_values[i]} → {self.out_values[i]}' for i in rows]
)
else:
rows0 = list(range(0, self._max_str_lines // 2))
rows1 = list(range(-self._max_str_lines // 2, 0))
string = '\n'.join(
['ArrayMap:']
+ [f' {self.in_values[i]} → {self.out_values[i]}' for i in rows0]
+ [' ...']
+ [f' {self.in_values[i]} → {self.out_values[i]}' for i in rows1]
)
return string
def __call__(self, arr):
return self.__getitem__(arr)
def __getitem__(self, index):
scalar = np.isscalar(index)
if scalar:
index = np.array([index])
elif isinstance(index, slice):
start = index.start or 0 # treat None or 0 the same way
stop = index.stop if index.stop is not None else len(self)
step = index.step
index = np.arange(start, stop, step)
if index.dtype == bool:
index = np.flatnonzero(index)
out = map_array(
index,
self.in_values.astype(index.dtype, copy=False),
self.out_values,
)
if scalar:
out = out[0]
return out
def __setitem__(self, indices, values):
if self._array is None:
self._array = self.__array__()
self._array[indices] = values
self.in_values = np.flatnonzero(self._array)
self.out_values = self._array[self.in_values]
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