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. ]])
The source values from which to map.
The destination values from which to map.
Class designed to mimic mapping by NumPy array indexing.
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.segmentation._join.relabel_sequential
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