scipy tutorial - Baustatik-Info-Server

... [0, 0, 0, 2, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 1]], numarray.Int8)
>>> print watershed_ift(input, markers)
[[1 1 1 1 1 1 1]
[1 1 1 1 1 1 1]
[1 1 2 2 2 1 1]
[1 1 2 2 2 1 1]
[1 1 2 2 2 1 1]
[1 1 1 1 1 1 1]
[1 1 1 1 1 1 1]]
SciPy Reference Guide, Release 0.8.dev
The result is that the object (marker = 2) is smaller because the second marker was processed earlier. This
may not be the desired effect if the first marker was supposed to designate a background object. Therefore
watershed_ift treats markers with a negative value explicitly as background markers and processes
them after the normal markers. For instance, replacing the first marker by a negative marker gives a result
similar to the first example:
>>> markers = array([[0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 2, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, -1]], numarray.Int8)
>>> print watershed_ift(input, markers)
[[-1 -1 -1 -1 -1 -1 -1]
[-1 -1 2 2 2 -1 -1]
[-1 2 2 2 2 2 -1]
[-1 2 2 2 2 2 -1]
[-1 2 2 2 2 2 -1]
[-1 -1 2 2 2 -1 -1]
[-1 -1 -1 -1 -1 -1 -1]]
The connectivity of the objects is defined by a structuring element. If no structuring element is provided,
one is generated by calling generate_binary_structure (see Binary morphology) using a connectivity
of one (which in 2D is a 4-connected structure.) For example, using an 8-connected structure
with the last example yields a different object:
>>> print watershed_ift(input, markers,
... structure = [[1,1,1], [1,1,1], [1,1,1]])
[[-1 -1 -1 -1 -1 -1 -1]
[-1 2 2 2 2 2 -1]
[-1 2 2 2 2 2 -1]
[-1 2 2 2 2 2 -1]
[-1 2 2 2 2 2 -1]
[-1 2 2 2 2 2 -1]
[-1 -1 -1 -1 -1 -1 -1]]
Note: The implementation of watershed_ift limits the data types of the input to UInt8 and
UInt16.
1.10. Multi-dimensional image processing (ndimage) 75