[Up] [mmlgranul] Additional Toolbox functions

mmbggmodel
Bernoulli germ-grain model realization.

Synopsis

y = mmbggmodel( dim, B = None, R = 3, p = 0.01 )

Implemented in Python.

Input

dim	mmIMAGE.
B	mmSE. Default: `None` (3x3 elementary cross)
R	mmDOUBLE. Default: `3`
p	mmDOUBLE. Default: `0.01`

Output

mmIMAGE.

Description

mmbggmodel generates a realization of a Bernoulli germ-grain model. The germs are distributed according to a Bernoulli process with probability p. A structuring element rB, with radius r uniformly distributed between 1 and R is assigned at each point of the Bernoulli process.

Examples

from handson import *

mmshow(mmbggmodel((100, 256)))

mmshow(mmbggmodel((100, 256),mmsebox(),5,0.05))

B=mmsedisk(1,'1D')

mmshow(mmbggmodel((100,256),B,5,0.03))

mmshow(mmbggmodel((100,256),mmserot(B,45),5,0.005))


mmbggmodel((100, 256))	mmbggmodel((100, 256),mmsebox(),5,0.05)


mmbggmodel((100,256),B,5,0.03)	mmbggmodel((100,256),mmserot(B,45),5,0.005)

Source Code
        def mmbggmodel(dim, B=None, R=3, p=0.01):
    from MLab import rand
    from morph import mmsecross, mmintersec, uint8, mmbinary, mmgray, mmskelmrec
    if B is None: B = mmsecross()
    dy,dx=dim
    a=mmbinary(rand(dy,dx)<p)
    b=uint8((rand(dy,dx)*(R-1))+1.5)
    a=mmintersec(mmgray(a),b)
    y=mmskelmrec(a,B)
    return y
    
      

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