I have an array that I've labeled using scipy.ndimage and I'd like to multiply each element by a factor specific to its corresponding label. I thought I could use ndimage.labeled_comprehension for this, however I can't seem to figure out how to pass an argument to the function. For example:
a = np.random.random(9).reshape(3,3)
lbls = np.repeat(np.arange(3),3).reshape(3,3)
ndx = np.arange(0,lbls.max()+1)
factors = np.random.randint(10,size=3)
>>> lbls
array([[0, 0, 0],
[1, 1, 1],
[2, 2, 2]])
>>> ndx
array([0, 1, 2])
>>> factors
array([5, 4, 8])
def fn(a, x):
return a*x
>>> b = ndimage.labeled_comprehension(a, labels=lbls, index=ndx, func=fn, out_dtype=float, default=0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/tgrant/anaconda/envs/python2/lib/python2.7/site-packages/scipy/ndimage/measurements.py", line 416, in labeled_comprehension
do_map([input], temp)
File "/Users/tgrant/anaconda/envs/python2/lib/python2.7/site-packages/scipy/ndimage/measurements.py", line 411, in do_map
output[i] = func(*[inp[l:h] for inp in inputs])
TypeError: fn() takes exactly 2 arguments (1 given)
As expected it gives an error since fn() needs factors fed into it somehow. Is labeled_comprehension able to do this?
Index into factors and then simply multiply with the image array -
a*factors[lbls]
Sample run -
In [483]: a # image/data array
Out[483]:
array([[ 0.10682998, 0.29631501, 0.08501469],
[ 0.46944505, 0.88346229, 0.75672908],
[ 0.11381292, 0.24096868, 0.86438641]])
In [484]: factors # scaling factors
Out[484]: array([8, 1, 1])
In [485]: lbls # labels
Out[485]:
array([[0, 0, 0],
[1, 1, 1],
[2, 2, 2]])
In [486]: factors[lbls] # factors populated based on the labels
Out[486]:
array([[8, 8, 8],
[1, 1, 1],
[1, 1, 1]])
In [487]: a*factors[lbls] # finally scale the image array
Out[487]:
array([[ 0.85463981, 2.37052006, 0.68011752],
[ 0.46944505, 0.88346229, 0.75672908],
[ 0.11381292, 0.24096868, 0.86438641]])