Hello all,

Based on the discussion here: https://groups.google.com/forum/#!topic/pythonvision/AVrnueiKKYI

It seems like this label function should be faster than the scipy version. However, I find it to be much slower:

from skimage import morphology

from scipy import ndarray as nd

k = np.zeros((4000,4000),dtype=int)

k[100,100] = 1

strel = np.ones((3,3))

%timeit morphology.label(k)

1 loops, best of 3: 1.46 s per loop

%timeit nd.label(k,strel)

1 loops, best of 3: 355 ms per loop

If you have any insight, I would be very grateful. I am using version 0.8.2 on the versions of python/numpy/cython, etc. included in the most recent edition of Enthought/Canopy.

Thanks,

Evan Daugharthy

It seems like this label function should be faster than the scipy version. However, I find it to be much slower:

from skimage import morphology

from scipy import ndarray as nd

k = np.zeros((4000,4000),dtype=int)

k[100,100] = 1

strel = np.ones((3,3))

%timeit morphology.label(k)

1 loops, best of 3: 1.46 s per loop

%timeit nd.label(k,strel)

1 loops, best of 3: 355 ms per loop

If you have any insight, I would be very grateful. I am using version 0.8.2 on the versions of python/numpy/cython, etc. included in the most recent edition of Enthought/Canopy.

Thanks,

Evan Daugharthy