def test_addm():
f = to_uint8([255, 255, 0, 10, 0, 255, 250])
g = to_uint8([0, 40, 80, 140, 250, 10, 30])
y = addm(f, g)
for fi, gi, yi in zip(f, g, y):
assert yi >= fi
assert yi >= gi
def test_addm():
f = to_uint8([255, 255, 0, 10, 0, 255, 250])
g = to_uint8([ 0, 40, 80, 140, 250, 10, 30])
y = addm(f,g)
for fi,gi,yi in zip(f,g,y):
assert yi >= fi
assert yi >= gi
def roysam_watershed(dna,thresh=None,blur_factor=3):
'''
Run watershed on mixed gradient & intensity image as suggested by Lin et al.
-Input
dna: DNA image
thresh: Gray value threshold (default: computed using Murphy's RC)
blur_factor: Blur factor (default: 3)
REFERENCE
Gang Lin, Umesh Adiga, Kathy Olson, John F. Guzowski, Carol A. Barnes, and Badrinath Roysam
"A Hybrid 3-D Watershed Algorithm Incorporating Gradient Cues & Object Models for Automatic
Segmentation of Nuclei in Confocal Image Stacks"
Vol. 56A, No. 1, pp. 23-36 Cytometry Part A, November 2003.
'''
if thresh is None:
thresh = 'murphy_rc'
M = (ndimage.gaussian_filter(dna,4) > thresholding.threshold(dna,thresh))
G = pymorph.gradm(dna)
D = ndimage.distance_transform_edt(M)
D *= np.exp(1-G/float(G.max()))
T = ndimage.gaussian_filter(D.max() - D,blur_factor)
if T.max() < 256:
T = pymorph.to_uint8(T)
else:
T = pymorph.to_uint8(T*(256.0/T.max()))
T *= M
R = pymorph.regmin(T)
R *= M
R,N = ndimage.label(R)
R[(R==0)&(M==0)] = N+1
W,WL = mahotas.cwatershed(T,R,return_lines=True)
W *= M
return W,WL
def gray16_to8(im):
i = 0.00390625 * im
return pymorph.to_uint8(i)
def gray12_to8(im):
i = 0.062271062 * im
return pymorph.to_uint8(i)
def gray16_to8(im):
i=0.00390625*im
return pymorph.to_uint8(i)
def gray12_to8(im):
i=0.062271062*im
return pymorph.to_uint8(i)
