def multithreshold(img,ignore_zeros=True,firstThreshold=20,nrThresholds=5):
'''
labeled,N = multithreshold(img, ignore_zeros = True)
Performs multi thresholding (which is a form of oversegmentation).
labeled is of the same size and type as img and contains different labels for
the N detected objects (the return of this function is similar to that of scipy.ndimage.label())
@param img: The input image
@param ignore_zeros: Don't take zero pixels into account
'''
output=numpy.zeros_like(img)
if ignore_zeros:
pmin=nonzeromin(img)
else:
pmin=img.min()
thresholds=pmin+firstThreshold+(img.max()-pmin-firstThreshold)//nrThresholds*numpy.arange(nrThresholds)
Ts=[majority_filter(img>T) for T in thresholds]
obj_count = 0
Ts.append(Ts[0]*0)
labeled0,N0=ndimage.label(Ts[0])
for T1 in Ts[1:]:
labeled1,N1=ndimage.label(T1)
for obj in xrange(N0):
binimg=(labeled0 == (obj+1))
objects1=(labeled1*binimg)
if not objects1.any() or ndimage.label(objects1)[1] == 1:
obj_count += 1
output[binimg]=obj_count
labeled0=labeled1
N0=N1
return output,obj_count
def threshold_segment(dna, threshold_method="otsu", smooth=None, median_size=5, min_obj_size=2500):
"""
labeled = threshold_method(dna, threshold_method='otsu',median_size=5,min_obj_size=2500)
Simple threshold-based segmentation
Params
------
* dna: either a pyslic.Image or a DNA image
* threshold_method: thresholding method to use. Can be either a function or
string which denotes the name of a function in pyslic.imageprocessing.thresholding (default: otsu)
* smooth: either None (no smoothing) or a sigma value for a gaussian blur (default: None)
* median_size: median filter size (default: 5). Set to None to skip filtering.
* min_obj_size: minimum object size (default: 2500)
"""
if type(dna) == Image:
with loadedimage(dna):
return threshold_segment(dna.get("dna"), threshold_method, smooth, median_size, min_obj_size)
if smooth is not None:
dna = ndimage.gaussian_filter(dna, smooth)
T = threshold(dna, threshold_method)
binimg = dna > T
if median_size is not None:
binimg = majority_filter(binimg, median_size)
L, N = ndimage.label(binimg)
if N == 0:
return L
sizes = numpy.array(ndimage.sum(binimg, L, numpy.arange(N + 1)))
for oid in numpy.where(sizes < min_obj_size)[0]:
L[L == oid] = 0
L, N = ndimage.label(L != 0)
return L
def preprocessimg(img):
if len(img.shape) > 2:
assert len(img.shape) == 3, "Cannot handle images of more than 3 dimensions."
if options.get('3d.mode','perslice') == 'perslice':
nr_slices=img.shape[0]
out_proc=img.copy()
out_res=img.copy()
for z in xrange(nr_slices):
proc,res=preprocessimg(img[z])
out_proc[z]=proc
out_res[z]=res
return out_proc,out_res
else:
raise Exception('pyslic.preprocessimg: Do not know how to handle 3d.mode: %s' % options['3d.mode'])
if do_bgsub:
regions = image.regions
img = img.copy()
if regions is not None:
rid = (regionid if regionid is not None else 1)
if options.get('bgsub.way','ml') == 'ml':
img *= (regions == rid)
img = bgsub(img, options)
else:
img = bgsub(img, options)
img *= (cropimg == rid)
else:
if regionid:
warn('Selecting a region different from 1 for an image without region information')
img = bgsub(img, options)
imgscaled = stretch(img, 255)
T = thresholdfor(imgscaled,options)
mask = (imgscaled > T)
mask = majority_filter(mask)
residual = img.copy()
img *= mask
residual *= ~mask
return img,residual
def preprocessimg(img):
if len(img.shape) > 2:
assert len(img.shape) == 3, "Cannot handle images of more than 3 dimensions."
if options.get("3d.mode", "perslice") == "perslice":
nr_slices = img.shape[0]
out_proc = img.copy()
out_res = img.copy()
for z in xrange(nr_slices):
proc, res = preprocessimg(img[z])
out_proc[z] = proc
out_res[z] = res
return out_proc, out_res
else:
raise Exception("pyslic.preprocessimg: Do not know how to handle 3d.mode: %s" % options["3d.mode"])
if do_bgsub:
regions = image.regions
img = img.copy()
if regions is not None:
if options.get("bgsub.way", "ml") == "ml":
img *= regions == regionid
img = bgsub(img, options)
else:
img = bgsub(img, options)
img *= cropimg == regionid
else:
if regionid:
warn("Selecting a region different from 1 for an image without region information")
img = bgsub(img, options)
imgscaled = stretch(img, 255)
T = thresholdfor(imgscaled, options)
mask = imgscaled > T
mask = majority_filter(mask)
residual = img.copy()
img *= mask
residual *= ~mask
return img, residual
