#from pythonvision.org

dist = nd.distance_transform_edt(bIm)

dist = dist.max() - dist

dist -= dist.min()

dist = dist/float(dist.ptp()) * 255

dist = dist.astype(np.uint8)

'''remove labelled objects touching the image border'''

#from pythonvision.org

whole = mahotas.segmentation.gvoronoi(imlabel)

borders = np.zeros(imlabel.shape, np.bool)

borders[ 0,:] = 1

borders[-1,:] = 1

borders[:, 0] = 1

borders[:,-1] = 1

at_border = np.unique(imlabel[borders])

for obj in at_border:

whole[whole == obj] = 0

blur=nd.gaussian_filter(im, 16)

rmax = pymorph.regmax(blur)

T = mahotas.thresholding.otsu(blur)

bImg0=im>T

#bImg01=nd.binary_closing(bImg0,iterations=2)

bImg01=pymorph.close(bImg0, pymorph.sedisk(3))

bImg=pymorph.open(bImg01, pymorph.sedisk(4))

#bImg=nd.binary_opening(bImg01,iterations=3)

b=pymorph.edgeoff(bImg)

d=distanceTranform(b)

seeds,nr_nuclei = nd.label(rmax)

lab=mahotas.cwatershed(d,seeds)

'''look for the modal value of an image'''

#print image.dtype

if image.dtype=="uint8":

depthmax=255

print "8bits"

if image.dtype=="uint16":

depthmax=65535

print "16bits"

histo=mahotas.fullhistogram(image)

countmax=histo.max()

print "countmax:",countmax

print "image max",image.max()

mig=image.min()#image min graylevel

mag=image.max()#image max gray level

mode=0

countmax=0#occurence of a given grayscale

print "mig=",mig," mag=",mag

for i in range(mig,mag-1,1):

test=histo[i]>countmax

#print "test:",test,"histo(",i,")=", histo[i],"max",countmax

if test:

countmax=histo[i]

mode=i

#print "mode",mode

mode=ModalValue(image)

back = np.zeros(image.shape, image.dtype)

back.fill(mode)

#print "def background:",back.mean()

im=pymorph.subm(image,back)

'''Perform a simple threshold after DoG filtering'''

noBack=RemoveModalBackground(image)

#print "from Segmeta noBack:",noBack.min(),noBack.mean()

blurLowRes=nd.filters.gaussian_filter(noBack,13)

blurHiRes=nd.filters.gaussian_filter(noBack,1)

midPass=pymorph.subm(blurHiRes,0.70*blurLowRes)

bin=(midPass>1.5*midPass.mean())

binLowRes=pymorph.open(bin,pymorph.sedisk(4))

''' give a grayscaled and a labelled image, extract the segmented particles

,returns a list of flattened particles'''

#grayIm and labIm should have the same size

def unflattenParticles(flatParticleList):

'''take a list of flat particles and unflat them to yield an image'''

unflatList=[]

lenFlatList=len(flatParticleList)

for i in range(0,lenFlatList):

#get the i particle:current Particle

curPart=flatParticleList[i]#current particle

#x values(col) are stored in the third col (3-1)

colmax=curPart[:,2].max()

colmin=curPart[:,2].min()

#y values(li) are stored in the fourth col (4-1)

limax=curPart[:,3].max()

limin=curPart[:,3].min()

unflatIm=np.zeros((limax-limin+1,colmax-colmin+1),np.int16)

#number of pixels in the particle

nbPixel=len(curPart[:,1])#count how many lines at col=1

for line in range(0,nbPixel):

col=curPart[line,2]

li=curPart[line,3]

pixVal=curPart[line,1]

unflatIm[li-limin,col-colmin]=pixVal

unflatList.append(unflatIm)

return unflatList

sx=grayIm.shape[0]

sy=grayIm.shape[1]

#flatten grayIm

fg=grayIm.flatten()

fl=labIm.flatten()

labmax=fl.max()

#print fg

#print fl

#build two 2D array containing x and y

#of each pixel of the grayIm

ax=np.zeros((sx,sy),np.int16)

ay=np.zeros((sx,sy),np.int16)

#vectorization with numpy may be

#more efficient than two loops

for j in range(0,sy):

for i in range(0,sx):

ax[i,j]=j#filling ax with x=col

ay[i,j]=i#filling ay with y values y=li

#flat arrays of coordinates

fax=ax.flatten()

fay=ay.flatten()

#1D merge graylevel, label and coordinates

#in one 1D array of 4-uplet

extract=np.vstack((fl,fg,fax,fay))

#transpose to watch it easily

eT=extract.T

#create a list of flatten particles

#labIndex takes the value from 1 (the first particle to labmax the\

#label of the last particle

flatParticleList=[]#from Matthieu Brucher

for labIndex in range(1,labmax+1):

flatParticleList.append(eT[eT[:,0]==labIndex])#from Matthieu Brucher