def analyseGradient(imagedict):
R = analysis_functions.getGradient(imagedict[RGB][:, :, RED])
G = analysis_functions.getGradient(imagedict[RGB][:, :, GREEN])
B = analysis_functions.getGradient(imagedict[RGB][:, :, BLUE])
GRAY = analysis_functions.getGradient(imagedict[grayscale])
colorlist = []
colorlist.append(mean(R))
colorlist.append(mean(G))
colorlist.append(mean(B))
colorlist.append(mean(GRAY))
colorlist.append(std(R))
colorlist.append(std(G))
colorlist.append(std(B))
colorlist.append(std(GRAY))
return colorlist
def runWalk(imgline,size,ML):
'''
Input:
The line containing the image
The window size
the object of type ml
Output:
A series of arrays of the different subtypes of the image
'''
fillThreshold=0.1
line = imgline.strip().split('\t')
img = line[0]
img = Image.open(img)
imgs = imageTransforms.normalizeImage(img)
x,y,step=getWalkerParameters(imgs[grayscale],size,factor=2)
arraydict = []
for key in ML.intdict.keys():
arraydict.append(np.zeros_like(imgs[grayscale],dtype=int))
temp=ML.intdict[key]
#make binary image mask by thresholding for each object label (neuron, astrocyte, ...)
tempColorImg=imgs['RGB']
imageMasks=ML.maskGen.getAllMasks(tempColorImg, 'LDA')
saveImages(imageMasks, ML.intdict)
'''
for i in x:
for j in y:
print
print i,i+size,j,j+size,
subMask=imageMask[j:j+size, i:i+size]
subMask=float(subMask.sum())
percentFilled=subMask/(size*size)
#if subregion has enough 1's in it, the go ahead, else do nothing
if(percentFilled>=fillThreshold):
try:
f=np.array(calculatefeatures(imgs,left=i,right=i+size,top=j+size,bottom=j),dtype=float)
labels = ML.getLabels(f)
for k in xrange(len(labels)):
print ML.intdict[labels[k]],
arraydict[labels[k]]=iterRegions(i,j,size,arraydict[labels[k]])
except ValueError:
for i in xrange(len(vector)):
print vector[i],
else:
print "skipping"
saveImages(nimages,ML.intdict,threshold=t)
print 'Finding Local Maxima'
markers = getSeeds(imageMask)
print 'Running Watershed'
watersheded = runWatershed(markers,imageMask)
print watersheded.shape
imsave('watersheded_image.tif', watersheded)
'''
savearray(imgs['grayscale'],'grayscale.tif')
gradient = analysis_functions.getGradient(imgs['grayscale'])
savearray(gradient,'gradient.tif')
print
#running watershed on GRADIENT images for all objects/ masks
segments = {}
for key in imageMasks.keys():
x = imageMasks[key]
gx = gradient*x
s, sb=getSeeds(x)
print 'Running Watershed on Gradient'
rwGradient = runWatershed(s,gx)
#savearray(sb, ML.intdict[key]+"centersBinary-Grad.tiff")
#savearray(s, ML.intdict[key]+"centers-Grad.tiff")
savearray(rwGradient,ML.intdict[key]+'_watershed_on_gradient.tif')
segments[key]=rwGradient
#running watershed on GRAYSCALE images for all objects/ masks
for key in imageMasks.keys():
x = imageMasks[key]
gx = imgs['grayscale']*x
s, sb=getSeeds(x)
print 'Running Watershed on Grayscale'
rwGray = runWatershed(s,gx)
#savearray(sb, ML.intdict[key]+"centersBinary-GrayScale.tiff")
#savearray(s, ML.intdict[key]+"centers-GrayScale.tiff")
savearray(rwGray,ML.intdict[key]+'_watershed_on_grayscale.tif')
segments[key]=rwGray
'''
