comparison UNetCoreograph.py @ 0:99308601eaa6 draft

"planemo upload for repository https://github.com/ohsu-comp-bio/UNetCoreograph commit fb90660a1805b3f68fcff80d525b5459c3f7dfd6-dirty"

0:99308601eaa6

import numpy as np
from scipy import misc as sm
import shutil
import scipy.io as sio
import os
import skimage.exposure as sk
import cv2
import argparse
import pytiff
import tifffile
import tensorflow as tf
from skimage.morphology import *
from skimage.exposure import rescale_intensity
from skimage.segmentation import chan_vese, find_boundaries, morphological_chan_vese
from skimage.measure import regionprops,label, find_contours
from skimage.transform import resize
from skimage.filters import gaussian
from skimage.feature import peak_local_max,blob_log
from skimage.color import label2rgb
import skimage.io as skio
from skimage import img_as_bool
from skimage.draw import circle_perimeter
from scipy.ndimage.filters import uniform_filter
from scipy.ndimage import gaussian_laplace
from os.path import *
from os import listdir, makedirs, remove



import sys
from typing import Any

#sys.path.insert(0, 'C:\\Users\\Public\\Documents\\ImageScience')
from toolbox.imtools import *
from toolbox.ftools import *
from toolbox.PartitionOfImage import PI2D


def concat3(lst):
		return tf.concat(lst,3)

class UNet2D:
	hp = None # hyper-parameters
	nn = None # network
	tfTraining = None # if training or not (to handle batch norm)
	tfData = None # data placeholder
	Session = None
	DatasetMean = 0
	DatasetStDev = 0

	def setupWithHP(hp):
		UNet2D.setup(hp['imSize'],
					 hp['nChannels'],
					 hp['nClasses'],
					 hp['nOut0'],
					 hp['featMapsFact'],
					 hp['downSampFact'],
					 hp['ks'],
					 hp['nExtraConvs'],
					 hp['stdDev0'],
					 hp['nLayers'],
					 hp['batchSize'])

	def setup(imSize,nChannels,nClasses,nOut0,featMapsFact,downSampFact,kernelSize,nExtraConvs,stdDev0,nDownSampLayers,batchSize):
		UNet2D.hp = {'imSize':imSize,
					 'nClasses':nClasses,
					 'nChannels':nChannels,
					 'nExtraConvs':nExtraConvs,
					 'nLayers':nDownSampLayers,
					 'featMapsFact':featMapsFact,
					 'downSampFact':downSampFact,
					 'ks':kernelSize,
					 'nOut0':nOut0,
					 'stdDev0':stdDev0,
					 'batchSize':batchSize}

		nOutX = [UNet2D.hp['nChannels'],UNet2D.hp['nOut0']]
		dsfX = []
		for i in range(UNet2D.hp['nLayers']):
			nOutX.append(nOutX[-1]*UNet2D.hp['featMapsFact'])
			dsfX.append(UNet2D.hp['downSampFact'])


		# --------------------------------------------------
		# downsampling layer
		# --------------------------------------------------

		with tf.name_scope('placeholders'):
			UNet2D.tfTraining = tf.placeholder(tf.bool, name='training')
			UNet2D.tfData = tf.placeholder("float", shape=[None,UNet2D.hp['imSize'],UNet2D.hp['imSize'],UNet2D.hp['nChannels']],name='data')

		def down_samp_layer(data,index):
			with tf.name_scope('ld%d' % index):
				ldXWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index], nOutX[index+1]], stddev=stdDev0),name='kernel1')
				ldXWeightsExtra = []
				for i in range(nExtraConvs):
					ldXWeightsExtra.append(tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name='kernelExtra%d' % i))
				
				c00 = tf.nn.conv2d(data, ldXWeights1, strides=[1, 1, 1, 1], padding='SAME')
				for i in range(nExtraConvs):
					c00 = tf.nn.conv2d(tf.nn.relu(c00), ldXWeightsExtra[i], strides=[1, 1, 1, 1], padding='SAME')

				ldXWeightsShortcut = tf.Variable(tf.truncated_normal([1, 1, nOutX[index], nOutX[index+1]], stddev=stdDev0),name='shortcutWeights')
				shortcut = tf.nn.conv2d(data, ldXWeightsShortcut, strides=[1, 1, 1, 1], padding='SAME')

				bn = tf.layers.batch_normalization(tf.nn.relu(c00+shortcut), training=UNet2D.tfTraining)

				return tf.nn.max_pool(bn, ksize=[1, dsfX[index], dsfX[index], 1], strides=[1, dsfX[index], dsfX[index], 1], padding='SAME',name='maxpool')

		# --------------------------------------------------
		# bottom layer
		# --------------------------------------------------

		with tf.name_scope('lb'):
			lbWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[UNet2D.hp['nLayers']], nOutX[UNet2D.hp['nLayers']+1]], stddev=stdDev0),name='kernel1')
			def lb(hidden):
				return tf.nn.relu(tf.nn.conv2d(hidden, lbWeights1, strides=[1, 1, 1, 1], padding='SAME'),name='conv')

		# --------------------------------------------------
		# downsampling
		# --------------------------------------------------

		with tf.name_scope('downsampling'):    
			dsX = []
			dsX.append(UNet2D.tfData)

			for i in range(UNet2D.hp['nLayers']):
				dsX.append(down_samp_layer(dsX[i],i))

			b = lb(dsX[UNet2D.hp['nLayers']])

		# --------------------------------------------------
		# upsampling layer
		# --------------------------------------------------

		def up_samp_layer(data,index):
			with tf.name_scope('lu%d' % index):
				luXWeights1    = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index+1], nOutX[index+2]], stddev=stdDev0),name='kernel1')
				luXWeights2    = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index]+nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name='kernel2')
				luXWeightsExtra = []
				for i in range(nExtraConvs):
					luXWeightsExtra.append(tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name='kernel2Extra%d' % i))
				
				outSize = UNet2D.hp['imSize']
				for i in range(index):
					outSize /= dsfX[i]
				outSize = int(outSize)

				outputShape = [UNet2D.hp['batchSize'],outSize,outSize,nOutX[index+1]]
				us = tf.nn.relu(tf.nn.conv2d_transpose(data, luXWeights1, outputShape, strides=[1, dsfX[index], dsfX[index], 1], padding='SAME'),name='conv1')
				cc = concat3([dsX[index],us]) 
				cv = tf.nn.relu(tf.nn.conv2d(cc, luXWeights2, strides=[1, 1, 1, 1], padding='SAME'),name='conv2')
				for i in range(nExtraConvs):
					cv = tf.nn.relu(tf.nn.conv2d(cv, luXWeightsExtra[i], strides=[1, 1, 1, 1], padding='SAME'),name='conv2Extra%d' % i)
				return cv

		# --------------------------------------------------
		# final (top) layer
		# --------------------------------------------------

		with tf.name_scope('lt'):
			ltWeights1    = tf.Variable(tf.truncated_normal([1, 1, nOutX[1], nClasses], stddev=stdDev0),name='kernel')
			def lt(hidden):
				return tf.nn.conv2d(hidden, ltWeights1, strides=[1, 1, 1, 1], padding='SAME',name='conv')


		# --------------------------------------------------
		# upsampling
		# --------------------------------------------------

		with tf.name_scope('upsampling'):
			usX = []
			usX.append(b)

			for i in range(UNet2D.hp['nLayers']):
				usX.append(up_samp_layer(usX[i],UNet2D.hp['nLayers']-1-i))

			t = lt(usX[UNet2D.hp['nLayers']])


		sm = tf.nn.softmax(t,-1)
		UNet2D.nn = sm


	def train(imPath,logPath,modelPath,pmPath,nTrain,nValid,nTest,restoreVariables,nSteps,gpuIndex,testPMIndex):
		os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex

		outLogPath = logPath
		trainWriterPath = pathjoin(logPath,'Train')
		validWriterPath = pathjoin(logPath,'Valid')
		outModelPath = pathjoin(modelPath,'model.ckpt')
		outPMPath = pmPath
		
		batchSize = UNet2D.hp['batchSize']
		imSize = UNet2D.hp['imSize']
		nChannels = UNet2D.hp['nChannels']
		nClasses = UNet2D.hp['nClasses']

		# --------------------------------------------------
		# data
		# --------------------------------------------------

		Train = np.zeros((nTrain,imSize,imSize,nChannels))
		Valid = np.zeros((nValid,imSize,imSize,nChannels))
		Test = np.zeros((nTest,imSize,imSize,nChannels))
		LTrain = np.zeros((nTrain,imSize,imSize,nClasses))
		LValid = np.zeros((nValid,imSize,imSize,nClasses))
		LTest = np.zeros((nTest,imSize,imSize,nClasses))

		print('loading data, computing mean / st dev')
		if not os.path.exists(modelPath):
			os.makedirs(modelPath)
		if restoreVariables:
			datasetMean = loadData(pathjoin(modelPath,'datasetMean.data'))
			datasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data'))
		else:
			datasetMean = 0
			datasetStDev = 0
			for iSample in range(nTrain+nValid+nTest):
				I = im2double(tifread('%s/I%05d_Img.tif' % (imPath,iSample)))
				datasetMean += np.mean(I)
				datasetStDev += np.std(I)
			datasetMean /= (nTrain+nValid+nTest)
			datasetStDev /= (nTrain+nValid+nTest)
			saveData(datasetMean, pathjoin(modelPath,'datasetMean.data'))
			saveData(datasetStDev, pathjoin(modelPath,'datasetStDev.data'))

		perm = np.arange(nTrain+nValid+nTest)
		np.random.shuffle(perm)

		for iSample in range(0, nTrain):
			path = '%s/I%05d_Img.tif' % (imPath,perm[iSample])
			im = im2double(tifread(path))
			Train[iSample,:,:,0] = (im-datasetMean)/datasetStDev
			path = '%s/I%05d_Ant.tif' % (imPath,perm[iSample])
			im = tifread(path)
			for i in range(nClasses):
				LTrain[iSample,:,:,i] = (im == i+1)

		for iSample in range(0, nValid):
			path = '%s/I%05d_Img.tif' % (imPath,perm[nTrain+iSample])
			im = im2double(tifread(path))
			Valid[iSample,:,:,0] = (im-datasetMean)/datasetStDev
			path = '%s/I%05d_Ant.tif' % (imPath,perm[nTrain+iSample])
			im = tifread(path)
			for i in range(nClasses):
				LValid[iSample,:,:,i] = (im == i+1)

		for iSample in range(0, nTest):
			path = '%s/I%05d_Img.tif' % (imPath,perm[nTrain+nValid+iSample])
			im = im2double(tifread(path))
			Test[iSample,:,:,0] = (im-datasetMean)/datasetStDev
			path = '%s/I%05d_Ant.tif' % (imPath,perm[nTrain+nValid+iSample])
			im = tifread(path)
			for i in range(nClasses):
				LTest[iSample,:,:,i] = (im == i+1)

		# --------------------------------------------------
		# optimization
		# --------------------------------------------------

		tfLabels = tf.placeholder("float", shape=[None,imSize,imSize,nClasses],name='labels')

		globalStep = tf.Variable(0,trainable=False)
		learningRate0 = 0.01
		decaySteps = 1000
		decayRate = 0.95
		learningRate = tf.train.exponential_decay(learningRate0,globalStep,decaySteps,decayRate,staircase=True)

		with tf.name_scope('optim'):
			loss = tf.reduce_mean(-tf.reduce_sum(tf.multiply(tfLabels,tf.log(UNet2D.nn)),3))
			updateOps = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
			# optimizer = tf.train.MomentumOptimizer(1e-3,0.9)
			optimizer = tf.train.MomentumOptimizer(learningRate,0.9)
			# optimizer = tf.train.GradientDescentOptimizer(learningRate)
			with tf.control_dependencies(updateOps):
				optOp = optimizer.minimize(loss,global_step=globalStep)

		with tf.name_scope('eval'):
			error = []
			for iClass in range(nClasses):
				labels0 = tf.reshape(tf.to_int32(tf.slice(tfLabels,[0,0,0,iClass],[-1,-1,-1,1])),[batchSize,imSize,imSize])
				predict0 = tf.reshape(tf.to_int32(tf.equal(tf.argmax(UNet2D.nn,3),iClass)),[batchSize,imSize,imSize])
				correct = tf.multiply(labels0,predict0)
				nCorrect0 = tf.reduce_sum(correct)
				nLabels0 = tf.reduce_sum(labels0)
				error.append(1-tf.to_float(nCorrect0)/tf.to_float(nLabels0))
			errors = tf.tuple(error)

		# --------------------------------------------------
		# inspection
		# --------------------------------------------------

		with tf.name_scope('scalars'):
			tf.summary.scalar('avg_cross_entropy', loss)
			for iClass in range(nClasses):
				tf.summary.scalar('avg_pixel_error_%d' % iClass, error[iClass])
			tf.summary.scalar('learning_rate', learningRate)
		with tf.name_scope('images'):
			split0 = tf.slice(UNet2D.nn,[0,0,0,0],[-1,-1,-1,1])
			split1 = tf.slice(UNet2D.nn,[0,0,0,1],[-1,-1,-1,1])
			if nClasses > 2:
				split2 = tf.slice(UNet2D.nn,[0,0,0,2],[-1,-1,-1,1])
			tf.summary.image('pm0',split0)
			tf.summary.image('pm1',split1)
			if nClasses > 2:
				tf.summary.image('pm2',split2)
		merged = tf.summary.merge_all()


		# --------------------------------------------------
		# session
		# --------------------------------------------------

		saver = tf.train.Saver()
		sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU

		if os.path.exists(outLogPath):
			shutil.rmtree(outLogPath)
		trainWriter = tf.summary.FileWriter(trainWriterPath, sess.graph)
		validWriter = tf.summary.FileWriter(validWriterPath, sess.graph)

		if restoreVariables:
			saver.restore(sess, outModelPath)
			print("Model restored.")
		else:
			sess.run(tf.global_variables_initializer())

		# --------------------------------------------------
		# train
		# --------------------------------------------------

		batchData = np.zeros((batchSize,imSize,imSize,nChannels))
		batchLabels = np.zeros((batchSize,imSize,imSize,nClasses))
		for i in range(nSteps):
			# train

			perm = np.arange(nTrain)
			np.random.shuffle(perm)

			for j in range(batchSize):
				batchData[j,:,:,:] = Train[perm[j],:,:,:]
				batchLabels[j,:,:,:] = LTrain[perm[j],:,:,:]

			summary,_ = sess.run([merged,optOp],feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 1})
			trainWriter.add_summary(summary, i)

			# validation

			perm = np.arange(nValid)
			np.random.shuffle(perm)

			for j in range(batchSize):
				batchData[j,:,:,:] = Valid[perm[j],:,:,:]
				batchLabels[j,:,:,:] = LValid[perm[j],:,:,:]

			summary, es = sess.run([merged, errors],feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 0})
			validWriter.add_summary(summary, i)

			e = np.mean(es)
			print('step %05d, e: %f' % (i,e))

			if i == 0:
				if restoreVariables:
					lowestError = e
				else:
					lowestError = np.inf

			if np.mod(i,100) == 0 and e < lowestError:
				lowestError = e
				print("Model saved in file: %s" % saver.save(sess, outModelPath))


		# --------------------------------------------------
		# test
		# --------------------------------------------------

		if not os.path.exists(outPMPath):
			os.makedirs(outPMPath)

		for i in range(nTest):
			j = np.mod(i,batchSize)

			batchData[j,:,:,:] = Test[i,:,:,:]
			batchLabels[j,:,:,:] = LTest[i,:,:,:]
		 
			if j == batchSize-1 or i == nTest-1:

				output = sess.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 0})

				for k in range(j+1):
					pm = output[k,:,:,testPMIndex]
					gt = batchLabels[k,:,:,testPMIndex]
					im = np.sqrt(normalize(batchData[k,:,:,0]))
					imwrite(np.uint8(255*np.concatenate((im,np.concatenate((pm,gt),axis=1)),axis=1)),'%s/I%05d.png' % (outPMPath,i-j+k+1))


		# --------------------------------------------------
		# save hyper-parameters, clean-up
		# --------------------------------------------------

		saveData(UNet2D.hp,pathjoin(modelPath,'hp.data'))

		trainWriter.close()
		validWriter.close()
		sess.close()

	def deploy(imPath,nImages,modelPath,pmPath,gpuIndex,pmIndex):
		os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex
		variablesPath = pathjoin(modelPath,'model.ckpt')
		outPMPath = pmPath

		hp = loadData(pathjoin(modelPath,'hp.data'))
		UNet2D.setupWithHP(hp)
		
		batchSize = UNet2D.hp['batchSize']
		imSize = UNet2D.hp['imSize']
		nChannels = UNet2D.hp['nChannels']
		nClasses = UNet2D.hp['nClasses']

		# --------------------------------------------------
		# data
		# --------------------------------------------------

		Data = np.zeros((nImages,imSize,imSize,nChannels))

		datasetMean = loadData(pathjoin(modelPath,'datasetMean.data'))
		datasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data'))

		for iSample in range(0, nImages):
			path = '%s/I%05d_Img.tif' % (imPath,iSample)
			im = im2double(tifread(path))
			Data[iSample,:,:,0] = (im-datasetMean)/datasetStDev

		# --------------------------------------------------
		# session
		# --------------------------------------------------

		saver = tf.train.Saver()
		sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU

		saver.restore(sess, variablesPath)
		print("Model restored.")

		# --------------------------------------------------
		# deploy
		# --------------------------------------------------

		batchData = np.zeros((batchSize,imSize,imSize,nChannels))

		if not os.path.exists(outPMPath):
			os.makedirs(outPMPath)

		for i in range(nImages):
			print(i,nImages)

			j = np.mod(i,batchSize)

			batchData[j,:,:,:] = Data[i,:,:,:]
		 
			if j == batchSize-1 or i == nImages-1:

				output = sess.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0})

				for k in range(j+1):
					pm = output[k,:,:,pmIndex]
					im = np.sqrt(normalize(batchData[k,:,:,0]))
					# imwrite(np.uint8(255*np.concatenate((im,pm),axis=1)),'%s/I%05d.png' % (outPMPath,i-j+k+1))
					imwrite(np.uint8(255*im),'%s/I%05d_Im.png' % (outPMPath,i-j+k+1))
					imwrite(np.uint8(255*pm),'%s/I%05d_PM.png' % (outPMPath,i-j+k+1))


		# --------------------------------------------------
		# clean-up
		# --------------------------------------------------

		sess.close()

	def singleImageInferenceSetup(modelPath,gpuIndex):
		os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex
		variablesPath = pathjoin(modelPath,'model.ckpt')
		hp = loadData(pathjoin(modelPath,'hp.data'))
		UNet2D.setupWithHP(hp)

		UNet2D.DatasetMean =loadData(pathjoin(modelPath,'datasetMean.data'))
		UNet2D.DatasetStDev =  loadData(pathjoin(modelPath,'datasetStDev.data'))
		print(UNet2D.DatasetMean)
		print(UNet2D.DatasetStDev)

		# --------------------------------------------------
		# session
		# --------------------------------------------------

		saver = tf.train.Saver()
		UNet2D.Session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU
		#UNet2D.Session = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
		saver.restore(UNet2D.Session, variablesPath)
		print("Model restored.")

	def singleImageInferenceCleanup():
		UNet2D.Session.close()

	def singleImageInference(image,mode,pmIndex):
		print('Inference...')

		batchSize = UNet2D.hp['batchSize']
		imSize = UNet2D.hp['imSize']
		nChannels = UNet2D.hp['nChannels']

		PI2D.setup(image,imSize,int(imSize/8),mode)
		PI2D.createOutput(nChannels)

		batchData = np.zeros((batchSize,imSize,imSize,nChannels))
		for i in range(PI2D.NumPatches):
			j = np.mod(i,batchSize)
			batchData[j,:,:,0] = (PI2D.getPatch(i)-UNet2D.DatasetMean)/UNet2D.DatasetStDev
			if j == batchSize-1 or i == PI2D.NumPatches-1:
				output = UNet2D.Session.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0})
				for k in range(j+1):
					pm = output[k,:,:,pmIndex]
					PI2D.patchOutput(i-j+k,pm)
					# PI2D.patchOutput(i-j+k,normalize(imgradmag(PI2D.getPatch(i-j+k),1)))

		return PI2D.getValidOutput()


def identifyNumChan(path):
   tiff = tifffile.TiffFile(path)
   shape = tiff.pages[0].shape
   numChan=None
   for i, page in enumerate(tiff.pages):
      if page.shape != shape:
         numChan = i
         return numChan
         break
#      else:
#         raise Exception("Did not find any pyramid subresolutions") 

   if not numChan:
      numChan = len(tiff.pages)
      return numChan

def getProbMaps(I,dsFactor,modelPath):
   hsize = int((float(I.shape[0]) * float(0.5)))
   vsize = int((float(I.shape[1]) * float(0.5)))
   imagesub = cv2.resize(I,(vsize,hsize),cv2.INTER_NEAREST)

   UNet2D.singleImageInferenceSetup(modelPath, 1)

   for iSize in range(dsFactor):
	   hsize = int((float(I.shape[0]) * float(0.5)))
	   vsize = int((float(I.shape[1]) * float(0.5)))
	   I = cv2.resize(I,(vsize,hsize),cv2.INTER_NEAREST)
   I = im2double(I)
   I = im2double(sk.rescale_intensity(I, in_range=(np.min(I), np.max(I)), out_range=(0, 0.983)))
   probMaps = UNet2D.singleImageInference(I,'accumulate',1)
   UNet2D.singleImageInferenceCleanup()
   return probMaps 

def coreSegmenterOutput(I,probMap,initialmask,preBlur,findCenter):
	hsize = int((float(I.shape[0]) * float(0.1)))
	vsize = int((float(I.shape[1]) * float(0.1)))
	nucGF = cv2.resize(I,(vsize,hsize),cv2.INTER_CUBIC)
#	Irs = cv2.resize(I,(vsize,hsize),cv2.INTER_CUBIC)
#	I=I.astype(np.float)
#	r,c = I.shape
#	I+=np.random.rand(r,c)*1e-6
#	c1 = uniform_filter(I, 3, mode='reflect')
#	c2 = uniform_filter(I*I, 3, mode='reflect')
#	nucGF = np.sqrt(c2 - c1*c1)*np.sqrt(9./8)
#	nucGF[np.isnan(nucGF)]=0
	#active contours
	hsize = int(float(nucGF.shape[0]))
	vsize = int(float(nucGF.shape[1]))
	initialmask = cv2.resize(initialmask,(vsize,hsize),cv2.INTER_NEAREST)
	initialmask = dilation(initialmask,disk(15)) >0
		
#	init=np.argwhere(eroded>0)
	nucGF = gaussian(nucGF,0.7)
	nucGF=nucGF/np.amax(nucGF)
	
   
#	initialmask = nucGF>0
	nuclearMask = morphological_chan_vese(nucGF, 100, init_level_set=initialmask, smoothing=10,lambda1=1.001, lambda2=1)
	
#	nuclearMask = chan_vese(nucGF, mu=1.5, lambda1=6, lambda2=1, tol=0.0005, max_iter=2000, dt=15, init_level_set=initialmask, extended_output=True)	
#	nuclearMask = nuclearMask[0]
  
	
	TMAmask = nuclearMask
#	nMaskDist =distance_transform_edt(nuclearMask)
#	fgm = peak_local_max(h_maxima(nMaskDist, 2*preBlur),indices =False)
#	markers= np.logical_or(erosion(1-nuclearMask,disk(3)),fgm)
#	TMAmask=watershed(-nMaskDist,label(markers),watershed_line=True)
#	TMAmask = nuclearMask*(TMAmask>0)
	TMAmask = remove_small_objects(TMAmask>0,round(TMAmask.shape[0])*round(TMAmask.shape[1])*0.005)
	TMAlabel = label(TMAmask)
# find object closest to center
	if findCenter==True:
		
		stats= regionprops(TMAlabel)
		counter=1
		minDistance =-1
		index =[]
		for props in stats:
			centroid = props.centroid
			distanceFromCenter = np.sqrt((centroid[0]-nucGF.shape[0]/2)**2+(centroid[1]-nucGF.shape[1]/2)**2)
	#		if distanceFromCenter<0.6/2*np.sqrt(TMAlabel.shape[0]*TMAlabel.shape[1]):
			if distanceFromCenter<minDistance or minDistance==-1 :
				minDistance =distanceFromCenter
				index = counter
			counter=counter+1
	#		dist = 0.6/2*np.sqrt(TMAlabel.shape[0]*TMAlabel.shape[1])
		TMAmask = morphology.binary_closing(TMAlabel==index,disk(3))

	return TMAmask

def overlayOutline(outline,img):
	img2 = img.copy()
	stacked_img = np.stack((img2,)*3, axis=-1)
	stacked_img[outline > 0] = [1, 0, 0]
	imshowpair(img2,stacked_img)

def imshowpair(A,B):
	plt.imshow(A,cmap='Purples')
	plt.imshow(B,cmap='Greens',alpha=0.5)
	plt.show()


if __name__ == '__main__':
	parser=argparse.ArgumentParser()
	parser.add_argument("--imagePath")
	parser.add_argument("--outputPath")
	parser.add_argument("--maskPath")
	parser.add_argument("--downsampleFactor",type = int, default = 5)
	parser.add_argument("--channel",type = int, default = 0)
	parser.add_argument("--buffer",type = float, default = 2)
	parser.add_argument("--outputChan", type=int, nargs = '+', default=[-1])
	parser.add_argument("--sensitivity",type = float, default=0.3)
	parser.add_argument("--useGrid",action='store_true')
	parser.add_argument("--cluster",action='store_true')
	args = parser.parse_args()

	outputPath = args.outputPath
	imagePath = args.imagePath
	sensitivity = args.sensitivity
	#scriptPath = os.path.dirname(os.path.realpath(__file__))
	#modelPath = os.path.join(scriptPath, 'TFModel - 3class 16 kernels 5ks 2 layers')
	#modelPath = 'D:\\LSP\\Coreograph\\model-4layersMaskAug20'
	scriptPath = os.path.dirname(os.path.realpath(__file__))
	modelPath = os.path.join(scriptPath, 'model')
#	outputPath = 'D:\\LSP\\cycif\\testsets\\exemplar-002\\dearrayPython' ############
	maskOutputPath = os.path.join(outputPath, 'masks')
#	imagePath = 'D:\\LSP\\cycif\\testsets\\exemplar-002\\registration\\exemplar-002.ome.tif'###########
#	imagePath = 'Y:\\sorger\\data\\RareCyte\\Connor\\TMAs\\CAJ_TMA11_13\\original_data\\TMA11\\registration\\TMA11.ome.tif'
#	imagePath = 'Y:\\sorger\\data\\RareCyte\\Connor\\TMAs\\Z124_TMA20_22\\TMA22\\registration\\TMA22.ome.tif'
#	classProbsPath = 'D:\\unetcoreograph.tif'
#	imagePath = 'Y:\\sorger\\data\\RareCyte\\Connor\\Z155_PTCL\\TMA_552\\registration\\TMA_552.ome.tif'
#	classProbsPath = 'Y:\\sorger\\data\\RareCyte\\Connor\\Z155_PTCL\\TMA_552\\probMapCore\\TMA_552_CorePM_1.tif'
#	imagePath = 'Y:\\sorger\\data\\RareCyte\\Zoltan\\Z112_TMA17_19\\190403_ashlar\\TMA17_1092.ome.tif'
#	classProbsPath = 'Z:\\IDAC\\Clarence\\LSP\\CyCIF\\TMA\\probMapCore\\1new_CorePM_1.tif'
#	imagePath = 'Y:\\sorger\\data\\RareCyte\\ANNIINA\\Julia\\2018\\TMA6\\julia_tma6.ome.tif'
#	classProbsPath = 'Z:\\IDAC\\Clarence\\LSP\\CyCIF\\TMA\\probMapCore\\3new_CorePM_1.tif'

	
#	if not os.path.exists(outputPath):
#		os.makedirs(outputPath)
#	else:
#		shutil.rmtree(outputPath)
	if not os.path.exists(maskOutputPath):
		os.makedirs(maskOutputPath)


	channel = args.channel 
	dsFactor = 1/(2**args.downsampleFactor)
#	I = tifffile.imread(imagePath, key=channel)
	I = skio.imread(imagePath, img_num=channel)

	imagesub = resize(I,(int((float(I.shape[0]) * dsFactor)),int((float(I.shape[1]) * dsFactor))))
	numChan = identifyNumChan(imagePath)
	
	outputChan = args.outputChan
	if len(outputChan)==1:
		if outputChan[0]==-1:
			outputChan = [0, numChan-1]
		else:
			outputChan.append(outputChan[0])
	
	classProbs = getProbMaps(I,args.downsampleFactor,modelPath)
#	classProbs = tifffile.imread(classProbsPath,key=0)
	preMask = gaussian(np.uint8(classProbs*255),1)>0.8
	
	P = regionprops(label(preMask),cache=False)
	area = [ele.area for ele in P]
	print(str(len(P)) + ' cores detected!')
	if len(P) <3:
		medArea = np.median(area)
		maxArea = np.percentile(area,99)
	else:
		count=0
		labelpreMask = np.zeros(preMask.shape,dtype=np.uint32)
		for props in P:
				count += 1
				yi = props.coords[:, 0]
				xi = props.coords[:, 1]
				labelpreMask[yi, xi] = count              
				P=regionprops(labelpreMask)
				area = [ele.area for ele in P]
		medArea =  np.median(area)
		maxArea = np.percentile(area,99)
	preMask = remove_small_objects(preMask,0.2*medArea)
	coreRad = round(np.sqrt(medArea/np.pi))
	estCoreDiam = round(np.sqrt(maxArea/np.pi)*1.2*args.buffer)

#preprocessing
	fgFiltered = blob_log(preMask,coreRad*0.6,threshold=sensitivity)
	Imax = np.zeros(preMask.shape,dtype=np.uint8)
	for iSpot in range(fgFiltered.shape[0]):
		yi = np.uint32(round(fgFiltered[iSpot, 0]))
		xi = np.uint32(round(fgFiltered[iSpot, 1]))
		Imax[yi, xi] = 1
	Imax = Imax*preMask
	Idist = distance_transform_edt(1-Imax)
	markers = label(Imax)
	coreLabel  = watershed(Idist,markers,watershed_line=True,mask = preMask)
	P = regionprops(coreLabel)
	centroids = np.array([ele.centroid for ele in P])/dsFactor
	numCores = len(centroids)
	estCoreDiamX = np.ones(numCores)*estCoreDiam/dsFactor
	estCoreDiamY = np.ones(numCores)*estCoreDiam/dsFactor

	if numCores ==0 & args.cluster:
		print('No cores detected. Try adjusting the downsample factor')
		sys.exit(255)

	singleMaskTMA = np.zeros(imagesub.shape)
	maskTMA = np.zeros(imagesub.shape)
	bbox = [None] * numCores

 
	x=np.zeros(numCores)
	xLim=np.zeros(numCores)
	y=np.zeros(numCores)
	yLim=np.zeros(numCores)
	
# segmenting each core   	
	#######################
	for iCore in range(numCores):
		x[iCore] = centroids[iCore,1] - estCoreDiamX[iCore]/2
		xLim[iCore] = x[iCore]+estCoreDiamX[iCore]
		if xLim[iCore] > I.shape[1]:
			xLim[iCore] = I.shape[1]
		if x[iCore]<1:
			x[iCore]=1

		y[iCore] = centroids[iCore,0] - estCoreDiamY[iCore]/2
		yLim[iCore] = y[iCore] + estCoreDiamY[iCore]
		if yLim[iCore] > I.shape[0]:
			yLim[iCore] = I.shape[0]
		if y[iCore]<1:
			y[iCore]=1

		bbox[iCore] = [round(x[iCore]), round(y[iCore]), round(xLim[iCore]), round(yLim[iCore])]
		
		for iChan in range(outputChan[0],outputChan[1]+1):
			with pytiff.Tiff(imagePath, "r", encoding='utf-8') as handle:
				handle.set_page(iChan)
				coreStack= handle[np.uint32(bbox[iCore][1]):np.uint32(bbox[iCore][3]-1), np.uint32(bbox[iCore][0]):np.uint32(bbox[iCore][2]-1)]
			skio.imsave(outputPath + os.path.sep + str(iCore+1)  + '.tif',coreStack,append=True)	

		with pytiff.Tiff(imagePath, "r", encoding='utf-8') as handle:
			handle.set_page(args.channel)
			coreSlice= handle[np.uint32(bbox[iCore][1]):np.uint32(bbox[iCore][3]-1), np.uint32(bbox[iCore][0]):np.uint32(bbox[iCore][2]-1)]

		core = (coreLabel ==(iCore+1))
		initialmask = core[np.uint32(y[iCore]*dsFactor):np.uint32(yLim[iCore]*dsFactor),np.uint32(x[iCore]*dsFactor):np.uint32(xLim[iCore]*dsFactor)]
		initialmask = resize(initialmask,size(coreSlice),cv2.INTER_NEAREST)

		singleProbMap = classProbs[np.uint32(y[iCore]*dsFactor):np.uint32(yLim[iCore]*dsFactor),np.uint32(x[iCore]*dsFactor):np.uint32(xLim[iCore]*dsFactor)]
		singleProbMap = resize(np.uint8(255*singleProbMap),size(coreSlice),cv2.INTER_NEAREST)
		TMAmask = coreSegmenterOutput(coreSlice,singleProbMap,initialmask,coreRad/20,False) 
		if np.sum(TMAmask)==0:
			TMAmask = np.ones(TMAmask.shape)
		vsize = int(float(coreSlice.shape[0]))
		hsize = int(float(coreSlice.shape[1]))
		masksub = resize(resize(TMAmask,(vsize,hsize),cv2.INTER_NEAREST),(int((float(coreSlice.shape[0])*dsFactor)),int((float(coreSlice.shape[1])*dsFactor))),cv2.INTER_NEAREST)
		singleMaskTMA[int(y[iCore]*dsFactor):int(y[iCore]*dsFactor)+masksub.shape[0],int(x[iCore]*dsFactor):int(x[iCore]*dsFactor)+masksub.shape[1]]=masksub
		maskTMA = maskTMA + resize(singleMaskTMA,maskTMA.shape,cv2.INTER_NEAREST)
		cv2.putText(imagesub, str(iCore+1), (int(P[iCore].centroid[1]),int(P[iCore].centroid[0])), 0, 0.5, (np.amax(imagesub), np.amax(imagesub), np.amax(imagesub)), 1, cv2.LINE_AA)
		
		skio.imsave(maskOutputPath + os.path.sep + str(iCore+1)  + '_mask.tif',np.uint8(TMAmask))
		print('Segmented core ' + str(iCore+1))	
		
	boundaries = find_boundaries(maskTMA)
	imagesub = imagesub/np.percentile(imagesub,99.9)
	imagesub[boundaries==1] = 1
	skio.imsave(outputPath + os.path.sep + 'TMA_MAP.tif' ,np.uint8(imagesub*255))
	print('Segmented all cores!')
	

#restore GPU to 0
	#image load using tifffile