--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/batchUNet2DtCycif.py	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,553 @@
+import numpy as np
+from scipy import misc
+import tensorflow as tf
+import shutil
+import scipy.io as sio
+import os,fnmatch,glob
+import skimage.exposure as sk
+
+import sys
+sys.path.insert(0, 'C:\\Users\\Clarence\\Documents\\UNet code\\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
+
+		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()
+
+
+if __name__ == '__main__':
+	logPath = 'C://Users//Clarence//Documents//UNet code//TFLogs'
+	modelPath = 'D:\\LSP\\UNet\\tonsil20x1bin1chan\\TFModel - 3class 16 kernels 5ks 2 layers'
+	pmPath = 'C://Users//Clarence//Documents//UNet code//TFProbMaps'
+
+
+
+	UNet2D.singleImageInferenceSetup(modelPath, 0)
+	imagePath = 'D:\\LSP\\cycif\\testsets'
+	sampleList = glob.glob(imagePath + '//exemplar-001*')
+	dapiChannel = 0
+	dsFactor = 1
+	for iSample in sampleList:
+		fileList = glob.glob(iSample + '//registration//*.tif')
+		print(fileList)
+		for iFile in fileList:
+			fileName = os.path.basename(iFile)
+			fileNamePrefix = fileName.split(os.extsep, 1)
+			I = tifffile.imread(iFile, key=dapiChannel)
+			rawI = I
+			hsize = int((float(I.shape[0])*float(dsFactor)))
+			vsize = int((float(I.shape[1])*float(dsFactor)))
+			I = resize(I,(hsize,vsize))
+			I = im2double(sk.rescale_intensity(I, in_range=(np.min(I), np.max(I)), out_range=(0, 0.983)))
+			rawI = im2double(rawI)/np.max(im2double(rawI))
+			outputPath = iSample + '//prob_maps'
+			if not os.path.exists(outputPath):
+				os.makedirs(outputPath)
+			K = np.zeros((2,rawI.shape[0],rawI.shape[1]))
+			contours = UNet2D.singleImageInference(I,'accumulate',1)
+			hsize = int((float(I.shape[0]) * float(1/dsFactor)))
+			vsize = int((float(I.shape[1]) * float(1/dsFactor)))
+			contours = resize(contours, (rawI.shape[0], rawI.shape[1]))
+			K[1,:,:] = rawI
+			K[0,:,:] = contours
+			tifwrite(np.uint8(255 * K),
+					 outputPath + '//' + fileNamePrefix[0] + '_ContoursPM_' + str(dapiChannel + 1) + '.tif')
+			del K
+			K = np.zeros((1, rawI.shape[0], rawI.shape[1]))
+			nuclei = UNet2D.singleImageInference(I,'accumulate',2)
+			nuclei = resize(nuclei, (rawI.shape[0], rawI.shape[1]))
+			K[0, :, :] = nuclei
+			tifwrite(np.uint8(255 * K),
+					 outputPath + '//' + fileNamePrefix[0] + '_NucleiPM_' + str(dapiChannel + 1) + '.tif')
+			del K
+	UNet2D.singleImageInferenceCleanup()
+