# HG changeset patch
# User perssond
# Date 1615508249 0
# Node ID 6bec4fef6b2e0dd816a2e9b481279b5f74395230

"planemo upload for repository https://github.com/ohsu-comp-bio/unmicst commit 73e4cae15f2d7cdc86719e77470eb00af4b6ebb7-dirty"

diff -r 000000000000 -r 6bec4fef6b2e UnMicst.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/UnMicst.py	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,674 @@
+import numpy as np
+from scipy import misc
+import tensorflow.compat.v1 as tf
+import shutil
+import scipy.io as sio
+import os, fnmatch, glob
+import skimage.exposure as sk
+import skimage.io
+import argparse
+import czifile
+from nd2reader import ND2Reader
+import tifffile
+import sys
+tf.disable_v2_behavior()
+#sys.path.insert(0, 'C:\\Users\\Public\\Documents\\ImageScience')
+
+from toolbox.imtools import *
+from toolbox.ftools import *
+from toolbox.PartitionOfImage import PI2D
+from toolbox import GPUselect
+
+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,mean,std):
+		variablesPath = pathjoin(modelPath, 'model.ckpt')
+
+		hp = loadData(pathjoin(modelPath, 'hp.data'))
+		UNet2D.setupWithHP(hp)
+		if mean ==-1:
+			UNet2D.DatasetMean = loadData(pathjoin(modelPath, 'datasetMean.data'))
+		else:
+			UNet2D.DatasetMean = mean
+
+		if std == -1:
+			UNet2D.DatasetStDev = loadData(pathjoin(modelPath, 'datasetStDev.data'))
+		else:
+			UNet2D.DatasetStDev = std
+		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__':
+	parser = argparse.ArgumentParser()
+	parser.add_argument("imagePath", help="path to the .tif file")
+	parser.add_argument("--model",  help="type of model. For example, nuclei vs cytoplasm",default = 'nucleiDAPI')
+	parser.add_argument("--outputPath", help="output path of probability map")
+	parser.add_argument("--channel", help="channel to perform inference on", type=int, default=0)
+	parser.add_argument("--classOrder", help="background, contours, foreground", type = int, nargs = '+', default=-1)
+	parser.add_argument("--mean", help="mean intensity of input image. Use -1 to use model", type=float, default=-1)
+	parser.add_argument("--std", help="mean standard deviation of input image. Use -1 to use model", type=float, default=-1)
+	parser.add_argument("--scalingFactor", help="factor by which to increase/decrease image size by", type=float,
+						default=1)
+	parser.add_argument("--stackOutput", help="save probability maps as separate files", action='store_true')
+	parser.add_argument("--GPU", help="explicitly select GPU", type=int, default = -1)
+	parser.add_argument("--outlier",
+						help="map percentile intensity to max when rescaling intensity values. Max intensity as default",
+						type=float, default=-1)
+	args = parser.parse_args()
+
+	logPath = ''
+	scriptPath = os.path.dirname(os.path.realpath(__file__))
+	modelPath = os.path.join(scriptPath, 'models', args.model)
+	# modelPath = os.path.join(scriptPath, 'models/cytoplasmINcell')
+	# modelPath = os.path.join(scriptPath, 'cytoplasmZeissNikon')
+	pmPath = ''
+
+	if os.system('nvidia-smi') == 0:
+		if args.GPU == -1:
+			print("automatically choosing GPU")
+			GPU = GPUselect.pick_gpu_lowest_memory()
+		else:
+			GPU = args.GPU
+		print('Using GPU ' + str(GPU))
+
+	else:
+		if sys.platform == 'win32':  # only 1 gpu on windows
+			if args.GPU==-1:
+				GPU = 0
+			else:
+				GPU = args.GPU
+			print('Using GPU ' + str(GPU))
+		else:
+			GPU=0
+			print('Using CPU')
+	os.environ['CUDA_VISIBLE_DEVICES'] = '%d' % GPU
+	UNet2D.singleImageInferenceSetup(modelPath, GPU,args.mean,args.std)
+	nClass = UNet2D.hp['nClasses']
+	imagePath = args.imagePath
+	dapiChannel = args.channel
+	dsFactor = args.scalingFactor
+	parentFolder = os.path.dirname(os.path.dirname(imagePath))
+	fileName = os.path.basename(imagePath)
+	fileNamePrefix = fileName.split(os.extsep, 1)
+	print(fileName)
+	fileType = fileNamePrefix[1]
+
+	if fileType=='ome.tif' or fileType == 'btf' :
+		I = skio.imread(imagePath, img_num=dapiChannel,plugin='tifffile')
+	elif fileType == 'tif' :
+		I = tifffile.imread(imagePath, key=dapiChannel)
+	elif fileType == 'czi':
+		with czifile.CziFile(imagePath) as czi:
+			image = czi.asarray()
+			I = image[0, 0, dapiChannel, 0, 0, :, :, 0]
+	elif fileType == 'nd2':
+		with ND2Reader(imagePath) as fullStack:
+			I = fullStack[dapiChannel]
+
+	if args.classOrder == -1:
+		args.classOrder = range(nClass)
+
+	rawI = I
+	print(type(I))
+	hsize = int((float(I.shape[0]) * float(dsFactor)))
+	vsize = int((float(I.shape[1]) * float(dsFactor)))
+	I = resize(I, (hsize, vsize))
+	if args.outlier == -1:
+		maxLimit = np.max(I)
+	else:
+		maxLimit = np.percentile(I, args.outlier)
+	I = im2double(sk.rescale_intensity(I, in_range=(np.min(I), maxLimit), out_range=(0, 0.983)))
+	rawI = im2double(rawI) / np.max(im2double(rawI))
+	if not args.outputPath:
+		args.outputPath = parentFolder + '//probability_maps'
+
+	if not os.path.exists(args.outputPath):
+		os.makedirs(args.outputPath)
+
+	append_kwargs = {
+		'bigtiff': True,
+		'metadata': None,
+		'append': True,
+	}
+	save_kwargs = {
+		'bigtiff': True,
+		'metadata': None,
+		'append': False,
+	}
+	if args.stackOutput:
+		slice=0
+		for iClass in args.classOrder[::-1]:
+			PM = np.uint8(255*UNet2D.singleImageInference(I, 'accumulate', iClass)) # backwards in order to align with ilastik...
+			PM = resize(PM, (rawI.shape[0], rawI.shape[1]))
+			if slice==0:
+				skimage.io.imsave(args.outputPath + '//' + fileNamePrefix[0] + '_Probabilities_' + str(dapiChannel) + '.tif', np.uint8(255 * PM),**save_kwargs)
+			else:
+				skimage.io.imsave(args.outputPath + '//' + fileNamePrefix[0] + '_Probabilities_' + str(dapiChannel) + '.tif',np.uint8(255 * PM),**append_kwargs)
+			if slice==1:
+				save_kwargs['append'] = False
+				skimage.io.imsave(args.outputPath + '//' + fileNamePrefix[0] + '_Preview_' + str(dapiChannel) + '.tif',	np.uint8(255 * PM), **save_kwargs)
+				skimage.io.imsave(args.outputPath + '//' + fileNamePrefix[0] + '_Preview_' + str(dapiChannel) + '.tif', np.uint8(255 * rawI), **append_kwargs)
+			slice = slice + 1
+
+	else:
+		contours = np.uint8(255*UNet2D.singleImageInference(I, 'accumulate', args.classOrder[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]))
+		skimage.io.imsave(args.outputPath + '//' + fileNamePrefix[0] + '_ContoursPM_' + str(dapiChannel) + '.tif',np.uint8(255 * contours),**save_kwargs)
+		skimage.io.imsave(args.outputPath + '//' + fileNamePrefix[0] + '_ContoursPM_' + str(dapiChannel) + '.tif',np.uint8(255 * rawI), **append_kwargs)
+		del contours
+		nuclei = np.uint8(255*UNet2D.singleImageInference(I, 'accumulate', args.classOrder[2]))
+		nuclei = resize(nuclei, (rawI.shape[0], rawI.shape[1]))
+		skimage.io.imsave(args.outputPath + '//' + fileNamePrefix[0] + '_NucleiPM_' + str(dapiChannel) + '.tif',np.uint8(255 * nuclei), **save_kwargs)
+		del nuclei
+	UNet2D.singleImageInferenceCleanup()
+
+#aligned output files to reflect ilastik
+#outputting all classes as single file
+#handles multiple formats including tif, ome.tif, nd2, czi
+#selectable models (human nuclei, mouse nuclei, cytoplasm)
+
+#added legacy function to save output files
+#append save function to reduce memory footprint
+#added --classOrder parameter to specify which class is background, contours, and nuclei respectively
\ No newline at end of file
diff -r 000000000000 -r 6bec4fef6b2e batchUNet2DTMACycif.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/batchUNet2DTMACycif.py	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,594 @@
+import numpy as np
+from scipy import misc
+import tensorflow as tf
+import shutil
+import scipy.io as sio
+import os,fnmatch,PIL,glob
+import skimage.exposure as sk
+
+import sys
+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
+
+		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 = 'D:\\LSP\\Sinem\\fromOlympus\\TFLogsssssssss'
+	modelPath = 'D:\\LSP\\UNet\\tonsil20x1bin1chan\\TFModel - 3class 16 kernels 5ks 2 layers'
+	pmPath = 'D:\\LSP\\Sinem\\fromOlympus\\TFProbMaps'
+
+	
+	# ----- test 1 -----
+
+	# imPath = 'D:\\LSP\\Sinem\\trainingSetContours\\trainingSetSmallLarge'
+	# # UNet2D.setup(128,1,2,8,2,2,3,1,0.1,2,8)
+	# # UNet2D.train(imPath,logPath,modelPath,pmPath,500,100,40,True,20000,1,0)
+	# UNet2D.setup(128, 1, 2, 12, 2, 2, 3, 4, 0.1, 4, 8)
+	# UNet2D.train(imPath, logPath, modelPath, pmPath, 1600, 400, 500, False, 150000, 1, 0)
+	# UNet2D.deploy(imPath,100,modelPath,pmPath,1,0)
+
+	# I = im2double(tifread('/home/mc457/files/CellBiology/IDAC/Marcelo/Etc/UNetTestSets/SinemSaka_NucleiSegmentation_SingleImageInferenceTest3.tif'))
+	# UNet2D.singleImageInferenceSetup(modelPath,0)
+	# J = UNet2D.singleImageInference(I,'accumulate',0)
+	# UNet2D.singleImageInferenceCleanup()
+	# # imshowlist([I,J])
+	# # sys.exit(0)
+	# # tifwrite(np.uint8(255*I),'/home/mc457/Workspace/I1.tif')
+	# # tifwrite(np.uint8(255*J),'/home/mc457/Workspace/I2.tif')
+	# K = np.zeros((2,I.shape[0],I.shape[1]))
+	# K[0,:,:] = I
+	# K[1,:,:] = J
+	# tifwrite(np.uint8(255*K),'/home/mc457/Workspace/Sinem_NucSeg.tif')
+
+	UNet2D.singleImageInferenceSetup(modelPath, 1)
+	imagePath ='Y:/sorger/data/RareCyte/Clarence/NKI_TMA'
+	sampleList = glob.glob(imagePath + '/ZTMA_18_810*')
+	dapiChannel = 0
+	for iSample in sampleList:
+		# fileList = glob.glob(iSample + '//dearray//*.tif')
+		fileList = [x for x in glob.glob(iSample + '/dearray/*.tif') if x != (iSample+'/dearray\\TMA_MAP.tif')]
+		print(fileList)
+		for iFile in fileList:
+			fileName = os.path.basename(iFile)
+			fileNamePrefix = fileName.split(os.extsep, 1)
+			I = tifffile.imread(iFile, key=dapiChannel)
+			I = im2double(sk.rescale_intensity(I, in_range=(np.min(I), np.max(I)), out_range=(0, 64424)))
+			# I=np.moveaxis(I,0,-1)
+			# I=I[:,:,0]
+			hsize = int((float(I.shape[0])*float(1)))
+			vsize = int((float(I.shape[1])*float(1)))
+			I = resize(I,(hsize,vsize))
+				#I = im2double(tifread('D:\\LSP\\cycif\\Unet\\Caitlin\\E - 04(fld 8 wv UV - DAPI)downsampled.tif'))
+			outputPath = iSample + '//prob_maps'
+			if not os.path.exists(outputPath):
+				os.makedirs(outputPath)
+			K = np.zeros((2,I.shape[0],I.shape[1]))
+			contours = UNet2D.singleImageInference(I,'accumulate',1)
+			K[1,:,:] = I
+			K[0,:,:] = contours
+			tifwrite(np.uint8(255 * K),
+					 outputPath + '//' + fileNamePrefix[0] + '_ContoursPM_' + str(dapiChannel + 1) + '.tif')
+			del K
+			K = np.zeros((1, I.shape[0], I.shape[1]))
+			nuclei = UNet2D.singleImageInference(I,'accumulate',2)
+			K[0, :, :] = nuclei
+			tifwrite(np.uint8(255 * K),
+					 outputPath + '//' + fileNamePrefix[0] + '_NucleiPM_' + str(dapiChannel + 1) + '.tif')
+			del K
+	UNet2D.singleImageInferenceCleanup()
+
+
+	# ----- test 2 -----
+
+	# imPath = '/home/mc457/files/CellBiology/IDAC/Marcelo/Etc/UNetTestSets/ClarenceYapp_NucleiSegmentation'
+	# UNet2D.setup(128,1,2,8,2,2,3,1,0.1,3,4)
+	# UNet2D.train(imPath,logPath,modelPath,pmPath,800,100,100,False,10,1)
+	# UNet2D.deploy(imPath,100,modelPath,pmPath,1)
+
+
+	# ----- test 3 -----
+
+	# imPath = '/home/mc457/files/CellBiology/IDAC/Marcelo/Etc/UNetTestSets/CarmanLi_CellTypeSegmentation'
+	# # UNet2D.setup(256,1,2,8,2,2,3,1,0.1,3,4)
+	# # UNet2D.train(imPath,logPath,modelPath,pmPath,1400,100,164,False,10000,1)
+	# UNet2D.deploy(imPath,164,modelPath,pmPath,1)
+
+
+	# ----- test 4 -----
+
+	# imPath = '/home/cicconet/Downloads/TrainSet1'
+	# UNet2D.setup(64,1,2,8,2,2,3,1,0.1,3,4)
+	# UNet2D.train(imPath,logPath,modelPath,pmPath,200,8,8,False,2000,1,0)
+	# # UNet2D.deploy(imPath,164,modelPath,pmPath,1)
\ No newline at end of file
diff -r 000000000000 -r 6bec4fef6b2e batchUNet2DtCycif.py
--- /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()
+
diff -r 000000000000 -r 6bec4fef6b2e batchUnMicst.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/batchUnMicst.py	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,588 @@
+import numpy as np
+from scipy import misc
+import tensorflow as tf
+import shutil
+import scipy.io as sio
+import os, fnmatch, PIL, glob
+import skimage.exposure as sk
+import argparse
+
+import sys
+
+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
+
+		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__':
+	parser = argparse.ArgumentParser()
+	parser.add_argument("imagePath", help="path to the .tif file")
+	parser.add_argument("--channel", help="channel to perform inference on", type=int, default=0)
+	parser.add_argument("--TMA", help="specify if TMA", action="store_true")
+	parser.add_argument("--scalingFactor", help="factor by which to increase/decrease image size by", type=float,
+						default=1)
+	args = parser.parse_args()
+
+	logPath = ''
+	modelPath = 'D:\\LSP\\UNet\\tonsil20x1bin1chan\\TFModel - 3class 16 kernels 5ks 2 layers'
+	pmPath = ''
+
+	UNet2D.singleImageInferenceSetup(modelPath, 1)
+	imagePath = args.imagePath
+	sampleList = glob.glob(imagePath + '/exemplar*')
+	dapiChannel = args.channel
+	dsFactor = args.scalingFactor
+	for iSample in sampleList:
+		if args.TMA:
+			fileList = [x for x in glob.glob(iSample + '\\dearray\\*.tif') if x != (iSample + '\\dearray\\TMA_MAP.tif')]
+			print(iSample)
+		else:
+			fileList = glob.glob(iSample + '//registration//*ome.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()
diff -r 000000000000 -r 6bec4fef6b2e macros.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,28 @@
+<?xml version="1.0"?>
+<macros>
+    <xml name="requirements">
+        <requirements>
+            <requirement type="package" version="3.7">python</requirement>
+            <requirement type="package" version="1.15.0">tensorflow</requirement>
+            <requirement type="package" version="1.15.1">tensorflow-estimator</requirement>
+            <requirement type="package">cudnn</requirement>
+            <requirement type="package" version="10.0">cudatoolkit</requirement>
+            <requirement type="package" version="0.17.2">scikit-image</requirement>
+            <requirement type="package" version="1.4.1">scipy</requirement>
+            <requirement type="package" version="2020.7.24">tifffile</requirement>
+            <requirement type="package" version="2019.7.2">czifile</requirement>
+            <requirement type="package" version="3.2.3">nd2reader</requirement>
+        </requirements>
+    </xml>
+
+    <xml name="version_cmd">
+        <version_command>echo @VERSION@</version_command>
+    </xml>
+    <xml name="citations">
+        <citations>
+        </citations>
+    </xml>
+
+    <token name="@VERSION@">3.1.1</token>
+    <token name="@CMD_BEGIN@">python ${__tool_directory__}/UnMicst.py</token>
+</macros>
diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell/checkpoint
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/CytoplasmIncell/checkpoint	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,2 @@
+model_checkpoint_path: "D:\\Dan\\CytoplasmIncell\\model.ckpt"
+all_model_checkpoint_paths: "D:\\Dan\\CytoplasmIncell\\model.ckpt"
diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell/datasetMean.data
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell/datasetStDev.data
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell/hp.data
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell/model.ckpt.data-00000-of-00001
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell/model.ckpt.index
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell/model.ckpt.meta
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell2/datasetMean.data
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/CytoplasmIncell2/datasetMean.data	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,1 @@
+�G?���Q�.
\ No newline at end of file
diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell2/datasetStDev.data
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/CytoplasmIncell2/datasetStDev.data	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,1 @@
+�G?���Q�.
\ No newline at end of file
diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell2/hp.data
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell2/model.ckpt.data-00000-of-00001
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell2/model.ckpt.index
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmIncell2/model.ckpt.meta
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmZeissNikon/checkpoint
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/CytoplasmZeissNikon/checkpoint	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,2 @@
+model_checkpoint_path: "D:\\Dan\\CytoplasmZeissNikon\\model.ckpt"
+all_model_checkpoint_paths: "D:\\Dan\\CytoplasmZeissNikon\\model.ckpt"
diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmZeissNikon/datasetMean.data
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diff -r 000000000000 -r 6bec4fef6b2e models/CytoplasmZeissNikon/model.ckpt.data-00000-of-00001
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diff -r 000000000000 -r 6bec4fef6b2e models/mousenucleiDAPI/checkpoint
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/mousenucleiDAPI/checkpoint	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,2 @@
+model_checkpoint_path: "D:\\Olesja\\UNet\\nuclei20x2bin1chan 3layers ks3 bs16 20input\\model.ckpt"
+all_model_checkpoint_paths: "D:\\Olesja\\UNet\\nuclei20x2bin1chan 3layers ks3 bs16 20input\\model.ckpt"
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diff -r 000000000000 -r 6bec4fef6b2e models/nucleiDAPI/checkpoint
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/nucleiDAPI/checkpoint	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,2 @@
+model_checkpoint_path: "D:\\LSP\\UNet\\tonsil20x1bin1chan\\TFModel\\model.ckpt"
+all_model_checkpoint_paths: "D:\\LSP\\UNet\\tonsil20x1bin1chan\\TFModel\\model.ckpt"
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diff -r 000000000000 -r 6bec4fef6b2e models/nucleiDAPI1-5/checkpoint
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/nucleiDAPI1-5/checkpoint	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,2 @@
+model_checkpoint_path: "D:\\LSP\\UNet\\TuuliaLPTBdapiTFv2\\model.ckpt"
+all_model_checkpoint_paths: "D:\\LSP\\UNet\\TuuliaLPTBdapiTFv2\\model.ckpt"
diff -r 000000000000 -r 6bec4fef6b2e models/nucleiDAPI1-5/datasetMean.data
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/nucleiDAPI1-5/datasetMean.data	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,1 @@
+�G?�\(��.
\ No newline at end of file
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diff -r 000000000000 -r 6bec4fef6b2e models/nucleiDAPILAMIN/checkpoint
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/nucleiDAPILAMIN/checkpoint	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,2 @@
+model_checkpoint_path: "/home/cy101/files/CellBiology/IDAC/Clarence/LSP/UNet models/LPTCdapilamin5-36/model.ckpt"
+all_model_checkpoint_paths: "/home/cy101/files/CellBiology/IDAC/Clarence/LSP/UNet models/LPTCdapilamin5-36/model.ckpt"
diff -r 000000000000 -r 6bec4fef6b2e models/nucleiDAPILAMIN/datasetMean.data
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/nucleiDAPILAMIN/datasetMean.data	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,3 @@
+�G?�
+=p��
+.
\ No newline at end of file
diff -r 000000000000 -r 6bec4fef6b2e models/nucleiDAPILAMIN/datasetStDev.data
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/models/nucleiDAPILAMIN/datasetStDev.data	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,1 @@
+�G?�\(��.
\ No newline at end of file
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diff -r 000000000000 -r 6bec4fef6b2e toolbox/GPUselect.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolbox/GPUselect.py	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,19 @@
+import subprocess, re
+import numpy as np
+
+def pick_gpu_lowest_memory():
+    output = subprocess.Popen("nvidia-smi", stdout=subprocess.PIPE, shell=True).communicate()[0]
+    output=output.decode("ascii")
+    gpu_output = output[output.find("Memory-Usage"):]
+        # lines of the form
+        # |    0      8734    C   python                                       11705MiB |
+    memory_regex = re.compile(r"[|]\s+?\D+?.+[ ](?P<gpu_memory>\d+)MiB /")
+    rows = gpu_output.split("\n")
+    result=[]
+    for row in gpu_output.split("\n"):
+        m = memory_regex.search(row)
+        if not m:
+            continue
+        gpu_memory = int(m.group("gpu_memory"))
+        result.append(gpu_memory)
+    return np.argsort(result)[0]
\ No newline at end of file
diff -r 000000000000 -r 6bec4fef6b2e toolbox/PartitionOfImage.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolbox/PartitionOfImage.py	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,305 @@
+import numpy as np
+from toolbox.imtools import *
+# from toolbox.ftools import *
+# import sys
+
+class PI2D:
+    Image = None
+    PaddedImage = None
+    PatchSize = 128
+    Margin = 14
+    SubPatchSize = 100
+    PC = None # patch coordinates
+    NumPatches = 0
+    Output = None
+    Count = None
+    NR = None
+    NC = None
+    NRPI = None
+    NCPI = None
+    Mode = None
+    W = None
+
+    def setup(image,patchSize,margin,mode):
+        PI2D.Image = image
+        PI2D.PatchSize = patchSize
+        PI2D.Margin = margin
+        subPatchSize = patchSize-2*margin
+        PI2D.SubPatchSize = subPatchSize
+
+        W = np.ones((patchSize,patchSize))
+        W[[0,-1],:] = 0
+        W[:,[0,-1]] = 0
+        for i in range(1,2*margin):
+            v = i/(2*margin)
+            W[i,i:-i] = v
+            W[-i-1,i:-i] = v
+            W[i:-i,i] = v
+            W[i:-i,-i-1] = v
+        PI2D.W = W
+
+        if len(image.shape) == 2:
+            nr,nc = image.shape
+        elif len(image.shape) == 3: # multi-channel image
+            nz,nr,nc = image.shape
+
+        PI2D.NR = nr
+        PI2D.NC = nc
+
+        npr = int(np.ceil(nr/subPatchSize)) # number of patch rows
+        npc = int(np.ceil(nc/subPatchSize)) # number of patch cols
+
+        nrpi = npr*subPatchSize+2*margin # number of rows in padded image 
+        ncpi = npc*subPatchSize+2*margin # number of cols in padded image 
+
+        PI2D.NRPI = nrpi
+        PI2D.NCPI = ncpi
+
+        if len(image.shape) == 2:
+            PI2D.PaddedImage = np.zeros((nrpi,ncpi))
+            PI2D.PaddedImage[margin:margin+nr,margin:margin+nc] = image
+        elif len(image.shape) == 3:
+            PI2D.PaddedImage = np.zeros((nz,nrpi,ncpi))
+            PI2D.PaddedImage[:,margin:margin+nr,margin:margin+nc] = image
+
+        PI2D.PC = [] # patch coordinates [r0,r1,c0,c1]
+        for i in range(npr):
+            r0 = i*subPatchSize
+            r1 = r0+patchSize
+            for j in range(npc):
+                c0 = j*subPatchSize
+                c1 = c0+patchSize
+                PI2D.PC.append([r0,r1,c0,c1])
+
+        PI2D.NumPatches = len(PI2D.PC)
+        PI2D.Mode = mode # 'replace' or 'accumulate'
+
+    def getPatch(i):
+        r0,r1,c0,c1 = PI2D.PC[i]
+        if len(PI2D.PaddedImage.shape) == 2:
+            return PI2D.PaddedImage[r0:r1,c0:c1]
+        if len(PI2D.PaddedImage.shape) == 3:
+            return PI2D.PaddedImage[:,r0:r1,c0:c1]
+
+    def createOutput(nChannels):
+        if nChannels == 1:
+            PI2D.Output = np.zeros((PI2D.NRPI,PI2D.NCPI),np.float16)
+        else:
+            PI2D.Output = np.zeros((nChannels,PI2D.NRPI,PI2D.NCPI),np.float16)
+        if PI2D.Mode == 'accumulate':
+            PI2D.Count = np.zeros((PI2D.NRPI,PI2D.NCPI),np.float16)
+
+    def patchOutput(i,P):
+        r0,r1,c0,c1 = PI2D.PC[i]
+        if PI2D.Mode == 'accumulate':
+            PI2D.Count[r0:r1,c0:c1] += PI2D.W
+        if len(P.shape) == 2:
+            if PI2D.Mode == 'accumulate':
+                PI2D.Output[r0:r1,c0:c1] += np.multiply(P,PI2D.W)
+            elif PI2D.Mode == 'replace':
+                PI2D.Output[r0:r1,c0:c1] = P
+        elif len(P.shape) == 3:
+            if PI2D.Mode == 'accumulate':
+                for i in range(P.shape[0]):
+                    PI2D.Output[i,r0:r1,c0:c1] += np.multiply(P[i,:,:],PI2D.W)
+            elif PI2D.Mode == 'replace':
+                PI2D.Output[:,r0:r1,c0:c1] = P
+
+    def getValidOutput():
+        margin = PI2D.Margin
+        nr, nc = PI2D.NR, PI2D.NC
+        if PI2D.Mode == 'accumulate':
+            C = PI2D.Count[margin:margin+nr,margin:margin+nc]
+        if len(PI2D.Output.shape) == 2:
+            if PI2D.Mode == 'accumulate':
+                return np.divide(PI2D.Output[margin:margin+nr,margin:margin+nc],C)
+            if PI2D.Mode == 'replace':
+                return PI2D.Output[margin:margin+nr,margin:margin+nc]
+        if len(PI2D.Output.shape) == 3:
+            if PI2D.Mode == 'accumulate':
+                for i in range(PI2D.Output.shape[0]):
+                    PI2D.Output[i,margin:margin+nr,margin:margin+nc] = np.divide(PI2D.Output[i,margin:margin+nr,margin:margin+nc],C)
+            return PI2D.Output[:,margin:margin+nr,margin:margin+nc]
+
+
+    def demo():
+        I = np.random.rand(128,128)
+        # PI2D.setup(I,128,14)
+        PI2D.setup(I,64,4,'replace')
+
+        nChannels = 2
+        PI2D.createOutput(nChannels)
+
+        for i in range(PI2D.NumPatches):
+            P = PI2D.getPatch(i)
+            Q = np.zeros((nChannels,P.shape[0],P.shape[1]))
+            for j in range(nChannels):
+                Q[j,:,:] = P
+            PI2D.patchOutput(i,Q)
+
+        J = PI2D.getValidOutput()
+        J = J[0,:,:]
+
+        D = np.abs(I-J)
+        print(np.max(D))
+
+        K = cat(1,cat(1,I,J),D)
+        imshow(K)
+
+
+class PI3D:
+    Image = None
+    PaddedImage = None
+    PatchSize = 128
+    Margin = 14
+    SubPatchSize = 100
+    PC = None # patch coordinates
+    NumPatches = 0
+    Output = None
+    Count = None
+    NR = None # rows
+    NC = None # cols
+    NZ = None # planes
+    NRPI = None
+    NCPI = None
+    NZPI = None
+    Mode = None
+    W = None
+
+    def setup(image,patchSize,margin,mode):
+        PI3D.Image = image
+        PI3D.PatchSize = patchSize
+        PI3D.Margin = margin
+        subPatchSize = patchSize-2*margin
+        PI3D.SubPatchSize = subPatchSize
+
+        W = np.ones((patchSize,patchSize,patchSize))
+        W[[0,-1],:,:] = 0
+        W[:,[0,-1],:] = 0
+        W[:,:,[0,-1]] = 0
+        for i in range(1,2*margin):
+            v = i/(2*margin)
+            W[[i,-i-1],i:-i,i:-i] = v
+            W[i:-i,[i,-i-1],i:-i] = v
+            W[i:-i,i:-i,[i,-i-1]] = v
+
+        PI3D.W = W
+
+        if len(image.shape) == 3:
+            nz,nr,nc = image.shape
+        elif len(image.shape) == 4: # multi-channel image
+            nz,nw,nr,nc = image.shape
+
+        PI3D.NR = nr
+        PI3D.NC = nc
+        PI3D.NZ = nz
+
+        npr = int(np.ceil(nr/subPatchSize)) # number of patch rows
+        npc = int(np.ceil(nc/subPatchSize)) # number of patch cols
+        npz = int(np.ceil(nz/subPatchSize)) # number of patch planes
+
+        nrpi = npr*subPatchSize+2*margin # number of rows in padded image 
+        ncpi = npc*subPatchSize+2*margin # number of cols in padded image 
+        nzpi = npz*subPatchSize+2*margin # number of plns in padded image 
+
+        PI3D.NRPI = nrpi
+        PI3D.NCPI = ncpi
+        PI3D.NZPI = nzpi
+
+        if len(image.shape) == 3:
+            PI3D.PaddedImage = np.zeros((nzpi,nrpi,ncpi))
+            PI3D.PaddedImage[margin:margin+nz,margin:margin+nr,margin:margin+nc] = image
+        elif len(image.shape) == 4:
+            PI3D.PaddedImage = np.zeros((nzpi,nw,nrpi,ncpi))
+            PI3D.PaddedImage[margin:margin+nz,:,margin:margin+nr,margin:margin+nc] = image
+
+        PI3D.PC = [] # patch coordinates [z0,z1,r0,r1,c0,c1]
+        for iZ in range(npz):
+            z0 = iZ*subPatchSize
+            z1 = z0+patchSize
+            for i in range(npr):
+                r0 = i*subPatchSize
+                r1 = r0+patchSize
+                for j in range(npc):
+                    c0 = j*subPatchSize
+                    c1 = c0+patchSize
+                    PI3D.PC.append([z0,z1,r0,r1,c0,c1])
+
+        PI3D.NumPatches = len(PI3D.PC)
+        PI3D.Mode = mode # 'replace' or 'accumulate'
+
+    def getPatch(i):
+        z0,z1,r0,r1,c0,c1 = PI3D.PC[i]
+        if len(PI3D.PaddedImage.shape) == 3:
+            return PI3D.PaddedImage[z0:z1,r0:r1,c0:c1]
+        if len(PI3D.PaddedImage.shape) == 4:
+            return PI3D.PaddedImage[z0:z1,:,r0:r1,c0:c1]
+
+    def createOutput(nChannels):
+        if nChannels == 1:
+            PI3D.Output = np.zeros((PI3D.NZPI,PI3D.NRPI,PI3D.NCPI))
+        else:
+            PI3D.Output = np.zeros((PI3D.NZPI,nChannels,PI3D.NRPI,PI3D.NCPI))
+        if PI3D.Mode == 'accumulate':
+            PI3D.Count = np.zeros((PI3D.NZPI,PI3D.NRPI,PI3D.NCPI))
+
+    def patchOutput(i,P):
+        z0,z1,r0,r1,c0,c1 = PI3D.PC[i]
+        if PI3D.Mode == 'accumulate':
+            PI3D.Count[z0:z1,r0:r1,c0:c1] += PI3D.W
+        if len(P.shape) == 3:
+            if PI3D.Mode == 'accumulate':
+                PI3D.Output[z0:z1,r0:r1,c0:c1] += np.multiply(P,PI3D.W)
+            elif PI3D.Mode == 'replace':
+                PI3D.Output[z0:z1,r0:r1,c0:c1] = P
+        elif len(P.shape) == 4:
+            if PI3D.Mode == 'accumulate':
+                for i in range(P.shape[1]):
+                    PI3D.Output[z0:z1,i,r0:r1,c0:c1] += np.multiply(P[:,i,:,:],PI3D.W)
+            elif PI3D.Mode == 'replace':
+                PI3D.Output[z0:z1,:,r0:r1,c0:c1] = P
+
+    def getValidOutput():
+        margin = PI3D.Margin
+        nz, nr, nc = PI3D.NZ, PI3D.NR, PI3D.NC
+        if PI3D.Mode == 'accumulate':
+            C = PI3D.Count[margin:margin+nz,margin:margin+nr,margin:margin+nc]
+        if len(PI3D.Output.shape) == 3:
+            if PI3D.Mode == 'accumulate':
+                return np.divide(PI3D.Output[margin:margin+nz,margin:margin+nr,margin:margin+nc],C)
+            if PI3D.Mode == 'replace':
+                return PI3D.Output[margin:margin+nz,margin:margin+nr,margin:margin+nc]
+        if len(PI3D.Output.shape) == 4:
+            if PI3D.Mode == 'accumulate':
+                for i in range(PI3D.Output.shape[1]):
+                    PI3D.Output[margin:margin+nz,i,margin:margin+nr,margin:margin+nc] = np.divide(PI3D.Output[margin:margin+nz,i,margin:margin+nr,margin:margin+nc],C)
+            return PI3D.Output[margin:margin+nz,:,margin:margin+nr,margin:margin+nc]
+
+
+    def demo():
+        I = np.random.rand(128,128,128)
+        PI3D.setup(I,64,4,'accumulate')
+
+        nChannels = 2
+        PI3D.createOutput(nChannels)
+
+        for i in range(PI3D.NumPatches):
+            P = PI3D.getPatch(i)
+            Q = np.zeros((P.shape[0],nChannels,P.shape[1],P.shape[2]))
+            for j in range(nChannels):
+                Q[:,j,:,:] = P
+            PI3D.patchOutput(i,Q)
+
+        J = PI3D.getValidOutput()
+        J = J[:,0,:,:]
+
+        D = np.abs(I-J)
+        print(np.max(D))
+
+        pI = I[64,:,:]
+        pJ = J[64,:,:]
+        pD = D[64,:,:]
+
+        K = cat(1,cat(1,pI,pJ),pD)
+        imshow(K)
+
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diff -r 000000000000 -r 6bec4fef6b2e toolbox/ftools.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolbox/ftools.py	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,55 @@
+from os.path import *
+from os import listdir, makedirs, remove
+import pickle
+import shutil
+
+def fileparts(path): # path = file path
+    [p,f] = split(path)
+    [n,e] = splitext(f)
+    return [p,n,e]
+
+def listfiles(path,token): # path = folder path
+    l = []
+    for f in listdir(path):
+        fullPath = join(path,f)
+        if isfile(fullPath) and token in f:
+            l.append(fullPath)
+    l.sort()
+    return l
+
+def listsubdirs(path): # path = folder path
+    l = []
+    for f in listdir(path):
+        fullPath = join(path,f)
+        if isdir(fullPath):
+            l.append(fullPath)
+    l.sort()
+    return l
+
+def pathjoin(p,ne): # '/path/to/folder', 'name.extension' (or a subfolder)
+    return join(p,ne)
+
+def saveData(data,path):
+    print('saving data')
+    dataFile = open(path, 'wb')
+    pickle.dump(data, dataFile)
+
+def loadData(path):
+    print('loading data')
+    dataFile = open(path, 'rb')
+    return pickle.load(dataFile)
+
+def createFolderIfNonExistent(path):
+    if not exists(path): # from os.path
+        makedirs(path)
+
+def moveFile(fullPathSource,folderPathDestination):
+    [p,n,e] = fileparts(fullPathSource)
+    shutil.move(fullPathSource,pathjoin(folderPathDestination,n+e))
+
+def copyFile(fullPathSource,folderPathDestination):
+    [p,n,e] = fileparts(fullPathSource)
+    shutil.copy(fullPathSource,pathjoin(folderPathDestination,n+e))
+
+def removeFile(path):
+    remove(path)
\ No newline at end of file
diff -r 000000000000 -r 6bec4fef6b2e toolbox/imtools.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolbox/imtools.py	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,312 @@
+import matplotlib.pyplot as plt
+import tifffile
+import os
+import numpy as np
+from skimage import io as skio
+from scipy.ndimage import *
+from skimage.morphology import *
+from skimage.transform import resize
+
+def tifread(path):
+    return tifffile.imread(path)
+
+def tifwrite(I,path):
+    tifffile.imsave(path, I)
+
+def imshow(I,**kwargs):
+    if not kwargs:
+        plt.imshow(I,cmap='gray')
+    else:
+        plt.imshow(I,**kwargs)
+        
+    plt.axis('off')
+    plt.show()
+
+def imshowlist(L,**kwargs):
+    n = len(L)
+    for i in range(n):
+        plt.subplot(1, n, i+1)
+        if not kwargs:
+            plt.imshow(L[i],cmap='gray')
+        else:
+            plt.imshow(L[i],**kwargs)
+        plt.axis('off')
+    plt.show()
+
+def imread(path):
+    return skio.imread(path)
+
+def imwrite(I,path):
+    skio.imsave(path,I)
+
+def im2double(I):
+    if I.dtype == 'uint16':
+        return I.astype('float64')/65535
+    elif I.dtype == 'uint8':
+        return I.astype('float64')/255
+    elif I.dtype == 'float32':
+        return I.astype('float64')
+    elif I.dtype == 'float64':
+        return I
+    else:
+        print('returned original image type: ', I.dtype)
+        return I
+
+def size(I):
+    return list(I.shape)
+
+def imresizeDouble(I,sizeOut): # input and output are double
+    return resize(I,(sizeOut[0],sizeOut[1]),mode='reflect')
+
+def imresize3Double(I,sizeOut): # input and output are double
+    return resize(I,(sizeOut[0],sizeOut[1],sizeOut[2]),mode='reflect')
+
+def imresizeUInt8(I,sizeOut): # input and output are UInt8
+    return np.uint8(resize(I.astype(float),(sizeOut[0],sizeOut[1]),mode='reflect',order=0))
+
+def imresize3UInt8(I,sizeOut): # input and output are UInt8
+    return np.uint8(resize(I.astype(float),(sizeOut[0],sizeOut[1],sizeOut[2]),mode='reflect',order=0))
+
+def normalize(I):
+    m = np.min(I)
+    M = np.max(I)
+    if M > m:
+        return (I-m)/(M-m)
+    else:
+        return I
+
+def snormalize(I):
+    m = np.mean(I)
+    s = np.std(I)
+    if s > 0:
+        return (I-m)/s
+    else:
+        return I
+
+def cat(a,I,J):
+    return np.concatenate((I,J),axis=a)
+
+def imerode(I,r):
+    return binary_erosion(I, disk(r))
+
+def imdilate(I,r):
+    return binary_dilation(I, disk(r))
+
+def imerode3(I,r):
+    return morphology.binary_erosion(I, ball(r))
+
+def imdilate3(I,r):
+    return morphology.binary_dilation(I, ball(r))
+
+def sphericalStructuralElement(imShape,fRadius):
+    if len(imShape) == 2:
+        return disk(fRadius,dtype=float)
+    if len(imShape) == 3:
+        return ball(fRadius,dtype=float)
+
+def medfilt(I,filterRadius):
+    return median_filter(I,footprint=sphericalStructuralElement(I.shape,filterRadius))
+
+def maxfilt(I,filterRadius):
+    return maximum_filter(I,footprint=sphericalStructuralElement(I.shape,filterRadius))
+
+def minfilt(I,filterRadius):
+    return minimum_filter(I,footprint=sphericalStructuralElement(I.shape,filterRadius))
+
+def ptlfilt(I,percentile,filterRadius):
+    return percentile_filter(I,percentile,footprint=sphericalStructuralElement(I.shape,filterRadius))
+
+def imgaussfilt(I,sigma,**kwargs):
+    return gaussian_filter(I,sigma,**kwargs)
+
+def imlogfilt(I,sigma,**kwargs):
+    return -gaussian_laplace(I,sigma,**kwargs)
+
+def imgradmag(I,sigma):
+    if len(I.shape) == 2:
+        dx = imgaussfilt(I,sigma,order=[0,1])
+        dy = imgaussfilt(I,sigma,order=[1,0])
+        return np.sqrt(dx**2+dy**2)
+    if len(I.shape) == 3:
+        dx = imgaussfilt(I,sigma,order=[0,0,1])
+        dy = imgaussfilt(I,sigma,order=[0,1,0])
+        dz = imgaussfilt(I,sigma,order=[1,0,0])
+        return np.sqrt(dx**2+dy**2+dz**2)
+
+def localstats(I,radius,justfeatnames=False):
+    ptls = [10,30,50,70,90]
+    featNames = []
+    for i in range(len(ptls)):
+        featNames.append('locPtl%d' % ptls[i])
+    if justfeatnames == True:
+        return featNames
+    sI = size(I)
+    nFeats = len(ptls)
+    F = np.zeros((sI[0],sI[1],nFeats))
+    for i in range(nFeats):
+        F[:,:,i] = ptlfilt(I,ptls[i],radius)
+    return F
+
+def localstats3(I,radius,justfeatnames=False):
+    ptls = [10,30,50,70,90]
+    featNames = []
+    for i in range(len(ptls)):
+        featNames.append('locPtl%d' % ptls[i])
+    if justfeatnames == True:
+        return featNames
+    sI = size(I)
+    nFeats = len(ptls)
+    F = np.zeros((sI[0],sI[1],sI[2],nFeats))
+    for i in range(nFeats):
+        F[:,:,:,i] = ptlfilt(I,ptls[i],radius)
+    return F
+
+def imderivatives(I,sigmas,justfeatnames=False):
+    if type(sigmas) is not list:
+        sigmas = [sigmas]
+    derivPerSigmaFeatNames = ['d0','dx','dy','dxx','dxy','dyy','normGrad','normHessDiag']
+    if justfeatnames == True:
+        featNames = [];
+        for i in range(len(sigmas)):
+            for j in range(len(derivPerSigmaFeatNames)):
+                featNames.append('derivSigma%d%s' % (sigmas[i],derivPerSigmaFeatNames[j]))
+        return featNames
+    nDerivativesPerSigma = len(derivPerSigmaFeatNames)
+    nDerivatives = len(sigmas)*nDerivativesPerSigma
+    sI = size(I)
+    D = np.zeros((sI[0],sI[1],nDerivatives))
+    for i in range(len(sigmas)):
+        sigma = sigmas[i]
+        dx = imgaussfilt(I,sigma,order=[0,1])
+        dy = imgaussfilt(I,sigma,order=[1,0])
+        dxx = imgaussfilt(I,sigma,order=[0,2])
+        dyy = imgaussfilt(I,sigma,order=[2,0])
+        D[:,:,nDerivativesPerSigma*i  ] = imgaussfilt(I,sigma)
+        D[:,:,nDerivativesPerSigma*i+1] = dx
+        D[:,:,nDerivativesPerSigma*i+2] = dy
+        D[:,:,nDerivativesPerSigma*i+3] = dxx
+        D[:,:,nDerivativesPerSigma*i+4] = imgaussfilt(I,sigma,order=[1,1])
+        D[:,:,nDerivativesPerSigma*i+5] = dyy
+        D[:,:,nDerivativesPerSigma*i+6] = np.sqrt(dx**2+dy**2)
+        D[:,:,nDerivativesPerSigma*i+7] = np.sqrt(dxx**2+dyy**2)
+    return D
+    # derivatives are indexed by the last dimension, which is good for ML features but not for visualization,
+    # in which case the expected dimensions are [plane,channel,y(row),x(col)]; to obtain that ordering, do
+    # D = np.moveaxis(D,[0,3,1,2],[0,1,2,3])
+
+def imderivatives3(I,sigmas,justfeatnames=False):
+    if type(sigmas) is not list:
+        sigmas = [sigmas]
+
+    derivPerSigmaFeatNames = ['d0','dx','dy','dz','dxx','dxy','dxz','dyy','dyz','dzz','normGrad','normHessDiag']
+
+    # derivPerSigmaFeatNames = ['d0','normGrad','normHessDiag']
+
+    if justfeatnames == True:
+        featNames = [];
+        for i in range(len(sigmas)):
+            for j in range(len(derivPerSigmaFeatNames)):
+                featNames.append('derivSigma%d%s' % (sigmas[i],derivPerSigmaFeatNames[j]))
+        return featNames
+    nDerivativesPerSigma = len(derivPerSigmaFeatNames)
+    nDerivatives = len(sigmas)*nDerivativesPerSigma
+    sI = size(I)
+    D = np.zeros((sI[0],sI[1],sI[2],nDerivatives)) # plane, channel, y, x
+    for i in range(len(sigmas)):
+        sigma = sigmas[i]
+        dx  = imgaussfilt(I,sigma,order=[0,0,1]) # z, y, x
+        dy  = imgaussfilt(I,sigma,order=[0,1,0])
+        dz  = imgaussfilt(I,sigma,order=[1,0,0])
+        dxx = imgaussfilt(I,sigma,order=[0,0,2])
+        dyy = imgaussfilt(I,sigma,order=[0,2,0])
+        dzz = imgaussfilt(I,sigma,order=[2,0,0])
+
+        D[:,:,:,nDerivativesPerSigma*i   ] = imgaussfilt(I,sigma)
+        D[:,:,:,nDerivativesPerSigma*i+1 ] = dx
+        D[:,:,:,nDerivativesPerSigma*i+2 ] = dy
+        D[:,:,:,nDerivativesPerSigma*i+3 ] = dz
+        D[:,:,:,nDerivativesPerSigma*i+4 ] = dxx
+        D[:,:,:,nDerivativesPerSigma*i+5 ] = imgaussfilt(I,sigma,order=[0,1,1])
+        D[:,:,:,nDerivativesPerSigma*i+6 ] = imgaussfilt(I,sigma,order=[1,0,1])
+        D[:,:,:,nDerivativesPerSigma*i+7 ] = dyy
+        D[:,:,:,nDerivativesPerSigma*i+8 ] = imgaussfilt(I,sigma,order=[1,1,0])
+        D[:,:,:,nDerivativesPerSigma*i+9 ] = dzz
+        D[:,:,:,nDerivativesPerSigma*i+10] = np.sqrt(dx**2+dy**2+dz**2)
+        D[:,:,:,nDerivativesPerSigma*i+11] = np.sqrt(dxx**2+dyy**2+dzz**2)
+
+        # D[:,:,:,nDerivativesPerSigma*i   ] = imgaussfilt(I,sigma)
+        # D[:,:,:,nDerivativesPerSigma*i+1 ] = np.sqrt(dx**2+dy**2+dz**2)
+        # D[:,:,:,nDerivativesPerSigma*i+2 ] = np.sqrt(dxx**2+dyy**2+dzz**2)
+    return D
+    # derivatives are indexed by the last dimension, which is good for ML features but not for visualization,
+    # in which case the expected dimensions are [plane,y(row),x(col)]; to obtain that ordering, do
+    # D = np.moveaxis(D,[2,0,1],[0,1,2])
+
+def imfeatures(I=[],sigmaDeriv=1,sigmaLoG=1,locStatsRad=0,justfeatnames=False):
+    if type(sigmaDeriv) is not list:
+        sigmaDeriv = [sigmaDeriv]
+    if type(sigmaLoG) is not list:
+        sigmaLoG = [sigmaLoG]
+    derivFeatNames = imderivatives([],sigmaDeriv,justfeatnames=True)
+    nLoGFeats = len(sigmaLoG)
+    locStatsFeatNames = []
+    if locStatsRad > 1:
+        locStatsFeatNames = localstats([],locStatsRad,justfeatnames=True)
+    nLocStatsFeats = len(locStatsFeatNames)
+    if justfeatnames == True:
+        featNames = derivFeatNames
+        for i in range(nLoGFeats):
+            featNames.append('logSigma%d' % sigmaLoG[i])
+        for i in range(nLocStatsFeats):
+            featNames.append(locStatsFeatNames[i])
+        return featNames
+    nDerivFeats = len(derivFeatNames)
+    nFeatures = nDerivFeats+nLoGFeats+nLocStatsFeats
+    sI = size(I)
+    F = np.zeros((sI[0],sI[1],nFeatures))
+    F[:,:,:nDerivFeats] = imderivatives(I,sigmaDeriv)
+    for i in range(nLoGFeats):
+        F[:,:,nDerivFeats+i] = imlogfilt(I,sigmaLoG[i])
+    if locStatsRad > 1:
+        F[:,:,nDerivFeats+nLoGFeats:] = localstats(I,locStatsRad)
+    return F
+
+def imfeatures3(I=[],sigmaDeriv=2,sigmaLoG=2,locStatsRad=0,justfeatnames=False):
+    if type(sigmaDeriv) is not list:
+        sigmaDeriv = [sigmaDeriv]
+    if type(sigmaLoG) is not list:
+        sigmaLoG = [sigmaLoG]
+    derivFeatNames = imderivatives3([],sigmaDeriv,justfeatnames=True)
+    nLoGFeats = len(sigmaLoG)
+    locStatsFeatNames = []
+    if locStatsRad > 1:
+        locStatsFeatNames = localstats3([],locStatsRad,justfeatnames=True)
+    nLocStatsFeats = len(locStatsFeatNames)
+    if justfeatnames == True:
+        featNames = derivFeatNames
+        for i in range(nLoGFeats):
+            featNames.append('logSigma%d' % sigmaLoG[i])
+        for i in range(nLocStatsFeats):
+            featNames.append(locStatsFeatNames[i])
+        return featNames
+    nDerivFeats = len(derivFeatNames)
+    nFeatures = nDerivFeats+nLoGFeats+nLocStatsFeats
+    sI = size(I)
+    F = np.zeros((sI[0],sI[1],sI[2],nFeatures))
+    F[:,:,:,:nDerivFeats] = imderivatives3(I,sigmaDeriv)
+    for i in range(nLoGFeats):
+        F[:,:,:,nDerivFeats+i] = imlogfilt(I,sigmaLoG[i])
+    if locStatsRad > 1:
+        F[:,:,:,nDerivFeats+nLoGFeats:] = localstats3(I,locStatsRad)
+    return F
+
+def stack2list(S):
+    L = []
+    for i in range(size(S)[2]):
+        L.append(S[:,:,i])
+    return L
+
+def thrsegment(I,wsBlr,wsThr): # basic threshold segmentation
+    G = imgaussfilt(I,sigma=(1-wsBlr)+wsBlr*5) # min 1, max 5
+    M = G > wsThr
+    return M
\ No newline at end of file
diff -r 000000000000 -r 6bec4fef6b2e unmicst.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/unmicst.xml	Fri Mar 12 00:17:29 2021 +0000
@@ -0,0 +1,104 @@
+<tool id="unmicst" name="UnMicst" version="@VERSION@.1" profile="17.09">
+    <description>UNet Model for Identifying Cells and Segmenting Tissue</description>
+    <macros>
+        <import>macros.xml</import>
+    </macros>
+ 
+    <expand macro="requirements"/>
+    @VERSION_CMD@
+
+    <command detect_errors="exit_code"><![CDATA[
+    #set $typeCorrected = str($image.name).replace('.ome.tiff','').replace('.ome.tif','').replace('.tiff','').replace('.tif','')+'.ome.tif'
+
+    ln -s $image '$typeCorrected';
+
+    @CMD_BEGIN@ '$typeCorrected'
+    
+    #if $stackoutput
+    --stackOutput
+    #end if
+
+    --outputPath `pwd`
+    --channel $channel
+    --model $model
+    --mean $mean
+    --std $stdev
+    --scalingFactor $scalingfactor;
+
+    ## Move files to different files for from_work_dir differentiation
+    #if $stackoutput
+    mv *Probabilities*.tif Probabilities.tif;
+    mv *Preview*.tif Preview.tif
+    #else
+    mv *ContoursPM*.tif ContoursPM.tif;
+    mv *NucleiPM*.tif NucleiPM.tif
+    #end if
+    ]]></command>
+
+    <inputs>
+        <param name="image" type="data" format="tiff" label="Registered TIFF"/>
+        <param name="model" type="select" label="Model">
+            <option value="nucleiDAPI">nucleiDAPI</option>
+            <option value="mousenucleiDAPI">mousenucleiDAPI</option>
+            <option value="CytoplasmIncell">CytoplasmIncell</option>
+            <option value="CytoplasmZeissNikon">CytoplasmZeissNikon</option>
+        </param>
+        <param name="mean" type="float" value="-1" label="Mean (-1 for model default)"/>
+        <param name="stdev" type="float" value="-1" label="Standard Deviation (-1 for model default)"/>
+        <param name="channel" type="integer" value="0" label="Channel to perform inference on"/>
+        <param name="stackoutput" type="boolean"  label="Stack probability map outputs"/>
+        <param name="scalingfactor" type="float" value="1.0" label="Factor to scale by"/>
+    </inputs>
+
+    <outputs>
+        <data format="tiff" name="previews" from_work_dir="Preview.tif" label="${tool.name} on ${on_string}: Preview">
+            <filter>stackoutput</filter>
+        </data>
+        <data format="tiff" name="probabilities" from_work_dir="Probabilities.tif" label="${tool.name} on ${on_string}: Probabilities">
+            <filter>stackoutput</filter>
+        </data>
+        <data format="tiff" name="contours" from_work_dir="ContoursPM.tif" label="${tool.name} on ${on_string}: ContoursPM">
+            <filter>not stackoutput</filter>
+        </data>
+        <data format="tiff" name="nuclei" from_work_dir="NucleiPM.tif" label="${tool.name} on ${on_string}: NucleiPM">
+            <filter>not stackoutput</filter>
+        </data>
+    </outputs>
+    <help><![CDATA[
+UnMicst - UNet Model for Identifying Cells and Segmenting Tissue
+Image Preprocessing
+Images can be preprocessed by inferring nuclei contours via a pretrained UNet model. The model is trained on 3 classes : background, nuclei contours and nuclei centers. The resulting probability maps can then be loaded into any modular segmentation pipeline that may use (but not limited to) a marker controlled watershed algorithm.
+
+The only input file is: an .ome.tif or .tif (preferably flat field corrected, minimal saturated pixels, and in focus. The model is trained on images acquired at 20x with binning 2x2 or a pixel size of 0.65 microns/px. If your settings differ, you can upsample/downsample to some extent.
+
+Running as a Docker container
+
+The docker image is distributed through Dockerhub and includes UnMicst with all of its dependencies. Parallel images with and without gpu support are available.
+
+docker pull labsyspharm/unmicst:latest
+docker pull labsyspharm/unmicst:latest-gpu
+Instatiate a container and mount the input directory containing your image.
+
+docker run -it --runtime=nvidia -v /path/to/data:/data labsyspharm/unmicst:latest-gpu bash
+When using the CPU-only image, --runtime=nvidia can be omitted:
+
+docker run -it -v /path/to/data:/data labsyspharm/unmicst:latest bash
+UnMicst resides in the /app directory inside the container:
+
+root@0ea0cdc46c8f:/# python app/UnMicst.py /data/input/my.tif --outputPath /data/results
+Running in a Conda environment
+
+If Docker is not available on your system, you can run the tool locally by creating a Conda environment. Ensure conda is installed on your system, then clone the repo and use conda.yml to create the environment.
+
+git clone https://github.com/HMS-IDAC/UnMicst.git
+cd UnMicst
+conda env create -f conda.yml
+conda activate unmicst
+python UnMicst.py /path/to/input.tif --outputPath /path/to/results/directory
+References:
+S Saka, Y Wang, J Kishi, A Zhu, Y Zeng, W Xie, K Kirli, C Yapp, M Cicconet, BJ Beliveau, SW Lapan, S Yin, M Lin, E Boyde, PS Kaeser, G Pihan, GM Church, P Yin, Highly multiplexed in situ protein imaging with signal amplification by Immuno-SABER, Nat Biotechnology (accepted)
+
+OHSU Wrapper Repo: https://github.com/ohsu-comp-bio/UnMicst
+    ]]></help>
+    <expand macro="citations" />
+</tool>