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()