text: from __future__ import division
  from __future__ import print_function
  
  import sys
  import os
  import argparse
  import cv2
  import editdistance
  from DataLoader import DataLoader, Batch
  from Model import Model, DecoderType
  from SamplePreprocessor import preprocess
  
  
  class FilePaths:
  	"filenames and paths to data"
  	my_path = os.path.abspath(os.path.dirname(__file__))
  	fnCharList = os.path.join(my_path,"../model/charList.txt")
  	fnAccuracy = os.path.join(my_path,"../model/accuracy.txt")
  	fnTrain = os.path.join(my_path,'../data/')
  	fnInfer = os.path.join(my_path,'../data/test.png')
  	fnCorpus = os.path.join(my_path,'../data/corpus.txt')
  
  
  def train(model, loader):
  	"train NN"
  	epoch = 0 # number of training epochs since start
  	bestCharErrorRate = float('inf') # best valdiation character error rate
  	noImprovementSince = 0 # number of epochs no improvement of character error rate occured
  	earlyStopping = 5 # stop training after this number of epochs without improvement
  	while True:
  		epoch += 1
  		print('Epoch:', epoch)
  
  		# train
  		print('Train NN')
  		loader.trainSet()
  		while loader.hasNext():
  			iterInfo = loader.getIteratorInfo()
  			batch = loader.getNext()
  			loss = model.trainBatch(batch)
  			print('Batch:', iterInfo[0],'/', iterInfo[1], 'Loss:', loss)
  
  		# validate
  		charErrorRate = validate(model, loader)
  		
  		# if best validation accuracy so far, save model parameters
  		if charErrorRate < bestCharErrorRate:
  			print('Character error rate improved, save model')
  			bestCharErrorRate = charErrorRate
  			noImprovementSince = 0
  			model.save()
  			open(FilePaths.fnAccuracy, 'w').write('Validation character error rate of saved model: %f%%' % (charErrorRate*100.0))
  		else:
  			print('Character error rate not improved')
  			noImprovementSince += 1
  
  		# stop training if no more improvement in the last x epochs
  		if noImprovementSince >= earlyStopping:
  			print('No more improvement since %d epochs. Training stopped.' % earlyStopping)
  			break
  
  
  def validate(model, loader):
  	"validate NN"
  	print('Validate NN')
  	loader.validationSet()
  	numCharErr = 0
  	numCharTotal = 0
  	numWordOK = 0
  	numWordTotal = 0
  	while loader.hasNext():
  		iterInfo = loader.getIteratorInfo()
  		print('Batch:', iterInfo[0],'/', iterInfo[1])
  		batch = loader.getNext()
  		(recognized, _) = model.inferBatch(batch)
  		
  		print('Ground truth -> Recognized')	
  		for i in range(len(recognized)):
  			numWordOK += 1 if batch.gtTexts[i] == recognized[i] else 0
  			numWordTotal += 1
  			dist = editdistance.eval(recognized[i], batch.gtTexts[i])
  			numCharErr += dist
  			numCharTotal += len(batch.gtTexts[i])
  			print('[OK]' if dist==0 else '[ERR:%d]' % dist,'"' + batch.gtTexts[i] + '"', '->', '"' + recognized[i] + '"')
  	
  	# print validation result
  	charErrorRate = numCharErr / numCharTotal
  	wordAccuracy = numWordOK / numWordTotal
  	print('Character error rate: %f%%. Word accuracy: %f%%.' % (charErrorRate*100.0, wordAccuracy*100.0))
  	return charErrorRate
  
  
  def infer(model, fnImg):
  	"recognize text in image provided by file path"
  	img = preprocess(cv2.imread(fnImg, cv2.IMREAD_GRAYSCALE), Model.imgSize)
  	batch = Batch(None, [img])
  	(recognized, probability) = model.inferBatch(batch, True)
  	print('Recognized:', '"' + recognized[0] + '"')
  	print('Probability:', probability[0])
  
  
  def main():
  	"main function"
  	# optional command line args
  	parser = argparse.ArgumentParser()
  	parser.add_argument('--train', help='train the NN', action='store_true')
  	parser.add_argument('--validate', help='validate the NN', action='store_true')
  	parser.add_argument('--beamsearch', help='use beam search instead of best path decoding', action='store_true')
  	parser.add_argument('--wordbeamsearch', help='use word beam search instead of best path decoding', action='store_true')
  	parser.add_argument('--dump', help='dump output of NN to CSV file(s)', action='store_true')
  
  	args = parser.parse_args()
  
  	decoderType = DecoderType.BestPath
  	if args.beamsearch:
  		decoderType = DecoderType.BeamSearch
  	elif args.wordbeamsearch:
  		decoderType = DecoderType.WordBeamSearch
  
  	# train or validate on IAM dataset	
  	if args.train or args.validate:
  		# load training data, create TF model
  		loader = DataLoader(FilePaths.fnTrain, Model.batchSize, Model.imgSize, Model.maxTextLen)
  
  		# save characters of model for inference mode
  		open(FilePaths.fnCharList, 'w').write(str().join(loader.charList))
  		
  		# save words contained in dataset into file
  		open(FilePaths.fnCorpus, 'w').write(str(' ').join(loader.trainWords + loader.validationWords))
  
  		# execute training or validation
  		if args.train:
  			model = Model(loader.charList, decoderType)
  			train(model, loader)
  		elif args.validate:
  			model = Model(loader.charList, decoderType, mustRestore=True)
  			validate(model, loader)
  
  	# infer text on test image
  	else:
  		print(open(FilePaths.fnAccuracy).read())
  		model = Model(open(FilePaths.fnCharList).read(), decoderType, mustRestore=True, dump=args.dump)
  		infer(model, FilePaths.fnInfer)
  
  
  if __name__ == '__main__':
  	main()
  
  