text: import cv2
  import os
  import numpy as np
  from sys import argv
  from lib import sauvola, linelocalization, pathfinder
  from WordSegmentation import wordSegmentation, prepareImg
  from time import time as timer
  from SamplePreprocessor import preprocess
  from DataLoader import Batch
  from Model import Model
  
  
  def draw_line(im, path):
  	for p in path:
  		im[p[0], p[1]] = 0
  
  def draw_box(im, path, prev):
  	curr=(path[0][1], path[0][0])
  	cv2.rectangle(im, prev, curr,0,3)
  
  
  def draw_map(im, mapy):
  	for m in mapy:
  		im[m[0], m[1]] = 255
  
  
  def print_path(path):
  	print('\t# path: ' + str(path[::-1]))
  
  
  def save(filename, imbw, immap):
  	imbw_filename = str.replace(filename, '.', '_bw.')
  	#imbw_filename = str.replace(imbw_filename, 'data', 'data/bw')
  	print('Saving image "' + imbw_filename + '"..\n')
  	cv2.imwrite(imbw_filename, imbw)
  	immap_filename = str.replace(imbw_filename, '_bw', '_map')
  	cv2.imwrite(immap_filename, immap)
  
  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])
  	return (recognized[0], probability[0])
  
  ######################
  # ------ MAIN ------ #
  ######################
  
  def identify_words(filepath, filenames, model):
  	begin = timer()
  	out_dict={}
  	out_path='../data/out/'
  	
  
  	print('############################')
  	print('#Line and Word Segmentation#')
  	print('############################')
  
  	for filename in filenames:
  		fullpath=os.path.join(filepath,filename)
  		f=filename.split('.')[0]
  		ext=filename.split('.')[1]
  		if ext=='pdf':
  			continue
  		out_dict[f] = []
  		print('Reading image "' + filename + '"..')
  		im = cv2.imread(fullpath, 0)
  
  		print('- Thresholding image..')
  		#imbw = sauvola.binarize(im, [20, 20], 128, 0.3)
  		pxmin = np.min(im)
  		pxmax = np.max(im)
  		im = (im - pxmin) / (pxmax - pxmin) * 255
  		im = im.astype('uint8')
  		#binarize
  		_ , imbw = cv2.threshold(im, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
  		#cv2.imshow('tempb', imbw)
  
  		# increase line width
  		kernel = np.ones((3, 3), np.uint8)
  		imbw = cv2.erode(imbw, kernel, iterations = 1)
  
  		print('- Localizing lines..')
  		lines = linelocalization.localize(imbw)
  		lines.append(imbw.shape[0])
  		print(' => ' + str(len(lines)) + ' lines detected.')
  
  		print('- Path planning with ')
  
  		immap = np.zeros((imbw.shape), dtype=np.int32)
  		# for i in range(0, 1):
  		prev=(0,0)
  		n_line=1
  		for line in lines:
  			# line = lines[i]
  			rline = imbw[int(prev[1]):int(line),:]
  			img = prepareImg(rline, 50)
  			# execute segmentation with given parameters
  			# -kernelSize: size of filter kernel (odd integer)
  			# -sigma: standard deviation of Gaussian function used for filter kernel
  			# -theta: approximated width/height ratio of words, filter function is distorted by this factor
  			# - minArea: ignore word candidates smaller than specified area
  			res = wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=100, increase_dim=10)
  			
  			# write output to 'out/inputFileName' directory
  			if not os.path.exists(out_path+'%s'%f):
  				os.mkdir(out_path+'%s'%f)
  			
  			# iterate over all segmented words
  			#print('Segmented into %d words'%len(res))
  			for (j, w) in enumerate(res):
  				(wordBox, wordImg) = w
  				(x, y, w, h) = wordBox
  				imgloc=out_path+'%s/%d.png'%(f, j)
  				# increase contrast
  				cv2.imwrite(imgloc, wordImg) # save word
  				#FilePaths.fnInfer = 'out/%s/%d.png'%(f,j)
  				try:
  					result, prob = infer(model, imgloc)
  				except:
  					print("Couldn't infer: image%d"%j)
  					result=""
  				#updating output dictionary
  				out_dict[f].append(result)
  				#deleting intermediate file
  				##os.remove(imgloc)
  				cv2.rectangle(img,(x,y),(x+w,y+h),0,1) # draw bounding box in summary image
  			
  			# output summary image with bounding boxes around words
  			cv2.imwrite(out_path+'%s/summary%d.png'%(f,n_line), img)
  
  			#path, mapy = pathfinder.search(imbw, 'A', line)
  			#path = [[int(line),0]]
  			path = [[int(line),rline.shape[1]]]
  			#print(rline.shape[1])
  			#print('path[0][0]: ',path[0][0], ' path[0][1]: ', path[0][1])
  			draw_box(im, path, prev)
  			#draw_map(immap, mapy)
  			prev=(0, path[0][0])
  			n_line+=1
  			# print_path(path)
  
  		#save(filename, imbw, immap)
  		cv2.imwrite(out_path+'%s/summary.png'%f, im)
  	return out_dict
  
  	print(' - Elapsed time: ' + str((timer() - begin)) + ' s')