[character, len(tagClassification[character])]) for onlySym in genTags: for i in range(len(genTags[onlySym])): if genTags[onlySym][i][1] < 0.05 * sum([ genTags[onlySym][caseID][1] for caseID in range(len(genTags[onlySym])) ]): del tagClassification[genTags[onlySym][i][0]] del average[genTags[onlySym][i][0]] filenom = 'algb02.inkml' Coord = ink2Traces.i2t(filenom) #Extreure les coordenades donades pel fitxer img, byAxis, difs = drawTraces.draw( Coord) #Mostrar resultat obtingut i montar imatge Symb, groupedStrokes = fileSeg.segment(Coord, byAxis, difs) #Agrupar traces en simbols Symb = drawRegions.drawS(Symb) symbol = spp.preprocessing([Symb[1]]) symbol[0].computeFeatures() #pprint(vars(symbol[0])) #for i in range(len(symbol[0].LP)): # print str(symbol[0].LP[i])+', ' auxAverage = copy.deepcopy(average) auxAverage['21'].computeFeatures() auxAverage['r1'].computeFeatures() os.remove('features.txt') report = open('features.txt', 'w') report.write('||||||||||||||||||||||||||||||||||||||||||||||||||||||\n') report.write('LP:\n') for i in range(len(tagClassification['21'])): for j in range(len(tagClassification['21'][i].LP)): report.write(str(tagClassification['21'][i].LP[j]) + ', ')
symboldB,tagClassification,average=temp.readTemplate() genTags={} for character in tagClassification: if character[:-1] not in genTags: genTags[character[:-1]]=[] genTags[character[:-1]].append([character,len(tagClassification[character])]) for onlySym in genTags: for i in range(len(genTags[onlySym])): if genTags[onlySym][i][1]<0.05*sum([genTags[onlySym][caseID][1] for caseID in range(len(genTags[onlySym]))]): del tagClassification[genTags[onlySym][i][0]] del average[genTags[onlySym][i][0]] filenom='algb02.inkml' Coord=ink2Traces.i2t(filenom) #Extreure les coordenades donades pel fitxer img,byAxis,difs=drawTraces.draw(Coord) #Mostrar resultat obtingut i montar imatge Symb,groupedStrokes=fileSeg.segment(Coord,byAxis,difs) #Agrupar traces en simbols Symb=drawRegions.drawS(Symb) symbol=spp.preprocessing([Symb[1]]) symbol[0].computeFeatures() #pprint(vars(symbol[0])) #for i in range(len(symbol[0].LP)): # print str(symbol[0].LP[i])+', ' auxAverage=copy.deepcopy(average) auxAverage['21'].computeFeatures() auxAverage['r1'].computeFeatures() os.remove('features.txt') report=open('features.txt','w') report.write('||||||||||||||||||||||||||||||||||||||||||||||||||||||\n') report.write('LP:\n') for i in range(len(tagClassification['21'])): for j in range(len(tagClassification['21'][i].LP)): report.write(str(tagClassification['21'][i].LP[j])+', ')
import repS import SClass from pprint import pprint import elasticMatching as eM import pytemplate as temp import featurePonderation as fp import pyStructural as stru #Sistema sencer, llegeix nom del fitxer inkml a analitzar i retorna l'expressio (ex: overAll.py fitxerAClassificar.inkml) filenom = sys.argv[1] #Llegir el nom del fitxer InkML de la consola Coord = ink2Traces.i2t(filenom) #Extreure les coordenades donades pel fitxer img, byAxis, difs = drawTraces.draw( Coord) #Mostrar resultat obtingut i montar imatge Symb, groupedStrokes = fileSeg.segment(Coord, byAxis, difs) #Agrupar traces en simbols Symb = drawRegions.drawS( Symb) #Buscar la bounding box i el centre de cada simbol Symb = spp.preprocessing(Symb) #Preprocessar tots els simbols print 'Computing features..........' for i in range(len(Symb)): Symb[i].computeFeatures() #Calcular les features de cada simbol print 'Features extracted.' symboldB, tagClassification, averages = temp.readTemplate( ) #Llegeix la base de dades per extreure tots els simbols etiquetats, totes les mostres ordenades per caracter(etiqueta) i el template de cada caracter genTags = { } #Busca el significat independent de cada caracter i si la proporcio del caracter respecte el significat independent es molt baixa ho considera soroll i elimina el caracter #Elimina components sorollosos de la base de dades for character in tagClassification: if character[:-1] not in genTags: genTags[character[:-1]] = [] genTags[character[:-1]].append( [character, len(tagClassification[character])])