[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])])
