def lengthInStroke(self): self.liS=np.zeros([self.tE.shape[0]],np.float64) anteriors=0 for i in range(self.tE.shape[0]): acDi=spp.lcomp(self.Coord,int(self.tE[i]+1)) self.liS[i]=acDi-anteriors anteriors=acDi
def lengthInStroke(self):
self.liS = np.zeros([self.tE.shape[0]], np.float64)
anteriors = 0
for i in range(self.tE.shape[0]):
acDi = spp.lcomp(self.Coord, int(self.tE[i] + 1))
self.liS[i] = acDi - anteriors
anteriors = acDi
def lengthPosition(self): self.LP=np.zeros([self.Coord.shape[0]],np.float64) Lan=0 for ik in range(self.tE.shape[0]): if ik==0: ini=-1 else: ini=int(self.tE[ik-1]) L=Lan+spp.lcomp(self.Coord,int(self.tE[ik])+1)-spp.lcomp(self.Coord,ini+1) Lan=L Lan=0 for ik in range(self.tE.shape[0]): if ik==0: ini=-1 else: ini=int(self.tE[ik-1]) for j in range(ini+1,int(self.tE[ik])+1): Lr=Lan+spp.lcomp(self.Coord,j+1)-spp.lcomp(self.Coord,ini+1) self.LP[j]=Lr/L Lan=Lr
def lengthPosition(self):
self.LP = np.zeros([self.Coord.shape[0]], np.float64)
Lan = 0
for ik in range(self.tE.shape[0]):
if ik == 0:
ini = -1
else:
ini = int(self.tE[ik - 1])
L = Lan + spp.lcomp(self.Coord,
int(self.tE[ik]) + 1) - spp.lcomp(
self.Coord, ini + 1)
Lan = L
Lan = 0
for ik in range(self.tE.shape[0]):
if ik == 0:
ini = -1
else:
ini = int(self.tE[ik - 1])
for j in range(ini + 1, int(self.tE[ik]) + 1):
Lr = Lan + spp.lcomp(self.Coord, j + 1) - spp.lcomp(
self.Coord, ini + 1)
self.LP[j] = Lr / L
Lan = Lr
def elasticMatching(charTemplates,symbolToRec,weights,amp,alt):
costCoord=[]
costLP=[]
costtAngle=[]
costtAD=[]
costliS=[]
costaA=[]
costqE=[]
costrSL=[]
costcog=[]
coststyle=[]
reference=[]
cost=[]
for character in charTemplates:
#Nomes si tenen el mateix nombre de traces ho decideix
if character[-1:]=='1':
tNum=1
elif character[-1:]=='2':
tNum=2
elif character[-1:]=='3':
tNum=3
if tNum==symbolToRec.tE.shape[0]:
adaptedSymbol=spp.altArcLengthResampling([symbolToRec],charTemplates[character].tE)[0]
adaptedSymbol.computeFeatures()
#Busca el cost de cada feature
costCoord.append(sum([math.sqrt(((charTemplates[character].Coord[i,0]-adaptedSymbol.Coord[i,0])**2)+((charTemplates[character].Coord[i,1]-adaptedSymbol.Coord[i,1])**2)) for i in range(adaptedSymbol.Coord.shape[0])]))
costLP.append(sum([abs(charTemplates[character].LP[i]-adaptedSymbol.LP[i]) for i in range(adaptedSymbol.LP.shape[0])]))
costtAngle.append(sum([abs(charTemplates[character].turningAngle[i]-adaptedSymbol.turningAngle[i]) for i in range(adaptedSymbol.turningAngle.shape[0])]))
costtAD.append(sum([abs(charTemplates[character].turningAngleDifference[i]-adaptedSymbol.turningAngleDifference[i]) for i in range(adaptedSymbol.turningAngleDifference.shape[0])]))
if adaptedSymbol.liS.shape[0]!=len(charTemplates[character].liS):
costliS.append(spp.lcomp(adaptedSymbol.Coord,adaptedSymbol.Coord.shape[0]))
else:
costliS.append(sum([abs(charTemplates[character].liS[i]-adaptedSymbol.liS[i]) for i in range(adaptedSymbol.liS.shape[0])]))
if adaptedSymbol.accAngle.shape[0]!=len(charTemplates[character].accAngle):
costaA.append(adaptedSymbol.tE.shape[0]*2*math.pi)
else:
costaA.append(sum([abs(charTemplates[character].accAngle[i]-adaptedSymbol.accAngle[i]) for i in range(adaptedSymbol.accAngle.shape[0])]))
if adaptedSymbol.quadraticError.shape[0]!=len(charTemplates[character].quadraticError):
costqE.append(math.sqrt(8)*adaptedSymbol.tE.shape[0])
else:
costqE.append(sum([abs(charTemplates[character].quadraticError[i]-adaptedSymbol.quadraticError[i]) for i in range(adaptedSymbol.quadraticError.shape[0])]))
if adaptedSymbol.relStrokeLength.shape[0]!=len(charTemplates[character].relStrokeLength):
costrSL.append(1)
else:
costrSL.append(sum([abs(charTemplates[character].relStrokeLength[i]-adaptedSymbol.relStrokeLength[i]) for i in range(adaptedSymbol.relStrokeLength.shape[0])]))
if adaptedSymbol.coG.shape[0]!=len(charTemplates[character].coG):
costcog.append(adaptedSymbol.tE.shape[0]*math.sqrt(8))
else:
costcog.append(sum([math.sqrt(((charTemplates[character].coG[i][0]-adaptedSymbol.coG[i][0])**2)+((charTemplates[character].coG[i][1]-adaptedSymbol.coG[i][1])**2)) for i in range(adaptedSymbol.coG.shape[0])]))
if charTemplates[character].Style==adaptedSymbol.Style:
coststyle.append(0)
else:
coststyle.append(1)
reference.append(character)
cost.append(0)
allThisCost={}
kindsOfCost={'Coord':costCoord,'LP':costLP,'turningAngle':costtAngle,'turningAngleDifference':costtAD,'liS':costliS,'accAngle':costaA,'quadraticError':costqE,'relStrokeLength':costrSL,'coG':costcog,'Style':coststyle}
probByCost={}
fiProb={}
#Segons el cost busca una probabilitat i la pondera
for kind in kindsOfCost:
allThisCost[kind]=np.nansum(kindsOfCost[kind])
probByCost[kind]=[allThisCost[kind]/float(ch) for ch in kindsOfCost[kind]]
totProb=np.nansum(probByCost[kind])
fiProb[kind]=[ch/float(totProb) for ch in probByCost[kind]]
probPonderada=[np.nansum([weights[kind]*fiProb[kind][i] for kind in fiProb]) for i in range(len(cost))]
while charTemplates[reference[np.argmax(probPonderada)]].tE.shape[0]!=adaptedSymbol.tE.shape[0]:
del reference[np.argmax(probPonderada)]
del probPonderada[np.argmax(probPonderada)]
etiqBelongs=reference[np.argmax(probPonderada)]
#Casos especials amb bounding box petita
if (symbolToRec.bBox[1]-symbolToRec.bBox[0])<0.0125*amp and (symbolToRec.bBox[3]-symbolToRec.bBox[2])<0.017*alt:
etiqBelongs='.1'
elif ((symbolToRec.bBox[1]-symbolToRec.bBox[0])>0.0125*amp and (symbolToRec.bBox[3]-symbolToRec.bBox[2])<0.017*alt):
etiqBelongs='-1'
elif (symbolToRec.bBox[1]-symbolToRec.bBox[0])<0.0125*amp and (symbolToRec.bBox[3]-symbolToRec.bBox[2])>0.017*alt:
if symbolToRec.tE.shape[0]==1:
etiqBelongs='11'
elif symbolToRec.tE.shape[0]==2:
if abs(symbolToRec.Coord[0,1]-symbolToRec.Coord[symbolToRec.tE[0],1])*(symbolToRec.bBox[3]-symbolToRec.bBox[2])<0.017*alt and abs(symbolToRec.Coord[symbolToRec.tE[0]+1,1]-symbolToRec.Coord[symbolToRec.tE[1],1])*(symbolToRec.bBox[3]-symbolToRec.bBox[2])<0.017*alt:
etiqBelongs='\ldots2'
elif (abs(symbolToRec.Coord[0,1]-symbolToRec.Coord[symbolToRec.tE[0],1])*(symbolToRec.bBox[3]-symbolToRec.bBox[2])>0.017*alt and abs(symbolToRec.Coord[symbolToRec.tE[0]+1,1]-symbolToRec.Coord[symbolToRec.tE[1],1])*(symbolToRec.bBox[3]-symbolToRec.bBox[2])<0.017*alt) or (abs(symbolToRec.Coord[0,1]-symbolToRec.Coord[symbolToRec.tE[0],1])*(symbolToRec.bBox[3]-symbolToRec.bBox[2])<0.017*alt and abs(symbolToRec.Coord[symbolToRec.tE[0]+1,1]-symbolToRec.Coord[symbolToRec.tE[1],1])*(symbolToRec.bBox[3]-symbolToRec.bBox[2])>0.017*alt):
if symbolToRec.coG[0,1]<symbolToRec.coG[1,1]:
etiqBelongs='i2'
else:
etiqBelongs='!2'
else:
etiqBelongs='\ldots3'
if (symbolToRec.bBox[1]-symbolToRec.bBox[0])>(symbolToRec.bBox[3]-symbolToRec.bBox[2])*8:
etiqBelongs='-1'
return etiqBelongs
def elasticMatching(charTemplates, symbolToRec, weights, amp, alt):
costCoord = []
costLP = []
costtAngle = []
costtAD = []
costliS = []
costaA = []
costqE = []
costrSL = []
costcog = []
coststyle = []
reference = []
cost = []
for character in charTemplates:
#Nomes si tenen el mateix nombre de traces ho decideix
if character[-1:] == '1':
tNum = 1
elif character[-1:] == '2':
tNum = 2
elif character[-1:] == '3':
tNum = 3
if tNum == symbolToRec.tE.shape[0]:
adaptedSymbol = spp.altArcLengthResampling(
[symbolToRec], charTemplates[character].tE)[0]
adaptedSymbol.computeFeatures()
#Busca el cost de cada feature
costCoord.append(
sum([
math.sqrt(((charTemplates[character].Coord[i, 0] -
adaptedSymbol.Coord[i, 0])**2) +
((charTemplates[character].Coord[i, 1] -
adaptedSymbol.Coord[i, 1])**2))
for i in range(adaptedSymbol.Coord.shape[0])
]))
costLP.append(
sum([
abs(charTemplates[character].LP[i] - adaptedSymbol.LP[i])
for i in range(adaptedSymbol.LP.shape[0])
]))
costtAngle.append(
sum([
abs(charTemplates[character].turningAngle[i] -
adaptedSymbol.turningAngle[i])
for i in range(adaptedSymbol.turningAngle.shape[0])
]))
costtAD.append(
sum([
abs(charTemplates[character].turningAngleDifference[i] -
adaptedSymbol.turningAngleDifference[i]) for i in
range(adaptedSymbol.turningAngleDifference.shape[0])
]))
if adaptedSymbol.liS.shape[0] != len(charTemplates[character].liS):
costliS.append(
spp.lcomp(adaptedSymbol.Coord,
adaptedSymbol.Coord.shape[0]))
else:
costliS.append(
sum([
abs(charTemplates[character].liS[i] -
adaptedSymbol.liS[i])
for i in range(adaptedSymbol.liS.shape[0])
]))
if adaptedSymbol.accAngle.shape[0] != len(
charTemplates[character].accAngle):
costaA.append(adaptedSymbol.tE.shape[0] * 2 * math.pi)
else:
costaA.append(
sum([
abs(charTemplates[character].accAngle[i] -
adaptedSymbol.accAngle[i])
for i in range(adaptedSymbol.accAngle.shape[0])
]))
if adaptedSymbol.quadraticError.shape[0] != len(
charTemplates[character].quadraticError):
costqE.append(math.sqrt(8) * adaptedSymbol.tE.shape[0])
else:
costqE.append(
sum([
abs(charTemplates[character].quadraticError[i] -
adaptedSymbol.quadraticError[i])
for i in range(adaptedSymbol.quadraticError.shape[0])
]))
if adaptedSymbol.relStrokeLength.shape[0] != len(
charTemplates[character].relStrokeLength):
costrSL.append(1)
else:
costrSL.append(
sum([
abs(charTemplates[character].relStrokeLength[i] -
adaptedSymbol.relStrokeLength[i])
for i in range(adaptedSymbol.relStrokeLength.shape[0])
]))
if adaptedSymbol.coG.shape[0] != len(charTemplates[character].coG):
costcog.append(adaptedSymbol.tE.shape[0] * math.sqrt(8))
else:
costcog.append(
sum([
math.sqrt(((charTemplates[character].coG[i][0] -
adaptedSymbol.coG[i][0])**2) +
((charTemplates[character].coG[i][1] -
adaptedSymbol.coG[i][1])**2))
for i in range(adaptedSymbol.coG.shape[0])
]))
if charTemplates[character].Style == adaptedSymbol.Style:
coststyle.append(0)
else:
coststyle.append(1)
reference.append(character)
cost.append(0)
allThisCost = {}
kindsOfCost = {
'Coord': costCoord,
'LP': costLP,
'turningAngle': costtAngle,
'turningAngleDifference': costtAD,
'liS': costliS,
'accAngle': costaA,
'quadraticError': costqE,
'relStrokeLength': costrSL,
'coG': costcog,
'Style': coststyle
}
probByCost = {}
fiProb = {}
#Segons el cost busca una probabilitat i la pondera
for kind in kindsOfCost:
allThisCost[kind] = np.nansum(kindsOfCost[kind])
probByCost[kind] = [
allThisCost[kind] / float(ch) for ch in kindsOfCost[kind]
]
totProb = np.nansum(probByCost[kind])
fiProb[kind] = [ch / float(totProb) for ch in probByCost[kind]]
probPonderada = [
np.nansum([weights[kind] * fiProb[kind][i] for kind in fiProb])
for i in range(len(cost))
]
while charTemplates[reference[np.argmax(
probPonderada)]].tE.shape[0] != adaptedSymbol.tE.shape[0]:
del reference[np.argmax(probPonderada)]
del probPonderada[np.argmax(probPonderada)]
etiqBelongs = reference[np.argmax(probPonderada)]
#Casos especials amb bounding box petita
if (symbolToRec.bBox[1] - symbolToRec.bBox[0]) < 0.0125 * amp and (
symbolToRec.bBox[3] - symbolToRec.bBox[2]) < 0.017 * alt:
etiqBelongs = '.1'
elif ((symbolToRec.bBox[1] - symbolToRec.bBox[0]) > 0.0125 * amp
and (symbolToRec.bBox[3] - symbolToRec.bBox[2]) < 0.017 * alt):
etiqBelongs = '-1'
elif (symbolToRec.bBox[1] - symbolToRec.bBox[0]) < 0.0125 * amp and (
symbolToRec.bBox[3] - symbolToRec.bBox[2]) > 0.017 * alt:
if symbolToRec.tE.shape[0] == 1:
etiqBelongs = '11'
elif symbolToRec.tE.shape[0] == 2:
if abs(symbolToRec.Coord[0, 1] -
symbolToRec.Coord[symbolToRec.tE[0], 1]) * (
symbolToRec.bBox[3] -
symbolToRec.bBox[2]) < 0.017 * alt and abs(
symbolToRec.Coord[symbolToRec.tE[0] + 1, 1] -
symbolToRec.Coord[symbolToRec.tE[1], 1]) * (
symbolToRec.bBox[3] -
symbolToRec.bBox[2]) < 0.017 * alt:
etiqBelongs = '\ldots2'
elif (
abs(symbolToRec.Coord[0, 1] -
symbolToRec.Coord[symbolToRec.tE[0], 1]) *
(symbolToRec.bBox[3] - symbolToRec.bBox[2]) > 0.017 * alt
and abs(symbolToRec.Coord[symbolToRec.tE[0] + 1, 1] -
symbolToRec.Coord[symbolToRec.tE[1], 1]) *
(symbolToRec.bBox[3] - symbolToRec.bBox[2]) < 0.017 * alt) or (
abs(symbolToRec.Coord[0, 1] -
symbolToRec.Coord[symbolToRec.tE[0], 1]) *
(symbolToRec.bBox[3] - symbolToRec.bBox[2]) < 0.017 * alt
and abs(symbolToRec.Coord[symbolToRec.tE[0] + 1, 1] -
symbolToRec.Coord[symbolToRec.tE[1], 1]) *
(symbolToRec.bBox[3] - symbolToRec.bBox[2]) > 0.017 * alt):
if symbolToRec.coG[0, 1] < symbolToRec.coG[1, 1]:
etiqBelongs = 'i2'
else:
etiqBelongs = '!2'
else:
etiqBelongs = '\ldots3'
if (symbolToRec.bBox[1] - symbolToRec.bBox[0]) > (symbolToRec.bBox[3] -
symbolToRec.bBox[2]) * 8:
etiqBelongs = '-1'
return etiqBelongs
