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envio arnau
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arkasarius committed Sep 4, 2019
1 parent 99bd88a commit d2cae4c
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37 changes: 37 additions & 0 deletions Comparador.py
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import calculos as c
import os

movie="Angeles y demonios"
m="moviesdata"
g="actordata"
s='/'
apidata=os.listdir(m+s+movie)
actordata=os.listdir(g+s+movie)
print(movie)
print(apidata)
print(actordata)
DM=0.75 # 0.0 distancia minima posible
AM=0.4 # 1.0 angle maxim posible
calculos=0
for unkownactor in apidata:
act=c.extraerSublistaArchivo(m+s+movie+s+unkownactor)
print("\n")
print('{} subcaras para el actor {}'.format(len(act),unkownactor.strip('.txt')))
for subactor in actordata:
currentactor=c.extraerSublistaArchivo(g+s+movie+s+subactor)
count=0
for subcara in act:
for caratest in currentactor:
calculos=calculos+1
if(c.similitudCoseno(caratest,subcara)>AM):
if(c.distanciaEuclidea(caratest,subcara)<DM):
count=count+1
if(count>0):
print('{} Positivo para el cluster {}, {} positivos para coeficientes AM: {} y DM: {}'.format(subactor.strip('.txt'),unkownactor.strip('.txt'),count,AM,DM))
# calculos=calculos+1
# else:
# calculos=calculos+1
# pass
# print("no hi han cares valides per els coeficients AM : {} i DM: {} per l'actor {}".format(AM,DM,subactor.strip('.txt')))
print("\n")
print('{} pares de caras comparados para la pelicula {}'.format(calculos,movie))
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15 changes: 15 additions & 0 deletions ids.txt
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Figura 1 Representación de convolución pagina 19
Figura 2: Clúster artista 00 Yo robot (Will Smith) pagina 28
Figura 3: Muestra de errores típicos en extracción de caras pagina 34
Figura 4: Muestra de código de IMDBPY pagina 36
Figura 5: Muestra de algunos de los actores de Matrix pagina 36
Figura 6: implementación de descarga de imágenes de actores pagina 37
Figura 7: muestra de imágenes de Keanu Reeves pagina 38
Figura 8: Representación de dos caras V y W en un espacio vectorial N = 128. pagina 44
Figura 9: Verdaderos positivos de Linda Fiorentino según coeficiente DM pagina 45
Figura 10: Verdaderos positivos de Rip Torn según coeficiente DM pagina 46
Figura 11: Imagen del sub set de comprobación para el actor Rip Torn pagina 47
Figura 12: Verdaderos positivos de Linda Fiorentino según coeficiente AM. pagina 48
Figura 13: Verdaderos positivos de Linda Fiorentino según coeficientes AM y DM. pagina 49
Figura 14: Falsos positivos de Linda Fiorentino según coeficientes AM y DM. pagina 50
Figura 15: muestra de resultados de los posibles casos para la película Ángeles y demonios. pagina 52
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4 changes: 2 additions & 2 deletions intepreter_AM.py
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Expand Up @@ -22,7 +22,7 @@

AM=AM-0.01
# DM=1.2
x.append(AM)
x.append(1-AM)

for unkownactor in apidata:
act=c.extraerSublistaArchivo(m+s+movie+s+unkownactor)
Expand All @@ -37,7 +37,7 @@
if(c.distanciaEuclidea(caratest,subcara)<DM):
count=count+1
if(count>0):
print('{} true per {}, {} vegades amb AM: {}'.format(subactor.strip('.txt'),unkownactor.strip('.txt'),count,AM))
print('{} true per {}, {} vegades amb AM: {}'.format(subactor.strip('.txt'),unkownactor.strip('.txt'),count,1-AM))
y.append(count)
else:
y.append(0)
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6 changes: 3 additions & 3 deletions intepreter_DM_AM.py
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Expand Up @@ -55,7 +55,7 @@

AM=AM-0.01
# DM=1.2
n.append(AM)
n.append(1-AM)

for unkownactor in apidata:
act=c.extraerSublistaArchivo(m+s+movie+s+unkownactor)
Expand All @@ -70,7 +70,7 @@
if(c.distanciaEuclidea(caratest,subcara)<DM):
count=count+1
if(count>0):
print('{} true per {}, {} vegades amb AM: {}'.format(subactor.strip('.txt'),unkownactor.strip('.txt'),count,AM))
print('{} true per {}, {} vegades amb AM: {}'.format(subactor.strip('.txt'),unkownactor.strip('.txt'),count,1-AM))
z.append(count)
else:
z.append(0)
Expand All @@ -83,7 +83,7 @@
plt.plot(n,z,label="AM")
plt.title(actordata[0].strip(".txt"))
plt.xlabel("Coeficiente DM AM")
plt.ylabel("Verdaderos positivos")
plt.ylabel("Falsos positivos")
plt.grid()
plt.legend()
plt.show()
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