if (len(sys.argv) == 3):
image = sys.argv[1]
shape = sys.argv[2]
kernel = 'rbf'
trainingSample = 500
validationSample = 1000
print "Default Value : kernel=rbf, trainingSample=500, validationSample=1000"
elif (len(sys.argv) == 6):
image = sys.argv[1]
shape = sys.argv[2]
kernel = sys.argv[3]
trainingSample = sys.argv[4]
validationSample = sys.argv[5]
else:
print "Usage : ./SVM_main_prog.py image.tif shape.shp [OPTIONS]"
print "OPTION : kernel(linear/rbf/poly/sigmoid) Trainig_Sample Validation_Sample"
exit()
print("Compute Statistics ... ")
svm.ComputeStat(image)
print("Train SVM ... ")
svm.TrainSVM(image, shape, kernel, trainingSample, validationSample)
print("Image Classification ... ")
svm.ImageClassif(image)
print("Colorizing Classification ... ")
svm.ColorMap(image)
band.append(src.GetRasterBand(bi + 1))
tab.append(bi)
tab = []
while len(tab) < len(band) :
line = "Sélection de la bande n° " + str(len(tab)) + "\n"
print line
finalRank.write(line)
tab.append(0)
nb_band = len(tab)
averageAccuracuy=[]
for i in range(len(band)):
tab[nb_band-1]=i
bandName = WriteImageFromBandNum (band, tab, shape, kernel, trainingSample, validationSample)
svm.ComputeStat(bandName)
svm.TrainSVM (bandName, shape, kernel,trainingSample,validationSample)
if os.path.exists("svmConfusionMatrixQB1.csv") == 0 :
exit("svmConfusionMatrixQB1.csv n'a pas été créé")
reader = csv.reader(open("svmConfusionMatrixQB1.csv","rb"))
next(reader, None)
next(reader, None)
x=list(reader)
confusionMatrix=np.array(x).astype('int')
nbGood=0
nbBad=0
for k in range(confusionMatrix.shape[0]) :
for j in range(confusionMatrix.shape[1]) :
if k==j :
nbGood+=confusionMatrix[k][j]
nbBad+=confusionMatrix[k][j]
