def __init__(self, size, makeData, noComp):
self.pca = eigenHands(size)
self.gabor = gaborFilters(False, size)
self.classify = classifyHands(False, size)
self.prep = preprocessing(size, noComp)
if (makeData == True):
self.pca.makeMatrix("garb")
self.pca.makeMatrix("hands")
self.pca.makeMatrix("rock")
self.pca.makeMatrix("paper")
self.pca.makeMatrix("scissors")
def __init__(self, size, makeData, noComp):
self.pca = eigenHands(size)
self.gabor = gaborFilters(False, size)
self.classify = classifyHands(False, size)
self.prep = preprocessing(size, noComp)
if(makeData == True):
self.pca.makeMatrix("garb")
self.pca.makeMatrix("hands")
self.pca.makeMatrix("rock")
self.pca.makeMatrix("paper")
self.pca.makeMatrix("scissors")
elif(int(choice) == 5):
noComp = raw_input('number of components for PCA ...')
dataset = raw_input('choose the dataset c= > rock & paper & scissors; h => hands vs garbage ...')
datas = {'c':['rock','paper','scissors'], 'h':['hands','garb']}
hands = eigenHands(int(sizeImg))
_,data,txtLabels = hands.justGetDataMat(datas[dataset][0],"",False)
prep = preprocessing(int(sizeImg),int(noComp))
prep.doSmallManyGabors(data,txtLabels,dataset,False)
#prep.doSmallManyGabors(data[0:1,:],None,"",False)
#____________________________________________________________________________________________________
elif(int(choice) == 6):
dataset = raw_input('classify h => hands vs garbage; r => rock vs paper & scissors; p => paper vb scissors ...')
typeu = raw_input('choose the data 1 => original images; 2 => PCA on initial images; 3 => multiple Gabor filters + orig img; 4 => just multiple Gabor Filters...')
datas = {'r':'rock', 'h':'hands', 'p':'paper'}
classi = classifyHands(buildOpt[str(build)],int(sizeImg))
classi.classifySVM(int(typeu), datas[dataset])
#____________________________________________________________________________________________________
elif(int(choice) == 7):
dataset = raw_input('classify h => hands vs garbage; c => rock & paper & scissors ...')
typeu = raw_input('choose the data 1 => original images; 2 => PCA on initial images; 3 => multiple Gabor filters + orig img; 4 => just multiple Gabor Filters...')
datas = {'c':'rock', 'h':'hands'}
classi = classifyHands(buildOpt[str(build)],int(sizeImg))
classi.classifyKNN(int(typeu), datas[dataset],4)
#____________________________________________________________________________________________________
elif(int(choice) == 8):
model = raw_input('model to built: s => svm; k => knn ...')
dataset = raw_input('classify h => hands vs garbage; c => rock & paper & scissors ...')
datas = {'c':'rock', 'h':'hands'}
