def saveClfOut(QuadDir,OutDir,halfwins,Numberofframe,svmclf,nbc):
for halfwin in halfwins:
outFile = '{}Classfication_nbc_{}_halfwin{}.mat'.format(OutDir,str(nbc),str(halfwin))
# if not os.path.isfile(outFile):
FVsFile = "{}FVS/FVsnbc_{}_halfwin_{}.mat".format(QuadDir,str(nbc),str(halfwin))
fvs = sio.loadmat(FVsFile)['fvs']
vecAllfvs = np.zeros((Numberofframe,nbc*13))
isFrame_labeled = np.zeros(Numberofframe)
i = 0;
for fnum in xrange(Numberofframe):
fvsum = np.sum(fvs[fnum])
if abs(fvsum)>0:
vecAllfvs[i,:] = fvs[i,:]
isFrame_labeled[fnum] = 1
i+=1
vecAllfvs = vecAllfvs[:i,:]
vecAllfvs = mytools.power_normalize(vecAllfvs,0.2)
frame_probs = svmclf.predict_proba(vecAllfvs)
frame_label = svmclf.predict(vecAllfvs)
frame_probstemp = np.zeros((Numberofframe,20))
frame_probstemp[isFrame_labeled>0,:] = frame_probs
frame_labelstemp = np.zeros(Numberofframe)
frame_labelstemp[isFrame_labeled>0]=frame_label
print 'saving to ' , outFile
sio.savemat(outFile,mdict={'frame_probs':frame_probstemp, 'frame_label':frame_labelstemp,
'isFrame_labeled':isFrame_labeled})
def saveClfOut(sample,QuadDir,OutDir,halfwin,Numberofframe,svmclf,nbc):
outFile = '{}binary_halfwin_{}.mat'.format(OutDir,str(halfwin))
# if not os.path.isfile(outFile):
FVsFile = "{}FVS/FVsnbc_{}_halfwin_{}.mat".format(QuadDir,str(nbc),str(halfwin))
fvs = sio.loadmat(FVsFile)['fvs']
vecAllfvs = np.zeros((Numberofframe,nbc*13))
isFrame_labeled = np.zeros(Numberofframe)
i = 0;
for fnum in xrange(Numberofframe):
fvsum = np.sum(fvs[fnum])
if abs(fvsum)>0:
vecAllfvs[i,:] = fvs[i,:]
isFrame_labeled[fnum] = 1
i+=1
vecAllfvs = vecAllfvs[:i,:]
vecAllfvs = mytools.power_normalize(vecAllfvs,0.4)
frame_label = svmclf.predict(vecAllfvs)
frame_labelstemp = np.zeros((Numberofframe))
frame_labelstemp[isFrame_labeled>0]=frame_label
print 'saving to ' , outFile
sio.savemat(outFile,mdict={'frame_label':frame_labelstemp,'isFrame_labeled':isFrame_labeled})
