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})