def SceneTrain(NumSub, ImagPerClassToRead, DataPath, TrainImgs, ResizeAmt):
# Read Images and Generate Image Features XNow
for i in range(0,NumSub):
for j in range(0, ImagPerClassToRead):
InitAll.tic()
#print DataPath+str(i+1)+'frame'+str(TrainImgs[i][j])+'.png'
XNow = cv2.imread(DataPath+str(i+1)+'frame'+str(TrainImgs[i][j])+'.png',0)
XNow = cv2.resize(XNow, ResizeAmt, interpolation = cv2.INTER_CUBIC)
XNow = InitAll.ComputeGIST(XNow)
#print("Sub " + str(i+1) + " Image " + str(j+1))
if(i==0 and j==0):
X = np.reshape(XNow, (1,np.product(XNow.shape)))
else:
X = np.vstack((X,np.reshape(XNow, (1,np.product(XNow.shape)))))
InitAll.toc()
print "Subject " + str(i+1) + " done...."
# Now Generate Class Labels Y
# Class labels start from 1 and not 0
Y = [i for i in range(1,NumSub+1)]*ImagPerClassToRead
Y = list(np.sort(Y))
SVMModel = svm.SVC()
SVMModel.fit(X, Y)
# Saving the objects:
with open('SceneTrainedSVMModel'+strftime("%Y-%m-%d %H:%M:%S", gmtime())+'.pickle', 'w') as f:
pickle.dump([X, Y, SVMModel], f)
return SVMModel
def SceneTest(TestImg, SVMModel):
# Use SVM to predict class label and return it
return SVMModel.predict(InitAll.ComputeGIST(TestImg))[0]
