def stackOF(chunk,img_rows,img_cols):
with open('../dataset/temporal_train_data.pickle','rb') as f1:
temporal_train_data=pickle.load(f1)
X_train,Y_train=ofp.stackOpticalFlow(chunk,temporal_train_data,img_rows,img_cols)
gc.collect()
return (X_train,Y_train)
def stackOF(chunk,img_rows,img_cols):
with open('../dataset/temporal_train_data.pickle','rb') as f1:
temporal_train_data=pickle.load(f1)
X_train,Y_train=ofp.stackOpticalFlow(chunk,temporal_train_data,img_rows,img_cols)
gc.collect()
return (X_train,Y_train)
def stackOF():
chunk_size=5000
with open('../dataset/temporal_train_data.pickle','rb') as f1:
temporal_train_data=pickle.load(f1)
chunk=chunks(temporal_train_data.keys(),chunk_size)
for blocks in chunk:
X_train,Y_train=ofp.stackOpticalFlow(blocks,temporal_train_data)
yield (X_train,Y_train)
def stackOF(chunk, img_rows, img_cols, jobType):
if jobType == 'train':
pickleFile = '../dataset/temporal_train_data.pickle'
else:
pickleFile = '../dataset/temporal_test_data.pickle'
with open(pickleFile, 'rb') as f1:
temporal_train_data = pickle.load(f1)
X_train, Y_train = ofp.stackOpticalFlow(chunk, temporal_train_data,
img_rows, img_cols)
gc.collect()
return (X_train, Y_train)