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)