def parse_data(path, shape, size, batch_size, time_step):
DataSet = []
i_Buffers = []
Labels = []
l_Buffers = []
cnt = 0
for i in range(time_step):
i_Buffers.append(None)
l_Buffers.append(None)
images = Img.Video_Read(path, shape, size)
labels = Img.Label_Read(path)
for image, label in zip(images, labels):
cnt += 1
for i in range(time_step):
i_Buffers[time_step -i - 1] = i_Buffers[time_step - i - 2]
l_Buffers[time_step -i - 1] = l_Buffers[time_step - i - 2]
i_Buffers[0] = image
l_Buffers[0] = label
if(cnt >= time_step):
DataSet.append(i_Buffers.copy())
Labels.append(l_Buffers.copy())
train = tf.data.Dataset.from_tensor_slices((DataSet, Labels))
train = train.batch(batch_size)
return train
