def __init__(self,
ply_dir,
device,
Normalize_=None,
is_full_size=False,
scale=None):
ply_numpy_list = []
ply_numpy_list_lable = []
for fil in ply_dir:
pc = ObjLoader.load_point_cloud(fil)
pc.vertices = pc.vertices.astype('float16')
#pc.colors = pc.colors.astype('float16')
if scale:
ply_numpy_list.append(
NNdataProcess.transform_scaling(pc.vertices,
Original_dimension=1024,
scale_factor=scale))
else:
ply_numpy_list.append(
NNdataProcess.transform(
pc.vertices, is_full_size=is_full_size)) #,pc.colors))
self.Tensor_ply_list = torch.from_numpy(np.array(ply_numpy_list)).type(
torch.FloatTensor).to(device)
def tensor_to_ply(tensor,
threshold,
save_dir,
scale_factor=None,
Original_dimension=1024):
return NNdataProcess.tensor_to_ply(tensor,
threshold,
save_dir,
scale_factor=scale_factor,
Original_dimension=Original_dimension)
def Create_Dataset():
raw_ply = config.pathToLongdress2Ply
lable_dir = config.pathToLongdress2Compressed_Ply
save_dir = config.pathToLongdress2RNNTrain
NNdataProcess.create_training_sequence_list_wise_for_RNN(raw_ply, lable_dir, save_dir)
def get_training_data(self, testPart, data_dir):
return NNdataProcess.get_data(testPart=testPart,
data_dir=data_dir,
is_return_dir=True)