def load_data(k):
for i, j in enumerate(range(k * n, (k + 1) * n)):
if i % 10 == 0:
print('reading number', i + 1,
'th lab' + str(k + 1) + ' point clouds')
if use_key_feature:
pc = np.loadtxt(
base_path + '/lab' + str(k + 1) + '/lab_project' + str(i) +
'.txt') # pc = tf.convert_to_tensor(pc, dtype=tf.float32)
pc = PointCloud(pc)
pc.normalize()
expand = np.expand_dims(pc.position, axis=0)
pc_tile[j, :, :] = expand
# print('*****************************************')
# print('reading point cloud cost time:{}'.format(t1 - t0))
pc_key_eig = get_local_eig_np(expand) # 1 x nb_keypoints x 9
# print('*****************************************')
# print('get local cost time:{}'.format(t2 - t1))
#pc_key_feature[i, :, :] = np.squeeze(sess.run(pc_key_eig, feed_dict={pc_pl: pc}))
pc_key_feature[j, :, :] = np.squeeze(pc_key_eig)
else:
pc_tile[j, :, :] = np.expand_dims(
np.loadtxt(base_path + '/lab' + str(k + 1) +
'/lab_project' + str(i) + '.txt'),
axis=0)
# print('-----------------------------------------')
# print('one pc cost total:{}second'.format(te-ts))
# print('----------------------------------------')
print('current thread ending： ', threading.current_thread().name)
def save_data(save_path='',
base_path='',
n=5000,
use_key_feature=True,
train_data=True,
nb_types=4,
show_result=False,
normalize=True,
shuffle=True):
"""
transform the txt point clouds into h5py dataset for simplicity. data augmentation of projection is implemented here
:param save_path:
:param n:
:param train_data whether it is training data or it is test data. if its testdata, label is random.
:param base_path: path contains txt or ply point cloud data
:param use_key_feature: if you want to use the local key features
:param nb_types: number of classes of used object
:return:
"""
compute_time = []
if train_data:
pc_tile = np.empty(shape=(nb_types * n, 1024, 3))
if use_key_feature:
pc_key_feature = np.empty(shape=(nb_types * n, int(
1024 * 0.1), 9)) # key feature space, 102=1024*0.1,
# 9 for multi-scale eigen-value
#pc_pl = tf.placeholder(tf.float32, shape=(1, 1024, 3))
for k in range(
nb_types
): # number of types of objects model, test data can ignore type label
for i, j in enumerate(range(k * n, (k + 1) * n)):
if i % 10 == 0:
print('reading number', i + 1,
'th lab' + str(k + 1) + ' point clouds')
if use_key_feature:
pc = np.loadtxt(
base_path + '/lab' + str(k + 1) + '/lab_project' +
str(i) + '.txt'
) # pc = tf.convert_to_tensor(pc, dtype=tf.float32)
pc = PointCloud(pc)
if normalize:
pc.normalize(
) # partial point cloud should not normalize
expand = np.expand_dims(pc.position, axis=0)
pc_tile[j, :, :] = expand
# print('*****************************************')
# print('reading point cloud cost time:{}'.format(t1 - t0))
pc_key_eig = get_local_eig_np(
expand, useiss=False) # 1 x nb_keypoints x 9
# print('*****************************************')
# print('get local cost time:{}'.format(t2 - t1))
#pc_key_feature[i, :, :] = np.squeeze(sess.run(pc_key_eig, feed_dict={pc_pl: pc}))
pc_key_feature[j, :, :] = np.squeeze(pc_key_eig)
else:
pc_tile[j, :, :] = np.expand_dims(
np.loadtxt(base_path + '/lab' + str(k + 1) +
'/lab_project' + str(i) + '.txt'),
axis=0)
# print('-----------------------------------------')
# print('one pc cost total:{}second'.format(te-ts))
# print('----------------------------------------')
pc_label = np.tile(np.arange(nb_types), (n, 1)).reshape((-1, ),
order='F')
train_set_shape = (nb_types * n, 1024, 3)
train_set_local_shape = (nb_types * n, 102, 9)
train_label_shape = (nb_types * n, )
else:
ply_list = [
base_path + '/' + i for i in os.listdir(base_path)
if os.path.splitext(i)[1] == '.ply'
or os.path.splitext(i)[1] == '.txt'
]
n = len(ply_list)
less_than_th = []
for i, j in enumerate(ply_list):
pc = PointCloud(j)
if pc.nb_points < 1024:
less_than_th.append(i)
n = n - len(less_than_th)
print("there are: ,", n, ' point clouds with # of points available')
pc_label = np.arange(n)
train_set_shape = (n, 1024, 3)
train_set_local_shape = (n, 102, 9)
train_label_shape = (n, )
pc_tile = np.empty(shape=(n, 1024, 3))
pc_key_feature = np.empty(shape=(n, int(
1024 * 0.1), 9)) # key feature space, 102=1024*0.1,
for i, j in enumerate(ply_list):
if i not in less_than_th:
print(j)
start_time = time.clock()
mypc = PointCloud(j)
if mypc.nb_points > 1024:
mypc.down_sample(number_of_downsample=1024)
if normalize:
mypc.normalize()
expand = np.expand_dims(mypc.position, axis=0)
pc_tile[i, :, :] = expand
pc_key_eig = get_local_eig_np(expand, useiss=False)
end_time = time.clock()
compute_time.append([end_time - start_time])
if use_key_feature:
pc_key_feature[i, :, :] = np.squeeze(pc_key_eig)
hdf5_file = h5py.File(save_path, mode='a')
hdf5_file.create_dataset('train_set', train_set_shape,
np.float32) # be careful about the dtype
hdf5_file.create_dataset('train_labels', train_label_shape, np.uint8)
hdf5_file.create_dataset('train_set_local', train_set_local_shape,
np.float32)
if shuffle:
idx = np.arange(np.shape(pc_tile)[0])
np.random.shuffle(idx)
pc_tile = pc_tile[idx, ...]
pc_label = pc_label[idx, ...]
pc_key_feature = pc_key_feature[idx, ...]
hdf5_file["train_set"][...] = pc_tile
hdf5_file["train_labels"][...] = pc_label
hdf5_file["train_set_local"][...] = pc_key_feature
hdf5_file.close()
return compute_time