def __getitem__(self, item):
pointcloud = self.data[item].astype(np.float32)[:, :3]
label = self.data[item].astype(np.long)[:, 3] - 1 # labels 1-8
# apply data rotation and augmentation on train samples
if self.partition == 'train':
pointcloud = jitter_pointcloud(
random_rotate_one_axis(pointcloud, "z"))
return (pointcloud, label)
def __getitem__(self, item):
pointcloud = np.load(self.pc_list[item])[:, :3].astype(np.float32)
label = np.copy(self.label[item])
pointcloud = scale_to_unit_cube(pointcloud)
# sample according to farthest point sampling
if pointcloud.shape[0] > NUM_POINTS:
pointcloud = np.swapaxes(np.expand_dims(pointcloud, 0), 1, 2)
_, pointcloud = farthest_point_sample_np(pointcloud, NUM_POINTS)
pointcloud = np.swapaxes(pointcloud.squeeze(), 1,
0).astype('float32')
# apply data rotation and augmentation on train samples
if self.partition == 'train' and item not in self.val_ind:
pointcloud = jitter_pointcloud(
random_rotate_one_axis(pointcloud, "z"))
return (pointcloud, label)
def __getitem__(self, item):
pointcloud = np.load(self.pc_list[item])[:, :3].astype(np.float32)
label = np.copy(self.centroid[self.pc_list[item].split('/')[-1][:-4]])
#label = np.copy(self.label[item])
#label = np.load(add_pd_path(self.pc_list[item], pre="pd4")) #load from npy file
#label = get_pd_vector(self.pc_list[item], self.rips)
pointcloud = scale_to_unit_cube(pointcloud)
# Rotate ShapeNet by -90 degrees
pointcloud = self.rotate_pc(pointcloud, label)
# sample according to farthest point sampling
if pointcloud.shape[0] > NUM_POINTS:
pointcloud = np.swapaxes(np.expand_dims(pointcloud, 0), 1, 2)
_, pointcloud = farthest_point_sample_np(pointcloud, NUM_POINTS)
pointcloud = np.swapaxes(pointcloud.squeeze(), 1,
0).astype('float32')
# apply data rotation and augmentation on train samples
if self.partition == 'train' and item not in self.val_ind:
pointcloud = jitter_pointcloud(
random_rotate_one_axis(pointcloud, "z"))
return (pointcloud, label)