def augment_fn(batch_xyz, batch_label):
bsize, num_point, _ = batch_xyz.shape
# shuffle the orders of samples in a batch
idx = np.arange(bsize)
np.random.shuffle(idx)
batch_xyz = batch_xyz[idx, :, :]
batch_label = batch_label[idx, :]
# shuffle the point orders of each sample
idx = np.arange(num_point)
np.random.shuffle(idx)
batch_xyz = batch_xyz[:, idx, :]
batch_label = batch_label[:, idx]
# perform augmentation on the first np.int32(augment_ratio*bsize) samples
augSize = np.int32(1 / 3.0 * bsize)
augment_xyz = batch_xyz[0:augSize]
augment_xyz = data_util.rotate_point_cloud(augment_xyz)
augment_xyz = data_util.rotate_perturbation_point_cloud(augment_xyz)
augment_xyz = data_util.random_scale_point_cloud(augment_xyz)
augment_xyz = data_util.shift_point_cloud(augment_xyz)
augment_xyz = data_util.jitter_point_cloud(augment_xyz)
batch_xyz[0:augSize] = augment_xyz
augment_xyz = batch_xyz[augSize:2 * augSize]
augment_xyz = data_util.random_scale_point_cloud(augment_xyz)
augment_xyz = data_util.shift_point_cloud(augment_xyz)
augment_xyz = data_util.jitter_point_cloud(augment_xyz)
batch_xyz[augSize:2 * augSize] = augment_xyz
return batch_xyz, batch_label
def augment_fn(batch_input, batch_feature, augment_ratio=0.5):
bsize, num_point, _ = batch_input.shape
# shuffle the orders of samples in a batch
idx = np.arange(bsize)
np.random.shuffle(idx)
batch_input = batch_input[idx, :, :]
# batch_rho = batch_rho[idx,:]
# batch_engy = batch_engy[idx,:]
batch_feature = batch_feature[idx]
# shuffle the point orders of each sample
idx = np.arange(num_point)
np.random.shuffle(idx)
batch_input = batch_input[:, idx, :]
# batch_rho = batch_rho[:,idx]
# batch_engy = batch_engy[:,idx]
# batch_xyz = data_util.shuffle_points(batch_xyz)
# perform augmentation on the first np.int32(augment_ratio*bsize) samples
augSize = np.int32(augment_ratio * bsize)
augment_xyz = batch_input[0:augSize, :, 0:3]
augment_xyz = data_util.rotate_point_cloud(augment_xyz)
augment_xyz = data_util.rotate_perturbation_point_cloud(augment_xyz)
augment_xyz = data_util.random_scale_point_cloud(augment_xyz)
augment_xyz = data_util.shift_point_cloud(augment_xyz)
batch_input[0:augSize, :, 0:3] = augment_xyz
return batch_input, batch_feature
def augment_fn(batch_xyz):
# perform augmentation on the first np.int32(augment_ratio*bsize) samples
augment_xyz = data_util.rotate_point_cloud(batch_xyz)
augment_xyz = data_util.rotate_perturbation_point_cloud(augment_xyz)
augment_xyz = data_util.random_scale_point_cloud(augment_xyz)
augment_xyz = data_util.shift_point_cloud(augment_xyz)
return augment_xyz
def augment_fn(batch_input):
augment_xyz = batch_input[:, :, 0:3]
augment_xyz = data_util.rotate_perturbation_point_cloud(augment_xyz)
augment_xyz = data_util.random_scale_point_cloud(augment_xyz)
augment_xyz = data_util.shift_point_cloud(augment_xyz)
augment_xyz = data_util.jitter_point_cloud(augment_xyz)
batch_input[:, :, 0:3] = augment_xyz
return batch_input
def augment_fn2(batch_xyz):
augment_xyz = batch_xyz
augment_xyz = data_util.rotate_perturbation_point_cloud(augment_xyz)
augment_xyz = data_util.random_scale_point_cloud(augment_xyz)
augment_xyz = data_util.shift_point_cloud(augment_xyz)
augment_xyz = data_util.jitter_point_cloud(augment_xyz)
batch_xyz = augment_xyz
return batch_xyz
def augment_fn1(batch_input):
# perform augmentation on the first np.int32(augment_ratio*bsize) samples
augment_xyz = batch_input[:, :, 0:3]
augment_xyz = data_util.rotate_perturbation_point_cloud(augment_xyz)
augment_xyz = data_util.random_scale_point_cloud(augment_xyz)
augment_xyz = data_util.shift_point_cloud(augment_xyz)
augment_xyz = data_util.jitter_point_cloud(augment_xyz)
batch_input[:, :, 0:3] = augment_xyz
return batch_input
def augment_fn(batch_input, batch_label):
bsize, num_point, _ = batch_input.shape
# shuffle the orders of samples in a batch
idx = np.arange(bsize)
np.random.shuffle(idx)
batch_input = batch_input[idx,:,:]
batch_label = batch_label[idx,:]
# shuffle the point orders of each sample
idx = np.arange(num_point)
np.random.shuffle(idx)
batch_input = batch_input[:,idx,:]
batch_label = batch_label[:,idx]
# perform augmentation on the first np.int32(augment_ratio*bsize) samples
augSize = np.int32(1/3.0 * bsize)
if INPUT_DIM==6:
augment_xyz = batch_input[0:augSize, :, 0:3]
augment_xyz = data_util.rotate_point_cloud(augment_xyz)
augment_xyz = data_util.rotate_perturbation_point_cloud(augment_xyz)
elif INPUT_DIM==9:
augment_xyz = batch_input[0:augSize, :, 0:6]
augment_xyz = data_util.rotate_point_cloud_with_normal(augment_xyz)
augment_xyz = data_util.rotate_perturbation_point_cloud_with_normal(augment_xyz)
augment_xyz[:,:,0:3] = data_util.random_scale_point_cloud(augment_xyz[:,:,0:3])
augment_xyz[:,:,0:3] = data_util.shift_point_cloud(augment_xyz[:,:,0:3])
augment_xyz[:,:,0:3] = data_util.jitter_point_cloud(augment_xyz[:,:,0:3])
if INPUT_DIM==6:
batch_input[0:augSize, :, 0:3] = augment_xyz
elif INPUT_DIM==9:
batch_input[0:augSize, :, 0:6] = augment_xyz
# one third of data without rotation augmentation
augment_xyz = batch_input[augSize:2*augSize,:,0:3]
augment_xyz = data_util.random_scale_point_cloud(augment_xyz)
augment_xyz = data_util.shift_point_cloud(augment_xyz)
augment_xyz = data_util.jitter_point_cloud(augment_xyz)
batch_input[augSize:2*augSize,:,0:3] = augment_xyz
return batch_input, batch_label
