def spatially_regular_gen():
# Generator loop
for i in range(num_per_epoch): # num_per_epoch
# Choose the cloud with the lowest probability
cloud_idx = int(np.argmin(self.min_possibility[split]))
# choose the point with the minimum of possibility in the cloud as query point
point_ind = np.argmin(self.possibility[split][cloud_idx])
# Get all points within the cloud from tree structure
points = np.array(self.input_trees[split][cloud_idx].data,
copy=False)
# Center point of input region
center_point = points[point_ind, :].reshape(1, -1)
# Add noise to the center point
noise = np.random.normal(scale=cfg.noise_init / 10,
size=center_point.shape)
pick_point = center_point + noise.astype(center_point.dtype)
query_idx = self.input_trees[split][cloud_idx].query(
pick_point, k=cfg.num_points)[1][0]
# Shuffle index
query_idx = DP.shuffle_idx(query_idx)
# Get corresponding points and colors based on the index
queried_pc_xyz = points[query_idx]
queried_pc_xyz[:,
0:2] = queried_pc_xyz[:, 0:2] - pick_point[:,
0:2]
#queried_pc_colors = self.input_colors[split][cloud_idx][query_idx]
if split == 'test':
queried_pc_labels = np.zeros(queried_pc_xyz.shape[0])
queried_pt_weight = 1
else:
queried_pc_labels = self.input_labels[split][cloud_idx][
query_idx]
queried_pc_labels = np.array(
[self.label_to_idx[l] for l in queried_pc_labels])
queried_pt_weight = np.array([
self.class_weight[split][0][n]
for n in queried_pc_labels
])
# Update the possibility of the selected points
dists = np.sum(np.square(
(points[query_idx] - pick_point).astype(np.float32)),
axis=1)
delta = np.square(1 -
dists / np.max(dists)) * queried_pt_weight
self.possibility[split][cloud_idx][query_idx] += delta
self.min_possibility[split][cloud_idx] = float(
np.min(self.possibility[split][cloud_idx]))
if True:
yield (queried_pc_xyz, queried_pc_labels,
query_idx.astype(np.int32),
np.array([cloud_idx], dtype=np.int32))
def crop_pc(points, labels, search_tree, pick_idx):
# crop a fixed size point cloud for training
center_point = points[pick_idx, :].reshape(1, -1)
select_idx = search_tree.query(center_point, k=cfg.num_points)[1][0]
select_idx = DP.shuffle_idx(select_idx)
select_points = points[select_idx]
select_labels = labels[select_idx]
return select_points, select_labels, select_idx
def spatially_regular_gen(self, item):
# Choose the cloud with the lowest probability
cloud_idx = int(np.argmin(self.min_possibility[self.mode]))
# choose the point with the minimum of possibility in the cloud as query point
point_ind = np.argmin(self.possibility[self.mode][cloud_idx])
# Get all points within the cloud from tree structure
points = np.array(self.input_trees[self.mode][cloud_idx].data,
copy=False)
# Center point of input region
center_point = points[point_ind, :].reshape(1, -1)
# Add noise to the center point
noise = np.random.normal(scale=cfg.noise_init / 10,
size=center_point.shape)
pick_point = center_point + noise.astype(center_point.dtype)
# Check if the number of points in the selected cloud is less than the predefined num_points
if len(points) < cfg.num_points:
# Query all points within the cloud
queried_idx = self.input_trees[self.mode][cloud_idx].query(
pick_point, k=len(points))[1][0]
else:
# Query the predefined number of points
queried_idx = self.input_trees[self.mode][cloud_idx].query(
pick_point, k=cfg.num_points)[1][0]
# Shuffle index
queried_idx = DP.shuffle_idx(queried_idx)
# Get corresponding points and colors based on the index
queried_pc_xyz = points[queried_idx]
queried_pc_xyz = queried_pc_xyz - pick_point
queried_pc_colors = self.input_colors[
self.mode][cloud_idx][queried_idx]
queried_pc_labels = self.input_labels[
self.mode][cloud_idx][queried_idx]
# Update the possibility of the selected points
dists = np.sum(np.square(
(points[queried_idx] - pick_point).astype(np.float32)),
axis=1)
delta = np.square(1 - dists / np.max(dists))
self.possibility[self.mode][cloud_idx][queried_idx] += delta
self.min_possibility[self.mode][cloud_idx] = float(
np.min(self.possibility[self.mode][cloud_idx]))
# up_sampled with replacement
if len(points) < cfg.num_points:
queried_pc_xyz, queried_pc_colors, queried_idx, queried_pc_labels = \
DP.data_aug(queried_pc_xyz, queried_pc_colors, queried_pc_labels, queried_idx, cfg.num_points)
return queried_pc_xyz.astype(np.float32), queried_pc_colors.astype(
np.float32), queried_pc_labels, queried_idx.astype(
np.int32), np.array([cloud_idx], dtype=np.int32)
def crop_pc(mode, points, labels, search_tree, pick_idx):
# crop a fixed size point cloud for training
center_point = points[pick_idx, :].reshape(1, -1) #[1,3]
# print('rail_dataset line 86 :',(points).shape)
# exit()
# cfg.num_points = 512*(points.shape[0]//512)
kk = points.shape[0]
# select_idx = search_tree.query(center_point, k=cfg.num_points)[1][0] #[45056,] 数值是[0-8w多]
if mode is 'test':
select_idx = np.random.randint(0, kk, (512 * (kk // 512), ))
else:
select_idx = np.random.randint(0, kk, (cfg.num_points, ))
select_idx = DP.shuffle_idx(select_idx)
select_points = points[select_idx]
select_labels = labels[select_idx]
return select_points, select_labels, select_idx
def spatially_regular_gen():
# Generator loop
for i in range(num_per_epoch): # num_per_epoch
# Choose a random cloud
cloud_idx = int(np.argmin(self.min_possibility[split]))
# choose the point with the minimum of possibility as query point
point_ind = np.argmin(self.possibility[split][cloud_idx])
# Get points from tree structure
points = np.array(self.input_trees[split][cloud_idx].data, copy=False)
# Center point of input region
center_point = points[point_ind, :].reshape(1, -1)
# Add noise to the center point
noise = np.random.normal(scale=cfg.noise_init / 10, size=center_point.shape)
pick_point = center_point + noise.astype(center_point.dtype)
if len(points) < cfg.num_points:
queried_idx = self.input_trees[split][cloud_idx].query(pick_point, k=len(points))[1][0]
else:
queried_idx = self.input_trees[split][cloud_idx].query(pick_point, k=cfg.num_points)[1][0]
queried_idx = DP.shuffle_idx(queried_idx)
# Collect points and colors
queried_pc_xyz = points[queried_idx]
queried_pc_xyz = queried_pc_xyz - pick_point
queried_pc_colors = self.input_colors[split][cloud_idx][queried_idx]
queried_pc_labels = self.input_labels[split][cloud_idx][queried_idx]
dists = np.sum(np.square((points[queried_idx] - pick_point).astype(np.float32)), axis=1)
delta = np.square(1 - dists / np.max(dists))
self.possibility[split][cloud_idx][queried_idx] += delta
self.min_possibility[split][cloud_idx] = float(np.min(self.possibility[split][cloud_idx]))
if len(points) < cfg.num_points:
queried_pc_xyz, queried_pc_colors, queried_idx, queried_pc_labels = \
DP.data_aug(queried_pc_xyz, queried_pc_colors, queried_pc_labels, queried_idx, cfg.num_points)
if True:
yield (queried_pc_xyz.astype(np.float32),
queried_pc_colors.astype(np.float32),
queried_pc_labels,
queried_idx.astype(np.int32),
np.array([cloud_idx], dtype=np.int32))
