def __getitem__(self, index):
lidar_file, label_file = self.dataset[index]
scan = SemLaserScan(self.nclasses,self.color_dict)
scan.open_scan(lidar_file)
scan.open_label(label_file)
ground_indices = np.zeros(scan.sem_label.shape)
# Find all lidar points associated with ground labels in semantic-kitti dataset
for label in self.ground_labels:
ground_indices += (scan.sem_label == label)
ground_points = scan.points[ground_indices.astype(np.bool)]
grid = BEVOccupancyGrid(scan.points,
n_x=self.n_x,
n_y=self.n_y,
n_z=self.n_z,
side_range = self.side_range,
fwd_range = self.fwd_range,
height_range = self.height_range,
filter_external = True
)
grid.compute()
# best-fit quadratic curve (2nd-order)
A = np.c_[np.ones(ground_points.shape[0]), ground_points[:,:2], np.prod(ground_points[:,:2], axis=1), ground_points[:,:2]**2]
C,_,_,_ = scipy.linalg.lstsq(A, ground_points[:,2])
# evaluate it on a grid
Y, X = np.meshgrid(np.flip(grid.midsegments[0]),np.flip(grid.midsegments[1]))
XX = X.flatten()
YY = Y.flatten()
Z = np.dot(np.c_[np.ones(XX.shape), XX, YY, XX*YY, XX**2, YY**2], C).reshape(X.shape)
return torch.FloatTensor(grid.grid).permute(2,0,1), torch.FloatTensor(Z).unsqueeze(dim=0)
print("U-Net MSE Average", np.mean(unet_losses))
print("U-Net MSE Standard deviation", np.std(unet_losses))
lidar_height_model_MSE = np.zeros(len(lidar_files))
lidar_height = -1.73
i = 0
for lidar_file in tqdm(lidar_files):
name = os.path.split(lidar_file)[-1]
label_file = os.path.join(label_dir,
os.path.splitext(name)[0] + '.label')
scan = SemLaserScan(nclasses, color_dict)
scan.open_scan(lidar_file)
scan.open_label(label_file)
ground_indices = np.zeros(scan.sem_label.shape)
# Find all lidar points associated with ground labels in semantic-kitti dataset
for label in ground_labels:
ground_indices += (scan.sem_label == label)
ground_points = scan.points[ground_indices.astype(np.bool)]
n, _ = ground_points.shape
ground_height = np.mean(ground_points[:, 2])
lidar_height_model_MSE[i] = MSE(np.repeat(lidar_height, n),
ground_points[:, 2])
i += 1
print("Lidar Height MSE", np.mean(lidar_height_model_MSE))
def __call__(self, label_desc):
label_dir, label_id, cfg = label_desc
laser = SemLaserScan(sem_color_dict=cfg['color_map'],
project=True,
H=cfg['sensor']['img_prop']['height'],
W=cfg['sensor']['img_prop']['width'],
fov_up=cfg['sensor']['fov_up'],
fov_down=cfg['sensor']['fov_down'])
laser.open_scan(
path.join(self.base_dir, label_dir, 'velodyne',
label_id.split('_')[1] + '.bin'))
laser.open_label(
path.join(self.base_dir, label_dir, 'labels',
label_id.split('_')[1] + '.label'))
if self.point_cloud:
ids = np.unique(laser.inst_label)
ids_sem = np.unique(laser.sem_label)
depth = laser.unproj_range
lbl_out = np.zeros(laser.inst_label.shape + (2, ), np.int32)
else:
ids = np.unique(laser.proj_inst_label)
ids_sem = np.unique(laser.proj_sem_label)
depth = laser.proj_range
lbl_out = np.zeros(laser.proj_inst_label.shape + (2, ), np.int32)
for id_sem in ids_sem:
cls_i = id_sem
iss_class_id = cfg['learning_map'][cls_i]
if cfg['instances'][iss_class_id]:
continue
else:
if self.point_cloud:
lbl_out[laser.sem_label == cls_i, 0] = iss_class_id
else:
lbl_out[laser.proj_sem_label == cls_i, 0] = iss_class_id
if self.point_cloud and self.depth_flag:
laser.inst_label[depth > self.depth_cutoff] = 0
laser.sem_label[depth > self.depth_cutoff] = 0
ids = np.unique(laser.inst_label)
ids_sem = np.unique(laser.sem_label)
elif self.depth_flag:
laser.proj_inst_label[depth > self.depth_cutoff] = 0
laser.proj_sem_label[depth > self.depth_cutoff] = 0
ids = np.unique(laser.proj_inst_label)
ids_sem = np.unique(laser.proj_sem_label)
for id_sem in ids_sem:
cls_i = id_sem
iss_class_id = cfg['learning_map'][cls_i]
if cfg['instances'][iss_class_id]:
for id_ in ids:
if self.point_cloud:
mask_i = np.logical_and(laser.sem_label == cls_i,
laser.inst_label == id_)
else:
mask_i = np.logical_and(laser.proj_sem_label == cls_i,
laser.proj_inst_label == id_)
if np.sum(mask_i) < 1:
continue
lbl_out[mask_i, 1] = int(cls_i) * 1000 + int(id_) + 1
lbl_out[mask_i, 0] = iss_class_id
np.save(path.join(self.out_dir, label_id + ".bin"), lbl_out)
return True