cosense3d.agents.utils package

Submodules

cosense3d.agents.utils.deco module

cosense3d.agents.utils.deco.save_ckpt_on_error(func)

cosense3d.agents.utils.transform module

class cosense3d.agents.utils.transform.DataOnlineProcessor

Bases: object

static adaptive_free_space_augmentation(data: dict, min_h: float = -1.5, steps: int = 20, alpha: float = 0.05, res: float = 0.5, time_idx: int = None)

Add free space points according to the distance of points to the origin.

lidar origin ->  *
              *  *
           *     * h
        *  ele   *
      ************
             d

Assume the $$\begin{gathered} \theta =\frac{ \\ pi}{2}-\text{ele} \end{gathered}$$ (elevation angle), $\alpha$ = average angle between two lidar rings, $d_k$ is the ground distance of the $n_{th}$ lidar ring to lidar origin, $k=1,...,n$, ${\delta }_d$ is the distance between two neighboring lidar rings, then

$$\begin{array}{rl}d& =h\tan (\theta )\\ {\delta }_d& =d_n-d_{n-1}=d_n-h\tan (\arctan (\frac{h}{d_n})-\alpha )\end{array}$$

we sample free space points in the ground distance of ${\delta }_d$ relative to each ring with the given ‘step’ distance.

Parameters:

• data – input data dict containing ‘points’.

• min_h – minimum sample height relative to lidar origin. Default is -1.5.

• steps – number of points to be sampled for each lidar ray. Default is 20.

• alpha – average angle offset between two neighboring lidar casting rays. Default is 0.05.

• res – resolution for down-sampling the free space points. Default is 0.5.

• time_idx – if provided, time will be copied from the original points to free space points.

Returns:

updated data.

static apply_transform(data, transform, apply_to=['points'])

static cav_aug_transform(data, transform, aug_params, apply_to=['points', 'imgs', 'annos_global'])

static filter_range(data, lidar_range, apply_to: list)

static free_space_augmentation(data, d: float = 10.0, h: float = -1.5, step: float = 1.5, res=0.25)

static generate_sparse_target_bev_points(data: dict, transform=None, sam_res=0.4, map_res=0.2, range=50, max_num_pts=3000, discrete=False)

static generate_sparse_target_roadline_points(data: dict, transform=None, map_res=0.2, range=50, kernel=3, max_num_pts=3000)

static update_transform_with_aug(transform, aug_params)

cosense3d.agents.utils.transform.add_flip(tf, flip_idx, flip_axis='xy')

cosense3d.agents.utils.transform.add_rotate(tf, rot)

cosense3d.agents.utils.transform.add_scale(tf, scale_ratio)

cosense3d.agents.utils.transform.apply_transform(data, transform, key)

cosense3d.agents.utils.transform.filter_range(data, lidar_range, key)

cosense3d.agents.utils.transform.filter_range_mask(points, lidar_range, eps=0.0001)

cosense3d.agents.utils.transform.generate_bev_tgt_pts(points, data, transform=None, sam_res=0.4, map_res=0.2, range=50, max_num_pts=5000, discrete=False)

Module contents