def test_draw_point_cloud_bev_smokescreen():
"""Test :ref:`draw_point_cloud_bev`"""
fig3d = plt.figure(figsize=(15, 8))
ax_bev = fig3d.add_subplot(111)
point_cloud = np.loadtxt(_TEST_DIR / "test_data/sample_argoverse_sweep.txt")
draw_point_cloud_bev(ax_bev, point_cloud, color="w", x_lim_3d=None, y_lim_3d=None, z_lim_3d=None)
plt.close("all")
def render_bev_labels_mpl_tmp(
self,
city_name: str,
ax: plt.Axes,
axis: str,
lidar_pts: np.ndarray,
local_lane_polygons: np.ndarray,
local_das: np.ndarray,
city_to_egovehicle_se3: SE3,
avm: ArgoverseMap,
) -> None:
"""Plot nearby lane polygons and nearby driveable areas (da) on the Matplotlib axes.
Args:
city_name: The name of a city, e.g. `"PIT"`
ax: Matplotlib axes
axis: string, either 'ego_axis' or 'city_axis' to demonstrate the
lidar_pts: Numpy array of shape (N,3)
local_lane_polygons: Polygons representing the local lane set
local_das: Numpy array of objects of shape (N,) where each object is of shape (M,3)
city_to_egovehicle_se3: Transformation from egovehicle frame to city frame
avm: ArgoverseMap instance
"""
if axis is not "city_axis":
# rendering instead in the egovehicle reference frame
for da_idx, local_da in enumerate(local_das):
local_da = city_to_egovehicle_se3.inverse_transform_point_cloud(local_da)
local_das[da_idx] = rotate_polygon_about_pt(local_da, city_to_egovehicle_se3.rotation, np.zeros(3))
for lane_idx, local_lane_polygon in enumerate(local_lane_polygons):
local_lane_polygon = city_to_egovehicle_se3.inverse_transform_point_cloud(local_lane_polygon)
local_lane_polygons[lane_idx] = rotate_polygon_about_pt(
local_lane_polygon, city_to_egovehicle_se3.rotation, np.zeros(3)
)
draw_lane_polygons(ax, local_lane_polygons)
draw_lane_polygons(ax, local_das, color="tab:pink")
if axis is not "city_axis":
lidar_pts = rotate_polygon_about_pt(lidar_pts, city_to_egovehicle_se3.rotation, np.zeros((3,)))
draw_point_cloud_bev(ax, lidar_pts)
def render_bev_labels_mpl(
self,
city_name: str,
ax: plt.Axes,
axis: str,
lidar_pts: np.ndarray,
local_lane_polygons: np.ndarray,
local_das: np.ndarray,
log_id: str,
timestamp: int,
city_to_egovehicle_se3: SE3,
avm: ArgoverseMap,
) -> None:
"""Plot nearby lane polygons and nearby driveable areas (da) on the Matplotlib axes.
Args:
city_name: The name of a city, e.g. `"PIT"`
ax: Matplotlib axes
axis: string, either 'ego_axis' or 'city_axis' to demonstrate the
lidar_pts: Numpy array of shape (N,3)
local_lane_polygons: Polygons representing the local lane set
local_das: Numpy array of objects of shape (N,) where each object is of shape (M,3)
log_id: The ID of a log
timestamp: In nanoseconds
city_to_egovehicle_se3: Transformation from egovehicle frame to city frame
avm: ArgoverseMap instance
"""
if axis is not "city_axis":
# rendering instead in the egovehicle reference frame
for da_idx, local_da in enumerate(local_das):
local_da = city_to_egovehicle_se3.inverse_transform_point_cloud(
local_da)
local_das[da_idx] = rotate_polygon_about_pt(
local_da, city_to_egovehicle_se3.rotation, np.zeros(3))
for lane_idx, local_lane_polygon in enumerate(local_lane_polygons):
local_lane_polygon = city_to_egovehicle_se3.inverse_transform_point_cloud(
local_lane_polygon)
local_lane_polygons[lane_idx] = rotate_polygon_about_pt(
local_lane_polygon, city_to_egovehicle_se3.rotation,
np.zeros(3))
draw_lane_polygons(ax, local_lane_polygons)
draw_lane_polygons(ax, local_das, color="tab:pink")
if axis is not "city_axis":
lidar_pts = rotate_polygon_about_pt(
lidar_pts, city_to_egovehicle_se3.rotation, np.zeros((3, )))
draw_point_cloud_bev(ax, lidar_pts)
objects = self.log_timestamp_dict[log_id][timestamp]
all_occluded = True
for frame_rec in objects:
if frame_rec.occlusion_val != IS_OCCLUDED_FLAG:
all_occluded = False
if not all_occluded:
for i, frame_rec in enumerate(objects):
bbox_city_fr = frame_rec.bbox_city_fr
bbox_ego_frame = frame_rec.bbox_ego_frame
color = frame_rec.color
if frame_rec.occlusion_val != IS_OCCLUDED_FLAG:
bbox_ego_frame = rotate_polygon_about_pt(
bbox_ego_frame, city_to_egovehicle_se3.rotation,
np.zeros((3, )))
if axis is "city_axis":
plot_bbox_2D(ax, bbox_city_fr, color)
if self.plot_lane_tangent_arrows:
bbox_center = np.mean(bbox_city_fr, axis=0)
tangent_xy, conf = avm.get_lane_direction(
query_xy_city_coords=bbox_center[:2],
city_name=city_name)
dx = tangent_xy[0] * LANE_TANGENT_VECTOR_SCALING
dy = tangent_xy[1] * LANE_TANGENT_VECTOR_SCALING
ax.arrow(bbox_center[0],
bbox_center[1],
dx,
dy,
color="r",
width=0.5,
zorder=2)
else:
plot_bbox_2D(ax, bbox_ego_frame, color)
cuboid_lidar_pts, _ = filter_point_cloud_to_bbox_2D_vectorized(
bbox_ego_frame[:, :2], copy.deepcopy(lidar_pts))
if cuboid_lidar_pts is not None:
draw_point_cloud_bev(ax, cuboid_lidar_pts, color)
else:
logger.info(f"all occluded at {timestamp}")
xcenter = city_to_egovehicle_se3.translation[0]
ycenter = city_to_egovehicle_se3.translation[1]
ax.text(xcenter - 146,
ycenter + 50,
"ALL OBJECTS OCCLUDED",
fontsize=30)
