def draw_local_map(avm, city_name, x_min, x_max, y_min, y_max, ax):
seq_lane_bbox = avm.city_halluc_bbox_table[city_name]
seq_lane_props = avm.city_lane_centerlines_dict[city_name]
# fig = plt.figure(figsize=(15,15))
# ax = fig.add_subplot(111)
ax.set_xlim([x_min, x_max])
ax.set_ylim([y_min, y_max])
map_range = np.array([x_min, x_max, y_min, y_max])
seq_lane_props = avm.city_lane_centerlines_dict[city_name]
lane_centerlines = map_Selection(map_range, seq_lane_props)
# print('PIT map visualization with highest traffic data density')
for lane_cl in lane_centerlines:
plt.plot(lane_cl[:, 0],
lane_cl[:, 1],
"--",
color="grey",
alpha=1,
linewidth=1,
zorder=0)
# use built in function to plot road info
local_lane_polygons = avm.find_local_lane_polygons(
[x_min, x_max, y_min, y_max], city_name)
draw_lane_polygons(ax, local_lane_polygons, color='#02383C')
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)
def plot_log_one_at_a_time(self, avm=None, log_num: int = 0):
df = self.afl[log_num].seq_df
city_name = df["CITY_NAME"].values[0]
time_step = self.afl[log_num].agent_traj.shape[0]
self.timestamps = list(sorted(set(df["TIMESTAMP"].values.tolist())))
self.log_agent_pose = self.afl[log_num].agent_traj
if not self.log_agent_pose.shape[0] == 50:
sys.exit("The agent's tacking time is less than 5 seconds!")
for i in range(time_step):
if i < AGENT_TIME_STEP or i >= time_step - FUTURE_TIME_STEP:
continue
if self.save_img:
file_num = self.filenames[log_num]
shard_ind = ceil(file_num / FRAME_IN_SHARD)
img_name = "{0}_{1:0>7d}_{2}.png".format(
city_name, file_num, i)
if (not self.overwrite_rendered_file) and \
Path(f"{self.dataset_dir}../rendered_image/{shard_ind}/{img_name}").exists():
continue
[xcenter, ycenter] = self.afl[log_num].agent_traj[i, :]
# Figure setting
fig = plt.figure(num=1, figsize=(4.3, 4.3), facecolor='k')
plt.clf()
plt.axis("off")
ax = fig.add_subplot(111)
ax.set_xlim([-FIELD_VIEW / 2 + xcenter, FIELD_VIEW / 2 + xcenter])
ax.set_ylim([-FIELD_VIEW / 2 + ycenter, FIELD_VIEW / 2 + ycenter])
fig.tight_layout()
# Draw map
xmin = xcenter - 60
xmax = xcenter + 60
ymin = ycenter - 60
ymax = ycenter + 60
local_lane_polygons = avm.find_local_lane_polygons(
[xmin, xmax, ymin, ymax], city_name)
local_das = avm.find_local_driveable_areas(
[xmin, xmax, ymin, ymax], city_name)
# Render and get the log info
viz = False
if self.save_img:
viz = True
draw_lane_polygons(ax, local_lane_polygons, color="tab:gray")
draw_lane_polygons(ax, local_das, color=(1, 0, 1))
if self.convert_tf_record:
# Save the map image
# plt.savefig("temp.png", dpi=100, facecolor='k', bbox_inches='tight', pad_inches=0)
self.get_map_with_one_channel(local_lane_polygons, local_das,
self.log_agent_pose[i, :])
past_traj = self.get_agent_past_traj(ax, i, viz)
future_traj = self.get_agent_future_traj(ax, i, viz)
center_lines = self.get_candidate_centerlines(
ax, self.log_agent_pose[:(AGENT_TIME_STEP + 1), :], avm,
city_name, viz)
surr_past_pos = self.get_surr_past_traj(ax, df, i,
self.log_agent_pose[i, :],
viz)
if self.save_img:
shard_ind = 1
plt.savefig(
f"{self.dataset_dir}../rendered_image/{shard_ind}/{img_name}",
dpi=100,
facecolor='k',
bbox_inches='tight',
pad_inches=0)
return past_traj, future_traj, center_lines, surr_past_pos
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,
timestamp_ind: 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))
local_das[da_idx] = local_da
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)
# )
local_lane_polygons[lane_idx] = local_lane_polygon
draw_lane_polygons(ax, local_lane_polygons, color="tab:gray")
draw_lane_polygons(ax, local_das, color=(1, 0, 1))
# render ego vehicle
bbox_ego = np.array([[-1.0, -1.0, 0.0], [3.0, -1.0, 0.0],
[-1.0, 1.0, 0.0], [3.0, 1.0, 0.0]])
# bbox_ego = rotate_polygon_about_pt(bbox_ego, city_to_egovehicle_se3.rotation, np.zeros((3,)))
plot_bbox_2D(ax, bbox_ego, 'g')
# render surrounding vehicle and pedestrians
objects = self.log_timestamp_dict[log_id][timestamp]
hist_objects = {}
all_occluded = True
for frame_rec in objects:
if frame_rec.occlusion_val != IS_OCCLUDED_FLAG:
all_occluded = False
hist_objects[frame_rec.track_uuid] = [
None for i in range(0, SURR_TIME_STEP + 1)
]
hist_objects[
frame_rec.track_uuid][SURR_TIME_STEP] = frame_rec.bbox_city_fr
if not all_occluded:
for ind, i in enumerate(
range(timestamp_ind - SURR_TIME_STEP, timestamp_ind)):
objects = self.log_timestamp_dict[log_id][self.timestamps[i]]
for frame_rec in objects:
if frame_rec.track_uuid in hist_objects:
hist_objects[
frame_rec.track_uuid][ind] = frame_rec.bbox_city_fr
# render color with decreasing lightness
color_lightness = np.linspace(1, 0,
SURR_TIME_STEP + 2)[1:].tolist()
for track_id in hist_objects:
for ind_color, bbox_city_fr in enumerate(
hist_objects[track_id]):
if bbox_city_fr is None:
continue
bbox_relative = city_to_egovehicle_se3.inverse_transform_point_cloud(
bbox_city_fr)
x = bbox_relative[:, 0].tolist()
y = bbox_relative[:, 1].tolist()
x[1], x[2], x[3], y[1], y[2], y[3] = x[2], x[3], x[1], y[
2], y[3], y[1]
ax.fill(x,
y,
c=(1, 1, color_lightness[ind_color]),
alpha=1,
zorder=1)
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)