def _radar(df: pd.DataFrame,
ax: plt.Axes,
label: str,
all_tags: Sequence[str],
color: str,
alpha: float = 0.2,
edge_alpha: float = 0.85,
zorder: int = 2,
edge_style: str = '-'):
"""Plot utility for generating the underlying radar plot."""
tmp = df.groupby('tag').mean().reset_index()
values = []
for curr_tag in all_tags:
score = 0.
selected = tmp[tmp['tag'] == curr_tag]
if len(selected) == 1:
score = float(selected['score'])
else:
print('{} bsuite scores found for tag {!r} with setting {!r}. '
'Replacing with zero.'.format(len(selected), curr_tag,
label))
values.append(score)
values = np.maximum(values, 0.05) # don't let radar collapse to 0.
values = np.concatenate((values, [values[0]]))
angles = np.linspace(0, 2 * np.pi, len(all_tags), endpoint=False)
angles = np.concatenate((angles, [angles[0]]))
ax.plot(angles,
values,
'-',
linewidth=5,
label=label,
c=color,
alpha=edge_alpha,
zorder=zorder,
linestyle=edge_style)
ax.fill(angles, values, alpha=alpha, color=color, zorder=zorder)
ax.set_thetagrids(angles * 180 / np.pi,
map(_tag_pretify, all_tags),
fontsize=18)
# To avoid text on top of gridlines, we flip horizontalalignment
# based on label location
text_angles = np.rad2deg(angles)
for label, angle in zip(ax.get_xticklabels()[:-1], text_angles[:-1]):
if 90 <= angle <= 270:
label.set_horizontalalignment('right')
else:
label.set_horizontalalignment('left')
def plot_polygon(geom: Union[Polygon, Iterable[Polygon]], *args,
ax: Axes=None, crs: CRS=None, **kwargs):
""" Plot a Polygon geometry, projected to the coordinate system `crs` """
kwargs.setdefault("facecolor", "none")
kwargs.setdefault("edgecolor", "black")
x, y = geom.get_coordinate_lists(crs=crs)
return ax.fill(x, y, *args, **kwargs)
def plot_polygon(geom: Union[Polygon, Iterable[Polygon]], *args,
ax: Axes=None, crs: CRS=None, **kwargs):
""" Plot a Polygon geometry, projected to the coordinate system `crs` """
kwargs.setdefault("facecolor", "none")
kwargs.setdefault("edgecolor", "black")
x, y = geom.get_coordinate_lists(crs=crs)
return ax.fill(x, y, *args, **kwargs)
def plot_multipolygon(geom: Union[Multipolygon, Iterable[Multipolygon]], *args,
ax: Axes=None, crs: CRS=None, **kwargs):
""" Plot a Line geometry, projected to the coordinate system `crs` """
kwargs.setdefault("facecolor", "none")
kwargs.setdefault("edgecolor", "black")
out = []
for polygon in geom:
x, y = polygon.get_coordinate_lists(crs=crs)
out.append(ax.fill(x, y, *args, **kwargs))
return out
def plot_multipolygon(geom: Union[Multipoint, Iterable[Multipoint]],
*args,
ax: Axes = None,
crs: CRS = None,
**kwargs):
""" Plot a Line geometry, projected to the coordinate system `crs` """
kwargs.setdefault("facecolor", "none")
kwargs.setdefault("edgecolor", "black")
out = []
for polygon in geom:
x, y = polygon.get_coordinate_lists(crs=crs)
out.append(ax.fill(x, y, *args, **kwargs))
return out
def plot(
self,
ax: plt.Axes,
component_x: str = 'x',
component_y: str = 'y',
component_z: str = 'z',
components: typ.Tuple[str, str] = ('x', 'y'),
# color: str = 'black',
transform_extra: typ.Optional[
kgpy.transforms.TransformList] = None,
to_global: bool = False,
**kwargs):
kwargs = {**self.plot_kwargs, **kwargs}
if to_global:
if transform_extra is None:
transform_extra = kgpy.transforms.TransformList()
transform_extra = transform_extra + self.transform
top = kgpy.vectors.Cartesian3D(
x=u.Quantity([0 * u.mm, 0 * u.mm, self.width, self.width]),
y=0 * u.mm,
z=u.Quantity([0 * u.mm, self.length, self.length, 0 * u.mm]),
)
bottom = top.copy()
bottom.y = -self.thickness
points = np.stack([top, bottom])
points = transform_extra(points)
c0, c1 = components
ax.fill(points.get_component(c0).T,
points.get_component(c1).T,
fill=False,
**kwargs)
ax.plot(points.get_component(c0), points.get_component(c1), **kwargs)
return ax
def plot_map(odr_map: Map, ax: plt.Axes = None, **kwargs) -> plt.Axes:
""" Draw the road layout of the map
Args:
odr_map: The Map to plot
ax: Axes to draw on
Keyword Args:
midline: True if the midline of roads should be drawn (default: False)
midline_direction: Whether to show directed arrows for the midline (default: False)
road_ids: If True, then the IDs of roads will be drawn (default: False)
markings: If True, then draw LaneMarkers (default: False)
road_color: Plot color of the road boundary (default: black)
junction_color: Face color of junctions (default: [0.941, 1.0, 0.420, 0.5])
midline_color: Color of the midline
Returns:
The axes onto which the road layout was drawn
"""
colors = plt.get_cmap("tab10").colors
if ax is None:
_, ax = plt.subplots(1, 1)
ax.set_xlim([odr_map.west, odr_map.east])
ax.set_ylim([odr_map.south, odr_map.north])
ax.set_facecolor("grey")
for road_id, road in odr_map.roads.items():
boundary = road.boundary.boundary
if boundary.geom_type == "LineString":
ax.plot(boundary.xy[0],
boundary.xy[1],
color=kwargs.get("road_color", "k"))
elif boundary.geom_type == "MultiLineString":
for b in boundary:
ax.plot(b.xy[0],
b.xy[1],
color=kwargs.get("road_color", "orange"))
color = kwargs.get(
"midline_color",
colors[road_id %
len(colors)] if kwargs.get("road_ids", False) else "r")
if kwargs.get("midline", False):
for lane_section in road.lanes.lane_sections:
for lane in lane_section.all_lanes:
if lane.id == 0:
continue
if kwargs.get("midline_direction", False):
x = np.array(lane.midline.xy[0])
y = np.array(lane.midline.xy[1])
ax.quiver(x[:-1],
y[:-1],
x[1:] - x[:-1],
y[1:] - y[:-1],
width=0.0025,
headwidth=2,
scale_units='xy',
angles='xy',
scale=1,
color="red")
else:
ax.plot(lane.midline.xy[0],
lane.midline.xy[1],
color=color)
if kwargs.get("road_ids", False):
mid_point = len(road.midline.xy) // 2
ax.text(road.midline.xy[0][mid_point],
road.midline.xy[1][mid_point],
road.id,
color=color,
fontsize=15)
if kwargs.get("markings", False):
for lane_section in road.lanes.lane_sections:
for lane in lane_section.all_lanes:
for marker in lane.markers:
line_styles = marker.type_to_linestyle
for i, style in enumerate(line_styles):
if style is None:
continue
df = 0.13 # Distance between parallel lines
side = "left" if lane.id <= 0 else "right"
line = lane.reference_line.parallel_offset(
i * df, side=side)
ax.plot(line.xy[0],
line.xy[1],
color=marker.color_to_rgb,
linestyle=style,
linewidth=marker.plot_width)
for junction_id, junction in odr_map.junctions.items():
if junction.boundary.geom_type == "Polygon":
ax.fill(junction.boundary.boundary.xy[0],
junction.boundary.boundary.xy[1],
color=kwargs.get("junction_color",
(0.941, 1.0, 0.420, 0.5)))
else:
for polygon in junction.boundary:
ax.fill(polygon.boundary.xy[0],
polygon.boundary.xy[1],
color=kwargs.get("junction_color",
(0.941, 1.0, 0.420, 0.5)))
return ax
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)
