def process_scene(self, data):
# Remove trajectory points of hidden vehicles.
# This method introduces occlusion to trajectories
comp_keys = pd.DataFrame.from_dict(data.vehicle_visibility,
orient='index')
comp_keys = comp_keys.stack().to_frame().reset_index().drop('level_1',
axis=1)
comp_keys.columns = ['frame_id', 'node_id']
data.trajectory_data = pd.merge(data.trajectory_data,
comp_keys,
how='inner',
on=['frame_id', 'node_id'])
# Trim points above/below certain Z levels.
points = data.overhead_points
z_mask = np.logical_and(points[:, 2] > self.Z_LOWERBOUND,
points[:, 2] < self.Z_UPPERBOUND)
points = data.overhead_points[z_mask]
labels = data.overhead_labels[z_mask]
# Select road LIDAR points.
road_label_mask = labels == SegmentationLabel.Road.value
road_points = points[road_label_mask]
# Remove trajectory points that are far from road LIDAR points.
# This method is just too computationally expensive
# X = traj_data[['x', 'y']].values
# D = scipy.spatial.distance_matrix(X, road_points[:, :2])
# traj_mask = np.min(D, axis=1) < self.DISTANCE_FROM_ROAD
# traj_data = traj_data[np.logical_or(traj_mask, traj_data['node_id'] == 'ego')]
# Get extent and other data
traj_data = data.trajectory_data
comp_key = np.logical_and(traj_data['node_id'] == 'ego',
traj_data['frame_id'] == 0)
s = traj_data[comp_key].iloc[0]
ego_initx, ego_inity = s['x'], s['y']
max_timesteps = traj_data['frame_id'].max()
x_min = round_to_int(traj_data['x'].min() - 50)
x_max = round_to_int(traj_data['x'].max() + 50)
y_min = round_to_int(traj_data['y'].min() - 50)
y_max = round_to_int(traj_data['y'].max() + 50)
# Form bitmap
bitmap = points_to_2d_histogram(road_points, x_min, x_max, y_min,
y_max, data.scene_config.pixels_per_m)
bitmap[bitmap > 0.] = 255.
bitmap = np.stack(
(bitmap, np.zeros(bitmap.shape), np.zeros(bitmap.shape)), axis=0)
bitmap = bitmap.astype(np.uint8)
# adjust trajectory data
traj_data['x'] = traj_data['x'] - x_min
traj_data['y'] = traj_data['y'] - y_min
# Create scene
dt = data.fixed_delta_seconds * data.scene_config.record_interval
scene = Scene(timesteps=max_timesteps + 1,
dt=dt,
name=data.scene_name,
aug_func=augment)
scene.ego_initx = ego_initx
scene.ego_inity = ego_inity
scene.x_min = x_min
scene.y_min = y_min
scene.x_max = x_max
scene.y_max = y_max
type_map = dict()
x_size = x_max - x_min
y_size = y_max - y_min
scene.map_name = data.map_name
scene.x_size = x_size
scene.y_size = y_size
patch_box = (x_min + 0.5 * (x_max - x_min),
y_min + 0.5 * (y_max - y_min), y_size, x_size)
patch_angle = 0
canvas_size = (np.round(3 * y_size).astype(int),
np.round(3 * x_size).astype(int))
homography = np.array([[3., 0., 0.], [0., 3., 0.], [0., 0., 3.]])
layer_names = ['drivable_area']
scene.patch_box = patch_box
scene.patch_angle = patch_angle
scene.canvas_size = canvas_size
scene.homography = homography
scene.layer_names = layer_names
type_map['VEHICLE'] = GeometricMap(data=bitmap,
homography=homography,
description=', '.join(layer_names))
type_map['VISUALIZATION'] = GeometricMap(
data=bitmap,
homography=homography,
description=', '.join(layer_names))
scene.map = type_map
scene_config = data.scene_config
del bitmap
del points
del labels
del road_points
del data
return process_trajectron_scene(scene, traj_data, max_timesteps,
scene_config)
def __process_carla_scene(self, data):
# Get extent and sizing from trajectory data.
traj_data = data.trajectory_data
comp_key = np.logical_and(traj_data['node_id'] == 'ego',
traj_data['frame_id'] == 0)
s = traj_data[comp_key].iloc[0]
ego_initx, ego_inity = s['x'], s['y']
max_timesteps = traj_data['frame_id'].max()
x_min = round_to_int(traj_data['x'].min() - 50)
x_max = round_to_int(traj_data['x'].max() + 50)
y_min = round_to_int(traj_data['y'].min() - 50)
y_max = round_to_int(traj_data['y'].max() + 50)
x_size = x_max - x_min
y_size = y_max - y_min
# Filter road from LIDAR points.
points = data.overhead_points
z_mask = np.logical_and(points[:, 2] > self.Z_LOWERBOUND,
points[:, 2] < self.Z_UPPERBOUND)
points = data.overhead_points[z_mask]
labels = data.overhead_labels[z_mask]
road_label_mask = np.logical_or(
labels == SegmentationLabel.Road.value,
labels == SegmentationLabel.Vehicle.value)
road_points = points[road_label_mask]
# Adjust and filter occlusions from trajectory data.
bitmap = points_to_2d_histogram(road_points, x_min, x_max, y_min,
y_max, data.scene_config.pixels_per_m)
bitmap[bitmap > 0] = 1
traj_data['x'] = traj_data['x'] - x_min
traj_data['y'] = traj_data['y'] - y_min
transform = scipy.ndimage.distance_transform_cdt(-bitmap + 1)
X = traj_data[['x', 'y']].values
Xind = (data.scene_config.pixels_per_m * X).astype(int)
vals = transform[Xind.T[0], Xind.T[1]]
comp_key = np.logical_or(traj_data['node_id'] == 'ego',
vals < self.DISTANCE_FROM_ROAD)
traj_data = traj_data[comp_key]
# Create bitmap from map data.
pixels_per_m = data.scene_config.pixels_per_m
road_polygons = data.map_data.road_polygons
yellow_lines = data.map_data.yellow_lines
white_lines = data.map_data.white_lines
dim = (int(pixels_per_m * y_size), int(pixels_per_m * x_size), 3)
bitmap = np.zeros(dim)
"""NuScenes bitmap format
scene.map[...].as_image() has shape (y, x, c)
Channel 1: lane, road_segment, drivable_area
Channel 2: road_divider
Channel 3: lane_divider"""
for polygon in road_polygons:
rzpoly = ( pixels_per_m*(polygon[:,:2] - np.array([x_min, y_min])) ) \
.astype(int).reshape((-1,1,2))
cv.fillPoly(bitmap, [rzpoly], (255, 0, 0))
for line in yellow_lines:
rzline = ( pixels_per_m*(line[:,:2] - np.array([x_min, y_min])) ) \
.astype(int).reshape((-1,1,2))
cv.polylines(bitmap, [rzline], False, (0, 255, 0), thickness=2)
for line in white_lines:
rzline = ( pixels_per_m*(line[:,:2] - np.array([x_min, y_min])) ) \
.astype(int).reshape((-1,1,2))
cv.polylines(bitmap, [rzline], False, (0, 0, 255), thickness=2)
bitmap = bitmap.astype(np.uint8).transpose((2, 1, 0))
# Create scene
dt = data.fixed_delta_seconds * data.scene_config.record_interval
scene = Scene(timesteps=max_timesteps + 1,
dt=dt,
name=data.scene_name,
aug_func=augment)
scene.ego_initx = ego_initx
scene.ego_inity = ego_inity
scene.x_min = x_min
scene.y_min = y_min
scene.x_max = x_max
scene.y_max = y_max
scene.map_name = data.map_name
scene.x_size = x_size
scene.y_size = y_size
patch_box = (x_min + 0.5 * (x_max - x_min),
y_min + 0.5 * (y_max - y_min), y_size, x_size)
patch_angle = 0
canvas_size = (np.round(3 * y_size).astype(int),
np.round(3 * x_size).astype(int))
homography = np.array([[3., 0., 0.], [0., 3., 0.], [0., 0., 3.]])
layer_names = ['drivable_area']
scene.patch_box = patch_box
scene.patch_angle = patch_angle
scene.canvas_size = canvas_size
scene.homography = homography
scene.layer_names = layer_names
type_map = dict()
type_map['VEHICLE'] = GeometricMap(data=bitmap,
homography=homography,
description=', '.join(layer_names))
type_map['VISUALIZATION'] = GeometricMap(
data=bitmap,
homography=homography,
description=', '.join(layer_names))
scene.map = type_map
scene_config = data.scene_config
return scene, traj_data, max_timesteps, scene_config
def process_scene(self, data):
# Get extent and other data
traj_data = data.trajectory_data
comp_key = np.logical_and(traj_data['node_id'] == 'ego',
traj_data['frame_id'] == 0)
s = traj_data[comp_key].iloc[0]
ego_initx, ego_inity = s['x'], s['y']
max_timesteps = traj_data['frame_id'].max()
x_min = round_to_int(traj_data['x'].min() - 50)
x_max = round_to_int(traj_data['x'].max() + 50)
y_min = round_to_int(traj_data['y'].min() - 50)
y_max = round_to_int(traj_data['y'].max() + 50)
x_size = x_max - x_min
y_size = y_max - y_min
traj_data['x'] = traj_data['x'] - x_min
traj_data['y'] = traj_data['y'] - y_min
# Trim points above/below certain Z levels.
points = data.overhead_points
z_mask = np.logical_and(points[:, 2] > self.Z_LOWERBOUND,
points[:, 2] < self.Z_UPPERBOUND)
points = data.overhead_points[z_mask]
labels = data.overhead_labels[z_mask]
# Select road LIDAR points.
road_label_mask = labels == SegmentationLabel.Road.value
road_points = points[road_label_mask]
# Form bitmap
bitmap = points_to_2d_histogram(road_points, x_min, x_max, y_min,
y_max, data.scene_config.pixels_per_m)
bitmap[bitmap > 0] = 1
# adjust trajectory data
transform = scipy.ndimage.distance_transform_cdt(-bitmap + 1)
X = traj_data[['x', 'y']].values
Xind = (data.scene_config.pixels_per_m * X).astype(int)
vals = transform[Xind.T[0], Xind.T[1]]
traj_data = traj_data[vals < self.DISTANCE_FROM_ROAD]
# Form color image bitmap
bitmap[bitmap > 0] = 255
bitmap = np.stack(
(bitmap, np.zeros(bitmap.shape), np.zeros(bitmap.shape)), axis=0)
bitmap = bitmap.astype(np.uint8)
# Create scene
dt = data.fixed_delta_seconds * data.scene_config.record_interval
scene = Scene(timesteps=max_timesteps + 1,
dt=dt,
name=data.scene_name,
aug_func=augment)
scene.ego_initx = ego_initx
scene.ego_inity = ego_inity
scene.x_min = x_min
scene.y_min = y_min
scene.x_max = x_max
scene.y_max = y_max
type_map = dict()
scene.map_name = data.map_name
scene.x_size = x_size
scene.y_size = y_size
patch_box = (x_min + 0.5 * (x_max - x_min),
y_min + 0.5 * (y_max - y_min), y_size, x_size)
patch_angle = 0
canvas_size = (np.round(3 * y_size).astype(int),
np.round(3 * x_size).astype(int))
homography = np.array([[3., 0., 0.], [0., 3., 0.], [0., 0., 3.]])
layer_names = ['drivable_area']
scene.patch_box = patch_box
scene.patch_angle = patch_angle
scene.canvas_size = canvas_size
scene.homography = homography
scene.layer_names = layer_names
type_map['VEHICLE'] = GeometricMap(data=bitmap,
homography=homography,
description=', '.join(layer_names))
type_map['VISUALIZATION'] = GeometricMap(
data=bitmap,
homography=homography,
description=', '.join(layer_names))
scene.map = type_map
scene_config = data.scene_config
del bitmap
del points
del labels
del road_points
del data
return process_trajectron_scene(scene, traj_data, max_timesteps,
scene_config)
