def rasterize(self,
history_frames: np.ndarray,
history_agents: List[np.ndarray],
history_tl_faces: List[np.ndarray],
agent: Optional[np.ndarray] = None) -> Dict[str, List]:
if agent is None:
ego_translation_m = history_frames[0]["ego_translation"]
ego_yaw_rad = rotation33_as_yaw(history_frames[0]["ego_rotation"])
else:
ego_translation_m = np.append(
agent["centroid"], history_frames[0]["ego_translation"][-1])
ego_yaw_rad = agent["yaw"]
raster_from_world = self.render_context.raster_from_world(
ego_translation_m, ego_yaw_rad)
world_from_raster = np.linalg.inv(raster_from_world)
# get XY of center pixel in world coordinates
center_in_raster_px = np.asarray(self.raster_size) * (0.5, 0.5)
center_in_world_m = transform_point(center_in_raster_px,
world_from_raster)
nodes = self.get_semantic_nodes(center_in_world_m, raster_from_world,
history_tl_faces[0])
nodes.update(
self.get_box_nodes(history_frames, history_agents, agent,
raster_from_world))
return nodes
def draw_arrowed_line(on_image: np.ndarray, position: np.ndarray, yaw: float, rgb_color: Tuple[int, int, int]) -> None:
"""
Draw a single arrowed line in an RGB image
Args:
on_image (np.ndarray): the RGB image to draw onto
position (np.ndarray): the starting position of the arrow
yaw (float): the arrow orientation
rgb_color (Tuple[int, int, int]): the arrow color
Returns: None
"""
start_pixel = np.array(position[:2])
rot = cv2.getRotationMatrix2D((0, 0), np.degrees(-yaw), 1.0) # minus here because of cv2 rotations convention
end_pixel = start_pixel + transform_point(np.asarray([ARROW_LENGTH_IN_PIXELS, 0]), rot)
cv2.arrowedLine(
on_image,
tuple(start_pixel.astype(np.int32)),
tuple(end_pixel.astype(np.int32)),
rgb_color,
thickness=ARROW_THICKNESS_IN_PIXELS,
tipLength=ARROW_TIP_LENGTH_IN_PIXELS,
)
def rasterize(
self,
history_frames: np.ndarray,
history_agents: List[np.ndarray],
history_tl_faces: List[np.ndarray],
agent: Optional[np.ndarray] = None,
) -> np.ndarray:
if agent is None:
ego_translation_m = history_frames[0]["ego_translation"]
ego_yaw_rad = rotation33_as_yaw(history_frames[0]["ego_rotation"])
else:
ego_translation_m = np.append(
agent["centroid"], history_frames[0]["ego_translation"][-1])
ego_yaw_rad = agent["yaw"]
raster_from_world = self.render_context.raster_from_world(
ego_translation_m, ego_yaw_rad)
world_from_raster = np.linalg.inv(raster_from_world)
# get XY of center pixel in world coordinates
center_in_raster_px = np.asarray(self.raster_size) * (0.5, 0.5)
center_in_world_m = transform_point(center_in_raster_px,
world_from_raster)
sem_im = self.render_semantic_map(center_in_world_m, raster_from_world,
history_tl_faces[0])
return sem_im.astype(np.float32) / 255
def rasterize_semantic(
self,
history_frames: np.ndarray,
history_agents: List[np.ndarray],
history_tl_faces: List[np.ndarray],
agent: Optional[np.ndarray] = None,
svg=False, svg_args=None,
):
if agent is None:
ego_translation_m = history_frames[0]["ego_translation"]
ego_yaw_rad = rotation33_as_yaw(history_frames[0]["ego_rotation"])
else:
ego_translation_m = np.append(agent["centroid"], history_frames[0]["ego_translation"][-1])
ego_yaw_rad = agent["yaw"]
raster_from_world = self.render_context.raster_from_world(ego_translation_m, ego_yaw_rad)
world_from_raster = np.linalg.inv(raster_from_world)
# get XY of center pixel in world coordinates
center_in_raster_px = np.asarray(self.raster_size) * (0.5, 0.5)
center_in_world_m = transform_point(center_in_raster_px, world_from_raster)
res = self.render_semantic_map(center_in_world_m, raster_from_world, history_tl_faces[0])
svg_args = svg_args or dict()
if svg:
res['path'] = torch.cat(
[linear_path_to_tensor(path, svg_args.get('pad_val', -1)) for path in res['path']], 0)
return res
def test_transform_single_point() -> None:
tf = np.asarray([[1.0, 0, 100], [0, 0.5, 50], [0, 0, 1]])
point = np.array([0, 10])
expected_point = np.array([100, 55])
output_point = transform_point(point, tf)
np.testing.assert_array_equal(output_point, expected_point)
def _create_targets_for_deep_prediction(
num_frames: int,
frames: np.ndarray,
selected_track_id:
int, # modified that ego car is -1, not None (None is used in l5kit)
agents: List[np.ndarray],
agent_from_world: np.ndarray,
current_agent_yaw: float,
agent_origin: np.ndarray,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Internal function that creates the targets and availability masks for deep prediction-type models.
The futures/history offset (in meters) are computed. When no info is available (e.g. agent not in frame)
a 0 is set in the availability array (1 otherwise).
Args:
num_frames (int): number of offset we want in the future/history
frames (np.ndarray): available frames. This may be less than num_frames
selected_track_id (Optional[int]): agent track_id or AV (-1)
agents (List[np.ndarray]): list of agents arrays (same len of frames)
agent_from_world (np.ndarray): local from world matrix
current_agent_yaw (float): angle of the agent at timestep 0
agent_origin (np.ndarray): (2,) xy coord
Returns:
Tuple[np.ndarray, np.ndarray, np.ndarray]: position offsets, angle offsets, availabilities
"""
# How much the coordinates differ from the current state in meters.
coords_offset = np.zeros((num_frames, 2), dtype=np.float32)
yaws_offset = np.zeros((num_frames, 1), dtype=np.float32)
availability = np.zeros((num_frames, ), dtype=np.float32)
for i, (frame, agents) in enumerate(zip(frames, agents)):
if selected_track_id == -1:
agent_centroid = frame["ego_translation"][:2]
agent_yaw = rotation33_as_yaw(frame["ego_rotation"])
else:
# it's not guaranteed the target will be in every frame
try:
agent = filter_agents_by_track_id(agents, selected_track_id)[0]
except IndexError:
availability[i] = 0.0 # keep track of invalid futures/history
continue
agent_centroid = agent["centroid"]
agent_yaw = agent["yaw"]
coords_offset[i] = transform_point(agent_centroid,
agent_from_world) - agent_origin
yaws_offset[i] = angular_distance(agent_yaw, current_agent_yaw)
availability[i] = 1.0
return coords_offset, yaws_offset, availability
def test_transform_single_point(self) -> None:
shape = (200, 200)
pixel_size = np.asarray((1.0, 0.5))
offset = np.asarray((0, -2))
point = np.array([10, 10])
expected_point = np.array([110, 124])
tf = world_to_image_pixels_matrix(shape, pixel_size, offset)
output_point = transform_point(point, tf)
np.testing.assert_array_equal(output_point, expected_point)
def get_sat_image_crop_scaled_from_ecef(
sat_image: np.ndarray,
crop_size: Union[Tuple[int, int], np.ndarray],
ecef_translation: np.ndarray,
ecef_to_sat: np.ndarray,
**kwargs: Any,
) -> np.ndarray:
"""Utility function, calls get_sat_image_crop_scaled, see that function for more details on additional
keyword arguments (such as ``yaw``).
Arguments:
sat_image (np.ndarray): satellite image
crop_size (Union[Tuple[int, int], np.ndarray]): size of desired crop in pixels
ecef_translation (np.ndarray): 2D or 3D vector where to center the cropped image
ecef_to_sat (np.ndarray): transform from ECEF to satellite image space
Returns:
np.ndarray -- a crop of satellite_image
"""
sat_pixel_translation = transform_point(ecef_translation, ecef_to_sat)
return get_sat_image_crop_scaled(sat_image, crop_size,
sat_pixel_translation, **kwargs)
def generate_multi_agent_sample(
state_index: int,
frames: np.ndarray,
agents: np.ndarray,
tl_faces: np.ndarray,
selected_track_id: Optional[int],
render_context: RenderContext,
history_num_frames: int,
history_step_size: int,
future_num_frames: int,
future_step_size: int,
filter_agents_threshold: float,
rasterizer: Optional[Rasterizer] = None,
perturbation: Optional[Perturbation] = None,
min_frame_history: int = MIN_FRAME_HISTORY,
min_frame_future: int = MIN_FRAME_FUTURE,
) -> dict:
"""Generates the inputs and targets to train a deep prediction model. A deep prediction model takes as input
the state of the world (here: an image we will call the "raster"), and outputs where that agent will be some
seconds into the future.
This function has a lot of arguments and is intended for internal use, you should try to use higher level classes
and partials that use this function.
Args:
state_index (int): The anchor frame index, i.e. the "current" timestep in the scene
frames (np.ndarray): The scene frames array, can be numpy array or a zarr array
agents (np.ndarray): The full agents array, can be numpy array or a zarr array
tl_faces (np.ndarray): The full traffic light faces array, can be numpy array or a zarr array
selected_track_id (Optional[int]): Either None for AV, or the ID of an agent that you want to
predict the future of. This agent is centered in the raster and the returned targets are derived from
their future states.
render_context (RenderContext):
raster_size (Tuple[int, int]): Desired output raster dimensions
pixel_size (np.ndarray): Size of one pixel in the real world
ego_center (np.ndarray): Where in the raster to draw the ego, [0.5,0.5] would be the center
history_num_frames (int): Amount of history frames to draw into the rasters
history_step_size (int): Steps to take between frames, can be used to subsample history frames
future_num_frames (int): Amount of history frames to draw into the rasters
future_step_size (int): Steps to take between targets into the future
filter_agents_threshold (float): Value between 0 and 1 to use as cutoff value for agent filtering
based on their probability of being a relevant agent
rasterizer (Optional[Rasterizer]): Rasterizer of some sort that draws a map image
perturbation (Optional[Perturbation]): Object that perturbs the input and targets, used
to train models that can recover from slight divergence from training set data
Raises:
ValueError: A ValueError is returned if the specified ``selected_track_id`` is not present in the scene
or was filtered by applying the ``filter_agent_threshold`` probability filtering.
Returns:
dict: a dict object with the raster array, the future offset coordinates (meters),
the future yaw angular offset, the future_availability as a binary mask
"""
# the history slice is ordered starting from the latest frame and goes backward in time., ex. slice(100, 91, -2)
history_slice = get_history_slice(state_index,
history_num_frames,
history_step_size,
include_current_state=True)
future_slice = get_future_slice(state_index, future_num_frames,
future_step_size)
history_frames = frames[history_slice].copy(
) # copy() required if the object is a np.ndarray
future_frames = frames[future_slice].copy()
sorted_frames = np.concatenate(
(history_frames[::-1], future_frames)) # from past to future
# get agents (past and future)
agent_slice = get_agents_slice_from_frames(sorted_frames[0],
sorted_frames[-1])
agents = agents[agent_slice].copy(
) # this is the minimum slice of agents we need
history_frames[
"agent_index_interval"] -= agent_slice.start # sync interval with the agents array
future_frames[
"agent_index_interval"] -= agent_slice.start # sync interval with the agents array
history_agents = filter_agents_by_frames(history_frames, agents)
future_agents = filter_agents_by_frames(future_frames, agents)
try:
tl_slice = get_tl_faces_slice_from_frames(
history_frames[-1], history_frames[0]) # -1 is the farthest
# sync interval with the traffic light faces array
history_frames["traffic_light_faces_index_interval"] -= tl_slice.start
history_tl_faces = filter_tl_faces_by_frames(history_frames,
tl_faces[tl_slice].copy())
except ValueError:
history_tl_faces = [
np.empty(0, dtype=TL_FACE_DTYPE) for _ in history_frames
]
if perturbation is not None:
history_frames, future_frames = perturbation.perturb(
history_frames=history_frames, future_frames=future_frames)
# State you want to predict the future of.
cur_frame = history_frames[0]
cur_agents = history_agents[0]
cur_agents = filter_agents_by_labels(cur_agents, filter_agents_threshold)
agent_track_ids_u64 = cur_agents["track_id"]
# uint64 --> int64
agent_track_ids = agent_track_ids_u64.astype(np.int64)
assert np.alltrue(agent_track_ids == agent_track_ids_u64)
agent_track_ids = np.concatenate(
[np.array([-1], dtype=np.int64), agent_track_ids])
# Draw image with Ego car in center
selected_agent = None
input_im = (None if not rasterizer else rasterizer.rasterize(
history_frames, history_agents, history_tl_faces, selected_agent))
future_coords_offset_list = []
future_yaws_offset_list = []
future_availability_list = []
history_coords_offset_list = []
history_yaws_offset_list = []
history_availability_list = []
agent_centroid_list = []
agent_yaw_list = []
agent_extent_list = []
filtered_track_ids_list = []
for selected_track_id in agent_track_ids:
if selected_track_id == -1:
agent_centroid = cur_frame["ego_translation"][:2]
agent_yaw_rad = rotation33_as_yaw(cur_frame["ego_rotation"])
agent_extent = np.asarray(
(EGO_EXTENT_LENGTH, EGO_EXTENT_WIDTH, EGO_EXTENT_HEIGHT))
world_from_agent = compute_agent_pose(agent_centroid,
agent_yaw_rad)
agent_from_world = np.linalg.inv(world_from_agent)
raster_from_world = render_context.raster_from_world(
agent_centroid, agent_yaw_rad)
agent_origin = np.zeros((2, ), dtype=np.float32)
else:
# this will raise IndexError if the agent is not in the frame or under agent-threshold
# this is a strict error, we cannot recover from this situation
try:
agent = filter_agents_by_track_id(cur_agents,
selected_track_id)[0]
except IndexError:
raise ValueError(
f" track_id {selected_track_id} not in frame or below threshold"
)
agent_centroid = agent["centroid"]
agent_yaw_rad = agent["yaw"]
agent_extent = agent["extent"]
agent_origin = transform_point(agent_centroid, agent_from_world)
future_coords_offset, future_yaws_offset, future_availability = _create_targets_for_deep_prediction(
future_num_frames, future_frames, selected_track_id, future_agents,
agent_from_world, agent_yaw_rad, agent_origin)
if selected_track_id != -1 and np.sum(
future_availability) < min_frame_future:
# Not enough future to predict, skip this agent.
continue
# history_num_frames + 1 because it also includes the current frame
history_coords_offset, history_yaws_offset, history_availability = _create_targets_for_deep_prediction(
history_num_frames + 1, history_frames, selected_track_id,
history_agents, agent_from_world, agent_yaw_rad, agent_origin)
if selected_track_id != -1 and np.sum(
history_availability) < min_frame_history:
# Not enough history to predict, skip this agent.
continue
future_coords_offset_list.append(future_coords_offset)
future_yaws_offset_list.append(future_yaws_offset)
future_availability_list.append(future_availability)
history_coords_offset_list.append(history_coords_offset)
history_yaws_offset_list.append(history_yaws_offset)
history_availability_list.append(history_availability)
agent_centroid_list.append(agent_centroid)
agent_yaw_list.append(agent_yaw_rad)
agent_extent_list.append(agent_extent)
filtered_track_ids_list.append(selected_track_id)
# Get pixel coordinate
agent_centroid_array = np.array(agent_centroid_list)
agent_centroid_in_pixel = transform_points(agent_centroid_array,
raster_from_world)
return {
"image": input_im, # (h, w, ch)
# --- All below is in world coordinate ---
"target_positions":
np.array(future_coords_offset_list), # (n_agents, num_frames, 2)
"target_yaws":
np.array(future_yaws_offset_list), # (n_agents, num_frames, 1)
"target_availabilities":
np.array(future_availability_list), # (n_agents, num_frames)
"history_positions":
np.array(history_coords_offset_list), # (n_agents, num_frames, 2)
"history_yaws":
np.array(history_yaws_offset_list), # (n_agents, num_frames, 1)
"history_availabilities":
np.array(history_availability_list), # (n_agents, num_frames)
# "world_to_image": raster_from_world, # (3, 3)
"raster_from_world": raster_from_world, # (3, 3)
"centroid": agent_centroid_array, # (n_agents, 2)
"yaw": np.array(agent_yaw_list), # (n_agents, 1)
"extent": np.array(agent_extent_list), # (n_agents, 3)
"track_ids": np.array(filtered_track_ids_list), # (n_agents)
"centroid_pixel": agent_centroid_in_pixel, # (n_agents, 2)
}
