from enum import IntEnum from typing import Any, Dict, List, Tuple import cv2 import numpy as np import numpy.typing as npt import torch from torchvision import transforms from shapely import affinity from shapely.geometry import Polygon, LineString from nuplan.common.maps.abstract_map import AbstractMap, SemanticMapLayer, MapObject from nuplan.common.actor_state.oriented_box import OrientedBox from nuplan.common.actor_state.state_representation import StateSE2 from nuplan.common.actor_state.tracked_objects_types import TrackedObjectType from navsim.agents.transfuser.transfuser_config import TransfuserConfig from navsim.common.dataclasses import AgentInput, Scene, Annotations from navsim.common.enums import BoundingBoxIndex, LidarIndex from navsim.planning.scenario_builder.navsim_scenario_utils import tracked_object_types from navsim.planning.training.abstract_feature_target_builder import ( AbstractFeatureBuilder, AbstractTargetBuilder, ) class TransfuserFeatureBuilder(AbstractFeatureBuilder): def __init__(self, config: TransfuserConfig): self._config = config def get_unique_name(self) -> str: """Inherited, see superclass.""" return "transfuser_feature" def compute_features(self, agent_input: AgentInput) -> Dict[str, torch.Tensor]: """Inherited, see superclass.""" features = {} features["camera_feature"] = self._get_camera_feature(agent_input) features["lidar_feature"] = self._get_lidar_feature(agent_input) features["status_feature"] = torch.concatenate( [ torch.tensor(agent_input.ego_statuses[-1].driving_command, dtype=torch.float32), torch.tensor(agent_input.ego_statuses[-1].ego_velocity, dtype=torch.float32), torch.tensor(agent_input.ego_statuses[-1].ego_acceleration, dtype=torch.float32), ], ) return features def _get_camera_feature(self, agent_input: AgentInput) -> torch.Tensor: """ Extract stitched camera from AgentInput :param agent_input: input dataclass :return: stitched front view image as torch tensor """ cameras = agent_input.cameras[-1] # Crop to ensure 4:1 aspect ratio l0 = cameras.cam_l0.image[28:-28, 416:-416] f0 = cameras.cam_f0.image[28:-28] r0 = cameras.cam_r0.image[28:-28, 416:-416] # stitch l0, f0, r0 images stitched_image = np.concatenate([l0, f0, r0], axis=1) resized_image = cv2.resize(stitched_image, (1024, 256)) tensor_image = transforms.ToTensor()(resized_image) return tensor_image def _get_lidar_feature(self, agent_input: AgentInput) -> torch.Tensor: """ Compute LiDAR feature as 2D histogram, according to Transfuser :param agent_input: input dataclass :return: LiDAR histogram as torch tensors """ # only consider (x,y,z) & swap axes for (N,3) numpy array lidar_pc = agent_input.lidars[-1].lidar_pc[LidarIndex.POSITION].T # NOTE: Code from # https://github.com/autonomousvision/carla_garage/blob/main/team_code/data.py#L873 def splat_points(point_cloud): # 256 x 256 grid xbins = np.linspace( self._config.lidar_min_x, self._config.lidar_max_x, (self._config.lidar_max_x - self._config.lidar_min_x) * int(self._config.pixels_per_meter) + 1, ) ybins = np.linspace( self._config.lidar_min_y, self._config.lidar_max_y, (self._config.lidar_max_y - self._config.lidar_min_y) * int(self._config.pixels_per_meter) + 1, ) hist = np.histogramdd(point_cloud[:, :2], bins=(xbins, ybins))[0] hist[hist > self._config.hist_max_per_pixel] = self._config.hist_max_per_pixel overhead_splat = hist / self._config.hist_max_per_pixel return overhead_splat # Remove points above the vehicle lidar_pc = lidar_pc[lidar_pc[..., 2] < self._config.max_height_lidar] below = lidar_pc[lidar_pc[..., 2] <= self._config.lidar_split_height] above = lidar_pc[lidar_pc[..., 2] > self._config.lidar_split_height] above_features = splat_points(above) if self._config.use_ground_plane: below_features = splat_points(below) features = np.stack([below_features, above_features], axis=-1) else: features = np.stack([above_features], axis=-1) features = np.transpose(features, (2, 0, 1)).astype(np.float32) return torch.tensor(features) class TransfuserTargetBuilder(AbstractTargetBuilder): def __init__(self, config: TransfuserConfig): self._config = config def get_unique_name(self) -> str: """Inherited, see superclass.""" return "transfuser_target" def compute_targets(self, scene: Scene) -> Dict[str, torch.Tensor]: """Inherited, see superclass.""" trajectory = torch.tensor( scene.get_future_trajectory( num_trajectory_frames=self._config.trajectory_sampling.num_poses ).poses ) frame_idx = scene.scene_metadata.num_history_frames - 1 annotations = scene.frames[frame_idx].annotations ego_pose = StateSE2(*scene.frames[frame_idx].ego_status.ego_pose) agent_states, agent_labels = self._compute_agent_targets(annotations) bev_semantic_map = self._compute_bev_semantic_map(annotations, scene.map_api, ego_pose) return { "trajectory": trajectory, "agent_states": agent_states, "agent_labels": agent_labels, "bev_semantic_map": bev_semantic_map, } def _compute_agent_targets(self, annotations: Annotations) -> Tuple[torch.Tensor, torch.Tensor]: """ Extracts 2D agent bounding boxes in ego coordinates :param annotations: annotation dataclass :return: tuple of bounding box values and labels (binary) """ max_agents = self._config.num_bounding_boxes agent_states_list: List[npt.NDArray[np.float32]] = [] def _xy_in_lidar(x: float, y: float, config: TransfuserConfig) -> bool: return (config.lidar_min_x <= x <= config.lidar_max_x) and ( config.lidar_min_y <= y <= config.lidar_max_y ) for box, name in zip(annotations.boxes, annotations.names): box_x, box_y, box_heading, box_length, box_width = ( box[BoundingBoxIndex.X], box[BoundingBoxIndex.Y], box[BoundingBoxIndex.HEADING], box[BoundingBoxIndex.LENGTH], box[BoundingBoxIndex.WIDTH], ) if name == "vehicle" and _xy_in_lidar(box_x, box_y, self._config): agent_states_list.append( np.array([box_x, box_y, box_heading, box_length, box_width], dtype=np.float32) ) agents_states_arr = np.array(agent_states_list) # filter num_instances nearest agent_states = np.zeros((max_agents, BoundingBox2DIndex.size()), dtype=np.float32) agent_labels = np.zeros(max_agents, dtype=bool) if len(agents_states_arr) > 0: distances = np.linalg.norm(agents_states_arr[..., BoundingBox2DIndex.POINT], axis=-1) argsort = np.argsort(distances)[:max_agents] # filter detections agents_states_arr = agents_states_arr[argsort] agent_states[: len(agents_states_arr)] = agents_states_arr agent_labels[: len(agents_states_arr)] = True return torch.tensor(agent_states), torch.tensor(agent_labels) def _compute_bev_semantic_map( self, annotations: Annotations, map_api: AbstractMap, ego_pose: StateSE2 ) -> torch.Tensor: """ Creates sematic map in BEV :param annotations: annotation dataclass :param map_api: map interface of nuPlan :param ego_pose: ego pose in global frame :return: 2D torch tensor of semantic labels """ bev_semantic_map = np.zeros(self._config.bev_semantic_frame, dtype=np.int64) for label, (entity_type, layers) in self._config.bev_semantic_classes.items(): if entity_type == "polygon": entity_mask = self._compute_map_polygon_mask(map_api, ego_pose, layers) elif entity_type == "linestring": entity_mask = self._compute_map_linestring_mask(map_api, ego_pose, layers) else: entity_mask = self._compute_box_mask(annotations, layers) bev_semantic_map[entity_mask] = label return torch.Tensor(bev_semantic_map) def _compute_map_polygon_mask( self, map_api: AbstractMap, ego_pose: StateSE2, layers: List[SemanticMapLayer] ) -> npt.NDArray[np.bool_]: """ Compute binary mask given a map layer class :param map_api: map interface of nuPlan :param ego_pose: ego pose in global frame :param layers: map layers :return: binary mask as numpy array """ map_object_dict = map_api.get_proximal_map_objects( point=ego_pose.point, radius=self._config.bev_radius, layers=layers ) map_polygon_mask = np.zeros(self._config.bev_semantic_frame[::-1], dtype=np.uint8) for layer in layers: for map_object in map_object_dict[layer]: polygon: Polygon = self._geometry_local_coords(map_object.polygon, ego_pose) exterior = np.array(polygon.exterior.coords).reshape((-1, 1, 2)) exterior = self._coords_to_pixel(exterior) cv2.fillPoly(map_polygon_mask, [exterior], color=255) # OpenCV has origin on top-left corner map_polygon_mask = np.rot90(map_polygon_mask)[::-1] return map_polygon_mask > 0 def _compute_map_linestring_mask( self, map_api: AbstractMap, ego_pose: StateSE2, layers: List[SemanticMapLayer] ) -> npt.NDArray[np.bool_]: """ Compute binary of linestring given a map layer class :param map_api: map interface of nuPlan :param ego_pose: ego pose in global frame :param layers: map layers :return: binary mask as numpy array """ map_object_dict = map_api.get_proximal_map_objects( point=ego_pose.point, radius=self._config.bev_radius, layers=layers ) map_linestring_mask = np.zeros(self._config.bev_semantic_frame[::-1], dtype=np.uint8) for layer in layers: for map_object in map_object_dict[layer]: linestring: LineString = self._geometry_local_coords( map_object.baseline_path.linestring, ego_pose ) points = np.array(linestring.coords).reshape((-1, 1, 2)) points = self._coords_to_pixel(points) cv2.polylines(map_linestring_mask, [points], isClosed=False, color=255, thickness=2) # OpenCV has origin on top-left corner map_linestring_mask = np.rot90(map_linestring_mask)[::-1] return map_linestring_mask > 0 def _compute_box_mask( self, annotations: Annotations, layers: TrackedObjectType ) -> npt.NDArray[np.bool_]: """ Compute binary of bounding boxes in BEV space :param annotations: annotation dataclass :param layers: bounding box labels to include :return: binary mask as numpy array """ box_polygon_mask = np.zeros(self._config.bev_semantic_frame[::-1], dtype=np.uint8) for name_value, box_value in zip(annotations.names, annotations.boxes): agent_type = tracked_object_types[name_value] if agent_type in layers: # box_value = (x, y, z, length, width, height, yaw) TODO: add intenum x, y, heading = box_value[0], box_value[1], box_value[-1] box_length, box_width, box_height = box_value[3], box_value[4], box_value[5] agent_box = OrientedBox(StateSE2(x, y, heading), box_length, box_width, box_height) exterior = np.array(agent_box.geometry.exterior.coords).reshape((-1, 1, 2)) exterior = self._coords_to_pixel(exterior) cv2.fillPoly(box_polygon_mask, [exterior], color=255) # OpenCV has origin on top-left corner box_polygon_mask = np.rot90(box_polygon_mask)[::-1] return box_polygon_mask > 0 @staticmethod def _query_map_objects( self, map_api: AbstractMap, ego_pose: StateSE2, layers: List[SemanticMapLayer] ) -> List[MapObject]: """ Queries map objects :param map_api: map interface of nuPlan :param ego_pose: ego pose in global frame :param layers: map layers :return: list of map objects """ # query map api with interesting layers map_object_dict = map_api.get_proximal_map_objects( point=ego_pose.point, radius=self, layers=layers ) map_objects: List[MapObject] = [] for layer in layers: map_objects += map_object_dict[layer] return map_objects @staticmethod def _geometry_local_coords(geometry: Any, origin: StateSE2) -> Any: """ Transform shapely geometry in local coordinates of origin. :param geometry: shapely geometry :param origin: pose dataclass :return: shapely geometry """ a = np.cos(origin.heading) b = np.sin(origin.heading) d = -np.sin(origin.heading) e = np.cos(origin.heading) xoff = -origin.x yoff = -origin.y translated_geometry = affinity.affine_transform(geometry, [1, 0, 0, 1, xoff, yoff]) rotated_geometry = affinity.affine_transform(translated_geometry, [a, b, d, e, 0, 0]) return rotated_geometry def _coords_to_pixel(self, coords): """ Transform local coordinates in pixel indices of BEV map :param coords: _description_ :return: _description_ """ # NOTE: remove half in backward direction pixel_center = np.array([[0, self._config.bev_pixel_width / 2.0]]) coords_idcs = (coords / self._config.bev_pixel_size) + pixel_center return coords_idcs.astype(np.int32) class BoundingBox2DIndex(IntEnum): _X = 0 _Y = 1 _HEADING = 2 _LENGTH = 3 _WIDTH = 4 @classmethod def size(cls): valid_attributes = [ attribute for attribute in dir(cls) if attribute.startswith("_") and not attribute.startswith("__") and not callable(getattr(cls, attribute)) ] return len(valid_attributes) @classmethod @property def X(cls): return cls._X @classmethod @property def Y(cls): return cls._Y @classmethod @property def HEADING(cls): return cls._HEADING @classmethod @property def LENGTH(cls): return cls._LENGTH @classmethod @property def WIDTH(cls): return cls._WIDTH @classmethod @property def POINT(cls): # assumes X, Y have subsequent indices return slice(cls._X, cls._Y + 1) @classmethod @property def STATE_SE2(cls): # assumes X, Y, HEADING have subsequent indices return slice(cls._X, cls._HEADING + 1)