import cv2 import numpy as np import matplotlib import matplotlib.pyplot as plt from pyquaternion import Quaternion from nuscenes.prediction import PredictHelper, convert_local_coords_to_global from tools.analysis_tools.visualize.render.base_render import BaseRender from tools.analysis_tools.visualize.utils import color_mapping, AgentPredictionData class BEVRender(BaseRender): """ Render class for BEV """ def __init__(self, figsize=(20, 20), margin: float = 50, view: np.ndarray = np.eye(4), show_gt_boxes=False): super(BEVRender, self).__init__(figsize) self.margin = margin self.view = view self.show_gt_boxes = show_gt_boxes def set_plot_cfg(self): self.axes.set_xlim([-self.margin, self.margin]) self.axes.set_ylim([-self.margin, self.margin]) self.axes.set_aspect('equal') self.axes.grid(False) def render_sample_data(self, canvas, sample_token): pass def render_anno_data( self, sample_token, nusc, predict_helper): sample_record = nusc.get('sample', sample_token) assert 'LIDAR_TOP' in sample_record['data'].keys( ), 'Error: No LIDAR_TOP in data, unable to render.' lidar_record = sample_record['data']['LIDAR_TOP'] data_path, boxes, _ = nusc.get_sample_data( lidar_record, selected_anntokens=sample_record['anns']) for box in boxes: instance_token = nusc.get('sample_annotation', box.token)[ 'instance_token'] future_xy_local = predict_helper.get_future_for_agent( instance_token, sample_token, seconds=6, in_agent_frame=True) if future_xy_local.shape[0] > 0: trans = box.center rot = Quaternion(matrix=box.rotation_matrix) future_xy = convert_local_coords_to_global( future_xy_local, trans, rot) future_xy = np.concatenate( [trans[None, :2], future_xy], axis=0) c = np.array([0, 0.8, 0]) box.render(self.axes, view=self.view, colors=(c, c, c)) self._render_traj(future_xy, line_color=c, dot_color=(0, 0, 0)) self.axes.set_xlim([-self.margin, self.margin]) self.axes.set_ylim([-self.margin, self.margin]) def show_lidar_data( self, sample_token, nusc): sample_record = nusc.get('sample', sample_token) assert 'LIDAR_TOP' in sample_record['data'].keys( ), 'Error: No LIDAR_TOP in data, unable to render.' lidar_record = sample_record['data']['LIDAR_TOP'] data_path, boxes, _ = nusc.get_sample_data( lidar_record, selected_anntokens=sample_record['anns']) LidarPointCloud.from_file(data_path).render_height( self.axes, view=self.view) self.axes.set_xlim([-self.margin, self.margin]) self.axes.set_ylim([-self.margin, self.margin]) self.axes.axis('off') self.axes.set_aspect('equal') def render_pred_box_data(self, agent_prediction_list): for pred_agent in agent_prediction_list: c = np.array([0, 1, 0]) if hasattr(pred_agent, 'pred_track_id') and pred_agent.pred_track_id is not None: # this is true tr_id = pred_agent.pred_track_id c = color_mapping[tr_id % len(color_mapping)] pred_agent.nusc_box.render( axis=self.axes, view=self.view, colors=(c, c, c)) if pred_agent.is_sdc: c = np.array([1, 0, 0]) pred_agent.nusc_box.render( axis=self.axes, view=self.view, colors=(c, c, c)) def render_pred_traj(self, agent_prediction_list, top_k=3): for pred_agent in agent_prediction_list: if pred_agent.is_sdc: continue sorted_ind = np.argsort(pred_agent.pred_traj_score)[ ::-1] # from high to low num_modes = len(sorted_ind) sorted_traj = pred_agent.pred_traj[sorted_ind, :, :2] sorted_score = pred_agent.pred_traj_score[sorted_ind] # norm_score = np.sum(np.exp(sorted_score)) norm_score = np.exp(sorted_score[0]) sorted_traj = np.concatenate( [np.zeros((num_modes, 1, 2)), sorted_traj], axis=1) trans = pred_agent.pred_center rot = Quaternion(axis=np.array([0, 0.0, 1.0]), angle=np.pi/2) vehicle_id_list = [0, 1, 2, 3, 4, 6, 7] if pred_agent.pred_label in vehicle_id_list: dot_size = 150 else: dot_size = 25 # print(sorted_score) for i in range(top_k-1, -1, -1): viz_traj = sorted_traj[i, :, :2] viz_traj = convert_local_coords_to_global(viz_traj, trans, rot) traj_score = np.exp(sorted_score[i])/norm_score # traj_score = [1.0, 0.01, 0.01, 0.01, 0.01, 0.01][i] self._render_traj(viz_traj, traj_score=traj_score, colormap='winter', dot_size=dot_size) def render_pred_map_data(self, predicted_map_seg): # rendered_map = map_color_dict # divider, crossing, contour map_color_dict = np.array( [(204, 128, 0), (102, 255, 102), (102, 255, 102)]) rendered_map = map_color_dict[predicted_map_seg.argmax( -1).reshape(-1)].reshape(200, 200, -1) bg_mask = predicted_map_seg.sum(-1) == 0 rendered_map[bg_mask, :] = 255 self.axes.imshow(rendered_map, alpha=0.6, interpolation='nearest', extent=(-51.2, 51.2, -51.2, 51.2)) def render_occ_map_data(self, agent_list): rendered_map = np.ones((200, 200, 3)) rendered_map_hsv = matplotlib.colors.rgb_to_hsv(rendered_map) occ_prob_map = np.zeros((200, 200)) for i in range(len(agent_list)): pred_agent = agent_list[i] if pred_agent.pred_occ_map is None: continue if hasattr(pred_agent, 'pred_track_id') and pred_agent.pred_track_id is not None: # this is true tr_id = pred_agent.pred_track_id c = color_mapping[tr_id % len(color_mapping)] pred_occ_map = pred_agent.pred_occ_map.max(0) update_mask = pred_occ_map > occ_prob_map occ_prob_map[update_mask] = pred_occ_map[update_mask] pred_occ_map *= update_mask hsv_c = matplotlib.colors.rgb_to_hsv(c) rendered_map_hsv[pred_occ_map > 0.1] = ( np.ones((200, 200, 1)) * hsv_c)[pred_occ_map > 0.1] max_prob = pred_occ_map.max() renorm_pred_occ_map = (pred_occ_map - max_prob) * 0.7 + 1 sat_map = (renorm_pred_occ_map * hsv_c[1]) rendered_map_hsv[pred_occ_map > 0.1, 1] = sat_map[pred_occ_map > 0.1] rendered_map = matplotlib.colors.hsv_to_rgb(rendered_map_hsv) self.axes.imshow(rendered_map, alpha=0.8, interpolation='nearest', extent=(-50, 50, -50, 50)) def render_occ_map_data_time(self, agent_list, t): rendered_map = np.ones((200, 200, 3)) rendered_map_hsv = matplotlib.colors.rgb_to_hsv(rendered_map) occ_prob_map = np.zeros((200, 200)) for i in range(len(agent_list)): pred_agent = agent_list[i] if pred_agent.pred_occ_map is None: continue if hasattr(pred_agent, 'pred_track_id') and pred_agent.pred_track_id is not None: # this is true tr_id = pred_agent.pred_track_id c = color_mapping[tr_id % len(color_mapping)] pred_occ_map = pred_agent.pred_occ_map[t] update_mask = pred_occ_map > occ_prob_map occ_prob_map[update_mask] = pred_occ_map[update_mask] pred_occ_map *= update_mask hsv_c = matplotlib.colors.rgb_to_hsv(c) rendered_map_hsv[pred_occ_map > 0.1] = ( np.ones((200, 200, 1)) * hsv_c)[pred_occ_map > 0.1] max_prob = pred_occ_map.max() renorm_pred_occ_map = (pred_occ_map - max_prob) * 0.7 + 1 sat_map = (renorm_pred_occ_map * hsv_c[1]) rendered_map_hsv[pred_occ_map > 0.1, 1] = sat_map[pred_occ_map > 0.1] rendered_map = matplotlib.colors.hsv_to_rgb(rendered_map_hsv) self.axes.imshow(rendered_map, alpha=0.8, interpolation='nearest', extent=(-50, 50, -50, 50)) def render_planning_data(self, predicted_planning, show_command=False): planning_traj = predicted_planning.pred_traj planning_traj = np.concatenate( [np.zeros((1, 2)), planning_traj], axis=0) self._render_traj(planning_traj, colormap='autumn', dot_size=50) if show_command: self._render_command(predicted_planning.command) def render_planning_attn_mask(self, predicted_planning): planning_attn_mask = predicted_planning.attn_mask planning_attn_mask = planning_attn_mask/planning_attn_mask.max() cmap_name = 'plasma' self.axes.imshow(planning_attn_mask, alpha=0.8, interpolation='nearest', extent=( -51.2, 51.2, -51.2, 51.2), vmax=0.2, cmap=matplotlib.colormaps[cmap_name]) def render_hd_map(self, nusc, nusc_maps, sample_token): sample_record = nusc.get('sample', sample_token) sd_rec = nusc.get('sample_data', sample_record['data']['LIDAR_TOP']) cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token']) pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token']) info = { 'lidar2ego_translation': cs_record['translation'], 'lidar2ego_rotation': cs_record['rotation'], 'ego2global_translation': pose_record['translation'], 'ego2global_rotation': pose_record['rotation'], 'scene_token': sample_record['scene_token'] } layer_names = ['road_divider', 'road_segment', 'lane_divider', 'lane', 'road_divider', 'traffic_light', 'ped_crossing'] map_mask = obtain_map_info(nusc, nusc_maps, info, patch_size=(102.4, 102.4), canvas_size=(1024, 1024), layer_names=layer_names) map_mask = np.flip(map_mask, axis=1) map_mask = np.rot90(map_mask, k=-1, axes=(1, 2)) map_mask = map_mask[:, ::-1] > 0 map_show = np.ones((1024, 1024, 3)) map_show[map_mask[0], :] = np.array([1.00, 0.50, 0.31]) map_show[map_mask[1], :] = np.array([159./255., 0.0, 1.0]) self.axes.imshow(map_show, alpha=0.2, interpolation='nearest', extent=(-51.2, 51.2, -51.2, 51.2)) def _render_traj(self, future_traj, traj_score=1, colormap='winter', points_per_step=20, line_color=None, dot_color=None, dot_size=25): total_steps = (len(future_traj)-1) * points_per_step + 1 dot_colors = matplotlib.colormaps[colormap]( np.linspace(0, 1, total_steps))[:, :3] dot_colors = dot_colors*traj_score + \ (1-traj_score)*np.ones_like(dot_colors) total_xy = np.zeros((total_steps, 2)) for i in range(total_steps-1): unit_vec = future_traj[i//points_per_step + 1] - future_traj[i//points_per_step] total_xy[i] = (i/points_per_step - i//points_per_step) * \ unit_vec + future_traj[i//points_per_step] total_xy[-1] = future_traj[-1] self.axes.scatter( total_xy[:, 0], total_xy[:, 1], c=dot_colors, s=dot_size) def _render_command(self, command): command_dict = ['TURN RIGHT', 'TURN LEFT', 'KEEP FORWARD'] self.axes.text(-48, -45, command_dict[int(command)], fontsize=45) def render_sdc_car(self): sdc_car_png = cv2.imread('sources/sdc_car.png') sdc_car_png = cv2.cvtColor(sdc_car_png, cv2.COLOR_BGR2RGB) self.axes.imshow(sdc_car_png, extent=(-1, 1, -2, 2)) def render_legend(self): legend = cv2.imread('sources/legend.png') legend = cv2.cvtColor(legend, cv2.COLOR_BGR2RGB) self.axes.imshow(legend, extent=(23, 51.2, -50, -40))