def render_bb_over_bev(bev_map, labels, configs, vis=False):
# convert BEV map from tensor to numpy array
bev_map_cpy = (bev_map.squeeze().permute(1, 2, 0).numpy() * 255).astype(
np.uint8)
bev_map_cpy = cv2.resize(bev_map_cpy,
(configs.bev_width, configs.bev_height))
# convert bounding box format format and project into bev
label_objects = tools.convert_labels_into_objects(labels, configs)
tools.project_detections_into_bev(bev_map_cpy, label_objects, configs,
[0, 255, 0])
# display bev map
if vis == True:
bev_map_cpy = cv2.rotate(bev_map_cpy, cv2.ROTATE_180)
cv2.imshow("BEV map", bev_map_cpy)
cv2.waitKey(0)
return bev_map_cpy
lidar_pcl = load_object_from_file(results_fullpath, data_filename,
'lidar_pcl', cnt_frame)
## Compute lidar birds-eye view (bev)
if 'bev_from_pcl' in exec_list:
print('computing birds-eye view from lidar pointcloud')
lidar_bev = pcl.bev_from_pcl(lidar_pcl, configs_det)
else:
print('loading birds-eve view from result file')
lidar_bev = load_object_from_file(results_fullpath, data_filename,
'lidar_bev', cnt_frame)
## 3D object detection
if (configs_det.use_labels_as_objects == True):
print('using groundtruth labels as objects')
detections = tools.convert_labels_into_objects(
frame.laser_labels, configs_det)
else:
if 'detect_objects' in exec_list:
print('detecting objects in lidar pointcloud')
detections = det.detect_objects(lidar_bev, model_det,
configs_det)
else:
print('loading detected objects from result file')
detections = load_object_from_file(
results_fullpath, data_filename, 'detections_' +
configs_det.arch + '_' + str(configs_det.conf_thresh),
cnt_frame)
## Validate object labels
if 'validate_object_labels' in exec_list:
print("validating object labels")