MotionNet SourceCode 解读[#4]-get_instance_boxes_multisweep_sa

def get_instance_boxes_multisweep_sample_data(cls,                                                  nusc: 'NuScenes',                                                  ref_sd_rec: Dict,                                                  instance_token: str,                                                  nsweeps_back: int = 5,                                                  nsweeps_forward: int = 5) -> \            Tuple[List['Box'], np.array, List[str], List[str]]:    """    Return the bounding boxes associated with the given instance. The bounding boxes are across different sweeps.    For each bounding box, we need to map its (global) coordinates to the reference frame.    For this function, the reference sweep is supposed to be from sample data record (not sample. ie, keyframe).    :param nusc: A NuScenes instance.    :param ref_sd_rec: The current sample data record.    :param instance_token: The current selected instance.    :param nsweeps_back: Number of sweeps to aggregate. The sweeps trace back.    :param nsweeps_forward: Number of sweeps to aggregate. The sweeps are obtained from the future.    :return: (list of bounding boxes, the time stamps of bounding boxes, attribute list, category list)    """    # Init    box_list = list()    all_times = list()    attr_list = list()  # attribute list    cat_list = list()  # category list    # Get reference pose and timestamp    ref_pose_rec = nusc.get('ego_pose', ref_sd_rec['ego_pose_token'])    ref_cs_rec = nusc.get('calibrated_sensor', ref_sd_rec['calibrated_sensor_token'])    ref_time = 1e-6 * ref_sd_rec['timestamp']    # Get the bounding boxes across different sweeps    boxes = list()    # Move backward to get the past annotations    current_sd_rec = ref_sd_rec    for _ in range(nsweeps_back):        box, attr, cat = nusc.get_instance_box(current_sd_rec['token'], instance_token)                      boxes.append(box)  # It is possible the returned box is None        attr_list.append(attr)        cat_list.append(cat)        time_lag = ref_time - 1e-6 * current_sd_rec['timestamp']  # positive difference        all_times.append(time_lag)        if current_sd_rec['prev'] == '':            break        else:            current_sd_rec = nusc.get('sample_data', current_sd_rec['prev'])    print("end")    

# Move forward to get the future annotationscurrent_sd_rec = ref_sd_rec# Abort if there are no future sweeps.if current_sd_rec['next'] != '':    current_sd_rec = nusc.get('sample_data', current_sd_rec['next'])    for _ in range(nsweeps_forward):        box, attr, cat = nusc.get_instance_box(current_sd_rec['token'], instance_token)        boxes.append(box)  # It is possible the returned box is None        attr_list.append(attr)        cat_list.append(cat)        time_lag = ref_time - 1e-6 * current_sd_rec['timestamp']  # negative difference        all_times.append(time_lag)        if current_sd_rec['next'] == '':            break        else:            current_sd_rec = nusc.get('sample_data', current_sd_rec['next'])# Map the bounding boxes to the local sensor coordinatefor box in boxes:    if box is not None:        # Move box to ego vehicle coord system        box.translate(-np.array(ref_pose_rec['translation']))        box.rotate(Quaternion(ref_pose_rec['rotation']).inverse)        # Move box to sensor coord system        box.translate(-np.array(ref_cs_rec['translation']))        box.rotate(Quaternion(ref_cs_rec['rotation']).inverse)    box_list.append(box)return box_list, all_times, attr_list, cat_list

这边先把每个instance的box, attribute, category都先收集起来

map the box to local sensor coordinate
这边的话就是把bounding box 做transformation 到ego_car 的frame
再从ego_car的frame转到sensor的frame

最后回传的是 boxes_list , all_time, attr_list, cat_list 这边的话在gen_data部份只会用到前面两个。