def _calculate_num_points_in_gt(data_path, infos, relative_path, remove_outside=True, num_features=4): for info in infos: if relative_path: v_path = str(pathlib.Path(data_path) / info["velodyne_path"]) else: v_path = info["velodyne_path"] points_v = np.fromfile(v_path, dtype=np.float32, count=-1).reshape([-1, num_features]) rect = info['calib/R0_rect'] Trv2c = info['calib/Tr_velo_to_cam'] P2 = info['calib/P2'] if remove_outside: points_v = remove_outside_points(points_v, rect, Trv2c, P2, info["img_shape"]) # points_v = points_v[points_v[:, 0] > 0] annos = info['annos'] num_obj = len([n for n in annos['name'] if n != 'DontCare']) # annos = kitti.filter_kitti_anno(annos, ['DontCare']) dims = annos['dimensions'][:num_obj] loc = annos['location'][:num_obj] rots = annos['rotation_y'][:num_obj] gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1) gt_boxes_lidar = box_camera_to_lidar(gt_boxes_camera, rect, Trv2c) indices = points_in_rbbox(points_v[:, :3], gt_boxes_lidar) num_points_in_gt = indices.sum(0) num_ignored = len(annos['dimensions']) - num_obj num_points_in_gt = np.concatenate( [num_points_in_gt, -np.ones([num_ignored])]) annos["num_points_in_gt"] = num_points_in_gt.astype(np.int32)
def _create_reduced_point_cloud(data_path, info_path, save_path=None, back=False): with open(info_path, 'rb') as f: kitti_infos = pickle.load(f) for info in prog_bar(kitti_infos): v_path = info['velodyne_path'] v_path = pathlib.Path(data_path) / v_path points_v = np.fromfile(str(v_path), dtype=np.float32, count=-1).reshape([-1, 4]) rect = info['calib/R0_rect'] P2 = info['calib/P2'] Trv2c = info['calib/Tr_velo_to_cam'] # first remove z < 0 points # keep = points_v[:, -1] > 0 # points_v = points_v[keep] # then remove outside. if back: points_v[:, 0] = -points_v[:, 0] points_v = remove_outside_points(points_v, rect, Trv2c, P2, info["img_shape"]) if save_path is None: save_filename = v_path.parent.parent / (v_path.parent.stem + "_reduced") / v_path.name # save_filename = str(v_path) + '_reduced' if back: save_filename += "_back" else: save_filename = str(pathlib.Path(save_path) / v_path.name) if back: save_filename += "_back" with open(save_filename, 'w') as f: points_v.tofile(f)
def create_groundtruth_database(data_path, info_path=None, used_classes=None, database_save_path=None, db_info_save_path=None, relative_path=True, lidar_only=False, bev_only=False, coors_range=None): root_path = pathlib.Path(data_path) if info_path is None: info_path = root_path / 'kitti_infos_train.pkl' if database_save_path is None: database_save_path = root_path / 'gt_database' else: database_save_path = pathlib.Path(database_save_path) if db_info_save_path is None: db_info_save_path = root_path / "kitti_dbinfos_train.pkl" database_save_path.mkdir(parents=True, exist_ok=True) with open(info_path, 'rb') as f: kitti_infos = pickle.load(f) all_db_infos = {} if used_classes is None: used_classes = list(kitti.get_classes()) used_classes.pop(used_classes.index('DontCare')) for name in used_classes: all_db_infos[name] = [] group_counter = 0 for info in prog_bar(kitti_infos): velodyne_path = info['velodyne_path'] if relative_path: # velodyne_path = str(root_path / velodyne_path) + "_reduced" velodyne_path = str(root_path / velodyne_path) num_features = 4 if 'pointcloud_num_features' in info: num_features = info['pointcloud_num_features'] points = np.fromfile(velodyne_path, dtype=np.float32, count=-1).reshape([-1, num_features]) image_idx = info["image_idx"] rect = info['calib/R0_rect'] P2 = info['calib/P2'] Trv2c = info['calib/Tr_velo_to_cam'] if not lidar_only: points = remove_outside_points(points, rect, Trv2c, P2, info["img_shape"]) annos = info["annos"] names = annos["name"] bboxes = annos["bbox"] difficulty = annos["difficulty"] gt_idxes = annos["index"] num_obj = np.sum(annos["index"] >= 0) rbbox_cam = kitti.anno_to_rbboxes(annos)[:num_obj] rbbox_lidar = box_camera_to_lidar(rbbox_cam, rect, Trv2c) if bev_only: # set z and h to limits assert coors_range is not None rbbox_lidar[:, 2] = coors_range[2] rbbox_lidar[:, 5] = coors_range[5] - coors_range[2] group_dict = {} group_ids = np.full([bboxes.shape[0]], -1, dtype=np.int64) if "group_ids" in annos: group_ids = annos["group_ids"] else: group_ids = np.arange(bboxes.shape[0], dtype=np.int64) point_indices = points_in_rbbox(points, rbbox_lidar) for i in range(num_obj): filename = f"{image_idx}_{names[i]}_{gt_idxes[i]}.bin" filepath = database_save_path / filename gt_points = points[point_indices[:, i]] gt_points[:, :3] -= rbbox_lidar[i, :3] with open(filepath, 'w') as f: gt_points.tofile(f) if names[i] in used_classes: if relative_path: db_path = str(database_save_path.stem + "/" + filename) else: db_path = str(filepath) db_info = { "name": names[i], "path": db_path, "image_idx": image_idx, "gt_idx": gt_idxes[i], "box3d_lidar": rbbox_lidar[i], "num_points_in_gt": gt_points.shape[0], "difficulty": difficulty[i], # "group_id": -1, # "bbox": bboxes[i], } local_group_id = group_ids[i] # if local_group_id >= 0: if local_group_id not in group_dict: group_dict[local_group_id] = group_counter group_counter += 1 db_info["group_id"] = group_dict[local_group_id] if "score" in annos: db_info["score"] = annos["score"][i] all_db_infos[names[i]].append(db_info) for k, v in all_db_infos.items(): print(f"load {len(v)} {k} database infos") with open(db_info_save_path, 'wb') as f: pickle.dump(all_db_infos, f)