import copy import pickle import numpy as np from skimage import io from . import kitti_utils from ...ops.roiaware_pool3d import roiaware_pool3d_utils from ...utils import box_utils, calibration_kitti, common_utils, object3d_kitti from ..dataset import DatasetTemplate class KittiDataset(DatasetTemplate): def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None): """ Args: root_path: dataset_cfg: class_names: training: logger: """ super().__init__( dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger ) self.split = self.dataset_cfg.DATA_SPLIT[self.mode] self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing') split_dir = self.root_path / 'ImageSets' / (self.split + '.txt') self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None self.kitti_infos = [] self.include_kitti_data(self.mode) def include_kitti_data(self, mode): if self.logger is not None: self.logger.info('Loading KITTI dataset') kitti_infos = [] for info_path in self.dataset_cfg.INFO_PATH[mode]: info_path = self.root_path / info_path if not info_path.exists(): continue with open(info_path, 'rb') as f: infos = pickle.load(f) kitti_infos.extend(infos) self.kitti_infos.extend(kitti_infos) if self.logger is not None: self.logger.info('Total samples for KITTI dataset: %d' % (len(kitti_infos))) def set_split(self, split): super().__init__( dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training, root_path=self.root_path, logger=self.logger ) self.split = split self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing') split_dir = self.root_path / 'ImageSets' / (self.split + '.txt') self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None def get_lidar(self, idx): lidar_file = self.root_split_path / 'velodyne' / ('%s.bin' % idx) assert lidar_file.exists() return np.fromfile(str(lidar_file), dtype=np.float32).reshape(-1, 4) def get_image(self, idx): """ Loads image for a sample Args: idx: int, Sample index Returns: image: (H, W, 3), RGB Image """ img_file = self.root_split_path / 'image_2' / ('%s.png' % idx) assert img_file.exists() image = io.imread(img_file) image = image.astype(np.float32) image /= 255.0 return image def get_image_shape(self, idx): img_file = self.root_split_path / 'image_2' / ('%s.png' % idx) assert img_file.exists() return np.array(io.imread(img_file).shape[:2], dtype=np.int32) def get_label(self, idx): label_file = self.root_split_path / 'label_2' / ('%s.txt' % idx) assert label_file.exists() return object3d_kitti.get_objects_from_label(label_file) def get_depth_map(self, idx): """ Loads depth map for a sample Args: idx: str, Sample index Returns: depth: (H, W), Depth map """ depth_file = self.root_split_path / 'depth_2' / ('%s.png' % idx) assert depth_file.exists() depth = io.imread(depth_file) depth = depth.astype(np.float32) depth /= 256.0 return depth def get_calib(self, idx): calib_file = self.root_split_path / 'calib' / ('%s.txt' % idx) assert calib_file.exists() return calibration_kitti.Calibration(calib_file) def get_road_plane(self, idx): plane_file = self.root_split_path / 'planes' / ('%s.txt' % idx) if not plane_file.exists(): return None with open(plane_file, 'r') as f: lines = f.readlines() lines = [float(i) for i in lines[3].split()] plane = np.asarray(lines) # Ensure normal is always facing up, this is in the rectified camera coordinate if plane[1] > 0: plane = -plane norm = np.linalg.norm(plane[0:3]) plane = plane / norm return plane @staticmethod def get_fov_flag(pts_rect, img_shape, calib): """ Args: pts_rect: img_shape: calib: Returns: """ pts_img, pts_rect_depth = calib.rect_to_img(pts_rect) val_flag_1 = np.logical_and(pts_img[:, 0] >= 0, pts_img[:, 0] < img_shape[1]) val_flag_2 = np.logical_and(pts_img[:, 1] >= 0, pts_img[:, 1] < img_shape[0]) val_flag_merge = np.logical_and(val_flag_1, val_flag_2) pts_valid_flag = np.logical_and(val_flag_merge, pts_rect_depth >= 0) return pts_valid_flag def get_infos(self, num_workers=4, has_label=True, count_inside_pts=True, sample_id_list=None): import concurrent.futures as futures def process_single_scene(sample_idx): print('%s sample_idx: %s' % (self.split, sample_idx)) info = {} pc_info = {'num_features': 4, 'lidar_idx': sample_idx} info['point_cloud'] = pc_info image_info = {'image_idx': sample_idx, 'image_shape': self.get_image_shape(sample_idx)} info['image'] = image_info calib = self.get_calib(sample_idx) P2 = np.concatenate([calib.P2, np.array([[0., 0., 0., 1.]])], axis=0) R0_4x4 = np.zeros([4, 4], dtype=calib.R0.dtype) R0_4x4[3, 3] = 1. R0_4x4[:3, :3] = calib.R0 V2C_4x4 = np.concatenate([calib.V2C, np.array([[0., 0., 0., 1.]])], axis=0) calib_info = {'P2': P2, 'R0_rect': R0_4x4, 'Tr_velo_to_cam': V2C_4x4} info['calib'] = calib_info if has_label: obj_list = self.get_label(sample_idx) annotations = {} annotations['name'] = np.array([obj.cls_type for obj in obj_list]) annotations['truncated'] = np.array([obj.truncation for obj in obj_list]) annotations['occluded'] = np.array([obj.occlusion for obj in obj_list]) annotations['alpha'] = np.array([obj.alpha for obj in obj_list]) annotations['bbox'] = np.concatenate([obj.box2d.reshape(1, 4) for obj in obj_list], axis=0) annotations['dimensions'] = np.array([[obj.l, obj.h, obj.w] for obj in obj_list]) # lhw(camera) format annotations['location'] = np.concatenate([obj.loc.reshape(1, 3) for obj in obj_list], axis=0) annotations['rotation_y'] = np.array([obj.ry for obj in obj_list]) annotations['score'] = np.array([obj.score for obj in obj_list]) annotations['difficulty'] = np.array([obj.level for obj in obj_list], np.int32) num_objects = len([obj.cls_type for obj in obj_list if obj.cls_type != 'DontCare']) num_gt = len(annotations['name']) index = list(range(num_objects)) + [-1] * (num_gt - num_objects) annotations['index'] = np.array(index, dtype=np.int32) loc = annotations['location'][:num_objects] dims = annotations['dimensions'][:num_objects] rots = annotations['rotation_y'][:num_objects] loc_lidar = calib.rect_to_lidar(loc) l, h, w = dims[:, 0:1], dims[:, 1:2], dims[:, 2:3] loc_lidar[:, 2] += h[:, 0] / 2 gt_boxes_lidar = np.concatenate([loc_lidar, l, w, h, -(np.pi / 2 + rots[..., np.newaxis])], axis=1) annotations['gt_boxes_lidar'] = gt_boxes_lidar info['annos'] = annotations if count_inside_pts: points = self.get_lidar(sample_idx) calib = self.get_calib(sample_idx) pts_rect = calib.lidar_to_rect(points[:, 0:3]) fov_flag = self.get_fov_flag(pts_rect, info['image']['image_shape'], calib) pts_fov = points[fov_flag] corners_lidar = box_utils.boxes_to_corners_3d(gt_boxes_lidar) num_points_in_gt = -np.ones(num_gt, dtype=np.int32) for k in range(num_objects): flag = box_utils.in_hull(pts_fov[:, 0:3], corners_lidar[k]) num_points_in_gt[k] = flag.sum() annotations['num_points_in_gt'] = num_points_in_gt return info sample_id_list = sample_id_list if sample_id_list is not None else self.sample_id_list with futures.ThreadPoolExecutor(num_workers) as executor: infos = executor.map(process_single_scene, sample_id_list) return list(infos) def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'): import torch database_save_path = Path(self.root_path) / ('gt_database' if split == 'train' else ('gt_database_%s' % split)) db_info_save_path = Path(self.root_path) / ('kitti_dbinfos_%s.pkl' % split) database_save_path.mkdir(parents=True, exist_ok=True) all_db_infos = {} with open(info_path, 'rb') as f: infos = pickle.load(f) for k in range(len(infos)): print('gt_database sample: %d/%d' % (k + 1, len(infos))) info = infos[k] sample_idx = info['point_cloud']['lidar_idx'] points = self.get_lidar(sample_idx) annos = info['annos'] names = annos['name'] difficulty = annos['difficulty'] bbox = annos['bbox'] gt_boxes = annos['gt_boxes_lidar'] num_obj = gt_boxes.shape[0] point_indices = roiaware_pool3d_utils.points_in_boxes_cpu( torch.from_numpy(points[:, 0:3]), torch.from_numpy(gt_boxes) ).numpy() # (nboxes, npoints) for i in range(num_obj): filename = '%s_%s_%d.bin' % (sample_idx, names[i], i) filepath = database_save_path / filename gt_points = points[point_indices[i] > 0] gt_points[:, :3] -= gt_boxes[i, :3] with open(filepath, 'w') as f: gt_points.tofile(f) if (used_classes is None) or names[i] in used_classes: db_path = str(filepath.relative_to(self.root_path)) # gt_database/xxxxx.bin db_info = {'name': names[i], 'path': db_path, 'image_idx': sample_idx, 'gt_idx': i, 'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0], 'difficulty': difficulty[i], 'bbox': bbox[i], 'score': annos['score'][i]} if names[i] in all_db_infos: all_db_infos[names[i]].append(db_info) else: all_db_infos[names[i]] = [db_info] for k, v in all_db_infos.items(): print('Database %s: %d' % (k, len(v))) with open(db_info_save_path, 'wb') as f: pickle.dump(all_db_infos, f) @staticmethod def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None): """ Args: batch_dict: frame_id: pred_dicts: list of pred_dicts pred_boxes: (N, 7), Tensor pred_scores: (N), Tensor pred_labels: (N), Tensor class_names: output_path: Returns: """ def get_template_prediction(num_samples): ret_dict = { 'name': np.zeros(num_samples), 'truncated': np.zeros(num_samples), 'occluded': np.zeros(num_samples), 'alpha': np.zeros(num_samples), 'bbox': np.zeros([num_samples, 4]), 'dimensions': np.zeros([num_samples, 3]), 'location': np.zeros([num_samples, 3]), 'rotation_y': np.zeros(num_samples), 'score': np.zeros(num_samples), 'boxes_lidar': np.zeros([num_samples, 7]) } return ret_dict def generate_single_sample_dict(batch_index, box_dict): pred_scores = box_dict['pred_scores'].cpu().numpy() pred_boxes = box_dict['pred_boxes'].cpu().numpy() pred_labels = box_dict['pred_labels'].cpu().numpy() pred_dict = get_template_prediction(pred_scores.shape[0]) if pred_scores.shape[0] == 0: return pred_dict calib = batch_dict['calib'][batch_index] image_shape = batch_dict['image_shape'][batch_index].cpu().numpy() pred_boxes_camera = box_utils.boxes3d_lidar_to_kitti_camera(pred_boxes, calib) pred_boxes_img = box_utils.boxes3d_kitti_camera_to_imageboxes( pred_boxes_camera, calib, image_shape=image_shape ) pred_dict['name'] = np.array(class_names)[pred_labels - 1] pred_dict['alpha'] = -np.arctan2(-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6] pred_dict['bbox'] = pred_boxes_img pred_dict['dimensions'] = pred_boxes_camera[:, 3:6] pred_dict['location'] = pred_boxes_camera[:, 0:3] pred_dict['rotation_y'] = pred_boxes_camera[:, 6] pred_dict['score'] = pred_scores pred_dict['boxes_lidar'] = pred_boxes return pred_dict annos = [] for index, box_dict in enumerate(pred_dicts): frame_id = batch_dict['frame_id'][index] single_pred_dict = generate_single_sample_dict(index, box_dict) single_pred_dict['frame_id'] = frame_id annos.append(single_pred_dict) if output_path is not None: cur_det_file = output_path / ('%s.txt' % frame_id) with open(cur_det_file, 'w') as f: bbox = single_pred_dict['bbox'] loc = single_pred_dict['location'] dims = single_pred_dict['dimensions'] # lhw -> hwl for idx in range(len(bbox)): print('%s -1 -1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f' % (single_pred_dict['name'][idx], single_pred_dict['alpha'][idx], bbox[idx][0], bbox[idx][1], bbox[idx][2], bbox[idx][3], dims[idx][1], dims[idx][2], dims[idx][0], loc[idx][0], loc[idx][1], loc[idx][2], single_pred_dict['rotation_y'][idx], single_pred_dict['score'][idx]), file=f) return annos def evaluation(self, det_annos, class_names, **kwargs): if 'annos' not in self.kitti_infos[0].keys(): return None, {} from .kitti_object_eval_python import eval as kitti_eval eval_det_annos = copy.deepcopy(det_annos) eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.kitti_infos] ap_result_str, ap_dict = kitti_eval.get_official_eval_result(eval_gt_annos, eval_det_annos, class_names) return ap_result_str, ap_dict def __len__(self): if self._merge_all_iters_to_one_epoch: return len(self.kitti_infos) * self.total_epochs return len(self.kitti_infos) def __getitem__(self, index): # index = 4 if self._merge_all_iters_to_one_epoch: index = index % len(self.kitti_infos) info = copy.deepcopy(self.kitti_infos[index]) sample_idx = info['point_cloud']['lidar_idx'] img_shape = info['image']['image_shape'] calib = self.get_calib(sample_idx) get_item_list = self.dataset_cfg.get('GET_ITEM_LIST', ['points']) input_dict = { 'frame_id': sample_idx, 'calib': calib, } if 'annos' in info: annos = info['annos'] annos = common_utils.drop_info_with_name(annos, name='DontCare') loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y'] gt_names = annos['name'] gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32) gt_boxes_lidar = box_utils.boxes3d_kitti_camera_to_lidar(gt_boxes_camera, calib) input_dict.update({ 'gt_names': gt_names, 'gt_boxes': gt_boxes_lidar }) if "gt_boxes2d" in get_item_list: input_dict['gt_boxes2d'] = annos["bbox"] road_plane = self.get_road_plane(sample_idx) if road_plane is not None: input_dict['road_plane'] = road_plane if "points" in get_item_list: points = self.get_lidar(sample_idx) if self.dataset_cfg.FOV_POINTS_ONLY: pts_rect = calib.lidar_to_rect(points[:, 0:3]) fov_flag = self.get_fov_flag(pts_rect, img_shape, calib) points = points[fov_flag] input_dict['points'] = points if "images" in get_item_list: input_dict['images'] = self.get_image(sample_idx) if "depth_maps" in get_item_list: input_dict['depth_maps'] = self.get_depth_map(sample_idx) if "calib_matricies" in get_item_list: input_dict["trans_lidar_to_cam"], input_dict["trans_cam_to_img"] = kitti_utils.calib_to_matricies(calib) input_dict['calib'] = calib data_dict = self.prepare_data(data_dict=input_dict) data_dict['image_shape'] = img_shape return data_dict def create_kitti_infos(dataset_cfg, class_names, data_path, save_path, workers=4): dataset = KittiDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False) train_split, val_split = 'train', 'val' train_filename = save_path / ('kitti_infos_%s.pkl' % train_split) val_filename = save_path / ('kitti_infos_%s.pkl' % val_split) trainval_filename = save_path / 'kitti_infos_trainval.pkl' test_filename = save_path / 'kitti_infos_test.pkl' print('---------------Start to generate data infos---------------') dataset.set_split(train_split) kitti_infos_train = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True) with open(train_filename, 'wb') as f: pickle.dump(kitti_infos_train, f) print('Kitti info train file is saved to %s' % train_filename) dataset.set_split(val_split) kitti_infos_val = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True) with open(val_filename, 'wb') as f: pickle.dump(kitti_infos_val, f) print('Kitti info val file is saved to %s' % val_filename) with open(trainval_filename, 'wb') as f: pickle.dump(kitti_infos_train + kitti_infos_val, f) print('Kitti info trainval file is saved to %s' % trainval_filename) dataset.set_split('test') kitti_infos_test = dataset.get_infos(num_workers=workers, has_label=False, count_inside_pts=False) with open(test_filename, 'wb') as f: pickle.dump(kitti_infos_test, f) print('Kitti info test file is saved to %s' % test_filename) print('---------------Start create groundtruth database for data augmentation---------------') dataset.set_split(train_split) dataset.create_groundtruth_database(train_filename, split=train_split) print('---------------Data preparation Done---------------') if __name__ == '__main__': import sys if sys.argv.__len__() > 1 and sys.argv[1] == 'create_kitti_infos': import yaml from pathlib import Path from easydict import EasyDict dataset_cfg = EasyDict(yaml.safe_load(open(sys.argv[2]))) ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve() create_kitti_infos( dataset_cfg=dataset_cfg, class_names=['Car', 'Pedestrian', 'Cyclist'], data_path=ROOT_DIR / 'data' / 'kitti', save_path=ROOT_DIR / 'data' / 'kitti' )