| import json |
| import os.path as osp |
| from collections import defaultdict |
| import cv2 |
| import numpy as np |
|
|
| class Octopus(object): |
| """ |
| dataset structure: |
| - data_root |
| - train_split.txt |
| - val_split.txt |
| - test_split.txt |
| - |
| """ |
| camera_names = ['cam01', 'cam03', 'cam05', 'cam06', 'cam07', 'cam08', 'cam09'] |
| camera_tags = ['top', 'top2', 'left_back', 'left_front', 'right_front', 'right_back', 'back'] |
|
|
| def __init__(self, dataset_root): |
| self.dataset_root = dataset_root |
| self.data_root = osp.join(self.dataset_root, 'data') |
| self._collect_basic_infos() |
|
|
| @property |
| def train_split_list(self): |
| if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'train_set.txt')): |
| train_split_list = None |
| else: |
| train_split_list = set(map(lambda x: x.strip(), |
| open(osp.join(self.data_root, 'train_set.txt')).readlines())) |
| return train_split_list |
|
|
| @property |
| def val_split_list(self): |
| if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'val_set.txt')): |
| val_split_list = None |
| else: |
| val_split_list = set(map(lambda x: x.strip(), |
| open(osp.join(self.data_root, 'val_set.txt')).readlines())) |
| return val_split_list |
|
|
| @property |
| def test_split_list(self): |
| if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'test_set.txt')): |
| test_split_list = None |
| else: |
| test_split_list = set(map(lambda x: x.strip(), |
| open(osp.join(self.data_root, 'test_set.txt')).readlines())) |
| return test_split_list |
|
|
| @property |
| def raw_split_list(self): |
| if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'raw_set.txt')): |
| raw_split_list = None |
| else: |
| raw_split_list = set(map(lambda x: x.strip(), |
| open(osp.join(self.data_root, 'raw_set.txt')).readlines())) |
| return raw_split_list |
|
|
| def _find_split_name(self, seq_id): |
| if seq_id in self.raw_split_list: |
| return 'raw' |
| if seq_id in self.train_split_list: |
| return 'train' |
| if seq_id in self.test_split_list: |
| return 'test' |
| if seq_id in self.val_split_list: |
| return 'val' |
| print("sequence id {} corresponding to no split".format(seq_id)) |
| raise NotImplementedError |
|
|
| def _collect_basic_infos(self): |
| self.train_info = defaultdict(dict) |
| if self.train_split_list is not None: |
| for train_seq in self.train_split_list: |
| anno_file_path = osp.join(self.data_root, train_seq, '{}.json'.format(train_seq)) |
| if not osp.isfile(anno_file_path): |
| print("no annotation file for sequence {}".format(train_seq)) |
| raise FileNotFoundError |
| anno_file = json.load(open(anno_file_path, 'r')) |
| for frame_anno in anno_file['frames']: |
| self.train_info[train_seq][frame_anno['frame_id']] = { |
| 'pose': frame_anno['pose'], |
| 'calib': anno_file['calib'], |
| } |
|
|
| def get_frame_anno(self, seq_id, frame_id): |
| split_name = self._find_split_name(seq_id) |
| frame_info = getattr(self, '{}_info'.format(split_name))[seq_id][frame_id] |
| if 'anno' in frame_info: |
| return frame_info['anno'] |
| return None |
|
|
| def load_point_cloud(self, seq_id, frame_id): |
| bin_path = osp.join(self.data_root, seq_id, 'lidar_roof', '{}.bin'.format(frame_id)) |
| points = np.fromfile(bin_path, dtype=np.float32).reshape(-1, 4) |
| return points |
|
|
| def load_image(self, seq_id, frame_id, cam_name): |
| cam_path = osp.join(self.data_root, seq_id, cam_name, '{}.jpg'.format(frame_id)) |
| img_buf = cv2.cvtColor(cv2.imread(cam_path), cv2.COLOR_BGR2RGB) |
| return img_buf |
|
|
| def project_lidar_to_image(self, seq_id, frame_id): |
| points = self.load_point_cloud(seq_id, frame_id) |
|
|
| split_name = self._find_split_name(seq_id) |
| frame_info = getattr(self, '{}_info'.format(split_name))[seq_id][frame_id] |
| points_img_dict = dict() |
| for cam_name in self.__class__.camera_names: |
| calib_info = frame_info['calib'][cam_name] |
| cam_2_velo = calib_info['cam_to_velo'] |
| cam_intri = calib_info['cam_intrinsic'] |
| point_xyz = points[:, :3] |
| points_homo = np.hstack( |
| [point_xyz, np.ones(point_xyz.shape[0], dtype=np.float32).reshape((-1, 1))]) |
| points_lidar = np.dot(points_homo, np.linalg.inv(cam_2_velo).T) |
| mask = points_lidar[:, 2] > 0 |
| points_lidar = points_lidar[mask] |
| points_img = np.dot(points_lidar, cam_intri.T) |
| points_img_dict[cam_name] = points_img |
| return points_img_dict |
|
|
| def undistort_image(self, seq_id, frame_id): |
| pass |
