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