125 lines
5.0 KiB
Python
125 lines
5.0 KiB
Python
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 |