Prepare data related to Lyft dataset. Related data consists of '.pkl' files recording basic infos. Although the ground truth database and 2D annotations are not used in Lyft, it can also be generated like nuScenes. Args: root_path (str): Path of dataset root. info_p
(root_path, info_prefix, version, max_sweeps=10)
| 91 | |
| 92 | |
| 93 | def lyft_data_prep(root_path, info_prefix, version, max_sweeps=10): |
| 94 | """Prepare data related to Lyft dataset. |
| 95 | |
| 96 | Related data consists of '.pkl' files recording basic infos. |
| 97 | Although the ground truth database and 2D annotations are not used in |
| 98 | Lyft, it can also be generated like nuScenes. |
| 99 | |
| 100 | Args: |
| 101 | root_path (str): Path of dataset root. |
| 102 | info_prefix (str): The prefix of info filenames. |
| 103 | version (str): Dataset version. |
| 104 | max_sweeps (int, optional): Number of input consecutive frames. |
| 105 | Defaults to 10. |
| 106 | """ |
| 107 | lyft_converter.create_lyft_infos( |
| 108 | root_path, info_prefix, version=version, max_sweeps=max_sweeps) |
| 109 | |
| 110 | |
| 111 | def scannet_data_prep(root_path, info_prefix, out_dir, workers): |