(self)
| 37 | self.n_out_samples = 0 |
| 38 | |
| 39 | def run(self): |
| 40 | n_filtered_out = defaultdict(int) |
| 41 | src_txn = self.src_lmdb_env.begin(write=False) |
| 42 | |
| 43 | # sampling and normalization |
| 44 | cursor = src_txn.cursor() |
| 45 | for key, value in cursor: |
| 46 | video = pyarrow.deserialize(value) |
| 47 | vid = video['vid'] |
| 48 | clips = video['clips'] |
| 49 | for clip_idx, clip in enumerate(clips): |
| 50 | filtered_result = self._sample_from_clip(vid, clip) |
| 51 | for type in filtered_result.keys(): |
| 52 | n_filtered_out[type] += filtered_result[type] |
| 53 | |
| 54 | # print stats |
| 55 | with self.dst_lmdb_env.begin() as txn: |
| 56 | print('no. of samples: ', txn.stat()['entries']) |
| 57 | n_total_filtered = 0 |
| 58 | for type, n_filtered in n_filtered_out.items(): |
| 59 | print('{}: {}'.format(type, n_filtered)) |
| 60 | n_total_filtered += n_filtered |
| 61 | print('no. of excluded samples: {} ({:.1f}%)'.format( |
| 62 | n_total_filtered, 100 * n_total_filtered / (txn.stat()['entries'] + n_total_filtered))) |
| 63 | |
| 64 | # close db |
| 65 | self.src_lmdb_env.close() |
| 66 | self.dst_lmdb_env.sync() |
| 67 | self.dst_lmdb_env.close() |
| 68 | |
| 69 | def _sample_from_clip(self, vid, clip): |
| 70 | clip_skeleton = clip['skeletons_3d'] |
no test coverage detected