Processing of video sensor data from the OREBA dataset for intake gesture detection.
Convert video files and labels to tfrecord format.
We use .tfrecord files to feed data into TensorFlow.
This includes TV-L1 optical flow, which is stored in the file alongside with raw frames and labels.
A dependency due to optical flow calculation is opencv.
Make sure that all requirements are fulfilled
$ brew install opencv
$ pip install -r requirements.txt
Then call main.py, pointing to the recordings directory for OREBA-DIS or OREBA-SHA.
$ python main.py --src_dir=OREBA_Dataset_Public_1_0/oreba_dis/recordings
The following flags can be set:
| Argument | Description | Default |
|---|---|---|
| --src_dir | Recordings directory | OREBA_Dataset_Public_1_0/oreba_dis/recordings |
| --exp_dir | Directory for data export | Export |
| --dataset | Which dataset is used {OREBA-DIS or OREBA-SHA} | OREBA-DIS |
| --label_spec | Filename of label specification | label_spec/OREBA_only_intake.xml |
| --label_spec_inherit | Inherit label specification for sublabels (if label not included, always keep sublabels as Idle) | True |
| --resolution | Resolution of the video {140p or 250p} | 140p |
| --exp_fps | Store video frames using this framerate (In fps; Should be able to divide original framerate by this) | 8 |
| --exp_optical_flow | Calculate optical flow | False |
| --organise_data | If True, organise data in train, valid, test subfolders | False |
| --organise_dir | Directory to copy train, val and test sets using data organiser | Organised |
| --organise_subfolders | Create sub folder per each file in validation and test set | False |
Control what labels are included by selecting or editing the appropriate label_spec file.
Templates are available in the label_spec directory.