| # Preparing Jester | |
| ## Introduction | |
| <!-- [DATASET] --> | |
| ```BibTeX | |
| @InProceedings{Materzynska_2019_ICCV, | |
| author = {Materzynska, Joanna and Berger, Guillaume and Bax, Ingo and Memisevic, Roland}, | |
| title = {The Jester Dataset: A Large-Scale Video Dataset of Human Gestures}, | |
| booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, | |
| month = {Oct}, | |
| year = {2019} | |
| } | |
| ``` | |
| For basic dataset information, you can refer to the dataset [website](https://developer.qualcomm.com/software/ai-datasets/jester). | |
| Before we start, please make sure that the directory is located at `$MMACTION2/tools/data/jester/`. | |
| ## Step 1. Prepare Annotations | |
| First of all, you have to sign in and download annotations to `$MMACTION2/data/jester/annotations` on the official [website](https://developer.qualcomm.com/software/ai-datasets/jester). | |
| ## Step 2. Prepare RGB Frames | |
| Since the [jester website](https://developer.qualcomm.com/software/ai-datasets/jester) doesn't provide the original video data and only extracted RGB frames are available, you have to directly download RGB frames from [jester website](https://developer.qualcomm.com/software/ai-datasets/jester). | |
| You can download all RGB frame parts on [jester website](https://developer.qualcomm.com/software/ai-datasets/jester) to `$MMACTION2/data/jester/` and use the following command to extract. | |
| ```shell | |
| cd $MMACTION2/data/jester/ | |
| cat 20bn-jester-v1-?? | tar zx | |
| cd $MMACTION2/tools/data/jester/ | |
| ``` | |
| For users who only want to use RGB frames, you can skip to step 5 to generate file lists in the format of rawframes. Since the prefix of official JPGs is "%05d.jpg" (e.g., "00001.jpg"), | |
| we add `"filename_tmpl='{:05}.jpg'"` to the dict of `data.train`, `data.val` and `data.test` in the config files related with jester like this: | |
| ``` | |
| data = dict( | |
| videos_per_gpu=16, | |
| workers_per_gpu=2, | |
| train=dict( | |
| type=dataset_type, | |
| ann_file=ann_file_train, | |
| data_prefix=data_root, | |
| filename_tmpl='{:05}.jpg', | |
| pipeline=train_pipeline), | |
| val=dict( | |
| type=dataset_type, | |
| ann_file=ann_file_val, | |
| data_prefix=data_root_val, | |
| filename_tmpl='{:05}.jpg', | |
| pipeline=val_pipeline), | |
| test=dict( | |
| type=dataset_type, | |
| ann_file=ann_file_test, | |
| data_prefix=data_root_val, | |
| filename_tmpl='{:05}.jpg', | |
| pipeline=test_pipeline)) | |
| ``` | |
| ## Step 3. Extract Flow | |
| This part is **optional** if you only want to use RGB frames. | |
| Before extracting, please refer to [install.md](/docs/en/get_started/installation.md) for installing [denseflow](https://github.com/open-mmlab/denseflow). | |
| If you have plenty of SSD space, then we recommend extracting frames there for better I/O performance. | |
| You can run the following script to soft link SSD. | |
| ```shell | |
| # execute these two line (Assume the SSD is mounted at "/mnt/SSD/") | |
| mkdir /mnt/SSD/jester_extracted/ | |
| ln -s /mnt/SSD/jester_extracted/ ../../../data/jester/rawframes | |
| ``` | |
| Then, you can run the following script to extract optical flow based on RGB frames. | |
| ```shell | |
| cd $MMACTION2/tools/data/jester/ | |
| bash extract_flow.sh | |
| ``` | |
| ## Step 4. Encode Videos | |
| This part is **optional** if you only want to use RGB frames. | |
| You can run the following script to encode videos. | |
| ```shell | |
| cd $MMACTION2/tools/data/jester/ | |
| bash encode_videos.sh | |
| ``` | |
| ## Step 5. Generate File List | |
| You can run the follow script to generate file list in the format of rawframes and videos. | |
| ```shell | |
| cd $MMACTION2/tools/data/jester/ | |
| bash generate_{rawframes, videos}_filelist.sh | |
| ``` | |
| ## Step 5. Check Directory Structure | |
| After the whole data process for Jester preparation, | |
| you will get the rawframes (RGB + Flow), and annotation files for Jester. | |
| In the context of the whole project (for Jester only), the folder structure will look like: | |
| ``` | |
| mmaction2 | |
| βββ mmaction | |
| βββ tools | |
| βββ configs | |
| βββ data | |
| β βββ jester | |
| β β βββ jester_{train,val}_list_rawframes.txt | |
| β β βββ jester_{train,val}_list_videos.txt | |
| β β βββ annotations | |
| β | βββ videos | |
| β | | βββ 1.mp4 | |
| β | | βββ 2.mp4 | |
| β | | βββ... | |
| β | βββ rawframes | |
| β | | βββ 1 | |
| β | | | βββ 00001.jpg | |
| β | | | βββ 00002.jpg | |
| β | | | βββ ... | |
| β | | | βββ flow_x_00001.jpg | |
| β | | | βββ flow_x_00002.jpg | |
| β | | | βββ ... | |
| β | | | βββ flow_y_00001.jpg | |
| β | | | βββ flow_y_00002.jpg | |
| β | | | βββ ... | |
| β | | βββ 2 | |
| β | | βββ ... | |
| ``` | |
| For training and evaluating on Jester, please refer to [Training and Test Tutorial](/docs/en/user_guides/train_test.md). | |