FacialActionLibras
Code and file structure
├ FacialActionLibras
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
| ├── examples <- Data used to test code examples.
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
| ├── examples <- Data generated from test codes.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- Make this project pip installable with `pip install -e`
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Forma de execução via docker antiga:
docker run --rm -it -v /home/jms2/Documentos/projetos/libras/FacialActionLibras/app:/home/work/app facialactionlibras:0.0.5 /bin/bash--ipc=host (nesni de --hostipc do x11docker) de permitir que o contêiner do docker se comunique com os processos do host e também acesse as memórias compartilhadas.
Procedimentos de execução de testes via Dlib através do Env Docker
Gere uma imagem.
Abra um terminal e deixe o serviço abaixo executando:
x11docker --hostipc --hostdisplay --webcam --share /home/jms2/Documentos/projetos/libras/FacialActionLibras facialactionlibras:0.0.8 bash
Em outro bash, liste o último nome do container gerado por último e copie:
docker ps -aAltere o comando abaixo, substituindo o nome do bash:
docker exec -it x11docker_X0_facialactionlibras-0-0-8-bash_27633084693 bashNavegue até o diretório e execute os scripts:
cd ../home/jms2/Documentos/projetos/libras/FacialActionLibras/src/features/python video_landmarks_detection.py