Hand data science

============================== Data science for hand shape and orientation

Run

• Run pipeline for small data but for code any path should be big/src/stage/*.py in big/dvc.yaml

dvc repro

• Run pipeline for small data but for code any path should be src/stage/*.py in dvc.yaml

dvc repro big/dvc.yaml

• Открыть камеру, показать руку камере и нажать на "Пробел", чтобы детектировать руку и через 1 сек выводить другое окно. 0 - номер камеры

python3 big/src/apps/word_image.py 0 1

• Найти слово в файлах

grep -i 's14c' folder/*.csv

• Определить статистику о метках по *.csv и ввод одна путь для json и несколько путей для csv

python3 big/src/features/stat_fsw_handshape.py

• Удобно ручное тестирование скелетной модели руки в браузере и пример кода codepen.io

Project Organization(Примерно)

---

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── big                <- Source code and data for runing use big data. 
│   ├── data               <- Big data.    
│   │   ├── 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.       
│   └── 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
├── data               <- Small data.    
│   ├── 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`.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── 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           <- makes project pip installable (pip install -e .) so src can be imported

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Project based on the cookiecutter data science project template. #cookiecutterdatascience