MedTracker Video Analytics Server 🚀

Introduction: Structuring of API

• api_template: Contains all the API related Code Base.

  • manage.py: Only entry point for API. Contains no logic.
  • .env: Most important file for your api and contains global configs. Acoid using application/variable level configs here.
  • application: It contains all your api related codes and test modules. I prefer keeping application folder at global.
  • logs: Logs is self-explanatory. FYI it will not contain any configuration information, just raw logs. Feel free to move according to your comfort but not inside the application folder.
  • models: As a part of Machine-Learning/ Deep-Learning app you might need to add model files here or if you have huge files on cloud add symlinks if possibles.
  • resources: To store any documentation, application related csv/txt/img files etc.
  • settings: Logger/DataBase/Model global settings files in yaml/json format.

• application:

  • main: priority folder of all your application related code.
    • 🏗 infrastructure: Data Base and ML/DL models related backbone code
    • 📮 routers: API routers and they strictly do not contain any business logic
    • 📡 services: All processing and business logic for routers here at service layer
    • ⚒ utility:
      • config_loader Load all application related config files from settings directory
      • logger Logging module for application
      • manager A manager utility for Data Related Task which can be common for different services
    • 🐍 config.py: Main config of application, inherits all details from .env file
  • test: Write test cases for your application here.
  • initializer.py: Preload/Initialisation of Models and Module common across application. Preloading model improves inferencing.

Running Locally ? 📍

```bash
    pip install -r requirements.txt

    python scripts/download_models.sh

    uvicorn "manage:app" "--host" "0.0.0.0" "--port" "4000" --workers 4
```
OR
```bash
    bash scripts/run.sh
```

Import the API collection into insomnia, and Test it 🚀

Docker Support 🐳

```bash
    docker build -t fastapi-image  .
    docker run -d --name fastapi-container -p 8000:8000 fastapi-image
```

Download Image Models

```bash
    bash scripts/download_models.sh
```

Results

``json
{
"message": "video analysed",
"result": {
"kissing": 13.440890341065824,
"fighting": 68.25806427001953,
"laughing": 18.226162994280457,
"clapping": 0.05865482428635005,
"calling": 0.02985033688958083,
"running": 0.0009125430551648606,
"dancing": 0.0010029199358996266,
"cycling": 0.00963106327582444,
"hugging": 0.00026188072297372855,
"drinking": 0.00023221400624606758,
"using-laptop": 0.07236006786115468
}
}
```

Notes: If results include drinking or eating, then the person in the video is probably taking a drug.

WARNING: The model is crappy and not accurate