semvec/pypi-packages huggingface dataset:
lgp-grader 0.4.0
Reference backend implementation for the LGP protocol

# Grader

Reference backend implementation for the [LGP protocol](https://github.com/LadueCS/LGP) written using Python's HTTPServer and SQLite. No external dependencies other than the Python standard library and optionally Firejail for sandboxing.


## Usage

It is highly recommended to put this server behind a reverse proxy like nginx because HTTPServer does not implement any security features.

Also, you should run this server with a sandboxing program. Currently, only Firejail is supported. You can also run this server without a sandbox, but make sure you have some other way of isolating it, such as running it inside a Docker container or virtual machine.

For debugging, you can adjust the logging configuration on the first few lines.

```
usage: grader.py [-h] [-p PORT] [-s SANDBOX]

Reference backend implementation for the LGP protocol

optional arguments:
  -h, --help            show this help message and exit
  -p PORT, --port PORT  which port to run the server on
  -s SANDBOX, --sandbox SANDBOX
                        which sandboxing program to use
```
videoanalytics 0.0.2
Library for prototyping video analytic applicatios.
Requires Python: >=3.6

# videoanalytics

![PyPI](https://img.shields.io/pypi/v/videoanalytics)

Python library for prototyping of video analytic applications. Relies on OpenCV, Keras, and other standard computer vision and machine learning python packages.

References:
- Code for YOLOv4 and DeepSORT was adapted from [yolov4-deepsort](https://github.com/theAIGuysCode/yolov4-deepsort).

## Component reference

Components are organized as Sources and sinks which are instanced and connected at execution time as pipelines.
Sources consume data from a camera or file and trigger the processing pipeline.
Sinks process data that was made available from other components and generate new.

- Sources
    - VideoReader
- Sinks
    - Object detection
        - YOLOv4Detector
        - DetectorCSV
    - Visualization
        - Bounding box annotation
        - Matplotlib
    - Outputs
        - Metadata
            - DetectionsCSVWriter
                - Store object detections as CSV.
            - TrackingCSVWriter
                - Store tracked objects as CSV.
        - Database
            - InfluxDB. 
            - ELasticSearch
        - Video
            - Write frames to video file.

## Instructions for developers

Import conda environment (GPU):

~~~bash
conda env create -f videoanalytics-gpu.yml
~~~

Some examples are provided as jupyter notebooks.

~~~bash
conda activate videoanalytics-gpu.yml
jupyter notebook .
~~~