Library for prototyping video analytic applicatios.
Project description
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.
Instructions for developers
Import conda environment (GPU):
conda env create -f videoanalytics-gpu.yml
Some examples are provided as jupyter notebooks.
conda activate videoanalytics-gpu.yml
jupyter notebook .
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.
- DetectionsCSVWriter
- Database
- InfluxDB.
- ELasticSearch
- Video
- Write frames to video file.
- Metadata
- Object detection
Notes for development and dependency management
Exporting conda environment:
conda env export --name videoanalytics-gpu > videoanalytics-gpu.yml
Exporting requirements for pip package:
pip freeze > requirements.txt
Generate documentation (Sphinx)
make
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