Skip to main content

Library for prototyping video analytic applicatios.

Project description



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


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):

conda env create -f videoanalytics-gpu.yml

Some examples are provided as jupyter notebooks.

conda activate videoanalytics-gpu.yml
jupyter notebook .

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

videoanalytics-0.0.2.tar.gz (26.2 kB view hashes)

Uploaded source

Built Distribution

videoanalytics-0.0.2-py3-none-any.whl (30.4 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page