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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:

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.
      • Database
        • InfluxDB.
        • ELasticSearch
      • Video
        • Write frames to video file.

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

Project details


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Files for videoanalytics, version 0.0.1
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Filename, size videoanalytics-0.0.1.tar.gz (14.2 kB) File type Source Python version None Upload date Hashes View
Filename, size videoanalytics-0.0.1-py3-none-any.whl (14.7 kB) File type Wheel Python version py3 Upload date Hashes View

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