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Webapp to help you understand and fine tune canny edge, hough line, and harris corner detection algorithm settings. Built with React, Flask, openCV, and Heroku

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#Online Computer Vision

##Repo Structure

  • The top level folder contains all the backend files. This is to facilitate Github integration with Heroku or AWS.
  • One of the top level folders is the frontend folder. This contains all the frontend source code.

##Run your own copy

  1. Download/clone repo
  2. Using command line interface (CLI) from top-level folder run: pip install -r requirements.txt
  3. The backend will require these environment variables:
  • FLASK_DEBUG (a 'true'/'false' boolean)
  • SECRET_KEY (some secret string)
  • ORIGIN (the url of where your frontend is hosted)
  1. Then run python application.py to start running the server locally
  2. Now in a new CLI, navigate to frontend folder and run: npm install
  3. Then while in the frontend folder, run either:
  • npm run start to run the frontend locally
  • npm run build to generate the bundled html, css, and javascript to deploy online

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Webapp to help you understand and fine tune canny edge, hough line, and harris corner detection algorithm settings. Built with React, Flask, openCV, and Heroku

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