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StreamLit web application for face detection, which includes eye detection, cartonize, and cannize images. We can also change the brightness, contrast, blur it, and convert it to gray-scale.

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krishnakaushik25/face-detection-opencv-streamlit

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face_detection_app

  • Streamlit App with OpenCV
  • To OPEN Face-Detecton-APP click here:

Streamlit APP

How to Deploy on Heroku

  • To deploy on heroku you will need the basic 3 requirements and a new requirement for the OpenCV
  • This is due to the fact that opencv requires few dependencies that you need to install.

3 Basic Requirements

  • Procfile
  • setup.sh
  • requirements.txt or pipfile

Requirements For OpenCV

  • Buildpack
  • To check if you have the buildpack installed you can use
heroku run bash
apt --help
  • If it shows the help, that means you have the buildpack for apt installed
  • If you do not have it you can use this command to install the buildpack
heroku buildpacks:add --index 1 https://github.com/heroku/heroku-buildpack-apt
  • Aptfile
  • You can then include a file called Aptfile which contains the basic dependences/copy and paste into Aptfile
libsm6
libxrender1
libfontconfig1
libice6
  • NB: This is the same as installing with apt on a paid account
apt-get install libsm6 libxrender1 libfontconfig1 libice6

Deploying

  • Then just like before you run after adding to your repo you can push to heroku to deploy by
git push heroku master

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StreamLit web application for face detection, which includes eye detection, cartonize, and cannize images. We can also change the brightness, contrast, blur it, and convert it to gray-scale.

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