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Jono-Gunti (The People Counter)

This is a people counter program that detects the number of people in a single in any road by analysing video via IP Camera of Drone or Mobile device or it can also analyze a recorded video file.I think it will be really very helpfull if anybody uses it in their drones to surveillance about that the no of peoples are roaming outside.

The People Counter IoT


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Getting Started

GitHub code size in bytes GitHub commit activity GitHub repo size

Requirements

  • Python3 (my version of development is Python 3.7.5 64-bit)
  • Pyimagesearch
  • Imutils
  • argparse
  • time
  • dlib
  • scipy
  • Numpy
  • Matplotlib
  • OpenCV

To use my program this requirements have to be fulfilled in your machine

To test my program just follow this steps .

  • First Clone/Download this repository to your computer.

  • After Downloading/Cloning the Repository Just Enter this code to the terminal (Open the terminal from inside this downloaded folder)

    ./test.sh

  • If all are set and you get no error then you can proceed to the next steps and connect it with the IP Camera if your chosen device (Drones , CCTVs or Mobile Devices ).

To Connect with another devices just follow this steps . (Here connection with a mobile device is shown ).

  1. Download IP Webcam app from Google
    Play store.

  2. Then Open the app.

  3. Select Start Server

  4. Note Down the IPv4 address.

  5. Enter the following code in terminal (Open the terminal from inside this downloaded folder).

    ./run.sh

  6. Enter the IPv4 address of the camera in the terminal when prompted and start Surveillancing.

 饾摙饾摻饾摢饾攤 饾摋饾摳饾摱饾摦 , 饾摙饾摻饾摳饾摴 饾摻饾摫饾摦 饾搾饾摳饾摱饾摱饾摼饾摲饾摬饾摻饾攤 饾摙饾摴饾摶饾摦饾摢饾摥. 饾晩饾晵饾暓饾晼 饾暘饾暊饾暒饾暎饾暏饾晼饾暆饾晽, 饾晩饾晵饾暓饾晼 饾暘饾暊饾暒饾暎 饾暏饾暊饾晹饾暁饾晼饾暐饾暘

Goals of this project :

  • To reduce the percentage of people roaming outside everyday
  • To provide better tracking system and proper measurement of people density in various areas

Citation

  1. The ML Model was built by taken help from http://www.pyimagesearch.com , and special thanks to Adrian Rosebrock for the awesome contribution in the image processing domain.

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