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Body measurements guidance system using computer vision and pose estimation

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Unit

Body measurements guidance system using computer vision and pose estimation

Solution

The solution built is a Python Flask application that processes images of through a custom mobilenet model that is adpated from the Pose Net model.

Application Image

Architecture

Architecture Diagram

Technologies

  • Python Flask
  • Tensorflow
  • Anaconda (Windows)
  • Bluetooth serial (Arduino BluNo)
  • Html/CSS/JS

System Requirements

  • Python >= 3.6.5
  • GPU (Optional)
  • PIP installed

Installation

  1. Install Anaconda from here (Anaconda >= 5.3)

  2. Install Swig from here here

  3. Open Anaconda prompt:

    Go with the mouse to the Windows Icon (lower left) and start typing "Anaconda". There should show up some matching entries. Select "Anaconda Prompt".

  4. Clone our repository and change directory:

    $ git clone https://github.com/Sri-vatsa/Unit
    $ cd Unit
    
  5. Create & Activate virtual env:

    $ conda create --prefix=unit python=3.6.5
    

    Note: allow conda to install all relevant packages for python 3.6.5 by typing y when prompted

    List all available conda environments: (Optional)

    $ conda info --envs
    

    Activate conda environment for Unit:

    $ conda activate PATH/TO/ENV
    

    e.g. PATH/TO/ENV : D:\Unit\unit

  6. Install dependencies:

    $ pip install -r requirements.txt
    
  7. Make paf for pose estimation post processing:

    a. Change directory to pafprocess:

    $ cd tf_pose_estimation/tf_pose/pafprocess
    

    b. Build the pafprocess module

    $ swig -python -c++ pafprocess.i && python3 setup.py build_ext --inplace
    
  8. change directory back to root directory:

    $ cd ../../..
    

Run application

  1. Run backend:
$ python main.py
  1. Find your local ip address:

In the anaconda prompt, type:

$ ipconfig 

IPv4 Address is your local ip address.Take note of this address.

E.g. 172.17.10.153

  1. Run the product demo in a browser

a. Open any browser (Chrome is preferred) b. In the addressbar, type : https://YOUR_IP_ADDRESS:5000 E.g. https://172.17.10.153:5000

Note: If the browser alerts that the site is insecure, click on advanced settings -> continue anyway

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