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Anxiety detection model

This repo uses code from the github repo-https://github.com/michalfaber/tensorflow_Realtime_Multi-Person_Pose_Estimation.git The pose model estimation is used is the program to detect movement. Further Anxiety and confidence score is based on the irregular movemnet of the candidate. Further models include trcaking eye movement and voice modulation to predict the Anxiety-Confidence score.

Progress uptil Now

Score predictor using movement is being implemented. For efficieny mobilenet model is being used

TO-DO

  1. Upgrade code to be compatible with tensorflow 2.0 [DONE]
  2. Add eye tracking module
  3. Add Voice modulation module
  4. Collect a good amount of datat to train on
  5. Improve speed and efficiency

Pre-requisites

You need to have tensorflow 2.0+ installed. And the dependencies listed in the requirments.txt Download the model and copy the weights.best.mobilenet.h5 file to the master directory of this repo.

  • install CUDNN and CUDA

    If you use Anaconda, there is a simpler way. Just install the precompiled libs:

    conda install -c anaconda cudatoolkit==10.0.130-0

How to install (with tensorflow-gpu)

Virtualenv

pip install virtualenv
virtualenv venv
source venv/bin/activate

pip install -r requirements.txt
# ...or
pip install -r requirements_all.txt # completely frozen environment with all dependent libraries

Anaconda

conda create --name tf_pose_estimation_env
conda activate tf_pose_estimation_env

bash requirements_conda.txt

Note: These instructions are also available in the originl repo from which these models and code was borrowed and further trained on.

Usage

To find required keypoints on a image In your python console or program, move to the Anxiety_Detection directory and

import pose_model
import cv2
img = cv2.imread("Imagepath")
keypoints_required = pose_model.get_points(img)

NOTE: The score predictor is yet to be updated.