STEP 1: Create an environment using the below commands
- Setup the pip package manager
$ pip install -U pip
- Install the virtualenv package
The virtualenv package is required to create virtual environments. You can install it with pip:
$ pip install virtualenv
- Create the virtual environment
To create a virtual environment, you must specify a path. For example to create one in the local directory called ‘mypython’, type the following:
$ virtualenv mypython
- Activate the virtual environment
You can activate the python environment by running the following command:
$ source mypython/bin/activate
STEP 2: Install Python (We've used Python3.7) using the command
$ sudo apt install python3.7
STEP 3: Install Jupyter Nodebook using the command
$ pip install notebook
STEP 4: Install The following required libraries
- Install 'OpenCV' using the command
$ pip install opencv-python
- Install 'Numpy' using the command
$ pip install numpy
- Install 'Pandas' using the command
$ pip install pandas
- Install 'Scipy' using the command
$ pip install scipy
- Install 'Dlib', after installing few prerequisites using the command
$ sudo apt-get install build-essential cmake
$ sudo apt-get install libgtk-3-dev
$ sudo apt-get install libboost-all-dev
$ pip install scikit-image
$ pip install dlib
STEP 4: After above installations
- Option 1: Either clone the repository using the below command, and move to directory ".../Smart_Monitoring_System/Code" and open 'classroom monitor.ipynb' in Jupyter Notebook. And pass the directory of 'shape_predictor_68_face_landmarks.dat' file, where it is required in the code.
https://github.com/dheeraj-2000/Smart_Monitoring_System.git
- Option 2: Or, After Unzipping this folder, move to ".../Smart_Monitoring_System/Code" and open 'classroom monitor.ipynb' in Jupyter Notebook. And pass the directory of 'shape_predictor_68_face_landmarks.dat' file, where it is required in the code.