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low-face-mode

This program uses opencv and osascript to brighten or dim your monitor screen (currently only on MacOS) based on whether or not you are behind your computer.

By doing this, it preserves battery life, as the screen is dimmed down to zero when a face has not been detected by the computer's webcamera for some time. However, as soon as a face is detected again, the screen is once more brightened with the help of applescript commands executed from python.

It also fully supports custom face recognition. This means that it will only brighten/dim the screen if it is your face that is in front of the screen.

Usage

low-face-mode utilizes the argparse library to run commands directly from terminal. This means that there is no need to run in an IDE.

The command-line commands are laid out as follows. You can run this code by typing python3 low_face_mode.py followed by the arguments you need (outline below). As an example, the simplest command for simple face detection is python3 low_face_mode.py -r False -u <yourname>. Make sure that you are in the current directory of the low_face_mode.py file before beginning.

usage: low_face_mode.py [-h] [-r RECOGNITION] [-d DATABASE] [-y YOURNAME] [-u USERS [USERS ...]]

optional arguments:
  -h, --help            show this help message and exit
  -r RECOGNITION, --recognition RECOGNITION
                        whether custom recognition is True/False
  -d DATABASE, --database DATABASE
                        create facial database; True/False
  -y YOURNAME, --yourname YOURNAME
                        name used for creating custom face database, MUST be same as in --users
  -u USERS [USERS ...], --users USERS [USERS ...]
                        list of verified users to brighten screen

With all of the listed arguments, we can either use a dash and the letter (e.g. -r' or two dashes and the name (shown on right, e.g. --recognition). We will use the shorter letters for simplicity and ease in this tutorial.

The -r tag means whether or not facial recognition is on. It is defaulted to True, and it is a necessary argument. Keep in mind that you MUST first set up your facial database before running, as otherwise the screen will always dim.

The -d tag is an optional argument; you can choose to ignore it and it is set to False by default. If you choose to set it to True, a startup script will intialize that will ask you to take some pictures of yourself. It will display a small window with your face in it, outlined in green. Now, you MUST make various faces and then press the k key. This creates samples of your face which are used with deep learning to later check it is you. When you are done taking photos, press the q key and wait a bit for them to be processed. Suggested image count: 5-10.

The y tag is an optional argument. It is set to none by default. However, if you are setting up your face database, it MUST be set to your name. This is because the database creates a folder with images of your name in it, and it will also later compare this name against the list of verified users. Example: python3 low_face_mode.py -r True -d True -y Simon -u Simon. If you set it to a previously unadded name, the image database will run again, and another user will be added to the facial database.

The u tag is required. You MUST include it, even if you are not running custom facial recognition. The u tag is the list of authorized users, ones who have the power to brighten/dim your screen. Make sure that your name is set to this. This list can be multiplefold long. Examples: ... -u Simon or ... -u Simon Frank. Ensure that this is your last argument.

If you are confused, you can type python3 low_face_mode.py -h to get the overview, which is also shown above.

Requirements

There's a lot to support the custom face recognition, if you just want face detection, don't worry about this.

opencv osascript argparse pickle imutils face_recognition

Code in Action!

Example GIF

About

Dim and brighten the screen based on whether or not you are present! OpenCV detects face with webcam and dims screen if not present.

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