This module detects and recognizes your face for MagicMirror². This module is mainly inspired by MMM-Facial-Recognition-OCV3 and a tutorial from Adrian Rosebrock. It uses the new DNN (Deep Neural Network) provided by OpenCV 4.1 and is much more accurate than the old Haar Cascade method. The old method is still available in this module because for a Raspberry Pi Zero the new way will be too heavy.
With this module you can show and hide all other modules depending on which person is recognized. For example, if you are in front of the mirror, the mirror will show you your agenda, but if a stranger is in front, the agenda will not be shown.
The module is finished as far as I can tell. I have tested the module on my development machine as well as on the MagicMirror and it works fine with the PiCamera.
This module works in the background, and so no screenshots are available.
- OpenCV 4.1
- dlib
- face_recognition
- imutils
- numpy
- Camera
I used a Raspberry Pi Spy Cam which i built into my mirror. It also works fine with the regular Raspberry Pi camera module.
You will find a lot of good tutorials to install OpenCV 4 for your computer / Raspberry Pi. I am not going to write another one here. The module has only been tested with OpenCV 4.1. If you use another version, please let me know whether it works so I can make a list of all compatible versions.
- https://www.pyimagesearch.com/2018/08/17/install-opencv-4-on-macos/
- https://www.pyimagesearch.com/2018/09/26/install-opencv-4-on-your-raspberry-pi/
- https://pysource.com/2019/03/15/how-to-install-python-3-and-opencv-4-on-windows/
If you install OpenCV in a virtual environment, please do not forget to set the correct pythonPath in the settings.
OpenCV 4.1.2 quick installation (wthout compliation) for Raspbian Buster
If you are planning to run OpenCV on Raspbian Buster, you can follow these steps to install pre-compilled OpenCV v.4.2.1 package. This will save time for OpenCV compilation.
wget https://github.com/cyysky/OpenCV-4.1.2-for-Raspbian/raw/master/opencv_4.1.2-1_armhf.deb
sudo dpkg -i opencv_4.1.2-1_armhf.deb
sudo apt-get -f install
sudo dpkg -i opencv_4.1.2-1_armhf.debIf you have both python v2 and python v3 installed then you may need to be specific in the use of either pip or pip3. If you are targetting python v3 then you will need to substitute pip3 instead of pip in the example commmands below. If you have only a single version of python installed then you may use pip3.
You can install dlib easily using pip with the following command.
pip3 install dlibYou can install the face_recognition library over pip too.
pip3 install face_recognitionIf you have issues installing, this tutorial may be helpful.
You can install imutils over pip.
pip3 install imutilsYou can install numpy over pip.
pip3 install numpyI have tested the following dependency versions. Let me know if the module works with another version so I can extend this list.
| Dependency | Versions |
|---|---|
| OpenCV | 4.1 |
| dlib | 19.17.0 |
| face_recognition | 1.2.3 |
| imutils | 0.5.3 |
Load this module directly from GitHub using the following commands.
cd ~/MagicMirror/modules/
git clone https://github.com/nischi/MMM-Face-Reco-DNN.git
cd MMM-Face-Reco-DNN
npm installHere is a summary of the most important points to use this module. If you are interested how it works, have a look at Adrian Rosebrocks' tutorial.
First of all you need to create your face dataset. The module is preconfigured such that you store your dataset in the folder dataset in the root of the module. It is possible to use another folder if you want to.
Put each person in a seperate subdirectory like this:
- dataset
- thierry
- img01.jpg
- img02.jpg
- img03.jpg
- adam
- img01.jpg
- img02.jpg
- img03.jpg
- james
- img01.jpg
- img02.jpg
- img03.jpg
- john
- img01.jpg
- img02.jpg
- img03.jpg
It's enough if you have around 10 pictures for each person. Try to use pictures in different settings and lighting if possible as it may help to build a better model of the person's face.
After you set up your dataset, we need to go to the embeddings for the recognitions. I prepared a script for that. It took a while to run on my Macbook and will take much longer on a Raspberry Pi, so if possible, use it on a computer with more power and then transfer the resulting file.
You will find a script called encode.py in folder tools of the module. The following arguments are default:
python3 encode.py -i ../dataset/ -e encodings.pickle -d hog
# Arguments can be omitted to use the defaults above
python3 encode.py| Argument | Description |
|---|---|
-i / --dataset |
Path to dataset directory containing face images Default Value: ../dataset/ |
-e / --encodings |
Path to serialized database of facial encodings Default Value: encodings.pickle |
-d / --detection-method |
Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate deep-learning model which is GPU/CUDA accelerated (if available). Default Value: hog |
After that you are ready to configure the module and use it on your MagicMirror.
Another option for building the encodings.pickle file is to use a Docker container. This way all dependencies and libraries are isolated from the host OS, you will only need make and docker.
cd tools;
make # generate an encodings.pickle with hog, or alternatively
make encoding=cnn # overide the default to use cnn encodingTo setup the module in MagicMirror², add the following section to the config.js file in the MagicMirror/config directory.
{
module: 'MMM-Face-Reco-DNN',
config: {
// Logout 15 seconds after user was not detected any more
// If they are detected within this period, the delay will start again
logoutDelay: 15000,
// How often the recognition starts in milliseconds
// With a Raspberry Pi 3+ it works well every 2 seconds
checkInterval: 2000,
// Module set used for when there is no face detected ie no one is in front of the camera
noFaceClass: 'noface',
// Module set used for when there is an unknown/unrecognised face detected
unknownClass: 'unknown',
// Module set used for when there is a known/recognised face detected
knownClass: 'known',
// Module set used for strangers and if no user is detected
defaultClass: 'default',
// Set of modules which should be shown for any user ie when there is any face detected
everyoneClass: 'everyone',
// Set of modules that are always shown - show if there is a face or no face detected
alwaysClass: 'always',
// XML to recognize with haarcascade
cascade: 'modules/MMM-Face-Reco-DNN/tools/haarcascade_frontalface_default.xml',
// Pre-encoded pickle with the faces
encodings: 'modules/MMM-Face-Reco-DNN/tools/encodings.pickle',
// Use Raspberry Pi camera or another type
// 1 = RasPi camera, 0 = other camera
usePiCamera: 1,
// If using another type of camera, you can choose
// i.e. 0 = /dev/video0 or 'http://link.to/live'
source: 0,
// Rotate camera
rotateCamera: 0,
// Method of facial recognition
// dnn = deep neural network, haar = haarcascade
method: 'dnn',
// Which face detection model to use
// "hog" is less accurate but faster on CPUs
// "cnn" is a more accurate deep-learning model which is GPU/CUDA accelerated
detectionMethod: 'hog',
// How long in milliseconds modules take to hide and show
animationSpeed: 0,
// Path to Python to run the face recognition
// null or '' means default path
pythonPath: null,
// Should a welcome message be shown using the MagicMirror alerts module?
welcomeMessage: true,
// Capture new pictures of recognized people, if unknown we save it in folder "unknown"
// So you can extend your dataset and retrain it afterwards for better recognitions
extendDataset: false,
// If extendDataset is true, you need to set the full path of the dataset
dataset: 'modules/MMM-Face-Reco-DNN/dataset/',
// How much distance between faces to consider it a match. Lower is more strict.
tolerance: 0.6,
// allow multiple concurrent user logins, 0=no, any other number is the maximum number of concurrent logins
multiUser: 0,
}
}The module sends notifications if a user is logged in or logged out. In addition you can request the list of logged-in users to check if somebody is in front of the mirror. You can then use it for your own module, such as MMM-MotionControl.
| Notification | Direction | Description |
|---|---|---|
USERS_LOGIN |
out | Sent if new users are detected in front of the mirror |
USERS_LOGOUT |
out | Sent if users leave the camera's view |
LOGGED_IN_USERS |
out | All logged in (in front of mirror) users |
GET_LOGGED_IN_USERS |
in | Request all logged in users |
| Option | Description |
|---|---|
logoutDelay |
Log out this long after user was not detected any more. If they are detected within this period, the delay will start again. Default Value: 15000 |
checkInterval |
How often the recognition starts in milliseconds. With a Raspberry Pi 3+ it works well every 2 seconds. Default Value: 2000 |
noFaceClass |
Module set used for when there is no face detected ie no one is in front of the camera. Default Value: noface |
unknownClass |
Module set used for when there is an unknown/unrecognised face detected. Default Value: unknown |
knownClass |
Module set used for when there is a known/recognised face detected Default Value: known |
defaultClass |
Module set used for strangers or if no user is detected. Default Value: default |
alwaysClass |
Set of modules that are always shown - show if there is a face or no face detected. Default Value: always |
everyoneClass |
Set of modules which should be shown for every recognised user. Default Value: everyone |
cascade |
XML to recognize with haarcascade. Default Value: modules/MMM-Face-Reco-DNN/tools/haarcascade_frontalface_default.xml. |
encodings |
Pre-encoded pickle with the faces. Default Value: modules/MMM-Face-Reco-DNN/tools/encodings.pickle |
usePiCamera |
Use Raspberry Pi camera or another type. (1 = RasPi camera, 0 = other camera) Default Value: 1 |
method |
Method of facial recognition. (dnn = deep neural network, haar = haarcascade) Default Value: dnn |
detectionMethod |
Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate deep-learning model which is GPU/CUDA accelerated (if available). Default Value: hog |
animationSpeed |
How long in milliseconds modules take to hide and show. Default Value: 0 |
pythonPath |
Path to Python to run the face recognition. Default Value: null |
welcomeMessage |
Should a welcome message be shown using the MagicMirror alerts module? Default Value: true |
extendDataset |
Capture new pictures of recognized people, if unknown we save it in folder "unknown". So you can extend your dataset and retrain it afterwards for better recognitions. Default Value: false |
dataset |
If extendDataset is true, you need to set the full path of the dataset as well. Default Value: modules/MMM-Face-Reco-DNN/dataset/ |
multiUser |
Allow multiple concurrent user logins, 0=no, 1=yes Default Value: 0 |
There are 3 states that of facial recogntion that this module runs in. The states are not directly configurable but rather you use the classes to define which modules are activated in the various states.
| State | Description |
|---|---|
| noface | This is the state when facial recognition has found no faces |
| unknown | This is the state when facial recognition has found a face that it does NOT recognise |
| known | This is the state when facial recognition has found a face that it does recognise |
In order for this module to do anything useful you have to assign classes to your modules. Do not set any classes on this module itself, it has no output and will not work.
There are a series of classes that drive the showing/hiding of modules based on the state of facial recognition. The following table gives a view of which classes drive which states.
When a state changes, the classes from the old state are hidden and the classes from the new state are shown. Anytime that a specific class appears in both states, nothing happens to the modules tagged with that specific class.
For example:
- starting from no faces detected, when an unknown face is detected, any modules tagged with the
nofaceclass will disappear and any modules tagged with theunknownclass will appear. - starting from no faces detected, when an unknown face is detected, any modules tagged with the
defaultclass will remain unchanged, becausedefaultprovides both the Noface and the Unknown states. - starting from no faces detected, when an known face is detected, any modules tagged with the
defaultclass will be hidden and modules tagged withknownor "specific user's name" ie to specify modules for a certain user, use their name as the class name
The state to class mappings are:
| States that the class is active in | |||
| Class | Noface | Unknown | Known |
noface |
Y | ||
unknown |
Y | ||
known |
Y | ||
default |
Y | Y | |
everyone |
Y | Y | |
always |
Y | Y | Y |
"specific user's name" |
Y | ||
{
module: 'example_module',
position: 'top_left',
// Set your classes here seperated by a space
// Always shown
classes: 'always'
},
{
module: 'example_module_2',
position: 'top_left',
// Only shown for Thierry and James
classes: 'thierry james'
},
{
module: 'example_module_3',
position: 'top_right',
// Only shown for James
classes: 'james'
}
{
module: 'example_module_4',
position: 'top_right',
// Only shown for known (recognised users)
classes: 'known'
}- Support for multiple concurrently logged in users is not yet complete.
- Adrian Rosebrock for the great tutorial: https://www.pyimagesearch.com/2018/06/25/raspberry-pi-face-recognition/
- Normyx for the first version of Face-Recognition for MagicMirror: https://github.com/normyx/MMM-Facial-Recognition-OCV3