Face Recognition With Raspberry Pi + OpenCV + Python
Summary
TLDRThis video from Crow Electronics demonstrates how to use a Raspberry Pi and open source software to create a facial recognition system. It covers the setup with necessary hardware, installing packages, capturing facial images, training the model with Python scripts, and finally, using the trained model for real-time face identification. The tutorial also extends the project by integrating a servo motor controlled by the Raspberry Pi's GPIO pins, responding to recognized faces. The video highlights the ease of implementing machine learning with accessible tools and open source software.
Takeaways
- 🤖 The video is a tutorial on how to use a Raspberry Pi to recognize faces with the help of open source software.
- 📷 OpenCV and the Python face recognition package are the two main tools used for real-time computer vision and face detection.
- 🛠️ A Raspberry Pi, official camera module, micro SD card, power supply, HDMI cord, monitor, mouse, and keyboard are the hardware requirements.
- 💻 The Raspberry Pi 4 Model B is recommended for its extra computing power.
- 🔍 The camera module needs to be enabled in the Raspberry Pi configuration menu for the project to work.
- 📝 The 'headshots_pycam.py' Python script is used to take photos of faces for training the face recognition model.
- 👤 Users should take around 10 photos of their face from different angles for better recognition.
- 📁 A separate folder is created for each person's face photos to store the training images.
- 🔧 The 'train_model.py' script is used for training the model with the collected face images.
- 📡 The 'facial_rec.py' script runs the identification process, displaying a live stream and identifying known faces.
- 🔄 The Raspberry Pi can differentiate between known and unknown faces, labeling them accordingly.
- 🛠️ By modifying the 'facial_rec.py' script, the Raspberry Pi can control a servo or other mechanisms using GPIO pins when a known face is detected.
- 👍 The video credits OpenCV, the face recognition package, and Carolyn Dunn for their contributions to the software used in the project.
Q & A
What is the main purpose of the video?
-The main purpose of the video is to demonstrate how to use a Raspberry Pi with open source software to recognize faces and control a servo based on face recognition.
Which open source software is mentioned for real-time computer vision problems?
-OpenCV is mentioned as the open source software for solving real-time computer vision problems.
What is the role of the Python face recognition package?
-The Python face recognition package computes bounding boxes around a face in real time, aiding in the face recognition process.
What are the hardware requirements for setting up the face recognition system with Raspberry Pi?
-The hardware requirements include a Raspberry Pi, an official camera module, a micro SD card, a power supply, an HDMI cord and monitor, a mouse and keyboard, and optionally a servo for additional control.
Why is a Raspberry Pi 4 Model B recommended for this project?
-The Raspberry Pi 4 Model B is recommended due to its extra computing power which is invaluable for running the machine learning algorithms required for face recognition.
How can one enable the camera interface on the Raspberry Pi?
-The camera interface can be enabled through the Raspberry Pi configuration menu, found under the 'Preferences' and then the 'Interfaces' tab.
What is the purpose of the 'headshots_pycam.py' Python script?
-The 'headshots_pycam.py' script is used to take photos of faces using the official Raspberry Pi camera for training the face recognition model.
How many photos of a face should be taken for the training process?
-It is suggested to take around 10 photos of a face, including different angles, for better recognition accuracy during the training process.
What is the command to start the face recognition training process?
-The command to start the training process is 'python train_model.py', run from the 'facial_recognition' directory.
How does the Raspberry Pi identify a known face and respond?
-When a known face is identified, the Raspberry Pi draws a yellow box around the face and writes the person's name next to it in the live stream window.
What can be done to control a servo using face recognition results?
-The Raspberry Pi can be programmed to send signals via the GPIO pins to control a servo, activating it when a known face is recognized and keeping it inactive for unknown faces or no face at all.
Outlines
🤖 Raspberry Pi Face Recognition Setup
This paragraph introduces a project where a Raspberry Pi is used to recognize faces using open source software. The narrator explains the synergy between open source software and Raspberry Pi, highlighting two key tools: OpenCV for real-time computer vision and the Python face recognition package. The project involves training the Raspberry Pi to identify a person's face and then using that recognition to control a servo. The required hardware is listed, including a Raspberry Pi 4 Model B for its computing power, an official camera module, a micro SD card, and other accessories. The process starts with setting up the Raspberry Pi, enabling the camera, and installing necessary packages. The user is guided to take photos of their face for training the model and to run a Python script for training the face recognition system.
🛠️ Enhancing Raspberry Pi with Face-Controlled Servo
The second paragraph delves into the practical application of the face recognition system by integrating it with a servo. The narrator demonstrates how to modify the Python code to send signals via the Raspberry Pi's GPIO pins when a known face is detected. This allows the servo to rotate, creating an interactive element to the project. The video shows a practical example where the servo only activates when the narrator's face is recognized, otherwise, it remains inactive. The narrator expresses gratitude to the developers of OpenCV and the face recognition package, as well as to Carolyn Dunn for creating software that integrates these systems effectively. The paragraph concludes by emphasizing the potential of this software for various projects and ends with a sign-off until the next video.
Mindmap
Keywords
💡Raspberry Pi
💡Open Source Software
💡OpenCV
💡Python Face Recognition Package
💡Machine Learning
💡Microprocessor
💡Camera Module
💡Training Model
💡GPIO Pins
💡Servo
Highlights
Using Raspberry Pi and open source software like OpenCV and the Python face recognition package to create a facial recognition system.
Raspberry Pi's open source nature makes it an excellent platform for real-time computer vision and machine learning projects.
The tutorial demonstrates how to train the Raspberry Pi to recognize a specific face using the official camera module.
The face recognition system uses bounding boxes to detect and identify faces in real-time.
List of required components for setting up the facial recognition system, including the Raspberry Pi 4 and official camera module.
Instructions on enabling the camera interface in the Raspberry Pi configuration menu.
Using the 'headshots_pycam.py' script to take photos of faces for training the model.
Creating a folder with the user's name to store the captured face images for training.
Running the 'train_model.py' script to analyze the dataset and train the model to recognize faces.
The trained model can identify known faces and write the person's name next to the detected face in real-time.
Demonstration of the live face recognition system using the Raspberry Pi camera and the trained model.
The system can differentiate between known and unknown faces, labeling them accordingly.
Exploring the potential of using the Raspberry Pi's GPIO pins to control external devices like servos based on face recognition.
Adding code to the 'facial_rec.py' script to activate a servo when a known face is detected.
The servo moves when the Raspberry Pi recognizes the user's face, demonstrating the practical application of the system.
The tutorial credits the open source communities behind OpenCV and the face recognition package for their contributions to the project.
A call to action for viewers to explore the potential of the software for their own projects, highlighting the limitless applications.
Transcripts
hey gang team here at crow electronics
and today we're making our raspberry pi
recognize who we are by our face
[Music]
open source software and raspberry pi's
go together hand in hand
the two excellent examples of this are
opencv which provides a huge
free resource to solve real-time
computer vision problems
and the python face recognition package
which computes
bounding boxes around a face in real
time
these are the two systems that we will
use to make this all come together
machine learning has never been more
accessible i will show you exactly how
to have your raspberry pi microprocessor
be able to spot human faces
how to train it to know your face and to
run code
so that it will successfully identify
you when it sees you
then i'll take it another step and show
you how you can use your face to control
the servo which is attached to
the raspberry pi
before me on the table is everything you
need to get this system
up and running really fast starting off
you're going to want a raspberry pi
official camera
module version 2. you can also use the
raspberry pi high quality camera
a micro sd card a official raspberry pi
power supply
a hdmi cord and monitor to connect to a
mouse and keyboard
and for this i'm using a raspberry pi 4
model b as the extra computing power
oomph that the raspberry pi provides is
invaluable
you're also going to want some way to
connect the micro sd card
to your computer so you can flash it
so we're going to get started with all
the packages installed already
to follow along at this point go to the
article to get the steps to install the
packages on your setup
once you power up the raspberry pi
you're going to see this familiar
background booted up
now first let's open up the raspberry pi
configuration menu
found by using the top left menu
scrolling over preferences
and make sure that the camera found
under the interfaces tab
is enabled if you had to enable this
setting go ahead and reset the raspberry
pi so that the changes can take
effect now open up the file explorer
which is the folder button on the top
left of the screen
jump into the folder located in the home
pi directory
facial recognition and then look for the
python code called headshots
underscore pycam.py this python code
will let us take some photos
of our faces using the official
raspberry pi camera right click
and open up that python script with
either funny or genie
both are just python language
interpreter softwares and alter the line
of code here
with your name in my case timmy
then save this script next go back into
the folder structure and open up
the photos folder here you're going to
add another folder with your name
this folder is then going to be the
location where the photo files will end
up then jump back into the python editor
where we had
saved that python code and run it this
will open up a little window and a
terminal window
which you can use to save images of your
face press the spacebar key to take an
image
take around 10 and then the q key to
close the window once you've done so
provide a couple of different angles of
your face so it can determine your
dimensions
better once you close the software
you're going to be able to see the
images of your face
stored in the folder you created for
your name you can add other faces using
this same method too
with all that sorted we can get into the
machine learning step
the pictures we took will now be used by
the python code train
underscore model dot py any pictures in
the dataset folder location will be
analyzed by this code when we run this
program
so open up a new terminal using the
black console button on the top left
and type the following pressing enter
after each line cd
space facial underscore recognition this
will get us into the right folder
and then python space train underscore
model dot py
which is going to run our desired code
this will start the training process
which you can see occurring for each
image
that i took of my face
then with that completed the raspberry
pi 4 model b will have learned what your
face looks like
so let's give it a go to run the
identification code that will identify
faces and when it finds a train face
will write their name next to it
start by opening up a new terminal just
the same as before
then type the following and press enter
after each step
cd facial underscore recognition and
then
python facial underscore req dot
py once you press enter it's going to
take around 5 seconds to boot up and run
then you're going to see a small window
pop up with a live stream of the
raspberry pi camera
aim the camera at your face and if it
puts a yellow box around your head
and names you correctly you have done it
the raspberry pi camera is now searching
live for faces
it will also determine if it's a known
or an unknown face if it's unknown it's
going to write
unknown next to it and if it's a face of
the name of the person that it knows
it's going to write that person's name
next to it
this example code is awesome and lets
you experiment to see
when the software can or cannot track
your face i find if you tip your head to
the side a couple of degrees it's going
to completely disable the facial
recognition and
if you cover your nose as well it
struggles close the terminal window or
press ctrl c
on the keyboard to stop it running
[Music]
so we can do many things with this now
simply to start we can now jump into the
folders with the python code and alter
just a couple of lines
in that code in the facial underscore
rec dot py
so that every time a known face is seen
it's going to send out signals via the
gpio pins of the raspberry pi
these general purpose input and output
pins can be used to control an almost
endless amount of sensors and mechanisms
so
for this i'm going to get a servo to
rotate when the raspberry pi system sees
my
face if it sees an unknown face or no
face at all
it's not going to activate this servo
all this
by adding just six lines of code to the
script
all the code i'll be adding here is
completely explained in the guide
controlling standard servers with
raspberry pi
linked down below so hopefully you can
see everything what i'm going to do is
hide my face from the camera by putting
my thumb just in front of it
the server you're going to be able to
see from the top and when i show up my
face
you're going to see the server move you
can see it's active
see down here
it's red because my skin is pink
and now i'm going to show my face
boom straight away as soon as it saw my
face
nice huge thanks go to the opencv
and facial recognition package teams
that work on the amazing machine
learning software
that we have running on this raspberry
pi both
are really good open source software
also a huge thank you goes to carolyn
dunn
who created the majority of the amazing
software which makes these two systems
work together
so well there's just so much potential
with this software to take projects to
amazing places
so that's it for today until next time
stay cozy
تصفح المزيد من مقاطع الفيديو ذات الصلة
Face recognition in real-time | with Opencv and Python
An Introduction to ROS, the Robot Operating System: Install and put together (3/6)
TASK 5.1P - EMBEDDED SYSTEMS DEVELOPMENT
AutoBill - An AI Powered Instant Checkout System | Edge Impulse | Raspberry Pi | Coders Cafe
Raspberry Pi Weather Station
How to use VirtualBox - Tutorial for Beginners
5.0 / 5 (0 votes)