Intelligent Facial Recognition with Django Restful IoT on Raspberry Pi
MIT License
This is a project of the facial recognition with Movidius on RaspberryPi 3B+ platform. It also uses Django and Django REST framework which providing the web platform. The project would like to build a safety and intelligent face recognition system in AI era.
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The code is tested using Tensorflow r1.7 and Movidius NCSDK2 under Debin 2018-06-27Kernel version:4.14 with django 2.1.5 and Python 3.5 & 3.6.
django_venv/ # django virtual env for RPI
face_recognition_model/ # Movidius NCS code
iot_control/ # iot control code
ran-django-template/ # django platform using my own model
requirements.txt # requirements for run the code
pip install -r requirements.txt
Install Neural Compute Application Zoo
Method attached below.
Install rpi-mjpg-streamer
Method attached below.
Run ran-django-template
cd ran-django-template
python manage.py runserver 0.0.0.0:8000
cd face_recognition_model
make run
cd iot_control
python iot_controller.py
Admin Account
python manage.py createsuperuser
username: ranxiaolang
email: YOUR EMAIL
password: ranxiaolang
Access the web page though this link: http://127.0.0.1:8000/admin .
The code requires Python 3.5 or Python 3.6, Tensorflow 1.7 or later, as well as the following python libraries:
Those modules can be installed using: pip3 install xxx
or pip install xxx
.
Follow How to run it
This repository is a place for any interested developers to share their projects (code and Neural Network content) that make use of the Intel Movidius Neural Compute Stick (Intel Movidius NCS) and associated Intel Movidius Neural Compute Software Development Kit.
You can use the following url(NC App Zoo) or git command to use the ncsdk2 branch of the NC App Zoo repo:
git clone -b ncsdk2 https://github.com/movidius/ncappzoo.git
pip3 -V
sudo pip3 install -U setuptools
sudo pip3 install -U django
sudo pip3 install -U djangorestframework
sudo pip3 install -U django-filter
sudo pip3 install -U markdown
sudo pip3 install -U requests
git clone https://github.com/adafruit/Adafruit_Python_DHT.git
cd Adafruit_Python_DHT
sudo python3 setup.py install
cd
git clone https://github.com/adafruit/Adafruit_Python_BMP.git
cd Adafruit_Python_BMP
sudo python3 setup.py install
cd
sudo pip3 install psutil
Instructions and helper scripts for running mjpg-streamer on Raspberry Pi.
$ sudo raspi-config
$ sudo apt-get update
$ sudo apt-get install build-essential libjpeg8-dev imagemagick libv4l-dev git cmake uvcdynctrl
$ sudo ln -s /usr/include/linux/videodev2.h /usr/include/linux/videodev.h
$ git clone https://github.com/jacksonliam/mjpg-streamer
$ cd mjpg-streamer/mjpg-streamer-experimental
$ cmake -DCMAKE_INSTALL_PREFIX:PATH=.. .
$ make install
$ git clone https://github.com/meinside/rpi-mjpg-streamer.git
# copy & edit run-mjpg-streamer.sh to your environment or needs
$ cp rpi-mjpg-streamer/run-mjpg-streamer.sh.sample somewhere/run-mjpg-streamer.sh
$ vi somewhere/run-mjpg-streamer.sh
# then run
$ somewhere/run-mjpg-streamer.sh
# copy & edit systemd/mjpg-streamer.service file,
$ sudo cp rpi-mjpg-streamer/systemd/mjpg-streamer.service.sample /lib/systemd/system/mjpg-streamer.service
$ sudo vi /lib/systemd/system/mjpg-streamer.service
# then register as a service
$ sudo systemctl enable mjpg-streamer.service
# or remove it
$ sudo systemctl disable mjpg-streamer.service
# and start/stop it
$ sudo systemctl start mjpg-streamer.service
$ sudo systemctl stop mjpg-streamer.service
Connect through the web browser:
Most modern browsers(including mobile browsers like Safari and Chrome) will show the live stream immediately.
Method 1
pip3 install virtualenv
Copy django_venv and activate
source venv/bin/activate
Method 2
pip install -r requirements.txt
chmod 666 db.sqlite3
chmod 777 xxx
Just can be used for non-business projects. If you use ran-django-template, please give me a star. Thanks!