Social Distancing System using NAO and AI, uses Django technology to create a website for the GUI and the use of YOLOv5 models to perfrom people and mask detection and tracking.
Show processing of images and videos for mask detection
On Mac OS X run:
python3 detect_mask_yolov5.py --view-img
On Windows run:
python detect_mask_yolov5.py --view-img
A Windows based installation and function guide is provided, Click here.
On Mac OS X run:
pip install pipenv
pipenv install -r requirements.txt
pipenv shell
pipenv run python3 manage.py makemigrations pages
pipenv run python3 manage.py migrate
On Windows run:
pip install pipenv
pipenv install -r requirements.txt
pipenv shell
pipenv run python manage.py makemigrations pages
pipenv run python manage.py migrate
In order to start NGX code clone this repository and run the two commands in parallel using two terminal windows:
pipenv run python3 manage.py runserver
pipenv run python3 manage.py runscript run_video
To start off the webpage, its good to create a superuser that has full access to the website and is able to manage the database of the GUI.
pipenv run python3 manage.py createsuperuser
Enter details:
Username: *****
Email address: admin@example.com
Password: **********
Password (again): *********
Superuser created successfully.
Its common over multiple executions of the pipenv run python3 manage.py runserver will produce redundant TCP connections after the attempts of closing down via CTRL + C. In order to counteract this problem, run the follow command:
sudo lsof -t -i tcp:8000 | xargs kill -9
This should kill any remaining TCP connection, allowing the established connection to the local webpage being served via the runserver command highlighted above.
In order to develop the system with usage of the NAOqi software, the Python SDK of Naoqi must be downloaded from here, you may find this installation guide helpful here.
Also the python path must be assigned to the respective Naoqi python directory:
PYTHONPATH = 'path/to/Naoqi/python/'
export PYTHONPATH
Implementation with the NAO robot can be achieved to produce the desired feedback functionality design within 'pages/detect_person_yolov5.py' that is being called within 'scripts/run_video'.
Parameters for different videos can be set in 'scripts/run_video', Such as running these two parameters seperately, will reproduce the results in 'runs/tests':
def run():
"""
run script for detection and tracking algoirthm
"""
# retrieve inputs for line trigger and data source
opt = {}
# Test with street video
# opt['start'] = (0, 500)
# opt['end'] = (1800, 950)
# opt['line-side'] = 'left'
# opt['source'] = 'data/images/street.mp4'
# opt['axis'] = 'horizontal'
# Test with mass_walking video
opt['start'] = (0, 500)
opt['end'] = (1344, 500)
opt['source'] = 'data/images/mass_walking.mp4'
opt['axis'] = 'horizontal'
opt['line-side'] = 'right'
# Test with walkingby video
#opt['start'] = (400, 0)
#opt['end'] = (400, 1000)
#opt['source'] = 'data/images/walkingby.mp4'
#opt['axis'] = 'vertical'
#opt['line-side'] = 'right'
On mac run:
pipenv run python3 test_graph.py
On windows run:
pipenv run python test_graph.py
By setting these parameters, we can then run the script with the GUI using these commands in parallel:
pipenv run python3 manage.py runserver
pipenv run python3 manage.py runscript run_video