DjangoObjdetection.mov
Note: This is not a full-fledged production ready app though can be scaled to work as one.
- Create/Edit ImageSets.
- Upload multiple images with dropzonejs to the selected ImageSet.
- Convert uploaded image size to 640 x 640. (For faster detection)
- Upload/update a custom pre-trained model.(If you have offline files of a model)
- YoloV5 models will download upon selection. (Active internet connection required for this step.)
- Detect object on an image with YoloV5/custom pre-trained model.
An image with the name default.png in media folder is required for user-profile. Create media folder and add any image file with this name 'default.png'.
clone the repo locally
create virtual environment
# install dependencies
pip install django
pip install django-crispy-forms
pip install crispy-bootstrap4
pip install django-cleanup
pip install django-debug-toolbar
pip install celery
pip install yolov5
# migrate
python manage.py migrate
# create super user
python manage.py createsuperuser # (it may show an error page if no 'default.png' in media folder. See note above.)
# run
python manage.py runserver
login
# Login at the web address 127.0.0.1:8000 using the superuser credentials.
Create ImageSet
# create an ImageSet first and then upload images into the ImageSet from ImageSet detail page.
# On images list page click on detect object.
# select a YoloV5 model
# the YoloV5 dependencies and pre-trained model will start downloading.- Detectobj
- images
- modelmanager
- users
- dropzonejs
- ekko-lightbox