Detect brazillian license plates from pictures using YOLOv3.This project uses pretrained weights from YOLOv3 and the brazillian alpr
Make sure you have Python 3 installed and then install the Pillow module for image cropping:
python3 -m pip install Pillow
In the last step of our pipeline we need to convert the image to grayscale. For this we use the convert utility of Imagemagick. Downloading imagemagick might differ slightly depending on the distro you are using, but it will generally be something like
sudo pacman -S imagemagick # for arch
sudo apt install imagemagick # for ubuntu
...
Just clone this repo
git clone https://github.com/gmelodie/bpd
And run the initialization script
cd bpd
./init-dir.sh
To run the project you will have to have the darknet framework installed as well as the yolo network.
For the darknet, clone the code with:
git clone https://github.com/pjreddie/darknet
We use a slightly different src/image.c file (in order to be able to save the bounding boxes to a file during execution), so you will need to change the default src/image.c for the one found in this project, and then compile the code:
cp our/src/image.c darknet/src/image.c
cd darknet
make
For this project we have the pre-trained weights for our specific case, which you can download by simply
./download-confs.sh
This will automatically download all the weights, data and other necessary configuration files into the local directory (make sure you are inside the darknet directory cloned earlier. More informations on using yolo with darknet here.
Running the code is as simple as typing
./detect-plate.sh your/image/file
As we didn't run extensive tests, we don't know which types of image files are supported, and which are not. To be safe, make sure you use a jpg file as input.
If everything goes well (fingers crossed) you should see the cropped images popping up as well as the predictions.jpg in the current directory with the predictions of the final cropped image (hopefully, the cropped license plate image).
- Detecting the front of a car
./darknet detector test fvlpd.data fvlpd-net.cfg fvlpd-net.weights -thresh .4 <<< <IMAGE_FILE>
- Detecting a plate
./darknet detector test fvlpd.data fvlpd-net.cfg fvlpd-net.weights -thresh .2 <<< <FRONTAL_VIEW_FILE>
- Detecting letters from the plate In order to be able to detect letters from the plate, we need to convert the image to grayscale:
convert <COLORED_PLATE_INPUT> -colorspace Gray <GRAYSCALE_PLATE_OUTPUT>
And then run the detector
./darknet detector test lpscr.data lpscr-net.cfg lpscr-net.weights -thresh .6 <<< <GRAYSCALE_PLATE_FILE>