|Roman Juranek 32d720c586 Uprava WB dema a navodu||11 months ago|
|data||11 months ago|
|matlab||1 year ago|
|opencv||11 months ago|
|waldboost||11 months ago|
|.gitignore||11 months ago|
|README.md||11 months ago|
License plate detection in using Haar Cascade (OpenCV), ACF (Matlab) and Waldboost.
.vec files which are used for training. The
.vec file contains training patches with common size extracted from the full images. Use
extract_training_samples.sh which extracts samples with two different sizes. Or, alternatively, you can execute
Then you can train detector with
opencv_traincascade by passing the extracted
.vec files and the list of background images
train_example_detectors.sh trains everything for you.
cascade.xml files contains the trained model which you can use with
CascadeClassifier class from OpenCV. See
You need to install Piotr's Computer Vision Matlab Toolbox and set paths in Matlab.
In Matlab, change directory to
matlab and open
LicensePlateModel.m. There are three code blocks that can be executed separately by
CTRL+Enter. First block sets training options. Second block trains the detector model and saves it as a
.mat file. The third block show detections in testing images.
Execute the blocks one by one and see what happens.
You need to install
waldboost package. It will also install few dependencies like opencv, skimage, etc. You might want to do that in virtualenv which is also possible.
# in waldbost directory > pip install .
Then run the training script. It will produce a new detector using data from
../data directory, and save it as
license_plate_detector.pb. The process tooks just a few minutes.
> python3 training.py
Finally run detection script which will show detection in images from
> python3 show_detections.py
The training data are for demonstration purposes only. Any non-academic exploitation is prohibited. Directory
background contains images extracted from Google Street View.