ALPR-OpenCV

Using OpenCV libraries to build an automatic number plate recognition system.

NOTE: This is an experimental project and is incomplete in a number of ways. This project is for be test it in Windows 10 64bits, and the code has been modify to generate licence plate patterns from Ecuador.

Dependencies:

• Python v3.6.+ (64bits)
• NumPy v1.14.+
• OpenCV v3.14.+ (for cv2)
• h5py v2.7.+
• keras v2.1.+
• MatPlotLib v2.1.+
• TensorFlow v1.+

Installations

Note: For install libraries use CMD terminal.

1. Download Python 3.6.+ (last version of 64bits), and install it. Guide Video
2. Installing numpy library (if not included)

py -m pip install numpy

3. Installing OpenCV library

py -m pip install opencv-python

4. Installing Keras library

py -m pip install keras

5. Installing h5py library

py -m pip install h5py

6. Installing MatPlotLib

py -m pip install matplotlib

7. Installing TensorFlow (CPU or GPU) library

#*CPU version*
py -m pip install --upgrade tensorflow

or

#*GPU version*
py -m pip install --upgrade tensorflow-gpu

Project

Create a folder name anpr and copy the following py files and folders:

• bgs\ (backgrounds images)
• fonts\ (ttf file)
• test\ (generated licence plate, empty)
• common.py (Common variables)
• model.py (py dependencie)
• gen.py (For generate test set images)
• train.py (For train the model with generate images)
• detect.py (For test result)

Using Networks

Usage is as follows:

1. (optional but recommended) ./extractbgs.py SUN397.tar.gz: Extract ~3GB of background images from the SUN database into bgs/ (bgs/ must be empty). The tar file (36GB) can be downloaded here. This step may take a while as it will extract 108,634 images.

2. ./gen.py: Locate variable generate_amount and set the number you want (default 100), it will safe the test set images in test/ (test/must be empty). This step requires a .ttf files to be in the fonts/ directory.

3. ./train.py: Train the model. A GPU is recommended for this step. It will take around 100,000 batches to converge. When you're satisfied that the network has learned enough press Ctrl+C once and the process will create a weights.npz file and write the weights.

4. ./detect.py in.png CPUweights.npz out.png: Detect number plates in an image and give and output image. if get a tensorflow gpu error, you should uninstall it py -m pip unistall tensorflow-gpu

Reproduce: Deevoluation