#How to use? insert a video path and output file in get_args method in main. The output will be then stored in output file.

License plate recognition template

This is the base template for the License Plate Recognition project from the CSE2225 Image Processing course.

The goal of the project is: given an input video you should recognize the license plates.

Input

You can see an example video under dataset/dummytestvideo.avi.
We recommend splitting the video into a training set and a testing set, and only using the testing part as a way to calculate your expected score.
This is important because for grading we use a totally different video.

Output

You can see an example of output under dataset/sampleOutput.csv.
Your output file should be in the same format.

Project structure

The shell script evaluator.sh is used for running the project and calculating scores.
This file initially runs your main.py file, followed by evaluation.py. Do not modify either one of evaluator.sh or evaluation.py if you want to see proper outputs.

Rest of the project:

• CaptureFrame_Process.py for reading the input video
• Localization.py for figuring out the location of the plate in a frame
• Recognize.py for figuring out what characters are in a plate
• helpers/ additional methods to help you get started (you do not have to use them, and are there for inspiration)
• requirements.txt if you want to use additional Python packages make sure to add them here as well
• .gitlab-ci.yml Gitlab pipeline file

Pipeline

If you want to see your score you can change the file in .gitlab-ci.yml to run on the trainingvideo.avi. We do NOT recommend always having this uncommented because running it on the full video makes the pipeline significantly slower.

Tips for setting up

1. Python projects normally have a requirements.txt file with all the packages you need to install. To install the ones we currently picked for you, use:

   pip install -r requirements.txt
   

   Feel free to add more packages to the requirements.txt file, but make sure to ask your TA if you are allowed to use them!

2. It is good practice to use a Virtual Environment such that you can easily switch between Python projects (projects usually have different requirements, and some of them don't work well together). Some commonly used ones are

   • venv (Built-in)

   python -m venv myenv
   source myenv/bin/activate  # Linux/Mac
   myenv\Scripts\activate     # Windows
   

   • or conda (Anaconda Prompt)

   conda create --name myenv python=3.9
   conda activate myenv
   conda install <package_name_here>
   pip install -r requirements.txt
   

3. Help: cv2 package not found!
   Try installing opencv-python, so pip install opencv-python.

4. Help: ffmpeg error (even though we don't explicitly make use of ffmpeg, you might get this error when running your project because opencv uses it in the backend). Check if ffmpeg is installed with ffmpeg -version, if not, install it:

   • Windows: Download from official website and add it to the PATH variable.
   • Linux: sudo apt install ffmpeg
   • Mac: brew install ffmpeg