visionComputer

License-plate detection examples and experiments (Jupyter notebooks)

This repository contains notebooks, images and code used for exploring and building license-plate detection solutions. It includes data examples, training/visualization assets, and experiments for detecting and localizing license plates using classical computer-vision and/or deep-learning approaches.

Demo

Repository layout

• notebooks/ or *.ipynb — Jupyter notebooks with experiments, preprocessing, training and evaluation (if present)
• data/ — sample images and dataset (if present)
• models/ — saved model weights or checkpoints (if present)
• scripts/ — helper scripts for training, inference or dataset preparation
• train.png — example training / visualization image

Features

• Example notebook(s) demonstrating license plate detection pipelines
• Data loading and preprocessing utilities
• Training and inference examples (notebook-based)
• Visualizations of detection results

Getting started

1. Clone the repository git clone https://github.com/AbhiRoy96/visionComputer.git cd visionComputer

2. Create and activate a Python virtual environment (recommended) python3 -m venv .venv source .venv/bin/activate # macOS / Linux .venv\Scripts\activate # Windows

3. Install dependencies If a requirements.txt exists: pip install -r requirements.txt

   If not, start with these commonly used packages: pip install jupyter numpy matplotlib opencv-python scikit-image scikit-learn

   Add a deep-learning framework if needed:

   • TensorFlow: pip install tensorflow
   • PyTorch: pip install torch torchvision torchaudio

4. Start Jupyter jupyter notebook or jupyter lab

Usage

• Open the notebooks in the repo (files ending with .ipynb) and run the cells to reproduce preprocessing, training and evaluation steps.
• If there are scripts for training or inference, run them from the command line (example): python scripts/train.py --config configs/train_config.yaml python scripts/infer.py --model models/latest.pth --input data/test.jpg

Notes on datasets and models

• If you used an external dataset (OpenALPR, SSIG, private dataset, etc.), provide dataset download/preparation instructions here.
• If models are large, consider adding instructions to download model weights or store them in a releases section.

Tips and troubleshooting

• Ensure OpenCV is installed for image I/O and visualization.
• Use a GPU-enabled environment for faster training if using deep learning frameworks.
• If notebooks fail because of missing packages, install them with pip or conda (look at the notebook import cells to see required libraries).

Examples of experiment steps (notebook workflow)

1. Explore sample images and annotations (visualize train.png)
2. Preprocess images (resize, normalize, data augmentation)
3. Train a detector or classification model (classical CV or CNN-based detector)
4. Evaluate on validation set and visualize predictions
5. Save best model and run inference on test images

Contributing

• Open issues for bugs or feature requests
• Fork the repo, create a branch, submit a pull request with clear description and tests (if applicable)

License No license specified. If you want this repo to be reusable, add a license (MIT, Apache-2.0, etc.). Let me know which license you prefer and I can include it and add a LICENSE file.

Contact Owner: @AbhiRoy96 (https://github.com/AbhiRoy96)

Suggested repository topics / tags

• python
• jupyter-notebook
• computer-vision
• license-plate-detection
• opencv
• deep-learning
• object-detection
• image-processing