📸 Computer Vision Repository

Welcome to the Computer Vision repository! This collection features several Jupyter Notebook files demonstrating various image processing techniques. Each notebook focuses on a specific topic, providing step-by-step explanations and code examples. 🚀

📂 Contents

1. cannyEdgeDetection.ipynb: 🖼️ Demonstrates the Canny edge detection algorithm, a popular technique for detecting edges in images.
2. harrisCornerDetection.ipynb: 📍 Learn about Harris corner detection, a method used to identify important features or corners in an image.
3. imgFlipping.ipynb: 🔄 Explores image-flipping techniques, including horizontal and vertical flipping, using various image-processing libraries.
4. img_contrastReduction.ipynb: 🌈 Focuses on reducing the contrast of an image, useful in applications such as medical imaging or enhancing specific image details.
5. img_enhancement.ipynb: ✨ Techniques to enhance the overall quality of an image by adjusting brightness, contrast, and other parameters.
6. img_processing.ipynb: 📚 A general introduction to image processing, covering essential concepts, image manipulation, and basic operations.

⚙️ Requirements

To run the notebooks and execute the code examples, you will need the following:

• 🐍 Python (version 3.x)
• 📓 Jupyter Notebook
• 📷 OpenCV (computer vision library)
• 🧮 Numpy (numerical computing library)
• 📊 Matplotlib (plotting library)
• 🖼️ Scikit-image (image processing library)

Please refer to the individual notebooks for more specific installation instructions and requirements.

🚀 Usage

1. 📥 Clone this repository to your local machine or download the notebook files directly.
2. ✅ Ensure you have all the necessary dependencies installed. You can use pip or conda to install the required libraries.
3. 🖥️ Open the desired notebook using Jupyter Notebook or JupyterLab.
4. 📝 Follow the instructions within each notebook to understand and experiment with the demonstrated image processing techniques.

🤝 Contributing

If you have any improvements or additional examples to contribute, please feel free to submit a pull request. Your contributions are greatly appreciated! 🙌

📄 License

This repository is licensed under the MIT License. Feel free to use the code and examples provided here for educational or commercial purposes. 🆓

Note: The images used in these examples are either publicly available or used for educational purposes only. 📸