{"id":24717899,"url":"https://github.com/ashithapallath/image-processing","last_synced_at":"2025-10-03T14:45:45.370Z","repository":{"id":261427074,"uuid":"868478137","full_name":"ashithapallath/Image-Processing","owner":"ashithapallath","description":"The Image Processing Lab repository showcases a variety of image processing techniques using Python. It covers essential concepts such as image manipulation (resizing, cropping), enhancement (adjusting brightness/contrast), analysis (edge detection, contours), color space conversions, and noise reduction. 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These techniques are widely used for manipulating, enhancing, and analyzing digital images using Python and associated libraries.\n\n\n\n## **Features**\n\nThis lab covers the following key concepts:\n- **Image Manipulation**: Operations like resizing, cropping, and geometric transformations.\n- **Image Enhancement**: Adjusting brightness, contrast, and applying filters to enhance image quality.\n- **Image Analysis**: Techniques for detecting edges, finding contours, and analyzing image structures.\n- **Color Space Conversions**: Working with RGB, grayscale, and other color models.\n- **Noise Reduction**: Applying filters (e.g., Gaussian and median) to reduce noise in images.\n\n\n\n## **Prerequisites**\n\nBefore running the notebook, ensure that the following tools and libraries are installed:\n\n- **Python 3.8 or higher**\n- **Jupyter Notebook**\n- **Required Python Libraries**:\n  - [OpenCV](https://opencv.org/)\n  - [NumPy](https://numpy.org/)\n  - [Matplotlib](https://matplotlib.org/)\n\nInstall the dependencies using pip:\n\n```bash\npip install opencv-python numpy matplotlib\n```\n\n\n\n## **How to Use**\n\n1. Clone this repository to your local machine:\n   ```bash\n   git clone https://github.com/ashithapallath/Image-Processing.git\n   cd Image-Processing\n   ```\n\n2. Launch the Jupyter Notebook:\n   ```bash\n   jupyter notebook Image_Processing.ipynb\n   ```\n\n3. Follow the step-by-step explanations in the notebook, executing the cells to explore the various image processing tasks.\n\n\n\n## **Examples**\n\n### **1. Grayscale Conversion**\nConvert an image to grayscale for simplicity in analysis:\n```python\ngray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nplt.imshow(gray_image, cmap='gray')\n```\n\n### **2. Edge Detection**\nDetect edges using the Canny algorithm:\n```python\nedges = cv2.Canny(image, threshold1=50, threshold2=150)\nplt.imshow(edges, cmap='gray')\n```\n\n\n\n## **Contribution Guidelines**\n\nWe welcome contributions to enhance the repository!  \n- **Fork the repository** to make changes.\n- Create a **pull request** with detailed explanations for your updates.\n- Ensure all contributions adhere to clean coding practices.\n\n\n\n## **License**\n\nThis project is distributed under the **MIT License**. Feel free to use, modify, and distribute this code with proper attribution. See the [LICENSE](LICENSE) file for more details.\n\n\n\n## **Acknowledgments**\n\nWe extend our gratitude to the open-source community for tools like:\n- **OpenCV** for efficient image processing.\n- **NumPy** for numerical computing.\n- **Matplotlib** for robust data visualization.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashithapallath%2Fimage-processing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fashithapallath%2Fimage-processing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashithapallath%2Fimage-processing/lists"}