https://github.com/ashithapallath/image-processing
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.
https://github.com/ashithapallath/image-processing
color-space-conversion image-analysis image-enhancement image-manipulation image-processing noise-reduction numpy opencv python
Last synced: 4 months ago
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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.
- Host: GitHub
- URL: https://github.com/ashithapallath/image-processing
- Owner: ashithapallath
- Created: 2024-10-06T13:41:58.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-14T04:53:12.000Z (about 1 year ago)
- Last Synced: 2025-03-22T10:33:28.883Z (11 months ago)
- Topics: color-space-conversion, image-analysis, image-enhancement, image-manipulation, image-processing, noise-reduction, numpy, opencv, python
- Language: Jupyter Notebook
- Homepage:
- Size: 22.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **Image Processing Lab**
This repository hosts a Jupyter Notebook showcasing a variety of image processing techniques. These techniques are widely used for manipulating, enhancing, and analyzing digital images using Python and associated libraries.
## **Features**
This lab covers the following key concepts:
- **Image Manipulation**: Operations like resizing, cropping, and geometric transformations.
- **Image Enhancement**: Adjusting brightness, contrast, and applying filters to enhance image quality.
- **Image Analysis**: Techniques for detecting edges, finding contours, and analyzing image structures.
- **Color Space Conversions**: Working with RGB, grayscale, and other color models.
- **Noise Reduction**: Applying filters (e.g., Gaussian and median) to reduce noise in images.
## **Prerequisites**
Before running the notebook, ensure that the following tools and libraries are installed:
- **Python 3.8 or higher**
- **Jupyter Notebook**
- **Required Python Libraries**:
- [OpenCV](https://opencv.org/)
- [NumPy](https://numpy.org/)
- [Matplotlib](https://matplotlib.org/)
Install the dependencies using pip:
```bash
pip install opencv-python numpy matplotlib
```
## **How to Use**
1. Clone this repository to your local machine:
```bash
git clone https://github.com/ashithapallath/Image-Processing.git
cd Image-Processing
```
2. Launch the Jupyter Notebook:
```bash
jupyter notebook Image_Processing.ipynb
```
3. Follow the step-by-step explanations in the notebook, executing the cells to explore the various image processing tasks.
## **Examples**
### **1. Grayscale Conversion**
Convert an image to grayscale for simplicity in analysis:
```python
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plt.imshow(gray_image, cmap='gray')
```
### **2. Edge Detection**
Detect edges using the Canny algorithm:
```python
edges = cv2.Canny(image, threshold1=50, threshold2=150)
plt.imshow(edges, cmap='gray')
```
## **Contribution Guidelines**
We welcome contributions to enhance the repository!
- **Fork the repository** to make changes.
- Create a **pull request** with detailed explanations for your updates.
- Ensure all contributions adhere to clean coding practices.
## **License**
This 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.
## **Acknowledgments**
We extend our gratitude to the open-source community for tools like:
- **OpenCV** for efficient image processing.
- **NumPy** for numerical computing.
- **Matplotlib** for robust data visualization.