Our Project

Wiki | About Us | Regarding Our Project |

PREVIEW¶

Our project primarily focuses on utilizing OpenCV for advanced image processing, supported by Python.
This project utilizes Unified Modeling Language (UML) for clear, structured design and Python for efficient coding, enhanced with OpenCV libraries for advanced image processing. Our code is thoroughly documented with Doxygen for clarity and maintenance, while project management and monitoring are streamlined using Redmine.

1. Image Processing with Python and OpenCV: We use Python, a flexible programming language, together with OpenCV, a detailed library for image processing. This pair helps us effectively carry out complex image analysis and processing tasks.
2. Structured Design with UML: We utilize Unified Modeling Language (UML) to create a clear and organized design. UML helps in visually representing the system's architecture and workflows, making it more accessible and easier to understand.
3. Documentation with Doxygen: Our code is thoroughly documented using Doxygen. This approach ensures clarity and ease of maintenance, enhancing the long-term usability and adaptability of our project.
4. Project Management with Redmine: For effective tracking and management, we use Redmine. This tool aids in monitoring project progress, task management, and team collaboration, keeping our project well organized and on schedule.

Each component of the project, from image processing to project management, has been thoughtfully integrated to ensure optimal efficiency and usability.

Alpha Correction: It is a technique used when computer graphics are laid on top of both layers and single or multiple objects contain some level of transparency portion.
It ensures that the visible pixels of the graphic that are underneath a transparent area and their color or brightness are adjusted based on the transparency degree on the upper object.
An alpha channel is a form of the mask that manages the amount of information to display from lower-lying graphics.

Gamma Correction: It is a nonlinear adaptation that is applied to each value of the pixel. In general, linear methods such as adding, subtracting, and multiplying are applied to all parts of pixels. Gamma correction is responsible for remodeling the saturation of the image by performing nonlinear methods on the input image's pixels. It is also necessary to maintain a stable gamma value, between too small or large.

IMAGES¶

Original Left Image	Original Right Image

Final Left Image	Final Right Image

End Goal