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Project Description 

Overview¶

The aim of this project is to output two different projector images merged into one singular display. We used techniques such as gamma correction, alpha blending, and modification of intensities on the images, in order to get the final result. Our project will have a project manager, team leader, Doxygen documentation team, wiki team, coding team, and UML design team.

Software and Tools

Redmine - We used Redmine to track our progress and tasks we need to finish
Python / OpenCV - For image processing we used OpenCV within Python
Doxygen - We used Doxygen for the code documentation
Astah - To create UML diagrams of the code

Technology

Alpha Blending Method

The code defines an image processing application using Tkinter for the GUI, OpenCV for image manipulation, and PIL for rendering. It allows users to load configuration files, process images with gamma correction and alpha blending, and display or save the results.

Gamma Correction Method

This project incorporates a method called gamma correction which adjusts the brightness of an image by altering its luminance values to each value of the pixel in a non-linear manner. This is helpful in matching to human perception of light. Compared to a camera, we are much more sensitive to changes in dark tones than we are to similar changes in bright tones. There's a biological reason for this peculiarity: it enables our vision to operate over a broader range of luminance.

Gamma correction involves applying a power-law function to the pixel values of an image. The formula used is described as below.

gamma_corrected = (image / 255.0)^gamma * 255

If the gamma is greater than 1, the image becomes darker; if it is less than 1, the image appears brighter.

Note: Gamma is the provided gamma value. Since pixel values range from 0 to 255, the original image is first normalized (divided by 255), then raised to the power of gamma, and finally rescaled to the 0-255 range.

Before Gamma Correction

After Gamma Correction