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About our project¶

Our project focuses on merging two images from separate projectors into one unified display. We utilize advanced image processing techniques including Gamma correction method and Alpha blending to achieve the desired output. The team is composed of a project manager, team leader, and various sub teams responsible for Doxyen documentation, wiki management, coding, commenting, and UML design.

Software and Tools¶

Python /Opencv: Used for efficient image processing.
Doxygen: Helps in documenting the code for clarity and future use.
Astah: Utilized for creating UML diagrams to visualize the project’s design.
Redmine: Employed for task tracking and progress management.¶

How system displays image¶

The system displays the image by following steps.¶

1. The system only accepts JPEG/PNG format files and from the picture. System also obtains the metadata as well when it loads the image file.
2. The system specifies the image file path inside the configuration file.
3. Once, system load the image the system masks the image and project to the screen¶

How to handle image masking¶

Our group handles the image masking by following steps.¶

1. Get the distance between the two projector. We set this distance as a symbol (ξ).
2. Get the distance of the total width of the projection. We set this distance as a symbol (η).
3. The system calculates the overlap percentage by the formlar below.¶

4. The system converts the overlapped pixel by multiplying the overlap percentage with the picture width in pixel.
5. we set the edge of the overlapped picture α　= 1, and it reduce the size of α by
1/overlapped pixel until α = 0. This is called linear interpolation.¶

see more: https://en.wikipedia.org/wiki/Linear_interpolation