Developed an Image Display Utility for Testing within the YuuTaTaNaKa/EMOBOT repository, focusing on streamlining computer vision validation workflows. The solution involved organizing test assets under a dedicated CV2 directory, standardizing the structure for easier onboarding and faster validation. Leveraging Python, OpenCV, and matplotlib, the utility reads images, converts color spaces for accurate visualization, and displays results to aid in debugging and testing. This work improved the visibility and velocity of computer vision feature validation by providing a lightweight, reproducible testing tool. The approach emphasized file organization, image processing, and testing best practices, though no major bugs were addressed.