The objective of this work is to develop and present ThermalDiabetesTools, an innovative Python library tailored for managing thermal and RGB images of diabetic feet. It encompasses image loading, post-processing, segmentation, and registration, addressing the unique challenges in diabetic foot imaging. These include small temperature differentials and the need for precise temperature gradient analysis and RGB image processing. The tool is intended not only to compile existing methodologies but also to calibrate images specifically for diabetic foot applications. This specialized approach ensures more accurate and efficient processing, essential for both research and clinical purposes. In the clinical domain, ThermalDiabetesTools facilitates the early detection of complications by providing detailed thermal analysis, crucial for preventive healthcare in diabetes management. From a research perspective, the library accelerates developments in diabetic foot care by simplifying initial yet critical image analysis steps. Its automation and standardization capabilities can significantly improve consistency in results, making it a valuable asset in scientific studies. Furthermore, ThermalDiabetesTools is engineered for seamless integration with various systems and workflows, ensuring its accessibility to a wide range of users. Its function interface bridges the gap between advanced image processing techniques and practical clinical applications. This accessibility is key to its wide adoption, thereby enhancing the overall quality of diabetic foot care. By offering a set of tools for specific applications, this platform contributes to both research and clinical practices in diabetes management.