PyFCS: A new Python library to create and manipulate Fuzzy Color Spaces

Color, as a fundamental element in digital imagery, plays a vital role across various domains, from art and design to scientific imaging. However, its representation and manipulation are challenged by the inherent subjectivity of human color perception and the limitations of traditional color naming and categorization methods. Addressing these challenges, this paper introduces PyFCS, a novel Python library developed to create and manipulate fuzzy color spaces, offering a more nuanced and realistic approach to color modeling. PyFCS leverages fuzzy logic and the conceptual spaces theory to allow flexible and accurate representation of color categories, overcoming the constraints of crisp boundaries. Building upon the groundwork laid by the Java-based JFCS software, PyFCS harnesses Python's strengths in computation and its rich ecosystem of libraries for enhanced data processing and image analysis capabilities. The library's user-friendly and scalable design, coupled with its integration with Python's computational resources, makes it accessible to a broad range of users, from beginners to advanced researchers. The efficacy of PyFCS is demonstrated through a case study involving the creation of fuzzy color spaces from a paint catalog and its subsequent application in mural painting, exemplifying its practical utility in real-world scenarios. Furthermore, this paper details how PyFCS seamlessly integrates with other libraries to offer a comprehensive color analysis, exemplified by linguistic description of colors present in an urban mural painting. This study introduces a sophisticated color space tool and enhances scientific collaboration by providing open-source access to PyFCS on GitHub.