RESUMO
Color data are often required for cultural heritage documentation. These data are typically acquired via standard digital cameras since they facilitate a quick and cost-effective way to extract RGB values from photos. However, cameras' absolute sensor responses are device-dependent and thus not colorimetric. One way to still achieve relatively accurate color data is via camera characterization, a procedure which computes a bespoke RGB-to-XYZ matrix to transform camera-dependent RGB values into the device-independent CIE XYZ color space. This article applies and assesses camera characterization techniques in heritage documentation, particularly graffiti photographed in the academic project INDIGO. To this end, this paper presents COOLPI (COlor Operations Library for Processing Images), a novel Python-based toolbox for colorimetric and spectral work, including white-point-preserving camera characterization from photos captured under diverse, real-world lighting conditions. The results highlight the colorimetric accuracy achievable through COOLPI's color-processing pipelines, affirming their suitability for heritage documentation.