Following the preparation of both the source and target ColorChecker datasets, the subsequent step involves generating a color transform through mathematical alignment. For this purpose, the tool Camera Match developed by Ethan Ou provides an effective and streamlined solution. This Python-based application enables the creation of LUTs by computationally matching the color responses of the source dataset (e.g., digital camera footage such as ARRI Alexa imagery) to a target dataset (e.g., film scans or alternative camera profiles).
Camera Match is accessible both as a downloadable script and via a browser-based interface (Camera Match GitHub Repository). The basic workflow for LUT generation using the browser interface is as follows:
- Initialization:
Execute the script by pressing the “Play” button, which installs all necessary Python libraries automatically. - Source Data Upload:
Load the source dataset (e.g., Alexa ColorChecker measurements) into the interface. - Target Data Upload:
Upload the corresponding target dataset representing the desired film look or alternative camera profile. - LUT Generation:
Initiate the LUT creation process by selecting the Radial Basis Function (RBF) algorithm as the matching function. The RBF method provides smooth and continuous color transitions, making it suitable for high-fidelity color transformations. - LUT Export:
Save the generated LUT to a local directory for further use.

Once created, the LUT can be implemented in post-production applications such as DaVinci Resolve, Lattice, or any system capable of ingesting standard LUT formats. The process is highly efficient, offering a rapid turnaround from dataset preparation to deployable LUT creation.

While this approach enables the user to quickly produce functional LUTs, it is important to acknowledge that the quality of the input datasets—particularly the preparation of the ColorChecker charts—significantly influences the final result. In subsequent discussions, we will explore more advanced methodologies for chart preparation, focusing on best practices for achieving scene-referred workflows compatible with color-managed environments such as DaVinci Wide Gamut Intermediate and ACES.
Although this preparation phase remains time-consuming, it is a critical component for those seeking the highest levels of color accuracy and transform reliability.
Reference:
DeMystify Colorgrading. (n.d.). Film Profile Journey: 20 – More Automation, Less Tedious Manual Work. Retrieved April 28, 2025, from https://www.demystify-color.com/post/film-profile-journey-20-more-automation-less-tedious-manual-work