The development of autocharacterization methods of materials is not novel, however, little literature exists on driving autocharacterization with computer vision to accelerate throughput and increase algorithm scalability to arbitrarily many samples. Escobedo-Morales et al. design an automated band gap calculation tool from reflectance data, however, their implementation does not employ automatic vision segmentation, hence, the approach is limited to single sample-by-sample processing. Furthermore, the process of band gap curve fitting using inclination angles developed by Escobedo-Morales et al. produces numerical inconsistencies and is removed in our implementation in favor of a simpler and more robust version of RMSE minimization. Keesey & Tiihonen et al. develop a procedure of optically extracting a figure of merit to quantify the stability of perovskites but this methodology requires the manual segmentation of samples one by one, which becomes infeasible for a human researcher when the number of samples increases significantly. In this paper, we build from these prior works by demonstrating the performance of three autocharacterization methods that are scalable to arbitrarily many samples using a computer vision-driven approach.