Through the use of high-fidelity computer vision segmentation, we demonstrate the scalability of accurate and automatic characterization for 80 unique FA 1-x MA x PbI 3 perovskite samples. The developed vision segmentation method avoids edge effects and removes aberrations to output a clean segmentation of optical data. From the segmented optical data, the automatic band gap computation is demonstrated to achieve an accuracy of 89.1%, the automatic stability measurement is demonstrated to achieve an accuracy of 87.8%, and the automatic composition extraction is demonstrated to achieve an accuracy of 78.8% across all 80 perovskite samples, relative to a human evaluation reference. Furthermore, the results extracted by the developed vision-driven autocharacterization tools are validated to align with traditional x-ray diffraction characterization. The results achieved by the methods developed in this paper support the advancement of scalable, high-throughput novel materials discovery by surmounting the obstacle of sample-by-sample characterization.