Solar energy resources are poised as the biggest renewable competitor of fossil fuels. Solar perovskite thin films are an emerging technology with efficiencies close to established photovoltaic technologies. However, structural and thermalinstability and an exponentially large number of possible compositions and processing parameters have led to reproducibility and scalability issues even at the lab scale. A DOE-funded collaboration across universities and national labs is creating a multi-scale statistical machine learning (ML) model to optimize the entire process from perovskite synthesis to device performance. The collaboration is called SPIRALs, which stands for Science and Processing Informed by Rational Algorithmic Learning (SPIRALs). A key challenge is integrating data from diverse data sources, such as experimental data and SEM images, as inputs to the ML algorithms. This work reports the Python code developed to reduce SEM images using spatial functions such as power spectral densities and autocorrelation functions. Quantitative analysis of these functions is done to derive spatial parameters such as the correlation length, grain size, fractal dimension, and Hurst exponent that describe the surface morphology of thin films. These parameters can tie synthesis methods to device performance and act as a check for ML framework predictions. The developed coding methodology and subsequent analysis can be applied to any thin film image and extended to different instruments to facilitate the SPIRALs project.