Studies of photosynthetic cyanobacteria are often carried out by growing the bacteria in large volume cultures. However, this results in an uneven amount of light received by the cells due to cell-cell shading. Fluorescence microscopy solves this issue as cells can be grown in a single layer, eliminating shading issues. However, microscopy brings its own challenge as hundreds of cells are imaged at the same time, making it difficult to analyze the resulting data by hand. Here we present CyAn, a toolbox to process and analyze microscope images of cyanobacteria. The toolbox automates cyanobacteria identification in microscope images, tracks moving and dividing cells, and computes relevant biological data such as growth rates. As proof-of-principle, we demonstrate that wild type and mutants can be grown on the same slide and identified using the software, allowing a much more accurate probe into photosynthetic processes that are often masked due to cellular heterogeneity or those requiring precise light control to prevent cell-cell shading.