Microscopic time-lapse a tool used in biology for tracking the growth and protein expression at single cell level. This requires reliable automated image segmentation, quantification and frame-to-frame cell identification. Here, we present an automated Python-based image processing package designed for quantitative analysis of live-cell fluorescence microscopy of bacteria in the mother machine microfluidic system. This software incorporates tools from OpenCV library to get cellular boundaries and a simple algorithm to link cells from frame-to-frame. The program exports a file with the size and fluorescence at single cell level along the time-course. A simple statistical analyzer is also shown. It can fit the growth-rate for a single-cell cycle, plot the protein expression dynamics and other statistical variables like mean and noise as well as correlation between two different reporters. Finally, we discuss some of the capabilities and limitations of these algorithms.