Droplet-based microfluidic technology is a powerful tool for generating large numbers of monodispersed nanoliter-sized droplets for ultra-high throughput screening of molecules or single cells. Yet further progress in the development of methods for the real-time detection and measurement of passing droplets is needed for achieving fully automated systems and ultimately scalability. Existing droplet monitoring technologies are either difficult to implement by non-experts or require complex experimentation setups. Moreover, commercially available monitoring equipment is expensive and therefore limited to a few laboratories worldwide. In this work, we validated for the first time an easy-to-use, open-source Bonsai visual programming language to accurately measure in real-time droplets generated in a microfluidic device. With this method, droplets are found and characterized from bright-field images with high processing speed. We used off-the-shelf components to achieve an optical system that allows sensitive image-based, label-free, and cost-effective monitoring. As a test of its use we present the results, in terms of droplet radius, circulation speed and production frequency, of our method and compared its performance with that of the widely-used ImageJ software. Moreover, we show that similar results are obtained regardless of the degree of expertise. Finally, our goal is to provide a robust, simple to integrate, and user-friendly tool for monitoring droplets, capable of helping researchers to get started in the laboratory immediately, even without programming experience, enabling analysis and reporting of droplet data in real-time and closed-loop experiments.