Resistance training is known to increase physical and mental health but requires a lot of knowledge and experience to be done effectively and safely. Personal trainers and physiotherapists provide their knowledge to athletes but their profession requires a lot of learning and experience, thus making their services often not affordable to the general public. Automating certain aspects of their work will make their services more available to the general population and therefore lead to more safe and more effective athletes. The first steps of automating personal training lie in observing a subject train and understanding their performed workout. This provides the basics for future work of automating providing feedback on exercise execution and improving their training regimes. In order to do so, we developed a proof-of-concept program, that uses a two-dimensional camera video as an input to classify what exercise a user performs and automatically counts the number of performed repetitions, in real-time. It should work without imposing requirements in the camera perspective or needing to know what exercise will be performed in advance. This is achieved by using a three-dimensional pose estimation model and defining a rule-based algorithm, that considers the position and angle of joints that characterize the performed exercises We evaluate our proof-of-concept program using videos of subjects performing squats and push-ups in order to understand the accuracy in a real-world scenario. Our program achieved an overall accuracy of 95.57% for the squats and 93.69% for the push-up evaluation.