Single molecule Förster resonance energy transfer (smFRET) plays a crucial role in revealing biomolecular dynamics over a wide range of timescales. Currently available tools that analyze smFRET data, however, remain limited in their scope as they are unable to simultaneously estimate: 1) the number of system states visited by a biomolecular complex; 2) the associated kinetic rates; and 3) uncertainties over the estimated parameters, while including features such as crosstalk, instrument response function (IRF) and background in the model. In this paper, we adapt the Bayesian nonparametrics (BNP) framework presented in the first paper to analyze dynamics from single photon smFRET traces generated under continuous illumination. Using this method, we learn the lifetimes/escape rates and the number of system states given a trace of photons. We benchmark our method by analyzing a range of synthetic and experimental data. Particularly, we apply our method to simultaneously learn the the number of system states and the corresponding dynamics for intrinsically disordered proteins(IDPs) using two-color FRET under varying chemical conditions. Moreover, using synthetic data, we show that our method can deduce the number of system states even when dynamics occur at timescales of interphoton intervals.