For materials exhibiting complex nonlinear elastic or inelastic behavior, the formulation and calibration of constitutive models remain challenging tasks. Accordingly, in the field of computational mechanics, novel techniques -- often referred to as data-driven or data-based methods -- have gained popularity in recent years. These methods, however, require an extensive amount of data, typically stresses and strains for solid mechanics problems. In this contribution, we present a consistent dual-stage approach for the automated calibration of hyperelastic constitutive models, which only requires experimentally measurable data. In the first step of our approach, data-driven identification (DDI) is applied to determine tuples consisting of stress and strain states [1]. By specifying only the displacement field and the applied boundary conditions -- which may be determined using full-field measurement techniques like digital image correlation (DIC) -- this method enables to identify these data. A physics-augmented neural network (PANN) is calibrated using the data in the second step of the suggested framework [2]. In addition to being extremely flexible, the introduced model fulfills all common conditions of hyperelasticity in a hard way. Moreover, the PANN model can be easily implemented into a finite element (FE) code. We demonstrate the applicability of our approach by several descriptive examples. Therefore, a reference constitutive model is used to generate two-dimensional synthetic data exemplarily. The calibrated PANN is then applied in three-dimensional FE simulations, where the solution is compared to the reference model. [1] Leygue, A., Coret, M., Réthoré, J., Stainier, L. and Verron, E., Data-based derivation of material response, Computer Methods in Applied Mechanics and Engineering 331 (2018). [2] Linden, L., Klein, D. K., Kalina, K. A., Brummund, J., Weeger, O. and Kästner, M., Neural networks meet hyperelasticity: A guide to enforcing physics, Journal of the Mechanics and Physics of Solids 179 (2023).