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This paper presents a toolbox for monocular and binocular depth estimation and an analysis of performance for some of these. At first, the definition of an appropriate depth indicator (DI) metric for 3D contents is discussed. To this aim, different algorithms from the literature for the characterization of 3D videos are compared. Results show that the simple 7.5 percentile of the disparity map values can already be an indicator, even though it may fail to address several perceptual aspects. In the latter cases, more advanced algorithms presented in this paper may be a better approach. In a second step, monocular depth indicators are described and analyzed in this paper. All code and tools enabling binocular and monocular depth estimation such as depth-map estimation, depth map characterization and monocular depth cue indicator computation is provided open-source. This will enable researchers to further characterize their 3D and 2D contents, for example before running a subjective experiment, or to automatically pre-screen 3D content that is to be presented to a larger number of viewers.