Hyperspectral imaging integrates spatial and spectral data, crucial for environmental and vision science applications. Existing spectroradiometers lack spatial resolution, and many hyperspectral systems are costly or unsuitable for fieldwork mimicking human vision. Here, we present the development of a portable, relatively low-cost hyperspectral camera based on a tunable filter and a commercial video camera, together with Python-based software capable of generating hyperspectral cubes from the acquired images, as a more accessible and adaptable alternative for specific applications. The proposed method includes assembling the acquisition system and processing raw images, which have been demosaiced, linearized, and labeled with their corresponding spectral band, to generate a hyperspectral cube. Our approach allows radiance retrieval through prior calibration or reflectance estimation using a white reference from a selected region of the hyperspectral cube. Additionally, it enables exporting the hyperspectral cube as a three-dimensional matrix. The developed software facilitates visualization and analysis of hyperspectral data. Validation against a spectroradiometer demonstrated reliable spectral radiance measurements under moderate to high light conditions. This adaptable approach enhances accessibility to hyperspectral imaging for research contexts with limited resources, supporting detailed visual environment characterization outside controlled laboratory settings.