Accurate localization of EEG scalp electrodes is crucial for precisely estimating the underlying neuronal activity from observed electrical potential changes on the scalp. Various techniques for electrode localization have been proposed, categorized into direct measurements on the subject's head or indirect methods utilizing structural MRI scans. Most direct measurement methods are expensive as they rely on complex stereo vision systems or are magnetic-field-driven, making them especially inconvenient for experiments involving simultaneous EEG-fMRI acquisition and cannot be used in the vicinity of the MRI scanner. Indirect methods are susceptible to artifacts, particularly with high-density EEG caps, where dense wire bundles cause signal dropouts on MR images. To address these challenges, we present an open-source Python environment for electrode localization. Our approach utilizes surface models of the human head generated from data acquired by affordable IR stereo structured light projection cameras and/or structural MRI head scans. Our primary objective was to streamline the electrode localization process, reducing both acquisition and processing time. We achieved automated electrode detection from head texture images and electrode labeling from template location files, reducing the acquisition and processing time together to under 5 minutes. Our environment enhances efficiency and accuracy in EEG electrode localization, facilitating seamless integration into neuroimaging workflows.