The visual cortex has been a heavily studied region in neuroscience due to many factors, not the least of which is its well-defined retinotopic organization. This organization makes it possible to predict the general location of cortical regions that stimuli will activate during visual tasks. However, the precise and accurate mapping of these regions in human patients takes time, effort, and participant compliance that can be difficult in many patient populations. In humans, this retino-cortical mapping has typically been done using functional localizers which maximally activate the area of interest, and then the activation profile is thresholded and converted to a binary mask region of interest (ROI). An alternative method involves performing population receptive field (pRF) mapping of the whole visual field and choosing vertices whose pRF centers fall within the stimulus. This method ignores the spatial extent of the pRF which changes dramatically between central and peripheral vision. Both methods require a dedicated functional scan and depend on participants' stable fixation. The aim of this project was to develop a user-friendly method that can transform a retinal object of interest (for example, an image, a retinal lesion, or a preferred locus for fixation) from retinal space to its expected representation on the cortical surface without a functional scan. We modeled the retinal representation of each cortical vertex as a 2D Gaussian with a location and spatial extent given by a previously published retinotopic atlas. To identify how affected any cortical vertex would be by a given retinal object, we took the product of the retinal object with the Gaussian pRF of that cortical vertex. Normalizing this value gives the expected response of a given vertex to the retinal object. This method was validated using BOLD data obtained using a localizer with discrete visual stimuli, and showed good agreement to predicted values. Cortical localization of a visual stimulus or retinal defect can be obtained using our publicly available software, without a functional scan. Our software may benefit research with disease populations who have trouble maintaining stable fixation.