Depth recovery from an image uses popular methods such as stereo-vision, defocus variation, focus or defocus variation, aperture variation, etc. Most of these methods demands extensive computational efforts. Further, the depth can also be obtained from a single image. Depth from single image requires less computational power and is simple to implement. In this work we propose an efficient method that relies on defocus or blur variation in an image to indicate the depth map for a given camera focus. The depth is derived by applying Gaussian filters on the contrast changes or edges in the image. The actual blur amount at the edges is then derived from the ratio of gradient magnitudes of these filtered images. This blur represents a depth map of the actual image. Our method differs from all other methods available in the literature and is based on the fact that we use real data, simple calibration method to extract the depth maps. This is unlike other authors who have mostly relied upon simulated datasets. A unique target is used to calibrate the derived depths by finding coefficients k between theoretical and actual blur. Additionally, the target also characterizes the blur range. © Springer Nature Singapore Pte Ltd 2020.