Python OpenCV Depth map from Stereo Images (original) (raw)

Last Updated : 15 Jul, 2025

OpenCV is the huge open-source library for the computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems.
Note: For more information, refer to Introduction to OpenCV

Depth Map : A depth map is a picture where every pixel has depth information(rather than RGB) and it normally represented as a grayscale picture. Depth information means the distance of surface of scene objects from a viewpoint. An example of pixel value depth map can be found here : Pixel Value Depth Map using Histograms

Stereo Images : Two images with slight offset. For example, take a picture of an object from the center. Move your camera to your right by 6cms while keeping the object at the center of the image. Look for the same thing in both pictures and infer depth from the difference in position. This is called stereo matching. To have best results, avoid distortions.

Approach

Example :
Sample Images:

Left

Right

Python3 `

import OpenCV and pyplot

import cv2 as cv from matplotlib import pyplot as plt

read left and right images

imgR = cv.imread('right.png', 0) imgL = cv.imread('left.png', 0)

creates StereoBm object

stereo = cv.StereoBM_create(numDisparities = 16, blockSize = 15)

computes disparity

disparity = stereo.compute(imgL, imgR)

displays image as grayscale and plotted

plt.imshow(disparity, 'gray') plt.show()

`

Output:

Disparity Map Output