# Stereo Vision with OpenCV: A Comprehensive Guide

In this tutorial, we will explore the concept of stereo vision, how it works, and how to implement it using OpenCV.

Updated March 25, 2023

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Welcome to this comprehensive tutorial on stereo vision using OpenCV! In this tutorial, we will explore the concept of stereo vision, how it works, and how to implement it using OpenCV. This tutorial is perfect for computer vision enthusiasts, developers, and researchers who want to dive into the world of stereo vision. Let’s get started!

## What is Stereo Vision?

Stereo vision is a technique used to estimate the depth information of a scene by analyzing the disparity between two images captured from slightly different viewpoints, similar to the way our human eyes perceive depth. The concept of stereo vision is based on triangulation, where the 3D position of a point is calculated by finding the intersection of the lines of sight from two cameras. Stereo vision has various applications, including robotics, 3D mapping, and autonomous navigation.

### How to Implement Stereo Vision with OpenCV: A Step-by-Step Guide

In this tutorial, we will use OpenCV’s built-in functions to perform stereo vision using a pair of rectified images (images that have been preprocessed to align the corresponding points). We’ll be using Python for our examples, but you can also use the OpenCV C++ API.

### Step 1: Install OpenCV and Other Dependencies

First, let’s install OpenCV and other required libraries:

``````pip install opencv-python opencv-python-headless numpy
``````

### Step 2: Load Rectified Images

``````import cv2

``````

### Step 3: Create a Stereo Block Matching (SBM) Object

Now, we will create a Stereo Block Matching (SBM) object, which is a popular method for estimating disparity. OpenCV provides the `StereoBM_create()` function to create an SBM object:

``````num_disparities = 16 * 5  # Must be divisible by 16
block_size = 15  # Must be an odd number

sbm = cv2.StereoBM_create(numDisparities=num_disparities, blockSize=block_size)
``````

The `num_disparities` parameter defines the number of disparity levels, and the block_size parameter specifies the size of the block window used for matching.

### Step 4: Compute the Disparity Map

Next, we’ll compute the disparity map using the compute() method of the SBM object:

``````disparity = sbm.compute(img_left, img_right)
``````

### Step 5: Normalize and Display the Disparity Map

Finally, we’ll normalize the disparity map to a range of 0 to 255 and display it using OpenCV’s imshow() function:

``````norm_disparity = cv2.normalize(disparity, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)

cv2.imshow('Disparity Map', norm_disparity)
cv2.waitKey(0)
cv2.destroyAllWindows()
``````

And there you have it! You’ve successfully implemented stereo vision using OpenCV. By understanding the underlying theory and leveraging OpenCV’s powerful built-in functions, you can now estimate depth information for various applications. Keep exploring the fascinating world of computer vision and enjoy working with stereo vision! Happy coding!