Exploring the Mysteries of the 3D World: A Detailed Explanation of OpenCV Stereo Vision Algorithms, from Binocular Stereo to Structured Light

# 1. Overview of OpenCV Stereo Vision

OpenCV (Open Source Computer Vision Library) is an open-source library that provides a plethora of algorithms and functions for image processing, video analysis, and machine learning. Stereo vision is a branch of computer vision that uses two or more cameras to obtain 3D information from a scene. OpenCV offers a range of stereo vision algorithms that extract depth information from stereo image pairs.

Stereo vision algorithms are generally divided into two categories: binocular stereo vision and structured light stereo vision. Binocular stereo vision employs two cameras placed side-by-side, while structured light stereo vision uses a single camera and a projector. Both methods utilize the principle of triangulation to calculate the depth of points in the scene.

# 2. Binocular Stereo Vision Algorithms

### 2.1 Principles of Binocular Stereo Imaging

#### 2.1.1 Stereo Matching Algorithms

Stereo matching is at the heart of binocular stereo vision algorithms, ***mon stereo matching algorithms include:

- **Block Matching Algorithm (BM)**: Divides the image into small blocks and matches based on block similarity.
- **Semi-Global Block Matching Algorithm (SGBM)**: Builds upon BM and uses global cost aggregation to optimize matching results.
- **Graph Cut Algorithm (GC)**: Models the matching problem as a graph cut problem and solves it with the min-cut algorithm.

### 2.2 Practice of Binocular Stereo Algorithms

#### 2.2.1 The SGBM Algorithm

The SGBM algorithm is a stereo matching algorithm based on global cost aggregation. Its algorithm flow is as follows:

import cv2

def sgbm_stereo(left_image, right_image, num_disparities):
     """
     Perform binocular stereo matching using the SGBM algorithm

     Args:
         left_image: Left image
         right_image: Right image
         num_disparities: Disparity range

     Returns:
         Disparity map
     """

     # Create an SGBM object
     sgbm = cv2.StereoSGBM_create(
         minDisparity=0,
         numDisparities=num_disparities,
         blockSize=5,
         P1=8 * 3 * window_size ** 2,
         P2=32 * 3 * window_size ** 2,
         disp12MaxDiff=1,
         uniquenessRatio=10,
         speckleWindowSize=100,
         speckleRange=32
     )

     # Compute the disparity map
     disparity_map = ***pute(left_image, right_image)

     return disparity_map

**Parameter Explanation:**

- `minDisparity`: Minimum disparity value
- `numDisparities`: Disparity range
- `blockSize`: Size of the matching block
- `P1`: Penalty coefficient for controlling the smoothness of matching cost
- `P2`: Penalty coefficient for controlling the difference in matching cost
- `disp12MaxDiff`: Maximum disparity difference between left and right disparity maps
- `uniquenessRatio`: Uniqueness ratio for controlling the uniqueness of matching results
- `speckleWindowSize`: Speckle window size for noise removal
- `speckleRange`: Speckle range for noise removal

**Code Logic Analysis:**

1. Create an SGBM object and set algorithm parameters.
2. Use the SGBM object to compute the disparity map.
3. Return the disparity map.

#### 2.2.2 The BM Algorithm

The BM algorithm is a block-matching based stereo matching algorithm. Its algorithm flow is as follows:

import cv2

def bm_stereo(left_image, right_image, num_disparities):
     """
     Perform binocular stereo matching using the BM algorithm

     Args:
         left_image: Left image
         right_image: Right image
         num_disparities: Disparity range

     Returns:
         Disparity map
     """

     # Create a BM object
     bm = cv2.StereoBM_create(
         numDisparities=num_disparities,
         blockSize=5
     )

     # Compute the disparity map
     disparity_map = ***pute(left_image, right_image)

     return disparity_map

**Parameter Explanation:**

- `numDisparities`: Disparity range
- `blockSize`: Size of the matching block

**Code Logic Analysis:**

1. Create a BM object and set algorithm parameters.
2. Use the BM object to compute the disparity map.
3. Return the disparity map.

# 3.1 Principles of Structured Light Imaging

Structured light stereo vision algorithms are a type of active stereo vision technique that obtains depth information. The basic principle involves projectin