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Computing Disparity between Stereo Images using OpenCV Stereo Module

The OpenCV stereo module provides a comprehensive set of functions for computing the disparity between two stereo images. In this article, we will explore how to use the OpenCV stereo module to compute the disparity between two stereo images.

What is Stereo Vision?

Stereo vision is a technique used in computer vision to estimate the depth of objects in a scene by analyzing the disparity between two images taken from different viewpoints. The disparity between the two images is calculated by finding the difference in the position of corresponding pixels in the two images.

Requirements

To compute the disparity between two stereo images using OpenCV, you will need:

  • OpenCV 3.x or later
  • Two stereo images (left and right)
  • A calibration file for the stereo camera (optional)

Step 1: Load the Stereo Images

Load the left and right stereo images using the `cv2.imread()` function.


import cv2

# Load the left and right stereo images
left_image = cv2.imread('left_image.jpg')
right_image = cv2.imread('right_image.jpg')

Step 2: Convert the Images to Grayscale

Convert the left and right images to grayscale using the `cv2.cvtColor()` function.


# Convert the images to grayscale
left_gray = cv2.cvtColor(left_image, cv2.COLOR_BGR2GRAY)
right_gray = cv2.cvtColor(right_image, cv2.COLOR_BGR2GRAY)

Step 3: Create a Stereo Matcher

Create a stereo matcher using the `cv2.StereoBM_create()` or `cv2.StereoSGBM_create()` function. The `cv2.StereoBM_create()` function uses the block matching algorithm, while the `cv2.StereoSGBM_create()` function uses the semi-global block matching algorithm.


# Create a stereo matcher using the block matching algorithm
stereo_matcher = cv2.StereoBM_create(numDisparities=16, blockSize=15)

# Create a stereo matcher using the semi-global block matching algorithm
# stereo_matcher = cv2.StereoSGBM_create(minDisparity=0, numDisparities=16, blockSize=15)

Step 4: Compute the Disparity

Compute the disparity between the left and right images using the `stereo_matcher.compute()` function.


# Compute the disparity
disparity = stereo_matcher.compute(left_gray, right_gray)

Step 5: Display the Disparity Map

Display the disparity map using the `cv2.imshow()` function.


# Display the disparity map
cv2.imshow('Disparity Map', disparity)
cv2.waitKey(0)
cv2.destroyAllWindows()

Example Code

Here is the complete example code:


import cv2

# Load the left and right stereo images
left_image = cv2.imread('left_image.jpg')
right_image = cv2.imread('right_image.jpg')

# Convert the images to grayscale
left_gray = cv2.cvtColor(left_image, cv2.COLOR_BGR2GRAY)
right_gray = cv2.cvtColor(right_image, cv2.COLOR_BGR2GRAY)

# Create a stereo matcher using the block matching algorithm
stereo_matcher = cv2.StereoBM_create(numDisparities=16, blockSize=15)

# Compute the disparity
disparity = stereo_matcher.compute(left_gray, right_gray)

# Display the disparity map
cv2.imshow('Disparity Map', disparity)
cv2.waitKey(0)
cv2.destroyAllWindows()

FAQs

Q: What is the difference between the block matching algorithm and the semi-global block matching algorithm?

A: The block matching algorithm is a simple and fast algorithm that computes the disparity by finding the best match between two blocks of pixels. The semi-global block matching algorithm is a more advanced algorithm that computes the disparity by finding the best match between two blocks of pixels and also considers the consistency of the disparity map.

Q: How do I calibrate the stereo camera?

A: You can calibrate the stereo camera using the `cv2.stereoCalibrate()` function. This function takes the images of a calibration pattern taken by the left and right cameras as input and returns the intrinsic and extrinsic parameters of the cameras.

Q: How do I rectify the stereo images?

A: You can rectify the stereo images using the `cv2.stereoRectify()` function. This function takes the intrinsic and extrinsic parameters of the cameras as input and returns the rectified images.

Q: What is the unit of the disparity map?

A: The unit of the disparity map is pixels. The disparity map represents the difference in the position of corresponding pixels in the left and right images.

Q: How do I convert the disparity map to depth map?

A: You can convert the disparity map to depth map using the `cv2.reprojectImageTo3D()` function. This function takes the disparity map and the intrinsic parameters of the camera as input and returns the depth map.

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