Stereo vision concept in Computer Vision

Introduction

Stereo vision helps computers see depth by using two images from slightly different views, like our eyes do.

To measure how far objects are in a scene using two cameras.

In robots to help them avoid obstacles by understanding distance.

For 3D reconstruction of environments from images.

In self-driving cars to detect nearby objects and their positions.

To create depth maps for augmented reality applications.

Syntax

Computer Vision

1. Capture two images from cameras placed side-by-side.
2. Find matching points between the two images.
3. Calculate the difference in position (disparity) of these points.
4. Use disparity to estimate depth using geometry.

The key idea is that closer objects shift more between the two images.

Disparity is inversely related to depth: bigger disparity means closer object.

Examples

This shows the main steps: capture, match, disparity, then depth.

Computer Vision

left_image = capture_camera_left()
right_image = capture_camera_right()
matches = find_feature_matches(left_image, right_image)
disparity_map = compute_disparity(matches)
depth_map = convert_disparity_to_depth(disparity_map)

Formula to convert disparity to depth using camera parameters.

Computer Vision

disparity = x_left - x_right
depth = (focal_length * baseline) / disparity

Sample Model

This code loads two images, computes the disparity map using OpenCV's StereoBM, and prints basic info about the disparity map.

Computer Vision

import numpy as np
import cv2

# Load left and right images (grayscale)
left_img = cv2.imread('left.png', cv2.IMREAD_GRAYSCALE)
right_img = cv2.imread('right.png', cv2.IMREAD_GRAYSCALE)

# Create stereo block matcher object
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)

# Compute disparity map
disparity = stereo.compute(left_img, right_img)

# Normalize disparity for display
disp_norm = cv2.normalize(disparity, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
disp_norm = np.uint8(disp_norm)

# Print some stats
print(f"Disparity map shape: {disparity.shape}")
print(f"Disparity min: {disparity.min()}")
print(f"Disparity max: {disparity.max()}")

Important Notes

Good lighting and camera calibration improve stereo vision accuracy.

Disparity maps can be noisy; smoothing or filtering helps.

Baseline is the distance between the two cameras; larger baseline improves depth accuracy but can cause matching difficulty.

Summary

Stereo vision uses two images to find depth by comparing positions of points.

Disparity is the key measure that relates to how far objects are.

It is useful in robotics, 3D mapping, and autonomous vehicles.

Practice

1. What is the main purpose of stereo vision in computer vision?

easy

A. To estimate the depth of objects by comparing two images

B. To enhance the color of images

C. To detect edges in a single image

D. To compress images for storage

Solution

1. Step 1: Understand stereo vision basics

   Stereo vision uses two images taken from slightly different viewpoints to find depth information.

2. Step 2: Identify the main goal

   The main goal is to estimate how far objects are by comparing their positions in the two images.

3. Final Answer:

   To estimate the depth of objects by comparing two images -> Option A

4. Quick Check:

   Stereo vision = Depth estimation [OK]

Hint: Stereo vision = depth from two images [OK]

Common Mistakes:

• Confusing stereo vision with color enhancement
• Thinking it works with only one image
• Mixing depth estimation with edge detection

2. Which of the following correctly describes 'disparity' in stereo vision?

easy

A. The difference in brightness between two images

B. The average color value of an image

C. The difference in pixel positions of the same point in two images

D. The total number of pixels in an image

Solution

1. Step 1: Define disparity in stereo vision

   Disparity is the horizontal difference in pixel positions of the same object point between the left and right images.

2. Step 2: Match the correct description

   It is not about brightness or color but about position difference to calculate depth.

3. Final Answer:

   The difference in pixel positions of the same point in two images -> Option C

4. Quick Check:

   Disparity = pixel position difference [OK]

Hint: Disparity = position difference between images [OK]

Common Mistakes:

• Confusing disparity with brightness or color
• Thinking disparity is total pixels count
• Mixing disparity with image resolution

3. Given two stereo images, the pixel of a point is at (x=150) in the left image and at (x=130) in the right image. What is the disparity value?

medium

A. 150

B. -20

C. 280

D. 20

Solution

1. Step 1: Calculate disparity from pixel positions

   Disparity = x_left - x_right = 150 - 130 = 20 pixels.

2. Step 2: Interpret the result

   Disparity is positive and represents how far the point shifted between images.

3. Final Answer:

   20 -> Option D

4. Quick Check:

   150 - 130 = 20 [OK]

Hint: Disparity = left x minus right x [OK]

Common Mistakes:

• Subtracting right from left incorrectly
• Using sum instead of difference
• Ignoring sign of disparity

4. You wrote code to compute disparity but always get zero values. What is the most likely error?

medium

A. Swapping the x and y coordinates in calculations

B. Using the same image for both left and right inputs

C. Using color images instead of grayscale

D. Calculating disparity as the sum of pixel positions

Solution

1. Step 1: Analyze zero disparity cause

   If both images are identical, the pixel positions match exactly, so disparity is zero everywhere.

2. Step 2: Check other options

   Swapping coordinates or color use won't cause all zero disparity; summing positions gives wrong values but not zero everywhere.

3. Final Answer:

   Using the same image for both left and right inputs -> Option B

4. Quick Check:

   Same images = zero disparity [OK]

Hint: Different images needed for disparity [OK]

Common Mistakes:

• Using identical images for stereo input
• Mixing x and y coordinates without correction
• Ignoring image color format effects

5. In a stereo vision system, if an object is very far away, how does its disparity value change and why?

hard

A. Disparity decreases because the pixel positions in both images become closer

B. Disparity increases because the object appears larger

C. Disparity stays the same regardless of distance

D. Disparity becomes negative because the object moves behind the cameras

Solution

1. Step 1: Understand disparity-distance relation

   Disparity is inversely related to distance; far objects have smaller disparity because their positions in both images are closer.

2. Step 2: Eliminate incorrect options

   Disparity does not increase with distance, nor does it become negative or stay constant.

3. Final Answer:

   Disparity decreases because the pixel positions in both images become closer -> Option A

4. Quick Check:

   Far object = small disparity [OK]

Hint: Farther objects have smaller disparity [OK]

Common Mistakes:

• Assuming disparity grows with distance
• Thinking disparity can be negative for far objects
• Believing disparity is constant