Stereo vision concept in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load two stereo images using OpenCV.

Computer Vision

import cv2

left_image = cv2.imread([1])
right_image = cv2.imread('right.jpg')

Drag options to blanks, or click blank then click option'

A'leftimage.jpg'

B'left_image.jpg'

C'left.jpg'

D'left.png'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong filename or extension.

Forgetting quotes around the filename.

The left stereo image filename is 'left.jpg'. We use cv2.imread() to load it.

2fill in blank

medium

Complete the code to convert the stereo images to grayscale.

Computer Vision

left_gray = cv2.cvtColor(left_image, [1])
right_gray = cv2.cvtColor(right_image, cv2.COLOR_BGR2GRAY)

Drag options to blanks, or click blank then click option'

Acv2.COLOR_BGR2RGB

Bcv2.COLOR_BGR2GRAY

Ccv2.COLOR_RGB2GRAY

Dcv2.COLOR_GRAY2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong color conversion code.

Confusing RGB and BGR color orders.

To convert a color image to grayscale, use cv2.COLOR_BGR2GRAY.

3fill in blank

hard

Fix the error in the code to compute disparity map using StereoBM.

Computer Vision

stereo = cv2.StereoBM_create(numDisparities=16, blockSize=[1])
disparity = stereo.compute(left_gray, right_gray)

Drag options to blanks, or click blank then click option'

A5

B15

C21

D7

Attempts:

3 left

💡 Hint

Common Mistakes

Using an even number for blockSize.

Using a blockSize too small or too large.

The blockSize must be an odd number between 5 and 255. 7 is a valid choice.

4fill in blank

hard

Fill both blanks to normalize and display the disparity map using OpenCV.

Computer Vision

disp_norm = cv2.normalize(disparity, None, [1], [2], cv2.NORM_MINMAX)
cv2.imshow('Disparity', disp_norm)
cv2.waitKey(0)
cv2.destroyAllWindows()

Drag options to blanks, or click blank then click option'

A0

B255

C-1

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using negative values for normalization range.

Confusing min and max values.

Normalization scales disparity to 0-255 for display.

5fill in blank

hard

Fill all three blanks to create a depth map from disparity and focal length.

Computer Vision

focal_length = 0.8  # in meters
baseline = 0.1       # distance between cameras in meters
depth_map = [1] * baseline / (disparity.astype(float) + [2])
depth_map[disparity == 0] = [3]

Drag options to blanks, or click blank then click option'

Afocal_length

B1e-6

C0

Dnp.inf

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by zero causing errors.

Not handling zero disparity pixels.

Depth = focal_length * baseline / disparity. Add a small number to avoid division by zero. Set depth to infinity where disparity is zero.

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