Computer vision basics in Intro to Computing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image using OpenCV.

Intro to Computing

import cv2
image = cv2.[1]('image.jpg')

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

Aimshow

Bresize

Cimwrite

Dimread

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow instead of imread

Using imwrite which saves images

Using resize which changes image size

The imread function loads an image from a file into memory.

2fill in blank

medium

Complete the code to convert a color image to grayscale using OpenCV.

Intro to Computing

gray_image = cv2.cvtColor(image, cv2.[1])

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

ACOLOR_BGR2GRAY

BCOLOR_GRAY2BGR

CCOLOR_BGR2RGB

DCOLOR_RGB2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using COLOR_BGR2RGB which changes color space but not to grayscale

Using COLOR_GRAY2BGR which converts grayscale to color

Using COLOR_RGB2BGR which swaps color channels

COLOR_BGR2GRAY converts a color image from BGR to grayscale.

3fill in blank

hard

Fix the error in the code to display an image using OpenCV.

Intro to Computing

cv2.[1]('Image Window', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

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

Aimread

Bimshow

Cresize

Dimwrite

Attempts:

3 left

💡 Hint

Common Mistakes

Using imread which loads images

Using imwrite which saves images

Using resize which changes image size

The imshow function displays an image in a window.

4fill in blank

hard

Fill both blanks to create a binary threshold on a grayscale image.

Intro to Computing

_, binary_image = cv2.threshold(gray_image, [1], [2], cv2.THRESH_BINARY)

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

A127

B255

C128

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 as threshold which makes all pixels black

Using 128 as max value which is not standard

Swapping threshold and max value

The threshold value is 127 and the max value for the binary image is 255.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps image filenames to their grayscale shapes.

Intro to Computing

shapes = {filename: cv2.imread(filename, [1]).shape[:[2]] for filename in files if cv2.imread(filename, [3]) is not None}

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

Acv2.IMREAD_GRAYSCALE

B2

Ccv2.IMREAD_COLOR

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using IMREAD_COLOR which loads 3 channels

Using 3 for slicing shape which is for color images

Using different flags for loading images in comprehension

Images are loaded in grayscale mode (IMREAD_GRAYSCALE), so shape has 2 dimensions (height, width).

Practice

1. What is the main goal of computer vision?

easy

A. To help computers understand images and videos

B. To write text documents automatically

C. To speed up internet connections

D. To create music from scratch

Solution

1. Step 1: Understand the purpose of computer vision

   Computer vision is about making computers see and understand visual data like images and videos.

2. Step 2: Compare options with this purpose

   Only To help computers understand images and videos matches this goal; others are unrelated to images or videos.

3. Final Answer:

   To help computers understand images and videos -> Option A

4. Quick Check:

   Computer vision = understanding images/videos [OK]

Hint: Computer vision means computers 'seeing' images [OK]

Common Mistakes:

• Confusing computer vision with text or audio processing
• Thinking it speeds up internet or unrelated tasks

2. Which Python library is commonly used to load and show images in computer vision?

easy

A. NumPy

B. OpenCV

C. Matplotlib

D. Pandas

Solution

1. Step 1: Identify libraries for image handling

   OpenCV is a popular library designed for image and video processing.

2. Step 2: Check other options

   NumPy handles arrays, Matplotlib plots graphs, Pandas manages tables; none focus on image loading/display like OpenCV.

3. Final Answer:

   OpenCV -> Option B

4. Quick Check:

   OpenCV = image loading/showing [OK]

Hint: OpenCV is the go-to for images/videos in Python [OK]

Common Mistakes:

• Choosing NumPy which is for arrays, not images
• Picking Matplotlib which is for plotting, not image loading

3. What will the following Python code print?

import cv2
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
print(img.shape)

medium

A. A tuple with 2 values (height, width)

B. A tuple with 3 values (height, width, channels)

C. A single integer representing total pixels

D. An error because of wrong syntax

Solution

1. Step 1: Understand cv2.imread with GRAYSCALE flag

   Loading an image in grayscale returns a 2D array with height and width only (no color channels).

2. Step 2: Check what img.shape returns

   For grayscale images, shape is a tuple of two values: (height, width).

3. Final Answer:

   A tuple with 2 values (height, width) -> Option A

4. Quick Check:

   Grayscale image shape = (height, width) [OK]

Hint: Grayscale images have 2D shape, color images have 3D [OK]

Common Mistakes:

• Expecting 3 values for grayscale images
• Thinking shape returns total pixels as integer
• Assuming code has syntax error

4. The code below tries to convert a color image to grayscale but throws an error. What is the mistake?

import cv2
img = cv2.imread('photo.png')
gray = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.imshow('Gray', gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

medium

A. cv2.waitKey() needs an argument

B. Missing cv2.imread() function

C. cv2.imshow() cannot show grayscale images

D. Using COLOR_GRAY2BGR instead of COLOR_BGR2GRAY

Solution

1. Step 1: Check color conversion code

   The code uses cv2.COLOR_GRAY2BGR which converts grayscale to color, but input is color image.

2. Step 2: Correct conversion for color to grayscale

   To convert color to grayscale, use cv2.COLOR_BGR2GRAY instead.

3. Final Answer:

   Using COLOR_GRAY2BGR instead of COLOR_BGR2GRAY -> Option D

4. Quick Check:

   Color to grayscale needs COLOR_BGR2GRAY [OK]

Hint: Use COLOR_BGR2GRAY to convert color to grayscale [OK]

Common Mistakes:

• Mixing up color conversion flags
• Thinking cv2.imshow can't show grayscale images
• Assuming waitKey needs argument

5. You want to build a simple system that detects if an image is mostly dark or bright. Which approach is best to start with?

hard

A. Count the number of colors in the image

B. Use a deep neural network without preprocessing

C. Convert the image to grayscale and calculate the average pixel brightness

D. Resize the image to a smaller size without changing colors

Solution

1. Step 1: Understand the problem of brightness detection

   Brightness relates to how light or dark pixels are; grayscale simplifies this by removing color.

2. Step 2: Choose a simple and effective method

   Converting to grayscale and averaging pixel values gives a direct measure of brightness, easy and fast.

3. Final Answer:

   Convert the image to grayscale and calculate the average pixel brightness -> Option C

4. Quick Check:

   Grayscale average brightness = brightness check [OK]

Hint: Grayscale average pixel value shows image brightness [OK]

Common Mistakes:

• Using complex models without simple preprocessing
• Counting colors which doesn't measure brightness
• Resizing without addressing brightness