Computer vision basics in Intro to Computing - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is computer vision?

Computer vision is a field of computer science that teaches computers to see, understand, and interpret images and videos like humans do.

beginner

Name three common tasks in computer vision.

Common tasks include image classification (telling what is in an image), object detection (finding objects and their locations), and image segmentation (dividing an image into meaningful parts).

beginner

What is an image represented as in computer vision?

An image is represented as a grid of pixels, where each pixel has color values, often in red, green, and blue (RGB) channels.

intermediate

Why do we use convolutional neural networks (CNNs) in computer vision?

CNNs are used because they can automatically find important patterns like edges and shapes in images, helping the computer understand visual data better.

beginner

What is the difference between image classification and object detection?

Image classification tells what is in the whole image (like 'cat' or 'dog'), while object detection finds where each object is in the image and labels them.

What does a pixel in an image represent?

A A small dot with color information

B A sound wave

C A text character

D A network connection

A pixel is the smallest unit in an image and holds color information.

Which task involves finding the location of objects in an image?

A Image classification

B Data cleaning

C Object detection

D Image enhancement

Object detection finds and locates objects within an image.

What type of neural network is most commonly used for image tasks?

A Convolutional Neural Network (CNN)

B Recurrent Neural Network (RNN)

C Feedforward Neural Network

D Transformer Network

CNNs are designed to process images by detecting patterns like edges and shapes.

What does image segmentation do?

A Converts images to text

B Divides an image into meaningful parts

C Compresses image size

D Removes noise from images

Image segmentation splits an image into parts that represent different objects or regions.

Which color channels are commonly used to represent images?

A Black, White, Gray

B Yellow, Cyan, Magenta

C Alpha, Beta, Gamma

D Red, Green, Blue

Images are usually represented using RGB color channels.

Explain in your own words what computer vision is and why it is useful.

Think about how computers can 'see' and help us with pictures or videos.

Describe the difference between image classification and object detection with simple examples.

Imagine telling what is in a photo versus pointing out where things are.

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