What computer vision encompasses - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image using OpenCV.

Computer Vision

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

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

Aresize

Bimshow

Cimwrite

Dimread

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow instead of imread

Using imwrite which saves images

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

2fill in blank

medium

Complete the code to convert an image to grayscale.

Computer Vision

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

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

Acv2.COLOR_BGR2RGB

Bcv2.COLOR_RGB2BGR

Ccv2.COLOR_BGR2GRAY

Dcv2.COLOR_GRAY2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using COLOR_BGR2RGB which changes color space but not to grayscale

cv2.COLOR_BGR2GRAY converts a color image to grayscale.

3fill in blank

hard

Fix the error in the code to detect edges using Canny.

Computer Vision

edges = cv2.Canny(image, [1], 100)

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

A50

B'50'

Cimage

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing threshold as a string

Passing None or image instead of a number

The first threshold for the Canny edge detector must be a number, not a string or None.

4fill in blank

hard

Fill both blanks to create a dictionary of image sizes for images with width greater than 100.

Computer Vision

sizes = {img: (img.shape[[1]], img.shape[[2]]) for img in images if img.shape[1] > 100}

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

A0

B1

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping height and width indices

Using invalid indices like 3

In OpenCV images, shape[1] is width and shape[0] is height.

5fill in blank

hard

Fill all three blanks to filter images by height and create a dictionary of their shapes.

Computer Vision

filtered = {img: img.shape for img in images if img.shape[[1]] [2] [3]

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

A0

B>

C200

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using width index 1 instead of height 0

Using '<' instead of '>'

Using wrong threshold value

This code filters images with height greater than 200 pixels and stores their shapes.

Practice

1. What is the main goal of computer vision?

easy

A. To help computers understand images and videos

B. To write programs faster

C. To improve internet speed

D. To create video games

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 computer vision.

3. Final Answer:

   To help computers understand images and videos -> Option A

4. Quick Check:

   Computer vision = understanding images/videos [OK]

Hint: Remember: computer vision means 'computer sees' [OK]

Common Mistakes:

• Confusing computer vision with programming speed
• Thinking it's about internet or games

2. Which of these is a common task in computer vision?

easy

A. Calculating taxes

B. Compiling code

C. Sending emails

D. Recognizing objects in images

Solution

1. Step 1: Identify tasks related to computer vision

   Computer vision tasks include recognizing objects, faces, and reading text from images or videos.

2. Step 2: Match options to these tasks

   Only Recognizing objects in images fits as it involves recognizing objects in images.

3. Final Answer:

   Recognizing objects in images -> Option D

4. Quick Check:

   Object recognition = computer vision task [OK]

Hint: Think about what computers 'see' in pictures [OK]

Common Mistakes:

• Choosing unrelated tasks like compiling or emailing
• Confusing computer vision with other computer tasks

3. Given this code snippet, what will it print?

import cv2
image = cv2.imread('cat.jpg')
print(type(image))

medium

A. <class 'numpy.ndarray'>

B. <class 'NoneType'>

C. <class 'str'>

D. Error: cv2 not found

Solution

1. Step 1: Understand cv2.imread output

   cv2.imread reads an image file and returns a numpy array representing the image pixels.

2. Step 2: Check the type printed

   Printing type(image) will show <class 'numpy.ndarray'> if the image loads correctly.

3. Final Answer:

   <class 'numpy.ndarray'> -> Option A

4. Quick Check:

   cv2.imread returns numpy array [OK]

Hint: cv2.imread returns image as numpy array [OK]

Common Mistakes:

• Thinking it returns NoneType if file exists
• Confusing with string type
• Assuming cv2 is missing

4. This code tries to detect faces. What is wrong?

import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface.xml')
image = cv2.imread('people.jpg')
faces = face_cascade.detectMultiScale(image)
print(len(faces))

medium

A. The cascade file name is incorrect or missing

B. cv2.imread should be cv2.readImage

C. detectMultiScale needs a grayscale image

D. print(len(faces)) should be print(faces.length)

Solution

1. Step 1: Check input type for detectMultiScale

   detectMultiScale requires a grayscale image, but the code passes a color image.

2. Step 2: Identify the fix

   Convert image to grayscale using cv2.cvtColor before detection.

3. Final Answer:

   detectMultiScale needs a grayscale image -> Option C

4. Quick Check:

   Face detection needs grayscale input [OK]

Hint: Face detection works on grayscale images only [OK]

Common Mistakes:

• Wrong cascade filename
• Using wrong cv2 function name
• Incorrect print syntax

5. You want to build a system that reads text from photos of street signs. Which computer vision task should you use?

hard

A. Image classification

B. Optical character recognition (OCR)

C. Object detection

D. Image segmentation

Solution

1. Step 1: Understand the task requirement

   Reading text from images means extracting characters and words from pictures.

2. Step 2: Match task to computer vision methods

   OCR is the process of recognizing text in images, perfect for reading street signs.

3. Final Answer:

   Optical character recognition (OCR) -> Option B

4. Quick Check:

   Text reading = OCR task [OK]

Hint: Text in images? Use OCR technology [OK]

Common Mistakes:

• Choosing object detection for text
• Confusing classification with text reading
• Using segmentation which separates regions