Why OpenCV is the standard CV library in Computer Vision - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the OpenCV library in Python.

Computer Vision

import [1] as cv

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

Acv2

Bcv

Copencv

Dopencv-python

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'opencv' instead of 'cv2' causes import errors.

Trying to import 'opencv-python' directly is incorrect.

The OpenCV library is imported in Python using 'cv2'.

2fill in blank

medium

Complete the code to read an image file using OpenCV.

Computer Vision

image = cv.[1]('image.jpg')

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

Aload

Bimread

Copen

Dread_image

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'load' or 'open' causes attribute errors.

Trying 'read_image' is not a valid OpenCV function.

The function 'imread' reads an image file into OpenCV.

3fill in blank

hard

Fix the error in the code to convert an image to grayscale.

Computer Vision

gray = cv.cvtColor(image, [1])

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

Acv.COLOR_BGR2GRAY

Bcv.COLOR_RGB2GRAY

Ccv.COLOR_BGR2RGB

Dcv.COLOR_GRAY2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'COLOR_RGB2GRAY' causes wrong color conversion.

Using 'COLOR_BGR2RGB' changes color space but not to grayscale.

To convert a BGR image to grayscale, use 'cv.COLOR_BGR2GRAY'.

4fill in blank

hard

Fill both blanks to resize an image to 100x100 pixels.

Computer Vision

resized = cv.resize(image, ([1], [2]))

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

A100

B200

C50

D150

Attempts:

3 left

💡 Hint

Common Mistakes

Using different numbers for width and height changes aspect ratio.

Using values other than 100 does not meet the requirement.

The size tuple (100, 100) resizes the image to 100 pixels width and height.

5fill in blank

hard

Fill all three blanks to display an image window and wait for a key press.

Computer Vision

cv.imshow('[1]', [2])
cv.[3](0)
cv.destroyAllWindows()

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

Awindow

Bimage

CwaitKey

Dshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'show' instead of 'waitKey' causes errors.

Passing wrong variable names causes runtime errors.

Use 'imshow' to show the image in a window named 'window', pass the image variable, then 'waitKey(0)' waits for a key press.

Practice

1. Why is OpenCV considered the standard library for computer vision tasks?

easy

A. Because it is free, easy to use, and works on many platforms

B. Because it only works on Windows

C. Because it requires expensive licenses

D. Because it only supports image editing, not video

Solution

1. Step 1: Understand OpenCV's accessibility

   OpenCV is free and open-source, making it easy for anyone to use without cost.

2. Step 2: Recognize platform support and usability

   It works on many platforms like Windows, Linux, and Mac, and supports many computer vision tasks.

3. Final Answer:

   Because it is free, easy to use, and works on many platforms -> Option A

4. Quick Check:

   OpenCV = Free + Easy + Cross-platform [OK]

Hint: Remember: free, easy, works everywhere [OK]

Common Mistakes:

• Thinking OpenCV is paid software
• Believing it only works on one OS
• Confusing it with image-only editors

2. Which of the following is the correct way to import OpenCV in Python?

easy

A. import cv2

B. import opencv

C. import cv

D. import open_cv

Solution

1. Step 1: Recall the official OpenCV Python package name

   The official Python package for OpenCV is called cv2.

2. Step 2: Check the import syntax

   The correct syntax to import OpenCV in Python is import cv2.

3. Final Answer:

   import cv2 -> Option A

4. Quick Check:

   OpenCV Python import = cv2 [OK]

Hint: OpenCV Python module is always cv2 [OK]

Common Mistakes:

• Using 'import opencv' which is incorrect
• Trying 'import cv' which is outdated
• Typing 'import open_cv' which does not exist

3. What will be the output of this OpenCV Python code snippet?

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

medium

A. <class 'NoneType'>

B. SyntaxError

C. <class 'list'>

D. <class 'numpy.ndarray'>

Solution

1. Step 1: Understand cv2.imread output

   The function cv2.imread reads an image and returns it as a NumPy array if the image is found.

2. Step 2: Check the type of the returned object

   Since the image is read successfully, type(img) will be numpy.ndarray.

3. Final Answer:

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

4. Quick Check:

   cv2.imread returns numpy.ndarray [OK]

Hint: cv2.imread returns a NumPy array if image loads [OK]

Common Mistakes:

• Assuming it returns NoneType without checking file existence
• Thinking it returns a list instead of ndarray
• Expecting a syntax error from correct code

4. Find the error in this OpenCV code snippet:

import cv2
img = cv2.imread('photo.png')
cvt_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
cv2.imshow('Image', cvt_img)
cv2.waitKey()

medium

A. cv2.COLOR_BGR2RGB is not a valid color conversion code

B. cv2.imread should be cv2.readImage

C. Missing cv2.destroyAllWindows() to close the window

D. cv2.waitKey() requires an argument

Solution

1. Step 1: Check function names and parameters

   cv2.imread and cv2.cvtColor usage are correct; cv2.COLOR_BGR2RGB is valid.

2. Step 2: Identify missing cleanup step

   After cv2.imshow and cv2.waitKey, it is best practice to call cv2.destroyAllWindows() to close the display window properly.

3. Final Answer:

   Missing cv2.destroyAllWindows() to close the window -> Option C

4. Quick Check:

   Always call destroyAllWindows() after waitKey() [OK]

Hint: Always add destroyAllWindows() after waitKey() [OK]

Common Mistakes:

• Thinking cv2.imread is misspelled
• Believing COLOR_BGR2RGB is invalid
• Assuming waitKey() must have argument

5. You want to detect faces in a video using OpenCV. Which feature makes OpenCV the best choice for this task?

hard

A. It requires manual coding of face detection algorithms from scratch

B. It has built-in pre-trained classifiers for face detection

C. It only supports static images, not video

D. It cannot process video frames in real-time

Solution

1. Step 1: Understand OpenCV's face detection capabilities

   OpenCV includes pre-trained classifiers like Haar cascades that simplify face detection.

2. Step 2: Recognize real-time video processing support

   OpenCV can process video frames quickly, enabling real-time face detection.

3. Final Answer:

   It has built-in pre-trained classifiers for face detection -> Option B

4. Quick Check:

   OpenCV = Pre-trained face detectors + real-time video [OK]

Hint: Look for built-in classifiers for fast face detection [OK]

Common Mistakes:

• Thinking OpenCV can't handle video
• Believing face detection needs full manual coding
• Assuming OpenCV is slow for real-time tasks