CV applications (autonomous driving, medical, retail) in Computer Vision - 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]('road.jpg')

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Aresize

Bimshow

Cimwrite

Dimread

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow instead of imread will cause an error because imshow displays images, not loads them.

Using imwrite tries to save an image, not load it.

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.

Computer Vision

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

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Acv2.COLOR_BGR2GRAY

Bcv2.COLOR_RGB2BGR

Ccv2.COLOR_GRAY2BGR

Dcv2.COLOR_BGR2RGB

Attempts:

3 left

💡 Hint

Common Mistakes

Using COLOR_BGR2RGB changes color format but does not convert to grayscale.

Using COLOR_GRAY2BGR converts grayscale to color, which is the opposite.

The cv2.COLOR_BGR2GRAY flag converts a color image to grayscale.

3fill in blank

hard

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

Computer Vision

edges = cv2.Canny(image, [1], 150)

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A100

B'100'

CNone

Dimage

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string instead of a number causes a type error.

Passing None will cause the function to fail.

The Canny function requires numeric thresholds. Using a string or None causes errors.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps image names to their sizes if width {{BLANK_1}} 500 and height {{BLANK_2}} 500.

Computer Vision

sizes = {name: (img.shape[1], img.shape[0]) for name, img in images.items() if img.shape[1] [1] 500 and img.shape[0] [2] 500}

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A>

B<

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operators reverses the filter logic.

Using >= or <= changes the condition boundaries.

The code filters images with width greater than 500 and height less than 500.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps patient IDs to their diagnosis if confidence {{BLANK_1}} 0.8 and diagnosis {{BLANK_2}} 'positive' and patient ID {{BLANK_3}} starts with 'P'.

Computer Vision

results = {pid: diag for pid, (diag, conf) in data.items() if conf [1] 0.8 and diag [2] 'positive' and pid.[3]('P')}

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A>=

B==

Cstartswith

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' instead of '==' for diagnosis causes wrong filtering.

Using '>' instead of '>=' excludes confidence exactly 0.8.

Using a wrong string method for patient ID causes errors.

The code filters patients with confidence at least 0.8, diagnosis equal to 'positive', and IDs starting with 'P'.