CV project workflow in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load images from a folder using OpenCV.

Computer Vision

import cv2
image = cv2.imread([1])
print(image.shape)

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

Afolder_path

Bimage.jpg

Ccv2.imread

D'image.jpg'

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename

Passing a variable name without defining it

We need to provide the image filename as a string to cv2.imread. So, the correct answer is a string with quotes.

2fill in blank

medium

Complete the code to resize an image to 100x100 pixels.

Computer Vision

resized_image = cv2.resize(image, [1])
print(resized_image.shape)

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A(100)

B(100, 100)

C[100, 100]

D100, 100

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single integer instead of a tuple

Using a list instead of a tuple

The cv2.resize function expects the new size as a tuple (width, height). So, (100, 100) is correct.

3fill in blank

hard

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

Computer Vision

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

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Acv2.COLOR_BGR2GRAY

Bcv2.COLOR_BGR2RGB

Ccv2.COLOR_RGB2GRAY

Dcv2.COLOR_GRAY2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using RGB instead of BGR conversion code

Using grayscale to BGR conversion code

To convert a color image to grayscale, use cv2.COLOR_BGR2GRAY because OpenCV loads images in BGR format.

4fill in blank

hard

Fill both blanks to create a dictionary of image filenames and their sizes (width, height).

Computer Vision

image_sizes = {filename: (image.shape[[1]], image.shape[[2]]) for filename, image in images.items()}

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A1

B0

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping width and height indexes

Using channel index 2 instead of width or height

In OpenCV images, shape[0] is height and shape[1] is width. The dictionary stores (width, height), so width is shape[1] and height is shape[0].

5fill in blank

hard

Fill all three blanks to filter images with width greater than 200 and create a new dictionary with filename and width.

Computer Vision

filtered_images = {filename: image.shape[[1]] for filename, image in images.items() if image.shape[[2]] [3] 200}

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

A0

B1

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using height index 0 instead of width

Using less than operator instead of greater than

Width is shape[1]. We want images where width is greater than 200, so the condition is image.shape[1] > 200. The dictionary stores filename and width.