Why processing prepares images for analysis in Computer Vision - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert an image to grayscale.

Computer Vision

gray_image = cv2.[1](color_image, cv2.COLOR_BGR2GRAY)

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

AcvtColor

Bresize

Cthreshold

Dblur

Attempts:

3 left

💡 Hint

Common Mistakes

Using resize instead of color conversion

Using blur which smooths images

The function cvtColor converts the image color space, here from BGR to grayscale.

2fill in blank

medium

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

Computer Vision

resized_image = cv2.[1](original_image, (100, 100))

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Athreshold

BcvtColor

Cresize

Dblur

Attempts:

3 left

💡 Hint

Common Mistakes

Using cvtColor which changes color space

Using threshold which changes pixel values

The resize function changes the image size to the given dimensions.

3fill in blank

hard

Fix the error in the code to apply a binary threshold to a grayscale image.

Computer Vision

_, binary_image = cv2.[1](gray_image, 127, 255, cv2.THRESH_BINARY)

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

Aresize

Bthreshold

CcvtColor

Dblur

Attempts:

3 left

💡 Hint

Common Mistakes

Using resize which changes image size

Using cvtColor which changes color space

The threshold function applies a binary threshold to the grayscale image.

4fill in blank

hard

Fill in the blank to create a dictionary of pixel intensities for pixels greater than 100.

Computer Vision

pixel_dict = { (x, y): image[x, y] for x in range(image.shape[0]) for y in range(image.shape[1]) if image[x, y] [1] 100 }

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A>

B==

C<

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which selects darker pixels

Using '==' which selects pixels equal to 100

The dictionary comprehension collects pixels with intensity greater than 100.

5fill in blank

hard

Fill all three blanks to create a dictionary of pixel intensities for pixels less than 50.

Computer Vision

pixel_dict = { ([1], [2]): image[[1], [2]] for [1] in range(image.shape[0]) for [2] in range(image.shape[1]) if image[[1], [2]] [3] 50 }

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

Ax

By

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' which selects brighter pixels

Using wrong variable names causing errors

The dictionary comprehension uses variables x and y to iterate over image pixels and selects pixels with intensity less than 50.