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))

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

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 }

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

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.

Practice

1. Why do we convert images to grayscale before analysis in many computer vision tasks?

easy

A. To reduce the amount of data and simplify processing

B. To add color information for better accuracy

C. To increase the image size for detailed analysis

D. To make the image brighter and easier to see

Solution

1. Step 1: Understand grayscale conversion

   Converting to grayscale reduces the image from three color channels (RGB) to one channel, lowering data size.

2. Step 2: Recognize impact on processing

   Less data means faster and simpler analysis without losing important shape or texture information.

3. Final Answer:

   To reduce the amount of data and simplify processing -> Option A

4. Quick Check:

   Grayscale reduces data size = A [OK]

Hint: Grayscale means less data, easier analysis [OK]

Common Mistakes:

• Thinking grayscale adds color details
• Believing grayscale increases image size
• Confusing brightness adjustment with grayscale

2. Which of the following Python code snippets correctly resizes an image using OpenCV?

easy

A. resized = cv2.resize(image, (100))

B. resized = cv2.resize(image, 100, 100)

C. resized = cv2.resize(image, size=(100, 100))

D. resized = cv2.resize(image, (100, 100))

Solution

1. Step 1: Check OpenCV resize syntax

   The correct syntax requires the second argument as a tuple for size: (width, height).

2. Step 2: Validate each option

   resized = cv2.resize(image, (100, 100)) uses cv2.resize(image, (100, 100)) which is correct. Others have wrong argument formats.

3. Final Answer:

   resized = cv2.resize(image, (100, 100)) -> Option D

4. Quick Check:

   Resize needs tuple size = D [OK]

Hint: Resize needs size as (width, height) tuple [OK]

Common Mistakes:

• Passing size as separate arguments
• Using keyword 'size' which is invalid
• Passing a single integer instead of tuple

3. What will be the output shape of the image after this code runs?

import cv2
image = cv2.imread('photo.jpg')
resized = cv2.resize(image, (64, 64))
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
print(gray.shape)

medium

A. (64, 64, 3)

B. (3, 64, 64)

C. (64, 64)

D. (128, 128)

Solution

1. Step 1: Analyze resizing step

   The image is resized to 64x64 pixels with 3 color channels initially.

2. Step 2: Analyze grayscale conversion

   Converting to grayscale removes color channels, leaving a 2D array of shape (64, 64).

3. Final Answer:

   (64, 64) -> Option C

4. Quick Check:

   Grayscale image shape = (height, width) = B [OK]

Hint: Grayscale images have 2D shape, no color channels [OK]

Common Mistakes:

• Assuming grayscale keeps 3 channels
• Confusing shape order (channels first vs last)
• Ignoring resize effect on dimensions

4. The following code is intended to normalize an image's pixel values to the range 0 to 1. What is the error?

normalized = image / 255

medium

A. Division by 255 is correct; no error

B. Image must be converted to float before division

C. Should multiply by 255 instead of dividing

D. Normalization requires subtracting mean, not dividing

Solution

1. Step 1: Understand data type impact

   If image is integer type, dividing by 255 does integer division, resulting in zeros.

2. Step 2: Fix with float conversion

   Convert image to float type before division to get decimal normalized values.

3. Final Answer:

   Image must be converted to float before division -> Option B

4. Quick Check:

   Integer division causes zero values = A [OK]

Hint: Convert to float before dividing pixel values [OK]

Common Mistakes:

• Ignoring data type before division
• Thinking multiplying normalizes pixels
• Confusing normalization with mean subtraction

5. You have a dataset of images with different sizes and color formats. Which sequence of processing steps best prepares them for a neural network model expecting 64x64 grayscale inputs normalized between 0 and 1?

hard

A. Resize to 64x64, convert to grayscale, convert to float, divide by 255

B. Convert to grayscale, resize to 64x64, divide by 255, convert to float

C. Divide by 255, resize to 64x64, convert to grayscale, convert to float

D. Convert to grayscale, divide by 255, resize to 64x64, convert to float

Solution

1. Step 1: Resize before color conversion

   Resizing first ensures consistent image size for the model input.

2. Step 2: Convert to grayscale and normalize

   Convert to grayscale to reduce channels, then convert to float and divide by 255 to normalize pixel values between 0 and 1.

3. Final Answer:

   Resize to 64x64, convert to grayscale, convert to float, divide by 255 -> Option A

4. Quick Check:

   Resize -> Grayscale -> Float -> Normalize = C [OK]

Hint: Resize first, then grayscale, then float and normalize [OK]

Common Mistakes:

• Normalizing before float conversion
• Changing order of resize and grayscale incorrectly
• Skipping float conversion before normalization