Image as 2D signal in Signal Processing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image as a 2D grayscale array.

Signal Processing

import numpy as np
from PIL import Image
img = Image.open('image.jpg').convert([1])
img_array = np.array(img)

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

AL

BRGBA

CCMYK

DRGB

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'RGB' mode which keeps 3 color channels.

Using 'RGBA' which includes an alpha channel.

The 'L' mode converts the image to grayscale, which is a 2D signal representation.

2fill in blank

medium

Complete the code to access the pixel value at row 10, column 15.

Signal Processing

pixel_value = img_array[[1], 15]

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

A15

B10

C0

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping row and column indices.

Using column index first.

Row index is 10, so use img_array[10, 15] to get the pixel value.

3fill in blank

hard

Fix the error in the code to compute the average pixel intensity.

Signal Processing

average_intensity = np.[1](img_array)

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

Asum

Bmax

Cmedian

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sum() which sums all pixels instead of averaging.

Using np.max() which finds the brightest pixel.

Use np.mean() to calculate the average intensity of the image pixels.

4fill in blank

hard

Fill both blanks to create a dictionary of pixel intensities for pixels greater than 128.

Signal Processing

bright_pixels = { (i, j): img_array[i, j] for i in range(img_array.shape[[1]]) for j in range(img_array.shape[[2]]) if img_array[i, j] > 128 }

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

A0

B1

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the indices for rows and columns.

Using shape[2] which does not exist for 2D arrays.

img_array.shape[0] is rows, img_array.shape[1] is columns.

5fill in blank

hard

Fill all three blanks to create a new image array with pixel values normalized between 0 and 1.

Signal Processing

normalized_img = (img_array - [1]) / ([2] - [3])

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

Aimg_array.min()

Bimg_array.max()

Dimg_array.mean()

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean instead of min or max.

Subtracting max instead of min.

Normalization subtracts the minimum and divides by the range (max - min).

Practice

1. What does it mean to say an image is a 2D signal?

easy

A. It is a sound wave recorded over time.

B. It is made of pixels arranged in rows and columns.

C. It is a list of numbers without any order.

D. It is a single value representing brightness.

Solution

1. Step 1: Understand the structure of an image

   An image consists of pixels arranged in a grid with rows and columns.

2. Step 2: Define 2D signal in context

   A 2D signal means data varies in two dimensions, matching the rows and columns of pixels.

3. Final Answer:

   It is made of pixels arranged in rows and columns. -> Option B

4. Quick Check:

   Image = 2D pixel grid [OK]

Hint: Remember: images have width and height as 2D grids [OK]

Common Mistakes:

• Confusing image with 1D signals like audio
• Thinking image is just one brightness value
• Ignoring the grid arrangement of pixels

2. Which of the following is the correct way to represent a grayscale image in Python using a 2D list?

easy

A. image = (0, 255, 128, 64)

B. image = [0, 255, 128, 64]

C. image = {0:255, 1:128}

D. image = [[0, 255], [128, 64]]

Solution

1. Step 1: Identify 2D structure for image

   A grayscale image is a 2D array where each element is a pixel value.

2. Step 2: Check each option's structure

   image = [[0, 255], [128, 64]] uses a list of lists, representing rows and columns correctly.

3. Final Answer:

   image = [[0, 255], [128, 64]] -> Option D

4. Quick Check:

   2D list = correct image format [OK]

Hint: Use nested lists for rows and columns [OK]

Common Mistakes:

• Using 1D list instead of 2D
• Using dictionary which is unordered
• Using tuple which is immutable but not 2D

3. Given the Python code:

image = [[10, 20], [30, 40]]
brightness_sum = sum(sum(row) for row in image)
print(brightness_sum)

What is the output?

medium

A. 100

B. 40

C. None

D. TypeError

Solution

1. Step 1: Calculate sum of each row

   First row sum: 10 + 20 = 30; second row sum: 30 + 40 = 70.

2. Step 2: Sum the row sums

   Total sum = 30 + 70 = 100.

3. Final Answer:

   100 -> Option A

4. Quick Check:

   Sum of all pixels = 100 [OK]

Hint: Sum rows first, then sum those results [OK]

Common Mistakes:

• Summing only one row
• Confusing nested sums
• Expecting print to show None

4. Find the error in this code snippet that tries to access the pixel at row 1, column 2:

image = [[5, 10, 15], [20, 25, 30]]
pixel = image[1][2]
print(pixel)

medium

A. IndexError because column 2 does not exist

B. SyntaxError due to wrong brackets

C. No error; output is 30

D. TypeError because image is not a list

Solution

1. Step 1: Check image dimensions

   Image has 2 rows and 3 columns (indices 0 to 2).

2. Step 2: Access pixel at row 1, column 2

   image[1][2] accesses second row, third column, which is 30.

3. Final Answer:

   No error; output is 30 -> Option C

4. Quick Check:

   Valid index access = 30 [OK]

Hint: Remember indexing starts at 0 in Python [OK]

Common Mistakes:

• Thinking index 2 is out of range
• Confusing row and column order
• Assuming syntax error with brackets

5. You want to increase the brightness of a grayscale image by adding 50 to each pixel value without exceeding 255. Which Python code correctly does this for a 2D list image?

hard

A. bright_image = [[min(pixel + 50, 255) for pixel in row] for row in image]

B. bright_image = [pixel + 50 if pixel < 255 else 255 for pixel in image]

C. bright_image = [[pixel + 50 for pixel in row] for row in image]

D. bright_image = [[max(pixel + 50, 255) for pixel in row] for row in image]

Solution

1. Step 1: Understand brightness increase with limit

   We add 50 to each pixel but cap values at 255 to avoid overflow.

2. Step 2: Analyze each option

   bright_image = [[min(pixel + 50, 255) for pixel in row] for row in image]

   uses min(pixel + 50, 255) to cap values correctly.

   bright_image = [pixel + 50 if pixel < 255 else 255 for pixel in image]

   is wrong because it treats image as 1D.

   bright_image = [[pixel + 50 for pixel in row] for row in image]

   can exceed 255.

   bright_image = [[max(pixel + 50, 255) for pixel in row] for row in image]

   uses max which increases values beyond 255.

3. Final Answer:

   bright_image = [[min(pixel + 50, 255) for pixel in row] for row in image] -> Option A

4. Quick Check:

   Use min to cap brightness at 255 [OK]

Hint: Use min(pixel+50, 255) to avoid overflow [OK]

Common Mistakes:

• Not capping pixel values at 255
• Treating 2D image as 1D list
• Using max instead of min for capping