Image as 2D signal in Signal Processing - Mini Project: Build & Apply

Image as 2D Signal

📖 Scenario: Imagine you have a small grayscale image represented as a grid of brightness values. Each value shows how bright a pixel is, from 0 (black) to 255 (white). We want to treat this image as a 2D signal and analyze it.

🎯 Goal: You will create a 2D array to hold the image data, set a brightness threshold, find pixels brighter than this threshold, and then print their positions and values.

📋 What You'll Learn

Create a 2D list called image with exact pixel brightness values

Create a variable called threshold with a specific brightness value

Use a nested for loop with variables row and col to find pixels brighter than threshold

Store the bright pixels as tuples of (row, col, brightness) in a list called bright_pixels

Print the bright_pixels list

💡 Why This Matters

🌍 Real World

Images are stored as grids of brightness or color values. Treating images as 2D signals helps in tasks like filtering, edge detection, and enhancement.

💼 Career

Understanding image data as 2D arrays is key for roles in computer vision, medical imaging, and any job involving image processing or analysis.

1

Create the 2D image data

Create a 2D list called image with these exact rows of pixel brightness values:
[100, 150, 200], [50, 255, 80], [90, 120, 60]

Signal Processing

# Create the 2D list called image with the exact pixel values
# Your code here

Hint

Use square brackets to create a list of lists. Each inner list is a row of pixels.

2

Set the brightness threshold

Create a variable called threshold and set it to 100 to select pixels brighter than this value.

Signal Processing

image = [
    [100, 150, 200],
    [50, 255, 80],
    [90, 120, 60]
]
# Create the threshold variable and set it to 100
# Your code here

Hint

Just assign the number 100 to the variable named threshold.

3

Find pixels brighter than threshold

Use nested for loops with variables row and col to go through image. Create a list called bright_pixels that stores tuples of (row, col, brightness) for pixels where brightness is greater than threshold.

Signal Processing

image = [
    [100, 150, 200],
    [50, 255, 80],
    [90, 120, 60]
]
threshold = 100
# Create bright_pixels list and fill it with (row, col, brightness) tuples for pixels > threshold
# Your code here

Hint

Use range(len(image)) to loop rows and range(len(image[row])) to loop columns. Check if pixel value is greater than threshold before adding.

4

Print the bright pixels

Write a print statement to display the bright_pixels list.

Signal Processing

image = [
    [100, 150, 200],
    [50, 255, 80],
    [90, 120, 60]
]
threshold = 100
bright_pixels = []
for row in range(len(image)):
    for col in range(len(image[row])):
        if image[row][col] > threshold:
            bright_pixels.append((row, col, image[row][col]))
# Print the bright_pixels list
# Your code here

Hint

Use print(bright_pixels) to show the list of bright pixels.

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