Basic image manipulation with arrays in NumPy - Mini Project: Build & Apply

Basic image manipulation with arrays

📖 Scenario: You have a small grayscale image represented as a 2D array of pixel brightness values. Each value ranges from 0 (black) to 255 (white). You want to perform simple image manipulations using arrays.

🎯 Goal: Learn how to create a numpy array to represent an image, set a brightness threshold, create a mask to highlight bright pixels, and display the result.

📋 What You'll Learn

Use numpy to create and manipulate arrays

Create a 2D numpy array with exact pixel values

Define a brightness threshold variable

Use array operations to create a mask of bright pixels

Print the resulting mask array

💡 Why This Matters

🌍 Real World

Image processing is used in photography, medical imaging, and computer vision to analyze and enhance pictures.

💼 Career

Understanding how to manipulate images as arrays is a key skill for data scientists working with image data or machine learning.

1

Create the image array

Create a 2D numpy array called image with these exact pixel values: [[10, 200, 30], [255, 100, 50], [0, 180, 220]].

NumPy

import numpy as np
# Create the 2D numpy array called image with the given pixel values
# Your code here

Hint

Use np.array() to create the 2D array with the exact values.

2

Set the brightness threshold

Create a variable called threshold and set it to 150.

NumPy

import numpy as np
image = np.array([[10, 200, 30], [255, 100, 50], [0, 180, 220]])
# Create the variable threshold and set it to 150
# Your code here

Hint

Just assign the number 150 to the variable threshold.

3

Create a mask for bright pixels

Create a variable called bright_mask that is a boolean numpy array. It should be True where image pixels are greater than threshold, and False otherwise.

NumPy

import numpy as np
image = np.array([[10, 200, 30], [255, 100, 50], [0, 180, 220]])
threshold = 150
# Create bright_mask as a boolean array where image > threshold
# Your code here

Hint

Use the comparison operator > between image and threshold to get a boolean array.

4

Print the bright pixel mask

Print the variable bright_mask to display the boolean mask array.

NumPy

import numpy as np
image = np.array([[10, 200, 30], [255, 100, 50], [0, 180, 220]])
threshold = 150
bright_mask = image > threshold
# Print the bright_mask array
# Your code here

Hint

Use print(bright_mask) to show the boolean array.

Practice

1. What does a 2D numpy array represent when used for a grayscale image?

easy

A. Pixel brightness values arranged in rows and columns

B. A list of image file names

C. The color channels of an image

D. The size of the image in bytes

Solution

1. Step 1: Understand image representation

   A grayscale image is made of pixels arranged in rows and columns, each pixel having a brightness value.

2. Step 2: Connect to numpy array structure

   A 2D numpy array stores these brightness values in a grid matching the image layout.

3. Final Answer:

   Pixel brightness values arranged in rows and columns -> Option A

4. Quick Check:

   Grayscale image = 2D pixel brightness array [OK]

Hint: Grayscale images use 2D arrays for pixel brightness [OK]

Common Mistakes:

• Confusing pixel values with file names
• Thinking 2D arrays store color channels
• Mixing image size with pixel data

2. Which numpy code correctly flips a 2D image array img vertically?

easy

A. np.fliplr(img)

B. np.flip(img, axis=1)

C. np.flip(img, axis=0)

D. np.flip(img)

Solution

1. Step 1: Understand vertical flip axis

   Vertical flip reverses rows, which corresponds to axis 0 in numpy arrays.

2. Step 2: Match numpy functions to axis

   np.flip(img, axis=0) flips vertically.

3. Final Answer:

   np.flip(img, axis=0) -> Option C

4. Quick Check:

   Vertical flip = axis 0 flip [OK]

Hint: Vertical flip uses axis=0 in np.flip [OK]

Common Mistakes:

• Using axis=1 flips horizontally
• np.flip(img) without axis reverses all axes
• Forgetting axis parameter in np.flip

3. Given this code:

import numpy as np
img = np.array([[10, 20], [30, 40]])
new_img = img + 5
print(new_img)

What is the output?

medium

A. [[10 20]
[30 40]]

B. [[5 15]
[25 35]]

C. [[50 60]
[70 80]]

D. [[15 25]
[35 45]]

Solution

1. Step 1: Understand numpy array addition

   Adding 5 to a numpy array adds 5 to every element (broadcasting).

2. Step 2: Calculate new array values

   Each element increases by 5: 10+5=15, 20+5=25, 30+5=35, 40+5=45.

3. Final Answer:

   [[15 25] [35 45]] -> Option D

4. Quick Check:

   Array + 5 adds 5 to all elements [OK]

Hint: Adding scalar adds to all pixels in array [OK]

Common Mistakes:

• Thinking addition changes shape
• Adding 5 only to first row or column
• Confusing addition with multiplication

4. What is wrong with this code to crop the top-left 2x2 part of a 3x3 image array img?:

cropped = img[0:2, 0:3]

medium

A. It crops 2 rows but 3 columns, not 2 columns

B. It causes an index error

C. It flips the image instead of cropping

D. It returns the whole image

Solution

1. Step 1: Analyze slicing indices

   The slice img[0:2, 0:3] selects rows 0 and 1 (2 rows) and columns 0,1,2 (3 columns).

2. Step 2: Compare to desired crop size

   We want a 2x2 crop, but this selects 2x3, so it includes one extra column.

3. Final Answer:

   It crops 2 rows but 3 columns, not 2 columns -> Option A

4. Quick Check:

   Slicing columns 0:3 selects 3 columns [OK]

Hint: Check slice ranges match desired rows and columns [OK]

Common Mistakes:

• Assuming slicing is exclusive of end index
• Expecting error from out-of-range slice
• Confusing rows and columns order

5. You have a color image as a 3D numpy array img with shape (100, 100, 3). How do you create a new image gray_img by averaging the color channels for each pixel?

hard

A. gray_img = img.sum(axis=0)

B. gray_img = img.mean(axis=2)

C. gray_img = img[:, :, 0]

D. gray_img = np.flip(img, axis=1)

Solution

1. Step 1: Understand image shape and channels

   The shape (100, 100, 3) means 100x100 pixels with 3 color channels (RGB) along axis 2.

2. Step 2: Average color channels per pixel

   Using img.mean(axis=2) calculates the average of the 3 channels for each pixel, producing a 2D grayscale image.

3. Final Answer:

   gray_img = img.mean(axis=2) -> Option B

4. Quick Check:

   Mean over axis 2 averages color channels [OK]

Hint: Use mean(axis=2) to average RGB channels [OK]

Common Mistakes:

• Summing over wrong axis
• Selecting only one color channel
• Using flip instead of averaging