Image interpolation in SciPy - Mini Project: Build & Apply

Image interpolation

📖 Scenario: You have a small grayscale image represented as a 2D array of pixel values. You want to make the image bigger smoothly by estimating new pixel values between the existing ones. This process is called image interpolation.Imagine you took a small photo and want to print it larger without it looking blocky or pixelated.

🎯 Goal: Use scipy to perform image interpolation and enlarge the image smoothly.

📋 What You'll Learn

Create a 2D numpy array called small_image with exact pixel values

Create a variable called zoom_factor to control the enlargement

Use scipy.ndimage.zoom with order=3 to interpolate the image

Print the shape of the original and the enlarged image

💡 Why This Matters

🌍 Real World

Image interpolation is used in photo editing, medical imaging, and satellite image processing to enlarge images without losing quality.

💼 Career

Understanding image interpolation is important for data scientists working with computer vision, image analysis, and machine learning tasks involving images.

1

Create the small image array

Create a 2D numpy array called small_image with these exact pixel values: [[10, 20, 30], [40, 50, 60], [70, 80, 90]].

SciPy

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

Hint

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

2

Set the zoom factor

Create a variable called zoom_factor and set it to 2 to enlarge the image by 2 times.

SciPy

import numpy as np
small_image = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
# Set zoom_factor to 2
# Your code here

Hint

Just assign the number 2 to the variable zoom_factor.

3

Interpolate the image using scipy

Import scipy.ndimage and use scipy.ndimage.zoom with order=3 to create a new variable large_image by zooming small_image by zoom_factor.

SciPy

import numpy as np
small_image = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
zoom_factor = 2
# Import scipy.ndimage and create large_image by zooming small_image with order=3
# Your code here

Hint

Use scipy.ndimage.zoom with order=3 for smooth cubic interpolation.

4

Print the shapes of original and enlarged images

Print the shape of small_image and the shape of large_image using two separate print statements.

SciPy

import numpy as np
import scipy.ndimage
small_image = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
zoom_factor = 2
large_image = scipy.ndimage.zoom(small_image, zoom=zoom_factor, order=3)
# Print the shapes of small_image and large_image
# Your code here

Hint

Use print(small_image.shape) and print(large_image.shape) to show the sizes.

Practice

1. What does image interpolation do when resizing an image using scipy.ndimage.zoom?

easy

A. It deletes pixels randomly to reduce image size.

B. It estimates new pixel values to make the resized image smooth.

C. It converts the image to grayscale automatically.

D. It changes the image format to JPEG.

Solution

1. Step 1: Understand image resizing

   When resizing, new pixels must be created or removed to fit the new size.

2. Step 2: Role of interpolation

   Interpolation estimates these new pixel values to keep the image smooth and avoid blockiness.

3. Final Answer:

   It estimates new pixel values to make the resized image smooth. -> Option B

4. Quick Check:

   Image interpolation = smooth pixel estimation [OK]

Hint: Interpolation fills new pixels smoothly when resizing images [OK]

Common Mistakes:

• Thinking interpolation deletes pixels randomly
• Confusing interpolation with color conversion
• Assuming interpolation changes image file format

2. Which of the following is the correct way to call scipy.ndimage.zoom to double the size of an image array img with linear interpolation?

easy

A. zoom(img, zoom=2, order=1)

B. zoom(img, scale=2, order=1)

C. zoom(img, zoom=2, interpolation='linear')

D. zoom(img, factor=2, order=1)

Solution

1. Step 1: Check parameter names in scipy.ndimage.zoom

   The correct parameter for resizing factor is zoom, not scale or factor.

2. Step 2: Check interpolation order

   Order=1 means linear interpolation, which is correct. The parameter interpolation does not exist.

3. Final Answer:

   zoom(img, zoom=2, order=1) -> Option A

4. Quick Check:

   zoom param + order=1 for linear [OK]

Hint: Use zoom= factor and order= interpolation level [OK]

Common Mistakes:

• Using wrong parameter names like scale or factor
• Using interpolation='linear' which is invalid
• Confusing order values with interpolation strings

3. Given the code below, what is the shape of zoomed_img?

import numpy as np
from scipy.ndimage import zoom
img = np.zeros((10, 10))
zoomed_img = zoom(img, zoom=1.5, order=3)

medium

A. (15, 10)

B. (10, 10)

C. (20, 20)

D. (15, 15)

Solution

1. Step 1: Understand zoom factor effect on shape

   The zoom factor 1.5 multiplies each dimension by 1.5. Original shape is (10, 10).

2. Step 2: Calculate new shape

   10 * 1.5 = 15 for both height and width, so new shape is (15, 15).

3. Final Answer:

   (15, 15) -> Option D

4. Quick Check:

   Shape scaled by 1.5 = (15, 15) [OK]

Hint: Multiply each dimension by zoom factor for new shape [OK]

Common Mistakes:

• Assuming shape stays same after zoom
• Rounding incorrectly to 20 instead of 15
• Mixing up dimensions and zoom factor

4. What is wrong with this code snippet for zooming an image with cubic interpolation?

from scipy.ndimage import zoom
zoomed = zoom(image, zoom=2, order='3')

medium

A. The zoom function does not support cubic interpolation.

B. The zoom parameter must be less than 1.

C. The order parameter should be an integer, not a string.

D. The image variable must be a list, not an array.

Solution

1. Step 1: Check the type of order parameter

   The order parameter expects an integer (0 to 5), not a string.

2. Step 2: Validate other parameters

   Zoom can be any positive number, cubic interpolation is order=3, and image can be an array.

3. Final Answer:

   The order parameter should be an integer, not a string. -> Option C

4. Quick Check:

   order must be int, not str [OK]

Hint: Use integer for order, not string [OK]

Common Mistakes:

• Passing order as string instead of int
• Thinking zoom must be less than 1
• Believing cubic interpolation unsupported
• Confusing image data type requirements

5. You want to resize a grayscale image stored in a 2D NumPy array img to 3 times its size using cubic interpolation. Which code snippet correctly achieves this and returns the resized image?

hard

A. zoomed_img = zoom(img, zoom=(3, 3), order=3)

B. zoomed_img = zoom(img, zoom=3, order='3')

C. zoomed_img = zoom(img, zoom=3, interpolation='cubic')

D. zoomed_img = zoom(img, scale=3, order=3)

Solution

1. Step 1: Understand zoom parameter for 2D arrays

   For 2D arrays, zoom can be a single float or a tuple for each axis. Using a tuple (3, 3) explicitly scales both dimensions by 3.

2. Step 2: Check interpolation order and parameter names

   Order=3 means cubic interpolation. Parameter interpolation and scale are invalid.

3. Step 3: Choose the best practice

   Using a tuple for zoom is clearer and recommended for 2D images.

4. Final Answer:

   zoomed_img = zoom(img, zoom=(3, 3), order=3) -> Option A

5. Quick Check:

   Tuple zoom + order=3 for cubic [OK]

Hint: Use tuple zoom for each axis and order=3 for cubic [OK]

Common Mistakes:

• Using invalid parameter names like scale or interpolation
• Passing zoom as single float without tuple (less explicit)
• Confusing order values with strings