Image interpolation in SciPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of resizing an image with scipy.ndimage.zoom

What is the shape of the output image after applying scipy.ndimage.zoom with a zoom factor of 2 on a 2D grayscale image of shape (50, 50)?

SciPy

import numpy as np
from scipy.ndimage import zoom

image = np.ones((50, 50))
zoomed_image = zoom(image, 2)
print(zoomed_image.shape)

A (51, 51)

B (50, 50)

C (25, 25)

D (100, 100)

💡 Hint

Think about how zoom factor affects each dimension size.

The zoom factor of 2 doubles each dimension size, so (50, 50) becomes (100, 100).

🧠 Conceptual

intermediate

Understanding interpolation order in scipy.ndimage.zoom

Which interpolation order in scipy.ndimage.zoom corresponds to cubic spline interpolation?

A order=1

B order=3

C order=0

D order=5

💡 Hint

Order 0 is nearest neighbor, order 1 is linear.

Order 3 corresponds to cubic spline interpolation in scipy.ndimage.zoom.

❓ data_output

advanced

Resulting pixel value after zoom with linear interpolation

Given a 1D array arr = np.array([0, 10, 20]), what is the output of scipy.ndimage.zoom(arr, 2, order=1)?

SciPy

import numpy as np
from scipy.ndimage import zoom

arr = np.array([0, 10, 20])
result = zoom(arr, 2, order=1)
print(result)

A [ 0. 5. 10. 15. 20.]

B [ 0. 10. 20.]

C [ 0. 7.5 15. 20.]

D [ 0. 3.3 6.6 10. 20.]

💡 Hint

Linear interpolation fills values evenly between points.

Zoom with factor 2 and linear interpolation doubles points, interpolating linearly between original values.

🔧 Debug

advanced

Identify the error in zooming a color image

What error occurs when running this code to zoom a color image with shape (100, 100, 3) using scipy.ndimage.zoom(image, 2)?

SciPy

import numpy as np
from scipy.ndimage import zoom

image = np.ones((100, 100, 3))
zoomed = zoom(image, 2)
print(zoomed.shape)

A ValueError: zoom() missing required argument 'zoom'

B TypeError: float object cannot be interpreted as an integer

C Output shape is (200, 200, 6) which is incorrect

D Output shape is (200, 200, 3) which is correct

💡 Hint

Check how zoom interprets a single float for multi-dimensional arrays.

Passing a single float applies zoom to all axes, including color channels, doubling channels from 3 to 6.

🚀 Application

expert

Choosing correct zoom parameters for RGB image resizing

You want to resize an RGB image of shape (120, 160, 3) to double its width and height but keep color channels unchanged using scipy.ndimage.zoom. Which zoom parameter is correct?

A (2, 2, 1)

B 2

C (2, 2, 2)

D (1, 1, 3)

💡 Hint

Color channels should not be scaled.

Zoom factor must be a tuple matching array dimensions; spatial axes doubled, channel axis kept 1.

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