Image interpolation in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the interpolation function from scipy.

SciPy

from scipy import [1]

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

Aintegrate

Boptimize

Csignal

Dinterpolate

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong scipy submodule like optimize or integrate.

Forgetting to import the interpolate module.

The interpolate module in scipy contains functions for image interpolation.

2fill in blank

medium

Complete the code to create a 2D interpolation function from image data.

SciPy

interp_func = scipy.interpolate.RegularGridInterpolator((x, y), [1])

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

Ax

Binterp_func

Cimage_data

Dy

Attempts:

3 left

💡 Hint

Common Mistakes

Passing coordinate arrays instead of image data.

Using the interpolation function variable itself as input.

The interpolation function needs the original image data array to create the interpolator.

3fill in blank

hard

Fix the error in the code to interpolate the image at new points.

SciPy

new_values = interp_func([1])

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A[[x_new, y_new]]

B[x_new, y_new]

C(x_new, y_new)

Dx_new, y_new

Attempts:

3 left

💡 Hint

Common Mistakes

Passing coordinates as a tuple or flat list instead of a 2D array.

Not wrapping the coordinates in an outer list.

The RegularGridInterpolator expects a 2D array of points, so the new points must be inside a list of lists.

4fill in blank

hard

Fill both blanks to create a dictionary of interpolated pixel values for points where x is greater than 5.

SciPy

interp_points = {point: interp_func([point])[1] point in points if point[0] [2] 5}

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

A for

B >

C <

D if

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'if' instead of 'for' for the loop.

Using '<' instead of '>' for the condition.

The dictionary comprehension syntax requires 'for' before the loop variable, and the condition checks if the x-coordinate (point[0]) is greater than 5.

5fill in blank

hard

Fill all three blanks to create a new image by interpolating at scaled coordinates.

SciPy

scaled_image = [1]([2] * scale_factor, [3] * scale_factor, method='linear')

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

Ascipy.interpolate.interpn

Bx_coords

Cy_coords

Dimage_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using the image data as the first argument instead of the interpolation function.

Not scaling both x and y coordinates.

Using the wrong interpolation method.

The interpn function interpolates on a grid defined by coordinate arrays. We scale the x and y coordinates to get new points for interpolation.

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