Image filtering (gaussian_filter) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the gaussian_filter function from scipy.ndimage.

SciPy

from scipy.ndimage import [1]

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

Amedian_filter

Bsobel_filter

Cgaussian_filter

Dlaplace_filter

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong filter function.

Forgetting to import from scipy.ndimage.

The gaussian_filter function is imported from scipy.ndimage to apply Gaussian blur to images.

2fill in blank

medium

Complete the code to apply a Gaussian filter with sigma=2 to the image array named 'img'.

SciPy

blurred_img = gaussian_filter(img, sigma=[1])

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

A2

B10

C5

D0.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using sigma=0.5 which blurs very little.

Using too large sigma values that blur too much.

The sigma parameter controls the amount of blur. Here, sigma=2 applies a moderate blur.

3fill in blank

hard

Fix the error in the code to correctly apply gaussian_filter to 'image' with sigma=1.5.

SciPy

filtered = gaussian_filter([1], sigma=1.5)

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

Aimage

Bpicture

Cimg

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable name that does not exist.

Typo in the variable name.

The variable holding the image data is named image. Using the correct variable name avoids errors.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each pixel value to its square if the value is greater than 100.

SciPy

{pixel: pixel[1]2 for pixel in pixels if pixel [2] 100}

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

A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of '**' for squaring.

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

Use ** to square the pixel value and > to filter pixels greater than 100.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each pixel's string representation to its value if the value is less than 50.

SciPy

{str([1]): [2] for [3] in pixels if [2] < 50}

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

Apixel

Bvalue

Dpix

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not converting the pixel to string for the key.

Use the same variable name pixel for key conversion, value, and iteration variable to keep the comprehension consistent.

Practice

1. What is the main purpose of using gaussian_filter in image processing?

easy

A. To detect edges in the image

B. To smooth the image by reducing noise

C. To convert the image to grayscale

D. To increase the image resolution

Solution

1. Step 1: Understand the function's role

   gaussian_filter applies a blur effect that smooths the image by averaging nearby pixels weighted by a Gaussian curve.

2. Step 2: Identify the effect on image quality

   This smoothing reduces noise and small details, making the image less sharp but cleaner.

3. Final Answer:

   To smooth the image by reducing noise -> Option B

4. Quick Check:

   Gaussian blur = noise reduction [OK]

Hint: Gaussian filter smooths images by blurring noise away [OK]

Common Mistakes:

• Thinking it increases resolution
• Confusing it with edge detection
• Assuming it changes color format

2. Which of the following is the correct way to import and apply a Gaussian filter with sigma=2 to a 2D numpy array named image?

easy

A. from scipy.ndimage import gaussian_filter
filtered = gaussian_filter(image, sigma=2)

B. import scipy
filtered = scipy.gaussian_filter(image, 2)

C. from scipy import gaussian_filter
filtered = gaussian_filter(image, 2)

D. import gaussian_filter from scipy.ndimage
filtered = gaussian_filter(image, sigma=2)

Solution

1. Step 1: Check the correct import statement

   The Gaussian filter is in scipy.ndimage, so import it with from scipy.ndimage import gaussian_filter.

2. Step 2: Verify function usage

   Apply it by calling gaussian_filter(image, sigma=2) to blur with sigma 2.

3. Final Answer:

   from scipy.ndimage import gaussian_filter filtered = gaussian_filter(image, sigma=2) -> Option A

4. Quick Check:

   Correct import and sigma usage = from scipy.ndimage import gaussian_filter filtered = gaussian_filter(image, sigma=2) [OK]

Hint: Import from scipy.ndimage, use sigma as keyword [OK]

Common Mistakes:

• Wrong import path for gaussian_filter
• Passing sigma as positional without keyword
• Using incorrect import syntax

3. Given the code below, what will be the output array after applying the Gaussian filter?

import numpy as np
from scipy.ndimage import gaussian_filter

image = np.array([[0, 0, 0],
                  [0, 10, 0],
                  [0, 0, 0]])
filtered = gaussian_filter(image, sigma=1)
print(np.round(filtered, 2))

medium

A. [[0.46 1.23 0.46]
[1.23 2.99 1.23]
[0.46 1.23 0.46]]

B. [[0 0 0]
[0 10 0]
[0 0 0]]

C. [[2.5 2.5 2.5]
[2.5 2.5 2.5]
[2.5 2.5 2.5]]

D. [[0.1 0.1 0.1]
[0.1 10 0.1]
[0.1 0.1 0.1]]

Solution

1. Step 1: Understand Gaussian filter effect on sparse image

   The single bright pixel (10) will blur into neighbors, spreading intensity smoothly.

2. Step 2: Calculate approximate blurred values

   Using sigma=1, the center pixel reduces from 10 to about 3, neighbors get values around 1.2 to 0.46.

3. Final Answer:

   [[0.46 1.23 0.46] [1.23 2.99 1.23] [0.46 1.23 0.46]] -> Option A

4. Quick Check:

   Blur spreads intensity smoothly = [[0.46 1.23 0.46] [1.23 2.99 1.23] [0.46 1.23 0.46]] [OK]

Hint: Gaussian blur spreads bright pixels softly to neighbors [OK]

Common Mistakes:

• Expecting no change in array
• Assuming uniform average instead of weighted blur
• Misreading sigma effect as sharpening

4. Identify the error in the following code snippet that applies a Gaussian filter:

import numpy as np
from scipy.ndimage import gaussian_filter

image = np.ones((5,5))
filtered = gaussian_filter(image, sigma='2')
print(filtered)

medium

A. gaussian_filter cannot be applied to numpy arrays

B. The array shape is invalid for gaussian_filter

C. Missing import for numpy

D. sigma should be a number, not a string

Solution

1. Step 1: Check parameter types

   The sigma parameter must be a numeric value (int or float), not a string.

2. Step 2: Identify the cause of error

   Passing '2' as a string causes a type error when the filter tries to compute the blur.

3. Final Answer:

   sigma should be a number, not a string -> Option D

4. Quick Check:

   Numeric sigma required, string causes error [OK]

Hint: Use numeric sigma, not string, to avoid errors [OK]

Common Mistakes:

• Passing sigma as string
• Assuming gaussian_filter needs special array types
• Ignoring import errors

5. You have a noisy grayscale image stored as a 2D numpy array. You want to smooth the image but keep edges relatively sharp. Which approach using gaussian_filter and its parameters is best?

hard

A. Apply gaussian_filter multiple times with sigma=3

B. Use a large sigma value like 5 to blur the image heavily

C. Use a small sigma value like 0.5 to reduce noise but preserve edges

D. Do not use gaussian_filter; use median filter instead

Solution

1. Step 1: Understand sigma effect on smoothing

   Small sigma values cause light blur, preserving edges better than large sigma which blurs heavily.

2. Step 2: Choose best sigma for noise reduction and edge preservation

   Using sigma=0.5 smooths noise but keeps edges sharper than larger sigma values or repeated blurring.

3. Final Answer:

   Use a small sigma value like 0.5 to reduce noise but preserve edges -> Option C

4. Quick Check:

   Small sigma = smooth noise + keep edges [OK]

Hint: Small sigma blurs less, preserving edges better [OK]

Common Mistakes:

• Using large sigma blurs edges too much
• Applying filter multiple times causes over-blur
• Confusing gaussian_filter with median filter