Sobel and Laplace edge detection in SciPy - Mini Project: Build & Apply

Sobel and Laplace Edge Detection

📖 Scenario: You are working with a simple grayscale image represented as a 2D array. You want to detect edges in this image using two popular methods: Sobel and Laplace filters. These filters help highlight where the image brightness changes sharply, which usually means edges.

🎯 Goal: Build a small program that applies Sobel and Laplace edge detection filters on a given 2D image array and shows the results.

📋 What You'll Learn

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

Create a variable called axis to select the Sobel filter direction

Use scipy.ndimage.sobel to compute the Sobel edges along the chosen axis

Use scipy.ndimage.laplace to compute the Laplace edges

Print the Sobel and Laplace filtered arrays

💡 Why This Matters

🌍 Real World

Edge detection is used in photo editing, computer vision, and robotics to find object boundaries and shapes.

💼 Career

Understanding edge detection is important for roles in image processing, machine learning, and AI development.

1

Create the image array

Create a 2D numpy array called image with these exact values: [[10, 10, 10, 10, 10], [10, 50, 50, 50, 10], [10, 50, 100, 50, 10], [10, 50, 50, 50, 10], [10, 10, 10, 10, 10]]

SciPy

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

Hint

Use np.array([...]) to create the 2D array with the exact numbers.

2

Set the Sobel filter axis

Create a variable called axis and set it to 0 to apply the Sobel filter vertically.

SciPy

import numpy as np
image = np.array([[10, 10, 10, 10, 10],
                  [10, 50, 50, 50, 10],
                  [10, 50, 100, 50, 10],
                  [10, 50, 50, 50, 10],
                  [10, 10, 10, 10, 10]])
# Create the variable axis and set it to 0
# Your code here

Hint

Just write axis = 0 to select vertical edges.

3

Apply Sobel and Laplace filters

Import scipy.ndimage and use scipy.ndimage.sobel with image and axis to get sobel_edges. Then use scipy.ndimage.laplace with image to get laplace_edges.

SciPy

import numpy as np
image = np.array([[10, 10, 10, 10, 10],
                  [10, 50, 50, 50, 10],
                  [10, 50, 100, 50, 10],
                  [10, 50, 50, 50, 10],
                  [10, 10, 10, 10, 10]])
axis = 0
# Import scipy.ndimage and apply sobel and laplace filters
# Your code here

Hint

Use scipy.ndimage.sobel(image, axis=axis) and scipy.ndimage.laplace(image).

4

Print the edge detection results

Print the variables sobel_edges and laplace_edges to see the edge detection results.

SciPy

import numpy as np
import scipy.ndimage

image = np.array([[10, 10, 10, 10, 10],
                  [10, 50, 50, 50, 10],
                  [10, 50, 100, 50, 10],
                  [10, 50, 50, 50, 10],
                  [10, 10, 10, 10, 10]])

axis = 0

sobel_edges = scipy.ndimage.sobel(image, axis=axis)
laplace_edges = scipy.ndimage.laplace(image)
# Print sobel_edges and laplace_edges
# Your code here

Hint

Use print(sobel_edges) and print(laplace_edges) to show the results.

Practice

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

easy

A. To detect edges by highlighting horizontal and vertical changes

B. To blur the image and reduce noise

C. To increase the brightness of the image

D. To convert the image to grayscale

Solution

1. Step 1: Understand Sobel filter function

   The Sobel filter detects edges by calculating gradients in horizontal and vertical directions separately.

2. Step 2: Identify the purpose of edge detection

   Edges are found by highlighting where brightness changes sharply, which Sobel does by combining horizontal and vertical gradients.

3. Final Answer:

   To detect edges by highlighting horizontal and vertical changes -> Option A

4. Quick Check:

   Sobel = edge detection [OK]

Hint: Sobel finds edges by checking horizontal and vertical changes [OK]

Common Mistakes:

• Confusing Sobel with blurring filters
• Thinking Sobel changes brightness directly
• Mixing Sobel with color conversion

2. Which of the following is the correct way to apply the Sobel filter on a 2D image array named img using SciPy?

easy

A. scipy.ndimage.sobel(img, axis=2)

B. scipy.ndimage.laplace(img, axis=1)

C. scipy.ndimage.sobel(img, axis=0)

D. scipy.ndimage.gaussian_filter(img, sigma=1)

Solution

1. Step 1: Recall Sobel filter usage in SciPy

   The Sobel filter is applied using scipy.ndimage.sobel with the image and axis (0 for vertical, 1 for horizontal).

2. Step 2: Check axis validity

   For a 2D image, valid axes are 0 or 1. Axis=2 is invalid and will cause an error.

3. Final Answer:

   scipy.ndimage.sobel(img, axis=0) -> Option C

4. Quick Check:

   Sobel syntax = scipy.ndimage.sobel(img, axis) [OK]

Hint: Use axis 0 or 1 for 2D images with sobel [OK]

Common Mistakes:

• Using axis=2 on 2D images causes errors
• Confusing laplace and sobel functions
• Using gaussian_filter instead of sobel for edges

3. Given the following code, what is the shape of the output array edges?

import numpy as np
from scipy import ndimage
img = np.array([[1, 2, 1], [0, 1, 0], [2, 1, 2]])
sobel_x = ndimage.sobel(img, axis=0)
sobel_y = ndimage.sobel(img, axis=1)
edges = np.hypot(sobel_x, sobel_y)

medium

A. (3, 3)

B. (2, 2)

C. (3, 2)

D. (1, 3)

Solution

1. Step 1: Check input image shape

   The input image img is a 3x3 array, so shape is (3, 3).

2. Step 2: Understand Sobel output shape

   Sobel filter preserves the input shape, so sobel_x and sobel_y are also (3, 3).

3. Step 3: Calculate edges shape

   Using np.hypot combines two (3, 3) arrays element-wise, resulting in (3, 3).

4. Final Answer:

   (3, 3) -> Option A

5. Quick Check:

   Input shape = output shape for sobel [OK]

Hint: Sobel keeps input shape; hypot combines same shapes [OK]

Common Mistakes:

• Assuming output shape shrinks after sobel
• Confusing axis with shape dimensions
• Thinking hypot changes array shape

4. The following code attempts to apply the Laplace filter but raises an error. What is the mistake?

from scipy import ndimage
img = [[1, 2, 3], [4, 5, 6], [7, 8]]
laplace_img = ndimage.laplace(img)

medium

A. ndimage.laplace does not exist in SciPy

B. Laplace filter requires axis parameter

C. The image must be grayscale

D. The input image must be a NumPy array, not a list

Solution

1. Step 1: Check input type for ndimage filters

   ndimage functions require NumPy arrays, not Python lists, for image input.

2. Step 2: Identify error cause

   Passing a list causes a TypeError because ndimage.laplace expects an array.

3. Final Answer:

   The input image must be a NumPy array, not a list -> Option D

4. Quick Check:

   ndimage needs np.array input [OK]

Hint: Convert lists to np.array before filtering [OK]

Common Mistakes:

• Passing lists instead of arrays
• Thinking laplace needs axis parameter
• Believing laplace function is missing

5. You want to detect edges in a noisy grayscale image using SciPy. Which approach best combines Sobel and Laplace filters to improve edge detection?

hard

A. Apply Gaussian blur, then Sobel filter on axis=0 only

B. Apply Sobel filters on both axes, combine results, then apply Laplace to sharpen edges

C. Use only Laplace filter because Sobel does not work on noisy images

D. Apply Laplace filter first, then Sobel on axis=2

Solution

1. Step 1: Understand Sobel and Laplace roles

   Sobel detects edges by gradients on horizontal and vertical axes; combining both gives full edge info.

2. Step 2: Use Laplace to enhance edges

   Applying Laplace after Sobel can sharpen edges by detecting second-order changes.

3. Step 3: Evaluate options

   Apply Sobel filters on both axes, combine results, then apply Laplace to sharpen edges correctly combines Sobel on both axes and Laplace for sharpening; others misuse axis or filters.

4. Final Answer:

   Apply Sobel filters on both axes, combine results, then apply Laplace to sharpen edges -> Option B

5. Quick Check:

   Sobel + Laplace = better edge detection [OK]

Hint: Combine Sobel axes then Laplace for sharper edges [OK]

Common Mistakes:

• Applying Sobel on invalid axis
• Skipping combination of horizontal and vertical Sobel
• Using Laplace alone without Sobel for noisy images