Edge detection (Canny) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read an image using OpenCV.

Computer Vision

import cv2

image = cv2.[1]('image.jpg')

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

Aimwrite

Bimshow

CcvtColor

Dimread

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.imshow instead of cv2.imread to load the image.

Using cv2.imwrite which saves an image instead of loading.

The cv2.imread function reads an image from a file into memory.

2fill in blank

medium

Complete the code to convert the image to grayscale.

Computer Vision

gray = cv2.[1](image, cv2.COLOR_BGR2GRAY)

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

Aimread

BcvtColor

Cblur

Dthreshold

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.imread again instead of converting color.

Using cv2.blur which smooths the image, not changes color.

cv2.cvtColor converts images between color spaces, here from BGR to grayscale.

3fill in blank

hard

Fix the error in the code to apply Canny edge detection.

Computer Vision

edges = cv2.Canny(gray, [1], 150)

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

A50

B'50'

CNone

Dgray

Attempts:

3 left

💡 Hint

Common Mistakes

Passing threshold as a string instead of integer.

Passing None which causes runtime error.

The first threshold must be a number, here 50, not a string or None.

4fill in blank

hard

Fill both blanks to display the edges image and wait for a key press.

Computer Vision

cv2.[1]('Edges', edges)
cv2.[2](0)

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

Aimshow

BwaitKey

Cimread

DdestroyAllWindows

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.imread instead of imshow to display.

Forgetting to wait for a key press causing window to close immediately.

cv2.imshow shows the image window, and cv2.waitKey(0) waits indefinitely for a key press.

5fill in blank

hard

Fill all three blanks to save the edges image, close windows, and print success message.

Computer Vision

cv2.[1]('edges_output.jpg', edges)
cv2.[2]()
print([3])

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

Aimwrite

BdestroyAllWindows

C'Edges image saved successfully!'

Dimshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow instead of imwrite to save image.

Not closing windows causing program to hang.

Printing without quotes causing error.

cv2.imwrite saves the image, cv2.destroyAllWindows() closes all windows, and print outputs the message.

Practice

1. What is the main purpose of the Canny edge detection algorithm in computer vision?

easy

A. To resize images without losing quality

B. To colorize black and white images

C. To blur an image for noise reduction

D. To find clear edges in an image by detecting boundaries

Solution

1. Step 1: Understand the goal of edge detection

   Edge detection aims to find where objects start and end by detecting sharp changes in brightness.

2. Step 2: Recognize Canny's role

   Canny edge detection specifically finds clear edges by using gradients and thresholds to highlight boundaries.

3. Final Answer:

   To find clear edges in an image by detecting boundaries -> Option D

4. Quick Check:

   Edge detection = finding boundaries [OK]

Hint: Edges show object borders clearly in images [OK]

Common Mistakes:

• Confusing edge detection with image coloring
• Thinking Canny blurs images
• Assuming it resizes images

2. Which of the following is the correct way to call the Canny edge detector function in OpenCV (Python)?

easy

A. cv2.Canny(image, threshold1, threshold2)

B. cv2.canny(image, threshold1, threshold2)

C. cv2.Canny(image, threshold2, threshold1)

D. cv2.Canny(image)

Solution

1. Step 1: Recall OpenCV function naming

   OpenCV functions are case-sensitive; the correct function is Canny with uppercase C.

2. Step 2: Check required parameters

   The function requires the image and two threshold values in order: low threshold first, then high threshold.

3. Final Answer:

   cv2.Canny(image, threshold1, threshold2) -> Option A

4. Quick Check:

   Correct function name and parameter order = A [OK]

Hint: Function names are case-sensitive; check parameter order [OK]

Common Mistakes:

• Using lowercase 'canny' instead of 'Canny'
• Swapping threshold1 and threshold2
• Omitting required threshold parameters

3. Given the following Python code snippet using OpenCV, what will be the shape of the output image after applying Canny edge detection?

import cv2
image = cv2.imread('photo.jpg')
edges = cv2.Canny(image, 100, 200)
print(edges.shape)

medium

A. (height, width)

B. (height, width, 3)

C. (width, height)

D. (height, width, 1)

Solution

1. Step 1: Understand input image shape

   Original image read by cv2.imread is usually (height, width, 3) for color images.

2. Step 2: Check output of cv2.Canny

   Canny outputs a single-channel (grayscale) edge map, so shape is (height, width) without color channels.

3. Final Answer:

   (height, width) -> Option A

4. Quick Check:

   Canny output is grayscale edges = (height, width) [OK]

Hint: Canny output is single-channel grayscale image [OK]

Common Mistakes:

• Assuming output keeps 3 color channels
• Confusing width and height order
• Expecting a 3D shape for edges

4. You run Canny edge detection with thresholds 50 and 150 but get too many noisy edges. Which fix below correctly reduces noise in the output?

medium

A. Use a color image instead of grayscale

B. Decrease both thresholds to lower values

C. Increase both thresholds to higher values

D. Remove Gaussian blur before Canny

Solution

1. Step 1: Understand threshold effect on noise

   Lower thresholds detect more edges including noise; higher thresholds reduce noise by ignoring weak edges.

2. Step 2: Choose correct adjustment

   Increasing thresholds filters out weak noisy edges, improving edge quality.

3. Final Answer:

   Increase both thresholds to higher values -> Option C

4. Quick Check:

   Higher thresholds reduce noise in edges [OK]

Hint: Higher thresholds filter out weak noisy edges [OK]

Common Mistakes:

• Lowering thresholds increases noise
• Using color images directly confuses Canny
• Skipping blur preprocessing increases noise

5. You want to detect edges on a noisy grayscale image using Canny. Which sequence of steps will best improve edge detection results?

hard

A. Apply median blur, then Canny with low thresholds, then erode edges

B. Apply Gaussian blur, then Canny with tuned thresholds, then dilate edges

C. Apply Canny directly with default thresholds, then convert to color

D. Resize image larger, then apply Canny with high thresholds, then invert edges

Solution

1. Step 1: Preprocess noisy image with Gaussian blur

   Gaussian blur smooths noise while preserving edges, improving Canny input.

2. Step 2: Apply Canny with tuned thresholds

   Adjust thresholds to balance edge detection and noise filtering.

3. Step 3: Use dilation to strengthen edges

   Dilation thickens edges, making them clearer for further processing.

4. Final Answer:

   Apply Gaussian blur, then Canny with tuned thresholds, then dilate edges -> Option B

5. Quick Check:

   Blur + tuned thresholds + dilation = best edge detection [OK]

Hint: Blur first, tune thresholds, then enhance edges [OK]

Common Mistakes:

• Using low thresholds increases noise
• Skipping blur causes noisy edges
• Converting to color after Canny is useless