Image thresholding (binary, adaptive, Otsu) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply a simple binary threshold to the grayscale image.

Computer Vision

_, binary_img = cv2.threshold(gray_img, [1], 255, cv2.THRESH_BINARY)

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

A1

B255

C0

D127

Attempts:

3 left

💡 Hint

Common Mistakes

Using 255 as threshold makes all pixels white.

Using 0 as threshold makes all pixels white.

The threshold value for binary thresholding is typically set around 127 for 8-bit images. Pixels above 127 become white (255), below become black (0).

2fill in blank

medium

Complete the code to apply adaptive mean thresholding with a block size of 11.

Computer Vision

adaptive_img = cv2.adaptiveThreshold(gray_img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, [1], 2)

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

A5

B11

C3

D7

Attempts:

3 left

💡 Hint

Common Mistakes

Using even numbers for block size causes errors.

Using too small block size may cause noisy results.

The block size defines the size of the neighborhood area used to calculate the threshold for adaptive thresholding. It must be an odd number, here 11.

3fill in blank

hard

Fix the error in the code to correctly compute Otsu's threshold value.

Computer Vision

otsu_thresh_val, otsu_img = cv2.threshold(gray_img, 0, 255, [1] + cv2.THRESH_OTSU)

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

Acv2.THRESH_BINARY

Bcv2.THRESH_TRUNC

Ccv2.THRESH_TOZERO

Dcv2.THRESH_BINARY_INV

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-binary threshold types with Otsu causes incorrect results.

Using THRESH_BINARY_INV changes foreground/background.

Otsu's method is combined with a binary threshold type, so cv2.THRESH_BINARY + cv2.THRESH_OTSU is correct.

4fill in blank

hard

Fill both blanks to create a dictionary with threshold types and their OpenCV constants.

Computer Vision

threshold_types = {'binary': [1], 'adaptive_mean': [2]

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

Acv2.THRESH_BINARY

Bcv2.ADAPTIVE_THRESH_MEAN_C

Ccv2.THRESH_BINARY_INV

Dcv2.ADAPTIVE_THRESH_GAUSSIAN_C

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing adaptive mean and Gaussian constants.

Using inverted binary threshold for 'binary' key.

Binary threshold uses cv2.THRESH_BINARY, adaptive mean threshold uses cv2.ADAPTIVE_THRESH_MEAN_C.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps image names to their thresholded images if mean pixel value is above 100.

Computer Vision

result = {name: img for name, img in images.items() if img[1]mean() [2] [3]

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

A.

B>

C100

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '>' for comparison.

Missing the '.' before mean().

The code accesses the mean method with '.', then checks if mean is greater than 100.

Practice

1. What is the main purpose of image thresholding in computer vision?

easy

A. To convert an image into black and white for easier analysis

B. To increase the color depth of an image

C. To blur the image for noise reduction

D. To resize the image to smaller dimensions

Solution

1. Step 1: Understand image thresholding

   Image thresholding simplifies images by turning pixels into black or white based on a cutoff value.

2. Step 2: Identify the purpose

   This simplification helps in easier analysis like object detection or segmentation.

3. Final Answer:

   To convert an image into black and white for easier analysis -> Option A

4. Quick Check:

   Image thresholding = black and white conversion [OK]

Hint: Thresholding means black and white conversion [OK]

Common Mistakes:

• Confusing thresholding with image resizing
• Thinking thresholding increases color depth
• Mixing thresholding with blurring

2. Which of the following is the correct syntax to apply binary thresholding using OpenCV in Python?

easy

A. ret, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

B. ret, thresh = cv2.adaptiveThreshold(image, 127, 255, cv2.THRESH_BINARY)

C. thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

D. ret, thresh = cv2.threshold(image, 255, 127, cv2.THRESH_BINARY)

Solution

1. Step 1: Recall OpenCV binary threshold syntax

   The function cv2.threshold returns two values: the threshold used and the thresholded image.

2. Step 2: Check parameter order and function call

   Correct call is cv2.threshold(image, threshold_value, max_value, threshold_type).

3. Final Answer:

   ret, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY) -> Option A

4. Quick Check:

   cv2.threshold returns two values [OK]

Hint: cv2.threshold returns two values: ret and image [OK]

Common Mistakes:

• Using adaptiveThreshold instead of threshold for binary
• Not unpacking two return values
• Swapping threshold and max values

3. Given the following code snippet, what will be the value of ret after applying Otsu's thresholding?

import cv2
image = cv2.imread('image.jpg', 0)
ret, thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
print(ret)

medium

A. The fixed threshold value 0

B. Always 255

C. The optimal threshold value found by Otsu's method

D. The maximum pixel value in the image

Solution

1. Step 1: Understand Otsu's thresholding output

   When using cv2.THRESH_OTSU, the function ignores the input threshold (0 here) and calculates an optimal threshold automatically.

2. Step 2: Identify what ret holds

   The variable ret stores the threshold value found by Otsu's method, not the input or max pixel value.

3. Final Answer:

   The optimal threshold value found by Otsu's method -> Option C

4. Quick Check:

   Otsu returns optimal threshold in ret [OK]

Hint: Otsu's ret is the best threshold found [OK]

Common Mistakes:

• Assuming ret is always 0 or max pixel value
• Confusing input threshold with output
• Thinking ret is the thresholded image

4. Identify the error in this adaptive thresholding code snippet and select the correct fix:

import cv2
image = cv2.imread('image.jpg', 0)
thresh = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 6, 2)

medium

A. Image must be read in color mode, not grayscale

B. Max value should be 127 instead of 255

C. Use cv2.THRESH_OTSU instead of cv2.THRESH_BINARY

D. Block size must be an odd number greater than 1; change 6 to 7

Solution

1. Step 1: Check adaptiveThreshold parameters

   The block size parameter must be an odd number greater than 1 to define the neighborhood size.

2. Step 2: Identify the error in block size

   The block size is 6, which is even and will cause a runtime error. It must be changed to an odd number greater than 1, such as 7.

3. Final Answer:

   Block size must be an odd number greater than 1; change 6 to 7 -> Option D

4. Quick Check:

   Block size odd and >1 [OK]

Hint: Block size in adaptiveThreshold must be odd > 1 [OK]

Common Mistakes:

• Using even block size causing runtime error
• Confusing max value with threshold value
• Reading image in color instead of grayscale

5. You have an image with uneven lighting. Which thresholding method should you choose to get the best binary segmentation, and why?

hard

A. Binary thresholding with a fixed value, because it is simple and fast

B. Adaptive thresholding, because it calculates thresholds locally for different regions

C. Otsu's thresholding, because it finds a global optimal threshold automatically

D. No thresholding, just use the original image

Solution

1. Step 1: Understand the problem of uneven lighting

   Uneven lighting means different parts of the image have different brightness levels, making a single global threshold ineffective.

2. Step 2: Compare thresholding methods

   Binary thresholding uses one fixed value, which fails with uneven lighting. Otsu's method finds one global threshold, also insufficient. Adaptive thresholding calculates thresholds for small regions, handling uneven lighting well.

3. Final Answer:

   Adaptive thresholding, because it calculates thresholds locally for different regions -> Option B

4. Quick Check:

   Uneven lighting = adaptive thresholding best [OK]

Hint: Uneven light? Use adaptive thresholding [OK]

Common Mistakes:

• Choosing global threshold methods for uneven lighting
• Ignoring lighting variation in images
• Skipping thresholding and using raw image