Image thresholding (binary, adaptive, Otsu) in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of binary thresholding with OpenCV

What is the shape and unique values of the output image after applying binary thresholding with threshold=127 on a grayscale image of shape (100, 100) with pixel values ranging from 0 to 255?

Computer Vision

import cv2
import numpy as np
img = np.random.randint(0, 256, (100, 100), dtype=np.uint8)
_, thresh_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
unique_vals = np.unique(thresh_img)
output_shape = thresh_img.shape
print(output_shape, unique_vals)

A (100, 100, 3) and [0 255]

B (100, 100) and [0 255]

C (100, 100) and [0 127]

D (100, 100) and [127 255]

💡 Hint

Binary thresholding converts pixels to either 0 or max value based on threshold.

Binary thresholding sets pixels above threshold to max value (255) and below to 0, keeping the image shape unchanged.

🧠 Conceptual

intermediate

Understanding adaptive thresholding

Which statement best describes adaptive thresholding compared to global binary thresholding?

A Adaptive thresholding requires the image to be color, not grayscale.

B Adaptive thresholding uses a fixed threshold for the entire image.

C Adaptive thresholding calculates threshold for small regions, handling varying lighting.

D Adaptive thresholding always produces a binary image with only zeros.

💡 Hint

Think about how lighting changes across an image.

Adaptive thresholding computes thresholds locally for small areas, making it good for images with uneven lighting.

❓ Metrics

advanced

Evaluating Otsu's thresholding output

After applying Otsu's thresholding on a bimodal grayscale image, what metric best indicates the quality of the threshold?

A Maximizing inter-class variance between foreground and background

B Minimizing mean squared error of pixel intensities

C Maximizing the number of pixels set to zero

D Minimizing the image histogram entropy

💡 Hint

Otsu's method tries to separate two classes clearly.

Otsu's method finds the threshold that maximizes the variance between two classes (foreground and background), improving separation.

🔧 Debug

advanced

Identifying error in adaptive thresholding code

What error will this code raise? import cv2 import numpy as np img = np.random.randint(0, 256, (50, 50), dtype=np.uint8) thresh = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 2, 5)

A cv2.error: blockSize must be odd and greater than 1

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C No error, code runs successfully

D ValueError: image must be 3-channel color

💡 Hint

Check the blockSize parameter requirements in adaptiveThreshold.

The blockSize parameter must be an odd number greater than 1; 2 is invalid and causes an OpenCV error.

❓ Model Choice

expert

Choosing thresholding method for uneven lighting

You have a grayscale image with strong shadows and bright spots. Which thresholding method is best to segment the foreground accurately?

A Global binary thresholding with a fixed threshold

B Otsu's thresholding

C No thresholding, use raw pixel values

D Adaptive thresholding with Gaussian weighting

💡 Hint

Consider how local lighting variations affect thresholding.

Adaptive thresholding with Gaussian weighting adjusts thresholds locally, handling uneven lighting better than global or Otsu's methods.

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