Image thresholding (binary, adaptive, Otsu) in Computer Vision

Introduction

Image thresholding helps turn a color or gray image into a simple black and white image. This makes it easier to find shapes or objects.

To separate objects from the background in a photo.

To prepare images for reading text or numbers automatically.

To detect edges or shapes in simple images.

When you want to reduce image details to just black and white.

To clean up noisy images by choosing the right threshold method.

Syntax

Computer Vision

import cv2

# Binary thresholding
_, binary_img = cv2.threshold(src, thresh, maxval, cv2.THRESH_BINARY)

# Adaptive thresholding
adaptive_img = cv2.adaptiveThreshold(src, maxval, adaptiveMethod, thresholdType, blockSize, C)

# Otsu's thresholding
_, otsu_img = cv2.threshold(src, 0, maxval, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

src is the input grayscale image.

For adaptive thresholding, blockSize is the size of the area to calculate the threshold, and C is a constant subtracted from the mean or weighted mean.

Examples

This sets pixels above 127 to white (255) and below to black (0).

Computer Vision

_, binary_img = cv2.threshold(gray_img, 127, 255, cv2.THRESH_BINARY)

Threshold is calculated for each 11x11 block using the mean minus 2.

Computer Vision

adaptive_img = cv2.adaptiveThreshold(gray_img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)

Otsu automatically finds the best threshold value to separate foreground and background.

Computer Vision

_, otsu_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

Sample Model

This code creates a simple image with a bright square on a dark background. It applies three types of thresholding and counts how many pixels are white after each method.

Computer Vision

import cv2
import numpy as np

# Create a simple grayscale image with a dark and bright area
img = np.zeros((100, 100), dtype=np.uint8)
img[25:75, 25:75] = 150  # Bright square in the middle

# Binary thresholding
_, binary_img = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY)

# Adaptive thresholding
adaptive_img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)

# Otsu's thresholding
_, otsu_img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

# Print threshold values and sum of white pixels to compare
print("Binary threshold sum:", np.sum(binary_img == 255))
print("Adaptive threshold sum:", np.sum(adaptive_img == 255))
print("Otsu threshold sum:", np.sum(otsu_img == 255))

Important Notes

Binary thresholding uses one fixed value for the whole image.

Adaptive thresholding changes the threshold for different parts of the image, useful for uneven lighting.

Otsu's method finds the best threshold automatically by looking at the image histogram.

Summary

Image thresholding turns images into black and white for easier analysis.

Binary thresholding uses a fixed cutoff value.

Adaptive and Otsu methods help when lighting or colors vary across the image.

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