Simple Thresholding using OpenCV

Thresholding is a foundational technique in computer vision and image processing used to segment objects from the background. It works by comparing each pixel value of a grayscale image against a specified threshold value. Based on this comparison, pixels are assigned new values, usually 0 (black) or 255 (white).In OpenCV with Python, the function cv2.threshold is used for thresholding. 

In thresholding, for every pixel at position (x,y) with an intensity value f(x,y): 

• If f(x,y) < T, set the pixel to 0 (black).
• If f(x,y) ≥ T, set it to the maximum value (typically 255, white).
• Here, T is the threshold value, and the process is usually performed on a grayscale version of the image

Step-by-Step Implementation

Let's implement the various types of simple thresholding techniques,

Step 1: Import libraries and Image Preparation

Sample image can be downloaded from here.

Let's import the required libraries and load our image on which we will perform the operations,

• cv2: Handles image reading, processing, and applies thresholding techniques.
• numpy: Supports efficient array operations, enabling fast image data handling.
• matplotlib.pyplot: Displays images and results in Colab notebooks.

import cv2
import numpy as np
import matplotlib.pyplot as plt

image = cv2.imread('input.png')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

Step 2: Helper Function

Define the helper function which helps in displaying the images,

def show_image(img, title):
    plt.imshow(img, cmap='gray')
    plt.title(title)
    plt.axis('off')
    plt.show()

Step 3: Display the Original Image

show_image(gray_image, 'Original Grayscale Image')

Output:

Step 4: Binary Threshold

• If a pixel’s intensity is above the threshold, it is assigned the maximum value (usually 255; white)—otherwise, it becomes 0 (black).
• Useful for separating bright objects from a dark background.

_, thresh_binary = cv2.threshold(gray_image, 120, 255, cv2.THRESH_BINARY)
show_image(thresh_binary, 'Binary Threshold ')

Output:

Step 5: Binary Threshold Inverted

• This works opposite to the binary threshold. Pixels above the threshold become 0 (black), and those below become the maximum value (255; white).
• Highlights background over foreground.

_, thresh_binary_inv = cv2.threshold(
    gray_image, 120, 255, cv2.THRESH_BINARY_INV)
show_image(thresh_binary_inv, 'Binary Threshold Inverted ')

Output:

Step 6: Truncated Threshold

• Pixels above the threshold take the threshold value itself. Pixels below the threshold remain unchanged.
• Limits the maximum brightness, useful for flattening overexposed regions.

_, thresh_trunc = cv2.threshold(gray_image, 120, 255, cv2.THRESH_TRUNC)
show_image(thresh_trunc, 'Truncated Threshold')

Output:

Step 7: To Zero Threshold

• Pixels below the threshold are set to 0. Pixels above the threshold keep their original intensity.
• Extracts the brighter parts of the image while suppressing darker regions.

_, thresh_tozero = cv2.threshold(gray_image, 120, 255, cv2.THRESH_TOZERO)
show_image(thresh_tozero, 'Set to 0 ')

Output:

Step 8: To Zero Inverted Threshold

• Pixels above the threshold are set to 0. Pixels below the threshold keep their original value.
• Retains darker details while masking the brighter ones.

_, thresh_tozero_inv = cv2.threshold(
    gray_image, 120, 255, cv2.THRESH_TOZERO_INV)
show_image(thresh_tozero_inv, 'Set to 0 Inverted')

Output: