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.

Step 2: Helper Function

Define the helper function which helps in displaying the images,

Step 3: Display the Original 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.

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.

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.

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.

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.

Output: