Simple Thresholding in OpenCV

Thresholding is a process in image processing where pixel values in an image are set to a specific value based on a certain condition. Simple thresholding as the name suggests involves applying a single threshold value. If the pixel value is above the threshold, it's set to one value (possibly white), and if it's below the threshold, it's set to another value (maybe black).

In OpenCV, this can be achieved with the cv2.threshold() function.

Simple Thresholding in OpenCV:

1. Setup:

Make sure you have OpenCV installed:

pip install opencv-python

Then, import the necessary libraries:

import cv2
import numpy as np

2. Read the Image & Convert to Grayscale:

For thresholding, it's usually necessary to work with a grayscale image:

# Read the image
image = cv2.imread('path_to_image.jpg')

# Convert to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

3. Apply Simple Thresholding:

Use the cv2.threshold() function:

# Apply thresholding
ret, thresh1 = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)

Here:

gray_image is the grayscale image.
127 is the threshold value.
255 is the max value that is used to represent white.
cv2.THRESH_BINARY is the type of thresholding. This will result in a binary image where pixels with a value greater than 127 are set to 255 (white) and others are set to 0 (black).

4. Display the Result:

cv2.imshow('Original Image', gray_image)
cv2.imshow('Binary Threshold', thresh1)
cv2.waitKey(0)
cv2.destroyAllWindows()

Other Thresholding Types:

OpenCV provides various thresholding methods, and you can experiment with them:

cv2.THRESH_BINARY
cv2.THRESH_BINARY_INV (Inverse binary thresholding)
cv2.THRESH_TRUNC (Truncates values above the threshold)
cv2.THRESH_TOZERO (Set to zero below the threshold)
cv2.THRESH_TOZERO_INV (Inverse of the above)

Complete Code:

Here's the complete code for simple binary thresholding:

import cv2
import numpy as np

# Load the image and convert to grayscale
image = cv2.imread('path_to_image.jpg')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply thresholding
ret, thresh1 = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)

# Display the result
cv2.imshow('Original Image', gray_image)
cv2.imshow('Binary Threshold', thresh1)
cv2.waitKey(0)
cv2.destroyAllWindows()

Replace 'path_to_image.jpg' with the path to your image file and run the script. The original grayscale and thresholded images will be displayed side by side.

Simple thresholding in OpenCV using Python:

import cv2
import numpy as np

# Read an image in grayscale
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Apply simple thresholding
_, thresholded = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)

# Display the original and thresholded images
cv2.imshow('Original Image', img)
cv2.imshow('Thresholded Image', thresholded)
cv2.waitKey(0)
cv2.destroyAllWindows()

Thresholding algorithms comparison in OpenCV:

import cv2
import numpy as np

img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Apply different thresholding methods
_, binary = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
_, binary_inv = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
_, trunc = cv2.threshold(img, 127, 255, cv2.THRESH_TRUNC)
_, tozero = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO)
_, tozero_inv = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO_INV)

# Display the results
cv2.imshow('Binary Threshold', binary)
cv2.imshow('Binary Inverted Threshold', binary_inv)
cv2.imshow('Truncated Threshold', trunc)
cv2.imshow('To Zero Threshold', tozero)
cv2.imshow('To Zero Inverted Threshold', tozero_inv)
cv2.waitKey(0)
cv2.destroyAllWindows()

Adaptive simple thresholding in OpenCV:

import cv2

img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Apply adaptive thresholding
adaptive_thresh = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)

# Display the original and adaptive thresholded images
cv2.imshow('Original Image', img)
cv2.imshow('Adaptive Thresholded Image', adaptive_thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

Simple thresholding for image segmentation in OpenCV:

import cv2

img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Apply simple thresholding for image segmentation
_, segmented_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)

# Perform additional processing on the segmented image as needed

# Display the original and segmented images
cv2.imshow('Original Image', img)
cv2.imshow('Segmented Image', segmented_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Otsu's method for thresholding in OpenCV:

import cv2

img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

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

# Display the original and Otsu's thresholded images
cv2.imshow('Original Image', img)
cv2.imshow('Otsu Thresholded Image', otsu_thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

Real-time simple thresholding in OpenCV:

import cv2

cap = cv2.VideoCapture(0)  # Use webcam, or replace with video path

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Convert the frame to grayscale
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Apply simple thresholding
    _, thresholded = cv2.threshold(gray_frame, 127, 255, cv2.THRESH_BINARY)

    # Display the original and thresholded frames
    cv2.imshow('Original Frame', frame)
    cv2.imshow('Thresholded Frame', thresholded)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Simple thresholding for noise reduction in OpenCV:

import cv2

img = cv2.imread('noisy_image.jpg', cv2.IMREAD_GRAYSCALE)

# Apply simple thresholding for noise reduction
_, denoised_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)

# Display the original and denoised images
cv2.imshow('Original Image', img)
cv2.imshow('Denoised Image', denoised_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Thresholding grayscale and color images with OpenCV:

import cv2

# Grayscale image thresholding
img_gray = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
_, thresh_gray = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)

# Color image thresholding
img_color = cv2.imread('color_image.jpg')
_, thresh_color = cv2.threshold(img_color, 127, 255, cv2.THRESH_BINARY)

# Display the results
cv2.imshow('Grayscale Thresholded Image', thresh_gray)
cv2.imshow('Color Thresholded Image', thresh_color)
cv2.waitKey(0)
cv2.destroyAllWindows()