This undertaking demonstrates a way how to get entry to and adjust character pixel values in an image by using OpenCV in Python. Moreover, Pixel manipulation is a fundamental component of photograph processing, allowing for tasks which include image modifying, function highlighting, and greater. In this challenge, you may discover ways to retrieve pixel values, adjust them, and show the up to date photo to visualize the changes. The steps below ruin down the technique and clearly explain for every operation in detail.

How to Get and Set Pixels Using OpenCV Python

 

Step 1: Import the OpenCV library

In this step, you import the OpenCV library, which offers crucial gear for picture processing. Notably, OpenCV offers capabilities for loading, reworking, and editing snap shots, including pixel manipulation.

import cv2

Step 2: Load the input image

Here, you use the cv2.Imread() feature to load the photo file into memory specially, this feature reads the photo in its default BGR (Blue, Green, Red) format and shops it as a multi-dimensional array consequently, this array represents the pixel values, which you may get right of entry to and regulate.

image = cv2.imread('example.jpg')

Step 3: Access a specific pixel value

In this step, you get entry to the pixel cost on the coordinate (50, one hundred) using array indexing. The fee is saved within the variable pixel_value and represents the BGR additives of that precise pixel. By printing it, you could view the depth levels for blue, inexperienced, and purple at this region.

pixel_value = image[50, 100]
print("Pixel Value at (50, 100):", pixel_value)

Step 4: Modify a specific pixel value

Here, you adjust the pixel price at coordinate (50, 100) through assigning a brand new BGR price [255, 0, 0]. This trade sets the pixel shade to blue. Subsequently printing the pixel fee again confirms that the update has been applied correctly.

image[50, 100] = [255, 0, 0]
print("Modified Pixel Value at (50, 100):", image[50, 100])

Step 5: Display the modified image

Finally, you operate cv2.Imshow() to show the modified picture in a new window. The modifications made to the pixel values are visually obtrusive in this output. The software waits for a key press (cv2.WaitKey(0)) earlier than remaining the display window with cv2.DestroyAllWindows() to make certain right cleanup of resources.

cv2.imshow('Modified Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

The project outputs the pixel values before and after modification in the console. Additionally, the updated image is displayed in a window, allowing you to verify the pixel manipulation visually.