Skip to main content

Image Modification with OpenCV

This section explains how to modify image pixel values using OpenCV and NumPy slicing in Python.

Image modification is a fundamental operation in computer vision and is commonly used for:

  • Region masking
  • Drawing overlays
  • Data preprocessing
  • Debug visualization

1. Implementation Principle​

Images loaded by OpenCV are stored as NumPy arrays.

This means you can directly:

  • Access pixels by index\
  • Slice regions of interest (ROI)\
  • Assign new pixel values

By modifying array values, the image content changes immediately.

2. Implementation Effect​

Navigate to the OpenCV working directory:

cd ~/opencv

Run the image modification script:

python3 03.image_modify.py
note

Select the image display window and press q to exit the program.

Image Modification Result

3. Implementation Code​

import cv2

def modify_image(input_path, output_path):
    image = cv2.imread(input_path)

    if image is None:
        print("Error: Unable to open image file.")
        return

    # Modify the top-left 50x50 region (set to white)
    image[:50, :50] = [255, 255, 255]

    if cv2.imwrite(output_path, image):
        print(f"Image saved to {output_path}")
        cv2.imshow('Image Preview', cv2.imread(output_path))
        cv2.waitKey(0)
        cv2.destroyAllWindows()
    else:
        print("Error: Unable to save image file.")

modify_image(
    '/home/jetson/opencv/images/hemihex_logo.png',
    '/home/jetson/opencv/images/hemihex_logo_modify.png'
)

4. Code Explanation​

  • cv2.imread() loads the image into memory as a NumPy array\
  • Array slicing selects the region of interest\
  • cv2.imwrite() saves the modified image\
  • Display functions preview the result

Maintained by HemiHex for OpenCV-based image processing workflows.