This script will pad an image with the common color. It will add extra space on all four sides of an image

pad_image.py

import cv2
import numpy as np

def pad_image(image, padding_percent):
    """
    Pad the image and return it
    :param image: Numpy array
    :param padding_percent: float
    :return: padded_image: Numpy array
    """
    # Find the most common color in the image
    most_common_color = find_most_common_color(image)

    if padding_percent < 0 or padding_percent > 1:
        padding_percent = 0.2

    desired_height = int(image.shape[0] * (2 + padding_percent))
    desired_width = int(image.shape[1] * (1 + padding_percent))

    # Determine the dimensions of the original image
    original_height, original_width = image.shape[:2]

    # Create a new padded image array with the desired dimensions
    padded_image = np.full((desired_height, desired_width, 3),
                           fill_value=most_common_color, dtype=np.uint8)

    # Calculate the position to place the original image in the center of the padded image
    start_height = (desired_height - original_height) // 2
    start_width = (desired_width - original_width) // 2

    # Place the original image in the center of the padded image
    padded_image[start_height:start_height + original_height,
                 start_width:start_width + original_width] = image

    return padded_image


def find_most_common_color(image):
    """
    Find the most common color in the image and return it
    :param image: Numpy array
    :return: most_common_color: tuple
    """
    # Flatten the image into a 2D array of pixels
    pixels = image.reshape(-1, 3)

    # Count the occurrence of each unique color value
    color_counts = np.unique(pixels, axis=0, return_counts=True)

    # Find the index of the color with the highest count
    most_common_index = np.argmax(color_counts[1])

    # Retrieve the most common color from the index
    most_common_color = color_counts[0][most_common_index]

    return tuple(most_common_color)


img_path = 'input_img_path.jpg'
img = cv2.imread(img_path)
img = pad_image(img, 0.2)
cv2.imwrite('output_img_path.jpg', img)