Task 3: High Boost filtering

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

def apply_high_boost_filter(image, mask, A):
    kernel = np.array(mask, dtype=np.float32)
    kernel = kernel + A
    filtered_image = cv.filter2D(image, -1, kernel)
    return filtered_image

def display_images(images, titles):
    plt.figure(figsize=(12, 6))
    for i, (image, title) in enumerate(zip(images, titles), start=1):
        plt.subplot(1, len(images), i)
        plt.imshow(cv.cvtColor(image, cv.COLOR_BGR2RGB))
        plt.title(title)
        plt.xticks([])
        plt.yticks([])
    plt.show()

def main():
    image = cv.imread("Electron_Microscope.jpg", cv.IMREAD_COLOR)

    mask1 = [[0, -1, 0], [-1, 4, -1], [0, -1, 0]]
    mask2 = [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]

    A_values = [0, 1]

    for A in A_values:
        filtered_image1 = apply_high_boost_filter(image, mask1, A)
        filtered_image2 = apply_high_boost_filter(image, mask2, A)

        display_images(
            [image, filtered_image1, filtered_image2],
            [
                f"Original Image",
                f"High Boost Filtered Image (A={A}, Mask1)",
                f"High Boost Filtered Image (A={A}, Mask2)",
            ],
        )

if __name__ == "__main__":
    main()