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import cv2
import numpy as np
def unsharp_masking(img, kernel_size=5, threshold=2.0):
if kernel_size % 2 == 0:
kernel_size += 1 # Ensure the kernel size is odd
gaussian = cv2.GaussianBlur(img, (kernel_size, kernel_size), 2.0)
unsharp_mask = cv2.addWeighted(img, threshold, gaussian, -1.0, 0)
# Clip the pixel values to the valid range [0, 255]
unsharp_mask = np.clip(unsharp_mask, 0, 255)
# Normalize the image to bring pixel values back to [0, 255]
cv2.normalize(unsharp_mask, unsharp_mask, 0, 255, cv2.NORM_MINMAX)
return unsharp_mask
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