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import cv2
import numpy as np
def unsharp_mask(img, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0, mask=None):
if amount == 0:
return img
# Return a sharpened version of the image, using an unsharp mask.
# If mask is not None, only areas under mask are handled
blurred = cv2.GaussianBlur(img, kernel_size, sigma)
sharpened = float(amount + 1) * img - float(amount) * blurred
sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
sharpened = sharpened.round().astype(np.uint8)
if threshold > 0:
low_contrast_mask = np.absolute(img - blurred) < threshold
np.copyto(sharpened, img, where=low_contrast_mask)
if mask is not None:
mask = np.array(mask)
masked_sharpened = cv2.bitwise_and(sharpened, sharpened, mask=mask)
masked_img = cv2.bitwise_and(img, img, mask=255-mask)
sharpened = cv2.add(masked_img, masked_sharpened)
return sharpened
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