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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 | |