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import cv2 |
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import numpy as np |
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def unsharp_mask(img, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0, mask=None): |
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if amount == 0: |
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return img |
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blurred = cv2.GaussianBlur(img, kernel_size, sigma) |
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sharpened = float(amount + 1) * img - float(amount) * blurred |
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sharpened = np.maximum(sharpened, np.zeros(sharpened.shape)) |
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sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape)) |
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sharpened = sharpened.round().astype(np.uint8) |
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if threshold > 0: |
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low_contrast_mask = np.absolute(img - blurred) < threshold |
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np.copyto(sharpened, img, where=low_contrast_mask) |
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if mask is not None: |
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mask = np.array(mask) |
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masked_sharpened = cv2.bitwise_and(sharpened, sharpened, mask=mask) |
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masked_img = cv2.bitwise_and(img, img, mask=255-mask) |
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sharpened = cv2.add(masked_img, masked_sharpened) |
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return sharpened |