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import numpy as np |
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def dice(img1: np.ndarray, img2: np.ndarray, epsilon=0.00001): |
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assert np.min(img1) >= 0. |
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assert np.max(img1) <= 1. |
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assert np.min(img2) >= 0. |
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assert np.max(img2) <= 1. |
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return (np.sum(np.multiply(img1, img2)) * 2 + epsilon) / (np.sum(img1) + np.sum(img2) + epsilon) |
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def harden(img: np.ndarray): |
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h_img = np.zeros_like(img, dtype=np.uint8) |
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h_img[img > 0.5] = 1 |
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return h_img |
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def restrict(m: np.ndarray): |
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rm = np.copy(m) |
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rm[rm<0]=0. |
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rm[rm>1]=1. |
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return rm |
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def check_mask(m): |
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if np.min(m) < 0.: |
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print("WARNING: Mask contains pixels below 0, constraining it to be over 0") |
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if np.max(m) > 1.: |
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print("WARNING: Mask contains pixels over 1, constraining them to be below 1") |
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assert np.min(m) >= 0. |
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assert np.max(m) <= 1. |
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def hard_dice(img1: np.ndarray, img2: np.ndarray, epsilon=0.00001): |
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check_mask(img1) |
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check_mask(img2) |
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return dice(harden(img1), harden(img2), epsilon) |
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def rel_size_diff(img_r: np.ndarray, img: np.ndarray, epsilon=0.00001): |
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assert np.min(img) >= 0. |
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assert np.max(img) <= 1. |
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s_r = np.sum(img_r) |
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s = np.sum(img) |
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return np.abs(s_r-s) / s_r |
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