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try: |
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from skimage.measure import compare_ssim, compare_psnr |
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except: |
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from skimage.metrics import peak_signal_noise_ratio as compare_psnr |
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from skimage.metrics import structural_similarity as compare_ssim |
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from functools import partial |
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import numpy as np |
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class Bandwise(object): |
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def __init__(self, index_fn): |
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self.index_fn = index_fn |
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def __call__(self, X, Y): |
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C = X.shape[-1] |
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bwindex = [] |
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for ch in range(C): |
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x = X[..., ch] |
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y = Y[..., ch] |
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index = self.index_fn(x, y) |
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bwindex.append(index) |
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return bwindex |
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cal_bwpsnr = Bandwise(partial(compare_psnr, data_range=255)) |
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cal_bwssim = Bandwise(partial(compare_ssim, data_range=255)) |
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def compare_ncc(x, y): |
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return np.mean((x - np.mean(x)) * (y - np.mean(y))) / (np.std(x) * np.std(y)) |
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def ssq_error(correct, estimate): |
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"""Compute the sum-squared-error for an image, where the estimate is |
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multiplied by a scalar which minimizes the error. Sums over all pixels |
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where mask is True. If the inputs are color, each color channel can be |
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rescaled independently.""" |
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assert correct.ndim == 2 |
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if np.sum(estimate ** 2) > 1e-5: |
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alpha = np.sum(correct * estimate) / np.sum(estimate ** 2) |
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else: |
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alpha = 0. |
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return np.sum((correct - alpha * estimate) ** 2) |
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def local_error(correct, estimate, window_size, window_shift): |
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"""Returns the sum of the local sum-squared-errors, where the estimate may |
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be rescaled within each local region to minimize the error. The windows are |
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window_size x window_size, and they are spaced by window_shift.""" |
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M, N, C = correct.shape |
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ssq = total = 0. |
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for c in range(C): |
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for i in range(0, M - window_size + 1, window_shift): |
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for j in range(0, N - window_size + 1, window_shift): |
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correct_curr = correct[i:i + window_size, j:j + window_size, c] |
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estimate_curr = estimate[i:i + window_size, j:j + window_size, c] |
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ssq += ssq_error(correct_curr, estimate_curr) |
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total += np.sum(correct_curr ** 2) |
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return ssq / total |
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def quality_assess(X, Y): |
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psnr = np.mean(cal_bwpsnr(Y, X)) |
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ssim = np.mean(cal_bwssim(Y, X)) |
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lmse = local_error(Y, X, 20, 10) |
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ncc = compare_ncc(Y, X) |
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return {'PSNR': psnr, 'SSIM': ssim, 'LMSE': lmse, 'NCC': ncc} |
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