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https://github.com/wassname/scikit-image.git
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Siva Prasad Varma
74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
"""
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===========================
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Structural similarity index
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===========================
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When comparing images, the mean squared error (MSE)--while simple to
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implement--is not highly indicative of perceived similarity. Structural
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similarity aims to address this shortcoming by taking texture into account
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[1]_, [2]_.
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The example shows two modifications of the input image, each with the same MSE,
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but with very different mean structural similarity indices.
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.. [1] Zhou Wang; Bovik, A.C.; ,"Mean squared error: Love it or leave it? A new
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look at Signal Fidelity Measures," Signal Processing Magazine, IEEE,
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vol. 26, no. 1, pp. 98-117, Jan. 2009.
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.. [2] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality
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assessment: From error visibility to structural similarity," IEEE
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Transactions on Image Processing, vol. 13, no. 4, pp. 600-612,
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Apr. 2004.
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"""
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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from skimage import data, img_as_float
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from skimage.measure import structural_similarity as ssim
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matplotlib.rcParams['font.size'] = 9
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img = img_as_float(data.camera())
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rows, cols = img.shape
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noise = np.ones_like(img) * 0.2 * (img.max() - img.min())
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noise[np.random.random(size=noise.shape) > 0.5] *= -1
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def mse(x, y):
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return np.linalg.norm(x - y)
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img_noise = img + noise
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img_const = img + abs(noise)
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fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(8, 4))
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mse_none = mse(img, img)
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ssim_none = ssim(img, img, dynamic_range=img.max() - img.min())
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mse_noise = mse(img, img_noise)
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ssim_noise = ssim(img, img_noise, dynamic_range=img_const.max() - img_const.min())
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mse_const = mse(img, img_const)
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ssim_const = ssim(img, img_const, dynamic_range=img_noise.max() - img_noise.min())
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label = 'MSE: %2.f, SSIM: %.2f'
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ax0.imshow(img, cmap=plt.cm.gray, vmin=0, vmax=1)
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ax0.set_xlabel(label % (mse_none, ssim_none))
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ax0.set_title('Original image')
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ax1.imshow(img_noise, cmap=plt.cm.gray, vmin=0, vmax=1)
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ax1.set_xlabel(label % (mse_noise, ssim_noise))
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ax1.set_title('Image with noise')
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ax2.imshow(img_const, cmap=plt.cm.gray, vmin=0, vmax=1)
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ax2.set_xlabel(label % (mse_const, ssim_const))
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ax2.set_title('Image plus constant')
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plt.show()
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