mirror of
https://github.com/wassname/scikit-image.git
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emmanuelle
55f5103dd8
Modified travis_script.sh to account for the new structure of the gallery Added README.txt files in directories of gallery examples Fixed references to gallery images in user guide pages Fixed broken links
76 lines
2.3 KiB
Python
76 lines
2.3 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), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
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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,
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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,
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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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