Create SSIM.py

SSIM.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import data, img_as_float
+from skimage.measure import compare_ssim as ssim
+
+
+img = img_as_float(data.camera())
+rows, cols = img.shape
+
+noise = np.ones_like(img) * 0.2 * (img.max() - img.min())
+noise[np.random.random(size=noise.shape) > 0.5] *= -1
+
+def mse(x, y):
+    return np.linalg.norm(x - y)
+
+img_noise = img + noise
+img_const = img + abs(noise)
+
+fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 4),
+                         sharex=True, sharey=True)
+ax = axes.ravel()
+
+mse_none = mse(img, img)
+ssim_none = ssim(img, img, data_range=img.max() - img.min())
+
+mse_noise = mse(img, img_noise)
+ssim_noise = ssim(img, img_noise,
+                  data_range=img_noise.max() - img_noise.min())
+
+mse_const = mse(img, img_const)
+ssim_const = ssim(img, img_const,
+                  data_range=img_const.max() - img_const.min())
+
+label = 'MSE: {:.2f}, SSIM: {:.2f}'
+
+ax[0].imshow(img, cmap=plt.cm.gray, vmin=0, vmax=1)
+ax[0].set_xlabel(label.format(mse_none, ssim_none))
+ax[0].set_title('Original image')
+
+ax[1].imshow(img_noise, cmap=plt.cm.gray, vmin=0, vmax=1)
+ax[1].set_xlabel(label.format(mse_noise, ssim_noise))
+ax[1].set_title('Image with noise')
+
+ax[2].imshow(img_const, cmap=plt.cm.gray, vmin=0, vmax=1)
+ax[2].set_xlabel(label.format(mse_const, ssim_const))
+ax[2].set_title('Image plus constant')
+
+plt.tight_layout()
+plt.show()