Update README.md

- Update the comparison example of our method with two other commonly used libraries.

Author: lartpang

README.md

@@ -10,74 +10,97 @@ At the same time, in this implementation, I have dealt with the problem that the
 
 > When comparing images, the mean squared error (MSE)–while simple to implement–is not highly indicative of perceived similarity. Structural similarity aims to address this shortcoming by taking texture into account. More details can be seen at https://scikit-image.org/docs/dev/auto_examples/transform/plot_ssim.html?highlight=structure+similarity
 
-![results](https://user-images.githubusercontent.com/26847524/174805728-81e8502b-2ecb-4b40-a2c4-b4f1e2361ea9.png)
+![results](https://user-images.githubusercontent.com/26847524/175031400-92426661-4536-43c7-8f6e-5c470fb9ccb5.png)
 
 ```python
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
 import torch.nn.functional as F
-from pytorch_ssim import SSIM, ssim
+from lartpang_ssim import SSIM
+from po_hsun_su_ssim import SSIM as PoHsunSuSSIM
+from vainf_ssim import MS_SSIM as VainFMSSSIM
+from vainf_ssim import SSIM as VainFSSIM
 from skimage import data, img_as_float
 
 img = img_as_float(data.camera())
 rows, cols = img.shape
 
-noise = np.ones_like(img) * 0.2 * (img.max() - img.min())
+noise = np.ones_like(img) * 0.3 * (img.max() - img.min())
 rng = np.random.default_rng()
 noise[rng.random(size=noise.shape) > 0.5] *= -1
 
 img_noise = img + noise
-img_const = img + abs(noise)
+img_const = np.zeros_like(img)
 
 img_tensor = torch.from_numpy(img).unsqueeze(0).unsqueeze(0).float()
 img_noise_tensor = torch.from_numpy(img_noise).unsqueeze(0).unsqueeze(0).float()
 img_const_tensor = torch.from_numpy(img_const).unsqueeze(0).unsqueeze(0).float()
 
-fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8), sharex=True, sharey=True)
+fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 7))
 ax = axes.ravel()
 
 mse_none = F.mse_loss(img_tensor, img_tensor, reduction="mean")
-ssim_none = ssim(img_tensor, img_tensor, L=img_tensor.max() - img_tensor.min())
-
 mse_noise = F.mse_loss(img_tensor, img_noise_tensor, reduction="mean")
-ssim_noise = ssim(img_tensor, img_noise_tensor, L=img_noise_tensor.max() - img_noise_tensor.min())
-
 mse_const = F.mse_loss(img_tensor, img_const_tensor, reduction="mean")
-ssim_const = ssim(img_tensor, img_const_tensor, L=img_const_tensor.max() - img_const_tensor.min())
+
+# https://github.com/VainF/pytorch-msssim
+vainf_ssim_none = VainFSSIM(channel=1, data_range=1)(img_tensor, img_tensor)
+vainf_ssim_noise = VainFSSIM(channel=1, data_range=1)(img_tensor, img_noise_tensor)
+vainf_ssim_const = VainFSSIM(channel=1, data_range=1)(img_tensor, img_const_tensor)
+vainf_ms_ssim_none = VainFMSSSIM(channel=1, data_range=1)(img_tensor, img_tensor)
+vainf_ms_ssim_noise = VainFMSSSIM(channel=1, data_range=1)(img_tensor, img_noise_tensor)
+vainf_ms_ssim_const = VainFMSSSIM(channel=1, data_range=1)(img_tensor, img_const_tensor)
+
+# use the settings of https://github.com/VainF/pytorch-msssim
+ssim_none_0 = SSIM(return_msssim=False, L=1, padding=0, ensemble_kernel=False)(img_tensor, img_tensor)
+ssim_noise_0 = SSIM(return_msssim=False, L=1, padding=0, ensemble_kernel=False)(img_tensor, img_noise_tensor)
+ssim_const_0 = SSIM(return_msssim=False, L=1, padding=0, ensemble_kernel=False)(img_tensor, img_const_tensor)
+ms_ssim_none_0 = SSIM(return_msssim=True, L=1, padding=0, ensemble_kernel=False)(img_tensor, img_tensor)
+ms_ssim_noise_0 = SSIM(return_msssim=True, L=1, padding=0, ensemble_kernel=False)(img_tensor, img_noise_tensor)
+ms_ssim_const_0 = SSIM(return_msssim=True, L=1, padding=0, ensemble_kernel=False)(img_tensor, img_const_tensor)
+
+# https://github.com/Po-Hsun-Su/pytorch-ssim
+pohsunsu_ssim_none = PoHsunSuSSIM()(img_tensor, img_tensor)
+pohsunsu_ssim_noise = PoHsunSuSSIM()(img_tensor, img_noise_tensor)
+pohsunsu_ssim_const = PoHsunSuSSIM()(img_tensor, img_const_tensor)
+
+# use the settings of https://github.com/Po-Hsun-Su/pytorch-ssim
+ssim_none_1 = SSIM(return_msssim=False, L=1, padding=None, ensemble_kernel=True)(img_tensor, img_tensor)
+ssim_noise_1 = SSIM(return_msssim=False, L=1, padding=None, ensemble_kernel=True)(img_tensor, img_noise_tensor)
+ssim_const_1 = SSIM(return_msssim=False, L=1, padding=None, ensemble_kernel=True)(img_tensor, img_const_tensor)
+
 
 ax[0].imshow(img, cmap=plt.cm.gray, vmin=0, vmax=1)
-ax[0].set_xlabel(f"MSE: {mse_none:.2f}, SSIM: {ssim_none:.2f}")
+ax[0].set_xlabel(
+    f"MSE: {mse_none:.6f}\n"
+    f"SSIM {ssim_none_0:.6f}, MS-SSIM {ms_ssim_none_0:.6f}\n"
+    f"(VainF) SSIM: {vainf_ssim_none:.6f}, MS-SSIM {vainf_ms_ssim_none:.6f}\n"
+    f"SSIM {ssim_none_1:.6f}\n"
+    f"(PoHsunSu) SSIM: {pohsunsu_ssim_none:.6f}\n"
+)
 ax[0].set_title("Original image")
 
 ax[1].imshow(img_noise, cmap=plt.cm.gray, vmin=0, vmax=1)
-ax[1].set_xlabel(f"MSE: {mse_noise:.2f}, SSIM: {ssim_noise:.2f}")
+ax[1].set_xlabel(
+    f"MSE: {mse_noise:.6f}\n"
+    f"SSIM {ssim_noise_0:.6f}, MS-SSIM {ms_ssim_noise_0:.6f}\n"
+    f"(VainF) SSIM: {vainf_ssim_noise:.6f}, MS-SSIM {vainf_ms_ssim_noise:.6f}\n"
+    f"SSIM {ssim_noise_1:.6f}\n"
+    f"(PoHsunSu) SSIM: {pohsunsu_ssim_noise:.6f}\n"
+)
 ax[1].set_title("Image with noise")
 
 ax[2].imshow(img_const, cmap=plt.cm.gray, vmin=0, vmax=1)
-ax[2].set_xlabel(f"MSE: {mse_const:.2f}, SSIM: {ssim_const:.2f}")
+ax[2].set_xlabel(
+    f"MSE: {mse_const:.6f}\n"
+    f"SSIM {ssim_const_0:.6f}, MS-SSIM {ms_ssim_const_0:.6f}\n"
+    f"(VainF) SSIM: {vainf_ssim_const:.6f}, MS-SSIM {vainf_ms_ssim_const:.6f}\n"
+    f"SSIM {ssim_const_1:.6f}\n"
+    f"(PoHsunSu) SSIM: {pohsunsu_ssim_const:.6f}\n"
+)
 ax[2].set_title("Image plus constant")
 
-mse_none = F.mse_loss(img_tensor, img_tensor, reduction="mean")
-ssim_none = SSIM(L=img_tensor.max() - img_tensor.min())(img_tensor, img_tensor)
-
-mse_noise = F.mse_loss(img_tensor, img_noise_tensor, reduction="mean")
-ssim_noise = SSIM(L=img_noise_tensor.max() - img_noise_tensor.min())(img_tensor, img_noise_tensor)
-
-mse_const = F.mse_loss(img_tensor, img_const_tensor, reduction="mean")
-ssim_const = SSIM(L=img_const_tensor.max() - img_const_tensor.min())(img_tensor, img_const_tensor)
-
-ax[3].imshow(img, cmap=plt.cm.gray, vmin=0, vmax=1)
-ax[3].set_xlabel(f"MSE: {mse_none:.2f}, SSIM: {ssim_none:.2f}")
-ax[3].set_title("Original image")
-
-ax[4].imshow(img_noise, cmap=plt.cm.gray, vmin=0, vmax=1)
-ax[4].set_xlabel(f"MSE: {mse_noise:.2f}, SSIM: {ssim_noise:.2f}")
-ax[4].set_title("Image with noise")
-
-ax[5].imshow(img_const, cmap=plt.cm.gray, vmin=0, vmax=1)
-ax[5].set_xlabel(f"MSE: {mse_const:.2f}, SSIM: {ssim_const:.2f}")
-ax[5].set_title("Image plus constant")
 
 [ax[i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]) for i in range(len(axes))]
 
@@ -247,5 +270,6 @@ plt.savefig("prediction.png")
 ## Reference
 
 - https://github.com/Po-Hsun-Su/pytorch-ssim
+- https://github.com/VainF/pytorch-msssim
 - https://scikit-image.org/docs/dev/auto_examples/transform/plot_ssim.html?highlight=structure+similarity
 - Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.