ms-ssim 计算相似度 pytorch实现
时间: 2023-11-02 16:03:12 浏览: 111
MS-SSIM(结构相似性索引度量)是一种用于测量图像相似性的指标。下面是如何使用PyTorch实现MS-SSIM的解释:
首先,我们需要导入必要的PyTorch库和方法:
```python
import torch
import torch.nn.functional as F
from torch.autograd import Variable
```
接下来,我们定义计算MS-SSIM的函数`ms_ssim`:
```python
def gaussian(window_size, sigma):
kernel = torch.FloatTensor([math.exp(-((x - window_size // 2)**2) / float(2 * sigma**2)) for x in range(window_size)])
return kernel / kernel.sum()
def create_window(window_size, channel):
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
return window
def ssim(img1, img2, window, window_size, channel):
mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel)
mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1*mu2
sigma1_sq = F.conv2d(img1*img1, window, padding = window_size//2, groups = channel) - mu1_sq
sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = channel) - mu2_sq
sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = channel) - mu1_mu2
C1 = (0.01)**2
C2 = (0.03)**2
ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2)) / ((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
def ms_ssim(img1, img2, window_size=11, channel=1):
weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]) # 权重
levels = weights.size(0)
msssim = []
for _ in range(levels):
ssim_map = ssim(img1, img2, window, window_size, channel)
msssim.append(ssim_map)
img1 = F.avg_pool2d(img1, (2, 2))
img2 = F.avg_pool2d(img2, (2, 2))
msssim = torch.stack(msssim, dim=0)
return torch.prod(msssim.pow(weights))
```
最后,我们可以使用以上函数计算两个图像的MS-SSIM相似度:
```python
img1 = torch.tensor(...) # 输入第一个图像
img2 = torch.tensor(...) # 输入第二个图像
window_size = 11 # 窗口大小,可调整
channel = 3 # 图像通道数
window = create_window(window_size, channel).to(img1.device)
similarity = ms_ssim(img1, img2, window_size, channel)
```
以上就是使用PyTorch实现MS-SSIM相似度计算的方法。希望能对你有所帮助。