pytorch代码,对超像素图像进行超像素池化,并输出超像素特征图
时间: 2024-05-16 22:15:02 浏览: 11
以下是一个使用PyTorch实现超像素池化的示例代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from skimage.segmentation import slic
class SuperPixelPooling(nn.Module):
def __init__(self, n_segments, in_channels):
super(SuperPixelPooling, self).__init__()
self.n_segments = n_segments
self.in_channels = in_channels
def forward(self, x):
# Get the superpixel segmentation of the input image
segments = slic(x, n_segments=self.n_segments, compactness=10)
segments = torch.tensor(segments).unsqueeze(0)
# Calculate the mean value of each superpixel
pooled = torch.zeros((1, self.in_channels, self.n_segments))
for i in range(self.n_segments):
mask = (segments == i).float().unsqueeze(0).repeat(self.in_channels, 1, 1)
pooled[:, :, i] = (x * mask).sum((-1, -2)) / mask.sum((-1, -2))
return pooled.squeeze()
```
这里我们使用了skimage库中的超像素分割算法slic对输入图像进行超像素分割,并计算每个超像素区域的均值作为该超像素的特征表示。最终返回一个形状为(1, in_channels, n_segments)的超像素特征图。
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