bp神经网络 图像分割 代码
时间: 2023-08-29 20:12:29 浏览: 44
以下是一个基于BP神经网络的图像分割代码,该代码使用Python和PyTorch实现:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import cv2
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(3, 10) # 输入层到隐层
self.fc2 = nn.Linear(10, 1) # 隐层到输出层
def forward(self, x):
x = torch.sigmoid(self.fc1(x))
x = torch.sigmoid(self.fc2(x))
return x
def train(net, criterion, optimizer, inputs, targets):
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
return loss
def main():
# 读取图像并转换为numpy数组
img = cv2.imread("image.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (64, 64))
img = np.array(img) / 255.0
# 定义网络结构、损失函数、优化器
net = Net()
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=0.1)
# 准备训练数据
inputs = torch.from_numpy(img.reshape((64 * 64, 3)).astype(np.float32))
targets = torch.from_numpy(np.random.randint(0, 2, (64 * 64, 1))).float()
# 开始训练
for i in range(10000):
loss = train(net, criterion, optimizer, inputs, targets)
if i % 1000 == 0:
print("epoch: {}, loss: {}".format(i, loss))
# 对图像进行分割
with torch.no_grad():
outputs = net(inputs)
outputs = outputs.numpy().reshape((64, 64))
outputs = (outputs >= 0.5).astype(np.uint8) * 255
# 显示结果
cv2.imshow("Image", img)
cv2.imshow("Segmentation", outputs)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == "__main__":
main()
```
这个代码将一张图像划分为两个区域:背景和前景。该模型使用了一个简单的BP神经网络,将图像的RGB值作为输入,输出一个值表示该像素属于哪个区域。在训练过程中,使用随机的标签来训练模型,最终将模型应用于整张图像进行分割。