深度学习进行人脸识别代码

以下是一个使用深度学习进行人脸识别的Python代码示例，基于PyTorch框架和OpenCV库：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import cv2

# 定义模型
class FaceNet(nn.Module):
    def __init__(self):
        super(FaceNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(64 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, 5)
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = F.max_pool2d(F.relu(self.conv3(x)), 2)
        x = x.view(-1, 64 * 8 * 8)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 读取数据集
X_train = np.load('X_train.npy')
X_test = np.load('X_test.npy')
y_train = np.load('y_train.npy')
y_test = np.load('y_test.npy')

# 数据预处理
X_train = torch.from_numpy(X_train).float()
X_test = torch.from_numpy(X_test).float()
y_train = torch.from_numpy(y_train).long()
y_test = torch.from_numpy(y_test).long()

# 创建模型、优化器和损失函数
model = FaceNet()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i in range(len(X_train)):
        optimizer.zero_grad()
        output = model(X_train[i].unsqueeze(0))
        loss = criterion(output, y_train[i].unsqueeze(0))
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(X_train)))

# 保存模型
torch.save(model.state_dict(), 'face_recognition_model.pth')

# 加载模型
model = FaceNet()
model.load_state_dict(torch.load('face_recognition_model.pth'))

# 测试模型
img = cv2.imread('test_image.jpg')
img = cv2.resize(img, (32, 32))
img = np.transpose(img, (2, 0, 1))
img = np.expand_dims(img, axis=0)
img = torch.from_numpy(img).float()
output = model(img)
result = torch.argmax(output).item()
print(result)

这段代码中，我们使用了一个简单的卷积神经网络（CNN）模型进行人脸识别，使用了一个包含5个人的数据集进行训练和测试。在测试阶段，我们读取一张测试图片，进行预处理后输入模型进行预测，输出预测结果。