概述
基于深度学习的人脸表情识别，数据集采用公开数据集fer2013，可直接运行，效果良好，可根据需求修改训练代码，自己训练模型。
详细
一、概述
本项目以PyTorch为框架，搭建卷积神经网络模型，训练后可直接调用py文件进行人脸检测与表情识别，默认开启摄像头实时检测识别。效果良好，可根据个人需求加以修改。
二、演示效果：
三、实现过程
-
搭建网络
def init(self):
super(FaceCNN, self).init()# 第一次卷积、池化 self.conv1 = nn.Sequential( nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1), # 卷积层 # BatchNorm2d进行数据的归一化处理，这使得数据在进行Relu之前不会因为数据过大而导致网络性能的不稳定 nn.BatchNorm2d(num_features=64), # 归一化 nn.RReLU(inplace=True), # 激活函数 nn.MaxPool2d(kernel_size=2, stride=2), # 最大值池化 ) # 第二次卷积、池化 self.conv2 = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(num_features=128), nn.RReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), ) # 第三次卷积、池化 self.conv3 = nn.Sequential( nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(num_features=256), nn.RReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), ) # 参数初始化 self.conv1.apply(gaussian_weights_init) self.conv2.apply(gaussian_weights_init) self.conv3.apply(gaussian_weights_init) # 全连接层 self.fc = nn.Sequential( nn.Dropout(p=0.2), nn.Linear(in_features=256 * 6 * 6, out_features=4096), nn.RReLU(inplace=True), nn.Dropout(p=0.5), nn.Linear(in_features=4096, out_features=1024), nn.RReLU(inplace=True), nn.Linear(in_features=1024, out_features=256), nn.RReLU(inplace=True), nn.Linear(in_features=256, out_features=7), ) -
训练模型
载入数据并分割batch
train_loader = data.DataLoader(train_dataset, batch_size)
损失函数
loss_function = nn.CrossEntropyLoss()
学习率衰减
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.8)
device = "cuda" if torch.cuda.is_available() else 'cpu'
构建模型
model = FaceCNN().to(device)
优化器
optimizer = optim.SGD(model.parameters(), lr=learning_rate, weight_decay=wt_decay)
逐轮训练
for epoch in range(epochs):
if (epoch + 1) % 10 == 0:
learning_rate = learning_rate * 0.1
# 记录损失值
loss_rate = 0
# scheduler.step() # 学习率衰减
model.train() # 模型训练
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
# 梯度清零
optimizer.zero_grad()
# 前向传播
output = model.forward(images)
# 误差计算
loss_rate = loss_function(output, labels)
# 误差的反向传播
loss_rate.backward()
# 更新参数
optimizer.step() -
模型预测
with torch.no_grad():
pred = model(face)
probability = torch.nn.functional.softmax(pred, dim=1)
probability = np.round(probability.cpu().detach().numpy(), 3)
max_prob = np.max(probability)
# print(max_prob)
predicted = classes[torch.argmax(pred[0])]
cv2.putText(img, predicted + " " + str(max_prob), (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (100, 255, 0), 1, cv2.LINE_AA)
cv2.imshow('frame', img)