AI算法与系统（4）：口罩佩戴检测

深度学习

实验要求

1. 建立深度学习模型，检测出图中的人是否佩戴了口罩，并将其尽可能调整到最佳状态。
2. 学习经典的模型MTCNN 和 MobileNet 的结构。
3. 学习训练时的方法。

实验环境

可以使用基于Python的Numpy等库进行数据处理，使用sklearn等框架建立深度学习模型，使用过程中请注意Python包（库）的版本。

参考资料

• 论文：Joint Face Detection and Alignmentusing Multi-task Cascaded Convolutional Networks：https://kpzhang93.github.io/MTCNN_face_detection_alignment/
• OpenCV：https://opencv-python-
  tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html
• PIL：https://pillow.readthedocs.io/en/stable/
• Numpy：https://www.numpy.org/
• Scikit-learn：https://scikit-learn.org/
• PyTorch：https://pytorch.org/

import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision import datasets
import cv2
from PIL import Image
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import os

# MTCNN人脸检测
from mtcnn import MTCNN

# MobileNet模型
from torchvision.models import mobilenet_v2

# 设备配置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 数据预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# 加载数据集
def load_dataset(data_dir):
    dataset = datasets.ImageFolder(root=data_dir, transform=transform)
    return dataset

# 划分训练集和测试集
def split_dataset(dataset):
    train_size = int(0.8 * len(dataset))
    test_size = len(dataset) - train_size
    train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])
    return train_dataset, test_dataset

# 数据加载器
def get_data_loaders(train_dataset, test_dataset):
    train_loader = DataLoader(dataset=train_dataset, batch_size=32, shuffle=True)
    test_loader = DataLoader(dataset=test_dataset, batch_size=32, shuffle=False)
    return train_loader, test_loader

# 初始化MobileNet模型
def init_model(num_classes=2):
    model = mobilenet_v2(pretrained=True)
    model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, num_classes)
    model.to(device)
    return model

# 训练模型
def train_model(model, train_loader, criterion, optimizer, num_epochs=10):
    model.train()
    for epoch in range(num_epochs):
        for images, labels in train_loader:
            images, labels = images.to(device), labels.to(device)
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# 评估模型
def evaluate_model(model, test_loader):
    model.eval()
    predictions, true_labels = [], []
    with torch.no_grad():
        for images, labels in test_loader:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs, 1)
            predictions.extend(predicted.tolist())
            true_labels.extend(labels.tolist())
    accuracy = accuracy_score(true_labels, predictions)
    return accuracy

# 口罩佩戴检测
def mask_detection(model, image_path):
    detector = MTCNN()
    image = cv2.imread(image_path)
    results = detector.detect_faces(image)
    for result in results:
        x, y, width, height = result['box']
        face = image[y:y+height, x:x+width]
        face = Image.fromarray(face)
        face = transform(face).unsqueeze(0).to(device)
        output = model(face)
        _, predicted = torch.max(output, 1)
        label = 'Mask' if predicted.item() == 1 else 'No Mask'
        cv2.rectangle(image, (x, y), (x+width, y+height), (0, 255, 0), 2)
        cv2.putText(image, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2)
    cv2.imshow('Mask Detection', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

def main():
    data_dir = 'data'
    dataset = load_dataset(data_dir)
    train_dataset, test_dataset = split_dataset(dataset)
    train_loader, test_loader = get_data_loaders(train_dataset, test_dataset)

    model = init_model(num_classes=2)
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

    train_model(model, train_loader, criterion, optimizer, num_epochs=10)
    accuracy = evaluate_model(model, test_loader)
    print(f'Accuracy: {accuracy:.4f}')

    # 检测一张图片
    mask_detection(model, 'test_image.jpg')

if __name__ == "main":
    main()