采集人员佩戴安全帽和未佩戴安全帽图片作为数据集。对数据集进行分析和预处理，从而得到相关图片特征。选取合适的模型在训练集上进行训练，得到分类模型。最后在测试数据集上进行图片分类识别，获取选择模型相关性能指标。以此编写代码

好的，以下是基于您提供的课程设计内容，编写的用于安全帽佩戴识别的数据采集、预处理、模型训练和测试的Python代码示例。我们将使用常见的深度学习框架TensorFlow和Keras来实现这个项目。

### 1. 数据采集
首先，需要收集人员佩戴安全帽和未佩戴安全帽的图片。这些图片可以来自公开数据集或自行拍摄。

### 2. 数据预处理

import os
import cv2
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical

# 设置数据路径
data_dir = 'path_to_your_data_directory'
categories = ['with_helmet', 'without_helmet']

# 图片尺寸
img_size = (128, 128)

# 加载和预处理数据
data = []
labels = []

for category in categories:
    path = os.path.join(data_dir, category)
    label = categories.index(category)
    for img_name in os.listdir(path):
        img_path = os.path.join(path, img_name)
        img = cv2.imread(img_path)
        img = cv2.resize(img, img_size)
        data.append(img)
        labels.append(label)

# 转换为NumPy数组
data = np.array(data) / 255.0  # 归一化
labels = np.array(labels)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)

# 将标签转换为one-hot编码
y_train = to_categorical(y_train, num_classes=len(categories))
y_test = to_categorical(y_test, num_classes=len(categories))

### 3. 模型构建

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout

model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dropout(0.5),
    Dense(len(categories), activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

### 4. 模型训练

history = model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test))

### 5. 模型评估

loss, accuracy = model.evaluate(X_test, y_test)
print(f'Test Loss: {loss}')
print(f'Test Accuracy: {accuracy}')

# 可视化训练过程
import matplotlib.pyplot as plt

plt.figure(figsize=(12, 4))

# 绘制训练和验证损失
plt.subplot(1, 2, 1)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.legend()
plt.title('Loss')

# 绘制训练和验证准确率
plt.subplot(1, 2, 2)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.legend()
plt.title('Accuracy')

plt.show()

### 6. 模型保存

model.save('helmet_detection_model.h5')

### 7. 模型预测

from tensorflow.keras.models import load_model

# 加载模型
model = load_model('helmet_detection_model.h5')

# 预测新图片
def predict_image(image_path):
    img = cv2.imread(image_path)
    img = cv2.resize(img, img_size)
    img = img / 255.0
    img = np.expand_dims(img, axis=0)
    
    prediction = model.predict(img)
    predicted_class = np.argmax(prediction, axis=1)[0]
    return categories[predicted_class]

# 示例
image_path = 'path_to_new_image.jpg'
result = predict_image(image_path)
print(f'Predicted class: {result}')

以上代码涵盖了从数据采集到模型训练、评估和预测的完整流程。希望这对你有所帮助！如果有任何问题，请随时提问。