提供一个完整详细的基于图片的安全帽佩戴识别代码示例，要求只使用sklearn，机器学习

基于图片的安全帽佩戴识别通常涉及图像处理和机器学习模型。由于 `scikit-learn` 主要用于传统的机器学习算法，而不是深度学习，因此我们需要先对图像进行预处理，提取有用的特征，然后使用这些特征来训练模型。以下是一个完整的示例代码，展示了如何使用 `scikit-learn` 实现基于图片的安全帽佩戴识别：

### 1. 导入必要的库

import os
import cv2
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report

### 2. 图像预处理

def preprocess_image(image_path):
    # 读取图像
    image = cv2.imread(image_path)
    # 转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 缩放图像到固定大小
    resized = cv2.resize(gray, (64, 64))
    # 将图像展平成一维数组
    flattened = resized.flatten()
    return flattened

def load_dataset(data_dir):
    images = []
    labels = []
    for label in ['with_helmet', 'without_helmet']:
        label_dir = os.path.join(data_dir, label)
        for image_name in os.listdir(label_dir):
            image_path = os.path.join(label_dir, image_name)
            features = preprocess_image(image_path)
            images.append(features)
            labels.append(1 if label == 'with_helmet' else 0)
    return np.array(images), np.array(labels)

### 3. 加载数据集

data_dir = 'path_to_your_dataset'
X, y = load_dataset(data_dir)

### 4. 划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

### 5. 特征标准化

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

### 6. 训练支持向量机（SVM）模型

model = SVC(kernel='linear')
model.fit(X_train, y_train)

### 7. 预测和评估

y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")
print("Classification Report:")
print(classification_report(y_test, y_pred))

### 8. 可视化结果（可选）

import matplotlib.pyplot as plt

def plot_results(images, true_labels, predicted_labels, num_samples=10):
    fig, axes = plt.subplots(2, num_samples // 2, figsize=(15, 6))
    for i in range(num_samples):
        ax = axes[i % 2, i // 2]
        ax.imshow(images[i].reshape(64, 64), cmap='gray')
        ax.set_title(f'True: {true_labels[i]}, Pred: {predicted_labels[i]}')
        ax.axis('off')
    plt.show()

plot_results(X_test[:10], y_test[:10], y_pred[:10])

### 9. 保存模型（可选）

import joblib

joblib.dump(model, 'safety_helmet_model.pkl')

### 10. 加载模型并进行预测（可选）

loaded_model = joblib.load('safety_helmet_model.pkl')
new_image_path = 'path_to_new_image.jpg'
new_features = preprocess_image(new_image_path).reshape(1, -1)
new_features = scaler.transform(new_features)
prediction = loaded_model.predict(new_features)
print(f"Prediction: {'With helmet' if prediction[0] == 1 else 'Without helmet'}")

这个示例代码展示了如何使用 `scikit-learn` 和传统机器学习方法（如支持向量机）进行基于图片的安全帽佩戴识别。请注意，对于更复杂的图像识别任务，建议使用深度学习框架（如 TensorFlow 或 PyTorch）。