k210实现识别交通标志的代码，涉及的交通标志仅限于：直行、左转、右转、掉头、禁止直行、禁止通行。用python写

在K210这样的嵌入式设备上实现交通标志识别通常需要将复杂的人工智能任务分解为几个步骤。这里提供一个简化版的Python代码框架，使用OpenCV进行图像预处理和HOG+SVM（Histogram of Oriented Gradients和支持向量机）作为特征提取与分类器。请注意，这只是一个基础示例，并未涵盖完整的模型训练过程，实际部署可能需要专业的深度学习模型或云服务。

import cv2
import numpy as np
from sklearn.svm import SVC
from skimage.feature import hog

# 加载预训练的交通标志分类模型
traffic_sign_model = load_trained_model('traffic_signs_svm.model')

def preprocess_image(image_path):
    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    img_resized = cv2.resize(img, (64, 64))  # 调整到合适的尺寸
    return img_resized

def extract_hog_features(image):
    orientations = 9
    pixels_per_cell = (8, 8)
    cells_per_block = (2, 2)
    feature_vector = hog(image, orientations, pixels_per_cell, cells_per_block, transform_sqrt=True, block_norm='L2-Hys')
    return feature_vector

def predict_traffic_sign(image):
    processed_img = preprocess_image(image)
    hog_features = extract_hog_features(processed_img)
    prediction = traffic_sign_model.predict(hog_features.reshape(1, -1))
    return prediction

def interpret_prediction(prediction):
    label_to_text = {
        0: '直行',
        1: '左转',
        2: '右转',
        3: '掉头',
        4: '禁止直行',
        5: '禁止通行',
    }
    return label_to_text[prediction]

# 主程序入口
if __name__ == "__main__":
    image_path = 'traffic_sign.jpg'  # 替换为实际图像路径
    sign = predict_traffic_sign(image_path)
    print(f"识别到的交通标志：{interpret_prediction(sign)}")