用C语言和3.4.1opencv库，代码设计一个禁止右转道路交通标识标志识别系统，要求具有一定的鲁棒性

好的，以下是一个简单的禁止右转道路交通标识标志识别系统的实现过程，具有一定的鲁棒性：

1. 导入opencv库和其他必要的头文件，包括cv.h、highgui.h、imgproc.h等。

#include <stdio.h>
#include <stdlib.h>
#include <opencv2/highgui/highgui_c.h>
#include <opencv2/opencv.hpp>
#include <opencv2/imgproc/imgproc.hpp>

2. 定义分类器的特征向量和大小，以及训练样本的数量和路径。

#define feature_size 99
int sample_num = 150;
char sample_path[] = "./samples/";

3. 定义一个函数，用于提取图像的LBP特征向量。

void extract_lbp_feature(IplImage* img, float* feature_vector) {
    int cell_size = 8;
    int block_size = 2;
    int lbp_size = 256;
    int step_x = img->width / cell_size;
    int step_y = img->height / cell_size;
    int feature_idx = 0;
    for (int i = 0; i < step_y - block_size + 1; i++) {
        for (int j = 0; j < step_x - block_size + 1; j++) {
            float block_feature[feature_size / 4] = { 0 };
            for (int m = 0; m < block_size; m++) {
                for (int n = 0; n < block_size; n++) {
                    int x = j * cell_size + n * cell_size;
                    int y = i * cell_size + m * cell_size;
                    int lbp_value = 0;
                    int center_value = CV_IMAGE_ELEM(img, uchar, y + cell_size / 2, x + cell_size / 2);
                    for (int k = 0; k < 8; k++) {
                        int neighbor_x = x + cell_size / 2 + cos(k * CV_PI / 4) * cell_size / 2;
                        int neighbor_y = y + cell_size / 2 - sin(k * CV_PI / 4) * cell_size / 2;
                        int neighbor_value = CV_IMAGE_ELEM(img, uchar, neighbor_y, neighbor_x);
                        lbp_value += (neighbor_value >= center_value) << k;
                    }
                    block_feature[lbp_value / 4]++;
                }
            }
            for (int m = 0; m < feature_size / 4; m++) {
                feature_vector[feature_idx++] = block_feature[m];
            }
        }
    }
}

4. 定义一个函数，用于读取训练样本并提取LBP特征向量。

void load_samples(float** samples, int* labels) {
    CvMat* sample_mat = cvCreateMat(sample_num, feature_size, CV_32FC1);
    CvMat* label_mat = cvCreateMat(sample_num, 1, CV_32FC1);
    for (int i = 0; i < sample_num; i++) {
        char file_path[100];
        sprintf(file_path, "%s%d.jpg", sample_path, i + 1);
        IplImage* img = cvLoadImage(file_path, CV_LOAD_IMAGE_GRAYSCALE);
        float* feature_vector = (float*)malloc(feature_size * sizeof(float));
        extract_lbp_feature(img, feature_vector);
        samples[i] = feature_vector;
        labels[i] = 1;
        cvSet1D(label_mat, i, cvScalar(labels[i]));
        for (int j = 0; j < feature_size; j++) {
            cvmSet(sample_mat, i, j, samples[i][j]);
        }
        cvReleaseImage(&img);
    }
    CvSVM svm;
    CvSVMParams params;
    svm_type = CvSVM::C_SVC;
    kernel_type = CvSVM::RBF;
    params.svm_type = svm_type;
    params.kernel_type = kernel_type;
    params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
    svm.train(sample_mat, label_mat, NULL, NULL, params);
    svm.save("svm.xml");
    cvReleaseMat(&sample_mat);
    cvReleaseMat(&label_mat);
}

5. 定义一个函数，用于读取图像并进行预处理，包括高斯模糊、二值化、腐蚀、膨胀等操作。

void preprocess_image(IplImage* img, IplImage* processed_img) {
    cvSmooth(img, processed_img, CV_GAUSSIAN, 3, 3);
    cvThreshold(processed_img, processed_img, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
    cvErode(processed_img, processed_img, NULL, 1);
    cvDilate(processed_img, processed_img, NULL, 1);
}

6. 定义主函数，用于读取图像、进行预处理、提取特征、调用分类器进行识别，并输出结果。

int main() {
    float* samples[sample_num];
    int labels[sample_num];
    load_samples(samples, labels);
    IplImage* img = cvLoadImage("test.jpg", CV_LOAD_IMAGE_GRAYSCALE);
    IplImage* processed_img = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 1);
    preprocess_image(img, processed_img);
    float feature_vector[feature_size];
    extract_lbp_feature(processed_img, feature_vector);
    CvMat* feature_mat = cvCreateMat(1, feature_size, CV_32FC1);
    for (int i = 0; i < feature_size; i++) {
        cvmSet(feature_mat, 0, i, feature_vector[i]);
    }
    CvSVM svm;
    svm.load("svm.xml");
    float result = svm.predict(feature_mat);
    if (result == 1) {
        printf("禁止右转道路交通标识标志\n");
    } else {
        printf("未识别出禁止右转道路交通标识标志\n");
    }
    cvReleaseImage(&img);
    cvReleaseImage(&processed_img);
    cvReleaseMat(&feature_mat);
    return 0;
}

这只是一个简单的实现过程，具体的实现方法需要根据实际情况进行调整。