opencv数字识别算法代码

基于OpenCV的数字识别算法代码如下所示[^1]：

#include <iostream>
#include <fstream>
#include <sstream>
#include <vector>
#include <opencv2/opencv.hpp>
#include <opencv2/ml.hpp>

using namespace std;
using namespace cv;
using namespace cv::ml;

// 读取训练数据
void readData(const string& filename, Mat& data, Mat& labels) {
    ifstream file(filename);
    if (!file.is_open()) {
        cerr << "无法打开文件: " << filename << endl;
        exit(-1);
    }

    vector<vector<float>> values;
    string line;
    while (getline(file, line)) {
        stringstream ss(line);
        vector<float> row;
        float value;
        while (ss >> value) {
            row.push_back(value);
        }
        values.push_back(row);
    }

    data = Mat(values.size(), values[0].size() - 1, CV_32F);
    labels = Mat(values.size(), 1, CV_32S);

    for (int i = 0; i < values.size(); i++) {
        for (int j = 0; j < values[i].size() - 1; j++) {
            data.at<float>(i, j) = values[i][j];
        }
        labels.at<int>(i, 0) = values[i][values[i].size() - 1];
    }
}

int main() {
    // 读取训练数据
    Mat trainData, trainLabels;
    readData("train.txt", trainData, trainLabels);

    // 创建SVM分类器
    Ptr<SVM> svm = SVM::create();
    svm->setType(SVM::C_SVC);
    svm->setKernel(SVM::LINEAR);
    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));

    // 训练SVM分类器
    svm->train(trainData, ROW_SAMPLE, trainLabels);

    // 保存训练好的模型
    svm->save("svm_model.xml");

    // 加载测试数据
    Mat testData, testLabels;
    readData("test.txt", testData, testLabels);

    // 预测测试数据
    Mat predictions;
    svm->predict(testData, predictions);

    // 计算准确率
    int correct = 0;
    for (int i = 0; i < predictions.rows; i++) {
        if (predictions.at<float>(i, 0) == testLabels.at<int>(i, 0)) {
            correct++;
        }
    }
    float accuracy = (float)correct / predictions.rows * 100;
    cout << "准确率: " << accuracy << "%" << endl;

    return 0;
}

请注意，这只是一个示例代码，实际使用时需要根据具体情况进行修改和调整。