数字识别（测试）

#include <windows.h>
#include <iostream>
#include <opencv2/opencv.hpp>

using namespace cv;
using namespace std;

char* WcharToChar(const wchar_t* wp)
{
    char *m_char;
    int len = WideCharToMultiByte(CP_ACP, 0, wp, wcslen(wp), NULL, 0, NULL, NULL);
    m_char = new char[len + 1];
    WideCharToMultiByte(CP_ACP, 0, wp, wcslen(wp), m_char, len, NULL, NULL);
    m_char[len] = '';
    return m_char;
}

wchar_t* CharToWchar(const char* c)
{
    wchar_t *m_wchar;
    int len = MultiByteToWideChar(CP_ACP, 0, c, strlen(c), NULL, 0);
    m_wchar = new wchar_t[len + 1];
    MultiByteToWideChar(CP_ACP, 0, c, strlen(c), m_wchar, len);
    m_wchar[len] = '';
    return m_wchar;
}

wchar_t* StringToWchar(const string& s)
{
    const char* p = s.c_str();
    return CharToWchar(p);
}

int main()
{
    const string fileform = "*.png";
    const string perfileReadPath = "charSamples";

    const int sample_mun_perclass = 20;//训练字符每类数量
    const int class_mun = 10;//训练字符类数

    const int image_cols = 8;
    const int image_rows = 16;
    string  fileReadName,
        fileReadPath;
    char temp[256];

    CvANN_MLP bp;
    bp.load("bpcharModel.yml");

    //测试神经网络
    cout << "测试：" << endl;
    Mat test_image = imread("2.png", CV_LOAD_IMAGE_GRAYSCALE);
    Mat test_temp;
    //使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现
    resize(test_image, test_temp, Size(image_cols, image_rows), (0, 0), (0, 0), CV_INTER_AREA);
    threshold(test_temp, test_temp, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
    Mat_<float>sampleMat(1, image_rows*image_cols);
    for (int i = 0; i<image_rows*image_cols; ++i)
    {
        sampleMat.at<float>(0, i) = (float)test_temp.at<uchar>(i / 8, i % 8);
    }

    Mat responseMat;
    bp.predict(sampleMat, responseMat);
    Point maxLoc;
    double maxVal = 0;
    minMaxLoc(responseMat, NULL, &maxVal, NULL, &maxLoc);
    cout << "识别结果：" << maxLoc.x << "    相似度:" << maxVal * 100 << "%" << endl;
    imshow("test_image", test_image);
    waitKey(0);

    return 0;
}