神经网络识别数字0-9

神经网络识别数字

1. opencv3.1.0+vs2013
2. 只允许单张图片识别
3. 训练集为数字样本0-9，每类样本内有50个数字图片。

#include
#include 
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/imgcodecs/imgcodecs.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/ml/ml.hpp"

using namespace cv;
using namespace std;
using namespace ml;
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] = '\0';
    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] = '\0';
    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 = "D:/OPENCV/实践小程序/pictures/charSamples";

    const int sample_mun_perclass = 50;//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];

    float trainingData[class_mun*sample_mun_perclass][image_rows*image_cols] = { { 0 } };//每一行一个训练样本
    float labels[class_mun*sample_mun_perclass][class_mun] = { { 0 } };//训练样本标签

    for (int i = 0; i <= class_mun - 1; i++)//不同类
    {
        //读取每个类文件夹下所有图像
        int j = 0;//每一类读取图像个数计数
        sprintf(temp, "%d", i);
        fileReadPath = perfileReadPath + "/" + temp+ "/"+fileform;
        cout << "文件夹" << i << endl;
        HANDLE hFile;
        LPCTSTR lpFileName = StringToWchar(fileReadPath);//指定搜索目录和文件类型，如搜索d盘的音频文件可以是"D:\\*.mp3"
        WIN32_FIND_DATA pNextInfo;  //搜索得到的文件信息将储存在pNextInfo中;
        hFile = FindFirstFile(lpFileName, &pNextInfo);//请注意是 &pNextInfo , 不是 pNextInfo;
        if (hFile == INVALID_HANDLE_VALUE)
        {
            cout << "搜索失败" << endl;
            cout << fileReadPath << endl;
            exit(-1);//搜索失败
        }
        //do-while循环读取
        do
        {
            if (pNextInfo.cFileName[0] == '.')//过滤.和..
                continue;
            j++;//读取一张图
            //wcout<
            printf("%s\n", WcharToChar(pNextInfo.cFileName));
            //对读入的图片进行处理
            Mat srcImage = imread(perfileReadPath + "/" + temp+ "/" + WcharToChar(pNextInfo.cFileName), CV_LOAD_IMAGE_GRAYSCALE);
            Mat resizeImage;
            Mat trainImage;

            resize(srcImage, resizeImage, Size(image_cols, image_rows), (0, 0), (0, 0), CV_INTER_AREA);//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
            threshold(resizeImage, trainImage, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);

            for (int k = 0; k1)][k] = (float)trainImage.data[k];

            }

        } while (FindNextFile(hFile, &pNextInfo) && j//如果设置读入的图片数量，则以设置的为准，如果图片不够，则读取文件夹下所有图片

    }

    // Set up training data Mat
    Mat trainingDataMat(class_mun*sample_mun_perclass, image_rows*image_cols, CV_32FC1, trainingData);
    cout << "trainingDataMat——OK！" << endl;

    // Set up label data 
    for (int i = 0; i <= class_mun - 1; ++i)
    {
        for (int j = 0; j <= sample_mun_perclass - 1; ++j)
        {
            for (int k = 0; kif (k == i)
                    labels[i*sample_mun_perclass + j][k] = 1;
                else labels[i*sample_mun_perclass + j][k] = 0;
            }
        }
    }
    Mat labelsMat(class_mun*sample_mun_perclass, class_mun, CV_32FC1, labels);
    //cout << "labelsMat:" << endl;
    //cout << labelsMat << endl;
    cout << "labelsMat——OK！" << endl;

    //训练代码

    cout << "training start...." << endl;
    Ptr bp = ANN_MLP::create();
    Mat layerSizes = (Mat_<int>(1, 5) << image_rows*image_cols, 128, 128, 128, class_mun);
    bp->setLayerSizes(layerSizes);
    bp->setActivationFunction(ANN_MLP::SIGMOID_SYM);

    cout << "training...." << endl;
    bp->setTrainMethod(ANN_MLP::BACKPROP, 0.01);
    PtrtData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
    bp->train(tData);

    bp->save("../bpcharModel.xml"); //save classifier
    cout << "training finish...bpModel1.xml saved " << endl;


    //测试神经网络
    cout << "测试：" << endl;
    Mat test_image = imread("D:/OPENCV/实践小程序/pictures/9.png", CV_LOAD_IMAGE_GRAYSCALE);
    Mat test_temp;
    resize(test_image, test_temp, Size(image_cols, image_rows), (0, 0), (0, 0), CV_INTER_AREA);//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
    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; ifloat>(0, i) = (float)test_temp.at(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;
}

运行效果图