ann

转自 http://blog.csdn.net/yiluoyan/article/details/45308785

这篇文章接着之前的车牌识别，从输入的车图片中分割识别出车牌之后，将进行下一步：车牌号的识别，这里主要使用光学字符识别车牌字符。对每个检测到的车牌，将其每个字符分割出来，然后使用人工神经网络（artificial neural network，ANN）学习算法识别字符。

1.字符分割
将获得的车牌图像进行直方图均衡，然后采用阈值滤波器对图像进行处理，然后查找字符轮廓。

原图像：

阈值图像：

查找轮廓，后画出其外接矩形图像：

然后将字符逐一分割，

分割code：



#include <iostream>

#include <stdlib.h>

#include <vector>

#include <cv.h>

#include <highgui.h>

#include <ml.h>

#include <cvaux.h>

using namespace std;

using namespace  cv;

#define HORIZONTAL 1

#define VERTICAL 0

bool verifySizes(Mat r)  //验证框出来的区域是否为字符

{

    //char sizes 45*77

    float aspect = 45.0f / 77.0f; //字符的宽高比为 45/77

    float charAspect = (float) r.cols / (float) r.rows;

    float error = 0.35;

    float minHeight = 15;

    float maxHeight = 28;

    float minAspect = 0.2;

    float maxAspect = aspect + aspect * error;

    float area = countNonZero(r); //统计区域像素

    float bbArea = r.cols * r.rows; //区域面积

    float percPixels = area / bbArea; //像素比值

    if(percPixels < 0.8 && charAspect > minAspect && charAspect < maxAspect && r.rows >= minHeight && r.rows < maxHeight)

        return true;

    else

        return false;

}

Mat preprocessChar(Mat in)

{

    int h = in.rows;

    int w = in.cols;

    int charSize = 20; //统一字符大小

    Mat transformMat = Mat :: eye(2, 3, CV_32F);

    int m = max (w, h);

    transformMat.at<float>(0,2) = m/2 -w/2;

    transformMat.at<float>(1,2) = m/2 -h/2;

    Mat warpImage(m, m, in.type());

    warpAffine(in, warpImage, transformMat, warpImage.size(), INTER_LINEAR, BORDER_CONSTANT,Scalar(0));

    Mat out;

    resize(warpImage, out, Size( charSize, charSize));

    return out;

}

//计算累积直方图

Mat ProjectedHistogram(Mat img, int t)

{

    int sz = (t) ? img.rows :img.cols;

    Mat mhist = Mat :: zeros(1, sz, CV_32F);

    for (int j =0; j < sz; j++)

    {

        Mat data = (t)? img.row(j) : img.col(j);

        mhist.at<float>(j) = countNonZero(data); //统计这一行/列中的非零元素个数，保存到mhist中

    }

    double min, max;

    minMaxLoc(mhist, &min, &max);

    if (max > 0)

    {

        mhist.convertTo(mhist, -1, 1.0f / max, 0); // 用mhist直方图的最大值，归一化直方图

    }

    return mhist;

}

Mat getVisualHistogram(Mat *hist, int type)

{

    int size =100;

    Mat imHist;

    if(type == HORIZONTAL)

        imHist.create(Size(size, hist->cols), CV_8UC3 );

    else

        imHist.create(Size(hist->cols, size), CV_8UC3);

    imHist = Scalar(55, 55, 55);

    for (int i = 0; i < hist->cols; i++)

    {

        float value = hist->at<float>(i);

        int maxval = (int) (value * size);

        Point pt1;

        Point pt2, pt3, pt4;

        if (type == HORIZONTAL)

        {

            pt1.x = pt3.x = 0;

            pt2.x = pt4.x = maxval;

            pt1.y = pt2.y = i;

            pt3.y = pt4.y = i+1;

            line(imHist, pt1, pt2, CV_RGB(220, 220, 220), 1, 8, 0);

            line(imHist, pt3, pt4, CV_RGB(34, 34, 34), 1, 8, 0);

            pt3.y = pt4.y = i+2;

            line(imHist, pt3, pt4, CV_RGB(44, 44, 44), 1, 8, 0);

            pt3.y = pt4.y = i+3;

            line(imHist, pt3, pt4, CV_RGB(50, 50, 50), 1, 8, 0);

        }

        else

        {

            pt1.x = pt2.x = i;

            pt3.x = pt4.x = i+1;

            pt1.y = pt3.y = 100;

            pt2.y = pt4.y = 100 - maxval;

            line(imHist, pt1, pt2, CV_RGB(220, 220, 220), 1, 8, 0);

            line(imHist, pt3, pt4, CV_RGB(34, 34, 34), 1, 8, 0);

            pt3.x = pt4.x = i+2;

            line(imHist, pt3, pt4, CV_RGB(44, 44, 44), 1, 8, 0);

            pt3.x = pt4.x =i + 3;

            line(imHist, pt3, pt4, CV_RGB(50, 50, 50), 1, 8, 0);

        }

    }

    return imHist;

}

void drawVisualFeatures(Mat charcter, Mat hhist, Mat vhist, Mat lowData, int count)

{

    Mat img(121, 121, CV_8UC3, Scalar(0,0,0));

    Mat ch;

    Mat ld;

    char res[20];

    cvtColor(charcter, ch, CV_GRAY2BGR);

    resize(lowData, ld, Size(100, 100), 0, 0, INTER_NEAREST); //将ld从15*15扩大到100*100

    cvtColor(ld, ld, CV_GRAY2BGR);

    Mat hh = getVisualHistogram(&hhist, HORIZONTAL);

    Mat hv = getVisualHistogram(&vhist, VERTICAL);

    Mat subImg = img(Rect(0, 101, 20, 20));  //ch:20*20

    ch.copyTo(subImg);

    subImg = img(Rect(21, 101, 100, 20));  //hh:100*hist.cols

    hh.copyTo(subImg);

    subImg = img(Rect(0, 0, 20, 100));  //hv:hist.cols*100

    hv.copyTo(subImg);

    subImg = img(Rect(21, 0, 100, 100));  //ld:100*100

    ld.copyTo(subImg);

    line( img, Point(0, 100), Point(121, 100), Scalar(0,0,255) );

    line( img, Point(20, 0), Point(20, 121), Scalar(0,0,255) );

    stringstream ss(stringstream::in | stringstream::out);

    ss << "E://opencvcodetext//ANPR//"<<"hist"<< "_" << count <<" .jpg";

    imwrite(ss.str(), img);

    imshow("visual feature",img); //显示特征

    cvWaitKey(0);

}

Mat features(Mat in, int sizeData, int count)

{

    //直方图特征

    Mat vhist = ProjectedHistogram(in, VERTICAL);

    Mat hhist = ProjectedHistogram(in, HORIZONTAL);

    Mat lowdata;  //低分辨图像特征 sizeData * sizeData

    resize(in, lowdata, Size(sizeData, sizeData));

    drawVisualFeatures(in, hhist, vhist, lowdata, count);  //画出直方图

    int numCols = vhist.cols + hhist.cols + lowdata.cols * lowdata.cols;

    Mat out = Mat::zeros(1, numCols, CV_32F);

    int j = 0;

    for (int i =0; i <vhist.cols; i++)

    {

        out.at<float>(j) = vhist.at<float>(i);

        j++;

    }

    for (int i = 0; i < hhist.cols; i++)

    {

        out.at<float>(j) = hhist.at<float>(i);

        j++;

    }

    for (int x = 0; x <lowdata.cols; x++)

    {

        for (int y = 0; y < lowdata.rows; y++)

        {

            out.at<float>(j) = (float)lowdata.at<unsigned char>(x, y);

            j++;

        }

    }

    return out;

}

int main(int argc, char const *argv[])

{ 

    Mat input = imread("E://opencvcodetext//ANPR//img_2.jpg");

    cvtColor(input,input,CV_RGB2GRAY);

    imshow("srcimg",input);

    Mat img_threshold; //存放二值化后的车牌

    threshold(input, img_threshold, 60, 255, CV_THRESH_BINARY_INV); //CV_THRESH_BINARY_INV参数可将白色点变为黑色，黑色点变为白色

    imshow("阈值化车牌",img_threshold);

    Mat img_contours ; //存放车牌号轮廓

    img_threshold.copyTo(img_contours);  //复制图像

    //查找字符的轮廓

    vector < vector <Point> > contours; //使用向量格式存储轮廓

    findContours(img_contours, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);

    //把找到的轮廓画出来

    Mat result;

    input.copyTo(result); //先复制车牌图像

    cvtColor(result, result, CV_GRAY2BGR); //转为彩色图像，方便看轮廓

    vector <vector <Point> >::iterator itc = contours.begin();//定义一个迭代器访问轮廓

    int i = 0;

    while (itc != contours.end())

    {

        Rect mr = boundingRect( Mat(* itc)); //创建框图

        rectangle(result, mr, Scalar(255, 0, 0), 2);

        Mat auxRoi(img_threshold, mr);  //根据轮廓裁剪出字符

        if (verifySizes(auxRoi))  //判断是否是字符

        {

            auxRoi = preprocessChar(auxRoi); //对字符进行处理

            //sprintf(res, "train_data_%d,jpg",i);

            i++;

            stringstream ss(stringstream::in | stringstream::out);

            ss << "E://opencvcodetext//ANPR//"<<"train_data"<< "_" << i <<" .jpg";

            imwrite(ss.str(), auxRoi);

            //imwrite(res,auxRoi);

            rectangle(result, mr, Scalar(0, 255, 0),2);

            Mat f = features(auxRoi, 15, i); //提取字符的直方图特征

        }

        ++itc;

    }

    imwrite("E://opencvcodetext//ANPR//result1.jpg",result);

    imshow("car_plate",result);

    cvWaitKey(20);

    system("pause");

    return 0;

}

2.人工神经网络（ANN）
分类字符这一步将使用人工神经网络，即使用多层感知器（Multi-Layer Perceptron, MLP). MLP 由一个输入层、一个输出层和一个或多个隐藏层的神经网络组成。每层有一个或多个神经元痛前一层和后一层相连。
如下图是一个3层的神经元感知器，有3个输入和2个输出，以及包含5个神经元的隐藏层。

每个神经元通过输入权重加上一个偏移项来计算输出值，并由所选择的激励函数进行转换。神经元结构为：

常见的激励函数有S型、高斯型、上图的hadrlim型。单层的单个神经元可以将输入向量分为两类，而一个有S个神经元的感知机，可以将输入向量分为2^S类

神经网络的详细理论可以参考：http://blog.csdn.net/zzwu/article/details/575050

3.特征提取及训练分类器

与SVM分类类似，使用ANN算法识别字符同样需要建立分类器、训练分类器，最后使用分类器识别字符。这里我们使用的特征不是像素值，而是分割字符水平和竖直的累积直方图与字符的低分辨率图像。

对每一个字符，通过使用countNonZero函数来按行或按列统计非零像素值个数，将其保存在mhist中，并对mhist进行归一化处理。

得到的特征图像如下图所示：（左下角为字符图像原始大小20*20，由上角为低分辨率采样后放大的图像100*100，右下角为水平直方图，左上角为垂直直方图）

提取特征完毕后，将特征与对应的标注写到xml文件中，code如下：



#include <iostream>

#include <stdlib.h>

#include <vector>

#include <cv.h>

#include <highgui.h>

#include <ml.h>

#include <cvaux.h>

using namespace std;

using namespace  cv;

#define HORIZONTAL 1

#define VERTICAL 0

//针对书本的西班牙车牌，一共有30个字符（10个数字和20个字母），下面的数组存储的是每个字符的图片个数

const int numFilesChar[] = {35, 40, 42, 41, 42, 33, 30, 31, 49, 44, 30, 24, 21, 20, 34, 9, 10, 3, 11, 3, 15, 4, 9, 12, 10, 21, 18, 8, 15, 7};

const char strCharacters[] = {'0','1','2','3','4','5','6','7','8','9','B', 'C', 'D', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'R', 'S', 'T', 'V', 'W', 'X', 'Y', 'Z'};

const int numCharacters = 30;

//下面这些参数是自己试验的时候设置的一个较为简单的验证

//const int numFilesChar[] = {1, 1, 1, 1, 1, 1, 1};

//const char strCharacters[] = {'1','2','5','7', 'D', 'T', 'Z'};

//const int numCharacters = 7;

bool verifySizes(Mat r)  //验证框出来的区域是否为字符

{

    //char sizes 45*77

    float aspect = 45.0f / 77.0f; //字符的宽高比为 45/77

    float charAspect = (float) r.cols / (float) r.rows;

    float error = 0.35;

    float minHeight = 15;

    float maxHeight = 28;

    float minAspect = 0.2;

    float maxAspect = aspect + aspect * error;

    float area = countNonZero(r); //统计区域像素

    float bbArea = r.cols * r.rows; //区域面积

    float percPixels = area / bbArea; //像素比值

    if(percPixels < 0.8 && charAspect > minAspect && charAspect < maxAspect && r.rows >= minHeight && r.rows < maxHeight)

        return true;

    else

        return false;

}

Mat preprocessChar(Mat in)

{

    int h = in.rows;

    int w = in.cols;

    int charSize = 20; //统一字符大小

    Mat transformMat = Mat :: eye(2, 3, CV_32F);

    int m = max (w, h);

    transformMat.at<float>(0,2) = m/2 -w/2;

    transformMat.at<float>(1,2) = m/2 -h/2;

    Mat warpImage(m, m, in.type());

    warpAffine(in, warpImage, transformMat, warpImage.size(), INTER_LINEAR, BORDER_CONSTANT,Scalar(0));

    Mat out;

    resize(warpImage, out, Size( charSize, charSize));

    return out;

}

//计算累积直方图

Mat ProjectedHistogram(Mat img, int t)

{

    int sz = (t) ? img.rows :img.cols;

    Mat mhist = Mat :: zeros(1, sz, CV_32F);

    for (int j =0; j < sz; j++)

    {

        Mat data = (t)? img.row(j) : img.col(j);

        mhist.at<float>(j) = countNonZero(data); //统计这一行/列中的非零元素个数，保存到mhist中

    }

    double min, max;

    minMaxLoc(mhist, &min, &max);

    if (max > 0)

    {

        mhist.convertTo(mhist, -1, 1.0f / max, 0); // 用mhist直方图的最大值，归一化直方图

    }

    return mhist;

}

Mat getVisualHistogram(Mat *hist, int type)

{

    int size =100;

    Mat imHist;

    if(type == HORIZONTAL)

        imHist.create(Size(size, hist->cols), CV_8UC3 );

    else

        imHist.create(Size(hist->cols, size), CV_8UC3);

    imHist = Scalar(55, 55, 55);

    for (int i = 0; i < hist->cols; i++)

    {

        float value = hist->at<float>(i);

        int maxval = (int) (value * size);

        Point pt1;

        Point pt2, pt3, pt4;

        if (type == HORIZONTAL)

        {

            pt1.x = pt3.x = 0;

            pt2.x = pt4.x = maxval;

            pt1.y = pt2.y = i;

            pt3.y = pt4.y = i+1;

            line(imHist, pt1, pt2, CV_RGB(220, 220, 220), 1, 8, 0);

            line(imHist, pt3, pt4, CV_RGB(34, 34, 34), 1, 8, 0);

            pt3.y = pt4.y = i+2;

            line(imHist, pt3, pt4, CV_RGB(44, 44, 44), 1, 8, 0);

            pt3.y = pt4.y = i+3;

            line(imHist, pt3, pt4, CV_RGB(50, 50, 50), 1, 8, 0);

        }

        else

        {

            pt1.x = pt2.x = i;

            pt3.x = pt4.x = i+1;

            pt1.y = pt3.y = 100;

            pt2.y = pt4.y = 100 - maxval;

            line(imHist, pt1, pt2, CV_RGB(220, 220, 220), 1, 8, 0);

            line(imHist, pt3, pt4, CV_RGB(34, 34, 34), 1, 8, 0);

            pt3.x = pt4.x = i+2;

            line(imHist, pt3, pt4, CV_RGB(44, 44, 44), 1, 8, 0);

            pt3.x = pt4.x =i + 3;

            line(imHist, pt3, pt4, CV_RGB(50, 50, 50), 1, 8, 0);

        }

    }

    return imHist;

}

void drawVisualFeatures(Mat charcter, Mat hhist, Mat vhist, Mat lowData, int count)

{

    Mat img(121, 121, CV_8UC3, Scalar(0,0,0));

    Mat ch;

    Mat ld;

    char res[20];

    cvtColor(charcter, ch, CV_GRAY2BGR);

    resize(lowData, ld, Size(100, 100), 0, 0, INTER_NEAREST); //将ld从15*15扩大到100*100

    cvtColor(ld, ld, CV_GRAY2BGR);

    Mat hh = getVisualHistogram(&hhist, HORIZONTAL);

    Mat hv = getVisualHistogram(&vhist, VERTICAL);

    Mat subImg = img(Rect(0, 101, 20, 20));  //ch:20*20

    ch.copyTo(subImg);

    subImg = img(Rect(21, 101, 100, 20));  //hh:100*hist.cols

    hh.copyTo(subImg);

    subImg = img(Rect(0, 0, 20, 100));  //hv:hist.cols*100

    hv.copyTo(subImg);

    subImg = img(Rect(21, 0, 100, 100));  //ld:100*100

    ld.copyTo(subImg);

    line( img, Point(0, 100), Point(121, 100), Scalar(0,0,255) );

    line( img, Point(20, 0), Point(20, 121), Scalar(0,0,255) );

    stringstream ss(stringstream::in | stringstream::out);

    ss << "E://opencvcodetext//ANPR//"<<"hist"<< "_" << count <<" .jpg";

    imwrite(ss.str(), img);

    /*sprintf(res, "hist%d.jpg",count);

    imwrite(res, img);*/

    imshow("visual feature",img);

    cvWaitKey(0);

}

Mat features(Mat in, int sizeData, int count)

{

    //直方图特征

    Mat vhist = ProjectedHistogram(in, VERTICAL);

    Mat hhist = ProjectedHistogram(in, HORIZONTAL);

    Mat lowdata;  //低分辨图像特征 sizeData * sizeData

    resize(in, lowdata, Size(sizeData, sizeData));

    drawVisualFeatures(in, hhist, vhist, lowdata, count);  //画出直方图

    int numCols = vhist.cols + hhist.cols + lowdata.cols * lowdata.cols;

    Mat out = Mat::zeros(1, numCols, CV_32F);

    int j = 0;

    for (int i =0; i <vhist.cols; i++)

    {

        out.at<float>(j) = vhist.at<float>(i);

        j++;

    }

    for (int i = 0; i < hhist.cols; i++)

    {

        out.at<float>(j) = hhist.at<float>(i);

        j++;

    }

    for (int x = 0; x <lowdata.cols; x++)

    {

        for (int y = 0; y < lowdata.rows; y++)

        {

            out.at<float>(j) = (float)lowdata.at<unsigned char>(x, y);

            j++;

        }

    }

    return out;

}

Mat Features(Mat in, int sizeData)

{

    Mat vhist = ProjectedHistogram(in, VERTICAL);

    Mat hhist = ProjectedHistogram(in, HORIZONTAL);

    Mat lowData;

    resize( in, lowData, Size (sizeData, sizeData));

    int numCols = vhist.cols + hhist.cols + lowData.cols *lowData.cols;

    Mat out = Mat ::zeros(1, numCols, CV_32F);

    //将特征写到矩阵中

    int j = 0;

    for (int i = 0; i < vhist.cols; i++)

    {

        out.at <float> (j) = vhist.at<float>(i);

        j++;

    }

    for (int i =0; i <  hhist.cols; i++)

    {

        out.at<float>(j) = (float) hhist.at <float> (i);

        j++;

    }

    for (int x = 0; x < lowData.cols; x++)

    {

        for (int y = 0; y < lowData.rows; y++)

        {

            out.at<float>(j) = (float) lowData.at<unsigned char>(x,y);

            j++;

        }

    }

    return out;

}

int main(int argc, char const *argv[])

{

    //char *path = "E://opencvcodetext//ANPR//characters";

    Mat classes;

    Mat trainingDataf5;

    Mat trainingDataf10;

    Mat trainingDataf15;

    Mat trainingDataf20;

    vector <int> trainingLabels;

    for (int i = 0; i < numCharacters; i++)

    {

        int numFiles = numFilesChar[i];

        for (int j = 0; j < numFiles; j++)

        {

            cout << "Character " << strCharacters[i] << " files" << j <<"\n";

            stringstream ss (stringstream::in |stringstream::out);

            ss << "E://opencvcodetext//ANPR//characters//" << strCharacters[i] <<"1.jpg";

            Mat img = imread(ss.str(), 0);

            //Mat img = imread("E://opencvcodetext//ANPR//characters//21.jpg",0);

            imshow("char",img);

            Mat f5 = Features(img, 5);

            Mat f10 = Features(img, 10);

            Mat f15 = Features(img, 15);

            Mat f20 = Features(img, 20);

            trainingDataf5.push_back(f5);

            trainingDataf10.push_back(f10);

            trainingDataf15.push_back(f15);

            trainingDataf20.push_back(f20);

            trainingLabels.push_back(i);         //每一幅字符图片所对应的字符类别索引下标

        }

    }

    //将矩阵转换成浮点矩阵

    trainingDataf5.convertTo(trainingDataf5, CV_32FC1);

    trainingDataf10.convertTo(trainingDataf10, CV_32FC1);

    trainingDataf15.convertTo(trainingDataf15,CV_32FC1);

    trainingDataf20.convertTo(trainingDataf20,CV_32FC1);

    Mat (trainingLabels).convertTo(classes, CV_32FC1);

    FileStorage   fs("E://opencvcodetext//ANPR//characters//OCR.xml",FileStorage::WRITE);

    fs << "trainingDataF5" << trainingDataf5;

    fs << "trainingDataF10" << trainingDataf10;

    fs <<"trainingDataF15" << trainingDataf15;

    fs << "trainingDataF20" << trainingDataf20;

    fs <<"classes" <<classes;

    cvWaitKey(20);

    system("pause");

    return 0;

}

字符分类

（1）得到ANN训练文件xml后，利用OpenCV中的CvANN_MLP类来使用ANN算法，识别字符。先通过create函数初始化该类，初始化时需要指定神经网络的层数、神经元数、激励函数、alpha和beta。

（2）训练完ANN分类器后，可以使用predict函数来对特征向量分类，该函数返回一行，大小为类的数量，该向量的每一个元素安阳了输入样本属于每个类的概率。字符的类别有向量中的最大值确定。
分类的code：



#include <iostream>

#include <stdlib.h>

#include <vector>

#include <cv.h>

#include <highgui.h>

#include <ml.h>

#include <cvaux.h>

using namespace std;

using namespace  cv;

#define HORIZONTAL 1

#define VERTICAL 0

CvANN_MLP ann;

//针对书本的西班牙车牌，一共有30个字符（10个数字和20个字母），下面的数组存储的是每个字符的图片个数

const int numFilesChar[] = {35, 40, 42, 41, 42, 33, 30, 31, 49, 44, 30, 24, 21, 20, 34, 9, 10, 3, 11, 3, 15, 4, 9, 12, 10, 21, 18, 8, 15, 7};

const char strCharacters[] = {'0','1','2','3','4','5','6','7','8','9','B', 'C', 'D', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'R', 'S', 'T', 'V', 'W', 'X', 'Y', 'Z'};

const int numCharacters = 30;

//const int numFilesChar[] = {1, 1, 1, 1, 1, 1, 1};

//const char strCharacters[] = {'1','2','5','7', 'D', 'T', 'Z'};

//const int numCharacters = 7;

bool verifySizes(Mat r)  //验证框出来的区域是否为字符

{

    //char sizes 45*77

    float aspect = 45.0f / 77.0f; //字符的宽高比为 45/77

    float charAspect = (float) r.cols / (float) r.rows;

    float error = 0.35;

    float minHeight = 15;

    float maxHeight = 28;

    float minAspect = 0.2;

    float maxAspect = aspect + aspect * error;

    float area = countNonZero(r); //统计区域像素

    float bbArea = r.cols * r.rows; //区域面积

    float percPixels = area / bbArea; //像素比值

    if(percPixels < 0.8 && charAspect > minAspect && charAspect < maxAspect && r.rows >= minHeight && r.rows < maxHeight)

        return true;

    else

        return false;

}

Mat preprocessChar(Mat in)

{

    int h = in.rows;

    int w = in.cols;

    int charSize = 20; //统一字符大小

    Mat transformMat = Mat :: eye(2, 3, CV_32F);

    int m = max (w, h);

    transformMat.at<float>(0,2) = m/2 -w/2;

    transformMat.at<float>(1,2) = m/2 -h/2;

    Mat warpImage(m, m, in.type());

    warpAffine(in, warpImage, transformMat, warpImage.size(), INTER_LINEAR, BORDER_CONSTANT,Scalar(0));

    Mat out;

    resize(warpImage, out, Size( charSize, charSize));

    return out;

}

//计算累积直方图

Mat ProjectedHistogram(Mat img, int t)

{

    int sz = (t) ? img.rows :img.cols;

    Mat mhist = Mat :: zeros(1, sz, CV_32F);

    for (int j =0; j < sz; j++)

    {

        Mat data = (t)? img.row(j) : img.col(j);

        mhist.at<float>(j) = countNonZero(data); //统计这一行/列中的非零元素个数，保存到mhist中

    }

    double min, max;

    minMaxLoc(mhist, &min, &max);

    if (max > 0)

    {

        mhist.convertTo(mhist, -1, 1.0f / max, 0); // 用mhist直方图的最大值，归一化直方图

    }

    return mhist;

}

Mat getVisualHistogram(Mat *hist, int type)

{

    int size =100;

    Mat imHist;

    if(type == HORIZONTAL)

        imHist.create(Size(size, hist->cols), CV_8UC3 );

    else

        imHist.create(Size(hist->cols, size), CV_8UC3);

    imHist = Scalar(55, 55, 55);

    for (int i = 0; i < hist->cols; i++)

    {

        float value = hist->at<float>(i);

        int maxval = (int) (value * size);

        Point pt1;

        Point pt2, pt3, pt4;

        if (type == HORIZONTAL)

        {

            pt1.x = pt3.x = 0;

            pt2.x = pt4.x = maxval;

            pt1.y = pt2.y = i;

            pt3.y = pt4.y = i+1;

            line(imHist, pt1, pt2, CV_RGB(220, 220, 220), 1, 8, 0);

            line(imHist, pt3, pt4, CV_RGB(34, 34, 34), 1, 8, 0);

            pt3.y = pt4.y = i+2;

            line(imHist, pt3, pt4, CV_RGB(44, 44, 44), 1, 8, 0);

            pt3.y = pt4.y = i+3;

            line(imHist, pt3, pt4, CV_RGB(50, 50, 50), 1, 8, 0);

        }

        else

        {

            pt1.x = pt2.x = i;

            pt3.x = pt4.x = i+1;

            pt1.y = pt3.y = 100;

            pt2.y = pt4.y = 100 - maxval;

            line(imHist, pt1, pt2, CV_RGB(220, 220, 220), 1, 8, 0);

            line(imHist, pt3, pt4, CV_RGB(34, 34, 34), 1, 8, 0);

            pt3.x = pt4.x = i+2;

            line(imHist, pt3, pt4, CV_RGB(44, 44, 44), 1, 8, 0);

            pt3.x = pt4.x =i + 3;

            line(imHist, pt3, pt4, CV_RGB(50, 50, 50), 1, 8, 0);

        }

    }

    return imHist;

}

void drawVisualFeatures(Mat charcter, Mat hhist, Mat vhist, Mat lowData, int count)

{

    Mat img(121, 121, CV_8UC3, Scalar(0,0,0));

    Mat ch;

    Mat ld;

    cvtColor(charcter, ch, CV_GRAY2BGR);

    resize(lowData, ld, Size(100, 100), 0, 0, INTER_NEAREST); //将ld从15*15扩大到100*100

    cvtColor(ld, ld, CV_GRAY2BGR);

    Mat hh = getVisualHistogram(&hhist, HORIZONTAL);

    Mat hv = getVisualHistogram(&vhist, VERTICAL);

    Mat subImg = img(Rect(0, 101, 20, 20));  //ch:20*20

    ch.copyTo(subImg);

    subImg = img(Rect(21, 101, 100, 20));  //hh:100*hist.cols

    hh.copyTo(subImg);

    subImg = img(Rect(0, 0, 20, 100));  //hv:hist.cols*100

    hv.copyTo(subImg);

    subImg = img(Rect(21, 0, 100, 100));  //ld:100*100

    ld.copyTo(subImg);

    line( img, Point(0, 100), Point(121, 100), Scalar(0,0,255) );

    line( img, Point(20, 0), Point(20, 121), Scalar(0,0,255) );

    stringstream ss(stringstream::in | stringstream::out);

    ss << "E://opencvcodetext//ANPR//"<<"hist"<< "_" << count <<" .jpg";

    imwrite(ss.str(), img);

    /*sprintf(res, "hist%d.jpg",count);

    imwrite(res, img);*/

    imshow("visual feature",img);

    cvWaitKey(0);

}

Mat features(Mat in, int sizeData, int count)

{

    //直方图特征

    Mat vhist = ProjectedHistogram(in, VERTICAL);

    Mat hhist = ProjectedHistogram(in, HORIZONTAL);

    Mat lowdata;  //低分辨图像特征 sizeData * sizeData

    resize(in, lowdata, Size(sizeData, sizeData));

    drawVisualFeatures(in, hhist, vhist, lowdata, count);  //画出直方图

    int numCols = vhist.cols + hhist.cols + lowdata.cols * lowdata.cols;

    Mat out = Mat::zeros(1, numCols, CV_32F);

    int j = 0;

    for (int i =0; i <vhist.cols; i++)

    {

        out.at<float>(j) = vhist.at<float>(i);

        j++;

    }

    for (int i = 0; i < hhist.cols; i++)

    {

        out.at<float>(j) = hhist.at<float>(i);

        j++;

    }

    for (int x = 0; x <lowdata.cols; x++)

    {

        for (int y = 0; y < lowdata.rows; y++)

        {

            out.at<float>(j) = (float)lowdata.at<unsigned char>(x, y);

            j++;

        }

    }

    return out;

}

Mat Features(Mat in, int sizeData)

{

    Mat vhist = ProjectedHistogram(in, VERTICAL);

    Mat hhist = ProjectedHistogram(in, HORIZONTAL);

    Mat lowData;

    resize( in, lowData, Size (sizeData, sizeData));

    int numCols = vhist.cols + hhist.cols + lowData.cols *lowData.cols;

    Mat out = Mat ::zeros(1, numCols, CV_32F);

    //将特征写到矩阵中

    int j = 0;

    for (int i = 0; i < vhist.cols; i++)

    {

        out.at <float> (j) = vhist.at<float>(i);

        j++;

    }

    for (int i =0; i <  hhist.cols; i++)

    {

        out.at<float>(j) = (float) hhist.at <float> (i);

        j++;

    }

    for (int x = 0; x < lowData.cols; x++)

    {

        for (int y = 0; y < lowData.rows; y++)

        {

            out.at<float>(j) = (float) lowData.at<unsigned char>(x,y);

            j++;

        }

    }

    return out;

}

void train(Mat TrainData, Mat classes, int nlayers)

{

    Mat layerSizes(1, 3, CV_32SC1);

    layerSizes.at< int > (0) = TrainData.cols;

    layerSizes.at <int> (1) = nlayers;

    layerSizes.at < int > (2) = numCharacters;

    ann.create(layerSizes, CvANN_MLP :: SIGMOID_SYM, 1, 1); 

    Mat trainClasses;

    trainClasses.create(TrainData.rows, numCharacters, CV_32FC1);

    for (int i =0; i < trainClasses.rows; i++)

    {

        for (int k = 0; k < trainClasses.cols; k++)

        {

            if( k == classes.at< int> (i))

                trainClasses.at <float>(i,k)=1;

            else

                trainClasses.at<float>(i, k) = 0;

        }

    }

    Mat weights( 1, TrainData.rows, CV_32FC1, Scalar::all(1));

    ann.train( TrainData, trainClasses, weights);

    //trained = true;

}

int Classify (Mat f)

{

    int result = -1;

    Mat output( 1, numCharacters, CV_32FC1);

    ann.predict(f, output);

    Point maxLoc;

    double maxVal;

    minMaxLoc(output, 0, &maxVal, 0, &maxLoc);

    return maxLoc.x;

}

int main(int argc, char const *argv[])

{

    //char *path = "E://opencvcodetext//ANPR//characters";

    Mat classes;

    Mat trainingData;

    FileStorage fs;

     fs.open("E:/opencvcodetext/ANPR/OCR.xml",FileStorage::READ);

    fs [ "TrainingDataF10"] >> trainingData;

    fs ["classes" ]>> classes;

    train(trainingData, classes, 10); //训练神经网络

    Mat input = imread("E:/opencvcodetext/ANPR/img_2.jpg",CV_LOAD_IMAGE_GRAYSCALE);

    Mat img_threshold ;

    threshold( input, img_threshold, 60, 255, CV_THRESH_BINARY_INV);

    Mat img_contours;

    img_threshold.copyTo(img_contours);

    vector < vector <Point> > contours;

    findContours( img_contours, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);

    vector < vector <Point> > :: iterator itc = contours.begin();

    while (itc != contours.end())

    {

        Rect mr = boundingRect( Mat (*itc));

        Mat auxRoi (img_threshold, mr);

        if (verifySizes(auxRoi))

        {

            auxRoi = preprocessChar(auxRoi);

            Mat f = Features(auxRoi, 10);

            int charcter = Classify(f);

            printf("%c", strCharacters[charcter]);

        }

        ++itc;

    }

    printf("\n");

    cvWaitKey(20);

    system("pause");

    return 0;

}

实验结果：这里的顺序改变了，应该要还原字符的位置。。而且识别也不是百分百的准确，出现了错误：将 7 识别为 T 了。
原图：

识别：

最后，写一下调试程序的错误总结：
1. 二值黑白图像不等同于灰度图像
2. 图像的像素值是unsight char的
3. 在测试时，注意提取特征的一一对应，注意TrainingDataF10以及后面的 Features(auxRoi, 10) 的关系。
4. 另外，检测出的字符与原车牌的顺序有乱，应该进行调整（待续）

ann的更多相关文章

从下往上看--新皮层资料的读后感第三部分 70年前的逆向推演- 从NN到ANN
第三部分 NN-ANN 70年前的逆向推演从这部分开始,调整一下视角主要学习神经网络算法,将其与生物神经网络进行横向的比较,以窥探一二. 现在基于NN的AI应用几乎是满地都是,效果也不错,这种貌似神 ...
机器学习笔记之人工神经网络(ANN)
人工神经网络(ANN)提供了一种普遍而且实际的方法从样例中学习值为实数.离散值或向量函数.人工神经网络由一系列简单的单元相互连接构成,其中每个单元有一定数量的实值输入,并产生单一的实值输出. 上面是一 ...
Codeforces Round #311 (Div. 2) E. Ann and Half-Palindrome 字典树/半回文串
E. Ann and Half-Palindrome Time Limit: 20 Sec Memory Limit: 256 MB 题目连接 http://codeforces.com/contes ...
利用Multi-Probe LSH构建ANN高维索引
感谢大神们的无私奉献精神........因此笔者要坚持开源,专注开源,开源就像在HPU的考试中不像其他人作弊一样,长远来看,会有巨大收获. 一.背景介绍 1.1 相似性搜索简介高维相似性搜索在音频. ...
【转】漫谈ANN(2)：BP神经网络
上一次我们讲了M-P模型,它实际上就是对单个神经元的一种建模,还不足以模拟人脑神经系统的功能.由这些人工神经元构建出来的网络,才能够具有学习.联想.记忆和模式识别的能力.BP网络就是一种简单的人工神经 ...
Codeforces Round #311 (Div. 2) E - Ann and Half-Palindrome（字典树+dp）
E. Ann and Half-Palindrome time limit per test 1.5 seconds memory limit per test 512 megabytes input ...
OpenCV——ANN神经网络
ANN-- Artificial Neural Networks 人工神经网络 //定义人工神经网络 CvANN_MLP bp; // Set up BPNetwork's parameters Cv ...
目前所有的ANN神经网络算法大全
http://blog.sina.com.cn/s/blog_98238f850102w7ik.html 目前所有的ANN神经网络算法大全 (2016-01-20 10:34:17) 转载▼ 标签: ...
【机器学习】人工神经网络ANN
神经网络是从生物领域自然的鬼斧神工中学习智慧的一种应用.人工神经网络(ANN)的发展经历的了几次高潮低谷,如今,随着数据爆发.硬件计算能力暴增.深度学习算法的优化,我们迎来了又一次的ANN雄起时代,以 ...
ANN实现
ANN核心数据结构: typedef struct { int input_n; /* number of input units */ int h ...

随机推荐

20165210 预习作业3 Linux安装及学习
Linux安装及学习一.Linux安装通过参考教程:基于VirtualBox虚拟机安装Ubuntu图文教程成功的安装上了Linux但在过程中还是有一些小问题的. 首先按着步骤走,一开始没什么毛病, ...
_Meta 部分用法
model.UserInfo._meta.app_label #获取该类所在app的app名称 model.UserInfo._meta.model_name #获取该类对应表名(字符串类型) mod ...
BZOJ - 4196 软件包管理器 (树链剖分+dfs序+线段树)
题目链接设白色结点为未安装的软件,黑色结点为已安装的软件,则: 安装软件i:输出结点i到根的路径上的白色结点的数量,并把结点i到根的路径染成黑色.复杂度$O(nlog^2n)$ 卸载软件i:输出结点 ...
asp.net core mcroservices 架构之分布式日志（二）之自定义日志开发
netcore日志原理 netcore的日志是作为一个扩展库存在的,每个组件都有它的入口,那么作为研究这个组件的入口是最好的,首先看两种方式: 这个是源码例子提供的. var loggingConfi ...
php 字符串的分割
http://blog.sina.com.cn/s/blog_71ed1b870102uysa.html
蓝桥杯算法训练 ALGO-152 8-2求完数
算法训练 8-2求完数时间限制:50.0s 内存限制:256.0MB 问题描述如果一个自然数的所有小于自身的因子之和等于该数,则称为完数.设计算法,打印1-9999之间的所有完数. 样例 ...
java继承子类重写父类方法
package com.addd; //多态 public class Sld { private String name = "zhangsan"; public Sld() { ...
java代码。从来没想过java里的继承是多么的难懂。如哲学
总结:实例.. 这里不加super("aaa",32); 运行:父类和子类的姓名,年龄是一样的.那这个super为什么没效果呢? 显示:class:Ji姓名是小红年龄是:20课程 ...
[置顶] strcpy()与strncpy()的区别
头文件:#include <string.h> strcpy() 函数用来复制字符串,其原型为: char *strcpy(char *dest, const char *src); [参 ...
showModalDialog()子窗口刷新父窗口
今天再次使用showModalDialog(),发现了两个问题,一是子窗口如何刷新父窗口,二是窗口的参数问题. 1 子窗口刷新父窗口如果是window.open();问题就好办,直接用window. ...

ann

ann的更多相关文章

随机推荐

热门专题