最邻近算法KNN识别字符

#include <io.h> #include <string> #include <iostream> #include <opencv2\opencv.hpp> #include <opencv2\ml.hpp> #include<numeric> using namespace cv; using namespace ml; //利用KNN识别字符 int main() { ////===============================读取训练数据===============================//// //图片共有10类 const int classSum = 26; //每类共50张图片 const int imagesSum = 10; //图片尺寸 const int imageRows = 33; const int imageCols = 26; //每一行一个训练图片 float trainingData[classSum*imagesSum][imageRows*imageCols] = {{0}}; //训练样本标签 float labels[classSum*imagesSum]={0}; for (int i = 0; i < classSum; i++) { //目标文件夹路径 std::string inPath = "E:\\image\\letters\\"; char label = 'a'; int k = 0; label = label + i; inPath = inPath + label + "\\*.jpg"; //用于查找的句柄 long handle; struct _finddata_t fileinfo; //第一次查找 handle = _findfirst(inPath.c_str(),&fileinfo); if(handle == -1) return -1; do { //找到的文件的文件名 std::string imgname = "E:/image/letters/"; imgname = imgname + label + "/" + fileinfo.name; //std::cout<<imgname<<std::endl; Mat src = imread(imgname, 0); if (src.empty()) { std::cout<<"can not load image \n"<<std::endl; return -1; } //序列化后放入作为样本矩阵的一行 for(int j = 0; j<imageRows*imageCols; j++) { trainingData[i*imagesSum + k][j] = (float)src.data[j]; } // 设置样本标签 labels[i*imagesSum + k] = label; k++; } while (!_findnext(handle, &fileinfo)); _findclose(handle); } //训练样本数据及对应标签 Mat trainingDataMat(classSum*imagesSum, imageRows*imageCols, CV_32FC1, trainingData); Mat labelsMat(classSum*imagesSum, 1, CV_32FC1, labels); //std::cout<<trainingDataMat<<std::endl; //std::cout<<labelsMat<<std::endl; ////===============================创建KNN模型===============================//// Ptr<KNearest>model = KNearest::create(); model->setDefaultK(classSum); model->setIsClassifier(true); Ptr<TrainData>trainData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat); model->train(trainData); //model->save("E:/image/KNearestModel.xml"); ////===============================预测部分===============================//// Mat src, dst; src = imread("E:/image/image/letters.png"); if (src.empty()) { std::cout<<"can not load image \n"<<std::endl; return -1; } dst = src.clone(); //创建感兴趣区域,选取右侧10列作为预测数据 cvtColor(src, src, COLOR_BGR2GRAY); blur(src, src, Size(9,9)); threshold(src, src, 210, 255, THRESH_BINARY); Canny(src, src, 20, 80, 3, false); std::vector<std::vector<Point>> contours; std::vector<Vec4i>hierarchy; findContours(src, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0,0)); for (int i = 0; i < contours.size(); i++) { Rect rect = boundingRect(contours[i]); //以矩形中心及指定的宽高作为字符区域 Rect reRect = Rect(Point(rect.x + rect.width/2.0 - imageCols/2.0,rect.y + rect.height/2.0 - imageRows/2.0), Point(rect.x + rect.width/2.0 + imageCols/2.0, rect.y + rect.height/2.0 + imageRows/2.0)); Mat sampleImg; cvtColor(dst, sampleImg, COLOR_BGR2GRAY); threshold(sampleImg, sampleImg, 0, 255, CV_THRESH_BINARY|CV_THRESH_OTSU); Mat sample = Mat::zeros(Size(imageCols,imageRows), sampleImg.type()); float sampleData[imageRows*imageCols]; int nub = 0; for (int r = 0; r < imageRows; r++) { for (int c = 0; c < imageCols; c++) { sampleData[nub] = sampleImg.at<uchar>(reRect.y+r,reRect.x+c); nub++; } } Mat sampleDataMat(1, imageRows*imageCols, CV_32FC1, sampleData); char f; f = model->predict(sampleDataMat); char temp[256]; sprintf_s(temp, "%c", f); std::cout<<temp<<"\n"<<std::endl; std::string text(temp); RNG rng(f); Scalar color = Scalar(rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255)); putText(dst, text, Point(reRect.x,reRect.y + reRect.height),1,1.5,color,2); } imshow("output",dst); waitKey(); return 0; }