树莓派 OpenCV 数码管数字识别

#include<iostream> #include<opencv2\opencv.hpp> #include<opencv2\core\core.hpp> #include<opencv2\highgui\highgui.hpp> #include<opencv2\ml\ml.hpp> #include<opencv2\imgproc\imgproc.hpp> using namespace std; using namespace cv; using namespace cv::ml; bool cmp(const Rect &a, const Rect &b); vector<Mat> get_mats(Mat mt); bool has_Point(Mat mt); int main(){ string changename(int i,int j); string get_name(int i); Mat traindata, train_label, tmp; for (int j = 0; j < 10; j++){ for (int i = 0; i < 40; i++){ string name = changename(j,i); //cout << name << endl; tmp = imread(name, 0); resize(tmp, tmp, Size(75, 125)); traindata.push_back(tmp.reshape(0, 1)); train_label.push_back(j); } } traindata.convertTo(traindata, CV_32F); Ptr<TrainData> tData = TrainData::create(traindata, ROW_SAMPLE, train_label); Ptr<KNearest> knn = KNearest::create(); knn->setDefaultK(3); knn->setIsClassifier(true); knn->train(tData); Mat mymt = imread("f:/RecordedImage_GO-5000C-USB__000.tif"); vector<Mat> dds=get_mats(mymt); ostringstream oss; for (int n = 0; n < dds.size(); n++){ dds[n].copyTo(tmp); resize(tmp, tmp, Size(75, 125)); tmp = tmp.reshape(0, 1); tmp.convertTo(tmp, CV_32F); int result = knn->predict(tmp); oss << result; if (has_Point(dds[n])) oss << "."; } cout << oss.str() << endl; } string changename(int i,int j){ ostringstream oss; oss <<"f:/"<< i << "_out/" <<j<< ".jpg"; string name = oss.str(); return name; } string get_name(int i){ ostringstream oss; oss << "f:/tmp/" << i << ".jpg"; return oss.str(); } vector<Mat> get_mats(Mat mt){ vector<Mat> channels,result_datas; Mat image_bin, result, ele, image_bin2, temp; split(mt, channels); //阈值选取很重要 threshold(channels[2], image_bin2, 50, 255, THRESH_BINARY); ele = getStructuringElement(MORPH_RECT, Size(5, 5)); // 膨胀 dilate(image_bin2, image_bin, ele); image_bin.copyTo(temp); vector<vector<Point>> conts; vector<Vec4i> hire; //轮廓提取 findContours(image_bin, conts, hire, RETR_EXTERNAL, CHAIN_APPROX_NONE); vector<Rect> rects; vector<Mat> datas; temp.copyTo(image_bin); //threshold(mt,image_bin,150,255,THRESH_BINARY); for (int i = 0; i<conts.size(); i++){ Rect t = boundingRect(Mat(conts[i])); if (t.width<40 || t.height<50) continue; rects.push_back(t); //rectangle(mt,t,(255,255,255)); } // imshow("kkkl",mt); //sort numbers sort(rects.begin(), rects.end(), cmp); //cut pictures for (int i = 0; i<rects.size(); i++){ image_bin(rects[i]).copyTo(result); result_datas.push_back(result); // datas.push_back(result); } return result_datas; } bool cmp(const Rect &a, const Rect &b){ if (a.x<b.x){ return true; } else{ return false; } } //取5X5方块检验小数点 bool has_Point(Mat mt){ int with = mt.rows; int height = mt.cols; Rect r(height - 5, with - 5, 5, 5); mt(r).copyTo(mt); Mat_<uchar>::iterator it = mt.begin<uchar>(); Mat_<uchar>::iterator itend = mt.end<uchar>(); int count = 0; for (; it != itend; ++it) { if ((*it) == 255) count += 1;//二值化后，像素点是0或者255 } return (!count ==0); }