opencv3+ hog+svm前車識別，程式碼裡有acfdetection的，但是效果不好
阿新 • • 發佈：2019-02-14
直接上程式碼把，利用opencv的hog和svm的庫函式實現識別，在我的資料集下正確率95%以上
訓練，save引數和load，load有一些問題SVM字首不行，得用StatModel，注意大小寫，谷歌說opencv3+不用線性核load會出問題，說是opencv的bug，我沒試過，大佬可以試試用其他核
#ifdef TRAIN // SVM::Params params; // params.svmType = SVM::C_SVC; // params.kernelType = SVM::POLY; // params.gamma = 3; // Ptr<SVM> svm = SVM::create(params); Mat train_data; Mat train_label; read_num_class_data(&train_data,&train_label,num); Ptr<SVM> svm = SVM::create(); svm->setType(SVM::C_SVC); //設定svm型別 //svm->setKernel(SVM::POLY); //設定核函式; svm->setKernel(SVM::LINEAR); /*these param can be ignore // // libsvm引數說明:http://blog.csdn.net/changyuanchn/article/details/7540014 // // svm->setDegree(0.5); // // svm->setGamma(1); // // svm->setCoef0(1); // // svm->setNu(0.5); // // svm->setP(0); // // svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01)); // // setTermCriteria是用來設定演算法的終止條件， // // SVM訓練的過程就是一個通過 迭代 方式解決約束條件下的二次優化問題， // // 這裡我們指定一個最大迭代次數和容許誤差，以允許演算法在適當的條件下停止計算 // //svm->setC(1); */ Ptr<TrainData> tData = TrainData::create(train_data, ROW_SAMPLE, train_label); //svm->trainAuto(tData); svm->train(tData); svm->save("mysvm.xml");//檔案形式為xml，可以儲存在txt或者xml檔案中 cout<< svm->getVarCount()<<endl;//獲取SVM支援向量的維數 // // svm->getSupportVectors;//返回Mat型別，獲取SVM的支援向量 #else // Ptr<SVM> svm1 = SVM::create(); // SVM::load<SVM>("mysvm.xml"); Ptr<ml::SVM> svm1 = StatModel::load<SVM>("mysvm.xml"); //svm=SVM::load<SVM>("mysvm.xml"); //cout<<svm->getVarCount()<<endl;//獲取SVM支援向量的維數 #endif
預測就是一句話
float r = svm1->predict(featureVector);
整體程式碼
#include <iostream> #include <string> #include <fstream> #include <stdio.h> #include <stdlib.h> //OpenCV include #include <opencv2/opencv.hpp> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <ml.h> using namespace std; using namespace cv; using namespace cv::ml; static const Size trainingPadding = Size(0, 0); //static const Size winStride = Size(8, 8); static const Size winStride = Size(64, 64); bool read_num_class_data(Mat* _data, Mat* _responses,int num){ int icount=0; _data->release(); _responses->release(); vector<int >train_label; vector< vector<float > > train_data; for (int count = 0; icount < num ; ++count) //while (count<100) { //capture>>im; char str[100]; sprintf(str,"/home/cx/Videos/data/vehicles/KITTI_extracted/%d.png",count); Mat im = imread(str,1); if(im.empty()){ //cout <<"loading image failed"<< endl; continue; //return 0; } icount++; Mat luvImage; Mat imageData; cvtColor(im, imageData, CV_RGB2GRAY); //hog HOGDescriptor hog; //HOGDescriptor hog(cvSize(64,64),cvSize(64,64),cvSize(64,64),cvSize(64,64),9); // Use standard parameters here hog.winSize = Size(64, 64); // Default training images size as used in paper //calculateFeaturesFromInput(currentImageFile, featureVector, hog); vector<float> featureVector; // Check for mismatching dimensions if (imageData.cols != hog.winSize.width || imageData.rows != hog.winSize.height) { featureVector.clear(); //printf("Error: Image '%s' dimensions (%u x %u) do not match HOG window size (%u x %u)!\n", imageFilename.c_str(), imageData.cols, imageData.rows, hog.winSize.width, hog.winSize.height); return 1; } //hog detect vector<Point> locations; hog.compute(imageData, featureVector, winStride, trainingPadding, locations); // Mat edge; // Canny(imageData,edge,70,200,3); // for (int i=0;i<im.rows;i++){ // for (int j=0;j<im.cols;j++){ // File_vector<<edge.at<uchar>(j,i)/255.0 <<" "; // } // } //Sobel(imageData,edge,imageData.depth(),1,1); // cvtColor(im, luvImage, CV_RGB2Luv); // vector<Mat> mv; // split(im,mv); // fstream f("test"); // f.open("test.dat", ios::out); // f<<mv[2]<<endl; // for (int k=0;k<3;k++){ // for (int i=0;i<im.rows;i++){ // for (int j=0;j<im.cols;j++){ // File_vector<<mv[k].at<uchar>(j,i)/255.0 <<" "; // } // } // } //myluv // uchar *tmp = mat2uchar(im); // int dim0, dim1, dim2; // float *I = rgbConvert(tmp, im.rows, im.cols, im.channels(), dim0, dim1, dim2, "luv"); // int nr=im.rows; // int nc=im.cols; // for (int i = 0; i < im.channels(); i++){ // for (int j = 0; j <im.rows; j++){ // for (int k = 0; k < im.cols; k++){ // File_vector << I[i*nr*nc + j*nc + k] << " "; // } // } // } // wrFree(I); //File_vector<<endl; train_data.push_back(featureVector); train_label.push_back(1); } icount=0; for (int count = 0; icount < num ; ++count) //while (count<100) { //capture>>im; char str[100]; //sprintf(str,"/home/cx/Videos/data/vehicles/KITTI_extracted/%d.png",count); //sprintf(str,"/home/cx/Desktop/Vehicle_test/pyramid_test/pic/%d.png",count); sprintf(str,"/home/cx/Videos/data/non-vehicles/Extras/extra%d.png",count); //sprintf(str,"/home/cx/Videos/data/vehicles/GTI_MiddleClose/image%04d.png",count); //sprintf(str,"/home/cx/Videos/2011_09_26/2011_09_26_drive_0027_sync/image_02/data/%010d.png",count); Mat im = imread(str,1); if(im.empty()){ //cout <<"loading image failed"<< endl; continue; //return 0; } icount++; Mat luvImage; Mat imageData; cvtColor(im, imageData, CV_RGB2GRAY); //hog HOGDescriptor hog; //HOGDescriptor hog(cvSize(64,64),cvSize(64,64),cvSize(64,64),cvSize(64,64),9); // Use standard parameters here hog.winSize = Size(64, 64); // Default training images size as used in paper //calculateFeaturesFromInput(currentImageFile, featureVector, hog); vector<float> featureVector; // Check for mismatching dimensions if (imageData.cols != hog.winSize.width || imageData.rows != hog.winSize.height) { featureVector.clear(); //printf("Error: Image '%s' dimensions (%u x %u) do not match HOG window size (%u x %u)!\n", imageFilename.c_str(), imageData.cols, imageData.rows, hog.winSize.width, hog.winSize.height); return 1; } //hog detect vector<Point> locations; hog.compute(imageData, featureVector, winStride, trainingPadding, locations); // Mat edge; // Canny(imageData,edge,70,200,3); // for (int i=0;i<im.rows;i++){ // for (int j=0;j<im.cols;j++){ // File_vector<<edge.at<uchar>(j,i)/255.0 <<" "; // } // } //Sobel(imageData,edge,imageData.depth(),1,1); // cvtColor(im, luvImage, CV_RGB2Luv); // vector<Mat> mv; // split(im,mv); // fstream f("test"); // f.open("test.dat", ios::out); // f<<mv[2]<<endl; // for (int k=0;k<3;k++){ // for (int i=0;i<im.rows;i++){ // for (int j=0;j<im.cols;j++){ // File_vector<<mv[k].at<uchar>(j,i)/255.0 <<" "; // } // } // } //myluv // uchar *tmp = mat2uchar(im); // int dim0, dim1, dim2; // float *I = rgbConvert(tmp, im.rows, im.cols, im.channels(), dim0, dim1, dim2, "luv"); // int nr=im.rows; // int nc=im.cols; // for (int i = 0; i < im.channels(); i++){ // for (int j = 0; j <im.rows; j++){ // for (int k = 0; k < im.cols; k++){ // File_vector << I[i*nr*nc + j*nc + k] << " "; // } // } // } // wrFree(I); //File_vector<<endl; train_data.push_back(featureVector); train_label.push_back(0); } srand((unsigned)time(0)); for (int i=0;i<num*2;i++){ int a=rand()%(num*2); int b=rand()%(num*2); swap(train_label[a],train_label[b]); swap(train_data[a],train_data[b]); } Mat(train_label).copyTo(*_responses); *_data=Mat::zeros(train_data.size(), train_data[0].size(), CV_32F); for (int i=0;i<train_data.size();i++){ for (int j=0;j<train_data[0].size();j++){ _data->at<float>(i,j)=train_data[i][j]; //cout<<_data->at<float>(i,j)<<" "<<train_data[i][j]<<endl; } } } int main(int argc, char** argv) { string pic; int count = 0; int num = atoi(argv[1]); //the num of picture // VideoCapture capture(input_video_file); // if ( !capture.isOpened() ) { // cout << "video could not be opened" << endl; // return -1; // } // double num_frames = capture.get(CV_CAP_PROP_FRAME_COUNT); Mat im; fstream File_vector; fstream File_label; clock_t start, end; start = clock(); //vector<vector<float> > train_data; //vector<int > train_label; Mat train_data; Mat train_label; read_num_class_data(&train_data,&train_label,num); Ptr<SVM> svm = SVM::create(); svm->setType(SVM::C_SVC); //設定svm型別 //svm->setKernel(SVM::POLY); //設定核函式; svm->setKernel(SVM::LINEAR); // svm->setDegree(0.5); // svm->setGamma(1); // svm->setCoef0(1); // svm->setNu(0.5); // svm->setP(0); // svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01)); svm->setC(1); Ptr<TrainData> tData = TrainData::create(train_data, ROW_SAMPLE, train_label); svm->train(tData); Mat sample1 = train_data.row(0); //cout<<sample1.rows<<endl; int icount=0; int ans=0; for (int count = 0; icount < num ; ++count){ //capture>>im; char str[100]; //sprintf(str,"/home/cx/Videos/data/vehicles/KITTI_extracted/%d.png",count+1000); //sprintf(str,"/home/cx/Desktop/Vehicle_test/pyramid_test/pic/%d.png",count); sprintf(str,"/home/cx/Videos/data/non-vehicles/Extras/extra%d.png",count+1000); //sprintf(str,"/home/cx/Videos/data/vehicles/GTI_MiddleClose/image%04d.png",count); //sprintf(str,"/home/cx/Videos/2011_09_26/2011_09_26_drive_0027_sync/image_02/data/%010d.png",count); Mat im = imread(str,1); if(im.empty()){ continue; } icount++; Mat luvImage; Mat imageData; cvtColor(im, imageData, CV_RGB2GRAY); //hog HOGDescriptor hog; //HOGDescriptor hog(cvSize(64,64),cvSize(64,64),cvSize(64,64),cvSize(64,64),9); // Use standard parameters here hog.winSize = Size(64, 64); // Default training images size as used in paper //calculateFeaturesFromInput(currentImageFile, featureVector, hog); vector<float> featureVector; // Check for mismatching dimensions if (imageData.cols != hog.winSize.width || imageData.rows != hog.winSize.height) { featureVector.clear(); //printf("Error: Image '%s' dimensions (%u x %u) do not match HOG window size (%u x %u)!\n", imageFilename.c_str(), imageData.cols, imageData.rows, hog.winSize.width, hog.winSize.height); return 1; } //hog detect vector<Point> locations; hog.compute(imageData, featureVector, winStride, trainingPadding, locations); //Mat sample(featureVector); //cout<<sample.rows<<endl; float r = svm->predict(featureVector); if (r==0) ans++; } cout<<ans<<endl; end = clock(); printf("time=%f\n", (double)(end - start) / CLOCKS_PER_SEC); //release_data(det25); return 0; }