opencv3.3 svm的使用

在OpenCV 3.3中取消了CvSVM类的定义，结构变成了这样的了：

具体可参考文档：

http://docs.opencv.org/3.3.0/d1/d2d/classcv_1_1ml_1_1SVM.html#a77d9a35898cae44ac9071c4b35bc96a8

下边将OpenCV老版本的例子用OpenCV3.3的重新写了一下，亲测通过：

#include <iostream>#include <stdio.h>#include <unistd.h>#include <stdlib.h>#include <string.h>#include <string>#include <dirent.h>#include <unistd.h>#include <vector>#include <sstream>#include <fstream>#include <sys/io.h>#include <sys/times.h>#include <iomanip> // setw()using namespace std;#include <opencv2/core/core.hpp>#include <opencv2/imgproc.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/ml/ml.hpp> // svm using namespace cv;int main(int argc, char** argv){cout << ">> ----" << "\n" << endl;int wid = 512;int hei = 512;Mat image = Mat::zeros(hei, wid, CV_8UC3);// Set up training data    int labels[4] = {1, -1, -1, -1};    Mat labelsMat(4, 1, CV_32SC1, labels);    float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };    Mat trainingDataMat(4, 2, CV_32FC1, trainingData);Ptr<ml::SVM> svm = ml::SVM::create();// Type of a SVM formulation. See SVM::Types. Default value is SVM::C_SVCsvm->setType(ml::SVM::C_SVC); // Initialize with one of predefined kernels. See SVM::KernelTypes// Linear kernel. No mapping is done, linear discrimination (or regression) // is done in the original feature space. It is the fastest option. if (0)svm->setKernel(ml::SVM::LINEAR);else{svm->setKernel(ml::SVM::POLY);svm->setDegree(1.0); }// Termination criteria of the iterative SVM training procedure which // solves a partial case of constrained quadratic optimization problem. // You can specify tolerance and/or the maximum number of iterations. // Default value is TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON );svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON));// train// The second parameter: int lyout. See:// cv::ml::SampleTypes has two values: ROW_SAMPLE, COL_SAMPLEsvm->train(trainingDataMat, ml::ROW_SAMPLE, labelsMat);Vec3b green(0,255,0), blue (255,0,0);    // Show the decision regions given by the SVM    for (int i = 0; i < image.rows; ++i)        for (int j = 0; j < image.cols; ++j) {            Mat sampleMat = (Mat_<float>(1,2) << i,j);            // predict            float response = svm->predict(sampleMat);            if (response == 1)                image.at<Vec3b>(j, i)  = green;            else if (response == -1)                  image.at<Vec3b>(j, i)  = blue;        }// Show the training data    int thickness = -1;    int lineType  =  8;    circle(image, Point(501,  10), 6, Scalar(  0,   0,   0), thickness, lineType);    circle(image, Point(255,  10), 6, Scalar(255, 255, 255), thickness, lineType);    circle(image, Point(501, 255), 6, Scalar(255, 255, 255), thickness, lineType);    circle(image, Point( 10, 501), 6, Scalar(255, 255, 255), thickness, lineType);// Show support vectors    thickness = 2;    lineType  = 8;    // The method returns all the support vectors as a floating-point matrix,     // where support vectors are stored as matrix rows.    Mat SupportVectorsMat = svm->getSupportVectors();    for (int r = 0; r < SupportVectorsMat.rows; r++) {        float* data = SupportVectorsMat.ptr<float>(r);        cout << r << "" << data[0] << "" << data[1] << endl;        circle(image, Point((int)data[0], (int)data[1]), 6, Scalar(255, 0, 255), thickness, lineType);    }imshow("SVM Simple Example", image); // show it to the user    waitKey(0);cout << "\n" << ">> ----" << endl;return 0;}

注意代码中这几行：

if (0)svm->setKernel(ml::SVM::LINEAR);else{svm->setKernel(ml::SVM::POLY);svm->setDegree(1.0); }

如果核函数用ml::SVM::LINEAR，下边Mat SupportVectorsMat = svm->getSupportVectors();将无法正常输出支持向量。这并非BUG，官方解释：

https://github.com/Itseez/opencv/blob/2.4/modules/ml/src/svm.cpp#L1531

将所有支持向量压缩为了一个，这样做可以极大地优化线性核时的运行效率。