opencv3.1.0 SVM
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//#include "stdafx.h"
#include "opencv2/opencv.hpp"
using namespace cv;
using namespace cv::ml;
using namespace std;
int main(int, char**)
{
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3); //创建窗口可视化
// 设置训练数据
int labels[10] = { 1, -1, 1, 1, -1, 1, -1, 1, 1, -1 };
Mat labelsMat(10, 1, CV_32SC1, labels);
float trainingData[10][2] = { { 501, 150 }, { 255, 10 }, { 501, 255 }, { 10, 501 }, { 25, 80 },
{ 150, 300 }, { 77, 200 }, { 300, 300 }, { 45, 250 }, { 200, 210 } };
Mat trainingDataMat(10, 2, CV_32FC1, trainingData);
// 创建分类器并设置参数
Ptr<SVM> model = SVM::create();
model->setType(SVM::C_SVC);
model->setKernel(SVM::LINEAR); //核函数
//设置训练数据
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
// 训练分类器
model->train(tData);
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) << j, i); //生成测试数据
float response = model->predict(sampleMat); //进行预测,返回1或-1
if (response == 1)
image.at<Vec3b>(i, j) = green;
else if (response == -1)
image.at<Vec3b>(i, j) = blue;
}
// 显示训练数据
int thickness = -1;
int lineType = 8;
Scalar c1 = Scalar::all(0); //标记为1的显示成黑点
Scalar c2 = Scalar::all(255); //标记成-1的显示成白点
//绘图时,先宽后高,对应先列后行
for (int i = 0; i < labelsMat.rows; i++)
{
const float* v = trainingDataMat.ptr<float>(i); //取出每行的头指针
Point pt = Point((int)v[0], (int)v[1]);
if (labels[i] == 1)
circle(image, pt, 5, c1, thickness, lineType);
else
circle(image, pt, 5, c2, thickness, lineType);
}
imshow("SVM Simple Example", image);
//cout << "image" << image << endl;
//cout << "labelsMat" << labelsMat << endl;
waitKey(0);
}
#include "opencv2/opencv.hpp"
using namespace cv;
using namespace cv::ml;
using namespace std;
int main(int, char**)
{
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3); //创建窗口可视化
// 设置训练数据
int labels[10] = { 1, -1, 1, 1, -1, 1, -1, 1, 1, -1 };
Mat labelsMat(10, 1, CV_32SC1, labels);
float trainingData[10][2] = { { 501, 150 }, { 255, 10 }, { 501, 255 }, { 10, 501 }, { 25, 80 },
{ 150, 300 }, { 77, 200 }, { 300, 300 }, { 45, 250 }, { 200, 210 } };
Mat trainingDataMat(10, 2, CV_32FC1, trainingData);
// 创建分类器并设置参数
Ptr<SVM> model = SVM::create();
model->setType(SVM::C_SVC);
model->setKernel(SVM::LINEAR); //核函数
//设置训练数据
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
// 训练分类器
model->train(tData);
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) << j, i); //生成测试数据
float response = model->predict(sampleMat); //进行预测,返回1或-1
if (response == 1)
image.at<Vec3b>(i, j) = green;
else if (response == -1)
image.at<Vec3b>(i, j) = blue;
}
// 显示训练数据
int thickness = -1;
int lineType = 8;
Scalar c1 = Scalar::all(0); //标记为1的显示成黑点
Scalar c2 = Scalar::all(255); //标记成-1的显示成白点
//绘图时,先宽后高,对应先列后行
for (int i = 0; i < labelsMat.rows; i++)
{
const float* v = trainingDataMat.ptr<float>(i); //取出每行的头指针
Point pt = Point((int)v[0], (int)v[1]);
if (labels[i] == 1)
circle(image, pt, 5, c1, thickness, lineType);
else
circle(image, pt, 5, c2, thickness, lineType);
}
imshow("SVM Simple Example", image);
//cout << "image" << image << endl;
//cout << "labelsMat" << labelsMat << endl;
waitKey(0);
}
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