void svm()
{
// 視覺表達數據的設置
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);
//建立訓練數據
int labels[4] = { 1, -1, -1, -1 };
Mat labelsMat(4, 1, CV_32SC1, labels);
InputArray svmOutput(labelsMat);
float trainingData[4][2] = { { 501, 10 },{ 255, 10 },{ 501, 255 },{ 10, 501 } };
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
InputArray svmInput(trainingDataMat);
//設置支持向量機的參數
Ptr<SVM> svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::LINEAR);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
// 訓練支持向量機
svm->train(svmInput, ROW_SAMPLE, svmOutput);
Vec3b green(0, 255, 0), blue(255, 0, 0);
//顯示由SVM給出的決定區域
for (int i = 0; i < image.rows; ++i)
for (int j = 0; j < image.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1, 2) << j, i);
float response = svm->predict(sampleMat);
if (response == 1)
image.at<Vec3b>(i, j) = green;
else if (response == -1)
image.at<Vec3b>(i, j) = blue;
}
//顯示訓練數據
int thickness = -1;
int lineType = 8;
circle(image, Point(501, 10), 5, Scalar(0, 0, 0), thickness, lineType);
circle(image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);
circle(image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
circle(image, Point(10, 501), 5, Scalar(255, 255, 255), thickness, lineType);
//顯示支持向量
thickness = 2;
lineType = 8;
Mat sv = svm->getSupportVectors();
for (int i = 0; i < sv.rows; ++i)
{
const float* v = sv.ptr<float>(i);
circle(image, Point((int)v[0], (int)v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
}
imwrite("result.png", image); // 保存圖像
imshow("SVM Simple Example", image); // 顯示圖像
waitKey(0);
return ;
}
void svm2()
{
#define NTRAINING_SAMPLES 100 // 每類訓練樣本的數量
#define FRAC_LINEAR_SEP 0.9f // 部分(Fraction)線性可分的樣本組成部分
//設置視覺表達的參數
const int WIDTH = 512, HEIGHT = 512;
Mat I = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);
//隨機建立訓練數據
Mat trainData(2 * NTRAINING_SAMPLES, 2, CV_32FC1);
InputArray svmInput(trainData);
Mat labels(2 * NTRAINING_SAMPLES, 1, CV_32SC1);
InputArray svmOutput(labels);
RNG rng(100); // 隨機生成值
//建立訓練數據的線性可分的組成部分
int nLinearSamples = (int)(FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
// 爲Class1生成隨機點
Mat trainClass = trainData.rowRange(0, nLinearSamples);
// 點的x座標爲[0,0.4)
Mat c = trainClass.colRange(0, 1);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * WIDTH));
// 點的Y座標爲[0,1)
c = trainClass.colRange(1, 2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
// 爲Class2生成隨機點
trainClass = trainData.rowRange(2 * NTRAINING_SAMPLES - nLinearSamples, 2 * NTRAINING_SAMPLES);
// 點的x座標爲[0.6, 1]
c = trainClass.colRange(0, 1);
rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH));
// 點的Y座標爲[0, 1)
c = trainClass.colRange(1, 2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
//建立訓練數據的非線性可分組成部分
// 隨機生成Class1和Class2的點
trainClass = trainData.rowRange(nLinearSamples, 2 * NTRAINING_SAMPLES - nLinearSamples);
// 點的x座標爲[0.4, 0.6)
c = trainClass.colRange(0, 1);
rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));
// 點的y座標爲[0, 1)
c = trainClass.colRange(1, 2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
//爲類設置標籤
labels.rowRange(0, NTRAINING_SAMPLES).setTo(1); // Class 1
labels.rowRange(NTRAINING_SAMPLES, 2 * NTRAINING_SAMPLES).setTo(2); // Class 2
//設置支持向量機的參數
Ptr<SVM> svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::LINEAR);
svm->setC(0.1);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
//訓練支持向量機
cout << "Starting training process" << endl;
svm->train(svmInput, ROW_SAMPLE, svmOutput);
cout << "Finished training process" << endl;
//標出決策區域
Vec3b green(0, 100, 0), blue(100, 0, 0);
for (int i = 0; i < I.rows; ++i)
for (int j = 0; j < I.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1, 2) << i, j);
float response = svm->predict(sampleMat);
if (response == 1) I.at<Vec3b>(j, i) = green;
else if (response == 2) I.at<Vec3b>(j, i) = blue;
}
//顯示訓練數據
int thick = -1;
int lineType = 8;
float px, py;
// Class 1
for (int i = 0; i < NTRAINING_SAMPLES; ++i)
{
px = trainData.at<float>(i, 0);
py = trainData.at<float>(i, 1);
circle(I, Point((int)px, (int)py), 3, Scalar(0, 255, 0), thick, lineType);
}
// Class 2
for (int i = NTRAINING_SAMPLES; i <2 * NTRAINING_SAMPLES; ++i)
{
px = trainData.at<float>(i, 0);
py = trainData.at<float>(i, 1);
circle(I, Point((int)px, (int)py), 3, Scalar(255, 0, 0), thick, lineType);
}
//顯示支持向量
thick = 2;
lineType = 8;
Mat sv = svm->getSupportVectors();
for (int i = 0; i < sv.rows; ++i)
{
const float* v = sv.ptr<float>(i);
circle(I, Point((int)v[0], (int)v[1]), 6, Scalar(128, 128, 128), thick, lineType);
}
imwrite("result.png", I); //保存圖像到文件
imshow("SVM for Non-Linear Training Data", I); // 顯示最終窗口
waitKey(0);
return ;
}
opencv svm分類
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