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svm test now works with non-linearly separable data

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neingeist 2014-01-19 21:44:34 +01:00
parent 7d283f1a2f
commit 70505f8c65
1 changed files with 20 additions and 13 deletions
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33
SVMTest.cpp
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@ -11,23 +11,30 @@ int main()
Mat image = Mat::zeros(height, width, CV_8UC3);
// Set up training data
float labels[4] = {1.0, -1.0, -1.0, -1.0};
Mat labelsMat(4, 1, CV_32FC1, labels);
Mat labelsMat = (Mat_<float>(9, 1) << 1.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 1.0, 1.0);
Mat trainingDataMat = (Mat_<float>(9, 2) <<
501, 10, 255, 255, 255, 305, 10, 1, 10, 500, 290, 290, 180, 290, 200, 200, 400, 400);
float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
assert(labelsMat.rows == trainingDataMat.rows);
// Set up SVM's parameters
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, 1e-6);
params.kernel_type = CvSVM::RBF; //CvSVM::RBF, CvSVM::LINEAR ...
params.degree = 1; // for poly
params.gamma = .0001; // for poly/rbf/sigmoid
params.coef0 = 0; // for poly/sigmoid
params.C = 7; // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
params.nu = 0.0; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
params.p = 0.0; // for CV_SVM_EPS_SVR
// Train the SVM
CvSVM SVM;
SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);
Vec3b green(0,255,0), blue (255,0,0);
Vec3b whiteish(200,200,200), blackish (55,55,55);
// Show the decision regions given by the SVM
for (int i = 0; i < image.rows; ++i)
for (int j = 0; j < image.cols; ++j)
@ -36,18 +43,18 @@ int main()
float response = SVM.predict(sampleMat);
if (response == 1)
image.at<Vec3b>(i,j) = green;
image.at<Vec3b>(i,j) = whiteish;
else if (response == -1)
image.at<Vec3b>(i,j) = blue;
image.at<Vec3b>(i,j) = blackish;
}
// Show the training data
int thickness = -1;
int lineType = 8;
for (int i = 0; i < trainingDataMat.rows; i++) {
const CvScalar color = (labels[i] == 1) ?
const CvScalar color = (labelsMat.at<float>(i) == 1) ?
CV_RGB(255, 255, 255) : CV_RGB(0, 0, 0);
circle(image, Point(trainingData[i][0], trainingData[i][1]), 5,
circle(image, Point(trainingDataMat.at<float>(i, 0), trainingDataMat.at<float>(i, 1)), 5,
color, thickness, lineType);
}
@ -59,12 +66,12 @@ int main()
for (int i = 0; i < c; ++i)
{
const float* v = SVM.get_support_vector(i);
circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(0, 0, 128), thickness, lineType);
}
imwrite("result.png", image); // save the image
imshow("SVM Simple Example", image); // show it to the user
imshow("SVM Non-Linear Example", image); // show it to the user
waitKey(0);
}