// Train svm on the count values in trainData and labels, then draw on
// image the classifications associated with each label value in green
// (+1.0) or blue (-1.0) according to what the resulting svm predicts.
//
static void drawSvmRegions(cv::Mat_<cv::Vec3b> &image, cv::SVM &svm,
int count, float trainData[][2], float labels[])
{
static const cv::Mat zeroIdx;
static const cv::Mat varIdx = zeroIdx;
static const cv::Mat sampleIdx = zeroIdx;
static const cv::SVMParams params = makeSvmParams();
const cv::Mat labelsMat(count, 1, CV_32FC1, labels);
const cv::Mat trainDataMat(count, 2, CV_32FC1, trainData);
svm.train(trainDataMat, labelsMat, varIdx, sampleIdx, params);
for (int i = 0; i < image.rows; ++i) {
for (int j = 0; j < image.cols; ++j) {
const cv::Mat sampleMat = (cv::Mat_<float>(1, 2) << i, j);
const float response = svm.predict(sampleMat);
if (response == 1.0) {
image(j, i) = green;
} else if (response == -1.0) {
image(j, i) = blue;
} else {
std::cerr << "Unexpected response from SVM::predict(): "
<< response << std::endl;
assert(!"expected response from SVM");
}
}
}
}
// Draw in red circles on image the support vectors in svm.
//
static void drawSvm(cv::Mat &image, const cv::SVM &svm)
{
static const int radius = 9;
static const int thickness = 4;
static const int lineType = 8;
const int count = svm.get_support_vector_count();
std::cout << "support vector count == " << count << std::endl;
for (int i = 0; i < count; ++i) {
const float *const v = svm.get_support_vector(i);
const cv::Point center(v[0], v[1]);
std::cout << i << ": center == " << center << std::endl;
cv::circle(image, center, radius, red, thickness, lineType);
}
}
int TestMultiClassClassifier(const cv::SVM &SVM, const cv::Mat &data, const cv::Mat labels, FILE *report_file) {
int confussion_matrix[ACTIONS_NUM][ACTIONS_NUM] = {0};
float accuracy = 0.0f;
for (int desc_idx = 0; desc_idx < data.rows; desc_idx++) {
float label;
int true_idx, predicted_idx;
true_idx = (int)labels.at<float>(desc_idx);
label = SVM.predict(data.row(desc_idx));
predicted_idx = (int)label;
confussion_matrix[predicted_idx][true_idx]++;
accuracy += (true_idx == predicted_idx)? 1: 0;
}
printf("============================================\n");
fprintf(report_file, "============================================\n");
accuracy /= data.rows;
printf("accuracy: %2.2f\n", accuracy);
fprintf(report_file, "accuracy: %2.2f\n", accuracy);
for (int row = 0; row < ACTIONS_NUM; row++) {
for (int col=0; col < ACTIONS_NUM; col++) {
printf("%d\t\t",confussion_matrix[row][col]);
fprintf(report_file,"%d\t\t",confussion_matrix[row][col]);
}
printf("\n");
fprintf(report_file,"\n");
}
return 0;
}
