int main()
{
srand(time(0));
SVMClassifier svm;
//Do a simple test of loading a pre-made model, testing 2 vectors, and saving it
double testVectorPos[] = {.7,1,1,-.32,-.105,-1,1,-.4198,-1,-.2258,1,-1};
double testVectorNeg[] = {.58,-1,.3333,-.6,1,-1,1,.358779,-1,-.484,-1,1};
unsigned int testVectorSize = 12;
svm.loadModel("svm.model");
double probability;
probability = svm.classifyVector(testVectorPos,testVectorSize);
cout << "Test VectorPos Probability is " << probability << endl;
probability = svm.classifyVector(testVectorNeg,testVectorSize);
cout << "Test VectorNeg Probability is " << probability << endl;
svm.saveModel("svmNew1.model");
//Create a new model that exists of a simple cluster in the second quadrant of a 2d grid being positive, and the rest being negative (x,y)
testVectorSize = 2;
unsigned int numSamples = 1000;
double **trainVec = new double*[numSamples];
bool *trainPolarities = new bool[numSamples];
for (unsigned int vecIter = 0; vecIter < numSamples; vecIter++)
{
trainVec[vecIter] = new double[2];
//Pos
if (vecIter <= (numSamples - 1)/2)
{
trainVec[vecIter][0] = -1*rand()/(double)RAND_MAX;
trainVec[vecIter][1] = rand()/(double)RAND_MAX;
trainPolarities[vecIter] = true;
}
//Neg
else
{
trainVec[vecIter][0] = rand()/(double)RAND_MAX;
trainVec[vecIter][1] = -1*rand()/(double)RAND_MAX;
trainPolarities[vecIter] = false;
}
}
svm.trainModel(trainVec, trainPolarities, numSamples, testVectorSize);
svm.saveModel("svmNew2.model");
for (unsigned int testVecIter = 0; testVecIter < numSamples; testVecIter++)
{
probability = svm.classifyVector(trainVec[testVecIter],testVectorSize);
cout << "Test Probability is " << probability << endl;
}
delete [] trainPolarities;
for (unsigned int vecIter = 0; vecIter < numSamples; vecIter++)
delete [] trainVec[vecIter];
delete [] trainVec;
return 0;
}
// run from build folder of the project (see path below)
int main (int argc, char** argv) {
Eigen::MatrixXf X;
Eigen::MatrixXf y;
eigen_extensions::loadASCII("../test/svmtestx.eig.txt", &X);
eigen_extensions::loadASCII("../test/svmtesty.eig.txt", &y);
// Classify
cout << X.topRows(5) << endl;
cout << y.topRows(5) << endl;
vector<int> yhat;
SVMClassifier svm;
svm.train(X, y);
svm.test(X, yhat);
Eigen::MatrixXf w;
float b;
svm.getw(w, b);
Eigen::MatrixXf margin= ((X * w).array() + b).matrix(); // ahh eigen...
// Evaluate accuracy and print results
int match=0;
for(int i=0;i<yhat.size();i++) {
if(yhat[i]==(int)y(i)) match++; else printf("WRONG! ");
printf("y= %d, yhat= %d margin= %f\n", (int)y(i), yhat[i], margin(i));
}
printf("MATLAB cross-check: last margin should be around -3.107\n");
printf("Accuracy= %f. From MATLAB cross-check, expect this to be around 0.945\n", 1.0*match/yhat.size());
// Save the model
svm.saveModel("temp.svmmodel");
// Test loading model in new instance of SVMClassifier
SVMClassifier svm2;
svm2.loadModel("temp.svmmodel");
yhat.clear();
svm2.test(X, yhat);
match=0;
for(int i=0;i<yhat.size();i++) if(yhat[i]==(int)y(i)) match++;
printf("Accuracy= %f from loaded model. Should be 0.945 again.\n", 1.0*match/yhat.size());
printf("you may want to rm the temporary file temp.svmmodel\n");
}
int main(int argc, const char *argv[]) {
/* if(argc < 2) {
std::cout << " Input Arguments required " << std::endl;
return -1;
}*/
std::cout<< atoi(argv[1]) << std::endl;
VideoProcessorClass* videoProcessor = VideoProcessorClass::getInstance(atoi(argv[1]));// new VideoProcessorClass(atoi(argv[1]));
CamshiftProcessing* camshift = new CamshiftProcessing();
HOGFeatureSelector* hogfeatureSelector = new HOGFeatureSelector();
SVMClassifier* svmClassifier = new SVMClassifier();
//HoughTransformClass* houghClass = new HoughTransformClass();
// MarkerLessAR* markerlessAR = new MarkerLessAR();
// markerlessAR->startDrawing(1);
// cv::Mat inputSource = cv::imread("/home/pratheba/workspace/QTProject/HandDetectionWithCamshift/Images/Test/circle.jpg",CV_LOAD_IMAGE_COLOR);
// houghClass->findCircles(inputSource);
hogfeatureSelector->ComputeHOGFeatures("/home/pratheba/workspace/QTProject/HandDetectionWithCamshift/Images/Training/Triangle/", 25, IMG_TRIANGLE);
hogfeatureSelector->ComputeHOGFeatures("/home/pratheba/workspace/QTProject/HandDetectionWithCamshift/Images/Training/Non/", 10, IMG_NONE);
hogfeatureSelector->ComputeHOGFeatures("/home/pratheba/workspace/QTProject/HandDetectionWithCamshift/Images/Training/Circle/", 38, IMG_CIRCLE);
/* Train the images to classify as triangle or circle using SVM classifier */
svmClassifier->TrainData(hogfeatureSelector->hogDescriptorMat, hogfeatureSelector->TrainingClass);
/* Compute features for SVM classifier using HOG feature selector */
/* Get the VideoProcessing */
videoProcessor->SetVideoCaptureInstance();
//videoProcessor->ChooseProcessorClass(atoi(argv[1]));
videoProcessor->SetframeToStopProcessing(30);
videoProcessor->displayInput("Input Frame");
videoProcessor->displayOutput("Output Frame");
//// // To get the time taken in terms of second
double initialTickCount = (double)cv::getTickCount();
videoProcessor->ProcessVideoFrame();
double finalTickCount = (double)cv::getTickCount();
double timeextended = (finalTickCount - initialTickCount)/cv::getTickFrequency();
std::cout << timeextended << std::endl;
/* Get the color Probability mask */
camshift->TrackRegionOfInterest();
std::string testImagefileName = "/home/pratheba/workspace/QTProject/HandDetectionWithCamshift/Images/Test/";
hogfeatureSelector->ComputeHOGFeaturesForTestImage(testImagefileName);
svmClassifier->ClassifyImage(hogfeatureSelector->TestClassMat);
videoProcessor->release();
delete camshift;
delete hogfeatureSelector;
delete svmClassifier;
// delete houghClass;
//delete markerlessAR;
return 0;
}
