int test_main(int, char* [] )
{
BOOST_MATH_CONTROL_FP;
// start by printing some information:
#ifdef isnan
std::cout << "Platform has isnan macro." << std::endl;
#endif
#ifdef fpclassify
std::cout << "Platform has fpclassify macro." << std::endl;
#endif
#ifdef BOOST_HAS_FPCLASSIFY
std::cout << "Platform has FP_NORMAL macro." << std::endl;
#endif
std::cout << "FP_ZERO: " << (int)FP_ZERO << std::endl;
std::cout << "FP_NORMAL: " << (int)FP_NORMAL << std::endl;
std::cout << "FP_INFINITE: " << (int)FP_INFINITE << std::endl;
std::cout << "FP_NAN: " << (int)FP_NAN << std::endl;
std::cout << "FP_SUBNORMAL: " << (int)FP_SUBNORMAL << std::endl;
// then run the tests:
test_classify(float(0), "float");
test_classify(double(0), "double");
#ifndef BOOST_MATH_NO_LONG_DOUBLE_MATH_FUNCTIONS
test_classify((long double)(0), "long double");
test_classify((boost::math::concepts::real_concept)(0), "real_concept");
#endif
return 0;
}
/**
* Main function
*/
int main(int argc, char **argv)
{
int err = FALSE;
/* Create config */
config_init(&cfg);
config_check(&cfg);
ftable_init();
err |= test_classify();
err |= test_stress();
ftable_destroy();
config_destroy(&cfg);
return err;
}
int main(int argc, char **argv[])
{
string name;
vector<Mat>Images(100), TestImages(50);
vector<Mat> Descriptor(100), TestDescriptor(50), TestPcafeature(50);
vector<vector<KeyPoint>>Keypoints(100), TestKeypoint(50);
Mat histogram = Mat::zeros(100, Cluster, CV_32F);
Mat Testhistogram = Mat::zeros(50, Cluster, CV_32F);
Mat Keyword = Mat::zeros(Cluster, 20, CV_32F);
Mat full_Descriptor, Pcafeature, Pcaduplicate, clusteridx, trainlabels(100, 1, CV_32F);
vector<vector<DMatch>> matches(50);
Mat predicted(Testhistogram.rows, 1, CV_32F);
// Read Training Images.
read_train(Images, name);
//Calculate SIFT features for the Training Images.
calculate_SIFT(Images,Keypoints,Descriptor);
merge_descriptor(full_Descriptor,Descriptor);
//Compute PCA for all the features across all Images.
PCA pca;
perform_PCA(full_Descriptor, Pcafeature, pca);
//Perform K-Means on all the PCA reduced features.
Pcafeature.convertTo(Pcaduplicate, CV_32F);
calculate_Kmeans(Pcaduplicate, clusteridx);
//Calculate the Keywords in the Feature Space.
make_dictionary(clusteridx, Pcaduplicate, Keyword);
//Get the Histogram for each Training Image.
hist(Descriptor, clusteridx, histogram);
//Read Test Image
read_test(TestImages, name);
//Calculate the SIFT feature for all the test Images.
calculate_SIFT(TestImages, TestKeypoint, TestDescriptor);
//Project the SIFT feature of each feature on the lower dimensional PCA plane calculated above.
pca_testProject(TestDescriptor, TestPcafeature, pca);
//Find the Label by searching for keywords closest to current feature.
get_matches(TestPcafeature,Keyword,matches);
//Calculate Histogram for each test Image.
hist_test(TestDescriptor, matches, Testhistogram);
//Perform classification through Knn Classifier.
train_labels(trainlabels);
KNearest knn;
train_classifier(histogram, trainlabels, knn);
test_classify(Testhistogram,predicted,knn);
//Calculate Accuracy for each class.
calculate_accuracy(predicted);
getchar();
return 0;
}
