bool makeBOWModel(std::vector<BOWImg> &images, Mat &vocabulary, Mat &trainData, Mat &response)
{
// Load training images
std::cout<<"--->Loading training images ... "<<std::endl;
int numImages = imgRead(images);
if(numImages < 0)
return false;
std::cout<<" "<<numImages<<" images loaded."<<std::endl;
// Random shuffle samples
std::random_shuffle (images.begin(), images.end());
/*
Get the ROI of images.
Feature detect and extract descriptors.
*/
printf("--->Extracting %s features ...\n", conf.extractor.c_str());
features(images, conf.extractor, conf.detector);
BOWKMeansTrainer trainer(conf.numClusters,TermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,10,1.0));
for(std::vector<BOWImg>::iterator iter = images.begin();iter != images.end(); iter++)
{
Mat tmp = iter->descriptor;
trainer.add(tmp);
}
std::cout<<"--->Constructing vocabulary list ..."<<std::endl;
vocabulary = trainer.cluster();
std::cout<<"--->Extracting BOW features ..."<<std::endl;
bowFeatures(images, vocabulary, conf.extractor);
// Prepare traning data.
Mat rawData;
for(std::vector<BOWImg>::iterator iter = images.begin();iter != images.end(); iter++)
{
rawData.push_back(iter->BOWDescriptor);
response.push_back(iter->label);
}
// PCA
#ifdef _USE_PCA_
float factor = 1;
int maxComponentsNum = static_cast<float>(conf.numClusters) * factor;
PCA pca(rawData, Mat(),CV_PCA_DATA_AS_ROW, maxComponentsNum);
Mat pcaData;
for(int i = 0;i<rawData.rows;i++)
{
Mat vec = rawData.row(i);
Mat coeffs = pca.project(vec);
pcaData.push_back(coeffs);
}
trainData = pcaData;
#else
trainData = rawData;
#endif
return true;
}
double* mitk::pa::Volume::GetData() const
{
mitk::ImageWriteAccessor imgRead(m_InternalMitkImage, m_InternalMitkImage->GetVolumeData());
return (double*)imgRead.GetData();
}
double test(cv::Mat &vocabulary, void *src)
{
// Test
std::vector<BOWImg> images;
conf.max_num = conf.max_num * 2;
std::cout<<"--->Loading testing images ... "<<std::endl;
int numImages = imgRead(images);
std::cout<<" "<<numImages<<" images loaded."<<std::endl;
if(numImages < 0)
return -1;
printf("--->Extracting %s features ...\n", conf.extractor.c_str());
features(images, conf.extractor, conf.detector);
std::cout<<"--->Extracting BOW features ..."<<std::endl;
bowFeatures(images, vocabulary, conf.extractor);
cv::Mat rawData;
for(std::vector<BOWImg>::iterator iter = images.begin();iter != images.end(); iter++)
rawData.push_back(iter->BOWDescriptor);
//PCA
#ifdef _USE_PCA_
float factor = 1;
int maxComponentsNum = static_cast<float>(conf.numClusters) * factor;
cv::PCA pca(rawData, Mat(),CV_PCA_DATA_AS_ROW, maxComponentsNum);
cv::Mat pcaData;
for(int i = 0;i<rawData.rows;i++)
{
cv::Mat vec = rawData.row(i);
cv::Mat coeffs = pca.project(vec);
pcaData.push_back(coeffs);
}
cv::Mat testData = pcaData;
#else
cv::Mat testData = rawData;
#endif
std::cout<<"--->Executing predictions ..."<<std::endl;
cv::Mat output;
double ac = 0;
double ac_rate = 0;
if(conf.classifier == "BP")
{
CvANN_MLP *classifier = (CvANN_MLP *)src;
classifier->predict(testData,output);
cout<<"--->Predict answer: "<<std::endl;
for(int i = 0;i < output.rows;i++)
{
float *p = output.ptr<float>(i);
int k = 0;
int tmp = 0;
for(int j = 0;j < output.cols;j++)
{
if(p[j] > tmp )
{
tmp = p[j];
k = j;
}
}
std::cout<<" "<<images[i].imgName<<" ---- "<<conf.classes[k]<<endl;
if(images[i].label == k+1)
ac++;
}
ac_rate = ac / static_cast<double>(output.rows);
}
else if(conf.classifier == "SVM")
{
CvSVM *classifier = (CvSVM *)src;
classifier->predict(testData,output);
cout<<"--->Predict answer: "<<std::endl;
for(int i = 0;i < output.rows;i++)
{
int k = (int)output.ptr<float>()[i]-1;
std::cout<<" "<<images[i].imgName<<" ---- "<<conf.classes[k]<<endl;
if(images[i].label == k+1)
ac++;
}
ac_rate = ac / static_cast<double>(output.rows);
}
else {
std::cout<<"--->Error: wrong classifier."<<std::endl;
}
return ac_rate;
}
