Person.detection

int main(int argc, char** argv) 
{ HOGDescriptor hog;
Hog.winSize = Size(64,128); 
# Training size of the samples used #
# Define variables and define valid extensions #
static vector<string> positiveTrainingImages; 
static vector<string> negativeTrainingImages; 
static vector<string> validExtensions; 
validExtensions.push_back("jpg"); 
validExtensions.push_back("png"); 
validExtensions.push_back("ppm");
#Read image files from directory defined earlier#
getFilesInDirectory(posSamplesDir, positiveTrainingImages, validExtensions);
getFilesInDirectory(negSamplesDir, negativeTrainingImages, validExtensions);
# Retrieve the descriptor vectors from the samples#
Unsigned long overallSamples = positiveTrainingImages.size() +  negativeTrainingImages.size();
# Make sure there are actually samples to train#
if (overallSamples == 0) {
printf("No training sample files found, nothing to do!\n"); return EXIT_SUCCESS;
}
fstream File;     
File.open(featuresFile.c_str(), ios::out);     
if (File.good() && File.is_open()) {         
# Iterate over sample images         
for (unsigned long currentFile = 0; currentFile < overallSamples; ++currentFile) {             
storeCursor();             
vector<float> featureVector; 
# Get positive or negative sample image file path# 
const string currentImageFile = (currentFile < positiveTrainingImages.size() ? positiveTrainingImages.at(currentFile) : negativeTrainingImages.at(currentFile - positiveTrainingImages.size())); 
# Output                        
if ( (currentFile+1) % 10 == 0 || (currentFile+1) == overallSamples ) {                 
percent = ((currentFile+1) * 100 / overallSamples);
# Calculate feature vector from current image file  
calculateFeaturesFromInput(currentImageFile, featureVector, hog);
if (!featureVector.empty()) {               
# Put positive or negative sample class to file,
* true=positive, false=negative,
* and convert positive class to +1 and negative class to -1 for SVMlight 
*/#
File << ((currentFile < positiveTrainingImages.size()) ? "+1" : "-1");
// Save feature vector components
for (unsigned int feature = 0; feature < featureVector.size(); ++feature) {
File << " " << (feature + 1) << ":" << featureVector.at(feature);
}   
File << endl;
}         
}
printf("\n");
File.flush(); 
File.close();
} else {  
printf("Error opening file '%s'!\n", featuresFile.c_str());
return EXIT_FAILURE;     
}
# Read in and train the calculated feature vectors #
printf("Calling SVMlight\n");
SVMlight::getInstance()->read_problem(const_cast<char*> (featuresFile.c_str()));
SVMlight::getInstance()->train();
 # Calling the SVMlight module for training  #
 printf("Training done, saving model file!\n");
 SVMlight::getInstance()->saveModelToFile(svmModelFile); 
 printf("Generating representative single HOG feature vector using svmlight!\n");
 vector<float> descriptorVector;
 vector<unsigned int> descriptorVectorIndices;
 SVMlight::getInstance()->getSingleDetectingVector(descriptorVector, descriptorVectorIndices); 
 # save the positive to file system #
 saveDescriptorVectorToFile(descriptorVector,descriptorVectorIndices,  descriptorVectorFile); 
 printf("Testing custom detection using camera\n");    
 hog.setSVMDetector(descriptorVector);      
 VideoCapture cap(0);
 # open the default camera #
 if(!cap.isOpened()) { 
 # check if code is working #
 printf("Error opening camera!\n");
 return EXIT_FAILURE; 
 }
 Mat testImage;
 while ((cvWaitKey(10) & 255) != 27) {
 cap >> testImage; 
 # get a new frame from camera #
 cvtColor(testImage, testImage, CV_BGR2GRAY);
 # train on grey scale image #
 detectTest(hog, testImage);   
 imshow("HOG custom detection", testImage); 
 }
 return EXIT_SUCCESS;
 }