JNIEXPORT void JNICALL Java_org_opencv_objdetect_HOGDescriptor_save_11
(JNIEnv* env, jclass , jlong self, jstring filename)
{
static const char method_name[] = "objdetect::save_11()";
try {
LOGD("%s", method_name);
HOGDescriptor* me = (HOGDescriptor*) self; //TODO: check for NULL
const char* utf_filename = env->GetStringUTFChars(filename, 0); String n_filename( utf_filename ? utf_filename : "" ); env->ReleaseStringUTFChars(filename, utf_filename);
me->save( n_filename );
return;
} catch(const std::exception &e) {
throwJavaException(env, &e, method_name);
} catch (...) {
throwJavaException(env, 0, method_name);
}
return;
}
/**
* Main program entry point
* @param argc unused
* @param argv unused
* @return EXIT_SUCCESS (0) or EXIT_FAILURE (1)
*/
int main(int argc, char** argv) {
// <editor-fold defaultstate="collapsed" desc="Init">
HOGDescriptor hog; // Use standard parameters here
hog.winSize = Size(64, 128); // Default training images size as used in paper
// Get the files to train from somewhere
static vector<string> positiveTrainingImages;
static vector<string> negativeTrainingImages;
static vector<string> validExtensions;
validExtensions.push_back("jpg");
validExtensions.push_back("png");
validExtensions.push_back("ppm");
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Read image files">
getFilesInDirectory(posSamplesDir, positiveTrainingImages, validExtensions);
getFilesInDirectory(negSamplesDir, negativeTrainingImages, validExtensions);
/// Retrieve the descriptor vectors from the samples
unsigned long overallSamples = positiveTrainingImages.size() + negativeTrainingImages.size();
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Calculate HOG features and save to file">
// Make sure there are actually samples to train
if (overallSamples == 0) {
printf("No training sample files found, nothing to do!\n");
return EXIT_SUCCESS;
}
/// @WARNING: This is really important, some libraries (e.g. ROS) seems to set the system locale which takes decimal commata instead of points which causes the file input parsing to fail
setlocale(LC_ALL, "C"); // Do not use the system locale
setlocale(LC_NUMERIC,"C");
setlocale(LC_ALL, "POSIX");
printf("Reading files, generating HOG features and save them to file '%s':\n", featuresFile.c_str());
float percent;
/**
* Save the calculated descriptor vectors to a file in a format that can be used by SVMlight for training
* @NOTE: If you split these steps into separate steps:
* 1. calculating features into memory (e.g. into a cv::Mat or vector< vector<float> >),
* 2. saving features to file / directly inject from memory to machine learning algorithm,
* the program may consume a considerable amount of main memory
*/
fstream File;
File.open(featuresFile.c_str(), ios::out);
if (File.good() && File.is_open()) {
#if TRAINHOG_USEDSVM == SVMLIGHT
// Remove following line for libsvm which does not support comments
File << "# Use this file to train, e.g. SVMlight by issuing $ svm_learn -i 1 -a weights.txt " << featuresFile.c_str() << endl;
#endif
// 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 progress
if ( (currentFile+1) % 10 == 0 || (currentFile+1) == overallSamples ) {
percent = ((currentFile+1) * 100 / overallSamples);
printf("%5lu (%3.0f%%):\tFile '%s'", (currentFile+1), percent, currentImageFile.c_str());
fflush(stdout);
resetCursor();
}
// 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;
}
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Pass features to machine learning algorithm">
/// Read in and train the calculated feature vectors
printf("Calling %s\n", TRAINHOG_SVM_TO_TRAIN::getInstance()->getSVMName());
TRAINHOG_SVM_TO_TRAIN::getInstance()->read_problem(const_cast<char*> (featuresFile.c_str()));
TRAINHOG_SVM_TO_TRAIN::getInstance()->train(); // Call the core libsvm training procedure
printf("Training done, saving model file!\n");
TRAINHOG_SVM_TO_TRAIN::getInstance()->saveModelToFile(svmModelFile);
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Generate single detecting feature vector from calculated SVM support vectors and SVM model">
printf("Generating representative single HOG feature vector using svmlight!\n");
vector<float> descriptorVector;
vector<unsigned int> descriptorVectorIndices;
// Generate a single detecting feature vector (v1 | b) from the trained support vectors, for use e.g. with the HOG algorithm
TRAINHOG_SVM_TO_TRAIN::getInstance()->getSingleDetectingVector(descriptorVector, descriptorVectorIndices);
// And save the precious to file system
saveDescriptorVectorToFile(descriptorVector, descriptorVectorIndices, descriptorVectorFile);
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Test detecting vector">
// Detector detection tolerance threshold
const double hitThreshold = TRAINHOG_SVM_TO_TRAIN::getInstance()->getThreshold();
// Set our custom detecting vector
hog.setSVMDetector(descriptorVector);
hog.save(cvHOGFile);
printf("Testing training phase using training set as test set (just to check if training is ok - no detection quality conclusion with this!)\n");
detectTrainingSetTest(hog, hitThreshold, positiveTrainingImages, negativeTrainingImages);
printf("Testing custom detection using camera\n");
VideoCapture cap(-1); // open the default camera
if(!cap.isOpened()) { // check if we succeeded
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); // Work on grayscale images as trained
detectTest(hog, hitThreshold, testImage);
imshow("HOG custom detection", testImage);
}
// </editor-fold>
return EXIT_SUCCESS;
}
