Hello Sir,
I want detect object from image. Study Hog from https://www.learnopencv.com/histogram-of-oriented-gradients/
1)Create 100 positive image . Please see below images as a some positive sample.
C:\fakepath\AppService.png C:\fakepath\filename=4075.png C:\fakepath\filename=12068.png C:\fakepath\filename=12936.png C:\fakepath\filename=31908.png
2)Create 30 negative image. Please see below images as a some negative sample.
C:\fakepath\filename=4037.png
C:\fakepath\filename=7277.png
C:\fakepath\Negative=2300.png
3)Using this example for train and test HOG https://github.com/opencv/opencv/blob/master/samples/cpp/train_HOG.cpp I modify main function only to give fix path for above image .
int main()
{
const char* keys =
{
"{help h| | show help message}"
"{pd | | path of directory contains positive images}"
"{nd | | path of directory contains negative images}"
"{td | | path of directory contains test images}"
"{tv | | test video file name}"
"{dw | | width of the detector}"
"{dh | | height of the detector}"
"{d |false| train twice}"
"{t |false| test a trained detector}"
"{v |false| visualize training steps}"
"{fn |my_detector.yml| file name of trained SVM}"
};
string pos_dir = "D:\\HogImage\\Positive ";
string neg_dir = "D:\\HogImage\\Negative";
//String pos_dir = parser.get< String >("pd");
//String neg_dir = parser.get< String >("nd");
String test_dir = "D:\\1\\P";// parser.get< String >("td");
String obj_det_filename = "D:\\1\\de.ylm";// parser.get< String >("fn");
int detector_width = 80;// 83;
int detector_height = 16;
bool test_detector = false; //parser.get< bool >("t");
bool train_twice = true;// parser.get< bool >("d");
bool visualization = true;// parser.get< bool >("v");
string videofilename = "D:\\1\\sa.png";
if (test_detector)
{
test_trained_detector(obj_det_filename, test_dir, videofilename);
return 0;
}
if (pos_dir.empty() || neg_dir.empty())
{
}
vector< Mat > pos_lst, full_neg_lst, neg_lst, gradient_lst;
vector< int > labels;
clog << "Positive images are being loaded...";
load_images(pos_dir, pos_lst, visualization);
if (pos_lst.size() > 0)
{
clog << "...[done]" << endl;
}
else
{
clog << "no image in " << pos_dir << endl;
return 1;
}
Size pos_image_size = pos_lst[0].size();
for (size_t i = 0; i < pos_lst.size(); ++i)
{
if (pos_lst[i].size() != pos_image_size)
{
cout << "All positive images should be same size!" << endl;
exit(1);
}
}
pos_image_size = pos_image_size / 8 * 8;
if (detector_width && detector_height)
{
pos_image_size = Size(detector_width, detector_height);
}
labels.assign(pos_lst.size(), +1);
const unsigned int old = (unsigned int)labels.size();
clog << "Negative images are being loaded...";
load_images(neg_dir, full_neg_lst, false);
sample_negex(full_neg_lst, neg_lst, pos_image_size);
clog << "...[done]" << endl;
labels.insert(labels.end(), neg_lst.size(), -1);
CV_Assert(old < labels.size());
clog << "Histogram of Gradients are being calculated for positive images...";
computeHOGs(pos_image_size, pos_lst, gradient_lst);
clog << "...[done]" << endl;
clog << "Histogram of Gradients are being calculated for negative images...";
computeHOGs(pos_image_size, neg_lst, gradient_lst);
clog << "...[done]" << endl;
Mat train_data;
convert_to_mlex(gradient_lst, train_data);
clog << "Training SVM...";
Ptr< SVM > svm = SVM::create();
/* Default values to train SVM */
svm->setCoef0(0.0);
svm->setDegree(3);
svm->setTermCriteria(TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, 1e-3));
svm->setGamma(0);
svm->setKernel(SVM::LINEAR);
svm->setNu(0.5);
svm->setP(0.1); // for EPSILON_SVR, epsilon in loss function?
svm->setC(0.01); // From paper, soft classifier
svm->setType(SVM::EPS_SVR); // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
svm->train(train_data, ROW_SAMPLE, Mat(labels));
clog << "...[done]" << endl;
if (train_twice)
{
clog << "Testing trained detector on negative images. This may take a few minutes...";
HOGDescriptor my_hog;
my_hog.winSize = pos_image_size;
// Set the trained svm to my_hog
vector< float > hog_detector;
get_svm_detectorex(svm, hog_detector);
my_hog.setSVMDetector(hog_detector);
vector< Rect > detections;
vector< double > foundWeights;
for (size_t i = 0; i < full_neg_lst.size(); i++)
{
my_hog.detectMultiScale(full_neg_lst[i], detections, foundWeights);
for (size_t j = 0; j < detections.size(); j++)
{
Mat detection = full_neg_lst[i](detections[j]).clone();
resize(detection, detection, pos_image_size);
neg_lst.push_back(detection);
}
if (visualization)
{
for (size_t j = 0; j < detections.size(); j++)
{
rectangle(full_neg_lst[i], detections[j], Scalar(0, 255, 0), 2);
}
imshow("testing trained detector on negative images", full_neg_lst[i]);
waitKey(5);
}
}
clog << "...[done]" << endl;
labels.clear();
labels.assign(pos_lst.size(), +1);
labels.insert(labels.end(), neg_lst.size(), -1);
gradient_lst.clear();
clog << "Histogram of Gradients are being calculated for positive images...";
computeHOGs(pos_image_size, pos_lst, gradient_lst);
clog << "...[done]" << endl;
clog << "Histogram of Gradients are being calculated for negative images...";
computeHOGs(pos_image_size, neg_lst, gradient_lst);
clog << "...[done]" << endl;
clog << "Training SVM again...";
convert_to_mlex(gradient_lst, train_data);
svm->train(train_data, ROW_SAMPLE, Mat(labels));
clog << "...[done]" << endl;
}
vector< float > hog_detector;
get_svm_detectorex(svm, hog_detector);
HOGDescriptor hog;
hog.winSize = pos_image_size;
hog.setSVMDetector(hog_detector);
hog.save(obj_det_filename);
test_trained_detector(obj_det_filename, test_dir, videofilename);
return 0;
}
VOID TrainHOGEx::TestEx()
{
mainTestEx();
}
4) Test result result. C:\fakepath\Result.png It suppose to detect this image C:\fakepath\AppService.png .
Please guide my how to fix this issue?
Thanks, Mahendra
