/**
* Test the trained detector against the same training set to get an approximate idea of the detector.
* Warning: This does not allow any statement about detection quality, as the detector might be overfitting.
* Detector quality must be determined using an independent test set.
* @param hog
*/
static void detectTrainingSetTest(const HOGDescriptor& hog, const double hitThreshold, const vector<string>& posFileNames, const vector<string>& negFileNames) {
unsigned int truePositives = 0;
unsigned int trueNegatives = 0;
unsigned int falsePositives = 0;
unsigned int falseNegatives = 0;
vector<Point> foundDetection;
// Walk over positive training samples, generate images and detect
for (vector<string>::const_iterator posTrainingIterator = posFileNames.begin(); posTrainingIterator != posFileNames.end(); ++posTrainingIterator) {
const Mat imageData = imread(*posTrainingIterator, 0);
hog.detect(imageData, foundDetection, hitThreshold, winStride, trainingPadding);
if (foundDetection.size() > 0) {
++truePositives;
falseNegatives += foundDetection.size() - 1;
} else {
++falseNegatives;
}
}
// Walk over negative training samples, generate images and detect
for (vector<string>::const_iterator negTrainingIterator = negFileNames.begin(); negTrainingIterator != negFileNames.end(); ++negTrainingIterator) {
const Mat imageData = imread(*negTrainingIterator, 0);
hog.detect(imageData, foundDetection, hitThreshold, winStride, trainingPadding);
if (foundDetection.size() > 0) {
falsePositives += foundDetection.size();
} else {
++trueNegatives;
}
}
printf("Results:\n\tTrue Positives: %u\n\tTrue Negatives: %u\n\tFalse Positives: %u\n\tFalse Negatives: %u\n", truePositives, trueNegatives, falsePositives, falseNegatives);
}
JNIEXPORT void JNICALL Java_org_opencv_objdetect_HOGDescriptor_detect_10
(JNIEnv* env, jclass , jlong self, jlong img_nativeObj, jlong foundLocations_mat_nativeObj, jlong weights_mat_nativeObj, jdouble hitThreshold, jdouble winStride_width, jdouble winStride_height, jdouble padding_width, jdouble padding_height, jlong searchLocations_mat_nativeObj)
{
static const char method_name[] = "objdetect::detect_10()";
try {
LOGD("%s", method_name);
vector<Point> foundLocations;
Mat& foundLocations_mat = *((Mat*)foundLocations_mat_nativeObj);
vector<double> weights;
Mat& weights_mat = *((Mat*)weights_mat_nativeObj);
vector<Point> searchLocations;
Mat& searchLocations_mat = *((Mat*)searchLocations_mat_nativeObj);
Mat_to_vector_Point( searchLocations_mat, searchLocations );
HOGDescriptor* me = (HOGDescriptor*) self; //TODO: check for NULL
Mat& img = *((Mat*)img_nativeObj);
Size winStride((int)winStride_width, (int)winStride_height);
Size padding((int)padding_width, (int)padding_height);
me->detect( img, foundLocations, weights, (double)hitThreshold, winStride, padding, searchLocations );
vector_Point_to_Mat( foundLocations, foundLocations_mat ); vector_double_to_Mat( weights, weights_mat );
return;
} catch(const std::exception &e) {
throwJavaException(env, &e, method_name);
} catch (...) {
throwJavaException(env, 0, method_name);
}
return;
}
/*compute sim based on the exsiting hog descriptor and detector apperance model*/
double particleFilter::computeSim(const HOGDescriptor& hog, Mat& frame, int x, int y, double hitThreshold){
vector<Point> searchLoc;
vector<Point> foundLoc;
vector<double> weights;
searchLoc.push_back(Point((double)x, (double)y));
hog.detect(frame, foundLoc, weights, hitThreshold, Size(24,48), Size(1,1), searchLoc);
if(weights.empty())
return 0;
return MIN(1,(MAX(0,weights.at(0)-2)));
}
void classify()
{
HOGDescriptor hog;
hog.winSize = Size(sampleSize);
SVMLight::SVMClassifier c("classifier.dat");
std::vector<float> descriptorVector = c.getDescriptorVector();
std::cout << descriptorVector.size() << std::endl;
hog.setSVMDetector(descriptorVector);
Mat m = imread("/Users/alberto/tmp/samples/fullframe9.png");
Mat m1 = m.clone();
std::vector<Rect> found;
std::vector<Point> foundPoint;
Size padding(Size(0, 0));
std::cout << "try to detect.." << std::endl;
//hog.detectMultiScale(m, found, 0.0, winStride, padding, 1.01, 0.1);
hog.detect(m, foundPoint, 0.0, winStride, padding);
std::cout << "found: " << foundPoint.size() << std::endl;
for(int i=0; i<foundPoint.size(); ++i) {
Rect r;
r.x = foundPoint[i].x;
r.y = foundPoint[i].y;
r.width = 48;
r.height = 48;
rectangle(m, r, Scalar(255,255,255));
Mat imageroi = m1(r);
std::stringstream ss;
ss << "/Users/alberto/tmp/samples/tmp/test";
ss << i;
ss << ".png";
cv::imwrite(ss.str(), imageroi);
}
imshow("result", m);
}
JNIEXPORT void JNICALL Java_org_opencv_objdetect_HOGDescriptor_detect_11
(JNIEnv* env, jclass , jlong self, jlong img_nativeObj, jlong foundLocations_mat_nativeObj, jlong weights_mat_nativeObj)
{
static const char method_name[] = "objdetect::detect_11()";
try {
LOGD("%s", method_name);
vector<Point> foundLocations;
Mat& foundLocations_mat = *((Mat*)foundLocations_mat_nativeObj);
vector<double> weights;
Mat& weights_mat = *((Mat*)weights_mat_nativeObj);
HOGDescriptor* me = (HOGDescriptor*) self; //TODO: check for NULL
Mat& img = *((Mat*)img_nativeObj);
me->detect( img, foundLocations, weights );
vector_Point_to_Mat( foundLocations, foundLocations_mat ); vector_double_to_Mat( weights, weights_mat );
return;
} catch(const std::exception &e) {
throwJavaException(env, &e, method_name);
} catch (...) {
throwJavaException(env, 0, method_name);
}
return;
}
int main (int argc, const char * argv[])
{
printf("Hello\n");
cout << "Hello Artyom" << endl << flush ;
VideoCapture cap;
cap.open("http://192.168.1.105:8080/?action=stream");
/*
cap.set(CV_CAP_PROP_FRAME_WIDTH, 320);
cap.set(CV_CAP_PROP_FRAME_HEIGHT, 240);
*/
if (!cap.isOpened())
return -1;
Mat img;
/* VideoCapture cap1(1);
cap.set(CV_CAP_PROP_FRAME_WIDTH, 320);
cap.set(CV_CAP_PROP_FRAME_HEIGHT, 240);
if (!cap1.isOpened())
return -1;
Mat img2; */
string Pos = "";
HOGDescriptor hog;
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
string posPoint = "";
string posRect ="";
while (true)
{
cap >> img;
// cap1 >> img2;
if (!img.data)
continue;
vector<Rect> found, found_filtered;
vector<Point> found1, found_filtered1;
hog.detectMultiScale(img, found, 0, Size(8,8), Size(32,32), 1.05, 2);
hog.detect(img, found1, 0, Size(8,8), Size(0,0));
size_t i, j;
for (i = 0 ; i < found1.size(); i++){
Point tempPoint = found1[i];
Rect r ;
if (tempPoint.x > 0 && tempPoint.y > 0) {
r.x += tempPoint.x;
r.y += tempPoint.y;
r.width = 10;
r.height = 10;
rectangle(img, r.tl(), r.br(), cv::Scalar(255,0,0), 2);
string x = to_string(r.x);
string y = to_string(r.y);
posPoint = "Pos: x:" + x+ " y: " + y;
}
}
for (i=0; i<found.size(); i++)
{
Rect r = found[i];
for (j=0; j<found.size(); j++)
if (j!=i && (r & found[j])==r)
break;
if (j==found.size())
found_filtered.push_back(r);
}
for (i=0; i<found_filtered.size(); i++)
{
Rect r = found_filtered[i];
r.x += cvRound(r.width*0.1);
r.width = cvRound(r.width*0.8);
r.y += cvRound(r.height*0.06);
r.height = cvRound(r.height*0.9);
// string x = to_string(r.x);
string y = to_string(r.y);
// posRect = "Pos: x:" + x+ " y: " + y;
rectangle(img, r.tl(), r.br(), cv::Scalar(0,255,0), 2);
}
int number = 5;
char text[255];
sprintf(text, "Score %d", (int)number);
CvFont font;
double hScale=1.0;
double vScale=1.0;
int lineWidth=1;
cvInitFont(&font,CV_FONT_HERSHEY_SIMPLEX|CV_FONT_ITALIC, hScale,vScale,0,lineWidth);
IplImage* img1 = new IplImage(img);
char* p = new char[posRect.length()+1];
memcpy(p, posRect.c_str(), posRect.length()+1);
cvPutText(img1, p, cvPoint(200,400), &font, cvScalar(0,255,0));
char* p2 = new char[posPoint.length()+1];
memcpy(p2, posPoint.c_str(), posPoint.length()+1);
cvPutText(img1, p2, cvPoint(200,430), &font, cvScalar(255,255,255));
imshow("video capture", img);
// imshow("video capture2", img2);
if (waitKey(1) >= 0)
break;
}
//namedWindow("video capture", CV_WINDOW_AUTOSIZE);
return 0;
}
