#define MaxCost 100000
-MultiTracker::MultiTracker()
+MultiTracker::MultiTracker(EngineWPtr e)
+: engine(e)
{
LOG_DEBUG(TAG, "init - loading model.pkl");
predictor = PredictorWrapper::create("./python/model.pkl");
trackers.clear();
}
+static double calc_iou_ratio(const Detection& d1, const Detection& d2)
+{
+ // TODO
+ return 0.1;
+}
static std::vector<double> similarity(const PatchPtr p1, const PatchPtr p2)
{
double center_distance = sqrt(pow((d1.center_x - d2.center_x), 2) + pow((d1.center_y - d2.center_y), 2));
feature.push_back(center_distance / (d1.width + d1.height + d2.width + d2.height) * 4);
- //TODO
- double iou_ratio = 0.03;
+ double iou_ratio = calc_iou_ratio(d1, d2);
feature.push_back(iou_ratio);
return feature;
return prob;
}
+static long cc = 0;
+
void MultiTracker::update(unsigned int total, const Detection* detections, const Mat& image)
{
+ //////
+ if ((cc % 50) == 0){
+ if (EnginePtr e = engine.lock()){
+ e->onStatusChanged();
+ }
+ }
+ cc++;
+
+ //////
int row = trackers.size();
int col = total;
Eigen::MatrixXi cost_matrix = Eigen::MatrixXi::Zero(row, col);
for (int i = 0; i < row; i++){
for (int j = 0; j < col; j++){
- // TODO
- cost_matrix(i, j) = distance(trackers[i], image, detections[j]);
+ //if (calc_iou_ratio(trackers[i], detections[j]) < -0.1)
+ // cost_matrix(i, j) = MaxCost;
+ //else
+ cost_matrix(i, j) = distance(trackers[i], image, detections[j]);
}
}
- // assignment
Eigen::VectorXi tracker_inds, bb_inds;
linear_sum_assignment(cost_matrix, tracker_inds, bb_inds);
// handle unmatched trackers
- vector<TrackerPtr> unmatched_trackers;
+ //vector<TrackerPtr> unmatched_trackers;
for (int i = 0; i < row; i++){
if (!(tracker_inds.array() == i).any()){
- unmatched_trackers.push_back(trackers[i]);
+ trackers[i]->updateState(image);
}
}
- for (auto t : unmatched_trackers){
- t->updateState(image);
- }
// handle unmatched detections
vector<int> unmatched_detection;
float sranges[] = {0, 256};
const float* ranges[] = {hranges, sranges};
calcHist(&hsv, 1, channels, Mat(), hist, 2, histSize, ranges, true, false);
- Size sm = hist.size();
patch->image_crop = im.clone();
patch->detection = detect;