X-Git-Url: http://47.100.26.94:8080/?a=blobdiff_plain;f=src%2FMultiTracker.cpp;h=c87395ef3415c1da78da878eaa40aa2b9fcc568d;hb=b5f0328f4054d19fcf8a6b870d5448be8087d29c;hp=d0e70487eb0bde35917f6b072c851be91baf9313;hpb=48adce31a0ffdb3757ee1be8a63ce7e769e87deb;p=trackerpp.git diff --git a/src/MultiTracker.cpp b/src/MultiTracker.cpp index d0e7048..c87395e 100644 --- a/src/MultiTracker.cpp +++ b/src/MultiTracker.cpp @@ -2,90 +2,186 @@ #include "Metrics.h" #include #include "hungarian.h" +#include "Logger.h" using namespace suanzi; using namespace cv; -using namespace Eigen; +using namespace std; -MultiTracker::MultiTracker(MetricsPtr m) : metrics(m) -{ -} +static const std::string TAG = "MultiTracker"; +static const cv::Size PREFERRED_SIZE = Size(64, 128); +#define MaxCost 100000 -MultiTracker::~MultiTracker() +MultiTracker::MultiTracker(EngineWPtr e) +: engine(e) { - trackers.clear(); + LOG_DEBUG(TAG, "init - loading model.pkl"); + predictor = PredictorWrapper::create("./python/model.pkl"); + predictor->dump(); + this->descriptor = {Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9}; + + std::vector ff (40, 1); + double prob = predictor->predict(4, ff); } -TrackerPtr MultiTracker::createTracker(int id) +MultiTracker::~MultiTracker() { - TrackerPtr t (new Tracker(id)); - addTracker(t); - return t; + predictor.reset(); + trackers.clear(); } -void MultiTracker::addTracker(TrackerPtr t) +static Rect getRectInDetection(const Detection& d) { - trackers.push_back(t); + Rect r; + r.x = d.center_x - d.width / 2; + r.y = d.center_y - d.height / 2; + r.width = d.width; + r.height = d.height; + return r; } -void MultiTracker::removeTracker(TrackerPtr t) +static double calc_iou_ratio(const Detection& d1, const Detection& d2) { -// trackers.erase(t); + Rect r1 = getRectInDetection (d1); + Rect r2 = getRectInDetection (d2); + Rect r_inner = r1 & r1; + Rect r_union = r1 | r2; + return 1.0 * r_inner.area() / r_union.area(); } -void MultiTracker::initNewTrackers(cv::Mat& iamge) +static std::vector similarity(const PatchPtr p1, const PatchPtr p2) { + std::vector feature; + cv::Mat im1(PREFERRED_SIZE, p1->image_crop.type()); + cv::Mat im2(PREFERRED_SIZE, p2->image_crop.type()); + cv::resize(p1->image_crop, im1, im1.size()); + cv::resize(p2->image_crop, im2, im2.size()); + cv::Mat result; + cv::matchTemplate(im1, im2, result, CV_TM_CCOEFF_NORMED); + feature.push_back(result.at(0, 0)); + cv::matchTemplate(im1, im2, result, CV_TM_CCORR_NORMED); + feature.push_back(result.at(0, 0)); + + + vector& f1_hog = p1->features.first; Mat f1_hue = p1->features.second; + vector& f2_hog = p1->features.first; Mat f2_hue = p1->features.second; + feature.push_back(distance_cosine(Eigen::Map(f1_hog.data(), f1_hog.size()), + Eigen::Map(f2_hog.data(), f2_hog.size()))); + feature.push_back(distance_euclidean(Eigen::Map(f1_hog.data(), f1_hog.size()), + Eigen::Map(f2_hog.data(), f2_hog.size()))); + feature.push_back(compareHist(f1_hue, f2_hue, HISTCMP_CORREL)); + feature.push_back(compareHist(f1_hue, f2_hue, HISTCMP_HELLINGER)); + + Detection& d1 = p1->detection; + Detection& d2 = p2->detection; + + 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); + + feature.push_back(calc_iou_ratio(d1, d2)); + + return feature; } -void MultiTracker::correctTrackers(MetricsPtr m, Mat& image) +double MultiTracker::distance(TrackerPtr tracker, const cv::Mat& image, const Detection& d) { -} + PatchPtr patch = createPatch(image, d); + std::vector features; -void calculate_edistance() -{ + std::vector ss; + for (auto i : tracker->patches){ + ss = similarity(i, patch); + features.insert(features.end(), ss.begin(), ss.end()); + } + double prob = predictor->predict(4, features); + return prob; } -#define MaxCost 100000 +static long cc = 0; void MultiTracker::update(unsigned int total, const Detection* detections, const Mat& image) { - // correct_trackers - // Generate cost matrix + ////// + if ((cc % 50) == 0){ + if (EnginePtr e = engine.lock()){ + e->onStatusChanged(); + } + } + cc++; + + ////// int row = trackers.size(); int col = total; - MatrixXi cost_matrix = MatrixXi::Zero(row, col); + Eigen::MatrixXi cost_matrix = Eigen::MatrixXi::Zero(row, col); for (int i = 0; i < row; i++){ for (int j = 0; j < col; j++){ - TrackerPtr tracker = trackers[i]; - Detection det = detections[j]; - - int cost = MaxCost; - - // TODO - cost_matrix(i, j) = cost; + //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 - VectorXi tracker_inds, bb_inds; + Eigen::VectorXi tracker_inds, bb_inds; linear_sum_assignment(cost_matrix, tracker_inds, bb_inds); - // handle the result - vector unmatched_trackers; - vector unmatched_detection; + // handle unmatched trackers + //vector 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); } } + + // handle unmatched detections + vector unmatched_detection; for(int j = 0; j < col; j++){ if (!(bb_inds.array() == j).any()){ - unmatched_detection.push_back(detections[j]); + unmatched_detection.push_back(j); } } + // create new trackers for new detections + for (auto i : unmatched_detection){ + TrackerPtr t (new Tracker(image)); + this->trackers.push_back(t); + } + Detection dd; - // create new trackers for new detections + PatchPtr pp = createPatch(image, dd); +} + +static cv::Mat image_crop(const cv::Mat& image, const Detection& bb) +{ + return image(getRectInDetection(bb)); +} + +PatchPtr MultiTracker::createPatch(const Mat& image, const Detection& detect) +{ + PatchPtr patch(new Patch()); + + // calculate hog descriptors, size is 3780 + Mat im, im2; + im = image_crop(image, detect); + resize(im, im2, PREFERRED_SIZE); + vector feature_hog; + this->descriptor.compute(im2, feature_hog); + + // calculate histogram, size is (64 x 45) + Mat hsv, hist; + cvtColor(im, hsv, COLOR_BGR2HSV); + int channels[] = {0, 1}; + int histSize[] = {45, 64}; + float hranges[] = {0, 180}; + float sranges[] = {0, 256}; + const float* ranges[] = {hranges, sranges}; + calcHist(&hsv, 1, channels, Mat(), hist, 2, histSize, ranges, true, false); + + patch->image_crop = im.clone(); + patch->detection = detect; + std::vector feature_hog_double (feature_hog.begin(), feature_hog.end()); // convert to double + patch->features = std::make_pair(feature_hog_double, hist); + return patch; }