X-Git-Url: http://47.100.26.94:8080/?a=blobdiff_plain;f=src%2FMultiTracker.cpp;h=a7f5e596a490bb16eb0215c34c43b2d22d3699f8;hb=db369d962b595544373b417ae9a76e7268eb12fb;hp=e52b8e2dfd6cf6bd7e7467a7f274399e3e147c33;hpb=804f325c3a26e6ff253c3eb490071434da9c3b3f;p=trackerpp.git diff --git a/src/MultiTracker.cpp b/src/MultiTracker.cpp index e52b8e2..a7f5e59 100644 --- a/src/MultiTracker.cpp +++ b/src/MultiTracker.cpp @@ -1,37 +1,160 @@ #include "MultiTracker.h" #include "Metrics.h" +#include +#include "hungarian.h" +#include "Logger.h" +#include "Utils.h" using namespace suanzi; +using namespace cv; +using namespace std; -MultiTracker::MultiTracker(MetricsPtr m) : metrics(m) +static const std::string TAG = "MultiTracker"; +static const cv::Size PREFERRED_SIZE = Size(64, 128); + +static const double MaxCost = 100000; +static const int MaxPath = 5; + +MultiTracker::MultiTracker(EngineWPtr e) +: engine(e) { + LOG_DEBUG(TAG, "init - loading model.pkl"); + predictor = PredictorWrapper::create("./python", "./python/model.pkl"); + predictor->dump(); + this->descriptor = {Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9}; } - MultiTracker::~MultiTracker() { + predictor.reset(); trackers.clear(); } -TrackerPtr MultiTracker::createTracker(int id) +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(getRectInDetection(d1), getRectInDetection(d2))); + + return feature; +} + +double MultiTracker::distance(TrackerPtr tracker, const cv::Mat& image, const Detection& d) { - TrackerPtr t (new Tracker(id)); - addTracker(t); - return t; + PatchPtr patch = createPatch(image, d); + std::vector features; + + 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; } -void MultiTracker::addTracker(TrackerPtr t) +static float calc_iou_ratio(const Detection& d1, const Detection& d2) { - trackers.insert(t); + return calc_iou_ratio(getRectInDetection(d1), getRectInDetection(d2)); } -void MultiTracker::removeTracker(TrackerPtr t) +void MultiTracker::update(unsigned int total, const Detection* detections, const Mat& image) { - trackers.erase(t); + 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++){ + if (calc_iou_ratio(trackers[i]->detection, detections[j]) < -0.1) + cost_matrix(i, j) = MaxCost; + else + cost_matrix(i, j) = distance(trackers[i], image, detections[j]); + } + } + + Eigen::VectorXi tracker_inds, bb_inds; + linear_sum_assignment(cost_matrix, tracker_inds, bb_inds); + + // handle unmatched trackers + //vector unmatched_trackers; + for (int i = 0; i < row; i++){ + if (!(tracker_inds.array() == i).any()){ + 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(j); + } + } + // create new trackers for new detections + for (auto i : unmatched_detection){ + TrackerPtr t (new Tracker(image)); + this->trackers.push_back(t); + } + + Detection dd; + + PatchPtr pp = createPatch(image, dd); } +static cv::Mat image_crop(const cv::Mat& image, const Detection& bb) +{ + return image(getRectInDetection(bb)); +} -void MultiTracker::update() +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; }