upgrade opencv to 3.3.1, complete patch similarity

[trackerpp.git] / src / MultiTracker.cpp

diff --git a/src/MultiTracker.cpp b/src/MultiTracker.cpp
index 267ba0f..b1f380e 100644 (file)
--- a/src/MultiTracker.cpp
+++ b/src/MultiTracker.cpp
@@ -6,7 +6,7 @@
 
 using namespace suanzi;
 using namespace cv;
-using namespace Eigen;
+using namespace std;
 
 static const std::string TAG = "MultiTracker";
 static const cv::Size PREFERRED_SIZE = Size(64, 128);
@@ -15,16 +15,18 @@ static const cv::Size PREFERRED_SIZE = Size(64, 128);
 
 MultiTracker::MultiTracker()
 {
-    LOG_DEBUG(TAG, "init - load model.pkl");
+    LOG_DEBUG(TAG, "init - loading model.pkl");
     predictor = PredictorWrapper::create("./python/model.pkl");
     predictor->dump();
-    this->descriptor = {cv::Size(64, 128), cv::Size(16, 16), cv::Size(8, 8), cv::Size(8, 8), 9};
+    this->descriptor = {Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9};
+
     std::vector<double> ff (40, 1);
-    predictor->predict(4, ff);
+    double prob = predictor->predict(4, ff);
 }
 
 MultiTracker::~MultiTracker()
 {
+    predictor.reset();
     trackers.clear();
 }
 
@@ -42,28 +44,49 @@ static std::vector<double> similarity(const PatchPtr p1, const PatchPtr p2)
     cv::matchTemplate(im1, im2, result, CV_TM_CCORR_NORMED);
     feature.push_back(result.at<double>(0, 0));
 
+
+    vector<double>& f1_hog = p1->features.first; Mat f1_hue = p1->features.second;
+    vector<double>& f2_hog = p1->features.first; Mat f2_hue = p1->features.second;
+    feature.push_back(distance_cosine(Eigen::Map<Eigen::VectorXd>(f1_hog.data(), f1_hog.size()), 
+                                    Eigen::Map<Eigen::VectorXd>(f2_hog.data(), f2_hog.size())));
+    feature.push_back(distance_euclidean(Eigen::Map<Eigen::VectorXd>(f1_hog.data(), f1_hog.size()), 
+                                    Eigen::Map<Eigen::VectorXd>(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);
+
+    //TODO
+    double iou_ratio = 0.03;
+    feature.push_back(iou_ratio);
+
     return feature;
 }
 
 
-double MultiTracker::distance(TrackerPtr t, const cv::Mat& image, const Detection& d)
+double MultiTracker::distance(TrackerPtr tracker, const cv::Mat& image, const Detection& d)
 {
-    PatchPtr patch = createPatch(image);
+    PatchPtr patch = createPatch(image, d);
     std::vector<double> features;
+
     std::vector<double> ss;
-    for (const auto i : t->patches){
+    for (auto i : tracker->patches){
         ss = similarity(i, patch);
         features.insert(features.end(),  ss.begin(), ss.end());
     }
-    //predictor->predict();
-    return 0.1; 
+    double prob = predictor->predict(4, features);
+    return prob; 
 }
 
 void MultiTracker::update(unsigned int total, const Detection* detections, const Mat& image)
 {
     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++){
             // TODO
@@ -72,7 +95,7 @@ void MultiTracker::update(unsigned int total, const Detection* detections, const
     }
 
     // assignment
-    VectorXi tracker_inds, bb_inds;
+    Eigen::VectorXi tracker_inds, bb_inds;
     linear_sum_assignment(cost_matrix, tracker_inds, bb_inds);
 
     // handle unmatched trackers
@@ -86,7 +109,6 @@ void MultiTracker::update(unsigned int total, const Detection* detections, const
         t->updateState(image);
     }
 
-
     // handle unmatched detections
     vector<int> unmatched_detection;
     for(int j = 0; j < col; j++){
@@ -99,18 +121,45 @@ void MultiTracker::update(unsigned int total, const Detection* detections, const
         TrackerPtr t (new Tracker(image));
         this->trackers.push_back(t);
     }
+
+    Detection dd;
+
+    PatchPtr pp = createPatch(image, dd);
 }
 
-PatchPtr MultiTracker::createPatch(const cv::Mat& image)
+// Get image crop from input image within given bounding box - Detecinon
+static cv::Mat image_crop(const cv::Mat& image, const Detection& bb)
+{
+    // RECT
+    // TODO;
+    return image.clone();
+}
+
+PatchPtr MultiTracker::createPatch(const Mat& image, const Detection& detect)
 {
     PatchPtr patch(new Patch());
-    std::vector<float> feature_hog;
-    cv::Mat img;
-    cv::resize(image, img, PREFERRED_SIZE);
-    this->descriptor.compute(img, feature_hog);
-
-    cv::Mat hist;
-    //cv::calcHist()
-    patch->image_crop = image;
+
+    // calculate hog descriptors, size is 3780
+    Mat im, im2;
+    im = image_crop(image, detect);
+    resize(im, im2, PREFERRED_SIZE);
+    vector<float> 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);
+    Size sm = hist.size();
+
+    patch->image_crop = im.clone();
+    patch->detection = detect;
+    std::vector<double> feature_hog_double (feature_hog.begin(), feature_hog.end()); // convert to double
+    patch->features = std::make_pair(feature_hog_double, hist);
     return patch;
 }