Add test for Predictor

[trackerpp.git] / src / MultiTracker.cpp

diff --git a/src/MultiTracker.cpp b/src/MultiTracker.cpp
index b1f380e..a7f5e59 100644 (file)
--- a/src/MultiTracker.cpp
+++ b/src/MultiTracker.cpp
@@ -3,6 +3,7 @@
 #include <algorithm>
 #include "hungarian.h"
 #include "Logger.h"
+#include "Utils.h"
 
 using namespace suanzi;
 using namespace cv;
@@ -11,17 +12,16 @@ using namespace std;
 static const std::string TAG = "MultiTracker";
 static const cv::Size PREFERRED_SIZE = Size(64, 128);
 
-#define MaxCost  100000
+static const double MaxCost  = 100000;
+static const int MaxPath = 5;
 
-MultiTracker::MultiTracker()
+MultiTracker::MultiTracker(EngineWPtr e)
+: engine(e)
 {
     LOG_DEBUG(TAG, "init - loading model.pkl");
-    predictor = PredictorWrapper::create("./python/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};
-
-    std::vector<double> ff (40, 1);
-    double prob = predictor->predict(4, ff);
 }
 
 MultiTracker::~MultiTracker()
@@ -30,7 +30,6 @@ MultiTracker::~MultiTracker()
     trackers.clear();
 }
 
-
 static std::vector<double> similarity(const PatchPtr p1, const PatchPtr p2)
 {
     std::vector<double> feature;
@@ -60,14 +59,11 @@ 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;
-    feature.push_back(iou_ratio);
+    feature.push_back(calc_iou_ratio(getRectInDetection(d1), getRectInDetection(d2)));
 
     return feature;
 }
 
-
 double MultiTracker::distance(TrackerPtr tracker, const cv::Mat& image, const Detection& d)
 {
     PatchPtr patch = createPatch(image, d);
@@ -82,6 +78,11 @@ double MultiTracker::distance(TrackerPtr tracker, const cv::Mat& image, const De
     return prob; 
 }
 
+static float calc_iou_ratio(const Detection& d1, const Detection& d2)
+{
+    return calc_iou_ratio(getRectInDetection(d1), getRectInDetection(d2));
+}
+
 void MultiTracker::update(unsigned int total, const Detection* detections, const Mat& image)
 {
     int row = trackers.size();
@@ -89,25 +90,23 @@ void MultiTracker::update(unsigned int total, const Detection* detections, const
     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]->detection, 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;
@@ -127,12 +126,9 @@ void MultiTracker::update(unsigned int total, const Detection* detections, const
     PatchPtr pp = createPatch(image, dd);
 }
 
-// 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();
+    return image(getRectInDetection(bb));
 }
 
 PatchPtr MultiTracker::createPatch(const Mat& image, const Detection& detect)
@@ -155,7 +151,6 @@ PatchPtr MultiTracker::createPatch(const Mat& image, const Detection& detect)
     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;