Merge

Signed-off-by: xavier <micaelaverucchi@gmail.com>

src/CenternetDetection.cpp

@@ -1,3 +1,6 @@
+#ifndef CENTERNETDETECTION_H
+#define CENTERNETDETECTION_H
+
 #include "CenternetDetection.h"
 #include "opencv2/imgproc/imgproc.hpp"
 #include <opencv2/cudawarping.hpp>
@@ -419,3 +422,5 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
     stats.push_back(t_ns);
 }
 }}
+
+#endif /*CENTERNETDETECTION_H*/

src/Yolo3Detection.cpp

@@ -60,6 +60,37 @@ bool Yolo3Detection::init(std::string tensor_path) {
     return true;
 } 
 
+cv::Mat Yolo3Detection::draw(cv::Mat &imageORIG) {
+
+    tk::dnn::box b;
+    int x0, w, x1, y0, h, y1;
+    int objClass;
+    std::string det_class;
+    float prob;
+    int baseline = 0;
+    float fontScale = 0.5;
+    int thickness = 2;   
+    // draw dets
+    for(int i=0; i<detected.size(); i++) {
+        b           = detected[i];
+        x0   		= b.x;
+        x1   		= b.x + b.w;
+        y0   		= b.y;
+        y1   		= b.y + b.h;
+        det_class 	= getYoloLayer()->classesNames[b.cl];
+        prob    	= b.prob;
+
+        // std::cout<<det_class<<" ("<<prob<<"): "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
+        // draw rectangle
+        cv::rectangle(imageORIG, cv::Point(x0, y0), cv::Point(x1, y1), colors[b.cl], 2); 
+
+        // draw label
+        cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
+        cv::rectangle(imageORIG, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), colors[b.cl], -1);                      
+        cv::putText(imageORIG, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
+    }
+    return imageORIG;
+}
 
 void Yolo3Detection::update(cv::Mat &imageORIG) {
 

src/evaluation.cpp

@@ -1,4 +1,5 @@
 #include "evaluation.h"
+#include <fstream>
 
 
 void BoundingBox::clear()
@@ -283,27 +284,51 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
     return mean_average_precision;
 }
 
-double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh, const float conf_thresh, const int map_points, const float map_step, const int map_levels, const bool verbose)
+double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh, const float conf_thresh, const int map_points, const float map_step, const int map_levels, const bool verbose, const bool write_on_file, std::string net)
 {
-    double AP = 0;
+    std::ofstream out_file;
+    if(write_on_file)
+    {
+	out_file.open("map.csv", std::ios_base::app);
+        out_file<<net<<";";
+    }
+
+    double AP = 0, cur_AP = 0;
     float IoU_thresh = i_IoU_thresh;
     for(int i=0; i<map_levels; ++i)
     {
         for(auto& img:images)
             for(auto & d:img.det)
                 d.clear();
-        AP += computeMap(images,classes,IoU_thresh,conf_thresh,map_points, verbose);
+        cur_AP = computeMap(images,classes,IoU_thresh,conf_thresh,map_points, verbose);
+	if(write_on_file)
+	    out_file<<cur_AP<<";";
+	AP += cur_AP;
         IoU_thresh +=map_step;
     }
     AP/=map_levels;
+
+    if(write_on_file)
+    {
+	out_file<<AP<<"\n";
+	out_file.close();
+    }
     return AP;
 }
 
-void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, bool verbose)
+void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, bool verbose, const bool write_on_file, std::string net)
 {
+
+    std::ofstream out_file;
+    if(write_on_file)
+    {
+	out_file.open("pr.csv", std::ios_base::app);
+        out_file<<net<<";";
+    }
+
     std::vector<int> truth_classes_count(classes,0);
     std::vector<int> dets_classes_count(classes,0);
-    std::vector<PR> pr( classes);
+    std::vector<PR> pr(classes);
     
     for(auto &img:images)
     {
@@ -355,6 +380,8 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
 
     double avg_precision = 0, avg_recall = 0, f1_score = 0;
 
+
+    int TP = 0, FP = 0, FN = 0;
     for(size_t i=0; i<classes; i++)
     {
         pr[i].precision = (pr[i].tp + pr[i].fp) > 0 ? (double)pr[i].tp / (double)(pr[i].tp +pr[i].fp) : 0;
@@ -364,13 +391,23 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
             // std::cout<<i<<"\t"<<pr[i].tp<<"\t"<<pr[i].fp<<"\t"<<pr[i].fn<<"\t"<<pr[i].precision<<"\t"<<pr[i].recall<<std::endl;
         avg_precision += pr[i].precision;
         avg_recall += pr[i].recall;
+
+	TP += pr[i].tp;
+	FP += pr[i].fp;
+	FN += pr[i].fn;
     }
     avg_precision /= classes;
     avg_recall /= classes;
 
     f1_score = avg_precision + avg_recall > 0 ? 2 * ( avg_precision * avg_recall ) / ( avg_precision + avg_recall ) : 0;
 
+    if(write_on_file)
+    {
+	out_file<<TP<<";"<<FP<<";"<<FN<<";"<<avg_precision<<";"<<avg_recall<<";"<<f1_score<<"\n";
+	out_file.close();
+    }
+
     std::cout<<"avg precision: "<<avg_precision<<"\tavg recall: "<<avg_recall<<"\tavg f1 score:"<<f1_score<<std::endl;
 
     
-}
+}