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Add avg precision, recall, f1score computation, other minors

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Signed-off-by: xavier <micaelaverucchi@gmail.com>
This commit is contained in:
xavier
2020-02-10 18:19:08 +01:00
parent 9007e25a00
commit d601e980f6
3 changed files with 96 additions and 14 deletions
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demo/demo/map.cpp
+4 -1
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@@ -164,11 +164,14 @@ int main(int argc, char *argv[])
int map_levels = 10;
float map_step = 0.05;
float IoU_thresh = 0.5;
float conf_thresh = 0.3;
bool verbose = false;
double AP = computeMapNIoULevels(images,classes,IoU_thresh, map_points, map_step, map_levels, verbose);
double AP = computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose);
std::cout<<"mAP "<<IoU_thresh<<":"<<IoU_thresh+map_step*(map_levels-1)<<" = "<<AP<<std::endl;
computeTPFPFN(images,classes,IoU_thresh,conf_thresh);
return 0;
}
include/evaluation.h
+4 -2
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@@ -45,7 +45,9 @@ float boxIntersection(const BoundingBox &a, const BoundingBox &b);
float boxUnion(const BoundingBox &a, const BoundingBox &b);
float boxIoU(const BoundingBox &a, const BoundingBox &b);
double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const int map_points, const bool verbose=false);
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh=0.5, const int map_points=101, const float map_step=0.05, const int map_levels=10, const bool verbose=false);
double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh=0.3, const int map_points=101, const bool verbose=false);
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh=0.5, const float conf_thresh=0.3, const int map_points=101, const float map_step=0.05, const int map_levels=10, const bool verbose=false);
void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh=0.5, const float conf_thresh=0.3, bool verbose=false);
#endif /*EVALUATION_H*/
src/evaluation.cpp
+88 -11
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@@ -77,11 +77,8 @@ float boxIoU(const BoundingBox &a, const BoundingBox &b)
}
/* Credits to https://github.com/AlexeyAB/darknet/blob/master/src/detector.c*/
double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const int map_points, const bool verbose)
double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, const int map_points, const bool verbose)
{
std::cout<<"Computing mAP"<<std::endl;
if(verbose)
for(auto img:images)
img.print();
@@ -103,8 +100,11 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
groundtruths_count += i.gt.size();
}
std::cout<<"gt_count: "<<groundtruths_count<<std::endl;
std::cout<<"det_count: "<<detections_count<<std::endl;
if(verbose)
{
std::cout<<"gt_count: "<<groundtruths_count<<std::endl;
std::cout<<"det_count: "<<detections_count<<std::endl;
}
std::vector<BoundingBox> all_dets;
std::vector<BoundingBox> all_gts;
@@ -117,7 +117,7 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
{
for(size_t i=0; i<img.det.size(); i++)
{
if(img.det[i].prob > 0)
if(img.det[i].prob > conf_thresh)
{
float maxIoU = 0;
int truth_index = -1;
@@ -257,17 +257,18 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
avg_precision = avg_precision / map_points;
}
std::cout<<"Class: "<<i<<" AP: "<< avg_precision<<std::endl;
if(verbose)
std::cout<<"Class: "<<i<<" AP: "<< avg_precision<<std::endl;
mean_average_precision += avg_precision;
}
mean_average_precision = mean_average_precision / classes;
std::cout<<"Classes: "<<classes<<" mAP " <<IoU_thresh<<": "<< mean_average_precision<<std::endl;
std::cout<<"Classes: "<<classes<<" mAP " <<IoU_thresh<<":\t"<< mean_average_precision<<std::endl;
return mean_average_precision;
}
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_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)
{
double AP = 0;
float IoU_thresh = i_IoU_thresh;
@@ -276,9 +277,85 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const f
for(auto& img:images)
for(auto & d:img.det)
d.clear();
AP += computeMap(images,classes,IoU_thresh,map_points, verbose);
AP += computeMap(images,classes,IoU_thresh,conf_thresh,map_points, verbose);
IoU_thresh +=map_step;
}
AP/=map_levels;
return AP;
}
void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, bool verbose)
{
std::vector<int> truth_classes_count(classes,0);
std::vector<int> dets_classes_count(classes,0);
std::vector<PR> pr( classes);
for(auto &img:images)
{
for(auto& tc: truth_classes_count)
tc = 0;
for(auto& dc: dets_classes_count)
dc = 0;
std::vector<bool> det_assigned(img.det.size(), false);
for(size_t j=0; j<img.gt.size(); j++)
{
truth_classes_count[img.gt[j].cl]++;
float maxIoU = 0;
int det_index = -1;
for(size_t i=0; i<img.det.size(); i++)
{
if(img.det[i].prob > conf_thresh)
{
float currentIoU = boxIoU(img.det[i], img.gt[j]);
if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl && !det_assigned[i])
{
maxIoU = currentIoU;
det_index = i;
}
}
}
if(det_index > -1 && maxIoU > IoU_thresh && !det_assigned[det_index])
{
img.det[det_index].unique_truth_index = j;
img.det[det_index].truth_flag = 1;
img.det[det_index].max_IoU = maxIoU;
det_assigned[det_index] = true;
dets_classes_count[img.det[det_index].cl]++;
}
}
for(size_t i=0; i<img.det.size(); i++)
{
if(img.det[i].truth_flag)
pr[img.det[i].cl].tp ++;
else
pr[img.det[i].cl].fp ++;
}
for(size_t i=0; i<classes; i++)
{
pr[i].fn += truth_classes_count[i] - dets_classes_count[i];
}
}
double avg_precision = 0, avg_recall = 0, f1_score = 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;
pr[i].recall = (pr[i].tp + pr[i].fn) > 0 ? (double)pr[i].tp / (double)(pr[i].tp +pr[i].fn) : 0;
if(verbose)
std::cout<<"Class "<<i<<"\tTP: "<<pr[i].tp<<"\tFP: "<<pr[i].fp<<"\tFN: "<<pr[i].fn<<"\tprecision: "<<pr[i].precision<<"\trecall: "<<pr[i].recall<<std::endl;
// 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;
}
avg_precision /= classes;
avg_recall /= classes;
f1_score = avg_precision + avg_recall > 0 ? 2 * ( avg_precision * avg_recall ) / ( avg_precision + avg_recall ) : 0;
std::cout<<"avg precision: "<<avg_precision<<"\tavg recall: "<<avg_recall<<"\tavg f1 score:"<<f1_score<<std::endl;
}