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Add evaluation to tk::dnn namespace, style fix also

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-04-06 18:12:38 +02:00
parent c0a978a480
commit c8308963df
3 changed files with 81 additions and 176 deletions
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demo/demo/map.cpp
+14 -14
View File
@@ -62,7 +62,7 @@ int main(int argc, char *argv[])
FatalError("Wrong labels file path."); FatalError("Wrong labels file path.");
//read mAP parameters //read mAP parameters
readParams( config_filename, classes, map_points, map_levels, map_step, tk::dnn::readmAPParams( config_filename, classes, map_points, map_levels, map_step,
IoU_thresh, conf_thresh, verbose); IoU_thresh, conf_thresh, verbose);
std::ofstream times, memory; std::ofstream times, memory;
@@ -104,7 +104,7 @@ int main(int argc, char *argv[])
std::ifstream all_labels(labels_path); std::ifstream all_labels(labels_path);
std::string l_filename; std::string l_filename;
std::vector<Frame> images; std::vector<tk::dnn::Frame> images;
std::vector<tk::dnn::box> detected_bbox; std::vector<tk::dnn::box> detected_bbox;
std::cout<<"Reading groundtruth and generating detections"<<std::endl; std::cout<<"Reading groundtruth and generating detections"<<std::endl;
@@ -117,16 +117,16 @@ int main(int argc, char *argv[])
{ {
std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\n"<<COL_END; std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\n"<<COL_END;
Frame f; tk::dnn::Frame f;
f.l_filename = l_filename; f.lFilename = l_filename;
f.i_filename = l_filename; f.iFilename = l_filename;
convertFilename(f.i_filename, "labels", "images", ".txt", ".jpg"); convertFilename(f.iFilename, "labels", "images", ".txt", ".jpg");
// read frame // read frame
if(!fileExist(f.i_filename.c_str())) if(!fileExist(f.iFilename.c_str()))
FatalError("Wrong image file path."); FatalError("Wrong image file path.");
cv::Mat frame = cv::imread(f.i_filename.c_str(), cv::IMREAD_COLOR); cv::Mat frame = cv::imread(f.iFilename.c_str(), cv::IMREAD_COLOR);
int height = frame.rows; int height = frame.rows;
int width = frame.cols; int width = frame.cols;
@@ -144,14 +144,14 @@ int main(int argc, char *argv[])
std::ofstream myfile; std::ofstream myfile;
if(write_dets) if(write_dets)
myfile.open ("det/"+f.l_filename.substr(l_filename.find("000"))); myfile.open ("det/"+f.lFilename.substr(f.lFilename.find("000")));
// save detections labels // save detections labels
for(auto d:detected_bbox) for(auto d:detected_bbox)
{ {
//convert detected bb in the same format as label //convert detected bb in the same format as label
//<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width> //<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width>
BoundingBox b; tk::dnn::BoundingBox b;
b.x = (d.x + d.w/2) / width; b.x = (d.x + d.w/2) / width;
b.y = (d.y + d.h/2) / height; b.y = (d.y + d.h/2) / height;
b.w = d.w / width; b.w = d.w / width;
@@ -175,10 +175,10 @@ int main(int argc, char *argv[])
for(std::string line; std::getline(labels, line); ) for(std::string line; std::getline(labels, line); )
{ {
std::istringstream in(line); std::istringstream in(line);
BoundingBox b; tk::dnn::BoundingBox b;
in >> b.cl >> b.x >> b.y >> b.w >> b.h; in >> b.cl >> b.x >> b.y >> b.w >> b.h;
b.prob = 1; b.prob = 1;
b.truth_flag = 1; b.truthFlag = 1;
f.gt.push_back(b); f.gt.push_back(b);
if(show)// draw rectangle for groundtruth if(show)// draw rectangle for groundtruth
@@ -206,11 +206,11 @@ int main(int argc, char *argv[])
//compute mAP //compute mAP
double AP = computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net); double AP = tk::dnn::computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net);
std::cout<<"mAP "<<IoU_thresh<<":"<<IoU_thresh+map_step*(map_levels-1)<<" = "<<AP<<std::endl; std::cout<<"mAP "<<IoU_thresh<<":"<<IoU_thresh+map_step*(map_levels-1)<<" = "<<AP<<std::endl;
//compute average precision, recall and f1score //compute average precision, recall and f1score
computeTPFPFN(images,classes,IoU_thresh,conf_thresh, verbose, write_res_on_file, net); tk::dnn::computeTPFPFN(images,classes,IoU_thresh,conf_thresh, verbose, write_res_on_file, net);
std::cout << "Avg VM[MB]: " << vm_total/images_done/1024.0 << ";Avg RSS[MB]: " << rss_total/images_done/1024.0 << std::endl; std::cout << "Avg VM[MB]: " << vm_total/images_done/1024.0 << ";Avg RSS[MB]: " << rss_total/images_done/1024.0 << std::endl;
include/evaluation.h
-59
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@@ -1,59 +0,0 @@
#ifndef EVALUATION_H
#define EVALUATION_H
#include <iostream>
#include <vector>
#include <algorithm>
#include <yaml-cpp/yaml.h>
#include "tkdnn.h"
struct BoundingBox : public tk::dnn::box
{
friend std::ostream& operator<<(std::ostream& os, const BoundingBox& bb);
int unique_truth_index = -1;
int truth_flag = 0;
float max_IoU = 0;
void clear();
};
std::ostream& operator<<(std::ostream& os, const BoundingBox& bb);
bool boxComparison (const BoundingBox& a,const BoundingBox& b) ;
struct Frame
{
std::string l_filename;
std::string i_filename;
std::vector<BoundingBox> gt;
std::vector<BoundingBox> det;
void print() const;
};
struct PR
{
double precision = 0;
double recall = 0;
int tp = 0, fp = 0, fn = 0;
void print();
};
float overlap(float x1, float w1, float x2, float w2);
float boxIntersection(const BoundingBox &a, const BoundingBox &b);
float boxUnion(const BoundingBox &a, const BoundingBox &b);
float boxIoU(const BoundingBox &a, const BoundingBox &b);
void readParams(char* config_filename, int& classes, int& map_points,
int& map_levels, float& map_step, float& IoU_thresh,
float& conf_thresh, bool& verbose);
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, const bool write_on_file = false, std::string net = "");
void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh=0.5, const float conf_thresh=0.3, bool verbose=false, const bool write_on_file=false, std::string net="");
#endif /*EVALUATION_H*/
src/evaluation.cpp
+59 -95
View File
@@ -1,12 +1,13 @@
#include "evaluation.h" #include "evaluation.h"
#include <fstream> #include <fstream>
namespace tk { namespace dnn {
void BoundingBox::clear() void BoundingBox::clear()
{ {
unique_truth_index = -1; uniqueTruthIndex = -1;
truth_flag = 0; truthFlag = 0;
max_IoU = 0; maxIoU = 0;
} }
bool boxComparison (const BoundingBox& a,const BoundingBox& b) bool boxComparison (const BoundingBox& a,const BoundingBox& b)
@@ -18,15 +19,15 @@ std::ostream& operator<<(std::ostream& os, const BoundingBox& bb)
{ {
os <<"w: "<< bb.w << ", h: "<< bb.h << ", x: "<< bb.x << ", y: "<< bb.y << os <<"w: "<< bb.w << ", h: "<< bb.h << ", x: "<< bb.x << ", y: "<< bb.y <<
", cat: "<< bb.cl << ", conf: "<< bb.prob<< ", truth: "<< ", cat: "<< bb.cl << ", conf: "<< bb.prob<< ", truth: "<<
bb.truth_flag<< ", assignedGT: "<< bb.unique_truth_index<< bb.truthFlag<< ", assignedGT: "<< bb.uniqueTruthIndex<<
", maxIoU: "<< bb.max_IoU<<"\n"; ", maxIoU: "<< bb.maxIoU<<"\n";
return os; return os;
} }
void Frame::print() const void Frame::print() const
{ {
std::cout<<"labels filename: "<<l_filename<<std::endl; std::cout<<"labels filename: "<<lFilename<<std::endl;
std::cout<<"image filename: "<<i_filename<<std::endl; std::cout<<"image filename: "<<iFilename<<std::endl;
std::cout<<"GT: "<<std::endl; std::cout<<"GT: "<<std::endl;
for(auto g: gt) std::cout<<g; for(auto g: gt) std::cout<<g;
std::cout<<"DET: "<<std::endl; std::cout<<"DET: "<<std::endl;
@@ -38,7 +39,7 @@ void PR::print()
std::cout<<"precision: "<<precision<<" recall: "<<recall<<" tp: "<<tp<<" fp:"<<fp<<" fn:"<<fn<<std::endl; std::cout<<"precision: "<<precision<<" recall: "<<recall<<" tp: "<<tp<<" fp:"<<fp<<" fn:"<<fn<<std::endl;
} }
float overlap(float x1, float w1, float x2, float w2) float boxOverlap(float x1, float w1, float x2, float w2)
{ {
float l1 = x1 - w1/2; float l1 = x1 - w1/2;
float l2 = x2 - w2/2; float l2 = x2 - w2/2;
@@ -51,8 +52,8 @@ float overlap(float x1, float w1, float x2, float w2)
float boxIntersection(const BoundingBox &a, const BoundingBox &b) float boxIntersection(const BoundingBox &a, const BoundingBox &b)
{ {
float w = overlap(a.x, a.w, b.x, b.w); float w = boxOverlap(a.x, a.w, b.x, b.w);
float h = overlap(a.y, a.h, b.y, b.h); float h = boxOverlap(a.y, a.h, b.y, b.h);
if(w < 0 || h < 0) if(w < 0 || h < 0)
return 0; return 0;
float area = w*h; float area = w*h;
@@ -69,15 +70,13 @@ float boxUnion(const BoundingBox &a, const BoundingBox &b)
float boxIoU(const BoundingBox &a, const BoundingBox &b) float boxIoU(const BoundingBox &a, const BoundingBox &b)
{ {
float I = boxIntersection(a, b); float I = boxIntersection(a, b);
// std::cout<<"I: "<<I<<std::endl;
float U = boxUnion(a, b); float U = boxUnion(a, b);
// std::cout<<"U: "<<U<<std::endl;
if (I == 0 || U == 0) if (I == 0 || U == 0)
return 0; return 0;
return I / U; return I / U;
} }
void readParams(char* config_filename, int& classes, int& map_points, void readmAPParams(char* config_filename, int& classes, int& map_points,
int& map_levels, float& map_step, float& IoU_thresh, int& map_levels, float& map_step, float& IoU_thresh,
float& conf_thresh, bool& verbose) float& conf_thresh, bool& verbose)
{ {
@@ -106,8 +105,7 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
std::vector<int> dets_classes_count(classes,0); std::vector<int> dets_classes_count(classes,0);
//count groundtruth and detections in total and for each class //count groundtruth and detections in total and for each class
for(auto i:images) for(auto i:images){
{
for(auto gt:i.gt) for(auto gt:i.gt)
truth_classes_count[gt.cl]++; truth_classes_count[gt.cl]++;
for(auto det:i.det) for(auto det:i.det)
@@ -116,8 +114,7 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
groundtruths_count += i.gt.size(); groundtruths_count += i.gt.size();
} }
if(verbose) if(verbose){
{
std::cout<<"gt_count: "<<groundtruths_count<<std::endl; std::cout<<"gt_count: "<<groundtruths_count<<std::endl;
std::cout<<"det_count: "<<detections_count<<std::endl; std::cout<<"det_count: "<<detections_count<<std::endl;
} }
@@ -129,30 +126,24 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
// for each detection comput IoU with groundtruth and match detetcion and // for each detection comput IoU with groundtruth and match detetcion and
// groundtruth with IoU greater than IoU_thresh // groundtruth with IoU greater than IoU_thresh
for(auto &img:images) for(auto &img:images){
{ for(size_t i=0; i<img.det.size(); i++){
for(size_t i=0; i<img.det.size(); i++) if(img.det[i].prob > conf_thresh){
{
if(img.det[i].prob > conf_thresh)
{
float maxIoU = 0; float maxIoU = 0;
int truth_index = -1; int truth_index = -1;
for(size_t j=0; j<img.gt.size(); j++) for(size_t j=0; j<img.gt.size(); j++){
{
float currentIoU = boxIoU(img.det[i], img.gt[j]); float currentIoU = boxIoU(img.det[i], img.gt[j]);
if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl) if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl){
{
maxIoU = currentIoU; maxIoU = currentIoU;
truth_index = j; truth_index = j;
} }
} }
// std::cout<<"det i:"<<i<<" maxIoU:"<<maxIoU<<" tIndex:"<<truth_index<<std::endl; // std::cout<<"det i:"<<i<<" maxIoU:"<<maxIoU<<" tIndex:"<<truth_index<<std::endl;
if(truth_index > -1 && maxIoU > IoU_thresh) if(truth_index > -1 && maxIoU > IoU_thresh){
{
// std::cout<<"(INSIDE) IoU thresh:"<<IoU_thresh<<" maxIoU:"<<maxIoU<<" maxIoU > IoU_thresh:"<<(maxIoU > IoU_thresh)<<std::endl; // std::cout<<"(INSIDE) IoU thresh:"<<IoU_thresh<<" maxIoU:"<<maxIoU<<" maxIoU > IoU_thresh:"<<(maxIoU > IoU_thresh)<<std::endl;
img.det[i].unique_truth_index = truth_index + gt_checked; img.det[i].uniqueTruthIndex = truth_index + gt_checked;
img.det[i].truth_flag = 1; img.det[i].truthFlag = 1;
img.det[i].max_IoU = maxIoU; img.det[i].maxIoU = maxIoU;
} }
} }
@@ -161,8 +152,7 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
gt_checked += img.gt.size(); gt_checked += img.gt.size();
} }
if(verbose) if(verbose){
{
for(auto img:images) for(auto img:images)
img.print(); img.print();
std::cout<<"\n\n\n\n"; std::cout<<"\n\n\n\n";
@@ -178,30 +168,24 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
//compute precision-recall curve //compute precision-recall curve
std::vector<std::vector<PR>> pr( classes, std::vector<PR>(detections_count)); std::vector<std::vector<PR>> pr( classes, std::vector<PR>(detections_count));
for(int rank = 0; rank< detections_count; ++rank) for(int rank = 0; rank< detections_count; ++rank){
{ if (rank > 0) {
if (rank > 0) for (int class_id = 0; class_id < classes; ++class_id) {
{
for (int class_id = 0; class_id < classes; ++class_id)
{
pr[class_id][rank].tp = pr[class_id][rank - 1].tp; pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
pr[class_id][rank].fp = pr[class_id][rank - 1].fp; pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
} }
} }
//if it was detected and never detected before //if it was detected and never detected before
if (all_dets[rank].truth_flag == 1 && truth_flags[all_dets[rank].unique_truth_index] == 0) if (all_dets[rank].truthFlag == 1 && truth_flags[all_dets[rank].uniqueTruthIndex] == 0) {
{ truth_flags[all_dets[rank].uniqueTruthIndex] = 1;
truth_flags[all_dets[rank].unique_truth_index] = 1;
pr[all_dets[rank].cl][rank].tp++; // true-positive pr[all_dets[rank].cl][rank].tp++; // true-positive
} }
else else {
{
pr[all_dets[rank].cl][rank].fp++; // false-positive pr[all_dets[rank].cl][rank].fp++; // false-positive
} }
for (int i = 0; i < classes; ++i) for (int i = 0; i < classes; ++i){
{
const int tp = pr[i][rank].tp; const int tp = pr[i][rank].tp;
const int fp = pr[i][rank].fp; const int fp = pr[i][rank].fp;
const int fn = truth_classes_count[i] - tp; // false-negative = objects - true-positive const int fn = truth_classes_count[i] - tp; // false-negative = objects - true-positive
@@ -217,17 +201,15 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
else else
pr[i][rank].recall = 0; pr[i][rank].recall = 0;
if (rank == (detections_count - 1) && dets_classes_count[i] != (tp + fp)) if (rank == (detections_count - 1) && dets_classes_count[i] != (tp + fp)) {
{ // check for last rank // check for last rank
printf(" class_id: %d - detections = %d, tp+fp = %d, tp = %d, fp = %d \n", i, dets_classes_count[i], tp+fp, tp, fp); printf(" class_id: %d - detections = %d, tp+fp = %d, tp = %d, fp = %d \n", i, dets_classes_count[i], tp+fp, tp, fp);
} }
} }
} }
if(verbose) if(verbose){
{ for(int i=0; i < pr.size(); i++) {
for(int i=0; i < pr.size(); i++)
{
std::cout<<"---------Class "<<i<<std::endl; std::cout<<"---------Class "<<i<<std::endl;
for(auto r:pr[i]) for(auto r:pr[i])
r.print(); r.print();
@@ -240,16 +222,13 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
double last_recall, last_precision, delta_recall; double last_recall, last_precision, delta_recall;
double cur_recall, cur_precision; double cur_recall, cur_precision;
double avg_precision = 0; double avg_precision = 0;
for (int i = 0; i < classes; ++i) for (int i = 0; i < classes; ++i) {
{
avg_precision = 0; avg_precision = 0;
if (map_points == 0) //mAP calculation: ImageNet, PascalVOC 2010-2012 if (map_points == 0){ //mAP calculation: ImageNet, PascalVOC 2010-2012
{
last_recall = pr[i][detections_count - 1].recall; last_recall = pr[i][detections_count - 1].recall;
last_precision = pr[i][detections_count - 1].precision; last_precision = pr[i][detections_count - 1].precision;
for (int rank = detections_count - 2; rank >= 0; --rank) for (int rank = detections_count - 2; rank >= 0; --rank){
{
delta_recall = last_recall - pr[i][rank].recall; delta_recall = last_recall - pr[i][rank].recall;
last_recall = pr[i][rank].recall; last_recall = pr[i][rank].recall;
@@ -259,8 +238,7 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
avg_precision += delta_recall * last_precision; avg_precision += delta_recall * last_precision;
} }
} }
else //MSCOCO - 101 Recall-points, PascalVOC - 11 Recall-points else {//MSCOCO - 101 Recall-points, PascalVOC - 11 Recall-points
{
for (int point = 0; point < map_points; ++point) { for (int point = 0; point < map_points; ++point) {
cur_recall = point * 1.0 / ( map_points - 1 ); cur_recall = point * 1.0 / ( map_points - 1 );
cur_precision = 0; cur_precision = 0;
@@ -287,16 +265,14 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
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 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)
{ {
std::ofstream out_file; std::ofstream out_file;
if(write_on_file) if(write_on_file){
{
out_file.open("map.csv", std::ios_base::app); out_file.open("map.csv", std::ios_base::app);
out_file<<net<<";"; out_file<<net<<";";
} }
double AP = 0, cur_AP = 0; double AP = 0, cur_AP = 0;
float IoU_thresh = i_IoU_thresh; float IoU_thresh = i_IoU_thresh;
for(int i=0; i<map_levels; ++i) for(int i=0; i<map_levels; ++i){
{
for(auto& img:images) for(auto& img:images)
for(auto & d:img.det) for(auto & d:img.det)
d.clear(); d.clear();
@@ -308,8 +284,7 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const f
} }
AP/=map_levels; AP/=map_levels;
if(write_on_file) if(write_on_file){
{
out_file<<AP<<"\n"; out_file<<AP<<"\n";
out_file.close(); out_file.close();
} }
@@ -320,8 +295,7 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
{ {
std::ofstream out_file; std::ofstream out_file;
if(write_on_file) if(write_on_file){
{
out_file.open("pr.csv", std::ios_base::app); out_file.open("pr.csv", std::ios_base::app);
out_file<<net<<";"; out_file<<net<<";";
} }
@@ -330,50 +304,42 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
std::vector<int> dets_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) for(auto &img:images){
{
for(auto& tc: truth_classes_count) for(auto& tc: truth_classes_count)
tc = 0; tc = 0;
for(auto& dc: dets_classes_count) for(auto& dc: dets_classes_count)
dc = 0; dc = 0;
std::vector<bool> det_assigned(img.det.size(), false); std::vector<bool> det_assigned(img.det.size(), false);
for(size_t j=0; j<img.gt.size(); j++) for(size_t j=0; j<img.gt.size(); j++){
{
truth_classes_count[img.gt[j].cl]++; truth_classes_count[img.gt[j].cl]++;
float maxIoU = 0; float maxIoU = 0;
int det_index = -1; int det_index = -1;
for(size_t i=0; i<img.det.size(); i++) for(size_t i=0; i<img.det.size(); i++){
{ if(img.det[i].prob > conf_thresh){
if(img.det[i].prob > conf_thresh)
{
float currentIoU = boxIoU(img.det[i], img.gt[j]); float currentIoU = boxIoU(img.det[i], img.gt[j]);
if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl && !det_assigned[i]) if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl && !det_assigned[i]){
{
maxIoU = currentIoU; maxIoU = currentIoU;
det_index = i; det_index = i;
} }
} }
} }
if(det_index > -1 && maxIoU > IoU_thresh && !det_assigned[det_index]) if(det_index > -1 && maxIoU > IoU_thresh && !det_assigned[det_index]){
{ img.det[det_index].uniqueTruthIndex = j;
img.det[det_index].unique_truth_index = j; img.det[det_index].truthFlag = 1;
img.det[det_index].truth_flag = 1; img.det[det_index].maxIoU = maxIoU;
img.det[det_index].max_IoU = maxIoU;
det_assigned[det_index] = true; det_assigned[det_index] = true;
dets_classes_count[img.det[det_index].cl]++; dets_classes_count[img.det[det_index].cl]++;
} }
} }
for(size_t i=0; i<img.det.size(); i++) for(size_t i=0; i<img.det.size(); i++){
{ if(img.det[i].truthFlag)
if(img.det[i].truth_flag)
pr[img.det[i].cl].tp ++; pr[img.det[i].cl].tp ++;
else else
pr[img.det[i].cl].fp ++; pr[img.det[i].cl].fp ++;
} }
for(size_t i=0; i<classes; i++) for(size_t i=0; i<classes; i++){
{
pr[i].fn += truth_classes_count[i] - dets_classes_count[i]; pr[i].fn += truth_classes_count[i] - dets_classes_count[i];
} }
} }
@@ -382,8 +348,7 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
int TP = 0, FP = 0, FN = 0; int TP = 0, FP = 0, FN = 0;
for(size_t i=0; i<classes; i++) 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].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; pr[i].recall = (pr[i].tp + pr[i].fn) > 0 ? (double)pr[i].tp / (double)(pr[i].tp +pr[i].fn) : 0;
if(verbose) if(verbose)
@@ -401,13 +366,12 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
f1_score = avg_precision + avg_recall > 0 ? 2 * ( avg_precision * avg_recall ) / ( avg_precision + avg_recall ) : 0; f1_score = avg_precision + avg_recall > 0 ? 2 * ( avg_precision * avg_recall ) / ( avg_precision + avg_recall ) : 0;
if(write_on_file) if(write_on_file){
{
out_file<<TP<<";"<<FP<<";"<<FN<<";"<<avg_precision<<";"<<avg_recall<<";"<<f1_score<<"\n"; out_file<<TP<<";"<<FP<<";"<<FN<<";"<<avg_precision<<";"<<avg_recall<<";"<<f1_score<<"\n";
out_file.close(); out_file.close();
} }
std::cout<<"avg precision: "<<avg_precision<<"\tavg recall: "<<avg_recall<<"\tavg f1 score:"<<f1_score<<std::endl; std::cout<<"avg precision: "<<avg_precision<<"\tavg recall: "<<avg_recall<<"\tavg f1 score:"<<f1_score<<std::endl;
} }
}}