Refactoring & documentation
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
+29
-88
@@ -3,29 +3,7 @@
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namespace tk { namespace dnn {
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void BoundingBox::clear()
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{
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uniqueTruthIndex = -1;
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truthFlag = 0;
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maxIoU = 0;
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}
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bool boxComparison (const BoundingBox& a,const BoundingBox& b)
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{
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return (a.prob>b.prob);
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}
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std::ostream& operator<<(std::ostream& os, const BoundingBox& bb)
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{
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os <<"w: "<< bb.w << ", h: "<< bb.h << ", x: "<< bb.x << ", y: "<< bb.y <<
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", cat: "<< bb.cl << ", conf: "<< bb.prob<< ", truth: "<<
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bb.truthFlag<< ", assignedGT: "<< bb.uniqueTruthIndex<<
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", maxIoU: "<< bb.maxIoU<<"\n";
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return os;
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}
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void Frame::print() const
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{
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void Frame::print() const{
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std::cout<<"labels filename: "<<lFilename<<std::endl;
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std::cout<<"image filename: "<<iFilename<<std::endl;
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std::cout<<"GT: "<<std::endl;
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@@ -34,52 +12,13 @@ void Frame::print() const
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for(auto d: det) std::cout<<d;
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}
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void PR::print()
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{
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void PR::print(){
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std::cout<<"precision: "<<precision<<" recall: "<<recall<<" tp: "<<tp<<" fp:"<<fp<<" fn:"<<fn<<std::endl;
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}
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float boxOverlap(float x1, float w1, float x2, float w2)
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{
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float l1 = x1 - w1/2;
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float l2 = x2 - w2/2;
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float left = l1 > l2 ? l1 : l2;
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float r1 = x1 + w1/2;
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float r2 = x2 + w2/2;
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float right = r1 < r2 ? r1 : r2;
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return right - left;
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}
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float boxIntersection(const BoundingBox &a, const BoundingBox &b)
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{
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float w = boxOverlap(a.x, a.w, b.x, b.w);
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float h = boxOverlap(a.y, a.h, b.y, b.h);
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if(w < 0 || h < 0)
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return 0;
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float area = w*h;
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return area;
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}
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float boxUnion(const BoundingBox &a, const BoundingBox &b)
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{
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float i = boxIntersection(a, b);
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float u = a.w*a.h + b.w*b.h - i;
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return u;
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}
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float boxIoU(const BoundingBox &a, const BoundingBox &b)
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{
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float I = boxIntersection(a, b);
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float U = boxUnion(a, b);
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if (I == 0 || U == 0)
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return 0;
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return I / U;
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}
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void readmAPParams(char* config_filename, int& classes, int& map_points,
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int& map_levels, float& map_step, float& IoU_thresh,
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float& conf_thresh, bool& verbose)
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{
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void readmAPParams( char* config_filename, int& classes, int& map_points,
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int& map_levels, float& map_step, float& IoU_thresh,
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float& conf_thresh, bool& verbose) {
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YAML::Node config = YAML::LoadFile(config_filename);
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classes = config["classes"].as<int>();
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map_points = config["map_points"].as<int>();
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@@ -88,12 +27,12 @@ void readmAPParams(char* config_filename, int& classes, int& map_points,
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IoU_thresh = config["IoU_thresh"].as<float>();
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conf_thresh = config["conf_thresh"].as<float>();
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verbose = config["verbose"].as<bool>();
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}
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/* Credits to https://github.com/AlexeyAB/darknet/blob/master/src/detector.c*/
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double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, const int map_points, const bool verbose)
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{
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double computeMap( std::vector<Frame> &images,const int classes,
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const float IoU_thresh, const float conf_thresh,
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const int map_points, const bool verbose) {
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if(verbose)
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for(auto img:images)
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img.print();
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@@ -132,15 +71,13 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
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float maxIoU = 0;
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int truth_index = -1;
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for(size_t j=0; j<img.gt.size(); j++){
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float currentIoU = boxIoU(img.det[i], img.gt[j]);
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float currentIoU = img.det[i].IoU(img.gt[j]);
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if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl){
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maxIoU = currentIoU;
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truth_index = j;
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}
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}
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// std::cout<<"det i:"<<i<<" maxIoU:"<<maxIoU<<" tIndex:"<<truth_index<<std::endl;
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if(truth_index > -1 && maxIoU > IoU_thresh){
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// std::cout<<"(INSIDE) IoU thresh:"<<IoU_thresh<<" maxIoU:"<<maxIoU<<" maxIoU > IoU_thresh:"<<(maxIoU > IoU_thresh)<<std::endl;
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img.det[i].uniqueTruthIndex = truth_index + gt_checked;
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img.det[i].truthFlag = 1;
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img.det[i].maxIoU = maxIoU;
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@@ -262,8 +199,11 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
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return mean_average_precision;
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}
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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)
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{
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double computeMapNIoULevels(std::vector<Frame> &images,const int classes,
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const float i_IoU_thresh, const float conf_thresh,
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const int map_points, const float map_step,
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const int map_levels, const bool verbose,
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const bool write_on_file, std::string net) {
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std::ofstream out_file;
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if(write_on_file){
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out_file.open("map.csv", std::ios_base::app);
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@@ -272,15 +212,18 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const f
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double AP = 0, cur_AP = 0;
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float IoU_thresh = i_IoU_thresh;
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for(int i=0; i<map_levels; ++i){
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//clear detection-grounthuth matching
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for(auto& img:images)
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for(auto & d:img.det)
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d.clear();
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//compute mAP for the new IoU threshold
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cur_AP = computeMap(images,classes,IoU_thresh,conf_thresh,map_points, verbose);
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if(write_on_file)
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out_file<<cur_AP<<";";
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AP += cur_AP;
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IoU_thresh +=map_step;
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if(write_on_file)
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out_file<<cur_AP<<";";
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AP += cur_AP;
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IoU_thresh +=map_step;
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}
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AP/=map_levels;
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@@ -291,8 +234,9 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const f
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return AP;
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}
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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)
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{
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void computeTPFPFN( std::vector<Frame> &images,const int classes,
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const float IoU_thresh, const float conf_thresh,
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bool verbose, const bool write_on_file, std::string net) {
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std::ofstream out_file;
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if(write_on_file){
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@@ -304,11 +248,10 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
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std::vector<int> dets_classes_count(classes,0);
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std::vector<PR> pr(classes);
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//compute TP, FP, FN for each image, for each class
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for(auto &img:images){
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for(auto& tc: truth_classes_count)
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tc = 0;
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for(auto& dc: dets_classes_count)
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dc = 0;
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for(auto& tc: truth_classes_count) tc = 0;
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for(auto& dc: dets_classes_count) dc = 0;
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std::vector<bool> det_assigned(img.det.size(), false);
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for(size_t j=0; j<img.gt.size(); j++){
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@@ -317,7 +260,7 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
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int det_index = -1;
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for(size_t i=0; i<img.det.size(); i++){
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if(img.det[i].prob > conf_thresh){
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float currentIoU = boxIoU(img.det[i], img.gt[j]);
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float currentIoU = img.det[i].IoU(img.gt[j]);
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if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl && !det_assigned[i]){
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maxIoU = currentIoU;
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det_index = i;
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@@ -344,16 +287,14 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
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}
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}
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//count all TP, FP, FN and compute precsion, recall and f1-score
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double avg_precision = 0, avg_recall = 0, f1_score = 0;
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int TP = 0, FP = 0, FN = 0;
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for(size_t i=0; i<classes; i++){
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pr[i].precision = (pr[i].tp + pr[i].fp) > 0 ? (double)pr[i].tp / (double)(pr[i].tp +pr[i].fp) : 0;
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pr[i].recall = (pr[i].tp + pr[i].fn) > 0 ? (double)pr[i].tp / (double)(pr[i].tp +pr[i].fn) : 0;
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if(verbose)
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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;
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// 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;
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avg_precision += pr[i].precision;
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avg_recall += pr[i].recall;
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