micaela
360 lines
13 KiB
C++
360 lines
13 KiB
C++
#include "evaluation.h"
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#include <fstream>
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namespace tk { namespace dnn {
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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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for(auto g: gt) std::cout<<g;
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std::cout<<"DET: "<<std::endl;
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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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std::cout<<"precision: "<<precision<<" recall: "<<recall<<" tp: "<<tp<<" fp:"<<fp<<" fn:"<<fn<<std::endl;
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}
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void readmAPParams( const 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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map_levels = config["map_levels"].as<int>();
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map_step = config["map_step"].as<float>();
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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,
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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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int detections_count = 0;
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int groundtruths_count = 0;
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int unique_truth_count = 0;
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std::vector<int> truth_classes_count(classes,0);
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std::vector<int> dets_classes_count(classes,0);
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//count groundtruth and detections in total and for each class
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for(auto i:images){
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for(auto gt:i.gt)
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truth_classes_count[gt.cl]++;
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for(auto det:i.det)
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dets_classes_count[det.cl]++;
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detections_count += i.det.size();
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groundtruths_count += i.gt.size();
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}
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if(verbose){
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std::cout<<"gt_count: "<<groundtruths_count<<std::endl;
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std::cout<<"det_count: "<<detections_count<<std::endl;
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}
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std::vector<BoundingBox> all_dets;
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std::vector<BoundingBox> all_gts;
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int gt_checked = 0;
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// for each detection compute IoU with groundtruth and match detetcion and
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// groundtruth with IoU greater than IoU_thresh
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for(auto &img:images){
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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 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 = 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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if(truth_index > -1 && maxIoU > IoU_thresh){
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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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}
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}
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all_dets.push_back(img.det[i]);
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}
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gt_checked += img.gt.size();
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}
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if(verbose){
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for(auto img:images)
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img.print();
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std::cout<<"\n\n\n\n";
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}
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//sort all detections by descending value of confidence
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std::sort(all_dets.begin(), all_dets.end(), boxComparison);
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std::vector<int> truth_flags(groundtruths_count,0);
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if(verbose)
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for(auto d:all_dets)
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std::cout<<d;
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//compute precision-recall curve
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std::vector<std::vector<PR>> pr( classes, std::vector<PR>(detections_count));
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for(int rank = 0; rank< detections_count; ++rank){
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if (rank > 0) {
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for (int class_id = 0; class_id < classes; ++class_id) {
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pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
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pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
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}
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}
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//if it was detected and never detected before
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if (all_dets[rank].truthFlag == 1 && truth_flags[all_dets[rank].uniqueTruthIndex] == 0) {
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truth_flags[all_dets[rank].uniqueTruthIndex] = 1;
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pr[all_dets[rank].cl][rank].tp++; // true-positive
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}
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else {
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pr[all_dets[rank].cl][rank].fp++; // false-positive
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}
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for (int i = 0; i < classes; ++i){
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const int tp = pr[i][rank].tp;
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const int fp = pr[i][rank].fp;
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const int fn = truth_classes_count[i] - tp; // false-negative = objects - true-positive
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pr[i][rank].fn = fn;
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if ((tp + fp) > 0)
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pr[i][rank].precision = (double)tp / (double)(tp + fp);
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else
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pr[i][rank].precision = 0;
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if ((tp + fn) > 0)
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pr[i][rank].recall = (double)tp / (double)(tp + fn);
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else
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pr[i][rank].recall = 0;
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if (rank == (detections_count - 1) && dets_classes_count[i] != (tp + fp)) {
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// check for last rank
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printf(" class_id: %d - detections = %d, tp+fp = %d, tp = %d, fp = %d \n", i, dets_classes_count[i], tp+fp, tp, fp);
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}
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}
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}
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if(verbose){
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for(int i=0; i < pr.size(); i++) {
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std::cout<<"---------Class "<<i<<std::endl;
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for(auto r:pr[i])
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r.print();
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}
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}
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//compute average precision for each class. Two methods are available,
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//based on map_points required
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double mean_average_precision = 0;
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double last_recall, last_precision, delta_recall;
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double cur_recall, cur_precision;
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double avg_precision = 0;
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for (int i = 0; i < classes; ++i) {
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avg_precision = 0;
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if (map_points == 0){ //mAP calculation: ImageNet, PascalVOC 2010-2012
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last_recall = pr[i][detections_count - 1].recall;
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last_precision = pr[i][detections_count - 1].precision;
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for (int rank = detections_count - 2; rank >= 0; --rank){
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delta_recall = last_recall - pr[i][rank].recall;
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last_recall = pr[i][rank].recall;
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if (pr[i][rank].precision > last_precision)
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last_precision = pr[i][rank].precision;
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avg_precision += delta_recall * last_precision;
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}
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}
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else {//MSCOCO - 101 Recall-points, PascalVOC - 11 Recall-points
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for (int point = 0; point < map_points; ++point) {
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cur_recall = point * 1.0 / ( map_points - 1 );
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cur_precision = 0;
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for (int rank = 0; rank < detections_count; ++rank)
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if (pr[i][rank].recall >= cur_recall && pr[i][rank].precision > cur_precision)
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cur_precision = pr[i][rank].precision;
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avg_precision += cur_precision;
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}
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avg_precision = avg_precision / map_points;
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}
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if(verbose)
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std::cout<<"Class: "<<i<<" AP: "<< avg_precision<<std::endl;
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mean_average_precision += avg_precision;
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}
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mean_average_precision = mean_average_precision / classes;
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std::cout<<"Classes: "<<classes<<" mAP " <<IoU_thresh<<":\t"<< mean_average_precision<<std::endl;
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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,
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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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out_file<<net<<";";
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}
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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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}
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AP/=map_levels;
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if(write_on_file){
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out_file<<AP<<"\n";
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out_file.close();
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}
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return AP;
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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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out_file.open("pr.csv", std::ios_base::app);
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out_file<<net<<";";
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}
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std::vector<int> truth_classes_count(classes,0);
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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) 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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truth_classes_count[img.gt[j].cl]++;
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float maxIoU = 0;
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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 = 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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}
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}
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}
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if(det_index > -1 && maxIoU > IoU_thresh && !det_assigned[det_index]){
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img.det[det_index].uniqueTruthIndex = j;
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img.det[det_index].truthFlag = 1;
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img.det[det_index].maxIoU = maxIoU;
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det_assigned[det_index] = true;
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dets_classes_count[img.det[det_index].cl]++;
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}
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}
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for(size_t i=0; i<img.det.size(); i++){
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if(img.det[i].truthFlag)
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pr[img.det[i].cl].tp ++;
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else
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pr[img.det[i].cl].fp ++;
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}
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for(size_t i=0; i<classes; i++){
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pr[i].fn += truth_classes_count[i] - dets_classes_count[i];
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}
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}
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//count all TP, FP, FN and compute precision, 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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avg_precision += pr[i].precision;
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avg_recall += pr[i].recall;
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TP += pr[i].tp;
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FP += pr[i].fp;
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FN += pr[i].fn;
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}
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avg_precision /= classes;
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avg_recall /= classes;
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f1_score = avg_precision + avg_recall > 0 ? 2 * ( avg_precision * avg_recall ) / ( avg_precision + avg_recall ) : 0;
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if(write_on_file){
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out_file<<TP<<";"<<FP<<";"<<FN<<";"<<avg_precision<<";"<<avg_recall<<";"<<f1_score<<"\n";
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out_file.close();
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}
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std::cout<<"avg precision: "<<avg_precision<<"\tavg recall: "<<avg_recall<<"\tavg f1 score:"<<f1_score<<std::endl;
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}
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void printJsonCOCOFormat(std::ofstream *out_file, const std::string image_path, std::vector<tk::dnn::box> bbox, const int classes, const int w, const int h)
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{
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int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 };
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std::string id = image_path.substr(image_path.find("images/")+7, image_path.find(".jpg") - image_path.find("images/") -7);
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int image_id = std::stoi(id);
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for (int i = 0; i < bbox.size(); ++i) {
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float xmin = bbox[i].x ;
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float xmax = bbox[i].x + float(bbox[i].w);
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float ymin = bbox[i].y;
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float ymax = bbox[i].y + float(bbox[i].h);
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//limit to image borders
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if (xmin < 0) xmin = 0;
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if (ymin < 0) ymin = 0;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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float bx = xmin;
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float by = ymin;
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float bw = xmax - xmin;
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float bh = ymax - ymin;
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if(bbox[i].probs.size() == classes)
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for (int j = 0; j < classes; ++j) {
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//min threshold confidence is set in DetectionNN.h
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if (bbox[i].probs[j] > 0) {
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*out_file << "{\"image_id\":" << image_id <<
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", \"category_id\":" << coco_ids[j] <<
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", \"bbox\":[" << bx << ", " << by << ", " << bw << ", " << bh <<
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"], \"score\":" << bbox[i].probs[j] << "},\n";
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}
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}
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else
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*out_file << "{\"image_id\":" << image_id <<
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", \"category_id\":" << coco_ids[bbox[i].cl] <<
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", \"bbox\":[" << bx << ", " << by << ", " << bw << ", " << bh <<
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"], \"score\":" << bbox[i].prob << "},\n";
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}
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}
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}}
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