Add mAP computation and demo
Signed-off-by: xavier <micaelaverucchi@gmail.com>
This commit is contained in:
xavier
@@ -114,6 +114,9 @@ target_link_libraries(yolo3_demo tkDNN)
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add_executable(centernet_demo demo/demo/demo_centernet.cpp)
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target_link_libraries(centernet_demo tkDNN)
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add_executable(map_demo demo/demo/map.cpp)
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target_link_libraries(map_demo tkDNN)
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#-------------------------------------------------------------------------------
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# Install
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@@ -0,0 +1,434 @@
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#include <iostream>
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#include <signal.h>
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#include <stdlib.h> /* srand, rand */
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#include <unistd.h>
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#include <mutex>
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#include "utils.h"
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include "Yolo3Detection.h"
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#include "CenternetDetection.h"
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#include <map>
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struct BoundigBox : public tk::dnn::box
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{
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friend std::ostream& operator<<(std::ostream& os, const BoundigBox& bb);
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int unique_truth_index = -1;
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int truth_flag = 0;
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};
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bool boxComparison (const BoundigBox& a,const BoundigBox& 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 BoundigBox& 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.truth_flag<< ", assignedGT: "<< bb.unique_truth_index<<"\n";
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return os;
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}
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struct Frame
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{
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void print() const
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{
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std::cout<<"labels filename: "<<l_filename<<std::endl;
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std::cout<<"image filename: "<<i_filename<<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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std::string l_filename;
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std::string i_filename;
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std::vector<BoundigBox> gt;
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std::vector<BoundigBox> det;
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};
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void convertFilename(std::string &filename,const std::string l_folder, const std::string i_folder, const std::string l_ext,const std::string i_ext)
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{
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filename.replace(filename.find(l_folder),l_folder.length(),i_folder);
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filename.replace(filename.find(l_ext),l_ext.length(),i_ext);
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}
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float overlap(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 BoundigBox &a, const BoundigBox &b)
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{
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float w = overlap(a.x, a.w, b.x, b.w);
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float h = overlap(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 BoundigBox &a, const BoundigBox &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 BoundigBox &a, const BoundigBox &b)
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{
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float I = boxIntersection(a, b);
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// std::cout<<"I: "<<I<<std::endl;
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float U = boxUnion(a, b);
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// std::cout<<"U: "<<U<<std::endl;
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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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struct PR
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{
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double precision = 0;
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double recall = 0;
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int tp = 0, fp = 0, fn = 0;
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void print()
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{
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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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};
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double computeMap(std::vector<Frame> &images,const int classes,const int IoU_thresh, const int map_points, const bool verbose=false)
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{
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std::cout<<"Computing mAP"<<std::endl;
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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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// std::vector<int> avg_iou_per_class(classes,0);
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// std::vector<int> tp_for_thresh_per_class(classes,0);
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// std::vector<int> fp_for_thresh_per_class(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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{
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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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std::cout<<"gt_count: "<<groundtruths_count<<std::endl;
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std::cout<<"det_count: "<<detections_count<<std::endl;
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std::vector<BoundigBox> all_dets;
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std::vector<BoundigBox> all_gts;
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int gt_checked = 0;
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// for each detection comput 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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{
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for(size_t i=0; i<img.det.size(); i++)
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{
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if(img.det[i].prob > 0)
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{
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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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{
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float currentIoU = boxIoU(img.det[i], img.gt[j]);
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if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl)
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{
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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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{
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img.det[i].unique_truth_index = truth_index + gt_checked;
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img.det[i].truth_flag = 1;
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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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{
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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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{
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if (rank > 0)
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{
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for (int class_id = 0; class_id < classes; ++class_id)
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{
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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].truth_flag == 1 && truth_flags[all_dets[rank].unique_truth_index] == 0)
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{
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truth_flags[all_dets[rank].unique_truth_index] = 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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{
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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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{
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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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{
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for(int i=0; i < pr.size(); i++)
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{
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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 avaible,
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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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{
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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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{
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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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{
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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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{
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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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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: "<< mean_average_precision<<std::endl;
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return mean_average_precision;
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}
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enum networkType_t { YOLO, CENTERNET};
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int main(int argc, char *argv[])
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{
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// char *net = "resnet101_cnet_FP32.rt";
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char *net = "yolo3.rt";
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if(argc > 1)
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net = argv[1];
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char *labels_path = "/media/887E650E7E64F67A/val2014/all_labels.txt";
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if(argc > 2)
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labels_path = argv[2];
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networkType_t ntype = YOLO;
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bool show = false;
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tk::dnn::Yolo3Detection yolo;
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tk::dnn::CenternetDetection cnet;
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switch(ntype)
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{
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case YOLO:
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yolo.init(net);
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break;
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case CENTERNET:
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cnet.init(net);
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break;
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default:
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FatalError("Network type not allowed ");
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}
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std::ifstream all_labels(labels_path);
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std::string l_filename;
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std::vector<Frame> images;
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std::cout<<"Reading groundtruth and generating detections"<<std::endl;
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if(show)
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cv::namedWindow("detection", cv::WINDOW_NORMAL);
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int i=0;
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while (std::getline(all_labels, l_filename) && i < 1000)
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{
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Frame f;
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f.l_filename = l_filename;
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f.i_filename = l_filename;
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convertFilename(f.i_filename, "labels", "images", ".txt", ".jpg");
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std::cout << f.i_filename << std::endl;
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std::cout << "images done:" << i++ << "\n";
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// generate detections
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cv::Mat frame = cv::imread(f.i_filename.c_str(), cv::IMREAD_COLOR);
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int height = frame.rows;
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int width = frame.cols;
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cv::Mat dnn_input;
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if(!frame.data)
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break;
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dnn_input = frame.clone();
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//inference
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std::vector<tk::dnn::box> detected_bbox;
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switch(ntype)
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{
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case YOLO:
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yolo.update(dnn_input);
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detected_bbox = yolo.detected;
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break;
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case CENTERNET:
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cnet.update(dnn_input);
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detected_bbox = cnet.detected;
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break;
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default:
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FatalError("Network type not allowed ");
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}
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// save detections labels
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for(auto d:detected_bbox)
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{
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//convert detected bb in the same format as label
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//<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width>
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BoundigBox b;
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b.x = (d.x + d.w/2) / width;
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b.y = (d.y + d.h/2) / height;
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b.w = d.w / width;
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b.h = d.h / height;
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b.prob = d.prob;
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b.cl = d.cl;
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f.det.push_back(b);
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if(show)// draw rectangle for detection
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cv::rectangle(frame, cv::Point(d.x, d.y), cv::Point(d.x + d.w, d.y + d.h), cv::Scalar(255, 0, 0), 2);
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}
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// read and save groundtruth labels
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std::ifstream labels(l_filename);
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for(std::string line; std::getline(labels, line); )
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{
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std::istringstream in(line);
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BoundigBox b;
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in >> b.cl >> b.x >> b.y >> b.w >> b.h;
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b.prob = 1;
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b.truth_flag = 1;
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f.gt.push_back(b);
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if(show)// draw rectangle for groundtruth
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cv::rectangle(frame, cv::Point((b.x-b.w/2)*width, (b.y-b.h/2)*height), cv::Point((b.x+b.w/2)*width,(b.y+b.h/2)*height), cv::Scalar(0, 255, 0), 2);
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}
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images.push_back(f);
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if(show)
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{
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cv::imshow("detection", frame);
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cv::waitKey(0);
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}
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}
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std::cout<<"Done."<<std::endl;
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int classes = 80;
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int IoU_thresh = 0.5;
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int map_points = 0;
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bool verbose = false;
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computeMap(images,classes,IoU_thresh,map_points, verbose);
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return 0;
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}
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@@ -434,7 +434,7 @@ public:
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dnnType *predictions;
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static const int MAX_DETECTIONS = 256;
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static const int MAX_DETECTIONS = 1024;
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static Yolo::detection *allocateDetections(int nboxes, int classes);
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static void mergeDetections(Yolo::detection *dets, int ndets, int classes);
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};
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