charles/monoloco
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
a37b5c7b6c
monoloco/kitti-eval/original.cpp
Lorenzo a37b5c7b6c first commit
2020-08-20 11:33:19 +02:00

1004 lines
35 KiB
C++
Raw Blame History

#include <iostream>
#include <algorithm>
#include <stdio.h>
#include <math.h>
#include <vector>
#include <numeric>
#include <strings.h>
#include <assert.h>
#include <dirent.h>
#include <fstream>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/io.hpp>
#include <boost/geometry.hpp>
#include <boost/geometry/geometries/point_xy.hpp>
#include <boost/geometry/geometries/polygon.hpp>
#include <boost/geometry/geometries/adapted/c_array.hpp>
BOOST_GEOMETRY_REGISTER_C_ARRAY_CS(cs::cartesian)
typedef boost::geometry::model::polygon<boost::geometry::model::d2::point_xy<double> > Polygon;
using namespace std;
/*=======================================================================
STATIC EVALUATION PARAMETERS
=======================================================================*/
// holds the number of test images on the server
//const int32_t N_TESTIMAGES = 7518;
int32_t N_TESTIMAGES = 7480; // Validation
const int32_t append_zeros = 6;
// easy, moderate and hard evaluation level
enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};
// evaluation metrics: image, ground or 3D
enum METRIC{IMAGE=0, GROUND=1, BOX3D=2};
// evaluation parameter
const int32_t MIN_HEIGHT[3] = {40, 25, 25}; // minimum height for evaluated groundtruth/detections
const int32_t MAX_OCCLUSION[3] = {0, 1, 2}; // maximum occlusion level of the groundtruth used for evaluation
const double MAX_TRUNCATION[3] = {0.15, 0.3, 0.5}; // maximum truncation level of the groundtruth used for evaluation
// evaluated object classes
enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};
const int NUM_CLASS = 3;
// parameters varying per class
vector<string> CLASS_NAMES;
vector<string> CLASS_NAMES_CAP;
// the minimum overlap required for 2D evaluation on the image/ground plane and 3D evaluation
const double MIN_OVERLAP[3][3] = {{0.7, 0.5, 0.5}, {0.7, 0.5, 0.5}, {0.7, 0.5, 0.5}};
//const double MIN_OVERLAP[3][3] = {{0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}};
// no. of recall steps that should be evaluated (discretized)
const double N_SAMPLE_PTS = 41;
// initialize class names
void initGlobals () {
CLASS_NAMES.push_back("car");
CLASS_NAMES.push_back("pedestrian");
CLASS_NAMES.push_back("cyclist");
CLASS_NAMES_CAP.push_back("Car");
CLASS_NAMES_CAP.push_back("Pedestrian");
CLASS_NAMES_CAP.push_back("Cyclist");
}
/*=======================================================================
DATA TYPES FOR EVALUATION
=======================================================================*/
// holding data needed for precision-recall and precision-aos
struct tPrData {
vector<double> v; // detection score for computing score thresholds
double similarity; // orientation similarity
int32_t tp; // true positives
int32_t fp; // false positives
int32_t fn; // false negatives
tPrData () :
similarity(0), tp(0), fp(0), fn(0) {}
};
// holding bounding boxes for ground truth and detections
struct tBox {
string type; // object type as car, pedestrian or cyclist,...
double x1; // left corner
double y1; // top corner
double x2; // right corner
double y2; // bottom corner
double alpha; // image orientation
tBox (string type, double x1,double y1,double x2,double y2,double alpha) :
type(type),x1(x1),y1(y1),x2(x2),y2(y2),alpha(alpha) {}
};
// holding ground truth data
struct tGroundtruth {
tBox box; // object type, box, orientation
double truncation; // truncation 0..1
int32_t occlusion; // occlusion 0,1,2 (non, partly, fully)
double ry;
double t1, t2, t3;
double h, w, l;
tGroundtruth () :
box(tBox("invalild",-1,-1,-1,-1,-10)),truncation(-1),occlusion(-1) {}
tGroundtruth (tBox box,double truncation,int32_t occlusion) :
box(box),truncation(truncation),occlusion(occlusion) {}
tGroundtruth (string type,double x1,double y1,double x2,double y2,double alpha,double truncation,int32_t occlusion) :
box(tBox(type,x1,y1,x2,y2,alpha)),truncation(truncation),occlusion(occlusion) {}
};
// holding detection data
struct tDetection {
tBox box; // object type, box, orientation
double thresh; // detection score
double ry;
double t1, t2, t3;
double h, w, l;
tDetection ():
box(tBox("invalid",-1,-1,-1,-1,-10)),thresh(-1000) {}
tDetection (tBox box,double thresh) :
box(box),thresh(thresh) {}
tDetection (string type,double x1,double y1,double x2,double y2,double alpha,double thresh) :
box(tBox(type,x1,y1,x2,y2,alpha)),thresh(thresh) {}
};
/*=======================================================================
FUNCTIONS TO LOAD DETECTION AND GROUND TRUTH DATA ONCE, SAVE RESULTS
=======================================================================*/
vector<tDetection> loadDetections(string file_name, bool &compute_aos,
vector<bool> &eval_image, vector<bool> &eval_ground,
vector<bool> &eval_3d, bool &success) {
// holds all detections (ignored detections are indicated by an index vector
vector<tDetection> detections;
FILE *fp = fopen(file_name.c_str(),"r");
if (!fp) {
success = false;
return detections;
}
while (!feof(fp)) {
tDetection d;
double trash;
char str[255];
if (fscanf(fp, "%s %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf",
str, &trash, &trash, &d.box.alpha, &d.box.x1, &d.box.y1,
&d.box.x2, &d.box.y2, &d.h, &d.w, &d.l, &d.t1, &d.t2, &d.t3,
&d.ry, &d.thresh)==16) {
// d.thresh = 1;
d.box.type = str;
detections.push_back(d);
// orientation=-10 is invalid, AOS is not evaluated if at least one orientation is invalid
if(d.box.alpha == -10)
compute_aos = false;
// a class is only evaluated if it is detected at least once
for (int c = 0; c < NUM_CLASS; c++) {
if (!strcasecmp(d.box.type.c_str(), CLASS_NAMES[c].c_str()) || !strcasecmp(d.box.type.c_str(), CLASS_NAMES_CAP[c].c_str())) {
if (!eval_image[c] && d.box.x1 >= 0)
eval_image[c] = true;
if (!eval_ground[c] && d.t1 != -1000 && d.t3 != -1000 && d.w > 0 && d.l > 0)
eval_ground[c] = true;
if (!eval_3d[c] && d.t1 != -1000 && d.t2 != -1000 && d.t3 != -1000 && d.h > 0 && d.w > 0 && d.l > 0)
eval_3d[c] = true;
break;
}
}
}
}
fclose(fp);
success = true;
return detections;
}
vector<tGroundtruth> loadGroundtruth(string file_name,bool &success) {
// holds all ground truth (ignored ground truth is indicated by an index vector
vector<tGroundtruth> groundtruth;
FILE *fp = fopen(file_name.c_str(),"r");
if (!fp) {
success = false;
return groundtruth;
}
while (!feof(fp)) {
tGroundtruth g;
char str[255];
if (fscanf(fp, "%s %lf %d %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf",
str, &g.truncation, &g.occlusion, &g.box.alpha,
&g.box.x1, &g.box.y1, &g.box.x2, &g.box.y2,
&g.h, &g.w, &g.l, &g.t1,
&g.t2, &g.t3, &g.ry )==15) {
g.box.type = str;
groundtruth.push_back(g);
}
}
fclose(fp);
success = true;
return groundtruth;
}
void saveStats (const vector<double> &precision, const vector<double> &aos, FILE *fp_det, FILE *fp_ori) {
// save precision to file
if(precision.empty())
return;
for (int32_t i=0; i<precision.size(); i++)
fprintf(fp_det,"%f ",precision[i]);
fprintf(fp_det,"\n");
// save orientation similarity, only if there were no invalid orientation entries in submission (alpha=-10)
if(aos.empty())
return;
for (int32_t i=0; i<aos.size(); i++)
fprintf(fp_ori,"%f ",aos[i]);
fprintf(fp_ori,"\n");
}
/*=======================================================================
EVALUATION HELPER FUNCTIONS
=======================================================================*/
// criterion defines whether the overlap is computed with respect to both areas (ground truth and detection)
// or with respect to box a or b (detection and "dontcare" areas)
inline double imageBoxOverlap(tBox a, tBox b, int32_t criterion=-1){
// overlap is invalid in the beginning
double o = -1;
// get overlapping area
double x1 = max(a.x1, b.x1);
double y1 = max(a.y1, b.y1);
double x2 = min(a.x2, b.x2);
double y2 = min(a.y2, b.y2);
// compute width and height of overlapping area
double w = x2-x1;
double h = y2-y1;
// set invalid entries to 0 overlap
if(w<=0 || h<=0)
return 0;
// get overlapping areas
double inter = w*h;
double a_area = (a.x2-a.x1) * (a.y2-a.y1);
double b_area = (b.x2-b.x1) * (b.y2-b.y1);
// intersection over union overlap depending on users choice
if(criterion==-1) // union
o = inter / (a_area+b_area-inter);
else if(criterion==0) // bbox_a
o = inter / a_area;
else if(criterion==1) // bbox_b
o = inter / b_area;
// overlap
return o;
}
inline double imageBoxOverlap(tDetection a, tGroundtruth b, int32_t criterion=-1){
return imageBoxOverlap(a.box, b.box, criterion);
}
// compute polygon of an oriented bounding box
template <typename T>
Polygon toPolygon(const T& g) {
using namespace boost::numeric::ublas;
using namespace boost::geometry;
matrix<double> mref(2, 2);
mref(0, 0) = cos(g.ry); mref(0, 1) = sin(g.ry);
mref(1, 0) = -sin(g.ry); mref(1, 1) = cos(g.ry);
static int count = 0;
matrix<double> corners(2, 4);
double data[] = {g.l / 2, g.l / 2, -g.l / 2, -g.l / 2,
g.w / 2, -g.w / 2, -g.w / 2, g.w / 2};
std::copy(data, data + 8, corners.data().begin());
matrix<double> gc = prod(mref, corners);
for (int i = 0; i < 4; ++i) {
gc(0, i) += g.t1;
gc(1, i) += g.t3;
}
double points[][2] = {{gc(0, 0), gc(1, 0)},{gc(0, 1), gc(1, 1)},{gc(0, 2), gc(1, 2)},{gc(0, 3), gc(1, 3)},{gc(0, 0), gc(1, 0)}};
Polygon poly;
append(poly, points);
return poly;
}
// measure overlap between bird's eye view bounding boxes, parametrized by (ry, l, w, tx, tz)
inline double groundBoxOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {
using namespace boost::geometry;
Polygon gp = toPolygon(g);
Polygon dp = toPolygon(d);
std::vector<Polygon> in, un;
intersection(gp, dp, in);
union_(gp, dp, un);
double inter_area = in.empty() ? 0 : area(in.front());
double union_area = area(un.front());
double o;
if(criterion==-1) // union
o = inter_area / union_area;
else if(criterion==0) // bbox_a
o = inter_area / area(dp);
else if(criterion==1) // bbox_b
o = inter_area / area(gp);
return o;
}
// measure overlap between 3D bounding boxes, parametrized by (ry, h, w, l, tx, ty, tz)
inline double box3DOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {
using namespace boost::geometry;
Polygon gp = toPolygon(g);
Polygon dp = toPolygon(d);
std::vector<Polygon> in, un;
intersection(gp, dp, in);
union_(gp, dp, un);
double ymax = min(d.t2, g.t2);
double ymin = max(d.t2 - d.h, g.t2 - g.h);
double inter_area = in.empty() ? 0 : area(in.front());
double inter_vol = inter_area * max(0.0, ymax - ymin);
double det_vol = d.h * d.l * d.w;
double gt_vol = g.h * g.l * g.w;
double o;
if(criterion==-1) // union
o = inter_vol / (det_vol + gt_vol - inter_vol);
else if(criterion==0) // bbox_a
o = inter_vol / det_vol;
else if(criterion==1) // bbox_b
o = inter_vol / gt_vol;
return o;
}
vector<double> getThresholds(vector<double> &v, double n_groundtruth){
// holds scores needed to compute N_SAMPLE_PTS recall values
vector<double> t;
// sort scores in descending order
// (highest score is assumed to give best/most confident detections)
sort(v.begin(), v.end(), greater<double>());
// get scores for linearly spaced recall
double current_recall = 0;
for(int32_t i=0; i<v.size(); i++){
// check if right-hand-side recall with respect to current recall is close than left-hand-side one
// in this case, skip the current detection score
double l_recall, r_recall, recall;
l_recall = (double)(i+1)/n_groundtruth;
if(i<(v.size()-1))
r_recall = (double)(i+2)/n_groundtruth;
else
r_recall = l_recall;
if( (r_recall-current_recall) < (current_recall-l_recall) && i<(v.size()-1))
continue;
// left recall is the best approximation, so use this and goto next recall step for approximation
recall = l_recall;
// the next recall step was reached
t.push_back(v[i]);
current_recall += 1.0/(N_SAMPLE_PTS-1.0);
}
return t;
}
void cleanData(CLASSES current_class, const vector<tGroundtruth> &gt, const vector<tDetection> &det, vector<int32_t> &ignored_gt, vector<tGroundtruth> &dc, vector<int32_t> &ignored_det, int32_t &n_gt, DIFFICULTY difficulty){
// extract ground truth bounding boxes for current evaluation class
for(int32_t i=0;i<gt.size(); i++){
// only bounding boxes with a minimum height are used for evaluation
double height = gt[i].box.y2 - gt[i].box.y1;
// neighboring classes are ignored ("van" for "car" and "person_sitting" for "pedestrian")
// (lower/upper cases are ignored)
int32_t valid_class;
// all classes without a neighboring class
if(!strcasecmp(gt[i].box.type.c_str(), CLASS_NAMES[current_class].c_str()))
valid_class = 1;
// classes with a neighboring class
else if(!strcasecmp(CLASS_NAMES[current_class].c_str(), "Pedestrian") && !strcasecmp("Person_sitting", gt[i].box.type.c_str()))
valid_class = 0;
else if(!strcasecmp(CLASS_NAMES[current_class].c_str(), "Car") && !strcasecmp("Van", gt[i].box.type.c_str()))
valid_class = 0;
// classes not used for evaluation
else
valid_class = -1;
// ground truth is ignored, if occlusion, truncation exceeds the difficulty or ground truth is too small
// (doesn't count as FN nor TP, although detections may be assigned)
bool ignore = false;
if(gt[i].occlusion>MAX_OCCLUSION[difficulty] || gt[i].truncation>MAX_TRUNCATION[difficulty] || height<=MIN_HEIGHT[difficulty])
ignore = true;
// set ignored vector for ground truth
// current class and not ignored (total no. of ground truth is detected for recall denominator)
if(valid_class==1 && !ignore){
ignored_gt.push_back(0);
n_gt++;
}
// neighboring class, or current class but ignored
else if(valid_class==0 || (ignore && valid_class==1))
ignored_gt.push_back(1);
// all other classes which are FN in the evaluation
else
ignored_gt.push_back(-1);
}
// extract dontcare areas
for(int32_t i=0;i<gt.size(); i++)
if(!strcasecmp("DontCare", gt[i].box.type.c_str()))
dc.push_back(gt[i]);
// extract detections bounding boxes of the current class
for(int32_t i=0;i<det.size(); i++){
// neighboring classes are not evaluated
int32_t valid_class;
if(!strcasecmp(det[i].box.type.c_str(), CLASS_NAMES[current_class].c_str()))
valid_class = 1;
else
valid_class = -1;
int32_t height = fabs(det[i].box.y1 - det[i].box.y2);
// set ignored vector for detections
if(height<MIN_HEIGHT[difficulty])
ignored_det.push_back(1);
else if(valid_class==1)
ignored_det.push_back(0);
else
ignored_det.push_back(-1);
}
}
tPrData computeStatistics(CLASSES current_class, const vector<tGroundtruth> &gt,
const vector<tDetection> &det, const vector<tGroundtruth> &dc,
const vector<int32_t> &ignored_gt, const vector<int32_t> &ignored_det,
bool compute_fp, double (*boxoverlap)(tDetection, tGroundtruth, int32_t),
METRIC metric, bool compute_aos=false, double thresh=0, bool debug=false){
tPrData stat = tPrData();
const double NO_DETECTION = -10000000;
vector<double> delta; // holds angular difference for TPs (needed for AOS evaluation)
vector<bool> assigned_detection; // holds wether a detection was assigned to a valid or ignored ground truth
assigned_detection.assign(det.size(), false);
vector<bool> ignored_threshold;
ignored_threshold.assign(det.size(), false); // holds detections with a threshold lower than thresh if FP are computed
// detections with a low score are ignored for computing precision (needs FP)
if(compute_fp)
for(int32_t i=0; i<det.size(); i++)
if(det[i].thresh<thresh)
ignored_threshold[i] = true;
// evaluate all ground truth boxes
for(int32_t i=0; i<gt.size(); i++){
// this ground truth is not of the current or a neighboring class and therefore ignored
if(ignored_gt[i]==-1)
continue;
/*=======================================================================
find candidates (overlap with ground truth > 0.5) (logical len(det))
=======================================================================*/
int32_t det_idx = -1;
double valid_detection = NO_DETECTION;
double max_overlap = 0;
// search for a possible detection
bool assigned_ignored_det = false;
for(int32_t j=0; j<det.size(); j++){
// detections not of the current class, already assigned or with a low threshold are ignored
if(ignored_det[j]==-1)
continue;
if(assigned_detection[j])
continue;
if(ignored_threshold[j])
continue;
// find the maximum score for the candidates and get idx of respective detection
double overlap = boxoverlap(det[j], gt[i], -1);
// for computing recall thresholds, the candidate with highest score is considered
if(!compute_fp && overlap>MIN_OVERLAP[metric][current_class] && det[j].thresh>valid_detection){
det_idx = j;
valid_detection = det[j].thresh;
}
// for computing pr curve values, the candidate with the greatest overlap is considered
// if the greatest overlap is an ignored detection (min_height), the overlapping detection is used
else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && (overlap>max_overlap || assigned_ignored_det) && ignored_det[j]==0){
max_overlap = overlap;
det_idx = j;
valid_detection = 1;
assigned_ignored_det = false;
}
else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && valid_detection==NO_DETECTION && ignored_det[j]==1){
det_idx = j;
valid_detection = 1;
assigned_ignored_det = true;
}
}
/*=======================================================================
compute TP, FP and FN
=======================================================================*/
// nothing was assigned to this valid ground truth
if(valid_detection==NO_DETECTION && ignored_gt[i]==0) {
stat.fn++;
}
// only evaluate valid ground truth <=> detection assignments (considering difficulty level)
else if(valid_detection!=NO_DETECTION && (ignored_gt[i]==1 || ignored_det[det_idx]==1))
assigned_detection[det_idx] = true;
// found a valid true positive
else if(valid_detection!=NO_DETECTION){
// write highest score to threshold vector
stat.tp++;
stat.v.push_back(det[det_idx].thresh);
// compute angular difference of detection and ground truth if valid detection orientation was provided
if(compute_aos)
delta.push_back(gt[i].box.alpha - det[det_idx].box.alpha);
// clean up
assigned_detection[det_idx] = true;
}
}
// if FP are requested, consider stuff area
if(compute_fp){
// count fp
for(int32_t i=0; i<det.size(); i++){
// count false positives if required (height smaller than required is ignored (ignored_det==1)
if(!(assigned_detection[i] || ignored_det[i]==-1 || ignored_det[i]==1 || ignored_threshold[i]))
stat.fp++;
}
// do not consider detections overlapping with stuff area
int32_t nstuff = 0;
for(int32_t i=0; i<dc.size(); i++){
for(int32_t j=0; j<det.size(); j++){
// detections not of the current class, already assigned, with a low threshold or a low minimum height are ignored
if(assigned_detection[j])
continue;
if(ignored_det[j]==-1 || ignored_det[j]==1)
continue;
if(ignored_threshold[j])
continue;
// compute overlap and assign to stuff area, if overlap exceeds class specific value
double overlap = boxoverlap(det[j], dc[i], 0);
if(overlap>MIN_OVERLAP[metric][current_class]){
assigned_detection[j] = true;
nstuff++;
}
}
}
// FP = no. of all not to ground truth assigned detections - detections assigned to stuff areas
stat.fp -= nstuff;
// if all orientation values are valid, the AOS is computed
if(compute_aos){
vector<double> tmp;
// FP have a similarity of 0, for all TP compute AOS
tmp.assign(stat.fp, 0);
for(int32_t i=0; i<delta.size(); i++)
tmp.push_back((1.0+cos(delta[i]))/2.0);
// be sure, that all orientation deltas are computed
assert(tmp.size()==stat.fp+stat.tp);
assert(delta.size()==stat.tp);
// get the mean orientation similarity for this image
if(stat.tp>0 || stat.fp>0)
stat.similarity = accumulate(tmp.begin(), tmp.end(), 0.0);
// there was neither a FP nor a TP, so the similarity is ignored in the evaluation
else
stat.similarity = -1;
}
}
return stat;
}
/*=======================================================================
EVALUATE CLASS-WISE
=======================================================================*/
bool eval_class (FILE *fp_det, FILE *fp_ori, CLASSES current_class,
const vector< vector<tGroundtruth> > &groundtruth,
const vector< vector<tDetection> > &detections, bool compute_aos,
double (*boxoverlap)(tDetection, tGroundtruth, int32_t),
vector<double> &precision, vector<double> &aos,
DIFFICULTY difficulty, METRIC metric) {
assert(groundtruth.size() == detections.size());
// init
int32_t n_gt=0; // total no. of gt (denominator of recall)
vector<double> v, thresholds; // detection scores, evaluated for recall discretization
vector< vector<int32_t> > ignored_gt, ignored_det; // index of ignored gt detection for current class/difficulty
vector< vector<tGroundtruth> > dontcare; // index of dontcare areas, included in ground truth
// for all test images do
for (int32_t i=0; i<groundtruth.size(); i++){
// holds ignored ground truth, ignored detections and dontcare areas for current frame
vector<int32_t> i_gt, i_det;
vector<tGroundtruth> dc;
// only evaluate objects of current class and ignore occluded, truncated objects
cleanData(current_class, groundtruth[i], detections[i], i_gt, dc, i_det, n_gt, difficulty);
ignored_gt.push_back(i_gt);
ignored_det.push_back(i_det);
dontcare.push_back(dc);
// compute statistics to get recall values
tPrData pr_tmp = tPrData();
pr_tmp = computeStatistics(current_class, groundtruth[i], detections[i], dc, i_gt, i_det, false, boxoverlap, metric);
// add detection scores to vector over all images
for(int32_t j=0; j<pr_tmp.v.size(); j++)
v.push_back(pr_tmp.v[j]);
}
// get scores that must be evaluated for recall discretization
thresholds = getThresholds(v, n_gt);
// compute TP,FP,FN for relevant scores
vector<tPrData> pr;
pr.assign(thresholds.size(),tPrData());
for (int32_t i=0; i<groundtruth.size(); i++){
// for all scores/recall thresholds do:
for(int32_t t=0; t<thresholds.size(); t++){
tPrData tmp = tPrData();
tmp = computeStatistics(current_class, groundtruth[i], detections[i], dontcare[i],
ignored_gt[i], ignored_det[i], true, boxoverlap, metric,
compute_aos, thresholds[t], t==38);
// add no. of TP, FP, FN, AOS for current frame to total evaluation for current threshold
pr[t].tp += tmp.tp;
pr[t].fp += tmp.fp;
pr[t].fn += tmp.fn;
if(tmp.similarity!=-1)
pr[t].similarity += tmp.similarity;
}
}
// compute recall, precision and AOS
vector<double> recall;
precision.assign(N_SAMPLE_PTS, 0);
if(compute_aos)
aos.assign(N_SAMPLE_PTS, 0);
double r=0;
for (int32_t i=0; i<thresholds.size(); i++){
r = pr[i].tp/(double)(pr[i].tp + pr[i].fn);
recall.push_back(r);
precision[i] = pr[i].tp/(double)(pr[i].tp + pr[i].fp);
if(compute_aos)
aos[i] = pr[i].similarity/(double)(pr[i].tp + pr[i].fp);
}
// filter precision and AOS using max_{i..end}(precision)
for (int32_t i=0; i<thresholds.size(); i++){
precision[i] = *max_element(precision.begin()+i, precision.end());
if(compute_aos)
aos[i] = *max_element(aos.begin()+i, aos.end());
}
// save statisics and finish with success
saveStats(precision, aos, fp_det, fp_ori);
return true;
}
void saveAndPlotPlots(string dir_name,string file_name,string obj_type,vector<double> vals[],bool is_aos){
char command[1024];
// save plot data to file
FILE *fp = fopen((dir_name + "/" + file_name + ".txt").c_str(),"w");
printf("save %s\n", (dir_name + "/" + file_name + ".txt").c_str());
for (int32_t i=0; i<(int)N_SAMPLE_PTS; i++)
fprintf(fp,"%f %f %f %f\n",(double)i/(N_SAMPLE_PTS-1.0),vals[0][i],vals[1][i],vals[2][i]);
fclose(fp);
// create png + eps
for (int32_t j=0; j<2; j++) {
// open file
FILE *fp = fopen((dir_name + "/" + file_name + ".gp").c_str(),"w");
// save gnuplot instructions
if (j==0) {
fprintf(fp,"set term png size 450,315 font \"Helvetica\" 11\n");
fprintf(fp,"set output \"%s.png\"\n",file_name.c_str());
} else {
fprintf(fp,"set term postscript eps enhanced color font \"Helvetica\" 20\n");
fprintf(fp,"set output \"%s.eps\"\n",file_name.c_str());
}
// set labels and ranges
fprintf(fp,"set size ratio 0.7\n");
fprintf(fp,"set xrange [0:1]\n");
fprintf(fp,"set yrange [0:1]\n");
fprintf(fp,"set xlabel \"Recall\"\n");
if (!is_aos) fprintf(fp,"set ylabel \"Precision\"\n");
else fprintf(fp,"set ylabel \"Orientation Similarity\"\n");
obj_type[0] = toupper(obj_type[0]);
fprintf(fp,"set title \"%s\"\n",obj_type.c_str());
// line width
int32_t lw = 5;
if (j==0) lw = 3;
// plot error curve
fprintf(fp,"plot ");
fprintf(fp,"\"%s.txt\" using 1:2 title 'Easy' with lines ls 1 lw %d,",file_name.c_str(),lw);
fprintf(fp,"\"%s.txt\" using 1:3 title 'Moderate' with lines ls 2 lw %d,",file_name.c_str(),lw);
fprintf(fp,"\"%s.txt\" using 1:4 title 'Hard' with lines ls 3 lw %d",file_name.c_str(),lw);
// close file
fclose(fp);
// run gnuplot => create png + eps
sprintf(command,"cd %s; gnuplot %s",dir_name.c_str(),(file_name + ".gp").c_str());
system(command);
}
// create pdf and crop
sprintf(command,"cd %s; ps2pdf %s.eps %s_large.pdf",dir_name.c_str(),file_name.c_str(),file_name.c_str());
system(command);
sprintf(command,"cd %s; pdfcrop %s_large.pdf %s.pdf",dir_name.c_str(),file_name.c_str(),file_name.c_str());
system(command);
sprintf(command,"cd %s; rm %s_large.pdf",dir_name.c_str(),file_name.c_str());
system(command);
}
bool eval(string result_sha,string input_dataset){
// set some global parameters
initGlobals();
// ground truth and result directories
string gt_dir = "data/object/label_2";
string result_dir = "results/" + result_sha;
string plot_dir = result_dir + "/plot";
string valid_imgs_path = "lists/" + input_dataset + ".txt";
std::cout << "Results list: " << valid_imgs_path << std::endl;
// create output directories
system(("mkdir " + plot_dir).c_str());
// hold detections and ground truth in memory
vector< vector<tGroundtruth> > groundtruth;
vector< vector<tDetection> > detections;
// holds wether orientation similarity shall be computed (might be set to false while loading detections)
// and which labels where provided by this submission
bool compute_aos=true;
vector<bool> eval_image(NUM_CLASS, false);
vector<bool> eval_ground(NUM_CLASS, false);
vector<bool> eval_3d(NUM_CLASS, false);
/* LB load only validation images
*/
std::cout << "Getting valid images... " << std::endl;
// Get image indices
ifstream valid_imgs(valid_imgs_path.c_str());
if (!valid_imgs.is_open()){
std::cout << valid_imgs_path << " not found" << std::endl;
exit(-1);
}
string line;
vector<int> indices;
while (!valid_imgs.eof())
{
getline (valid_imgs,line);
if (atoi(line.c_str())!=0){
indices.push_back(atoi(line.c_str()));
}
}
std::cout << "Validation file loaded" << std::endl;
N_TESTIMAGES = indices.size();
std::cout << indices.size() << std::endl;
// Just to get stats for each class
vector<int> count, count_gt;
count.assign(CLASS_NAMES.size(), 0);
count_gt.assign(CLASS_NAMES.size(), 0);
// for all images read groundtruth and detections
std::cout << "Loading detections... " << std::endl;
for (int32_t i=0; i<N_TESTIMAGES; i++) {
// file name
char file_name[256];
switch (append_zeros){
case 6:
sprintf(file_name,"%06d.txt",indices[i]);
break;
case 8:
sprintf(file_name,"%08d.txt",indices[i]);
break;
default:
std::cout << "ERROR: Undefined number of zeros to append" << std::endl;
}
// read ground truth and result poses
bool gt_success,det_success;
vector<tGroundtruth> gt = loadGroundtruth(gt_dir + "/" + file_name,gt_success);
vector<tDetection> det = loadDetections(result_dir + "/data/" + file_name,
compute_aos, eval_image, eval_ground, eval_3d, det_success);
groundtruth.push_back(gt);
detections.push_back(det);
// check for errors
if (!gt_success) {
std::cout << "ERROR: Couldn't read: " << gt_dir + "/" + file_name << " of ground truth." << std::endl;;
return false;
}
if (!det_success) {
std::cout << "ERROR: Couldn't read: " << result_dir + "/data/" + file_name <<std::endl;
return false;
}
}
std::cout << " done." << std::endl;
// holds pointers for result files
FILE *fp_det=0, *fp_ori=0;
// eval image 2D bounding boxes
for (int c = 0; c < NUM_CLASS; c++) {
CLASSES cls = (CLASSES)c;
//mail->msg("Checking 2D evaluation (%s) ...", CLASS_NAMES[c].c_str());
if (eval_image[c]) {
cout << "Starting 2D evaluation (" << CLASS_NAMES[c] << ") ..." << endl;
fp_det = fopen((result_dir + "/stats_" + CLASS_NAMES[c] + "_detection.txt").c_str(), "w");
if(compute_aos)
fp_ori = fopen((result_dir + "/stats_" + CLASS_NAMES[c] + "_orientation.txt").c_str(),"w");
vector<double> precision[3], aos[3];
if( !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[0], aos[0], EASY, IMAGE)
|| !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[1], aos[1], MODERATE, IMAGE)
|| !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[2], aos[2], HARD, IMAGE)) {
cout << CLASS_NAMES[c].c_str() << " evaluation failed." << endl;
return false;
}
fclose(fp_det);
saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_detection", CLASS_NAMES[c], precision, 0);
if(compute_aos){
saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_orientation", CLASS_NAMES[c], aos, 1);
fclose(fp_ori);
}
cout << " done." << endl;
}
}
// don't evaluate AOS for birdview boxes and 3D boxes
compute_aos = false;
// eval bird's eye view bounding boxes
for (int c = 0; c < NUM_CLASS; c++) {
CLASSES cls = (CLASSES)c;
//mail->msg("Checking bird's eye evaluation (%s) ...", CLASS_NAMES[c].c_str());
if (eval_ground[c]) {
cout << "Starting bird's eye evaluation (" << CLASS_NAMES[c] << ") ...";
fp_det = fopen((result_dir + "/stats_" + CLASS_NAMES[c] + "_detection_ground.txt").c_str(), "w");
vector<double> precision[3], aos[3];
if( !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[0], aos[0], EASY, GROUND)
|| !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[1], aos[1], MODERATE, GROUND)
|| !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[2], aos[2], HARD, GROUND)) {
cout << CLASS_NAMES[c].c_str() << " evaluation failed." << endl;
return false;
}
fclose(fp_det);
saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_detection_ground", CLASS_NAMES[c], precision, 0);
cout << " done." << endl;
}
}
// eval 3D bounding boxes
for (int c = 0; c < NUM_CLASS; c++) {
CLASSES cls = (CLASSES)c;
//mail->msg("Checking 3D evaluation (%s) ...", CLASS_NAMES[c].c_str());
if (eval_3d[c]) {
cout << "Starting 3D evaluation (" << CLASS_NAMES[c] << ") ..." << endl;
fp_det = fopen((result_dir + "/stats_" + CLASS_NAMES[c] + "_detection_3d.txt").c_str(), "w");
vector<double> precision[3], aos[3];
if( !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[0], aos[0], EASY, BOX3D)
|| !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[1], aos[1], MODERATE, BOX3D)
|| !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[2], aos[2], HARD, BOX3D)) {
cout << CLASS_NAMES[c].c_str() << " evaluation failed." << endl;
return false;
}
fclose(fp_det);
saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_detection_3d", CLASS_NAMES[c], precision, 0);
cout << " done." << endl;
}
}
// success
return true;
}
int32_t main (int32_t argc,char *argv[]) {
// we need 2 or 4 arguments!
if (argc!=3) {
cout << "Usage: ./eval_detection result_sha val_dataset" << endl;
return 1;
}
// read arguments
string result_sha = argv[1];
string input_dataset = argv[2];
std:cout << "Starting evaluation..." << std::endl;
// run evaluation
if(eval(result_sha,input_dataset)){
cout << "Evaluation finished successfully" << endl;
}else{
cout << "Something happened..." << endl;
};
return 0;
}
//Original
//int32_t main (int32_t argc,char *argv[]) {
//
// // we need 2 or 4 arguments!
// if (argc!=2 && argc!=4) {
// cout << "Usage: ./eval_detection result_sha [user_sha email]" << endl;
// return 1;
// }
//
// // read arguments
// string result_sha = argv[1];
//
// // init notification mail
// Mail *mail;
// if (argc==4) mail = new Mail(argv[3]);
// else mail = new Mail();
// mail->msg("Thank you for participating in our evaluation!");
//
// // run evaluation
// if (eval(result_sha,mail)) {
// mail->msg("Your evaluation results are available at:");
// mail->msg("http://www.cvlibs.net/datasets/kitti/user_submit_check_login.php?benchmark=object&user=%s&result=%s",argv[2], result_sha.c_str());
// } else {
// system(("rm -r results/" + result_sha).c_str());
// mail->msg("An error occured while processing your results.");
// mail->msg("Please make sure that the data in your zip archive has the right format!");
// }
//
// // send mail and exit
// delete mail;
//
// return 0;
//}
Reference in New Issue View Git Blame Copy Permalink