bool eval_class()

in tools/kitti_eval/evaluate_object_3d_offline_ap11.cpp [622:704]

• 59 lines of code
• 11 McCabe index (conditional complexity)

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;
}