Chapter7: Training R-CNN Notebook

[MuhammedM294]

There is a missing parameter (epsilon) when calculating the IOUs of all candidates using extract_iou function (code line 8).

                  for ix, (im, bbs, labels, fpath) in enumerate(ds):
                      if(ix==N):
                          break
                      H, W, _ = im.shape
                      candidates = extract_candidates(im)
                      candidates = np.array([(x,y,x+w,y+h) for x,y,w,h in candidates])
                      ious, rois, clss, deltas = [], [], [], []
                      ious = np.array([[extract_iou(candidate, _bb_) for candidate in candidates] for _bb_ in bbs]).T
                      for jx, candidate in enumerate(candidates):
                          cx,cy,cX,cY = candidate
                          candidate_ious = ious[jx]
                          best_iou_at = np.argmax(candidate_ious)
                          best_iou = candidate_ious[best_iou_at]
                          best_bb = _x,_y,_X,_Y = bbs[best_iou_at]
                          if best_iou > 0.3: clss.append(labels[best_iou_at])
                          else : clss.append('background')
                          delta = np.array([_x-cx, _y-cy, _X-cX, _Y-cY]) / np.array([W,H,W,H])
                          deltas.append(delta)
                          rois.append(candidate / np.array([W,H,W,H]))
                      FPATHS.append(fpath)
                      IOUS.append(ious)
                      ROIS.append(rois)
                      CLSS.append(clss)
                      DELTAS.append(deltas)
                      GTBBS.append(bbs)