// We have to substract 1 because of the offset of the background class.
proposals_label_with_target = proposals_label[proposal_with_target_idx] - 1
// We cast them as ints since we want to use it for indexing.
proposals_label_with_target = proposals_label_with_target.astype(np.int32)
// We create a numpy array with shape (num_positive_proposals, 4),
// having the ideal GT boxes for each proposal.
proposals_gt_boxes = gt_boxes[proposals_label_with_target]
// We create the same array but with the proposals
After Change
proposal_with_target_idx = np.nonzero(proposals_label > 0)[0]
// Get top gt_box for every proposal, top_gt_idx shape (1000,) with values < gt_boxes.shape[0]
top_gt_idx = overlaps.argmax(axis=1)
// Get the corresponding ground truth box only for the proposals with target.
gt_boxes_ids = top_gt_idx[proposal_with_target_idx]
// Get the values of the ground truth boxes. This is shaped (num_proposals, 5) because we also have the label.
proposals_gt_boxes = gt_boxes[gt_boxes_ids]
