def _sample_rois(all_rois, gt_boxes, fg_rois_per_image, rois_per_image, num_classes):
    """Generate a random sample of RoIs comprising foreground and background
    examples.
    """
    # overlaps: (rois x gt_boxes)
    overlaps = bbox_overlaps(
        np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float),
        np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float))
    gt_assignment = overlaps.argmax(axis=1)
    max_overlaps = overlaps.max(axis=1)
    labels = gt_boxes[gt_assignment, 4]

    # Select foreground RoIs as those with >= FG_THRESH overlap
    fg_inds = np.where(max_overlaps >= cfg.TRAIN.FG_THRESH)[0]
    # Guard against the case when an image has fewer than fg_rois_per_image
    # foreground RoIs
    fg_rois_per_this_image = int(min(fg_rois_per_image, fg_inds.size))
    # Sample foreground regions without replacement
    if fg_inds.size > 0:
        fg_inds = npr.choice(fg_inds, size=fg_rois_per_this_image, replace=False)

    # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
    bg_inds = np.where((max_overlaps < cfg.TRAIN.BG_THRESH_HI) &
                       (max_overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
    # Compute number of background RoIs to take from this image (guarding
    # against there being fewer than desired)
    bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
    bg_rois_per_this_image = min(bg_rois_per_this_image, bg_inds.size)
    # Sample background regions without replacement
    if bg_inds.size > 0:
        bg_inds = npr.choice(bg_inds, size=bg_rois_per_this_image, replace=False)

    # The indices that we're selecting (both fg and bg)
    keep_inds = np.append(fg_inds, bg_inds)
    # Select sampled values from various arrays:
    labels = labels[keep_inds]
    # Clamp labels for the background RoIs to 0
    labels[fg_rois_per_this_image:] = 0
    rois = all_rois[keep_inds]

    bbox_target_data = _compute_targets(
        rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels)

    bbox_targets, bbox_inside_weights = \
        _get_bbox_regression_labels(bbox_target_data, num_classes)

    return labels, rois, bbox_targets, bbox_inside_weights
Generate a random sample of RoIs comprising foreground and background
    examples.
    
lib/rpn_msr/proposal_target_layer_tf.py