def apply_nms(all_boxes, thresh):
    """Apply non-maximum suppression to all predicted boxes output by the
    test_net method.
    """
    num_classes = len(all_boxes)
    num_images = len(all_boxes[0])
    nms_boxes = [[[] for _ in xrange(num_images)]
                 for _ in xrange(num_classes)]
    for cls_ind in xrange(num_classes):
        for im_ind in xrange(num_images):
            dets = all_boxes[cls_ind][im_ind]
            if dets == []:
                continue

            x1 = dets[:, 0]
            y1 = dets[:, 1]
            x2 = dets[:, 2]
            y2 = dets[:, 3]
            scores = dets[:, 4]
            inds = np.where((x2 > x1) & (y2 > y1) & (scores > cfg.TEST.DET_THRESHOLD))[0]
            dets = dets[inds,:]
            if dets == []:
                continue

            keep = nms(dets, thresh)
            if len(keep) == 0:
                continue
            nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
    return nms_boxes
Apply non-maximum suppression to all predicted boxes output by the
    test_net method.
    
lib/fast_rcnn/test.py