def apply_nms(all_boxes, thresh):
 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
   keep = nms(dets, thresh)
   if (len(keep) == 0):
    continue
   nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
 return nms_boxes

def apply_nms ( all_boxes , thresh ) : 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 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 .