text: from __future__ import print_function
  import sys
  import os
  import datetime
  import time
  import numpy as np
  import mxnet as mx
  from mxnet import ndarray as nd
  import cv2
  #from rcnn import config
  #from rcnn.processing.bbox_transform import nonlinear_pred, clip_boxes, landmark_pred
  from rcnn.processing.bbox_transform import clip_boxes
  from rcnn.processing.generate_anchor import generate_anchors_fpn, anchors_plane
  from rcnn.processing.nms import gpu_nms_wrapper, cpu_nms_wrapper
  from rcnn.processing.bbox_transform import bbox_overlaps
  
  class RetinaFaceCoV:
    def __init__(self, prefix, epoch, ctx_id=0, network='net3', nms=0.4, nocrop=False):
      self.ctx_id = ctx_id
      self.network = network
      self.nms_threshold = nms
      self.nocrop = nocrop
      self.debug = False
      self.fpn_keys = []
      self.anchor_cfg = None
      pixel_means=[0.0, 0.0, 0.0]
      pixel_stds=[1.0, 1.0, 1.0]
      pixel_scale = 1.0
      self.bbox_stds = [1.0, 1.0, 1.0, 1.0]
      self.landmark_std = 1.0
      self.preprocess = False
      _ratio = (1.,)
      fmc = 3
      if network=='ssh' or network=='vgg':
        pixel_means=[103.939, 116.779, 123.68]
        self.preprocess = True
      elif network=='net3':
        _ratio = (1.,)
      elif network=='net3l':
        _ratio = (1.,)
        self.landmark_std = 0.2
      elif network=='net3a':
        _ratio = (1.,1.5)
      elif network=='net6': #like pyramidbox or s3fd
        fmc = 6
      elif network=='net5': #retinaface
        fmc = 5
      elif network=='net5a':
        fmc = 5
        _ratio = (1.,1.5)
      elif network=='net4':
        fmc = 4
      elif network=='net4a':
        fmc = 4
        _ratio = (1.,1.5)
      elif network=='x5':
        fmc = 5
        pixel_means=[103.52, 116.28, 123.675]
        pixel_stds=[57.375, 57.12, 58.395]
      elif network=='x3':
        fmc = 3
        pixel_means=[103.52, 116.28, 123.675]
        pixel_stds=[57.375, 57.12, 58.395]
      elif network=='x3a':
        fmc = 3
        _ratio = (1.,1.5)
        pixel_means=[103.52, 116.28, 123.675]
        pixel_stds=[57.375, 57.12, 58.395]
      else:
        assert False, 'network setting error %s'%network
  
      if fmc==3:
        self._feat_stride_fpn = [32, 16, 8]
        self.anchor_cfg = {
            '32': {'SCALES': (32,16), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '16': {'SCALES': (8,4), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '8': {'SCALES': (2,1), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
        }
      elif fmc==4:
        self._feat_stride_fpn = [32, 16, 8, 4]
        self.anchor_cfg = {
            '32': {'SCALES': (32,16), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '16': {'SCALES': (8,4), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '8': {'SCALES': (2,1), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '4': {'SCALES': (2,1), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
        }
      elif fmc==6:
        self._feat_stride_fpn = [128, 64, 32, 16, 8, 4]
        self.anchor_cfg = {
            '128': {'SCALES': (32,), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '64': {'SCALES': (16,), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '32': {'SCALES': (8,), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '16': {'SCALES': (4,), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '8': {'SCALES': (2,), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
            '4': {'SCALES': (1,), 'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999},
        }
      elif fmc==5:
        self._feat_stride_fpn = [64, 32, 16, 8, 4]
        self.anchor_cfg = {}
        _ass = 2.0**(1.0/3)
        _basescale = 1.0
        for _stride in [4, 8, 16, 32, 64]:
          key = str(_stride)
          value = {'BASE_SIZE': 16, 'RATIOS': _ratio, 'ALLOWED_BORDER': 9999}
          scales = []
          for _ in range(3):
            scales.append(_basescale)
            _basescale *= _ass
          value['SCALES'] = tuple(scales)
          self.anchor_cfg[key] = value
  
      #print(self._feat_stride_fpn, self.anchor_cfg)
  
      for s in self._feat_stride_fpn:
          self.fpn_keys.append('stride%s'%s)
  
  
      dense_anchor = False
      #self._anchors_fpn = dict(zip(self.fpn_keys, generate_anchors_fpn(base_size=fpn_base_size, scales=self._scales, ratios=self._ratios)))
      self._anchors_fpn = dict(zip(self.fpn_keys, generate_anchors_fpn(dense_anchor=dense_anchor, cfg=self.anchor_cfg)))
      for k in self._anchors_fpn:
        v = self._anchors_fpn[k].astype(np.float32)
        self._anchors_fpn[k] = v
  
      self._num_anchors = dict(zip(self.fpn_keys, [anchors.shape[0] for anchors in self._anchors_fpn.values()]))
      #self._bbox_pred = nonlinear_pred
      #self._landmark_pred = landmark_pred
      sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
      if self.ctx_id>=0:
        self.ctx = mx.gpu(self.ctx_id)
        self.nms = gpu_nms_wrapper(self.nms_threshold, self.ctx_id)
      else:
        self.ctx = mx.cpu()
        self.nms = cpu_nms_wrapper(self.nms_threshold)
      self.pixel_means = np.array(pixel_means, dtype=np.float32)
      self.pixel_stds = np.array(pixel_stds, dtype=np.float32)
      self.pixel_scale = float(pixel_scale)
      #print('means', self.pixel_means)
      self.use_landmarks = True
      #print('use_landmarks', self.use_landmarks)
      self.cascade = 0
  
      if self.debug:
        c = len(sym)//len(self._feat_stride_fpn)
        sym = sym[(c*0):]
        self._feat_stride_fpn = [32,16,8]
      #print('sym size:', len(sym))
  
      image_size = (640, 640)
      self.model = mx.mod.Module(symbol=sym, context=self.ctx, label_names = None)
      self.model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))], for_training=False)
      self.model.set_params(arg_params, aux_params)
  
    def get_input(self, img):
      im = img.astype(np.float32)
      im_tensor = np.zeros((1, 3, im.shape[0], im.shape[1]))
      for i in range(3):
          im_tensor[0, i, :, :] = (im[:, :, 2 - i]/self.pixel_scale - self.pixel_means[2 - i])/self.pixel_stds[2-i]
      #if self.debug:
      #  timeb = datetime.datetime.now()
      #  diff = timeb - timea
      #  print('X2 uses', diff.total_seconds(), 'seconds')
      data = nd.array(im_tensor)
      return data
  
    def detect(self, img, threshold=0.5, scales=[1.0], do_flip=False):
      #print('in_detect', threshold, scales, do_flip, do_nms)
      proposals_list = []
      scores_list = []
      mask_scores_list = []
      landmarks_list = []
      strides_list = []
      timea = datetime.datetime.now()
      flips = [0]
      if do_flip:
        flips = [0, 1]
  
      imgs = [img]
      if isinstance(img, list):
        imgs = img
      for img in imgs:
        for im_scale in scales:
          for flip in flips:
            if im_scale!=1.0:
              im = cv2.resize(img, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
            else:
              im = img.copy()
            if flip:
              im = im[:,::-1,:]
            if self.nocrop:
              if im.shape[0]%32==0:
                h = im.shape[0]
              else:
                h = (im.shape[0]//32+1)*32
              if im.shape[1]%32==0:
                w = im.shape[1]
              else:
                w = (im.shape[1]//32+1)*32
              _im = np.zeros( (h, w, 3), dtype=np.float32 )
              _im[0:im.shape[0], 0:im.shape[1], :] = im
              im = _im
            else:
              im = im.astype(np.float32)
            if self.debug:
              timeb = datetime.datetime.now()
              diff = timeb - timea
              print('X1 uses', diff.total_seconds(), 'seconds')
            #self.model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))], for_training=False)
            #im_info = [im.shape[0], im.shape[1], im_scale]
            im_info = [im.shape[0], im.shape[1]]
            im_tensor = np.zeros((1, 3, im.shape[0], im.shape[1]))
            for i in range(3):
                im_tensor[0, i, :, :] = (im[:, :, 2 - i]/self.pixel_scale - self.pixel_means[2 - i])/self.pixel_stds[2-i]
            if self.debug:
              timeb = datetime.datetime.now()
              diff = timeb - timea
              print('X2 uses', diff.total_seconds(), 'seconds')
            data = nd.array(im_tensor)
            db = mx.io.DataBatch(data=(data,), provide_data=[('data', data.shape)])
            if self.debug:
              timeb = datetime.datetime.now()
              diff = timeb - timea
              print('X3 uses', diff.total_seconds(), 'seconds')
            self.model.forward(db, is_train=False)
            net_out = self.model.get_outputs()
            #post_nms_topN = self._rpn_post_nms_top_n
            #min_size_dict = self._rpn_min_size_fpn
  
            sym_idx = 0
  
            for _idx,s in enumerate(self._feat_stride_fpn):
                #if len(scales)>1 and s==32 and im_scale==scales[-1]:
                #  continue
                _key = 'stride%s'%s
                stride = int(s)
                is_cascade = False
                #if self.vote and stride==4 and len(scales)>2 and (im_scale==scales[0]):
                #  continue
                #print('getting', im_scale, stride, idx, len(net_out), data.shape, file=sys.stderr)
                scores = net_out[sym_idx].asnumpy()
                type_scores = net_out[sym_idx+3].asnumpy()
                print(scores.shape, type_scores.shape)
                if self.debug:
                  timeb = datetime.datetime.now()
                  diff = timeb - timea
                  print('A uses', diff.total_seconds(), 'seconds')
                A = self._num_anchors['stride%s'%s]
                #print(scores.shape)
                #print('scores',stride, scores.shape, file=sys.stderr)
                scores = scores[:, A:, :, :]
                mask_scores = type_scores[:, A*2:, :, :] #x, A, x, x
  
                bbox_deltas = net_out[sym_idx+1].asnumpy()
  
                #if DEBUG:
                #    print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
                #    print 'scale: {}'.format(im_info[2])
  
                #_height, _width = int(im_info[0] / stride), int(im_info[1] / stride)
                height, width = bbox_deltas.shape[2], bbox_deltas.shape[3]
  
                K = height * width
                anchors_fpn = self._anchors_fpn['stride%s'%s]
                anchors = anchors_plane(height, width, stride, anchors_fpn)
                #print((height, width), (_height, _width), anchors.shape, bbox_deltas.shape, scores.shape, file=sys.stderr)
                anchors = anchors.reshape((K * A, 4))
                #print('num_anchors', self._num_anchors['stride%s'%s], file=sys.stderr)
                #print('HW', (height, width), file=sys.stderr)
                #print('anchors_fpn', anchors_fpn.shape, file=sys.stderr)
                #print('anchors', anchors.shape, file=sys.stderr)
                #print('bbox_deltas', bbox_deltas.shape, file=sys.stderr)
                #print('scores', scores.shape, file=sys.stderr)
  
  
                #scores = self._clip_pad(scores, (height, width))
                scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
                mask_scores = mask_scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
  
                #print('pre', bbox_deltas.shape, height, width)
                #bbox_deltas = self._clip_pad(bbox_deltas, (height, width))
                #print('after', bbox_deltas.shape, height, width)
                bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1))
                bbox_pred_len = bbox_deltas.shape[3]//A
                #print(bbox_deltas.shape)
                bbox_deltas = bbox_deltas.reshape((-1, bbox_pred_len))
                bbox_deltas[:, 0::4] = bbox_deltas[:,0::4] * self.bbox_stds[0]
                bbox_deltas[:, 1::4] = bbox_deltas[:,1::4] * self.bbox_stds[1]
                bbox_deltas[:, 2::4] = bbox_deltas[:,2::4] * self.bbox_stds[2]
                bbox_deltas[:, 3::4] = bbox_deltas[:,3::4] * self.bbox_stds[3]
                proposals = self.bbox_pred(anchors, bbox_deltas)
  
  
  
                proposals = clip_boxes(proposals, im_info[:2])
  
                #if self.vote:
                #  if im_scale>1.0:
                #    keep = self._filter_boxes2(proposals, 160*im_scale, -1)
                #  else:
                #    keep = self._filter_boxes2(proposals, -1, 100*im_scale)
                #  if stride==4:
                #    keep = self._filter_boxes2(proposals, 12*im_scale, -1)
                #    proposals = proposals[keep, :]
                #    scores = scores[keep]
  
                #keep = self._filter_boxes(proposals, min_size_dict['stride%s'%s] * im_info[2])
                #proposals = proposals[keep, :]
                #scores = scores[keep]
                #print('333', proposals.shape)
                if stride==4 and self.decay4<1.0:
                  scores *= self.decay4
  
                scores_ravel = scores.ravel()
                #mask_scores_ravel = mask_scores.ravel()
                #print('__shapes', proposals.shape, scores_ravel.shape)
                #print('max score', np.max(scores_ravel))
                order = np.where(scores_ravel>=threshold)[0]
                  #_scores = scores_ravel[order]
                  #_order = _scores.argsort()[::-1]
                  #order = order[_order]
                proposals = proposals[order, :]
                scores = scores[order]
                mask_scores = mask_scores[order]
                if flip:
                  oldx1 = proposals[:, 0].copy()
                  oldx2 = proposals[:, 2].copy()
                  proposals[:, 0] = im.shape[1] - oldx2 - 1
                  proposals[:, 2] = im.shape[1] - oldx1 - 1
  
                proposals[:,0:4] /= im_scale
  
                proposals_list.append(proposals)
                scores_list.append(scores)
                mask_scores_list.append(mask_scores)
  
                landmark_deltas = net_out[sym_idx+2].asnumpy()
                #landmark_deltas = self._clip_pad(landmark_deltas, (height, width))
                landmark_pred_len = landmark_deltas.shape[1]//A
                landmark_deltas = landmark_deltas.transpose((0, 2, 3, 1)).reshape((-1, 5, landmark_pred_len//5))
                landmark_deltas *= self.landmark_std
                #print(landmark_deltas.shape, landmark_deltas)
                landmarks = self.landmark_pred(anchors, landmark_deltas)
                landmarks = landmarks[order, :]
  
                if flip:
                  landmarks[:,:,0] = im.shape[1] - landmarks[:,:,0] - 1
                  #for a in range(5):
                  #  oldx1 = landmarks[:, a].copy()
                  #  landmarks[:,a] = im.shape[1] - oldx1 - 1
                  order = [1,0,2,4,3]
                  flandmarks = landmarks.copy()
                  for idx, a in enumerate(order):
                    flandmarks[:,idx,:] = landmarks[:,a,:]
                    #flandmarks[:, idx*2] = landmarks[:,a*2]
                    #flandmarks[:, idx*2+1] = landmarks[:,a*2+1]
                  landmarks = flandmarks
                landmarks[:,:,0:2] /= im_scale
                #landmarks /= im_scale
                #landmarks = landmarks.reshape( (-1, landmark_pred_len) )
                landmarks_list.append(landmarks)
                #proposals = np.hstack((proposals, landmarks))
                sym_idx += 4
  
      if self.debug:
        timeb = datetime.datetime.now()
        diff = timeb - timea
        print('B uses', diff.total_seconds(), 'seconds')
      proposals = np.vstack(proposals_list)
      landmarks = None
      if proposals.shape[0]==0:
        landmarks = np.zeros( (0,5,2) )
        return np.zeros( (0,6) ), landmarks
      scores = np.vstack(scores_list)
      mask_scores = np.vstack(mask_scores_list)
      #print('shapes', proposals.shape, scores.shape)
      scores_ravel = scores.ravel()
      order = scores_ravel.argsort()[::-1]
      #if config.TEST.SCORE_THRESH>0.0:
      #  _count = np.sum(scores_ravel>config.TEST.SCORE_THRESH)
      #  order = order[:_count]
      proposals = proposals[order, :]
      scores = scores[order]
      mask_scores = mask_scores[order]
      landmarks = np.vstack(landmarks_list)
      landmarks = landmarks[order].astype(np.float32, copy=False)
  
      pre_det = np.hstack((proposals[:,0:4], scores)).astype(np.float32, copy=False)
      keep = self.nms(pre_det)
      det = np.hstack( (pre_det, mask_scores) )
      det = det[keep, :]
      landmarks = landmarks[keep]
  
  
      if self.debug:
        timeb = datetime.datetime.now()
        diff = timeb - timea
        print('C uses', diff.total_seconds(), 'seconds')
      return det, landmarks
  
    def detect_center(self, img, threshold=0.5, scales=[1.0], do_flip=False):
      det, landmarks = self.detect(img, threshold, scales, do_flip)
      if det.shape[0]==0:
        return None, None
      bindex = 0
      if det.shape[0]>1:
        img_size = np.asarray(img.shape)[0:2]
        bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1])
        img_center = img_size / 2
        offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])
        offset_dist_squared = np.sum(np.power(offsets,2.0),0)
        bindex = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering
      bbox = det[bindex,:]
      landmark = landmarks[bindex, :, :]
      return bbox, landmark
  
    @staticmethod
    def check_large_pose(landmark, bbox):
      assert landmark.shape==(5,2)
      assert len(bbox)==4
      def get_theta(base, x, y):
        vx = x-base
        vy = y-base
        vx[1] *= -1
        vy[1] *= -1
        tx = np.arctan2(vx[1], vx[0])
        ty = np.arctan2(vy[1], vy[0])
        d = ty-tx
        d = np.degrees(d)
        #print(vx, tx, vy, ty, d)
        #if d<-1.*math.pi:
        #  d+=2*math.pi
        #elif d>math.pi:
        #  d-=2*math.pi
        if d<-180.0:
          d+=360.
        elif d>180.0:
          d-=360.0
        return d
      landmark = landmark.astype(np.float32)
  
      theta1 = get_theta(landmark[0], landmark[3], landmark[2])
      theta2 = get_theta(landmark[1], landmark[2], landmark[4])
      #print(va, vb, theta2)
      theta3 = get_theta(landmark[0], landmark[2], landmark[1])
      theta4 = get_theta(landmark[1], landmark[0], landmark[2])
      theta5 = get_theta(landmark[3], landmark[4], landmark[2])
      theta6 = get_theta(landmark[4], landmark[2], landmark[3])
      theta7 = get_theta(landmark[3], landmark[2], landmark[0])
      theta8 = get_theta(landmark[4], landmark[1], landmark[2])
      #print(theta1, theta2, theta3, theta4, theta5, theta6, theta7, theta8)
      left_score = 0.0
      right_score = 0.0
      up_score = 0.0
      down_score = 0.0
      if theta1<=0.0:
        left_score = 10.0
      elif theta2<=0.0:
        right_score = 10.0
      else:
        left_score = theta2/theta1
        right_score = theta1/theta2
      if theta3<=10.0 or theta4<=10.0:
        up_score = 10.0
      else:
        up_score = max(theta1/theta3, theta2/theta4)
      if theta5<=10.0 or theta6<=10.0:
        down_score = 10.0
      else:
        down_score = max(theta7/theta5, theta8/theta6)
      mleft = (landmark[0][0]+landmark[3][0])/2
      mright = (landmark[1][0]+landmark[4][0])/2
      box_center = ( (bbox[0]+bbox[2])/2,  (bbox[1]+bbox[3])/2 )
      ret = 0
      if left_score>=3.0:
        ret = 1
      if ret==0 and left_score>=2.0:
        if mright<=box_center[0]:
          ret = 1
      if ret==0 and right_score>=3.0:
        ret = 2
      if ret==0 and right_score>=2.0:
        if mleft>=box_center[0]:
          ret = 2
      if ret==0 and up_score>=2.0:
        ret = 3
      if ret==0 and down_score>=5.0:
        ret = 4
      return ret, left_score, right_score, up_score, down_score
  
    @staticmethod
    def _filter_boxes(boxes, min_size):
        """ Remove all boxes with any side smaller than min_size """
        ws = boxes[:, 2] - boxes[:, 0] + 1
        hs = boxes[:, 3] - boxes[:, 1] + 1
        keep = np.where((ws >= min_size) & (hs >= min_size))[0]
        return keep
  
    @staticmethod
    def _filter_boxes2(boxes, max_size, min_size):
        """ Remove all boxes with any side smaller than min_size """
        ws = boxes[:, 2] - boxes[:, 0] + 1
        hs = boxes[:, 3] - boxes[:, 1] + 1
        if max_size>0:
          keep = np.where( np.minimum(ws, hs)<max_size )[0]
        elif min_size>0:
          keep = np.where( np.maximum(ws, hs)>min_size )[0]
        return keep
  
    @staticmethod
    def _clip_pad(tensor, pad_shape):
        """
        Clip boxes of the pad area.
        :param tensor: [n, c, H, W]
        :param pad_shape: [h, w]
        :return: [n, c, h, w]
        """
        H, W = tensor.shape[2:]
        h, w = pad_shape
  
        if h < H or w < W:
          tensor = tensor[:, :, :h, :w].copy()
  
        return tensor
  
    @staticmethod
    def bbox_pred(boxes, box_deltas):
        """
        Transform the set of class-agnostic boxes into class-specific boxes
        by applying the predicted offsets (box_deltas)
        :param boxes: !important [N 4]
        :param box_deltas: [N, 4 * num_classes]
        :return: [N 4 * num_classes]
        """
        if boxes.shape[0] == 0:
            return np.zeros((0, box_deltas.shape[1]))
  
        boxes = boxes.astype(np.float, copy=False)
        widths = boxes[:, 2] - boxes[:, 0] + 1.0
        heights = boxes[:, 3] - boxes[:, 1] + 1.0
        ctr_x = boxes[:, 0] + 0.5 * (widths - 1.0)
        ctr_y = boxes[:, 1] + 0.5 * (heights - 1.0)
  
        dx = box_deltas[:, 0:1]
        dy = box_deltas[:, 1:2]
        dw = box_deltas[:, 2:3]
        dh = box_deltas[:, 3:4]
  
        pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
        pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
        pred_w = np.exp(dw) * widths[:, np.newaxis]
        pred_h = np.exp(dh) * heights[:, np.newaxis]
  
        pred_boxes = np.zeros(box_deltas.shape)
        # x1
        pred_boxes[:, 0:1] = pred_ctr_x - 0.5 * (pred_w - 1.0)
        # y1
        pred_boxes[:, 1:2] = pred_ctr_y - 0.5 * (pred_h - 1.0)
        # x2
        pred_boxes[:, 2:3] = pred_ctr_x + 0.5 * (pred_w - 1.0)
        # y2
        pred_boxes[:, 3:4] = pred_ctr_y + 0.5 * (pred_h - 1.0)
  
        if box_deltas.shape[1]>4:
          pred_boxes[:,4:] = box_deltas[:,4:]
  
        return pred_boxes
  
    @staticmethod
    def landmark_pred(boxes, landmark_deltas):
        if boxes.shape[0] == 0:
            return np.zeros((0, landmark_deltas.shape[1]))
        boxes = boxes.astype(np.float, copy=False)
        widths = boxes[:, 2] - boxes[:, 0] + 1.0
        heights = boxes[:, 3] - boxes[:, 1] + 1.0
        ctr_x = boxes[:, 0] + 0.5 * (widths - 1.0)
        ctr_y = boxes[:, 1] + 0.5 * (heights - 1.0)
        pred = landmark_deltas.copy()
        for i in range(5):
          pred[:,i,0] = landmark_deltas[:,i,0]*widths + ctr_x
          pred[:,i,1] = landmark_deltas[:,i,1]*heights + ctr_y
        return pred
        #preds = []
        #for i in range(landmark_deltas.shape[1]):
        #  if i%2==0:
        #    pred = (landmark_deltas[:,i]*widths + ctr_x)
        #  else:
        #    pred = (landmark_deltas[:,i]*heights + ctr_y)
        #  preds.append(pred)
        #preds = np.vstack(preds).transpose()
        #return preds
  
    def vote(self, det):
        #order = det[:, 4].ravel().argsort()[::-1]
        #det = det[order, :]
        if det.shape[0] == 0:
          return np.zeros( (0, 5) )
            #dets = np.array([[10, 10, 20, 20, 0.002]])
            #det = np.empty(shape=[0, 5])
        dets = None
        while det.shape[0] > 0:
            if dets is not None and dets.shape[0]>=750:
                break
            # IOU
            area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)
            xx1 = np.maximum(det[0, 0], det[:, 0])
            yy1 = np.maximum(det[0, 1], det[:, 1])
            xx2 = np.minimum(det[0, 2], det[:, 2])
            yy2 = np.minimum(det[0, 3], det[:, 3])
            w = np.maximum(0.0, xx2 - xx1 + 1)
            h = np.maximum(0.0, yy2 - yy1 + 1)
            inter = w * h
            o = inter / (area[0] + area[:] - inter)
  
            # nms
            merge_index = np.where(o >= self.nms_threshold)[0]
            det_accu = det[merge_index, :]
            det = np.delete(det, merge_index, 0)
            if merge_index.shape[0] <= 1:
                if det.shape[0] == 0:
                    try:
                        dets = np.row_stack((dets, det_accu))
                    except:
                        dets = det_accu
                continue
            det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
            max_score = np.max(det_accu[:, 4])
            det_accu_sum = np.zeros((1, 5))
            det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4],
                                          axis=0) / np.sum(det_accu[:, -1:])
            det_accu_sum[:, 4] = max_score
            if dets is None:
                dets = det_accu_sum
            else:
                dets = np.row_stack((dets, det_accu_sum))
        dets = dets[0:750, :]
        return dets
  
  