          for scale in self._pnet_scales:
              rwidth, rheight = int(width * scale), int(height * scale)
              batch = np.empty((batch_items, rheight, rwidth, 3), dtype="float32")
              for idx in range(batch_items):
                  batch[idx, ...] = cv2.resize(images[idx, ...],  # pylint:disable=no-member
                                               (rwidth, rheight))
              output = self.pnet.predict(batch)
              cls_prob = output[0][..., 1]
              roi = output[1]
              out_h, out_w = cls_prob.shape[1:3]
              out_side = max(out_h, out_w)
 
</s> remove         interpln = cv2.INTER_CUBIC if scale > 1.0 else cv2.INTER_AREA  # pylint:disable=no-member
 </s> add         interpln = cv2.INTER_CUBIC if scale > 1.0 else cv2.INTER_AREA </s> remove             image = cv2.resize(image, dims, interpolation=interpln)  # pylint:disable=no-member
 </s> add             image = cv2.resize(image, dims, interpolation=interpln) </s> remove                 scale_img = cv2.resize(crop_img, (48, 48))  # pylint:disable=no-member
 </s> add                 scale_img = cv2.resize(crop_img, (48, 48)) </s> remove                 scale_img = cv2.resize(crop_img, (24, 24))  # pylint:disable=no-member
 </s> add                 scale_img = cv2.resize(crop_img, (24, 24)) </s> remove             cv2.putText(header_box,  # pylint: disable=no-member
 </s> add             cv2.putText(header_box,
                batch[idx, ...] = cv2.resize(images[idx, ...], (rwidth, rheight))
          for scale in self._pnet_scales:
              rwidth, rheight = int(width * scale), int(height * scale)
              batch = np.empty((batch_items, rheight, rwidth, 3), dtype="float32")
              for idx in range(batch_items):
                batch[idx, ...] = cv2.resize(images[idx, ...], (rwidth, rheight))
                batch[idx, ...] = cv2.resize(images[idx, ...], (rwidth, rheight))
              output = self.pnet.predict(batch)
              cls_prob = output[0][..., 1]
              roi = output[1]
              out_h, out_w = cls_prob.shape[1:3]
              out_side = max(out_h, out_w)
keep keep keep keep replace replace keep keep keep keep keep
 # pylint:disable=no-member cleanup (#927)

# pylint:disable=no-member cleanup 
plugins/extract/detect/mtcnn.py