code: def _bbox_pred(boxes, box_deltas):
      """Transform the set of class-agnostic boxes into class-specific boxes
      by applying the predicted offsets (box_deltas)
      """
      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] + cfg.EPS
      heights = boxes[:, 3] - boxes[:, 1] + cfg.EPS
      ctr_x = boxes[:, 0] + 0.5 * widths
      ctr_y = boxes[:, 1] + 0.5 * heights
  
      dx = box_deltas[:, 0::4]
      dy = box_deltas[:, 1::4]
      dw = box_deltas[:, 2::4]
      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::4] = pred_ctr_x - 0.5 * pred_w
      # y1
      pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
      # x2
      pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w
      # y2
      pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h
  
      return pred_boxes
  
file_path: CNTK/Examples/Image/Detection/FastRCNN/BrainScript/fastRCNN/test.py
imports: from __future__ import print_function
  import os, sys, cv2, numpy, pickle, heapq
  from .nms import nms
  from .utils.cython_nms import nms
  from .timer import Timer
  from cntk_helpers import im_detect, apply_nms
  from builtins import range
label_desc: Use image node_id 5 for calling a global function with example usage: _bbox_pred(boxes, box_deltas) and returns: pred_boxes, np