code: def _get_image_blob(im):
      """Converts an image into a network input.
  
      Arguments:
          im (ndarray): a color image in BGR order
  
      Returns:
          blob (ndarray): a data blob holding an image pyramid
          im_scale_factors (list): list of image scales (relative to im) used
              in the image pyramid
      """
      im_orig = im.astype(np.float32, copy=True)
      im_orig -= cfg.PIXEL_MEANS
  
      im_shape = im_orig.shape
      im_size_min = np.min(im_shape[0:2])
      im_size_max = np.max(im_shape[0:2])
  
      processed_ims = []
      im_scale_factors = []
  
      for target_size in cfg.TEST.SCALES:
          im_scale = float(target_size) / float(im_size_min)
          # Prevent the biggest axis from being more than MAX_SIZE
          if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
              im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
          im = cv2.resize(
              im_orig,
              None,
              None,
              fx=im_scale,
              fy=im_scale,
              interpolation=cv2.INTER_LINEAR,
          )
          im_scale_factors.append(im_scale)
          processed_ims.append(im)
  
      # Create a blob to hold the input images
      blob = im_list_to_blob(processed_ims)
  
      return blob, np.array(im_scale_factors)
  
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 1 for calling a global function with example usage: _get_image_blob(im) and returns: blob,  np