code: def evaluate_single_image(model, img_path, cfg):
      """
      Computes detection results for the given model on the provided image
      :param model: the model
      :param img_path: the path to the image
      :param cfg: the configuration
      :return:
          regressed_rois - the predicted bounding boxes
          cls_probs - class probabilities per bounding box
      """
  
      detector_name = _get_detector_name(cfg)
      regressed_rois = None
      cls_probs = None
      print("detecting objects in image {}".format(img_path))
      if detector_name == "FastRCNN":
          from FastRCNN.FastRCNN_eval import FastRCNN_Evaluator
  
          evaluator = FastRCNN_Evaluator(model, cfg)
          regressed_rois, cls_probs = evaluator.process_image(img_path)
      elif detector_name == "FasterRCNN":
          from FasterRCNN.FasterRCNN_eval import FasterRCNN_Evaluator
  
          evaluator = FasterRCNN_Evaluator(model, cfg)
          regressed_rois, cls_probs = evaluator.process_image(img_path)
      else:
          print("Unknown detector: {}".format(detector_name))
  
      return regressed_rois, cls_probs
  
file_path: CNTK/Examples/Image/Detection/utils/od_utils.py
imports: import easydict
  from utils.nms_wrapper import apply_nms_to_single_image_results
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate_single_image(model, img_path, cfg) and returns: regressed_rois,  cls_probs