code: def process_image_detailed(self, img_path):
      img = cv2.imread(img_path)
      _, cntk_img_input, dims = resize_and_pad(
          img, self._img_shape[2], self._img_shape[1]
      )
  
      # compute ROI proposals and apply scaling and padding to them
      # [target_w, target_h, img_width, img_height, top, bottom, left, right, scale_factor]
      img_stats = compute_image_stats(
          len(img[0]), len(img), self._img_shape[2], self._img_shape[1]
      )
      scale_factor = img_stats[-1]
      top = img_stats[4]
      left = img_stats[6]
  
      num_proposals = self._cfg["NUM_ROI_PROPOSALS"]
      proposals = compute_proposals(img, num_proposals, self._cfg)
      proposals = proposals * scale_factor
      proposals += (left, top, left, top)
  
      output = self._eval_model.eval(
          {
              self._eval_model.arguments[0]: [cntk_img_input],
              self._eval_model.arguments[1]: np.array(
                  proposals, dtype=np.float32
              ),
          }
      )
  
      out_dict = dict([(k.name, k) for k in output])
      out_cls_pred = output[out_dict["cls_pred"]][0]
      out_rpn_rois = proposals
      out_bbox_regr = output[out_dict["bbox_regr"]][0]
  
      return out_cls_pred, out_rpn_rois, out_bbox_regr, dims
  
file_path: CNTK/Examples/Image/Detection/FastRCNN/FastRCNN_eval.py
imports: import os
  import cv2
  import numpy
  import cntk
  from cntk import input_variable, Axis
  from utils.map_helpers import evaluate_detections
  from utils.plot_helpers import resize_and_pad
  from utils.rpn.bbox_transform import regress_rois
  from utils.od_mb_source import ObjectDetectionMinibatchSource
  from utils.proposal_helpers import ProposalProvider, compute_image_stats, compute_proposals
label_desc: Use image node_id 3 for calling the FastRCNN_Evaluator obj's underlying member method code with example usage: obj.process_image_detailed(img_path) and returns: out_cls_pred,  out_rpn_rois,  out_bbox_regr,  dims