code: def detect(self, srcimg):
      img, im_shape, scale_factor = self.resize_image(srcimg)
      img = self._normalize(img)
  
      blob = np.expand_dims(np.transpose(img, (2, 0, 1)), axis=0)
  
      inputs_dict = {
          "im_shape": im_shape,
          "image": blob,
          "scale_factor": scale_factor,
      }
      inputs_name = [a.name for a in self.net.get_inputs()]
      net_inputs = {k: inputs_dict[k] for k in inputs_name}
  
      outs = self.net.run(None, net_inputs)
  
      outs = np.array(outs[0])
      expect_boxes = (outs[:, 1] > 0.5) & (outs[:, 0] > -1)
      np_boxes = outs[expect_boxes, :]
  
      color_list = self.get_color_map_list(self.num_classes)
      clsid2color = {}
  
      for i in range(np_boxes.shape[0]):
          classid, conf = int(np_boxes[i, 0]), np_boxes[i, 1]
          xmin, ymin, xmax, ymax = (
              int(np_boxes[i, 2]),
              int(np_boxes[i, 3]),
              int(np_boxes[i, 4]),
              int(np_boxes[i, 5]),
          )
  
          if classid not in clsid2color:
              clsid2color[classid] = color_list[classid]
          color = tuple(clsid2color[classid])
  
          cv2.rectangle(
              srcimg, (xmin, ymin), (xmax, ymax), color, thickness=2
          )
          print(self.classes[classid] + ": " + str(round(conf, 3)))
          cv2.putText(
              srcimg,
              self.classes[classid] + ":" + str(round(conf, 3)),
              (xmin, ymin - 10),
              cv2.FONT_HERSHEY_SIMPLEX,
              0.8,
              (0, 255, 0),
              thickness=2,
          )
  
      return srcimg
  
file_path: PaddleDetection/deploy/third_engine/demo_onnxruntime/infer_demo.py
imports: import cv2
  import numpy
  import argparse
  import onnxruntime
  from pathlib import Path
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling the PicoDet obj's underlying member method code with example usage: obj.detect(srcimg) and returns: srcimg