content: import cv2
  import numpy as np
  from faster_rcnn import network
  from faster_rcnn.faster_rcnn import FasterRCNN
  from faster_rcnn.utils.timer import Timer
  
  
  def test():
      import os
      im_file = 'demo/004545.jpg'
      # im_file = 'data/VOCdevkit2007/VOC2007/JPEGImages/009036.jpg'
      # im_file = '/media/longc/Data/data/2DMOT2015/test/ETH-Crossing/img1/000100.jpg'
      image = cv2.imread(im_file)
  
      model_file = '/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5'
      # model_file = '/media/longc/Data/models/faster_rcnn_pytorch3/faster_rcnn_100000.h5'
      # model_file = '/media/longc/Data/models/faster_rcnn_pytorch2/faster_rcnn_2000.h5'
      detector = FasterRCNN()
      network.load_net(model_file, detector)
      detector.cuda()
      detector.eval()
      print('load model successfully!')
  
      # network.save_net(r'/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5', detector)
      # print('save model succ')
  
      t = Timer()
      t.tic()
      # image = np.zeros(shape=[600, 800, 3], dtype=np.uint8) + 255
      dets, scores, classes = detector.detect(image, 0.7)
      runtime = t.toc()
      print('total spend: {}s'.format(runtime))
  
      im2show = np.copy(image)
      for i, det in enumerate(dets):
          det = tuple(int(x) for x in det)
          cv2.rectangle(im2show, det[0:2], det[2:4], (255, 205, 51), 2)
          cv2.putText(im2show, '%s: %.3f' % (classes[i], scores[i]), (det[0], det[1] + 15), cv2.FONT_HERSHEY_PLAIN,
                      1.0, (0, 0, 255), thickness=1)
      cv2.imwrite(os.path.join('demo', 'out.jpg'), im2show)
      cv2.imshow('demo', im2show)
      cv2.waitKey(0)
  
  
  if __name__ == '__main__':
      test()