text: import numpy as np
  import cv2,os,sys
  
  caffe_root = '../'
  os.chdir(caffe_root)
  sys.path.insert(0, 'python')
  import caffe
  caffe.set_device(0)
  caffe.set_mode_gpu()
  model_def = 'models/faceboxes/deploy.prototxt'
  model_weights = 'models/faceboxes/faceboxes.caffemodel'
  net = caffe.Net(model_def, model_weights, caffe.TEST)
  
  count = 0
  Path = './examples/images/pascal_images/'
  f = open('./examples/faceboxes/pascal-dets.txt', 'wt')
  for Name in open('./examples/faceboxes/pascal_img_list.txt'):
      Image_Path = Path + Name[:-1]
      image = caffe.io.load_image(Image_Path)
      im_scale = 2.5
      if im_scale != 1:
          image = cv2.resize(image, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
      net.blobs['data'].reshape(1, 3, image.shape[0], image.shape[1])
      transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
      transformer.set_transpose('data', (2, 0, 1))
      transformer.set_mean('data', np.array([104, 117, 123])) # mean pixel
      transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]
      transformer.set_channel_swap('data', (2, 1, 0))  # the reference model has channels in BGR order instead of RGB
      transformed_image = transformer.preprocess('data', image)
      net.blobs['data'].data[...] = transformed_image
  
      detections = net.forward()['detection_out']
      det_label = detections[0, 0, :, 1]
      det_conf = detections[0, 0, :, 2]
      det_xmin = detections[0, 0, :, 3]
      det_ymin = detections[0, 0, :, 4]
      det_xmax = detections[0, 0, :, 5]
      det_ymax = detections[0, 0, :, 6]
  
      for i in xrange(det_conf.shape[0]):
          xmin = int(round(det_xmin[i] * image.shape[1]))
          ymin = int(round(det_ymin[i] * image.shape[0]))
          xmax = int(round(det_xmax[i] * image.shape[1]))
          ymax = int(round(det_ymax[i] * image.shape[0]))
          ymin += 0.2 * (ymax - ymin + 1)
          score = det_conf[i]
          f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
                  format(Name[:-1], score, xmin/im_scale, ymin/im_scale, xmax/im_scale, ymax/im_scale))
      count += 1
      print('%d' % count)