content: #!/usr/bin/env python
  
  # --------------------------------------------------------
  # Faster R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  
  """
  Demo script showing detections in sample images.
  
  See README.md for installation instructions before running.
  """
  
  import _init_paths
  from fast_rcnn.config import cfg
  from fast_rcnn.test import im_detect
  from fast_rcnn.nms_wrapper import nms
  from utils.timer import Timer
  import matplotlib.pyplot as plt
  import numpy as np
  import scipy.io as sio
  import caffe, os, sys, cv2
  import argparse
  
  CLASSES = ('__background__',
             'aeroplane', 'bicycle', 'bird', 'boat',
             'bottle', 'bus', 'car', 'cat', 'chair',
             'cow', 'diningtable', 'dog', 'horse',
             'motorbike', 'person', 'pottedplant',
             'sheep', 'sofa', 'train', 'tvmonitor')
  
  NETS = {'vgg16': ('VGG16',
                    'VGG16_faster_rcnn_final.caffemodel'),
          'zf': ('ZF',
                    'ZF_faster_rcnn_final.caffemodel')}
  
  
  def vis_detections(im, class_name, dets, thresh=0.5):
      """Draw detected bounding boxes."""
      inds = np.where(dets[:, -1] >= thresh)[0]
      if len(inds) == 0:
          return
  
      im = im[:, :, (2, 1, 0)]
      fig, ax = plt.subplots(figsize=(12, 12))
      ax.imshow(im, aspect='equal')
      for i in inds:
          bbox = dets[i, :4]
          score = dets[i, -1]
  
          ax.add_patch(
              plt.Rectangle((bbox[0], bbox[1]),
                            bbox[2] - bbox[0],
                            bbox[3] - bbox[1], fill=False,
                            edgecolor='red', linewidth=3.5)
              )
          ax.text(bbox[0], bbox[1] - 2,
                  '{:s} {:.3f}'.format(class_name, score),
                  bbox=dict(facecolor='blue', alpha=0.5),
                  fontsize=14, color='white')
  
      ax.set_title(('{} detections with '
                    'p({} | box) >= {:.1f}').format(class_name, class_name,
                                                    thresh),
                    fontsize=14)
      plt.axis('off')
      plt.tight_layout()
      plt.draw()
  
  def demo(net, image_name):
      """Detect object classes in an image using pre-computed object proposals."""
  
      # Load the demo image
      im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
      im = cv2.imread(im_file)
  
      # Detect all object classes and regress object bounds
      timer = Timer()
      timer.tic()
      scores, boxes = im_detect(net, im)
      timer.toc()
      print ('Detection took {:.3f}s for '
             '{:d} object proposals').format(timer.total_time, boxes.shape[0])
  
      # Visualize detections for each class
      CONF_THRESH = 0.8
      NMS_THRESH = 0.3
      for cls_ind, cls in enumerate(CLASSES[1:]):
          cls_ind += 1 # because we skipped background
          cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
          cls_scores = scores[:, cls_ind]
          dets = np.hstack((cls_boxes,
                            cls_scores[:, np.newaxis])).astype(np.float32)
          keep = nms(dets, NMS_THRESH)
          dets = dets[keep, :]
          vis_detections(im, cls, dets, thresh=CONF_THRESH)
  
  def parse_args():
      """Parse input arguments."""
      parser = argparse.ArgumentParser(description='Faster R-CNN demo')
      parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
                          default=0, type=int)
      parser.add_argument('--cpu', dest='cpu_mode',
                          help='Use CPU mode (overrides --gpu)',
                          action='store_true')
      parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
                          choices=NETS.keys(), default='vgg16')
  
      args = parser.parse_args()
  
      return args
  
  if __name__ == '__main__':
      cfg.TEST.HAS_RPN = True  # Use RPN for proposals
  
      args = parse_args()
  
      prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
                              'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')
      caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
                                NETS[args.demo_net][1])
  
      if not os.path.isfile(caffemodel):
          raise IOError(('{:s} not found.\nDid you run ./data/script/'
                         'fetch_faster_rcnn_models.sh?').format(caffemodel))
  
      if args.cpu_mode:
          caffe.set_mode_cpu()
      else:
          caffe.set_mode_gpu()
          caffe.set_device(args.gpu_id)
          cfg.GPU_ID = args.gpu_id
      net = caffe.Net(prototxt, caffemodel, caffe.TEST)
  
      print '\n\nLoaded network {:s}'.format(caffemodel)
  
      # Warmup on a dummy image
      im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
      for i in xrange(2):
          _, _= im_detect(net, im)
  
      im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
                  '001763.jpg', '004545.jpg']
      for im_name in im_names:
          print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
          print 'Demo for data/demo/{}'.format(im_name)
          demo(net, im_name)
  
      plt.show()
  