py-faster-rcnn_caffemodel对人脸进行标注

本程序在py-faster-rcnn/tools/demo.py的基础上进行修改

程序功能：利用训练好的caffemodel，对人脸进行标注

 #!/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')

 CLASSES = ('__background__','face')

 NETS = {'vgg16': ('VGG16',
                   'VGG16_faster_rcnn_final.caffemodel'),
         'myvgg': ('VGG_CNN_M_1024',
                   'VGG_CNN_M_1024_faster_rcnn_final.caffemodel'),
         'zf': ('ZF',
                   'ZF_faster_rcnn_final.caffemodel'),
         'myzf': ('ZF',
                   'zf_rpn_stage1_iter_80000.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

     #write_file.write(array[current_image] + ' ') #add by zhipeng
     #write_file.write('face' + ' ') #add by zhipeng
     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]

         write_file.write(array[current_image] + ' ') #add by zhipeng
         #write_file.write('face' + ' ')
         ##########   add by zhipeng for write rectange to txt   ########
         #bbox[0]:x, bbox[1]:y, bbox[2]:x+w, bbox[3]:y+h
         write_file.write( "{} {} {} {}\n".format(str(int(bbox[0])), str(int(bbox[1])),
                                                         str(int(bbox[2])-int(bbox[0])),
                                                         str(int(bbox[3])-int(bbox[1]))))
         #print "zhipeng, bbox:", bbox, "score:",score
         ##########   add by zhipeng for write rectange to txt   ########



 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(image_name)

     # 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']'''

     ##########   add by zhipeng for write rectange to txt   ########
     #write_file_name = '/home/xiao/code/py-faster-rcnn-master/py-faster-rcnn/tools/detections/out.txt'
     #write_file = open(write_file_name, "w")
     ##########   add by zhipeng for write rectange to txt   ########

 #    for current_file in range(1,11):      #orginal range(1, 11)

 #    print 'Processing file ' + str(current_file) + ' ...'

     read_file_name = '/home/xiao/code/py-faster-rcnn-master/py-faster-rcnn/data/pos_fold/name.txt'
     write_file_name = '/home/xiao/code/py-faster-rcnn-master/py-faster-rcnn/data/pos_fold/annotate.txt'
     write_file = open(write_file_name, "w")

     with open(read_file_name, "r") as ins:
         array = []
         for line in ins:
             line = line[:-1]
             array.append(line)      # list of strings

     number_of_images = len(array)

     for current_image in range(number_of_images):
         if current_image % 100 == 0:
             print 'Processing image : ' + str(current_image)
         # load image and convert to gray
         read_img_name = '/home/xiao/code/py-faster-rcnn-master/py-faster-rcnn/data/pos/' + array[current_image].rstrip()
         #write_file.write(array[current_image]) #add by zhipeng
         demo(net, read_img_name)

     write_file.close()

     '''''for im_name in im_names:
         print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
         print 'Demo for data/demo/{}'.format(im_name)
         write_file.write(im_name + '\n') #add by zhipeng
         demo(net, im_name)'''

     #write_file.close()  # add by zhipeng,close file
     plt.show()