py-faster-rcnn标注FDDB人脸便于其在FDDB上进行测试_fddb 未检测到人脸怎么标注

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

程序功能：用训练好的caffemodel，对FDDB人脸进行标注，便于其在FDDB上进行测试

<span style="font-size:24px;">#!/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__',  #背景 + 类
#           'aeroplane', 'bicycle', 'bird', 'boat',
#           'bottle', 'bus', 'car', 'cat', 'chair',
#           'cow', 'diningtable', 'dog', 'horse',
#           'motorbike', 'person', 'pottedplant',
#           'sheep', 'sofa', 'train', 'tvmonitor')
 
CLASSES = ('__background__','face') #只有一类：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(str(len(inds)) + '\n') #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]
 
        ##########   add by zhipeng for write rectange to txt   ########
        write_file.write( "{} {} {} {} {}\n".format(str(bbox[0]), str(bbox[1]),
                                                        str(bbox[2] - bbox[0]),
                                                        str(bbox[3] - bbox[1]),
                                                        str(score)))
        #print "zhipeng, bbox:", bbox, "score:",score
        ##########   add by zhipeng for write rectange to txt   ########
 
        '''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(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/tools/FDDB-fold/FDDB-fold-' + str(current_file).zfill(2) + '.txt'
        write_file_name = '/home/xiao/code/py-faster-rcnn-master/py-faster-rcnn/tools/detections/fold-' + str(current_file).zfill(2) + '-out.txt'
        write_file = open(write_file_name, "w")
 
        with open(read_file_name, "r") as ins:
            array = []
            for line in ins:
                array.append(line)      # list of strings
 
        number_of_images = len(array)
 
        for current_image in range(number_of_images):
            if current_image % 10 == 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/FDDB/originalPics/' + array[current_image].rstrip() + '.jpg'
            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()
</span>