py-faster-rcnn标注FDDB人脸便于其在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>