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face++,人脸识别考勤机第一版本
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‎1.py‎

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‎\‎

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‎face++人脸识别考勤机/LAN-jiankong/lianxuzhibo.py‎

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import subprocess
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import time
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file_number = 0
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dir = "/home/pi/images/"
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while True:
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file_name = dir + format(file_number,"05d")+".jpg"
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file_number = file_number + 1
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subprocess.call(["raspistill","-w","400","-h","400","-e","jpg","-n","-t","1","-o",file_name])
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subprocess.call(["cp","-f",file_name,dir + "live.jpg"])
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time.sleep(2)
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‎face++人脸识别考勤机/LAN-jiankong/live-image-server.py‎

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import tornado.ioloop
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import tornado.web
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import subprocess
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class MainHandler(tornado.web.RequestHandler):
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def get(self):
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self.write('<! DOCTYPE html><head>'+
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'<META HTTP_EQUIV="refresh"' +
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'CONTENT="5"></head><body>' +
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'<img src="/images/live.jpg"></body>')
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class ImageHandler(tornado.web.StaticFileHandler):
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def set_extra_headers(self, path):
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self.set_header('Cache-Control',
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'no-store, no-cache,must-revalidate, max-age=0')
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application = tornado.web.Application([
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(r"/",MainHandler),
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(r"/images/(.*)", ImageHandler,{"path":"/home/pi/images"})])
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if __name__== "__main__":
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subprocess.Popen(["python","lianxuzhibo.py"])
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application.listen(8899)
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tornado.ioloop.IOLoop.instance().start()
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‎face++人脸识别考勤机/LAN-jiankong/zhibo.py‎

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import tornado.ioloop
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import tornado.web
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import subprocess
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import time
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class MainHandler(tornado.web.RequestHandler):
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def get(self):
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subprocess.call(["raspistill", "-w", "200", "-h", "200","-e", "jpg", "-n", "-t", "1", "-o", "/home/pi/images/live.jpg"])
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time.sleep(2)
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self.write('<! DOCTYPE html><head>'+
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'<META HTTP_EQUIV="refresh"' +
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'CONTENT="5"></head><body>' +
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'<img src="/images/live.jpg"></body>')
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class ImageHandler(tornado.web.StaticFileHandler):
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def set_extra_headers(self, path):
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self.set_header('Cache-Control',
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'no-store, no-cache,must-revalidate,'+
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' max-age=0')
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application = tornado.web.Application([
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(r"/",MainHandler),
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(r"/images/(.*)", ImageHandler,{"path":"/home/pi/images"})])
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if __name__== "__main__":
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application.listen(8889)
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tornado.ioloop.IOLoop.instance().start()
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‎face++人脸识别考勤机/README.md‎

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之前的人脸识别考勤系统，已经依靠ｆａｃｅ＋＋和ｏｐｅｎｃｖ基本完成了功能初步测试。最后调试下的情况是：
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管理员：使用python进行文件夹遍历，获取每个目录下的每个图片，以及name.txt文件获得名字。。。然后统一先行上传到服务进行识别训练。这时，我需要一份返回的识别组的ｉｄ。
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摄像头监听：使用python调用建立新进程指令。从而反复进行拍摄命令，获取的每一张图片均调用ｏｐｅｎｃｖ进行人脸识别判定，如果有人脸则暂停进程，发出语音沟通，再次拍照进行二次识别。
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活取到二次照片，进行文件沟通，首先移动到ｅｎｄ.jpg　然后将end.txt　设定为１。。在循环的主文件中，再将文件上传进行人脸准确识别。。返回的人脸名字，将当前时间记录下来，也存储到签到表中。
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漏洞说明：使用文件沟通，可能引发读写故障，实用ｆａｃｅ＋＋考虑出ｔｒｙ的情况，可能返回错误信息，那时候的处理比较复杂。
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现在重新整理文件情况。
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程序文件的结构：
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admin.py　　　管理员
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‎face++人脸识别考勤机/__pycache__/facepp.cpython-34.pyc‎

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‎face++人脸识别考勤机/admin.py‎

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#!/usr/bin/env python2
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# -*- coding: utf-8 -*-
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# $File: admin.py
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API_KEY = '187aa2ee8fc784972b7302f3b31becab'
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API_SECRET = 'F1czaFXaRJ5VzRcdVNZppUwJpDg4zjUY'
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# 导入系统库并定义辅助函数
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import time
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from pprint import pformat
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def print_result(hint, result):
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def encode(obj):
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if type(obj) is unicode:
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return obj.encode('utf-8')
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if type(obj) is dict:
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return {encode(k): encode(v) for (k, v) in obj.iteritems()}
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if type(obj) is list:
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return [encode(i) for i in obj]
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return obj
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print hint
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result = encode(result)
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print '\n'.join([' ' + i for i in pformat(result, width = 75).split('\n')])
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# 首先，导入SDK中的API类,还有本地文件读取所需要的ｆａｃｅｐｐ类
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from facepp import API
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import facepp
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import os
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api = API(API_KEY, API_SECRET)
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#face = api.detection.detect(img = facepp.File('3.jpg'))
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# 步骤０：人名及其脸部图片,此部分，读取目录下ｐｉｃｔｕｒｅ文件夹，中通过获取每文件夹的路径和名字，完成ＰＥＲＳＯＮＳ字典的构建。
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dir = os.getcwd()+'/picture'
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a = os.listdir(dir)
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print(a)
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PERSONS = []
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for name in a:
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print (name)
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dir_temp = dir +'/'+name
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b = os.listdir(dir_temp)
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print(b)
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for img_name in b:
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if img_name[-1] != 'g':
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continue
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dir_img =dir_temp+'/'+img_name
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print (dir_img)
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f = open(dir_temp+'/name.txt')
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a = f.read()
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f.close()
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dir_temp
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yuan = (a,facepp.File(dir_img))
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PERSONS.append(yuan)
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print (PERSONS)
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# IMAGE_DIR　此部分则是选择出需要进行分析的图片。。管理员模式下不用设置实用。（可能有文件打开错误的ｂｕｇ，如果本身没有end.jpg的话。）
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TARGET_IMAGE = facepp.File('end.jpg')
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# 步骤1：检测出输入图片中的Face，找出图片中Face的位置及属性
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FACES = {name: api.detection.detect(img = url)
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for name, url in PERSONS}
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for name, face in FACES.iteritems():
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print_result(name, face)
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# 步骤2：引用face_id，创建新的person
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for name, face in FACES.iteritems():
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rst = api.person.create(
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person_name = name, face_id = face['face'][0]['face_id'])
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print_result('create person {}'.format(name), rst)
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# 步骤3：.创建Group，将之前创建的Person加入这个Group
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rst = api.group.create(group_name = 'test')
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print_result('创建一个群组－－ｔｅｓｔ', rst)
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rst = api.group.add_person(group_name = 'test', person_name = FACES.iterkeys())
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print_result('添加人到群组中', rst)
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# 步骤4：训练模型
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rst = api.train.identify(group_name = 'test')
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print_result('开始训练请等待', rst)
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# 等待训练完成
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rst = api.wait_async(rst['session_id'])
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print_result('等待异步训练完成', rst)
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'''
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# 步骤5：识别新图中的Face
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rst = api.recognition.identify(group_name = 'test', img = TARGET_IMAGE)
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print_result('recognition result', rst)
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print '=' * 60
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print 'The person with highest confidence:', \
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rst['face'][0]['candidate'][0]['person_name']
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'''
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# 最终，删除无用的person和group
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#api.group.delete(group_name = 'test')
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#api.person.delete(person_name = FACES.iterkeys())
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python_face++/apikey.cfg renamed to face++人脸识别考勤机/apikey.cfg

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‎face++人脸识别考勤机/chaxun.py‎

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#!/usr/bin/env python2
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# -*- coding: utf-8 -*-
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# $File: chaxun.py
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API_KEY = '187aa2ee8fc784972b7302f3b31becab'
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API_SECRET = 'F1czaFXaRJ5VzRcdVNZppUwJpDg4zjUY'
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# 导入系统库并定义辅助函数
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import time
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from pprint import pformat
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from facepp import API
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import facepp
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import os
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#此部分是为了中文的处理
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import sys
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reload(sys)
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sys.setdefaultencoding( "utf-8" )
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def print_result(hint, result):
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def encode(obj):
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if type(obj) is unicode:
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return obj.encode('utf-8')
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if type(obj) is dict:
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return {encode(k): encode(v) for (k, v) in obj.iteritems()}
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if type(obj) is list:
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return [encode(i) for i in obj]
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return obj
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print hint
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result = encode(result)
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print '\n'.join([' ' + i for i in pformat(result, width = 75).split('\n')])
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# 首先，导入SDK中的API类,还有本地文件读取所需要的ｆａｃｅｐｐ类
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api = API(API_KEY, API_SECRET)
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#face = api.detection.detect(img = facepp.File('3.jpg'))
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# IMAGE_DIR　此部分则是选择出需要进行分析的图片。。管理员模式下不用设置实用。（可能有文件打开错误的ｂｕｇ，如果本身没有end.jpg的话。）
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TARGET_IMAGE = facepp.File('end.jpg')
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print ("已经读取图片，正在进行人脸识别，根据网络环境速度不定。")
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#识别end.jpg图中的Face
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rst = api.recognition.identify(group_name = 'test', img = TARGET_IMAGE)
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#print_result('识别结果:', rst)
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print '=' * 60
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print '识别结果最匹配的对象为:', \
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rst['face'][0]['candidate'][0]['person_name']
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a = rst['face'][0]['candidate'][0]['person_name']
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print ("正在写入kaoqin.txt中,当前时间为："+" "+ time.asctime())
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print '=' * 60
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fp = open("kaoqin.txt",'a')
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fp.write("姓名:"+" "+rst['face'][0]['candidate'][0]['person_name']+"签到时间:"+" "+time.asctime()+"\n")
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fp.close()
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# 最终，删除无用的person和group
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#api.group.delete(group_name = 'test')
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#api.person.delete(person_name = FACES.iterkeys())
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