class PersonFace(BasicPart):
"""PersonFace，人物人脸录入及比对模块
Arguments:
BasicPart {[type]} -- [description]
"""
def __init__(self,
threshold = None,
logfile = None,
picShow = False,
isShow = False):
'''
threshold: 人脸大小阈值
logfile: 日志文件路径
isShow: 显示图片处理过程
'''
BasicPart.__init__(self, logfile=logfile, isShow=isShow)
self.picShow = picShow
from DBHandler import DBHandler
self.handler = DBHandler()
self.initFR()
# 缓存
self.dbcache = {}
# self.initFI()
def initFI(self):
"""初始化索引模块
"""
from FeatureIndex import FeatureIndex
prefixs = ['Person']
self.fi = FeatureIndex(index_prefixs=prefixs,isShow = self.isShow)
def initFR(self):
"""初始化人脸特征提取模块
"""
from FaceRecog import FaceRecog
self.fr = FaceRecog(isShow = self.isShow, picShow = self.picShow)
def read_config(self):
"""读配置文件
"""
self.dir = {}
self.maxdistance = self.config.getint('facerecog','max_distance')
self.thresh = self.config.getint('facerecog','threshold')
self.dir['faces'] = self.config.get('datadir','faces')
self.dir['faces_feat'] = self.config.get('datadir','faces_feat')
self.dir['faces_sample'] = self.config.get('datadir','faces_sample')
def storePersonToDB(self):
"""将人物存到PersonInfo表，返回id
"""
self.person_ids = self.handler.addmanyPerson(self.person_names)
def index_person(self, person_list = [], prefix="Person"):
"""对sample文件夹下所有人物人脸图片建立索引
或只建立某个人的索引
"""
if len(person_list) == 0:
self.person_names = os.listdir(self.dir['faces_sample'])
else:
self.person_names = person_list
# 存数据库，获取id
self.storePersonToDB()
self.person_pic_feats = []
self.person_pic_ids = []
for index, person_name in enumerate(self.person_names):
# 每一个人物对应一个文件夹
full_path = self.dir['faces_sample'] + '/' + person_name
pic_list = os.listdir(full_path)
num_faces = len(pic_list)
for face_pic in pic_list:
face_pic = full_path + '/' + face_pic
pic_face_dic = self.fr.extract_image_face_feature(face_pic)
feat = pic_face_dic['feats'][0]
self.person_pic_feats.append(feat)
self.person_pic_ids.append(self.person_ids[index])
# 建立索引
self.initFI()
self.fi.create_person_index(self.person_pic_feats, self.person_pic_ids, prefix)
def setFeatureIndex(self, fi):
"""设置FI对象，用于查询时，从外部导入已载入索引的索引对象
Arguments:
fi {FeatureIndex} -- 索引检索对象
"""
self.fi = fi
def identify_pic_person(self, imagename):
"""确定图片中人物名字以及id
Arguments:
imagename {string} -- 图片名
Returns:
personid, personname
"""
face_dic = self.fr.extract_image_face_feature(imagename)
if len(face_dic['feats']) > 0:
personid, personname = self.idenity(face_dic['feats'][0])
else:
personid=-1
personname="无人物"
return personid, personname
# print(result)
def idenity(self, facefeat):
"""确定人物身份，返回人物名及人物id
Arguments:
facefeat {data} -- 待确定的人脸特征
-1, unknown 未识别人物
"""
# 首先进行query
results, distance = self.fi.queryPerson(facefeat)
# 若高于最远距离,则为未知人物,返回None,''
if distance[0] > self.maxdistance:
personid = -1
personname = 'unknown'
else:
# 确定人物身份:
# kmean
max_count_id = pd.value_counts(results, sort=True).index[0]
# 计算每个结果的得分
personid = int(max_count_id)
# 首先查询缓存,若miss后再查询数据库
if personid in self.dbcache.keys():
personname = self.dbcache[personid]
else:
personname = self.handler.queryPersonById(personid)[0][1]
self.dbcache[personid] = personname
return personid, personname
