print "Fator: {}".format(fator)
#fator = 0.5
reduzido = cv2.resize(roiframe,(0,0),fx=fator,fy=fator)
#===============================================================
bb = align.getLargestFaceBoundingBox(reduzido)
if bb is not None:
xd1 = bb.left()
yd1 = bb.top()
xd2 = bb.right()
yd2 = bb.bottom()
wb = xd2 - xd1
xd1,yd1,xd2,yd2 = (np.array((xd1,yd1,xd2,yd2))/fator).astype(int)
draw_rects(anotado,[(x1+xd1,y1+yd1,xd2-xd1,yd2-yd1)],(0,255,255))
# print "Largest B:{}".format(bb)
landmarks = align.findLandmarks(reduzido, bb)
distl = calcDistPoints(landmarks[0],landmarks[16])
#w = x2 - x1
frac = distl/(w*fator)
soma += frac
contad += 1
print "Valor de distl:{} , w: {} wb: {} e distl/w: {} media: {}".format(distl,w,wb,frac,soma/contad)
alignedFace = align.align(96, reduzido, bb,
landmarks=landmarks,
landmarkIndices=align.OUTER_EYES_AND_NOSE)
cv2.imshow("Extraido",alignedFace)
# #print "Landmarks: {}".format(landmarks)
# reff = [0,8,16,27,30,33,36,45,48,51,54,57,62,66]
# for idxp in landmarks:
class RecogServerProtocol(WebSocketServerProtocol):
def __init__(self):
#conexao onde buscara imagem para ser classificada
self.mdb = None
#classe usada para processamento e classificacao
self.classif = None
# alocado globalmente o extrador dlib
self.align = AlignDlib(os.path.join(basedir,"models","dlib", "shape_predictor_68_face_landmarks.dat"))
self.net = openface.TorchNeuralNet(os.path.join(basedir,"models", 'openface', #'treinado-jun16.t7'))
'nn4.small2.v1.t7')
,imgDim=96,
cuda=False)
def onConnect(self, request):
print("Client connecting: {0}".format(request.peer))
def onMessage(self, payload, isBinary):
raw = payload.decode('utf8')
msg = json.loads(raw)
print msg['type']
if msg['type'] == "MONGOSRVINI":
# inicializar o servidor mongodb onde sera baixado os arquivos
resposta = self.initMongo(msg)
elif msg['type'] == "MONGOPOSEDB":
resposta = self.initMongoPose(msg)
elif msg['type'] == "QUERYIMGREP":
# processar uma imagem
resposta = self.processImgRep(msg)
elif msg['type'] == "QUERYIDREP":
# processar as imagens de uma determinada identidade
resposta = self.processIdRep(msg)
elif msg['type'] == "QUERYIMGINFO":
resposta = self.processImgInfo(msg)
elif msg['type'] == "QUERYIDINFO":
resposta = self.processIdInfo(msg)
else:
resposta = {'REASON':"UNKNOW parameters {}".format(msg.keys()),'RESPONSE':'ERROR'}
resposta['type']=msg['type']
self.sendMessage(json.dumps(resposta))
def initMongo(self,msg):
#resposta = resposta = {'RESPONSE':'ERROR','REASON':'MONGOSRVINI:Unknow parameters'+msg.keys()}
if 'host' not in msg.keys() or 'port' not in msg.keys():
resposta = {'RESPONSE':'ERROR','REASON':'Not have host or port field'}
else:
if 'base' not in msg.keys():
msg['base']='facecoleta' # default database from libColeta
print "mongodb://{}:{}".format(msg['host'],msg['port'])
self.mdb = MongoConn(url="mongodb://{}:{}".format(msg['host'],msg['port']),
dbs=msg['base'])
if self.mdb is not None:
resposta = {'RESPONSE':'OK'}
else:
resposta = {'RESPONSE':'ERROR'}
return resposta
def initMongoPose(self,msg):
#resposta = resposta = {'RESPONSE':'ERROR','REASON':'MONGOSRVINI:Unknow parameters'+msg.keys()}
if 'host' not in msg.keys() or 'port' not in msg.keys():
resposta = {'RESPONSE':'ERROR','REASON':'Not have host or port field'}
else:
texto = "Load face database classifier from mongodb://{}:{}".format(msg['host'],msg['port'])
print texto
try:
self.classif=Classificador(self.align,self.net,host=msg['host'],port=msg['port'])
except pymongo.errors.ServerSelectionTimeoutError:
print "Can not connect to server mongodb://{}:{}".format(msg['host'],msg['port'])
self.classif=None
if self.classif is not None:
resposta = {'RESPONSE':'OK','INFO':texto}
try:
self.classif.loadClassif()
except pymongo.errors.ServerSelectionTimeoutError:
texto= "Can not load data from server mongodb://{}:{}".format(msg['host'],msg['port'])
self.classif=None
resposta = {'RESPONSE':'ERROR','REASON':texto}
else:
resposta = {'RESPONSE':'ERROR'}
return resposta
def processImgRep(self,msg):
#resposta = {'RESPONSE':'ERROR','REASON':'QUERYIMGREP:Unknow parameters:'+msg.keys()}
if self.mdb is None:
resposta = {'RESPONSE':'ERROR','REASON':'mongodb serve is not connected'}
else:
if 'filename' not in msg.keys():
resposta = {'RESPONSE':'ERROR','REASON':'Not have filename field'}
else:
keys={'filename':msg['filename']}
imgs=self.mdb.readImFromDB(keys=keys)
if imgs is None:
resposta = {'RESPONSE':'ERROR','REASON':'Something wrong in mongodb'}
elif len(imgs)<1:
resposta = {'RESPONSE':'ERROR','REASON':'Do not have file in db'}
elif len(imgs) == 1:
for doc,img in imgs:
bb = self.align.getLargestFaceBoundingBox(img)
if bb is None:
print "O arquivo {} nao detectou face".format(doc["filename"])
resposta = {'RESPONSE':'ERROR','REASON':'Not found face'}
break
landmarks = self.align.findLandmarks(img, bb)
angcab,angvcab,pp = RepUtil.calcHVAngRosto(landmarks[0],landmarks[16],landmarks[27])
alignedFace = self.align.align(96, img, bb,
landmarks=landmarks,
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
rep = self.net.forward(alignedFace)
resposta = {'RESPONSE':'OK','rep':rep.tolist(),
'identity':doc['identity'],
'angh':angcab,
'angv':angvcab}
else:
resposta = {'RESPONSE':'ERROR','REASON':'Multiples results docs'}
return resposta
def processImgInfo(self,msg):
#resposta = {'RESPONSE':'ERROR','REASON':'QUERYIMGREP:Unknow parameters:'+msg.keys()}
if self.mdb is None:
resposta = {'RESPONSE':'ERROR','REASON':'mongodb serve is not connected'}
else:
if 'filename' not in msg.keys():
resposta = {'RESPONSE':'ERROR','REASON':'Not have filename field'}
else:
keys={'filename':msg['filename']}
imgs=self.mdb.readImFromDB(keys=keys)
if imgs is None:
resposta = {'RESPONSE':'ERROR','REASON':'Something wrong in mongodb'}
elif len(imgs)<1:
resposta = {'RESPONSE':'ERROR','REASON':'Do not have file in db'}
elif len(imgs) == 1:
for doc,img in imgs:
bb = self.align.getLargestFaceBoundingBox(img)
if bb is None:
print "O arquivo {} nao detectou face".format(doc["filename"])
resposta = {'RESPONSE':'ERROR','REASON':'Not found face'}
break
landmarks = self.align.findLandmarks(img, bb)
angcab,angvcab,pp = RepUtil.calcHVAngRosto(landmarks[0],landmarks[16],landmarks[27])
alignedFace = self.align.align(96, img, bb,
landmarks=landmarks,
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
rep = self.net.forward(alignedFace)
#repF = self.net.forward(cv2.flip(alignedFace))
# Somente sobre a imagem base se o espelhamento
# Reduz a metade a possibilidades
resp,nc = self.classif.buscaCandidato(rep, angcab, angvcab)
resposta = {'RESPONSE':'OK','rep':rep.tolist(),
'identity':doc['identity'],
'angh':angcab,
'angv':angvcab}
countMat = 0
for nome,pb,reffile,tipoc,angs in resp:
if nome != "Desconhecido":
resposta["name{:02d}".format(countMat)]=nome
resposta["prob{:02d}".format(countMat)]=pb
resposta["tycl{:02d}".format(countMat)]=tipoc
anghe,angve = angs
resposta["angh{:02d}".format(countMat)]=anghe
resposta["angv{:02d}".format(countMat)]=angve
print "A imagem {} pode ser de {} com prob {:4.1f}".format(doc["filename"],nome,pb*100)
countMat += 1
resposta['nclassif']=countMat
else:
resposta = {'RESPONSE':'ERROR','REASON':'Multiples results docs'}
return resposta
def processIdRep(self,msg):
#resposta = {'RESPONSE':'ERROR','REASON':'QUERYIDREP:Unknow parameters:'+msg.keys()}
if self.mdb is None:
resposta = {'RESPONSE':'ERROR','REASON':'mongodb serve is not connected'}
else:
if 'identity' not in msg.keys():
resposta = {'RESPONSE':'ERROR','REASON':'Not have filename field'}
else:
keys={'identity':msg['identity']}
imgs=self.mdb.readImFromDB(keys=keys)
if imgs is None:
resposta = {'RESPONSE':'ERROR','REASON':'Something wrong in mongodb'}
elif len(imgs)<1:
resposta = {'RESPONSE':'ERROR','REASON':'Do not have file in db'}
else:
resposta = {'RESPONSE':'OK'}
contap = 0
contaf = 0
seqf=[]
for doc,img in imgs:
bb = self.align.getLargestFaceBoundingBox(img)
if bb is None:
print "O arquivo {} nao detectou face".format(doc["filename"])
seqf.append(doc['seq'])
contaf += 1
else:
if "rep{:03d}".format(doc['seq']) in doc.keys():
resposta["rep{:03d}".format(doc['seq'])] = doc["rep{:03d}".format(doc['seq'])]
resposta["angh{:03d}".format(doc['seq'])] = doc["angh{:03d}".format(doc['seq'])]
resposta["angv{:03d}".format(doc['seq'])] = doc["angv{:03d}".format(doc['seq'])]
else:
landmarks = self.align.findLandmarks(img, bb)
angcab,angvcab,pp = RepUtil.calcHVAngRosto(landmarks[0],landmarks[16],landmarks[27])
alignedFace = self.align.align(96, img, bb,
landmarks=landmarks,
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
rep = self.net.forward(alignedFace)
resposta["rep{:03d}".format(doc['seq'])] = rep.tolist()
resposta["angh{:03d}".format(doc['seq'])] = angcab
resposta["angv{:03d}".format(doc['seq'])] = angvcab
self.mdb.db.fs.files.update({'_id':doc['_id']},{ "$set":
{"rep{:03d}".format(doc['seq']):rep.tolist(),
"angh{:03d}".format(doc['seq']):angcab,
"angv{:03d}".format(doc['seq']):angvcab}})
contap += 1
if (contap+contaf)%10 == 0:
texto = "Processed {} images".format((contap+contaf))
print texto
parcial={'RESPONSE':'INPROGRESS',
'INFO':texto}
self.sendMessage(json.dumps(parcial))
resposta['countp']=contap
resposta['seqf']=np.array(seqf).tolist()
resposta['countn']=contaf
resposta['identity']=doc['identity']
return resposta
def processIdInfo(self,msg):
#resposta = {'RESPONSE':'ERROR','REASON':'QUERYIDREP:Unknow parameters:'+msg.keys()}
if self.mdb is None:
resposta = {'RESPONSE':'ERROR','REASON':'mongodb serve is not connected'}
else:
if 'identity' not in msg.keys():
resposta = {'RESPONSE':'ERROR','REASON':'Not have filename field'}
else:
keys={'identity':msg['identity']}
imgs=self.mdb.readImFromDB(keys=keys)
if imgs is None:
resposta = {'RESPONSE':'ERROR','REASON':'Something wrong in mongodb'}
elif len(imgs)<1:
resposta = {'RESPONSE':'ERROR','REASON':'Do not have file in db'}
else:
resposta = {'RESPONSE':'OK'}
contap = 0
contaf = 0
seqf=[]
for doc,img in imgs:
bb = self.align.getLargestFaceBoundingBox(img)
if bb is None:
print "O arquivo {} nao detectou face".format(doc["filename"])
seqf.append(doc['seq'])
contaf += 1
else:
landmarks = self.align.findLandmarks(img, bb)
angcab,angvcab,pp = RepUtil.calcHVAngRosto(landmarks[0],landmarks[16],landmarks[27])
alignedFace = self.align.align(96, img, bb,
landmarks=landmarks,
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
rep = self.net.forward(alignedFace)
resposta["rep{:03d}".format(doc['seq'])] = rep.tolist()
resposta["angh{:03d}".format(doc['seq'])] = angcab
resposta["angv{:03d}".format(doc['seq'])] = angvcab
resp,nc = self.classif.buscaCandidato(rep, angcab, angvcab)
countMat = 0
for nome,pb,reffile,tipoc,angs in resp:
if nome != "Desconhecido":
resposta["name{:03d}.{:02d}".format(doc['seq'],countMat)]=nome
resposta["prob{:03d}.{:02d}".format(doc['seq'],countMat)]=pb
resposta["tycl{:03d}.{:02d}".format(doc['seq'],countMat)]=tipoc
anghe,angve = angs
resposta["angh{:02d}".format(countMat)]=anghe
resposta["angv{:02d}".format(countMat)]=angve
print "A imagem {} pode ser de {} com prob {:4.1f}".format(doc["filename"],nome,pb*100)
countMat += 1
resposta["nclassif".format(doc['seq'])]=countMat
contap += 1
if (contap+contaf)%10 == 0:
texto = "Processed {} images".format((contap+contaf))
print texto
parcial={'RESPONSE':'INPROGRESS',
'INFO':texto}
self.sendMessage(json.dumps(parcial))
resposta['countp']=contap
resposta['seqf']=np.array(seqf).tolist()
resposta['countn']=contaf
resposta['identity']=doc['identity']
return resposta
def onClose(self,wasClean,code,reason):
print "Connection closed with reason:{} code:{} is clean:{}".format(reason,code,wasClean)
#!/usr/bin/env python
import cv2
import numpy as np
from align_dlib import AlignDlib
from os.path import join
# Read Image
im = cv2.imread("headl.png");
size = im.shape
align = AlignDlib(join("..","..","data","models","dlib","shape_predictor_68_face_landmarks.dat"))
bb = align.getLargestFaceBoundingBox(im)
if bb is not None:
landmarks = align.findLandmarks(im, bb)
for idx,p in enumerate(landmarks):
cv2.circle(im, p , 3, (0,0,255), -1)
cv2.putText(im,str(idx),p,cv2.FONT_HERSHEY_SIMPLEX,
0.6,(0,255,255),1)
# Display image
cv2.imshow("Output", im)
cv2.waitKey(0)
