def __init__(self):
##
self.faceCascade = cv2.CascadeClassifier('FaceRecognitionWorker/config/haarcascade_frontalface_alt2.xml')
##
# TODO: DB 형식의 csv 파일 포맷 생성
self.db = pd.read_csv('FaceRecognitionWorker/db/db.csv')
lines = self.db['file_dir'].values.tolist()
# TODO: index 값 저장! index 기반으로 유저 판단 -> 나중에 내뱉는 index 값에서 판단할 것!
self.refImgs = []
self.refImgs_for_send = []
self.refVecs = []
self.data = b''
for line in lines:
temp = cv2.imread(line)
self.refImgs.append(temp)
self.refImgs_for_send.append('img/'+line.split('/')[-1])
temp = cv2.cvtColor(temp, cv2.COLOR_RGB2GRAY)
faces = self.faceCascade.detectMultiScale(
temp,
scaleFactor=1.3,
minNeighbors=5,
minSize=(100, 100)
)
if len(faces) == 0:
log.debug('{}: No Face!'.format(line))
self.refVecs.append(np.zeros(512))
else:
log.debug('{}: Okay'.format(line))
(x, y, w, h) = faces[0]
faceROI = temp[y:y + h, x:x + w]
faceROI = cv2.resize(faceROI, (128, 128))
faceROI = cv2.equalizeHist(faceROI)
log.debug(faceROI)
cnnOut = cnnpRun(faceROI)
log.debug(cnnOut)
self.refVecs.append(cnnOut)
def main(self, HOST, PORT, requester_name):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((HOST, PORT))
s.listen(5)
while True:
try:
requester_socket = s.accept()
log.debug(requester_socket)
self.inloop_recieveFile(*requester_socket, requester_name)
except Exception as e:
log.debug(str(e))
log.debug('pass')
continue
from worker import FaceRecognitionWorker
from FaceRecognitionWorker.log import log
if __name__ == "__main__":
log.debug('Face recognition worker run')
_face_worker = FaceRecognitionWorker()
_face_worker.run()
def inloop_recieveFile(self, socket, addr, req_name, cnt):
# Send Ready Signal
socket.sendall('Ready'.encode())
# Receive filename
msg = socket.recv(4096)
time_stamp = msg.decode('utf-8')[:-4]
log.debug(time_stamp)
# Receive File Data
while True:
# get file bytes
data = socket.recv(4096)
self.data = self.data + data
if not data:
break
image = Image.open(io.BytesIO(self.data))
img_name = req_name+ str(cnt)
file_dir = '/home/haze/다운로드/apache-tomcat-7.0.92/webapps/HAZE/img/'+ img_name + '.jpg'
image.save(file_dir, "JPEG")
# write bytes on file
frame = cv2.imdecode(np.frombuffer(self.data, np.uint8), -1)
# send API server image bytes
data = {'req_addr': req_name,
'image_packet': 'img/' + img_name + '.jpg'}
log.debug(data)
requests.post('http://127.0.0.1:5000/image_packet', json=data)
log.debug('send image packet')
log.debug(frame.shape)
self.data = b''
frame_gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
log.debug(frame_gray.shape)
faces = self.faceCascade.detectMultiScale(
frame_gray,
scaleFactor=1.3,
minNeighbors=5,
minSize=(100, 100)
)
log.debug(faces)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
##
if len(faces):
faceROI = frame_gray[y:y + h, x:x + w]
faceROI = cv2.resize(faceROI, (128, 128))
faceROI = cv2.equalizeHist(faceROI)
log.debug(faceROI)
cnnOut = cnnpRun(faceROI)
inx, minval = self.find_Match(cnnOut)
log.debug("{}: {}".format(inx, minval))
log.debug("{} : {} : {}".format(*self.db.ix[inx]))
if minval < 1:
# status 변경 기준으로 가정
log.debug(type(cnnOut))
log.debug(type(addr))
data = {'req_addr': req_name,
'data': {'hash': hash(str(cnnOut)),
'sol': {'name': self.db.ix[inx][1],
'age': str(self.db.ix[inx][2])}
},
'time_stamp': time_stamp,
'db_image': self.refImgs_for_send[inx]
}
log.debug(data)
response = requests.post('http://127.0.0.1:5000/send_detect_data', json=data)
log.debug('send detected image')
log.debug(response.json())
def inloop_recieveFile(self, socket, addr, req_name):
# Send Ready Signal
socket.sendall('Ready'.encode())
# Receive filename
msg = socket.recv(4096)
time_stamp = msg.decode('utf-8')[:-4]
log.debug(time_stamp)
# Receive File Data
while True:
# get file bytes
data = socket.recv(4096)
self.data = self.data + data
if not data:
break
temp_data = self.data
# write bytes on file
frame = cv2.imdecode(np.frombuffer(self.data, np.uint8), -1)
# send API server image bytes
data = {'req_addr': req_name, 'image_packet': self.data}
# requests.post('http://127.0.0.1:5000/image_packet', json=data)
log.debug('send image packet')
frame_gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
log.debug(frame_gray.shape)
faces = self.faceCascade.detectMultiScale(frame_gray,
scaleFactor=1.3,
minNeighbors=5,
minSize=(100, 100))
log.debug(faces)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
##
if len(faces):
faceROI = frame_gray[y:y + h, x:x + w]
faceROI = cv2.resize(faceROI, (128, 128))
faceROI = cv2.equalizeHist(faceROI)
log.debug(faceROI)
cnnOut = cnnpRun(faceROI)
if self.db_count == 0:
headers = ['file_dir', 'name', 'age']
image = Image.open(io.BytesIO(temp_data))
img_name = str(self.db_count) + '.jpg'
file_dir = '/home/haze/다운로드/apache-tomcat-7.0.92/webapps/HAZE/img/' + img_name
image.save(file_dir, "JPEG")
# TODO: byte 파일 저장 로직 여기 추가!
image = Image.open(io.BytesIO(temp_data))
img_dir = 'FaceRecognitionWorker/db/' + str(
self.db_count) + '.jpg'
image.save(img_dir, "JPEG")
name = input('name: ')
age = input('age: ')
fields = [img_dir, name, age]
with open('FaceRecognitionWorker/db/db.csv', 'w') as myfile:
writer = csv.writer(myfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
writer.writerow(headers)
writer.writerow(fields)
self.db_count = self.db_count + 1
self.refVecs.append(cnnOut)
else:
inx, minval = self.find_Match(cnnOut)
log.debug("{}: {}".format(inx, minval))
log.debug("{} : {} : {}".format(*self.db.ix[inx]))
if minval > 1.0:
# status 변경 기준으로 가정
log.debug(type(cnnOut))
log.debug(type(addr))
# TODO: byte 파일 저장 로직 여기 추가!
image = Image.open(io.BytesIO(temp_data))
img_name = str(self.db_count) + '.jpg'
file_dir = '/home/haze/다운로드/apache-tomcat-7.0.92/webapps/HAZE/img/' + img_name
image.save(file_dir, "JPEG")
image = Image.open(io.BytesIO(temp_data))
img_dir = 'FaceRecognitionWorker/db/' + str(
self.db_count) + '.jpg'
image.save(img_dir, "JPEG")
name = input('name: ')
age = input('age: ')
fields = [img_dir, name, age]
with open('FaceRecognitionWorker/db/db.csv', 'a') as f:
writer = csv.writer(f, delimiter=',')
writer.writerow(fields)
self.db_count = self.db_count + 1
self.refVecs.append(cnnOut)
# 인식 성공했을 경우 1초 delay
# time.sleep(1)
self.data = b''