def handle_conn(self):
"""
连接处理
:return:
"""
log.info('Socket conn eastablished with %s:%s' % (self.addr))
# 检查美学模型是否是ok的，如果不是ok的，那么告诉客户端你凉了
if self.aes_model == None or (not self.aes_model.initFinish()):
self.sock.send(Config.CODE_ERROR)
self.sock.close()
log.info('Aes not init, return CODE_ERROR')
return
# 美学检测通过，返回CODE_OK，进行下一步
self.sock.send(Config.CODE_OK.encode('utf-8'))
# 正式接受数据
received_path = self.recv_end()
log.info('Received path at -> %s' % (received_path))
# 路径校验
suc, normed_path, msg = FileUtils.valid_path(received_path,
relative_path=False)
if not suc:
self.sock.send(Config.CODE_FILE_ERROR.encode('utf-8'))
self.sock.close()
log.info('Received path at -> %s is invalid!' % (received_path))
return
# 进行美学评估
aesScore = self.aes_model.runModelForSingleImg(normed_path, True)
self.sock.send(aesScore.encode('utf-8'))
self.sock.close()
log.info('Aes over, score = %s for img->%s' % (aesScore, normed_path))
def launch_model():
"""
启动美学模型
:return:
"""
from core.PhotoAesModel import aes_model
aes_model.initModel()
log.info('Init aes_model over')
return aes_model
def start_listen():
"""
开启端口监听
:return:
"""
# 使用ipV4
socket_conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# 本机服务，端口8848
socket_conn.bind((Config.LISTEN_IP, Config.LISTEN_PORT))
# 并发连接数目为4
socket_conn.listen(Config.CONCURRENT_NUM)
log.info('Start to listener at port %d' % (Config.LISTEN_PORT))
return socket_conn
def initModel(self):
"""
模型初始化
全局只会初始化一次
包括：
1.inceptionV2结构化定义
2.加载模型权重
:param weightsFilePath:
:return:
"""
from keras.models import Model
from keras.layers import Dense, Dropout
from keras.applications.inception_resnet_v2 import InceptionResNetV2
# 初始化过，就不要再初始化了
if self.initFinish():
log.info('the model has already init before, now re-use it')
return
# 同步Graph
self.graph = tf.Graph()
# session = tf.Session()
# 模型构建
log.info(('start to build model based on weights in -> %s' %
(self.weightsFile)))
# with tf.device('/CPU:0'):
with self.graph.as_default():
# with session.as_default():
# 模型参数定义
base_model = InceptionResNetV2(input_shape=(None, None, 3),
include_top=False,
pooling='avg',
weights=None)
log.info('suc create v2 model')
x = Dropout(0.75)(base_model.output)
x = Dense(10, activation='softmax')(x)
self.model = Model(base_model.input, x)
# 读取权重数据
self.model.load_weights(self.weightsFile)
self.model._make_predict_function()
# graph = tf.get_default_graph()
self.hasModelInit = True
log.info('model init complete!')
return
def runModelForSingleImg(self, imgFilePath, resize):
"""
运行模式 - 单照片运行
这个函数必须在模型已经初始化完毕之后才能使用
注意，此函数线程安全，进程不安全
:param imgFilePathLst:
:param resize:
:return:
:param imgFilePath:
:param resize:
:return:
"""
if not self.initFinish:
log.error(
'Failed to calculate aes score : model is not initialized!')
return
# with tf.device('/CPU:0'):
# with graph.as_default():
with self.graph.as_default():
log.info('Now processing img -> %s' % (imgFilePath))
# 预处理
imgArr = self._preImgProcess(imgFilePath, resize)
try:
self.lock.acquire()
log.info('Now preprocessing img -> %s' % (imgFilePath))
# 预测
scores = self.model.predict(imgArr, batch_size=1, verbose=0)[0]
aesScore = score.calculateAesScore(scores)
aesScore = aesScore * 10
log.info('The score for img -> %s is %.2f' %
(imgFilePath, aesScore))
finally:
self.lock.release()
return str(aesScore)