def runModel(imgFilePathLst, resize):
"""
运行模式 - 多照片运行
这个函数必须在模型已经初始化完毕之后才能使用
:param imgFilePathLst:
:param resize:
:return:
"""
global model, hasModelInit
if model == None or (not hasModelInit):
log.error('Failed to calculate aes score : model is not initialized!')
return
with tf.device('/CPU:0'):
scoreLst = []
for imgPath in imgFilePathLst:
log.info('Now processing img -> %s' % (imgPath))
# 预处理
imgArr = _preImgProcess(imgPath, resize)
# 预测
scores = model.predict(imgArr, batch_size=1, verbose=0)[0]
aesScore = score.calculateAesScore(scores)
aesScore = aesScore * 10
# log.success('The aes score for img -> %s is %0.3f' % (imgPath, aesScore))
_, imgName = os.path.split(imgPath)
scoreLst.append((imgName, aesScore))
# 进行排序
sortedScoreLst = sorted(scoreLst, key=lambda s: s[1], reverse=True)
return sortedScoreLst
def runModelForSingleImg(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
:param imgFilePathLst:
:param resize:
:return:
:param imgFilePath:
:param resize:
:return:
"""
global model, hasModelInit, graph
if model == None or (not hasModelInit):
log.error('Failed to calculate aes score : model is not initialized!')
return
with tf.device('/CPU:0'):
with graph.as_default():
log.info('Now processing img -> %s' % (imgFilePath))
# 预处理
imgArr = _preImgProcess(imgFilePath, resize)
# 预测
scores = model.predict(imgArr, batch_size=1, verbose=0)[0]
aesScore = score.calculateAesScore(scores)
aesScore = aesScore * 10
return aesScore
def addAesInfo(user_id, file_name, aes_score, db):
"""
向DB增加一条美学数据
这里的检查交给调用者去做
:param user_id:
:param file_name:
:param db:
:return:
"""
user_aes_info = AesUserInfo(user_id, file_name, aes_score)
db.session.add(user_aes_info)
db.session.commit()
log.info('Add aes info into db for img -> %s' % file_name)
def make_request(abs_img_path):
"""
使用Socket进行图片美学打分
真正的模型预测在美学微服务中
:param abs_img_path:
:return:
"""
# 进行文件检查
suc, normed_path, msg = FileUtils.valid_path(abs_img_path,
relative_path=False)
if not suc:
log.error('Invalid File path : %s' % abs_img_path)
return 'Invalid File'
abs_img_path = normed_path
try:
# 使用ipV4
socket_conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# 连接
socket_conn.connect((LISTEN_IP, LISTEN_PORT))
rtn_code = (socket_conn.recv(2048)).decode('utf-8')
if rtn_code == CODE_ERROR:
log.error('Aes model not init')
return 'Model Init Error'
elif rtn_code == CODE_OK:
log.info('Socket established!')
# 添加END_MAKRER给微服务进行内容识别
test_img_path = abs_img_path
test_img_path += END_MARKER
log.info('Send info -> %s' % test_img_path)
# 发送请求
socket_conn.send(test_img_path)
aes_score = (socket_conn.recv(2048)).decode('utf-8')
log.info('The aes score is %s' % aes_score)
socket_conn.close()
return aes_score
else:
socket_conn.close()
return 'Unknow Error'
finally:
if socket_conn:
socket_conn.close()
def queryAesInfoById(user_id):
"""
通过user_id
查询对应用户已经进行过美学评测的所有照片
:param user_id:
:param db:
:return:
"""
aes_score_container = {}
aes_info_lst = AesUserInfo.query.filter_by(userId=user_id)
if aes_info_lst is None:
return aes_score_container
for aesInfo in aes_info_lst:
aes_score_container[aesInfo.photoName] = aesInfo.photoScore
log.info('The aes score for img=%s is %s' %
(aesInfo.photoName, aesInfo.photoScore))
return aes_score_container
def initModel(weightsFilePath):
"""
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:
"""
# 初始化过，就不要再初始化了
global model, hasModelInit, graph
if model != None and hasModelInit:
log.info('the model has already init before, now re-use it')
return
# 模型构建
log.info(
('start to build model based on weights in -> %s' % (weightsFilePath)))
with tf.device('/CPU:0'):
# 模型参数定义
base_model = InceptionResNetV2(input_shape=(None, None, 3),
include_top=False,
pooling='avg',
weights=None)
x = Dropout(0.75)(base_model.output)
x = Dense(10, activation='softmax')(x)
model = Model(base_model.input, x)
# 读取权重数据
model.load_weights(weightsFilePath)
graph = tf.get_default_graph()
hasModelInit = True
log.info('model init complete!')
return
from core.helper import FileUtils from core.helper import LogUtils as log from core.photoaes import PhotoAesModel as AesModel # flask 后台相关 app = Flask(__name__) app.config['SECRET_KEY'] = 'gaokuaidian' app.config['UPLOAD_FOLDER'] = 'data/' app.config['LOG_FOLDER'] = 'logs/' app.config['THUMBNAIL_FOLDER_NAME'] = 'thumbnail' app.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024 app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///data/aesinfo.db' app.config['AES_MODEL_PATH'] = 'weights/weights.h5' db = SQLAlchemy(app) bootstrap = Bootstrap(app) log.register_logger(app) app.logger.name = "PhotoAes" # 数据库初始化 db.create_all() # 照片美学相关环境启动 AesModel.initModel(app.config['AES_MODEL_PATH']) """ db对象，这里因为只是用来存储一下文件名，用户id和照片美学得分的 所以简单做，只是一个简单存储而已 """ class AesUserInfo(db.Model):