from text.opencv_dnn_detect import text_detect
elif yoloTextFlag == 'darknet':
scale, maxScale = IMGSIZE
from text.darknet_detect import text_detect
elif yoloTextFlag == 'keras':
scale, maxScale = IMGSIZE[0], 2048
from text.keras_detect import text_detect
else:
print("err,text engine in keras\opencv\darknet")
from text.opencv_dnn_detect import angle_detect
if ocr_redis:
##多任务并发识别
from apphelper.redisbase import redisDataBase
ocr = redisDataBase().put_values
else:
from crnn.keys import alphabetChinese, alphabetEnglish
if ocrFlag == 'keras':
from crnn.network_keras import CRNN
if chineseModel:
alphabet = alphabetChinese
if LSTMFLAG:
ocrModel = ocrModelKerasLstm
else:
ocrModel = ocrModelKerasDense
else:
ocrModel = ocrModelKerasEng
alphabet = alphabetEnglish
LSTMFLAG = True
alphabet = alphabetChinese
if LSTMFLAG:
ocrModel = ocrModelTorchLstm
else:
ocrModel = ocrModelTorchDense
else:
ocrModel = ocrModelTorchEng
alphabet = alphabetEnglish
LSTMFLAG = True
nclass = len(alphabet) + 1
ocr = CRNN(32,
1,
nclass,
256,
leakyRelu=False,
lstmFlag=LSTMFLAG,
GPU=GPU,
alphabet=alphabet)
if os.path.exists(ocrModel):
ocr.load_weights(ocrModel)
else:
print("download model or tranform model with tools!")
if __name__ == '__main__':
redisJob = redisDataBase()
while True:
redisJob.get_job(ocr.predict)