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)