整个流程介绍

1. 拉取 ufoym/deepo 镜像 -- 因为包含了主流深度学习框架，镜像4G出头。
2. 拉取 chineseocr 项目代码。
3. 修改代码，不使用web，增加命令行传入图片路径的功能
4. 打包成docker镜像。

开始

拉取 ufoym/deepo 镜像 ：cpu版本为例

docker hub地址：（好像需要梯子）

https://hub.docker.com/r/ufoym/deepo

docker pull ufoym/deepo:cpu

拉取 chineseocr 项目代码

项目：https://github.com/chineseocr/chineseocr

git clone https://github.com/chineseocr/chineseocr.git

修改代码，不使用web，增加命令行传入图片路径的功能

先 run 一个容器：

docker run -itd -v /home/wind/winds/ocr/chineseocr-app:/data --name ocr ufoym/deepo:cpu

 进入容器开发：

docker exec -it ocr /bin/bash

安装一些库：

 ps. 把web注释掉

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt

把官方的 test.ipynb 文件改为 .py 文件，使用argparse解析命令行输入的命令：

下面代码保存为 run.py 文件：

import os
import json
import time
from config import *
from application import trainTicket,idcard import argparseif __name__ == "__main__":parser = argparse.ArgumentParser()parser.add_argument('--img_path','-img_path', type=str, default='test.jpeg')args = parser.parse_args()if yoloTextFlag =='keras' or AngleModelFlag=='tf' or ocrFlag=='keras':os.environ["CUDA_VISIBLE_DEVICES"] = ''if yoloTextFlag=='opencv':scale,maxScale = IMGSIZEfrom text.opencv_dnn_detect import text_detectelif yoloTextFlag=='darknet':scale,maxScale = IMGSIZEfrom text.darknet_detect import text_detectelif yoloTextFlag=='keras':scale,maxScale = IMGSIZE[0],2048from text.keras_detect import  text_detectelse:print( "err,text engine in keras\opencv\darknet")from text.opencv_dnn_detect import angle_detectfrom crnn.keys import alphabetChinese,alphabetEnglishif ocrFlag=='keras':from crnn.network_keras import CRNNif chineseModel:alphabet = alphabetChineseif LSTMFLAG:ocrModel = ocrModelKerasLstmelse:ocrModel = ocrModelKerasDenseelse:ocrModel = ocrModelKerasEngalphabet = alphabetEnglishLSTMFLAG = Trueelif ocrFlag=='torch':from crnn.network_torch import CRNNif chineseModel:alphabet = alphabetChineseif LSTMFLAG:ocrModel = ocrModelTorchLstmelse:ocrModel = ocrModelTorchDenseelse:ocrModel = ocrModelTorchEngalphabet = alphabetEnglishLSTMFLAG = Trueelif ocrFlag=='opencv':from crnn.network_dnn import CRNNocrModel = ocrModelOpencvalphabet = alphabetChineseelse:print( "err,ocr engine in keras\opencv\darknet")nclass = len(alphabet)+1   if ocrFlag=='opencv':crnn = CRNN(alphabet=alphabet)else:crnn = CRNN( 32, 1, nclass, 256, leakyRelu=False,lstmFlag=LSTMFLAG,GPU=GPU,alphabet=alphabet)if os.path.exists(ocrModel):crnn.load_weights(ocrModel)else:print("download model or tranform model with tools!")ocr = crnn.predict_jobfrom main import TextOcrModelmodel =  TextOcrModel(ocr,text_detect,angle_detect)import cv2import timep = args.img_pathimg = cv2.imread(p)h,w = img.shape[:2]timeTake = time.time()scale=608maxScale=2048result,angle= model.model(img,detectAngle=True,##是否进行文字方向检测scale=scale,maxScale=maxScale,MAX_HORIZONTAL_GAP=80,##字符之间的最大间隔，用于文本行的合并MIN_V_OVERLAPS=0.6,MIN_SIZE_SIM=0.6,TEXT_PROPOSALS_MIN_SCORE=0.1,TEXT_PROPOSALS_NMS_THRESH=0.7,TEXT_LINE_NMS_THRESH = 0.9,##文本行之间测iou值LINE_MIN_SCORE=0.1,                                             leftAdjustAlph=0,##对检测的文本行进行向左延伸rightAdjustAlph=0.1,##对检测的文本行进行向右延伸)timeTake = time.time()-timeTakeprint('It take:{:.3f}s'.format(timeTake))for line in result:print(line['text'])

ps. 因为TensorFlow版本的问题，会出现2-3和错误，下面列出可能遇到的错误和解决办法：

报错1：tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_367' with dtype float and shape [2]
[[{{node Placeholder_367}}]] -- 就是这类Placeholder_xxx的问题

解决1：https://github.com/chineseocr/chineseocr/issues/496：

报错2：AttributeError: module ‘keras.backend‘ has no attribute 'get_session'

解决2：AttributeError: module ‘keras.backend‘ has no attribute ‘get_session‘ 问题解决_叶庭云的博客-CSDN博客

报错3：ValueError: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)

解决3：Tensorflow v1到v2版本兼容指南 - 知乎

最后，成功：

打包成docker镜像

docker commit -m 'cpu inference' -a 'hongrun' cdfaa57d915d ocr:cpu

以上。

---

附注：有一个轻量化的 chineseocr 项目，叫做 chineseocr_lite，这个也可以打包成镜像，但好像封装的有点死，不容易改动（比如不想用web），所以就抛弃它。

chineseocr_lite 项目：

https://github.com/DayBreak-u/chineseocr_lite/tree/master