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 argparse

if __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 = IMGSIZE
        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

    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
            
    elif ocrFlag=='torch':
        from crnn.network_torch import CRNN
        if chineseModel:
            alphabet = alphabetChinese
            if LSTMFLAG:
                ocrModel = ocrModelTorchLstm
            else:
                ocrModel = ocrModelTorchDense
                
        else:
            ocrModel = ocrModelTorchEng
            alphabet = alphabetEnglish
            LSTMFLAG = True
    elif ocrFlag=='opencv':
        from crnn.network_dnn import CRNN
        ocrModel = ocrModelOpencv
        alphabet = alphabetChinese
    else:
        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_job
    
    from main import TextOcrModel

    model =  TextOcrModel(ocr,text_detect,angle_detect)

    import cv2
    import time
    p = args.img_path
    img = cv2.imread(p)

    h,w = img.shape[:2]
    timeTake = time.time()
    scale=608
    maxScale=2048

    result,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()-timeTake

    print('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