使用EasyOCR库进行OCR文字识别介绍与实践

[TOC]

0x00 快速了解

EasyOCR 介绍

Q: 什么是 EasyOCR ?

描述: EasyOCR 是一个用于从图像中提取文本的 python 模块, 它是一种通用的 OCR，既可以读取自然场景文本，也可以读取文档中的密集文本。目前支持 80 多种语言和所有流行的书写脚本，包括：拉丁文、中文、阿拉伯文、梵文、西里尔文等。

Q: 使用 EasyOCR 可以干什么?

描述: EasyOCR 支持两种方式运行一种是常用的CPU，而另外一种是需要GPU支持并且需安装CUDA环境, 我们使用其可以进行图片中语言文字识别, 例如小程序里图片识别、车辆车牌识别(即车债管理系统)。

WeiyiGeek.Examples

Tips: 在其官网有demo演示，我们可以使用其进行简单图片ocr识别,地址为https://www.jaided.ai/easyocr/ 或者 https://huggingface.co/spaces/tomofi/EasyOCR

WeiyiGeek.官网Demo演示

EasyOCR Framework

WeiyiGeek.EasyOCR Framework

温馨提示: 图中 灰色插槽是可更换的浅蓝色模块的占位符，我们可以重构代码以支持可交换的检测和识别算法 api

EasyOCR 参考来源

官网地址: https://www.jaided.ai/easyocr/

项目地址: https://github.com/JaidedAI/EasyOCR

实践项目源码地址：https://github.com/WeiyiGeek/SecOpsDev/tree/master/Project/Python/EasyOCR/Travelcodeocr

文档原文地址: https://www.bilibili.com/read/cv16911816

实践视频地址: https://www.bilibili.com/video/BV1nY4y1x7JG

温馨提示: 该项目基于来自多篇论文和开源存储库的研究和代码，所有深度学习执行都基于 Pytorch ，识别模型是 CRNN 它由 3 个主要部分组成：特征提取（我们目前使用 Resnet ）和 VGG、序列标记（ LSTM ）和解码​​（ CTC ）。 ❤️

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0x01 安装部署

环境依赖

环境依赖

• Python 建议 3.8 x64 以上版本 (原本我的环境是 Python 3.7 安装时各种稀奇古怪的错误都出来，不得已abandon放弃)
• easyocr 包 -> 依赖 torch 、torchvision 第三方包

注意事项:

Note 1.本章是基于 cpu 与 GPU 下使用 EasyOCR, 如果你需要使用 GPU 跑, 那么请你安装相应的CUDA环境。

代码语言：javascript复制

$ nvidia-smi -l
Fri May 27 14:57:57 2022
 ----------------------------------------------------------------------------- 
| NVIDIA-SMI 465.19.01    Driver Version: 465.19.01    CUDA Version: 11.3     |
|------------------------------- ---------------------- ---------------------- 
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|=============================== ====================== ======================|
|   0  NVIDIA Tesla V1...  Off  | 00000000:1B:00.0 Off |                    0 |
| N/A   41C    P0    36W / 250W |      0MiB / 32510MiB |      0%      Default |
|                               |                      |                  N/A |
 ------------------------------- ---------------------- ---------------------- 

Note 2.最好在Python 3.8 x64 位系统上安装使用 easyocr , 非常注意其不支持32位的python。

Note 3.对于 Windows，请先按照 https://pytorch.org 的官方说明安装 torch 和 torchvision。 在 pytorch 网站上，请务必选择您拥有的正确 CUDA 版本。 如果您打算仅在 CPU 模式下运行，请选择 CUDA = None。

环境安装

描述: 此处我们使用 pip 安装 easyocr 使用以及通过官方提供的Dockerfile。

pip 方式 对于最新的稳定版本：

代码语言：javascript复制

pip install easyocr -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com

对于最新的开发版本：

代码语言：javascript复制

pip install git git://github.com/jaidedai/easyocr.git

Dockerfile 描述: 由于国内网络环境因素, 此处我将官方提供的Dockerfile稍作更改。

代码语言：javascript复制

$ cd /opt/images/easyocr && git clone https://github.com/JaidedAI/EasyOCR.git --depth=1
$ ls
Dockerfile  EasyOCR

$ cat Dockerfile
# pytorch OS is Ubuntu 18.04
FROM pytorch/pytorch
LABEL DESC="EasyOCR Enviroment Build with Containerd Images"
ARG service_home="/home/EasyOCR" 

# Enviroment && Software
RUN sed -i -e "s#archive.ubuntu.com#mirrors.aliyun.com#g" -e "s#security.ubuntu.com#mirrors.aliyun.com#g" /etc/apt/sources.list  && 
    apt-get update -y && 
    apt-get install -y 
    libglib2.0-0 
    libsm6 
    libxext6 
    libxrender-dev 
    libgl1-mesa-dev 
    git 
    vim 
    # cleanup
    && apt-get autoremove -y 
    && apt-get clean -y 
    && rm -rf /var/lib/apt/lists

# COPY EasyOCR is Github(https://github.com/JaidedAI/EasyOCR.git)
COPY ./EasyOCR "$service_home"

# Build
RUN cd "$service_home" 
  && pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ 
  && python setup.py build_ext --inplace -j 4 
  && python -m pip install -e .

环境验证

代码语言：javascript复制

# Windows 环境
pip freeze | findstr "easyocr"
easyocr @ file:///E:/迅雷下载/easyocr-1.4.2-py3-none-any.whl
# Linux & 容器环境
$ pip freeze | grep "EasyOCR"
-e git https://github.com/JaidedAI/EasyOCR.git@7a685cb8c4ba14f2bc246f89c213f1a56bbc2107#egg=easyocr

# python 命令行中使用
>>> from pprint import pprint  # 方便格式化输出
>>> import easyocr
>>> reader = easyocr.Reader(['ch_sim','en'])
CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU.
>>> result = reader.readtext('00e336dbde464c809ef1f6ea568d4621.png')
>>> pprint(result)
[([[354, 46], [444, 46], [444, 76], [354, 76]], '中国移动', 0.981803297996521),
 ([[477, 55], [499, 55], [499, 75], [477, 75]], '46', 0.3972922105840435),
 ([[533, 55], [555, 55], [555, 75], [533, 75]], '5G', 0.5360637875500641),
 ([[354, 76], [474, 76], [474, 104], [354, 104]],
  '中国移动四 ',
  0.25950584649873865),
 ([[489, 57], [625, 57], [625, 95], [489, 95]],
  'GMl s @',
  0.011500043801327683),
 ([[693, 55], [801, 55], [801, 95], [693, 95]], 'Q92%', 0.022083675488829613),
 ([[864, 60], [950, 60], [950, 92], [864, 92]], '09:03', 0.9793587315696877),
 ([[884, 158], [938, 158], [938, 214], [884, 214]], '@', 0.29484160211053734),
 ([[123, 298], [592, 298], [592, 361], [123, 361]],
  '通信行程卡提供服务>',
  0.6739866899213806),
 ([[115, 429], [384, 429], [384, 497], [115, 497]],
  '通信行程卡',
  0.9159307714297187),
 ([[153, 596], [848, 596], [848, 704], [153, 704]],
  '通信大数据行程卡',
  0.2522292283860262),
 ([[303, 723], [699, 723], [699, 785], [303, 785]],
  '疫情防控;人人有责',
  0.7030201163942564),
 ([[347, 844], [653, 844], [653, 892], [347, 892]],
  '请收下绿色行程卡',
  0.9120484515458063),
 ([[248, 950], [754, 950], [754, 1004], [248, 1004]],
  '157****2966的动态行程卡',
  0.9868984946820241),
 ([[173, 1045], [345, 1045], [345, 1105], [173, 1105]],
  '更新于:',
  0.972654586401667),
 ([[360, 1049], [829, 1049], [829, 1100], [360, 1100]],
  '2022.05.2509:03:56',
  0.9411191664033213),
 ([[110, 1670], [633, 1670], [633, 1732], [110, 1732]],
  '您于前14夭内到达或途经:',
  0.8531442220608394),
 ([[648, 1674], [788, 1674], [788, 1730], [648, 1730]],
  '重庆市',
  0.9605511910615995),
 ([[104, 1778], [898, 1778], [898, 1810], [104, 1810]],
  '结果包含您在前14天内到访的国家(地区) 与停留4小时以上的国内城市',
  0.6574011574316847),
 ([[272, 1825], [729, 1825], [729, 1863], [272, 1863]],
  '色卡仅对到访地作提醒。不关联健康状况',
  0.8806245499955613),
 ([[383, 1891], [607, 1891], [607, 1933], [383, 1933]],
  '本服务联合提供',
  0.9781898210349773),
 ([[119, 1966], [337, 1966], [337, 2006], [119, 2006]],
  'CAICT 中国信通院',
  0.3636917908522541),
 ([[435, 1963], [533, 1963], [533, 1999], [435, 1999]],
  '中国电信',
  0.08182162046432495),
 ([[624, 1966], [702, 1966], [702, 1990], [624, 1990]],
  '中国移动',
  0.9323447942733765),
 ([[812, 1966], [892, 1966], [892, 1990], [812, 1990]],
  '中国联通',
  0.9082608819007874),
 ([[441, 1993], [531, 1993], [531, 2005], [441, 2005]],
  'CINA TUUUC0',
  0.028013896371299665),
 ([[629, 1987], [701, 1987], [701, 2003], [629, 2003]],
  'ChnaMobile',
  0.7021787396208221),
 ([[815, 1989], [893, 1989], [893, 2003], [815, 2003]],
  'Chnoumco',
  0.19655737186726854),
 ([[107, 2077], [281, 2077], [281, 2119], [107, 2119]],
  '证通查来了!',
  0.9745880948510078),
 ([[467, 2075], [825, 2075], [825, 2117], [467, 2117]],
  '全国移动电话卡"一证通查',
  0.9208412317655043),
 ([[79, 2131], [269, 2131], [269, 2173], [79, 2173]],
  '立即点击进入',
  0.6082888941606105),
 ([[510, 2128], [644, 2128], [644, 2172], [510, 2172]],
  '防范诈骗',
  0.952128529548645),
 ([[663, 2129], [793, 2129], [793, 2173], [663, 2173]],
  '保护你我',
  0.9819014668464661)]
# 设置 --detail=0 输出更简单
>>> result = reader.readtext('00e336dbde464c809ef1f6ea568d4621.png', detail = 0)

使用说明

• Note 1.在使easyocr.Reader(['ch_sim','en'])于将模型加载到内存中(可能会耗费一些时间), 并且我们需要设定默认阅读的语言列表, 可以同时使用多种语言，但并非所有语言都可以一起使用, 而通常会采用英语与其他语言联合。

下面列举出可用语言及其语言对应列表 (https://www.jaided.ai/easyocr/) :

代码语言：javascript复制

# 对于我们来说常用语言如下:
# Language	Code Name
Simplified Chinese	ch_sim
Traditional Chinese	ch_tra
English	en

温馨提示: 所选语言的模型权重将自动下载，或者您可以从模型中心 并将它们放在~/.EasyOCR/model文件夹中

Note 2.如果--gpu=True设置为True, 而机器又没有GPU支持的化将默认采用 CPU ，所以通常你会看到如下提示:

代码语言：javascript复制

# 如果您没有 GPU，或者您的 GPU 内存不足，您可以通过添加 gpu=False. 
CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU.

Note 3.在reader.readtext(‘参数值’)函数中的参数值,可以是图片路径、也可是图像文件字节或者 OpenCV 图像对象（numpy 数组）以及互联网上图像的URL 等几种方式.

代码语言：javascript复制

# 图像路径
reader.readtext('chinese.jpg')

# 图像URL
reader.readtext('https://www.weiyigeek.top/wechat.jpg')

# 图形字节
with open("chinese_tra.jpg", "rb") as f:
  img = f.read()
result = reader.readtext(img)

# 图像作为 numpy 数组（来自 opencv）传递
img = cv2.imread('chinese_tra.jpg')
result = reader.readtext(img)

Note 3.从上面结果可以看出输出结果将采用列表格式，每个项目分别代表一个边界框(四个点)、检测到的文本和可信度。

代码语言：javascript复制

([[347, 844], [653, 844], [653, 892], [347, 892]],  # 边界 1 --> 2 -> 3 -> 4
 '请收下绿色行程卡',       # 文本
 0.9120484515458063),     # 可信度

Note 4.我们也可以在命令行中直接调用easyocr。

代码语言：javascript复制

# 语法示例:
usage: easyocr [-h] -l LANG [LANG ...] [--gpu {True,False}] [--model_storage_directory MODEL_STORAGE_DIRECTORY]
  [--user_network_directory USER_NETWORK_DIRECTORY] [--recog_network RECOG_NETWORK]
  [--download_enabled {True,False}] [--detector {True,False}] [--recognizer {True,False}]
  [--verbose {True,False}] [--quantize {True,False}] -f FILE
  [--decoder {greedy,beamsearch,wordbeamsearch}] [--beamWidth BEAMWIDTH] [--batch_size BATCH_SIZE]
  [--workers WORKERS] [--allowlist ALLOWLIST] [--blocklist BLOCKLIST] [--detail {0,1}]
  [--rotation_info ROTATION_INFO] [--paragraph {True,False}] [--min_size MIN_SIZE]
  [--contrast_ths CONTRAST_THS] [--adjust_contrast ADJUST_CONTRAST] [--text_threshold TEXT_THRESHOLD]
  [--low_text LOW_TEXT] [--link_threshold LINK_THRESHOLD] [--canvas_size CANVAS_SIZE]
  [--mag_ratio MAG_RATIO] [--slope_ths SLOPE_THS] [--ycenter_ths YCENTER_THS] [--height_ths HEIGHT_THS]
  [--width_ths WIDTH_THS] [--y_ths Y_THS] [--x_ths X_THS] [--add_margin ADD_MARGIN]

# 案例:
$ easyocr -l ch_sim en -f chinese.jpg --detail=1 --gpu=False
$ easyocr -l ch_sim en -f .a1e948e90964d42b435d63c9f0aa268.png --detail=0 --gpu=True
  # CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU.
....
请收下绿色行程卡
191****8499的动态行程卡
更新于:2022.05.2510:49:21
您于前14夭内到达或途经:  重庆市
结果包含您在前14天内到访的国家(地区)与停留4小时以上的国内城市
.....

方法参数

描述: 官方提供的包的模块方法以及参数说明, 参考地址 ( https://www.jaided.ai/easyocr/documentation/ )

1.EasyOCR 的基类

代码语言：javascript复制

easyocr.Reader(['ch_sim','en'], gpu=False, model_storage_directory="~/.EasyOCR/.",download_enabled=True, user_network_directory="~/.EasyOCR/user_network",recog_network="recog_network",detector=True,recognizer=True)
# download_enabled ：如果 EasyOCR 无法找到模型文件，则启用下载
# model_storage_directory: 模型数据目录的路径
# user_network_directory: 用户定义识别网络的路径
# detector : 加载检测模型到内存中
# recognizer : 加载识别模型到内存中

2.Reader 对象的主要方法, 有 4 组参数：General、Contrast、Text Detection 和 Bounding Box Merging, 其返回值为列表形式。

代码语言：javascript复制

reader.readtext(
  'chinese.jpg',image,decoder='greedy',beamWidth=5,batch_size=1,workers=0,allowlist="ch_sim",blocklist="ch_tra",detail=1,paragraph=False,min_size=10,rotation_info=[90, 180 ,270],
  contrast_ths = 0.1, adjust_contrast = 0.5,
  text_threshold = 0.7, low_text = 0.4,link_threshold = 0.4, canvas_size = 2560, mag_ratio = 1,
  slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5, width_ths = 0.5, add_margin = 0.1, x_ths = 1.0, y_ths = 0.5

)

# Parameters 1: General
--batch_size : 当其值大于 1 时将使 EasyOCR 更快，但使用更多内存。
--allowlist : 强制 EasyOCR 仅识别字符子集。  对特定问题有用（例如车牌等）
--detail : 将此设置为 0 以进行简单输出.
--paragraph ：将结果合并到段落中
--min_size: 过滤小于像素最小值的文本框
--rotation_info:允许 EasyOCR 旋转每个文本框并返回具有最高置信度分数的文本框。例如，对所有可能的文本方向尝试 [90, 180 ,270]。

# Parameters 2: Contrast
--contrast_ths : 对比度低于此值的文本框将被传入模型 2 次,首先是原始图像，其次是对比度调整为“adjust_contrast”值,结果将返回具有更高置信度的那个。
--adjust_contrast : 低对比度文本框的目标对比度级别


# Parameters 3: Text Detection (from CRAFT)
--text_threshold: 文本置信度阈值
--link_threshold: 链接置信度阈值
--canvas_size: 最大图像尺寸,大于此值的图像将被缩小。
--mag_ratio: 图像放大率

# Parameters 4: Bounding Box Merging
height_ths (float, default = 0.5) - 盒子高度的最大差异,不应合并文本大小差异很大的框。
width_ths (float, default = 0.5) - 合并框的最大水平距离。
x_ths (float, default = 1.0) - 当段落 = True 时合并文本框的最大水平距离。
y_ths (float, default = 0.5) - 当段落 = True 时合并文本框的最大垂直距离。

3.detect method, 检测文本框的方法。

代码语言：javascript复制

Parameters
  image (string, numpy array, byte) - Input image
  min_size (int, default = 10) - Filter text box smaller than minimum value in pixel
  text_threshold (float, default = 0.7) - Text confidence threshold
  low_text (float, default = 0.4) - Text low-bound score
  link_threshold (float, default = 0.4) - Link confidence threshold
  canvas_size (int, default = 2560) - Maximum image size. Image bigger than this value will be resized down.
  mag_ratio (float, default = 1) - Image magnification ratio
  slope_ths (float, default = 0.1) - Maximum slope (delta y/delta x) to considered merging. Low value means tiled boxes will not be merged.
  ycenter_ths (float, default = 0.5) - Maximum shift in y direction. Boxes with different level should not be merged.
  height_ths (float, default = 0.5) - Maximum different in box height. Boxes with very different text size should not be merged.
  width_ths (float, default = 0.5) - Maximum horizontal distance to merge boxes.
  add_margin (float, default = 0.1) - Extend bounding boxes in all direction by certain value. This is important for language with complex script (E.g. Thai).
  optimal_num_chars (int, default = None) - If specified, bounding boxes with estimated number of characters near this value are returned first.

Return horizontal_list, free_list - horizontal_list is a list of regtangular text boxes. The format is [x_min, x_max, y_min, y_max]. free_list is a list of free-form text boxes. The format is [[x1,y1],[x2,y2],[x3,y3],[x4,y4]].

4.recognize method, 从文本框中识别字符的方法,如果未给出 Horizo​​ntal_list 和 free_list,它将整个图像视为一个文本框。

代码语言：javascript复制

Parameters
  image (string, numpy array, byte) - Input image
  horizontal_list (list, default=None) - see format from output of detect method
  free_list (list, default=None) - see format from output of detect method
  decoder (string, default = 'greedy') - options are 'greedy', 'beamsearch' and 'wordbeamsearch'.
  beamWidth (int, default = 5) - How many beam to keep when decoder = 'beamsearch' or 'wordbeamsearch'
  batch_size (int, default = 1) - batch_size>1 will make EasyOCR faster but use more memory
  workers (int, default = 0) - Number thread used in of dataloader
  allowlist (string) - Force EasyOCR to recognize only subset of characters. Useful for specific problem (E.g. license plate, etc.)
  blocklist (string) - Block subset of character. This argument will be ignored if allowlist is given.
  detail (int, default = 1) - Set this to 0 for simple output
  paragraph (bool, default = False) - Combine result into paragraph
  contrast_ths (float, default = 0.1) - Text box with contrast lower than this value will be passed into model 2 times. First is with original image and second with contrast adjusted to 'adjust_contrast' value. The one with more confident level will be returned as a result.
  adjust_contrast (float, default = 0.5) - target contrast level for low contrast text box

Return list of results

---

0x02 实践案例

1.批量识别行程码图片

描述: 公司有业务需求做一个行程码识别, 当前是调用某云的文字识别接口来识别行程码, 而其按照调用次数进行计费, 所以为了节约成本就要Python参考了Github上大佬的们项目, 截取部分函数，并使用Flask Web 框架进行封装，从而实现通过网页进行请求调用，并返回JSON字符串。

项目源码Github地址：https://github.com/WeiyiGeek/SecOpsDev/tree/master/Project/Python/EasyOCR/Travelcodeocr

项目实践 步骤 01.安装flask及其依赖模块的。

代码语言：javascript复制

pip install flask -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com

步骤 02.项目路径以及图片路径 D:StudyProject

代码语言：javascript复制

PS D:StudyProject> ls
    目录: D:StudyProject
Mode                 LastWriteTime         Length Name
----                 -------------         ------ ----
d-----         2022/5/25     15:59                img
d-----         2022/5/26     21:17                templates
-a----         2022/5/25     19:34           3966 setup.py

步骤 03.基于Flask web框架下进行调用EasyOCR执行图片文字识别的python代码(v1 版本).

代码语言：javascript复制

# -*- coding: utf-8 -*-
# ####################################################################
# Author: WeiyiGeek
# Description: 基于easyocr实现大数据通信行程卡图片识别信息获取-Flask项目。
# Time: 2022年5月25日 17点31分
# Blog: https://www.weiyigeek.top
# Email: master@weiyigeek.top
# ====================================================================
# 环境依赖与模块安装, 建议 Python 3.8.x 的环境下进行
# pip install flask
# pip install easyocr
# ====================================================================
# 行程码有绿色、黄色、橙色、红色四种颜色。
# 1、红卡：行程中的中高风险地市将标记为红色字体作提示。
# 2、橙卡：新冠肺炎确诊或疑似患者的密切接触者。
# 3、黄卡：海外国家和地区。
# 4、绿卡：其他地区。行程卡结果包含在前14天内到访的国家（地区）与停留4小时以上的国内城市。色卡仅对到访地作提醒，不关联健康状况。
# #####################################################################
import os,sys
import cv2
import re
import glob
import json
import easyocr
from flask import Flask, jsonify, request,render_template
from datetime import datetime
from werkzeug.utils import secure_filename
import numpy as np
import collections

app = Flask(__name__)

# 项目运行路径与行程码图片路径定义
RUNDIR = None
IMGDIR = None
colorDict= {"red": "红色", "red1": "红色", "orange": "橙色", "yellow": "黄色", "green": "绿色"}

def getColorList():
  """
  函数说明: 定义字典存放 HSV 颜色分量上下限 (HSV-RGB)
  例如：{颜色: [min分量, max分量]}
      {'red': [array([160, 43, 46]), array([179, 255, 255])]}
  返回值: 专门的容器数据类型，提供Python通用内置容器、dict、list、set和tuple的替代品。
  """
  dict = collections.defaultdict(list)

  # 红色
  lower_red = np.array([156, 43, 46])
  upper_red = np.array([180, 255, 255])
  color_list = []
  color_list.append(lower_red)
  color_list.append(upper_red)
  dict['red']=color_list
 
  # 红色2
  lower_red = np.array([0, 43, 46])
  upper_red = np.array([10, 255, 255])
  color_list = []
  color_list.append(lower_red)
  color_list.append(upper_red)
  dict['red2'] = color_list

  # 橙色
  lower_orange = np.array([11, 43, 46])
  upper_orange = np.array([25, 255, 255])
  color_list = []
  color_list.append(lower_orange)
  color_list.append(upper_orange)
  dict['orange'] = color_list
 
  # 黄色
  lower_yellow = np.array([26, 43, 46])
  upper_yellow = np.array([34, 255, 255])
  color_list = []
  color_list.append(lower_yellow)
  color_list.append(upper_yellow)
  dict['yellow'] = color_list

  # 绿色
  lower_green = np.array([35, 43, 46])
  upper_green = np.array([77, 255, 255])
  color_list = []
  color_list.append(lower_green)
  color_list.append(upper_green)
  dict['green'] = color_list

  return dict

def getTravelcodeColor(img_np):
  """
  函数说明: 利用阈值返回行程码主页颜色
  参数值: cv2.imread() 读取的图像对象(np数组)
  返回值: 行程卡颜色{红、橙、绿}
  """
  hsv = cv2.cvtColor(img_np, cv2.COLOR_BGR2HSV)
  maxsum = -100
  color = None
  color_dict = getColorList()
  for d in color_dict:
    mask = cv2.inRange(hsv,color_dict[d][0],color_dict[d][1])
    # cv2.imwrite(os.path.join(os.path.abspath(os.curdir),"img",d '.jpg')  ,mask)
    binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
    binary = cv2.dilate(binary,None,iterations=2)
    cnts, hiera = cv2.findContours(binary.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    sum = 0
    for c in cnts:
      sum =cv2.contourArea(c)
    if sum > maxsum :
      maxsum = sum
      color = d

  return colorDict[color]


def information_filter(file_path,img_np,text_str):
  """
  函数说明: 提出ocr识别的行程码
  参数值：字符串,文件名称
  返回值：有效信息组成的字典
  """
  # 健康码字段
  try:
    re_healthcode = re.compile('请收下(.{,2})行程卡')
    healthcode = re_healthcode.findall(text_str)[0]
  except Exception as _:
    healthcode = getTravelcodeColor(img_np)  # 文字无法识别时采用图片颜色识别
    print("[*] Get Photo Color = ",healthcode)

  # 电话字段
  re_phone = re.compile('[0-9]{3}*{4}[0-9]{4}')
  phone_str = re_phone.findall(text_str)[0]

  # 日期字段
  re_data = re.compile('2022.[0-1][0-9].[0-3][0-9]')
  data_str = re_data.findall(text_str)[0]

  # 时间字段
  re_time = re.compile('[0-9][0-9]:[0-9][0-9]:[0-9][0-9]')
  time_str = re_time.findall(text_str)[0]

  # 地区城市字段
  citys_re = re.compile('到达或途经:(. )结果包含')
  citys_str = citys_re.findall(text_str)[0].strip().split('(')[0]

  result_dic = {"status": "succ", "file": file_path ,"类型": healthcode, "电话": phone_str, "日期": data_str, "时间": time_str, "行程": citys_str}
  print("33[032m",result_dic,"33[0m")
  return result_dic


def getTravelcodeInfo(filename, img_np):
  """
  函数说明: 返回以JSON字符串格式过滤后结果
  参数值：文件名称,图像作为 numpy 数组（来 opencv传递
  返回值：JSON字符串格式
  """
  # 灰度处理
  img_gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
  # 阈值二进制 - > 127 设置为255(白)，否则0(黑) -> 淡白得更白,淡黑更黑
  _,img_thresh = cv2.threshold(img_gray,180,255,cv2.THRESH_BINARY)
  # 图像 OCR 识别
  text = reader.readtext(img_thresh, detail=0, batch_size=10) 
  result_dic = information_filter(filename, img_np, "".join(text))
  return result_dic

# Flask 路由 - 首页
@app.route('/')
@app.route('/index')
def Index():
  return "<h4 style='text-algin:center'>https://www.weiyigeek.top</h4><script>window.location.href='https://www.weiyigeek.top'</script>"

# Flask 路由 - /tools/ocr
@app.route('/tools/ocr',methods=["GET"])
def Travelcodeocr():
  """
  请求路径: /tools/ocr
  请求参数: (/tools/ocr?file=20220520/test.png, /tools/ocr?dir=20220520)
  """
  filename = request.args.get("file")
  dirname = request.args.get("dir")
  if (filename):
    img_path = os.path.join(IMGDIR, filename)
    if (os.path.exists(img_path)):
      print(img_path)  # 打印路径
      img_np = cv2.imread(img_path)

      try:
        result_dic_succ = getTravelcodeInfo(filename,img_np)
      except Exception as err:
        print("33[31m"  img_path   " -->> "   str(err)   "33[0m")
        return json.dumps({"status":"err", "img": filename}).encode('utf-8'), 200, {"Content-Type":"application/json"} 
      
      return json.dumps(result_dic_succ, ensure_ascii=False).encode('utf-8'), 200, {"Content-Type":"application/json"}
    else:
      return jsonify({"status": "err","msg": "文件" img_path "路径不存在."})

  elif (dirname and os.path.join(IMGDIR, dirname)):
    result_dic_all = []
    result_dic_err = []
    img_path_all =  glob.iglob(os.path.join(os.path.join(IMGDIR,dirname) "/*.[p|j]*g"))   # 正则匹配 png|jpg|jpeg 后缀的后缀,返回的是迭代器。
    for img_path in img_path_all:
      print(img_path) # 打印路径
      img_np = cv2.imread(img_path)

      try:
        result_dic_succ = getTravelcodeInfo(os.path.join(dirname,os.path.basename(img_path)),img_np)
      except Exception as err:
        print("33[31m"  img_path   " -->> "   str(err)   "33[0m") # 输出识别错误的图像
        result_dic_err.append(img_path)
        continue

      # 成功则加入到List列表中
      result_dic_all.append(result_dic_succ)

    res_succ_json=json.dumps(result_dic_all, ensure_ascii=False)
    res_err_json=json.dumps(result_dic_err, ensure_ascii=False)

    with open(os.path.join(IMGDIR, dirname, dirname   "-succ.json"),'w') as succ:
      succ.write(res_succ_json)
    with open(os.path.join(IMGDIR, dirname,  dirname   "-err.json"),'w') as error:
      error.write(res_err_json)

    return res_succ_json.encode('utf-8'), 200, {"Content-Type":"application/json"}
  else:
    return jsonify({"status": "err","msg": "请求参数有误!"})


# Flask 路由 - /tools/upload/ocr
@app.route('/tools/upload/ocr',methods=["GET","POST"])
def TravelcodeUploadocr():
  if request.method == 'POST':
    unix = datetime.now().strftime('%Y%m%d-%H%M%S%f')
    f = request.files['file']
    if (f.mimetype == 'image/jpeg' or f.mimetype == 'image/png'):
      filedate = unix.split("-")[0]
      filesuffix = f.mimetype.split("/")[-1]
      uploadDir = os.path.join('img',filedate)

      # 判断上传文件目录是否存在
      if (not os.path.exists(uploadDir)):
        os.makedirs(uploadDir)

      img_path = os.path.join(uploadDir,secure_filename(unix "." filesuffix))  # 图片路径拼接
      print(img_path)     # 打印路径
      f.save(img_path)    # 写入图片

      # 判断上传文件是否存在
      if (os.path.exists(img_path)):
        img_np = cv2.imread(img_path)
        try:
          result_dic_succ = getTravelcodeInfo(os.path.join(filedate,os.path.basename(img_path)),img_np)
        except Exception as err:
          print("33[31m"  err   "33[0m")
          return json.dumps({"status":"err", "img": img_path}).encode('utf-8'), 200, {"Content-Type":"application/json"}
        return json.dumps(result_dic_succ, ensure_ascii=False).encode('utf-8'), 200, {"Content-Type":"application/json"}
      else:
        return jsonify({"status": "err","msg": "文件" img_path "路径不存在!"})
    else:
      return jsonify({"status": "err","msg": "不能上传除 jpg 与 png 格式以外的图片"})
  else:
    return render_template('index.html')

# 程序入口
if __name__ == '__main__':
  try:
    RUNDIR = sys.argv[1]
    IMGDIR = sys.argv[2]
  except Exception as e:
    print("[*] Uage:"  sys.argv[0]   " RUNDIR IMGDIR")
    print("[*] Default:"  sys.argv[0]   " ./ ./img"   "n" )
    RUNDIR = os.path.abspath(os.curdir)
    IMGDIR = os.path.join(RUNDIR,"img")
  # finally:
  #   if os.path.exists(RUNDIR):
  #     RUNDIR = os.path.abspath(os.curdir)
  #   if os.path.exists(IMGDIR):
  #     IMGDIR = os.path.join(RUNDIR,"img")

  # 使用easyocr模块中的Reader方法, 设置识别中英文两种语言
  reader = easyocr.Reader(['ch_sim', 'en'], gpu=False) 
  # 使用Flask模块运行web
  app.run(host='0.0.0.0', port=8000, debug=True)

步骤 03.运行该脚本并使用浏览进行指定行程码图片路径以及识别提取。

代码语言：javascript复制

python .setup.py
  # Using CPU. Note: This module is much faster with a GPU.
  # * Serving Flask app 'index' (lazy loading)
  # * Environment: production
  #   WARNING: This is a development server. Do not use it in a production deployment.
  #   Use a production WSGI server instead.
  # * Debug mode: on
  # * Running on all addresses (0.0.0.0)
  #   WARNING: This is a development server. Do not use it in a production deployment.
  # * Running on http://127.0.0.1:8000
  # * Running on http://10.20.172.106:8000 (Press CTRL C to quit)
  # * Restarting with stat
  # Using CPU. Note: This module is much faster with a GPU.
  # * Debugger is active!
  # * Debugger PIN: 115-313-307

温馨提示: 从上面的Python脚本中可以看出我们可使用file参数指定图片路径或者使用dir参数指定行程码图片存放目录(默认在img目录下的子目录)。

例如，获取单个行程码图片信息，我本地浏览器访问http://127.0.0.1:8000/tools/ocr?file=20220530/00e336dbde464c809ef1f6ea568d4621.png地址,将会返回如下JSON字符串。

代码语言：javascript复制

D:StudyProjectimg202205300e336dbde464c809ef1f6ea568d4621.png
127.0.0.1 - - [01/Jun/2022 16:58:58] "GET /tools/upload/ocr HTTP/1.1" 200 -
{'status': 'succ', 'file': '20220530\00e336dbde464c809ef1f6ea568d4621.png', '类型': '绿色', '电话': '157****2966', '日期': '2022.05.25', '时间': '09:03:56', '行程': '重庆市'}

例如，获取多个行程码图片识别信息，我本地浏览器访问http://127.0.0.1:8000/tools/ocr?dir=20220530地址,将会返回如下图所示结果。

WeiyiGeek.批量获取行程码图片信息

例如, 我们可以上传并识别行程码图片信息，本地浏览器访问http://127.0.0.1:8000/tools/upload/ocr地址,将会返回如下图所示结果。

WeiyiGeek.上传并识别行程码图片信息

2.在k8s集群中运行

---

0x03 代码迭代

温馨提示: 如下环境依赖与模块安装, 建议 Python 3.8.x 的环境下进行.

代码语言：javascript复制

# pip install flask
# pip install easyocr
# pip install gevent
# pip3 install gevent-websocket

mylogger.py

代码语言：javascript复制

import os
import logging
from logging.handlers import TimedRotatingFileHandler

LOG_PATH = "log"
LOG_INFO = '_info.log'
LOG_ERROR = '_error.log'

class logger:
  def __init__(self, prefix_name = "flask"):
    if (not os.path.exists(LOG_PATH)):
      os.makedirs(LOG_PATH)

    self.prefix = prefix_name

    # 创建logger日志对象
    self.info_logger = logging.getLogger("info")
    self.error_logger = logging.getLogger("error")

    # 日志的最低输出级别
    self.info_logger.setLevel(logging.DEBUG)
    self.error_logger.setLevel(logging.ERROR)

    # 日志格式化
    self.format = logging.Formatter('[%(asctime)s][%(threadName)s:%(thread)d][task_id:%(name)s][%(filename)s:%(lineno)d]' '[%(levelname)s] : %(message)s')
    
    # 按照时间切割文件 Handler 配置
    TimeFileHandlerINFO = TimedRotatingFileHandler("%s/%s%s" % (LOG_PATH, prefix_name, LOG_INFO), when='MIDNIGHT', encoding="utf-8", backupCount=8760, delay=True)
    TimeFileHandlerINFO.suffix = "%Y-%m-%d.log"
    TimeFileHandlerERROR = TimedRotatingFileHandler("%s/%s%s" % (LOG_PATH, prefix_name, LOG_ERROR), when='MIDNIGHT', encoding="utf-8", backupCount=8760, delay=True)
    TimeFileHandlerERROR.suffix = "%Y-%m-%d.log"
    LoggerStream = logging.StreamHandler()
    
    # 设置日志格式化
    TimeFileHandlerINFO.setFormatter(self.format)
    TimeFileHandlerERROR.setFormatter(self.format)
    LoggerStream.setFormatter(self.format)

    # 添加设置的句柄
    self.info_logger.addHandler(TimeFileHandlerINFO)
    # self.info_logger.addHandler(LoggerStream)
    self.error_logger.addHandler(TimeFileHandlerERROR)
    # self.error_logger.addHandler(LoggerStream)

  def debug(self, msg, *args, **kwargs):
    self.info_logger.debug(msg, *args, **kwargs)

  def info(self, msg, *args, **kwargs):
    self.info_logger.info(msg, *args, **kwargs)

  def warn(self, msg, *args, **kwargs):
    self.info_logger.warning(msg, *args, **kwargs)

  def error(self, msg, *args, **kwargs):
    self.error_logger.error(msg, *args, **kwargs)

  def fatal(self, msg, *args, **kwargs):
    self.error_logger.fatal(msg, *args, **kwargs)

  def critical(self, msg, *args, **kwargs):
    self.error_logger.critical(msg, *args, **kwargs)

setup.py

代码语言：javascript复制

# -*- coding: utf-8 -*-
# ####################################################################
# Author: WeiyiGeek
# Description: 基于easyocr实现大数据通信行程卡图片识别信息获取-Flask项目。
# Time: 2022年5月25日 17点31分
# Blog: https://www.weiyigeek.top
# Email: master@weiyigeek.top
# Modity: 2022年8月10日 14点53分
# ====================================================================
# 环境依赖与模块安装, 建议 Python 3.8.x 的环境下进行
# pip install flask
# pip install easyocr
# pip install gevent
# pip3 install gevent-websocket
# ====================================================================
# 行程码有绿色、黄色、橙色、红色四种颜色。
# 1、红卡：行程中的中高风险地市将标记为红色字体作提示。
# 2、橙卡：新冠肺炎确诊或疑似患者的密切接触者。
# 3、黄卡：海外国家和地区。
# 4、绿卡：其他地区。行程卡结果包含在前14天内到访的国家（地区）与停留4小时以上的国内城市。色卡仅对到访地作提醒，不关联健康状况。
# #####################################################################


import os
import cv2
import re
import glob
import json
import easyocr
import logging
import collections
import argparse
from flask import Flask, jsonify, request,render_template
from datetime import datetime
from werkzeug.utils import secure_filename
import numpy as np
from gevent import pywsgi
from mylogger import logger
from logging.handlers import RotatingFileHandler
from geventwebsocket.handler import WebSocketHandler

app = Flask(__name__)

# 运行参数
parser = argparse.ArgumentParser(description="本程序利用easyocr进行图像文字识别，实现行程码与健康码的识别",prog='Easyocr')
parser.add_argument('--rundir',dest="rundir", type=str, help="指定程序运行目录", required=False, default="./")
parser.add_argument('--imgdir',dest="imgdir", type=str, help="指定图像存放目录", required=False, default="./img")
parser.add_argument('--gpu',dest="gpu", type=bool, help="指定是否使用GPU执行计算(缺省: Flase)", required=False, default=False)
parser.add_argument('--ip',dest="ip", type=str, help="指定服务监听网卡(缺省: 0.0.0.0)", required=False, default="0.0.0.0")
parser.add_argument('--port',dest="port", type=int, help="指定服务的端口(缺省: 8000)", required=False, default=8000)
args = parser.parse_args()

# 常量定义
RUNDIR = args.rundir
IMGDIR = args.imgdir
codeDict = {"green": "绿码", "yellow": "黄码", "red": "红码", "other": "暂时无法确认"}
colorDict = {"red": "红色", "red1": "红色", "orange": "橙色", "yellow": "黄色", "green": "绿色"}

def getColorList():
  """
  函数说明: 定义字典存放 HSV 颜色分量上下限 (HSV-RGB)
  例如：{颜色: [min分量, max分量]}
      {'red': [array([160, 43, 46]), array([179, 255, 255])]}
  返回值: 专门的容器数据类型，提供Python通用内置容器、dict、list、set和tuple的替代品。
  """
  dict = collections.defaultdict(list)

  # 红色
  lower_red = np.array([156, 43, 46])
  upper_red = np.array([180, 255, 255])
  color_list = []
  color_list.append(lower_red)
  color_list.append(upper_red)
  dict['red']=color_list
 
  # 红色2
  lower_red = np.array([0, 43, 46])
  upper_red = np.array([10, 255, 255])
  color_list = []
  color_list.append(lower_red)
  color_list.append(upper_red)
  dict['red2'] = color_list

  # 橙色
  lower_orange = np.array([11, 43, 46])
  upper_orange = np.array([25, 255, 255])
  color_list = []
  color_list.append(lower_orange)
  color_list.append(upper_orange)
  dict['orange'] = color_list
 
  # 黄色
  lower_yellow = np.array([26, 43, 46])
  upper_yellow = np.array([34, 255, 255])
  color_list = []
  color_list.append(lower_yellow)
  color_list.append(upper_yellow)
  dict['yellow'] = color_list

  # 绿色
  lower_green = np.array([35, 43, 46])
  upper_green = np.array([77, 255, 255])
  color_list = []
  color_list.append(lower_green)
  color_list.append(upper_green)
  dict['green'] = color_list

  return dict

def getTravelcodeColor(img_np):
  """
  函数说明: 利用阈值返回行程码主页颜色
  参数值: cv2.imread() 读取的图像对象(np数组)
  返回值: 行程卡颜色{红、橙、绿}
  """
  hsv = cv2.cvtColor(img_np, cv2.COLOR_BGR2HSV)
  maxsum = -100
  color = None
  color_dict = getColorList()
  for d in color_dict:
    mask = cv2.inRange(hsv,color_dict[d][0],color_dict[d][1])
    # cv2.imwrite(os.path.join(os.path.abspath(os.curdir),"img",d '.jpg')  ,mask)
    binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
    binary = cv2.dilate(binary,None,iterations=2)
    cnts, hiera = cv2.findContours(binary.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    sum = 0
    for c in cnts:
      sum =cv2.contourArea(c)
    if sum > maxsum :
      maxsum = sum
      color = d

  return colorDict[color]

def health_filter(file_path, img_np ,text_str):
  """
  函数说明: ocr识别的健康码
  参数值：字符串,文件名称
  返回值：有效信息组成的字典
  """
  # 健康码字段
  try:
    result_dic = None
    for i in codeDict:
      if (text_str.find(codeDict[i]) > 0) :
        result_dic = {"code": 200,"msg" : "成功获取健康码图片数据为有健康码状态.", "data" :{"file": file_path ,"type": codeDict[i]}}
        if ( i == "other" ):
          result_dic = {"code": 200,"msg" : "成功获取健康码图片数据为无码.", "data" :{"file": file_path ,"type": "无码"}}
        break
      else:
        result_dic = {"code": 0,"msg" : "识别健康码图片数据未在字典范围内.", "data" :{"file": file_path ,"type": "未知"}}
    return result_dic
  except Exception as _:
    # healthcode = getTravelcodeColor(img_np)  # 文字无法识别时采用图片颜色识别
    # print("[*] Get Photo Color = ",healthcode)
    result_dic = {"code": 0,"msg" : "识别健康码图片数据失败.", "data" :{"file": file_path ,"type": "未知"}}
    return result_dic
    
def getHealthCodeInfo(filename, img_np):
  """
  函数说明: 获取健康码信息
  参数值：文件名称, 图像作为 numpy 数组进行opencv传递
  返回值：返回以JSON字符串格式过滤后结果
  """
  # 灰度处理
  # img_gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
  # 阈值二进制 - > 127 设置为255(白)，否则0(黑) -> 淡白得更白,淡黑更黑
  # _,img_thresh = cv2.threshold(img_gray,176,255,cv2.THRESH_BINARY)
  # 图像 OCR 识别
  text = reader.readtext(img_np, detail=0, batch_size=64) 
  result_dic = health_filter(filename, img_np, "".join(text))
  return result_dic
  

def travel_filter(file_path,img_np,text_str):
  """
  函数说明: ocr识别的行程码
  参数值：字符串,文件名称
  返回值：有效信息组成的字典
  """
  # 健康码字段
  try:
    re_healthcode = re.compile('请收下(.{,2})行程卡')
    healthcode = re_healthcode.findall(text_str)[0]
  except Exception as _:
    healthcode = getTravelcodeColor(img_np)  # 文字无法识别时采用图片颜色识别
    print("[*] Get Photo Color = ",healthcode)

  # 电话字段
  re_phone = re.compile('[0-9]{3}*{4}[0-9]{4}')
  phone_str = re_phone.findall(text_str)[0]

  # 日期字段
  re_data = re.compile('2022.[0-1][0-9].[0-3][0-9]')
  data_str = re_data.findall(text_str)[0]

  # 时间字段
  re_time = re.compile('[0-9][0-9]:[0-9][0-9]:[0-9][0-9]')
  time_str = re_time.findall(text_str)[0]

  # 地区城市字段
  citys_re = re.compile('到达或途经:(. )结果包含')
  citys_str = citys_re.findall(text_str)[0].strip().split('(')[0]

  result_dic = {"code": 200,"msg" : "成功获取行程码数据.", "data" :{"file": file_path ,"type": healthcode, "phone": phone_str, "date": data_str, "time": time_str, "travel": citys_str}}
  return result_dic


def getTravelCodeInfo(filename, img_np):
  """
  函数说明: 返回以JSON字符串格式过滤后结果
  参数值：文件名称,图像作为numpy数组进行opencv传递
  返回值：JSON字符串格式
  """
  # 灰度处理
  img_gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
  # 阈值二进制 - > 127 设置为255(白)，否则0(黑) -> 淡白得更白,淡黑更黑
  _,img_thresh = cv2.threshold(img_gray,176,255,cv2.THRESH_BINARY)
  # 图像 OCR 识别
  text = reader.readtext(img_thresh, detail=0, batch_size=64)
  result_dic = travel_filter(filename, img_np, "".join(text))
  return result_dic

# Flask 路由 - 首页
@app.route('/')
@app.route('/index')
def Index():
  return "<h4 style='text-algin:center'>https://www.weiyigeek.top/wechat.html?key=欢迎关注</h4><br/><b style='text-algin:center'>/api/v1/ocr/health?action=jkm&file=20220520/test.png <br> /api/v1/ocr/health?action=xcm&file=20220520/test.png </b>"

# Flask 路由
@app.route('/api/v1/ocr/health',methods=["GET"])
def Travelcodeocr():
  """
  请求路径: /api/v1/ocr/health
  请求参数: (/api/v1/ocr/health?action=['jkm','xcm']&file=20220520/test.png, /tools/ocr?dir=20220520)
  """
  action = request.args.get("action")
  filename = request.args.get("file")
  dirname = request.args.get("dir")
  if (action and filename):
    img_path = os.path.join(IMGDIR, filename)
    if (os.path.exists(img_path)):
      # 打印路径
      log.info("图像路径: "  img_path)
      img_np = cv2.imread(img_path)
      try:
        if (action == "jkm"):
          result_dic_succ = getHealthCodeInfo(filename,img_np)
        else:
          result_dic_succ = getTravelCodeInfo(filename,img_np)
      except Exception as err:
        result_dic_err = {"code": 0,"msg" : "图像数据获取异常, 请调用第三方接口识别.","data" :{"action": action,"file": filename ,"type": "未知"}}  
        log.error(str(err)   "-"   str(result_dic_err))
        return json.dumps(result_dic_err, ensure_ascii=False).encode('utf-8'), 200, {"Content-Type":"application/json"} 

      log.info(result_dic_succ)
      return json.dumps(result_dic_succ, ensure_ascii=False).encode('utf-8'), 200, {"Content-Type":"application/json"}
    else:
      result_dic_err={"code": 0,"msg": "图像数据获取异常, 文件" filename "或者路径不对, 请检查."}
      log.error(str(result_dic_err))
      return json.dumps(result_dic_err, ensure_ascii=False).encode('utf-8'), 200, {"Content-Type":"application/json"}

  elif (action and dirname and os.path.join(IMGDIR, dirname)):
    result_dic_all = []
    result_dic_err = []
    img_path_all =  glob.iglob(os.path.join(os.path.join(IMGDIR,dirname) "/*.[p|j]*g"))   # 正则匹配 png|jpg|jpeg 后缀的后缀,返回的是迭代器。
    for img_path in img_path_all:
      img_np = cv2.imread(img_path)
      try:
        result_dic_succ = getTravelCodeInfo(os.path.join(dirname,os.path.basename(img_path)),img_np)
      except Exception as err:
        print("33[31m"  img_path   " -->> "   str(err)   "33[0m") # 输出识别错误的图像
        result_dic_err.append(img_path)
        continue

      # 成功则加入到List列表中
      result_dic_all.append(result_dic_succ)

    res_succ_json=json.dumps(result_dic_all, ensure_ascii=False)
    res_err_json=json.dumps(result_dic_err, ensure_ascii=False)

    with open(os.path.join(IMGDIR, dirname, dirname   "-succ.json"),'w') as succ:
      succ.write(res_succ_json)
    with open(os.path.join(IMGDIR, dirname,  dirname   "-err.json"),'w') as error:
      error.write(res_err_json)

    return res_succ_json.encode('utf-8'), 200, {"Content-Type":"application/json"}
  else:
    return jsonify({"code": "0","msg": "请求参数有误!"})


# Flask 路由 - /tools/upload/ocr
@app.route('/tools/upload/ocr',methods=["GET","POST"])
def TravelcodeUploadocr():
  if request.method == 'POST':
    unix = datetime.now().strftime('%Y%m%d-%H%M%S%f')
    f = request.files['file']
    if (f.mimetype == 'image/jpeg' or f.mimetype == 'image/png'):
      filedate = unix.split("-")[0]
      filesuffix = f.mimetype.split("/")[-1]
      uploadDir = os.path.join('img',filedate)

      # 判断上传文件目录是否存在
      if (not os.path.exists(uploadDir)):
        os.makedirs(uploadDir)
      # 图片路径拼接与写入图片
      img_path = os.path.join(uploadDir,secure_filename(unix "." filesuffix)) 
      f.save(img_path) 
      # log.info("上传路径:"   img_path)

      # 判断上传文件是否存在
      if (os.path.exists(img_path)):
        img_np = cv2.imread(img_path)
        try:
          result_dic_succ = getTravelCodeInfo(os.path.join(filedate,os.path.basename(img_path)),img_np)
        except Exception as err:
          result_dic_err = {"code": 0,"msg" : "图像数据获取异常, 请调用第三方接口识别.","data" :{"action": "upload","img_path": img_path ,"type": "未知"}}  
          log.error(str(err)   "-"   str(result_dic_err))
          return json.dumps(result_dic_err, ensure_ascii=False).encode('utf-8'), 200, {"Content-Type":"application/json"} 

        log.info(str(result_dic_succ))
        return json.dumps(result_dic_succ, ensure_ascii=False).encode('utf-8'), 200, {"Content-Type":"application/json"}
      else:
        result_dic_err={"code": 0,"msg": "图像数据获取异常, 文件" img_path "或者路径不对, 请检查."}
        log.error(str(result_dic_err))
        return json.dumps(result_dic_err, ensure_ascii=False).encode('utf-8'), 200, {"Content-Type":"application/json"}
    else:
      return jsonify({"status": "err","msg": "不能上传除 jpg 与 png 格式以外的图片"})
  else:
    return render_template('index.html')


# 程序入口
if __name__ == '__main__':
  log = logger("app")
  # 全局设置日志的记录等级，调试INFO级
  logging.basicConfig(level=logging.INFO) 
  # 创建日志记录器，指明日志保存的路径、每个日志文件的最大大小、保存的日志文件个数上限 (100 兆)
  file_log_handler = RotatingFileHandler("log/console.log", maxBytes=1024 * 1024 * 100, backupCount=10, encoding="utf-8")
  # 创建日志记录的格式 日志等级 输入日志信息的文件名 行数 日志信息
  formatter = logging.Formatter('%(levelname)s - %(message)s')
  # 为刚创建的日志记录器设置日志记录格式
  file_log_handler.setFormatter(formatter)
  # 为全局的日志工具对象（flask app使用的）添加日志记录器
  logging.getLogger().addHandler(file_log_handler)

  # try:
  #   RUNDIR = sys.argv[1]
  #   IMGDIR = sys.argv[2]
  # except Exception as e:
  #   print("[*] Uage:"  sys.argv[0]   " RUNDIR IMGDIR")
  #   print("[*] Default:"  sys.argv[0]   " ./ ./img"   "n" )
  #   RUNDIR = os.path.abspath(os.curdir)
  #   IMGDIR = os.path.join(RUNDIR,"img")
  # finally:
  #   if os.path.exists(RUNDIR):
  #     RUNDIR = os.path.abspath(os.curdir)
  #   if os.path.exists(IMGDIR):
  #     IMGDIR = os.path.join(RUNDIR,"img")

  # 使用easyocr模块中的Reader方法, 设置识别中英文两种语言
  reader = easyocr.Reader(['ch_sim', 'en'], gpu=args.gpu, detector=True, recognizer=True) 
  # 使用原生app运行web(单线程)
  # app.run(host='0.0.0.0', port=8000, debug=False)
  # 使用WSGIServer多线程(实际上是异步)
  server = pywsgi.WSGIServer((args.ip, args.port), app, handler_class=WebSocketHandler)
  server.serve_forever()

代码执行:

代码语言：javascript复制

---

0x04 入坑出坑

问题1.通过pip install 安装easyocr离线的whl包是报ERROR: No matching distribution found for torch

错误信息:

代码语言：javascript复制

pip install ./easyocr-1.4.2-py3-none-any.whl -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
ERROR: Could not find a version that satisfies the requirement torch (from easyocr) (from versions: none)
ERROR: No matching distribution found for torch

解决办法: python.exe -m pip install --upgrade pip

问题2.在Python3.7的环境中安装easyocr依赖的torch模块的whl安装包报not a supported wheel on this platform.错误

错误信息:

代码语言：javascript复制

$ pip install torch-1.8.0 cpu-cp37-cp37m-win_amd64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/
WARNING: Requirement 'torch-1.8.0 cpu-cp37-cp37m-win_amd64.whl' looks like a filename, but the file does not exist
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple/
ERROR: torch-1.8.0 cpu-cp37-cp37m-win_amd64.whl is

错误原因: 平台与下载的whl不符合, 此处我遇到的问题明显不是这个导致的，百度后我想是由于pip版本与python版本、以及系统平台联合导致。

解决办法:

代码语言：javascript复制

# 解决1.假如,你是linux你可以通过 https://download.pytorch.org/whl/torch_stable.html 找到所需版本。
文件名解释：cpu或显卡/文件名-版本号-python版本-应该是编译格式-平台-cpu类型（intel也选amd64）
# torch-1.8.0 cpu-cp37-cp37m-win_amd64.whl

# 解决2.将 torch-1.8.0 cpu-cp37-cp37m-win_amd64.whl 更名为 torch-1.8.0 cpu-cp37-cp37m-win32.whl

问题3.在执行调用torch模块的py脚本时报`Error loading “D:**libsite-packagestorchlibasmjit.dll” or one of its dependencies.`错误**

错误信息:

代码语言：javascript复制

Microsoft Visual C   Redistributable is not installed, this may lead to the DLL load failure.
It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe
Traceback (most recent call last):
.....
OSError: [WinError 193] <no description> Error loading "D:Program Files (x86)Python37-32libsite-packagestorchlibasmjit.dll" or one of its dependencies.

解决办法: 在你的电脑上下载安装 https://aka.ms/vs/16/release/vc_redist.x64.exe 缺少的C 运行库，重启电脑。

问题4.在安装opencv_python_headless进行依赖模块安装时报ERROR: No matching distribution found for torchvision>=0.5错误

错误信息:

代码语言：javascript复制

Using cached https://mirrors.aliyun.com/pypi/packages/a4/0a/39b102047bcf3b1a58ee1cc83a9269b2a2c4c1ab3062a65f5292d8df6594/opencv_python_headless-4.5.4.60-cp37-cp37m-win32.whl (25.8 MB)
ERROR: Could not find a version that satisfies the requirement torchvision>=0.5 (from easyocr) (from versions: 0.1.6, 0.1.7, 0.1.8, 0.1.9, 0.2.0, 0.2.1, 0.2.2, 0.2.2.post2, 0.2.2.post3)
ERROR: No matching distribution found for torchvision>=0.5

解决办法: 如果你的 python 版本为3.7.x,那么你只能安装 torch 1.5 和 torchvision0.6。

问题5.在执行easyocr文字识别时出现Downloading detection model, please wait. This may take several minutes depending upon your network connection.提示

问题描述: 在首次使用时会自动下载EasyOCR模块所需的模型, 而由于国内网络环境，通常会报出超时错误，此时我们提前从官网下载其所需的数据模型，并安装在指定目录中。

模型下载: https://www.jaided.ai/easyocr/modelhub/

代码语言：javascript复制

# 主要下载以下模型(如有其它需要请自行选择下载)
english_g2 : https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/english_g2.zip
zh_sim_g2 : https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/zh_sim_g2.zip
CRAFT : https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/craft_mlt_25k.zip

# 模型安装位置
# windows
C:UsersWeiyiGeek.EasyOCRmodel

# Linux
/home/weiyigeek/.EasyOCRmodel

https pytorch python 通用文字识别

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