189 8069 5689
关注「WeiyiGeek」点我，点我
10年的资源网站建设经验，针对设计、前端、开发、售后、文案、推广等六对一服务，响应快，48小时及时工作处理。营销型网站建设的优势是能够根据用户设备显示端的尺寸不同，自动调整资源建站的显示方式，使网站能够适用不同显示终端，在浏览器中调整网站的宽度，无论在任何一种浏览器上浏览网站，都能展现优雅布局与设计，从而大程度地提升浏览体验。创新互联公司从事“资源网站设计”,“资源网站推广”以来，每个客户项目都认真落实执行。
设为「特别关注」，每天带你在B站玩转网络安全运维、应用开发、物联网IOT学习！
希望各位看友【关注、点赞、评论、收藏、投币】，助力每一个梦想。
文章目录
0x00 快速了解
0x01 安装部署
0x02 实践案例
0x03 入坑出坑
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)。
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
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
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/cv
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 ）。 ❤️
环境依赖
注意事项:
$ 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 / MiB | 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。
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：
pip install easyocr -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
对于最新的开发版本：
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。
$ 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
# Windows 环境
pip freeze | findstr "easyocr"
easyocr @ file:///E:/%E8%BF%85%E9%9B%B7%E4%B8%8B%E8%BD%BD/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.),
([[477, 55], [499, 55], [499, 75], [477, 75]], '46', 0.),
([[533, 55], [555, 55], [555, 75], [533, 75]], '5G', 0.),
([[354, 76], [474, 76], [474, 104], [354, 104]],
'中国移动四 ',
0.),
([[489, 57], [625, 57], [625, 95], [489, 95]],
'GMl s @',
0.0),
([[693, 55], [801, 55], [801, 95], [693, 95]], 'Q92%', 0.0),
([[864, 60], [950, 60], [950, 92], [864, 92]], '09:03', 0.),
([[884, 158], [938, 158], [938, 214], [884, 214]], '@', 0.),
([[123, 298], [592, 298], [592, 361], [123, 361]],
'通信行程卡提供服务>',
0.),
([[115, 429], [384, 429], [384, 497], [115, 497]],
'通信行程卡',
0.),
([[153, 596], [848, 596], [848, 704], [153, 704]],
'通信大数据行程卡',
0.),
([[303, 723], [699, 723], [699, 785], [303, 785]],
'疫情防控;人人有责',
0.),
([[347, 844], [653, 844], [653, 892], [347, 892]],
'请收下绿色行程卡',
0.),
([[248, 950], [754, 950], [754, 1004], [248, 1004]],
'157****2966的动态行程卡',
0.),
([[173, 1045], [345, 1045], [345, 1105], [173, 1105]],
'更新于:',
0.),
([[360, 1049], [829, 1049], [829, 1100], [360, 1100]],
'2022.05.2509:03:56',
0.),
([[110, 1670], [633, 1670], [633, 1732], [110, 1732]],
'您于前14夭内到达或途经:',
0.),
([[648, 1674], [788, 1674], [788, 1730], [648, 1730]],
'重庆市',
0.),
([[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.),
([[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.),
([[383, 1891], [607, 1891], [607, 1933], [383, 1933]],
'本服务联合提供',
0.),
([[119, 1966], [337, 1966], [337, 2006], [119, 2006]],
'CAICT 中国信通院',
0.),
([[435, 1963], [533, 1963], [533, 1999], [435, 1999]],
'中国电信',
0.0),
([[624, 1966], [702, 1966], [702, 1990], [624, 1990]],
'中国移动',
0.),
([[812, 1966], [892, 1966], [892, 1990], [812, 1990]],
'中国联通',
0.),
([[441, 1993], [531, 1993], [531, 2005], [441, 2005]],
'CINA TUUUC0',
0.0),
([[629, 1987], [701, 1987], [701, 2003], [629, 2003]],
'ChnaMobile',
0.),
([[815, 1989], [893, 1989], [893, 2003], [815, 2003]],
'Chnoumco',
0.),
([[107, 2077], [281, 2077], [281, 2119], [107, 2119]],
'证通查来了!',
0.),
([[467, 2075], [825, 2075], [825, 2117], [467, 2117]],
'全国移动电话卡"一证通查',
0.),
([[79, 2131], [269, 2131], [269, 2173], [79, 2173]],
'立即点击进入',
0.),
([[510, 2128], [644, 2128], [644, 2172], [510, 2172]],
'防范诈骗',
0.),
([[663, 2129], [793, 2129], [793, 2173], [663, 2173]],
'保护你我',
0.)]
# 设置 --detail=0 输出更简单
>>> result = reader.readtext('00e336dbde464c809ef1f6ea568d4621.png', detail = 0)
使用说明
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/) :
# 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
--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:# 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.
# 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)
边界框(四个点)、检测到的文本和可信度。 ([[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.), # 可信度
# 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 .\0a1e948ed42b435d63c9f0aa268.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
.....
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/ )
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
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。
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]].
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
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。
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:\Study\Project
PS D:\Study\Project> ls
目录: D:\Study\Project
Mode LastWriteTime Length Name
---- ------------- ------ ----
d----- 2022/5/25 15:59 img
-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.
# -*- 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("\033[032m",result_dic,"\033[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 "https://www.weiyigeek.top
"
# 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=/test.png, /tools/ocr?dir=)
"""
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("\033[31m"+ img_path + " -->> " + str(err) + "\033[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("\033[31m"+ img_path + " -->> " + str(err) + "\033[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("\033[31m"+ err + "\033[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。
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=/00e336dbde464c809ef1f6ea568d4621.png地址,将会返回如下JSON字符串。
D:\Study\Project\img\\00e336dbde464c809ef1f6ea568d4621.png
127.0.0.1 - - [01/Jun/2022 16:58:58] "GET /tools/upload/ocr HTTP/1.1" 200 -
{'status': 'succ', 'file': '\\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=地址,将会返回如下图所示结果。
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。
问题1.通过pip install 安装easyocr离线的whl包是报ERROR: No matching distribution found for torch
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
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
$ 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
# 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:\****\lib\site-packages\torch\lib\asmjit.dll" or one of its dependencies.错误
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] Error loading "D:\Program Files (x86)\Python37-32\lib\site-packages\torch\lib\asmjit.dll" or one of its dependencies.
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
Using cached https://mirrors.aliyun.com/pypi/packages/a4/0a/39bbcf3b1a58ee1cc83a9269b2a2c4c1ab3062a65f5292d8df6594/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
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
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。# 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:\Users\WeiyiGeek\.EasyOCR\model
# Linux
/home/weiyigeek/.EasyOCR\model
文章书写不易，如果您觉得这篇文章还不错的，请给这篇专栏 【点个赞、投个币、收个藏、关个注，转个发】(人间五大情)，这将对我的肯定，谢谢！。
本文章来源 我的Blog站点 或 WeiyiGeek 公众账号 以及 我的BiliBili专栏 (
技术交流、友链交换请邮我哟),谢谢支持！(๑′ᴗ‵๑) ❤
欢迎各位志同道合的朋友一起学习交流，如文章有误请留下您宝贵的知识建议，通过邮箱【master#weiyigeek.top】联系我哟！