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python opencv pytesseract 验证码识别的实现

  • 作者: 楼下真是一个人才
  • 来源: 51数据库
  • 2022-04-07

一、环境配置

需要 pillow 和 pytesseract 这两个库,pip install 安装就好了。

install pillow -i http://www.51sjk.com/Upload/Articles/1/0/300/300236_20210728153553550.jpg --trusted-host pypi.douban.com
pip install pytesseract -i http://www.51sjk.com/Upload/Articles/1/0/300/300236_20210728153553550.jpg --trusted-host pypi.douban.com

安装好tesseract-ocr.exe

pytesseract 库的配置:搜索找到pytesseract.py,打开该.py文件,找到 tesseract_cmd,改变它的值为刚才安装 tesseract.exe 的路径。

二、验证码识别

识别验证码,需要先对图像进行预处理,去除会影响识别准确度的线条或噪点,提高识别准确度。

实例1

import cv2 as cv
import pytesseract
from pil import image


def recognize_text(image):
  # 边缘保留滤波 去噪
  dst = cv.pyrmeanshiftfiltering(image, sp=10, sr=150)
  # 灰度图像
  gray = cv.cvtcolor(dst, cv.color_bgr2gray)
  # 二值化
  ret, binary = cv.threshold(gray, 0, 255, cv.thresh_binary_inv | cv.thresh_otsu)
  # 形态学操作  腐蚀 膨胀
  erode = cv.erode(binary, none, iterations=2)
  dilate = cv.dilate(erode, none, iterations=1)
  cv.imshow('dilate', dilate)
  # 逻辑运算 让背景为白色 字体为黑 便于识别
  cv.bitwise_not(dilate, dilate)
  cv.imshow('binary-image', dilate)
  # 识别
  test_message = image.fromarray(dilate)
  text = pytesseract.image_to_string(test_message)
  print(f'识别结果:{text}')


src = cv.imread(r'./test/044.png')
cv.imshow('input image', src)
recognize_text(src)
cv.waitkey(0)
cv.destroyallwindows()

运行效果如下:

识别结果:3n3d

process finished with exit code 0

实例2

import cv2 as cv
import pytesseract
from pil import image


def recognize_text(image):
  # 边缘保留滤波 去噪
  blur =cv.pyrmeanshiftfiltering(image, sp=8, sr=60)
  cv.imshow('dst', blur)
  # 灰度图像
  gray = cv.cvtcolor(blur, cv.color_bgr2gray)
  # 二值化
  ret, binary = cv.threshold(gray, 0, 255, cv.thresh_binary_inv | cv.thresh_otsu)
  print(f'二值化自适应阈值:{ret}')
  cv.imshow('binary', binary)
  # 形态学操作 获取结构元素 开操作
  kernel = cv.getstructuringelement(cv.morph_rect, (3, 2))
  bin1 = cv.morphologyex(binary, cv.morph_open, kernel)
  cv.imshow('bin1', bin1)
  kernel = cv.getstructuringelement(cv.morph_open, (2, 3))
  bin2 = cv.morphologyex(bin1, cv.morph_open, kernel)
  cv.imshow('bin2', bin2)
  # 逻辑运算 让背景为白色 字体为黑 便于识别
  cv.bitwise_not(bin2, bin2)
  cv.imshow('binary-image', bin2)
  # 识别
  test_message = image.fromarray(bin2)
  text = pytesseract.image_to_string(test_message)
  print(f'识别结果:{text}')


src = cv.imread(r'./test/045.png')
cv.imshow('input image', src)
recognize_text(src)
cv.waitkey(0)
cv.destroyallwindows()

运行效果如下:

二值化自适应阈值:181.0
识别结果:8a62n1

process finished with exit code 0

实例3

import cv2 as cv
import pytesseract
from pil import image


def recognize_text(image):
  # 边缘保留滤波 去噪
  blur = cv.pyrmeanshiftfiltering(image, sp=8, sr=60)
  cv.imshow('dst', blur)
  # 灰度图像
  gray = cv.cvtcolor(blur, cv.color_bgr2gray)
  # 二值化 设置阈值 自适应阈值的话 黄色的4会提取不出来
  ret, binary = cv.threshold(gray, 185, 255, cv.thresh_binary_inv)
  print(f'二值化设置的阈值:{ret}')
  cv.imshow('binary', binary)
  # 逻辑运算 让背景为白色 字体为黑 便于识别
  cv.bitwise_not(binary, binary)
  cv.imshow('bg_image', binary)
  # 识别
  test_message = image.fromarray(binary)
  text = pytesseract.image_to_string(test_message)
  print(f'识别结果:{text}')


src = cv.imread(r'./test/045.jpg')
cv.imshow('input image', src)
recognize_text(src)
cv.waitkey(0)
cv.destroyallwindows()

运行效果如下:

二值化设置的阈值:185.0
识别结果:7364

process finished with exit code 0

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