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python基础教程python opencv pytesseract 验证码识别的实现

2020-11-08 11:42:19 python秋枫

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://pypi.douban.com/simple --trusted-host pypi.douban.com
pip install pytesseract -i http://pypi.douban.com/simple --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。

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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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本文为[python秋枫]所创,转载请带上原文链接,感谢
https://www.cnblogs.com/chenyunhui1994/p/13943786.html