python利用pytesseract 实现本地识别图片文字【3】（多线程）

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import glob
from os import path
import os
import pytesseract
from PIL import Image
from queue import Queue
import threading
import datetime
import cv2

def convertimg(picfile, outdir):
    '''调整图片大小，对于过大的图片进行压缩
    picfile:    图片路径
    outdir：    图片输出路径
    '''
    img = Image.open(picfile)
    width, height = img.size
    while (width * height > 4000000):  # 该数值压缩后的图片大约 两百多k
        width = width // 2
        height = height // 2
    new_img = img.resize((width, height), Image.BILINEAR)
    new_img.save(path.join(outdir, os.path.basename(picfile)))


def baiduOCR(ts_queue):
    while not ts_queue.empty():
        picfile = ts_queue.get()
        filename = path.basename(picfile)
        outfile = 'D:\Study\pythonProject\scrapy\IpProxy\port_zidian.txt'
        img = cv2.imread(picfile, cv2.IMREAD_COLOR)
        print("正在识别图片：\t" + filename)
        message = pytesseract.image_to_string(img,lang = 'eng')
        message = message.replace('', '')
        message = message.replace('\n', '')
        # message = client.basicAccurate(img)   # 通用文字高精度识别，每天 800 次免费
        #print("识别成功！"))
        try:
            filename1 = filename.split('.')[0]
            filename1 = ''.join(filename1)
            with open(outfile, 'a+') as fo:
                fo.writelines('\'' + filename1 + '\'' + ':' + message + ',')
                fo.writelines('\n')
                # fo.writelines("+" * 60 + '\n')
                # fo.writelines("识别图片：\t" + filename + "\n" * 2)
                # fo.writelines("文本内容：\n")
                # # 输出文本内容
                # for text in message.get('words_result'):
                #     fo.writelines(text.get('words') + '\n')
                # fo.writelines('\n' * 2)
            os.remove(filename)
            print("识别成功！")
        except:
            print('识别失败')



        print("文本导出成功！")
        print()
def duqu_tupian(dir):
    ts_queue = Queue(10000)

    outdir = dir
    # if path.exists(outfile):
    #     os.remove(outfile)
    if not path.exists(outdir):
        os.mkdir(outdir)
    print("压缩过大的图片...")
    # 首先对过大的图片进行压缩，以提高识别速度，将压缩的图片保存与临时文件夹中
    try:
        for picfile in glob.glob(r"D:\Study\pythonProject\scrapy\IpProxy\tmp\*"):
            convertimg(picfile, outdir)
        print("图片识别...")
        for picfile in glob.glob("tmp1/*"):
            ts_queue.put(picfile)
            #baiduOCR(picfile, outfile)
            #os.remove(picfile)
        print('图片文本提取结束！文本输出结果位于文件中。' )
        #os.removedirs(outdir)
        return ts_queue
    except:
        print('失败')

if __name__ == "__main__":

    start = datetime.datetime.now().replace(microsecond=0)
    t = 'tmp1'
    s = duqu_tupian(t)
    threads = []
    try:
        for i in range(100):
            t = threading.Thread(target=baiduOCR, name='th-' + str(i), kwargs={'ts_queue': s})
            threads.append(t)
        for t in threads:
            t.start()
        for t in threads:
            t.join()
        end = datetime.datetime.now().replace(microsecond=0)
        print('删除耗时：' + str(end - start))
    except:
        print('识别失败')

实测速度慢，但用了多线程明显提高了速度，但准确度稍低，同样高清图片，90百分识别率。还时不时出现乱码文字，乱空格，这里展现不了，自己实践吧，重点免费的，随便识别，通向100张图片，用时快6分钟了，速度慢了一倍，但是是免费的，挺不错的了。

标签：img,python,queue,pytesseract,print,import,多线程,os,picfile
来源： https://www.cnblogs.com/aotumandaren/p/14128765.html