#!/usr/bin/env python # -*- encoding: utf-8 -*- ''' @Contact : liuyuqi.gov@msn.cn @Time : 2023/07/11 18:52:28 @License : Copyright © 2017-2022 liuyuqi. All Rights Reserved. @Desc : 验证码识别 ''' import sys import cv2 import numpy as np from keras import models import pretreatment from mlearn_for_image import preprocess_input def get_text(img, offset=0): text = pretreatment.get_text(img, offset) text = cv2.cvtColor(text, cv2.COLOR_BGR2GRAY) text = text / 255.0 h, w = text.shape text.shape = (1, h, w, 1) return text def main(fn): # 读取并预处理验证码 img = cv2.imread(fn) text = get_text(img) imgs = np.array(list(pretreatment._get_imgs(img))) imgs = preprocess_input(imgs) # 识别文字 model = models.load_model('model.v2.0.h5') label = model.predict(text) label = label.argmax() texts = ['打字机', '调色板', '跑步机', '毛线', '老虎', '安全帽', '沙包', '盘子', '本子', '药片', '双面胶', '龙舟', '红酒', '拖把', '卷尺', '海苔', '红豆', '黑板', '热水袋', '烛台', '钟表', '路灯', '沙拉', '海报', '公交卡', '樱桃', '创可贴', '牌坊', '苍蝇拍', '高压锅', '电线', '网球拍', '海鸥', '风铃', '订书机', '冰箱', '话梅', '排风机', '锅铲', '绿豆', '航母', '电子秤', '红枣', '金字塔', '鞭炮', '菠萝', '开瓶器', '电饭煲', '仪表盘', '棉棒', '篮球', '狮子', '蚂蚁', '蜡烛', '茶盅', '印章', '茶几', '啤酒', '档案袋', '挂钟', '刺绣', '铃铛', '护腕', '手掌印', '锦旗', '文具盒', '辣椒酱', '耳塞', '中国结', '蜥蜴', '剪纸', '漏斗', '锣', '蒸笼', '珊瑚', '雨靴', '薯条', '蜜蜂', '日历', '口哨'] text = texts[label] print(text) # 获取下一个词 # 根据第一个词的长度来定位第二个词的位置 if len(text) == 1: offset = 27 elif len(text) == 2: offset = 47 else: offset = 60 text = get_text(img, offset=offset) if text.mean() < 0.95: label = model.predict(text) label = label.argmax() text = texts[label] print(text) # 加载图片分类器 model = models.load_model('12306.image.model.h5') labels = model.predict(imgs) labels = labels.argmax(axis=1) for pos, label in enumerate(labels): print(pos // 4, pos % 4, texts[label]) if __name__ == '__main__': main(sys.argv[1]) # 运行方式 python3 main.py # 训练好的模型地址奉上: # 链接: https://pan.baidu.com/s/1-Q-084F5g_ga1LXdBto-6w 提取码: rnrf