| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768 |
- # coding: utf-8
- 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 <img.jpg>
- # 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
|
