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.dockerignore

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+venv

.gitignore

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+.idea
+.DS_Store
+venv
+__pycache__
+config.toml
+docker-compose.yml

Dockerfile

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+FROM python:3.6.6-slim
+
+MAINTAINER <pjialin admin@pjialin.com>
+ENV TZ Asia/Shanghai
+
+WORKDIR /code
+
+COPY requirements.txt .
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends gcc python-dev libglib2.0-0 libsm6 libxext6 libxrender-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && pip install --no-cache-dir -r requirements.txt \
+    && apt-get purge -y --auto-remove gcc python-dev
+
+COPY . .
+COPY config.toml.example config.toml
+
+EXPOSE 8000
+
+CMD [ "python", "main.py"]
+

LICENSE

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+
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config.py

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+import logging
+import os
+
+
+class Config:
+    class AppEnvType:
+        DEV = 'dev'
+        PRODUCTION = 'production'
+        TEST = 'test'
+
+    APP_NAME = '12306-ocr'
+    APP_ENV = AppEnvType.PRODUCTION
+    LOADED = False
+
+    PROJECT_DIR = os.path.abspath(__file__ + '/../') + '/'
+    CONFIG_FILE = PROJECT_DIR + 'config.toml'
+    IMAGE_MODEL_FILE = PROJECT_DIR + 'ocr/image.model.h5'
+    TEXT_MODEL_FILE = PROJECT_DIR + 'ocr/text.model.h5'
+    TEXTS_FILE = PROJECT_DIR + 'ocr/texts.txt'
+
+    WEB = {
+        'host': '0.0.0.0',
+        'port': 8000
+    }
+    OCR = {
+    }
+
+    @classmethod
+    def load(cls):
+        """
+        Load configs from toml file
+        :return:
+        """
+        import toml
+        configs = toml.load(cls.CONFIG_FILE)
+
+        web = configs.get('web', {})
+        cls.WEB.update(web)
+
+        ocr = configs.get('ocr', {})
+        cls.OCR.update(ocr)
+
+
+if not Config.LOADED:
+    Config.load()
+
+# Logger
+def set_up_logger():
+    logger = logging.getLogger(Config.APP_NAME)
+
+    logger.setLevel(logging.DEBUG if Config.APP_ENV == Config.AppEnvType.DEV else logging.INFO)
+    handler = logging.StreamHandler()
+    formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s')
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+    return logger
+
+
+Logger = set_up_logger()

config.toml.example

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+[web]
+host = "0.0.0.0"
+port = 8000

main.py

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+from web import Web
+
+
+def main():
+    Web().run()
+
+
+if __name__ == '__main__':
+    main()

ocr/image.model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea2ea0aaa152009af0e8bed2aa1ce26a1f5c90d174b63b068b95b205e93cbcae
+size 912208

ocr/ml_predict.py

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+# coding: utf-8
+import cv2
+import tensorflow as tf
+import numpy as np
+from keras import models
+
+from config import Logger, Config
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+
+class ShareInstance():
+    __session = None
+
+    @classmethod
+    def share(cls, **kwargs):
+        if not cls.__session:
+            cls.__session = cls(**kwargs)
+        return cls.__session
+
+
+class Predict(ShareInstance):
+    def __init__(self):
+        # 识别文字
+        self.model = models.load_model(Config.TEXT_MODEL_FILE, compile=False)
+
+        with open(Config.TEXTS_FILE, encoding='utf-8') as f:
+            self.texts = [text.rstrip('\n') for text in f]
+
+        # 加载图片分类器
+        self.code_model = models.load_model(Config.IMAGE_MODEL_FILE, compile=False)
+
+    def get_text(self, img, offset=0):
+        text = img[3:22, 120 + offset:177 + 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 get_coordinate(self, img_str):
+        # 储存最终坐标结果
+        result = ''
+
+        try:
+            # 读取并预处理验证码
+            img = cv2.imdecode(np.fromstring(img_str, np.uint8), cv2.IMREAD_COLOR)
+            text = self.get_text(img)
+            images = np.array(list(self._get_imgs(img)))
+            images = self.preprocess_input(images)
+
+            label = self.model.predict(text)
+            label = label.argmax()
+            text = self.texts[label]
+
+            # list放文字
+            titles = [text]
+
+            position = []
+
+            # 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
+            text2 = self.get_text(img, offset=offset)
+            if text2.mean() < 0.95:
+                label = self.model.predict(text2)
+                label = label.argmax()
+                text2 = self.texts[label]
+                titles.append(text2)
+
+            labels = self.code_model.predict(images)
+            labels = labels.argmax(axis=1)
+
+            for pos, label in enumerate(labels):
+                if self.texts[label] in titles:
+                    position.append(pos + 1)
+
+            # 没有识别到结果
+            if len(position) == 0:
+                return result
+            result = position
+            Logger.info('识别结果: %s' % result)
+        except:
+            pass
+        return result
+
+    def preprocess_input(self, x):
+        x = x.astype('float32')
+        # 我是用cv2来读取的图片，其已经是BGR格式了
+        mean = [103.939, 116.779, 123.68]
+        x -= mean
+        return x
+
+    def _get_imgs(self, img):
+        interval = 5
+        length = 67
+        for x in range(40, img.shape[0] - length, interval + length):
+            for y in range(interval, img.shape[1] - length, interval + length):
+                yield img[x:x + length, y:y + length]
+
+
+if __name__ == '__main__':
+    for i in range(10):
+        with open('test.jpg', 'r') as f:
+            print(Predict.share().get_coordinate(f.buffer.read()))

ocr/text.model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:885a2ac8496d96d852035cd443ec633f59ca8a87bc9f5b57575be2ba3027d444
+size 361824

ocr/texts.txt

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+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

requirements.txt

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+-i http://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com
+absl-py==0.8.0
+aiofiles==0.4.0
+astor==0.8.0
+certifi==2019.6.16
+chardet==3.0.4
+Click==7.0
+Flask==1.1.1
+gast==0.3.1
+google-pasta==0.1.7
+grpcio==1.23.0
+h11==0.8.1
+h2==3.1.1
+h5py==2.10.0
+hpack==3.0.0
+httpcore==0.3.0
+hyperframe==5.2.0
+idna==2.8
+itsdangerous==1.1.0
+Jinja2==2.10.3
+Keras==2.2.4
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+Markdown==3.1.1
+MarkupSafe==1.1.1
+multidict==4.5.2
+numpy==1.16.4
+opencv-python==4.1.1.26
+protobuf==3.9.1
+PyYAML==5.4
+requests==2.22.0
+requests-async==0.5.0
+rfc3986==1.3.2
+scipy==1.3.1
+six==1.12.0
+tensorboard==1.14.0
+tensorflow==1.14.0
+tensorflow-estimator==1.14.0
+termcolor==1.1.0
+toml==0.10.0
+urllib3==1.25.3
+Werkzeug==0.15.6
+wrapt==1.11.2

web.py

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+import base64
+
+from flask import Flask, jsonify, request
+from config import Config
+from ocr.ml_predict import Predict
+
+app = Flask(__name__)
+
+
+class Web:
+    def run(self):
+        app.run(**Config.WEB)
+
+
+@app.route('/check', methods=['POST'], strict_slashes=False)
+def check():
+    img = request.form.get('img')
+    try:
+        img = base64.b64decode(img)
+    except Exception as e:
+        pass
+    if not img:
+        return jsonify({'msg': 'Wrong format. '})
+    result = Predict.share().get_coordinate(img)
+    return jsonify({
+        'msg': 'success',
+        'result': result
+    })
+
+
+if __name__ == '__main__':
+    Web().run()