优化model，改为tf加载

UnitTest/TestAll.py

@@ -1,4 +1,6 @@
 # coding=utf-8
+import base64
+import threading
 import unittest
 from collections import OrderedDict
 
@@ -63,6 +65,19 @@ class testAll(unittest.TestCase):
             ua = UserAgent(verify_ssl=False)
             print(ua.random)
 
+    def testVerfyImage(self):
+        """
+        测试模型加载识别
+        :return:
+        """
+        from verify.localVerifyCode import Verify
+        v = Verify()
+        with open('../tkcode.png', 'rb') as f:
+            base64Image = base64.b64encode(f.read())
+            for i in range(5):
+                t = threading.Thread(target=v.verify, args=(base64Image,))
+                t.start()
+
 
 if __name__ == '__main__':
     unittest.main()

inter/GetRandCode.py

@@ -3,7 +3,9 @@ from PIL import Image
 
 from config.urlConf import urls
 from myUrllib.httpUtils import HTTPClient
-from verify.localVerifyCode import verify
+from verify.localVerifyCode import Verify
+
+v = Verify()
 
 
 def getRandCode(is_auto_code, auto_code_type, result):
@@ -17,7 +19,7 @@ def getRandCode(is_auto_code, auto_code_type, result):
                 print(u"打码兔已关闭, 如需使用自动识别，请使用如果平台 auto_code_type == 2")
                 return
             elif auto_code_type == 2:
-                Result = verify(result)
+                Result = v.verify(result)
                 return codexy(Ofset=Result, is_raw_input=False)
             elif auto_code_type == 3:
                 print("您已设置使用云打码，但是服务器资源有限，请尽快改为本地打码")

verify/localVerifyCode.py

@@ -1,19 +1,27 @@
 # coding: utf-8
 import TickerConfig
+
 if TickerConfig.AUTO_CODE_TYPE == 2:
     import base64
     import os
     import cv2
     import numpy as np
     from keras import models, backend
+    import tensorflow as tf
     from verify import pretreatment
     from verify.mlearn_for_image import preprocess_input
 
+    graph = tf.get_default_graph()
+
 PATH = lambda p: os.path.abspath(
     os.path.join(os.path.dirname(__file__), p)
 )
 
 
+TEXT_MODEL = ""
+IMG_MODEL = ""
+
+
 def get_text(img, offset=0):
     text = pretreatment.get_text(img, offset)
     text = cv2.cvtColor(text, cv2.COLOR_BGR2GRAY)
@@ -34,48 +42,74 @@ def base64_to_image(base64_code):
     return img
 
 
-def verify(fn):
+
-    backend.clear_session()
+
-    verify_titles = ['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']
+class Verify:
-    # 读取并预处理验证码
+    def __init__(self):
-    img = base64_to_image(fn)
+        self.textModel = ""
-    text = get_text(img)
+        self.imgModel = ""
-    imgs = np.array(list(pretreatment._get_imgs(img)))
+        self.loadImgModel()
-    imgs = preprocess_input(imgs)
+        self.loadTextModel()
-    text_list = []
+
-    # 识别文字
+    def loadTextModel(self):
-    model = models.load_model(PATH('../model.v2.0.h5'))
+        if not self.textModel:
-    label = model.predict(text)
+            self.textModel = models.load_model(PATH('../model.v2.0.h5'))
-    label = label.argmax()
+
-    text = verify_titles[label]
+    def loadImgModel(self):
-    text_list.append(text)
+        if not self.imgModel:
-    # 获取下一个词
+            self.imgModel = models.load_model(PATH('../12306.image.model.h5'))
-    # 根据第一个词的长度来定位第二个词的位置
+
-    if len(text) == 1:
+    def verify(self, fn):
-        offset = 27
+        verify_titles = ['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',
-    elif len(text) == 2:
+                         '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',
-        offset = 47
+                         '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',
-    else:
+                         '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',
-        offset = 60
+                         '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',
-    text = get_text(img, offset=offset)
+                         '蜜蜂', '日历', '口哨']
-    if text.mean() < 0.95:
+        # 读取并预处理验证码
-        label = model.predict(text)
+        img = base64_to_image(fn)
+        text = get_text(img)
+        imgs = np.array(list(pretreatment._get_imgs(img)))
+        imgs = preprocess_input(imgs)
+        text_list = []
+        # 识别文字
+        self.loadTextModel()
+        global graph
+        with graph.as_default():
+            label = self.textModel.predict(text)
         label = label.argmax()
         text = verify_titles[label]
         text_list.append(text)
-    print("题目为{}".format(text_list))
+        # 获取下一个词
-    # 加载图片分类器
+        # 根据第一个词的长度来定位第二个词的位置
-    model = models.load_model(PATH('../12306.image.model.h5'))
+        if len(text) == 1:
-    labels = model.predict(imgs)
+            offset = 27
-    labels = labels.argmax(axis=1)
+        elif len(text) == 2:
-    results = []
+            offset = 47
-    for pos, label in enumerate(labels):
+        else:
-        l = verify_titles[label]
+            offset = 60
-        print(pos+1, l)
+        text = get_text(img, offset=offset)
-        if l in text_list:
+        if text.mean() < 0.95:
-            results.append(str(pos+1))
+            with graph.as_default():
-    return results
+                label = self.textModel.predict(text)
+            label = label.argmax()
+            text = verify_titles[label]
+            text_list.append(text)
+        print("题目为{}".format(text_list))
+        # 加载图片分类器
+        self.loadImgModel()
+        with graph.as_default():
+            labels = self.imgModel.predict(imgs)
+        labels = labels.argmax(axis=1)
+        results = []
+        for pos, label in enumerate(labels):
+            l = verify_titles[label]
+            print(pos + 1, l)
+            if l in text_list:
+                results.append(str(pos + 1))
+        return results
 
 
 if __name__ == '__main__':
-    verify("verify-img1.jpeg")
+    pass
+    # verify("verify-img1.jpeg")