dev init commit and rebase recognoze object code

network.py

@@ -0,0 +1,111 @@
+import tensorflow as tf
+import numpy as np
+import os
+from PIL import Image
+
+
+class CNN(object):
+    def __init__(self, image_height, image_width, max_captcha, char_set, model_save_dir):
+        # 初始值
+        self.image_height = image_height
+        self.image_width = image_width
+        self.max_captcha = max_captcha
+        self.char_set = char_set
+        self.char_set_len = len(char_set)
+        self.model_save_dir = model_save_dir  # 模型路径
+        self.w_alpha = 0.01
+        self.b_alpha = 0.1
+        # tf初始化占位符
+        self.X = tf.placeholder(tf.float32, [None, self.image_height * self.image_width])  # 特征向量
+        self.Y = tf.placeholder(tf.float32, [None, self.max_captcha * self.char_set_len])  # 标签
+        self.keep_prob = tf.placeholder(tf.float32)  # dropout值
+
+    @staticmethod
+    def gen_captcha_text_image(img_path, img_name):
+        """
+        返回一个验证码的array形式和对应的字符串标签
+        :return:tuple (str, numpy.array)
+        """
+        # 标签
+        label = img_name.split("_")[0]
+        # 文件
+        img_file = os.path.join(img_path, img_name)
+        captcha_image = Image.open(img_file)
+        captcha_array = np.array(captcha_image)  # 向量化
+        return label, captcha_array
+
+    @staticmethod
+    def convert2gray(img):
+        """
+        图片转为灰度图，如果是3通道图则计算，单通道图则直接返回
+        :param img:
+        :return:
+        """
+        if len(img.shape) > 2:
+            r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
+            gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
+            return gray
+        else:
+            return img
+
+    def text2vec(self, text):
+        """
+        转标签为oneHot编码
+        :param text: str
+        :return: numpy.array
+        """
+        text_len = len(text)
+        if text_len > self.max_captcha:
+            raise ValueError('验证码最长{}个字符'.format(self.max_captcha))
+
+        vector = np.zeros(self.max_captcha * self.char_set_len)
+
+        for i, ch in enumerate(text):
+            idx = i * self.char_set_len + self.char_set.index(ch)
+            vector[idx] = 1
+        return vector
+
+    def model(self):
+        x = tf.reshape(self.X, shape=[-1, self.image_height, self.image_width, 1])
+        print(">>> input x: {}".format(x))
+
+        # 卷积层1
+        wc1 = tf.get_variable(name='wc1', shape=[3, 3, 1, 32], dtype=tf.float32,
+                              initializer=tf.contrib.layers.xavier_initializer())
+        bc1 = tf.Variable(self.b_alpha * tf.random_normal([32]))
+        conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, wc1, strides=[1, 1, 1, 1], padding='SAME'), bc1))
+        conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+        conv1 = tf.nn.dropout(conv1, self.keep_prob)
+
+        # 卷积层2
+        wc2 = tf.get_variable(name='wc2', shape=[3, 3, 32, 64], dtype=tf.float32,
+                              initializer=tf.contrib.layers.xavier_initializer())
+        bc2 = tf.Variable(self.b_alpha * tf.random_normal([64]))
+        conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, wc2, strides=[1, 1, 1, 1], padding='SAME'), bc2))
+        conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+        conv2 = tf.nn.dropout(conv2, self.keep_prob)
+
+        # 卷积层3
+        wc3 = tf.get_variable(name='wc3', shape=[3, 3, 64, 128], dtype=tf.float32,
+                              initializer=tf.contrib.layers.xavier_initializer())
+        bc3 = tf.Variable(self.b_alpha * tf.random_normal([128]))
+        conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, wc3, strides=[1, 1, 1, 1], padding='SAME'), bc3))
+        conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+        conv3 = tf.nn.dropout(conv3, self.keep_prob)
+        print(">>> convolution 3: ", conv3.shape)
+        next_shape = conv3.shape[1] * conv3.shape[2] * conv3.shape[3]
+
+        # 全连接层1
+        wd1 = tf.get_variable(name='wd1', shape=[next_shape, 1024], dtype=tf.float32,
+                              initializer=tf.contrib.layers.xavier_initializer())
+        bd1 = tf.Variable(self.b_alpha * tf.random_normal([1024]))
+        dense = tf.reshape(conv3, [-1, wd1.get_shape().as_list()[0]])
+        dense = tf.nn.relu(tf.add(tf.matmul(dense, wd1), bd1))
+        dense = tf.nn.dropout(dense, self.keep_prob)
+
+        # 全连接层2
+        wout = tf.get_variable('name', shape=[1024, self.max_captcha * self.char_set_len], dtype=tf.float32,
+                               initializer=tf.contrib.layers.xavier_initializer())
+        bout = tf.Variable(self.b_alpha * tf.random_normal([self.max_captcha * self.char_set_len]))
+        y_predict = tf.add(tf.matmul(dense, wout), bout)
+        return y_predict

recognition_object.py

@@ -11,18 +11,13 @@
 import numpy as np
 from PIL import Image
 from sample import sample_conf
+from network import CNN
 
 
-class Recognizer(object):
+class Recognizer(CNN):
     def __init__(self, image_height, image_width, max_captcha, char_set, model_save_dir):
-        self.w_alpha = 0.01
-        self.b_alpha = 0.1
-        self.image_height = image_height
-        self.image_width = image_width
-        self.max_captcha = max_captcha
-        self.char_set = char_set
-        self.char_set_len = len(self.char_set)
-        self.model_save_dir = model_save_dir
+        # 初始化变量
+        super(Recognizer, self).__init__(image_height, image_width, max_captcha, char_set, model_save_dir)
 
         # 新建图和会话
         self.g = tf.Graph()
@@ -45,82 +40,6 @@ def __init__(self, image_height, image_width, max_captcha, char_set, model_save_
     #     self.sess.close()
     #     print("session close")
 
-    @staticmethod
-    def convert2gray(img):
-        """
-        图片转为灰度图，如果是3通道图则计算，单通道图则直接返回
-        :param img:
-        :return:
-        """
-        if len(img.shape) > 2:
-            r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
-            gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
-            return gray
-        else:
-            return img
-
-    def text2vec(self, text):
-        """
-        转标签为oneHot编码
-        :param text: str
-        :return: numpy.array
-        """
-        text_len = len(text)
-        if text_len > self.max_captcha:
-            raise ValueError('验证码最长{}个字符'.format(self.max_captcha))
-
-        vector = np.zeros(self.max_captcha * self.char_set_len)
-
-        for i, ch in enumerate(text):
-            idx = i * self.char_set_len + self.char_set.index(ch)
-            vector[idx] = 1
-        return vector
-
-    def model(self):
-        x = tf.reshape(self.X, shape=[-1, self.image_height, self.image_width, 1])
-        print(">>> input x: {}".format(x))
-
-        # 卷积层1
-        wc1 = tf.get_variable(name='wc1', shape=[3, 3, 1, 32], dtype=tf.float32,
-                              initializer=tf.contrib.layers.xavier_initializer())
-        bc1 = tf.Variable(self.b_alpha * tf.random_normal([32]))
-        conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, wc1, strides=[1, 1, 1, 1], padding='SAME'), bc1))
-        conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
-        conv1 = tf.nn.dropout(conv1, self.keep_prob)
-
-        # 卷积层2
-        wc2 = tf.get_variable(name='wc2', shape=[3, 3, 32, 64], dtype=tf.float32,
-                              initializer=tf.contrib.layers.xavier_initializer())
-        bc2 = tf.Variable(self.b_alpha * tf.random_normal([64]))
-        conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, wc2, strides=[1, 1, 1, 1], padding='SAME'), bc2))
-        conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
-        conv2 = tf.nn.dropout(conv2, self.keep_prob)
-
-        # 卷积层3
-        wc3 = tf.get_variable(name='wc3', shape=[3, 3, 64, 128], dtype=tf.float32,
-                              initializer=tf.contrib.layers.xavier_initializer())
-        bc3 = tf.Variable(self.b_alpha * tf.random_normal([128]))
-        conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, wc3, strides=[1, 1, 1, 1], padding='SAME'), bc3))
-        conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
-        conv3 = tf.nn.dropout(conv3, self.keep_prob)
-        print(">>> convolution 3: ", conv3.shape)
-        next_shape = conv3.shape[1] * conv3.shape[2] * conv3.shape[3]
-
-        # 全连接层1
-        wd1 = tf.get_variable(name='wd1', shape=[next_shape, 1024], dtype=tf.float32,
-                              initializer=tf.contrib.layers.xavier_initializer())
-        bd1 = tf.Variable(self.b_alpha * tf.random_normal([1024]))
-        dense = tf.reshape(conv3, [-1, wd1.get_shape().as_list()[0]])
-        dense = tf.nn.relu(tf.add(tf.matmul(dense, wd1), bd1))
-        dense = tf.nn.dropout(dense, self.keep_prob)
-
-        # 全连接层2
-        wout = tf.get_variable('name', shape=[1024, self.max_captcha * self.char_set_len], dtype=tf.float32,
-                               initializer=tf.contrib.layers.xavier_initializer())
-        bout = tf.Variable(self.b_alpha * tf.random_normal([self.max_captcha * self.char_set_len]))
-        y_predict = tf.add(tf.matmul(dense, wout), bout)
-        return y_predict
-
     def rec_image(self, img):
         # 读取图片
         img_array = np.array(img)

requirements.txt

@@ -0,0 +1,7 @@
+tensorflow==1.7.0
+flask==1.0.2
+requests==2.19.1
+Pillow==4.3.0
+matplotlib==2.1.0
+easydict==1.8
+numpy==1.16.2

test_batch.py

@@ -6,14 +6,14 @@
 import random
 import os
 from sample import sample_conf
+from network import CNN
 
 
 class TestError(Exception):
     pass
 
 
-class TestBatch(object):
-
+class TestBatch(CNN):
     def __init__(self, img_path, char_set, model_save_dir, total):
         # 模型路径
         self.model_save_dir = model_save_dir
@@ -24,7 +24,7 @@ def __init__(self, img_path, char_set, model_save_dir, total):
         random.shuffle(self.img_list)
 
         # 获得图片宽高和字符长度基本信息
-        label, captcha_array = self.gen_captcha_text_image()
+        label, captcha_array = self.gen_captcha_text_image(self.img_path, self.img_list[0])
 
         captcha_shape = captcha_array.shape
         captcha_shape_len = len(captcha_shape)
@@ -37,15 +37,7 @@ def __init__(self, img_path, char_set, model_save_dir, total):
             raise TestError("图片转换为矩阵时出错，请检查图片格式")
 
         # 初始化变量
-        # 图片尺寸
-        self.image_height = image_height
-        self.image_width = image_width
-        # 验证码长度（位数）
-        self.max_captcha = len(label)
-        # 验证码字符类别
-        self.char_set = char_set
-        self.char_set_len = len(char_set)
-        # 测试个数
+        super(TestBatch, self).__init__(image_height, image_width, len(label), char_set, model_save_dir)
         self.total = total
 
         # 相关信息打印
@@ -54,104 +46,6 @@ def __init__(self, img_path, char_set, model_save_dir, total):
         print("-->验证码共{}类 {}".format(self.char_set_len, char_set))
         print("-->使用测试集为 {}".format(img_path))
 
-        # tf初始化占位符
-        self.X = tf.placeholder(tf.float32, [None, image_height * image_width])  # 特征向量
-        self.Y = tf.placeholder(tf.float32, [None, self.max_captcha * self.char_set_len])  # 标签
-        self.keep_prob = tf.placeholder(tf.float32)  # dropout值
-        self.w_alpha = 0.01
-        self.b_alpha = 0.1
-
-    def gen_captcha_text_image(self):
-        """
-        返回一个验证码的array形式和对应的字符串标签
-        :return:tuple (str, numpy.array)
-        """
-        img_name = random.choice(self.img_list)
-        # 标签
-        label = img_name.split("_")[0]
-        # 文件
-        img_file = os.path.join(self.img_path, img_name)
-        captcha_image = Image.open(img_file)
-        captcha_array = np.array(captcha_image)  # 向量化
-
-        return label, captcha_array
-
-    @staticmethod
-    def convert2gray(img):
-        """
-        图片转为灰度图，如果是3通道图则计算，单通道图则直接返回
-        :param img:
-        :return:
-        """
-        if len(img.shape) > 2:
-            r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
-            gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
-            return gray
-        else:
-            return img
-
-    def text2vec(self, text):
-        """
-        转标签为oneHot编码
-        :param text: str
-        :return: numpy.array
-        """
-        text_len = len(text)
-        if text_len > self.max_captcha:
-            raise ValueError('验证码最长{}个字符'.format(self.max_captcha))
-
-        vector = np.zeros(self.max_captcha * self.char_set_len)
-
-        for i, ch in enumerate(text):
-            idx = i * self.char_set_len + self.char_set.index(ch)
-            vector[idx] = 1
-        return vector
-
-    def model(self):
-        x = tf.reshape(self.X, shape=[-1, self.image_height, self.image_width, 1])
-        print(">>> input x: {}".format(x))
-
-        # 卷积层1
-        wc1 = tf.get_variable(name='wc1', shape=[3, 3, 1, 32], dtype=tf.float32,
-                              initializer=tf.contrib.layers.xavier_initializer())
-        bc1 = tf.Variable(self.b_alpha * tf.random_normal([32]))
-        conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, wc1, strides=[1, 1, 1, 1], padding='SAME'), bc1))
-        conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
-        conv1 = tf.nn.dropout(conv1, self.keep_prob)
-
-        # 卷积层2
-        wc2 = tf.get_variable(name='wc2', shape=[3, 3, 32, 64], dtype=tf.float32,
-                              initializer=tf.contrib.layers.xavier_initializer())
-        bc2 = tf.Variable(self.b_alpha * tf.random_normal([64]))
-        conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, wc2, strides=[1, 1, 1, 1], padding='SAME'), bc2))
-        conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
-        conv2 = tf.nn.dropout(conv2, self.keep_prob)
-
-        # 卷积层3
-        wc3 = tf.get_variable(name='wc3', shape=[3, 3, 64, 128], dtype=tf.float32,
-                              initializer=tf.contrib.layers.xavier_initializer())
-        bc3 = tf.Variable(self.b_alpha * tf.random_normal([128]))
-        conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, wc3, strides=[1, 1, 1, 1], padding='SAME'), bc3))
-        conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
-        conv3 = tf.nn.dropout(conv3, self.keep_prob)
-        print(">>> convolution 3: ", conv3.shape)
-        next_shape = conv3.shape[1]*conv3.shape[2]*conv3.shape[3]
-
-        # 全连接层1
-        wd1 = tf.get_variable(name='wd1', shape=[next_shape, 1024], dtype=tf.float32,
-                              initializer=tf.contrib.layers.xavier_initializer())
-        bd1 = tf.Variable(self.b_alpha * tf.random_normal([1024]))
-        dense = tf.reshape(conv3, [-1, wd1.get_shape().as_list()[0]])
-        dense = tf.nn.relu(tf.add(tf.matmul(dense, wd1), bd1))
-        dense = tf.nn.dropout(dense, self.keep_prob)
-
-        # 全连接层2
-        wout = tf.get_variable('name', shape=[1024, self.max_captcha * self.char_set_len], dtype=tf.float32,
-                               initializer=tf.contrib.layers.xavier_initializer())
-        bout = tf.Variable(self.b_alpha * tf.random_normal([self.max_captcha * self.char_set_len]))
-        y_predict = tf.add(tf.matmul(dense, wout), bout)
-        return y_predict
-
     def test_batch(self):
         y_predict = self.model()
         total = self.total
@@ -179,7 +73,7 @@ def test_batch(self):
                 else:
                     pass
             e = time.time()
-        rate = str(right/total) + "%"
+        rate = str(right/total * 100) + "%"
         print("测试结果： {}/{}".format(right, total))
         print("{}个样本识别耗时{}秒，准确率{}".format(total, e-s, rate))