test_batch.py

# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import time
from PIL import Image
import random
import os
from sample import sample_conf


class TestBatch(object):

    def __init__(self, img_path, char_set, model_save_dir, total):
        # 模型路径
        self.model_save_dir = model_save_dir
        # 打乱文件顺序
        self.img_path = img_path
        self.img_list = os.listdir(img_path)
        random.seed(time.time())
        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()
        image_height, image_width, channel = captcha_array.shape

        # 初始化变量
        # 图片尺寸
        self.image_height = image_height
        self.image_width = image_width
        self.channel = channel
        # 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
        self.total = total

        # 相关信息打印
        print("-->图片尺寸: {} X {}".format(image_height, image_width))
        print("-->验证码长度: {}".format(self.max_captcha))
        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
        right = 0

        saver = tf.train.Saver()
        with tf.Session() as sess:
            saver.restore(sess, self.model_save_dir)
            s = time.time()
            for i in range(total):
                # test_text, test_image = gen_special_num_image(i)
                test_text, test_image = self.gen_captcha_text_image()  # 随机
                test_image = self.convert2gray(test_image)
                test_image = test_image.flatten() / 255

                predict = tf.argmax(tf.reshape(y_predict, [-1, self.max_captcha, self.char_set_len]), 2)
                text_list = sess.run(predict, feed_dict={self.X: [test_image], self.keep_prob: 1.})
                predict_text = text_list[0].tolist()
                p_text = ""
                for p in predict_text:
                    p_text += str(self.char_set[p])
                print("origin: {} predict: {}".format(test_text, p_text))
                if test_text == p_text:
                    right += 1
                else:
                    pass
            e = time.time()
        rate = str(right/total) + "%"
        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))


def main():
    test_image_dir = sample_conf["test_image_dir"]
    model_save_dir = sample_conf["model_save_dir"]
    char_set = sample_conf["char_set"]
    total = 100
    tb = TestBatch(test_image_dir, char_set, model_save_dir, total)
    tb.test_batch()


if __name__ == '__main__':
    main()