# -*- coding: utf-8 -*-
import json

import tensorflow as tf
import numpy as np
import time
from PIL import Image
import random
import os
from cnn import CNN


class TestError(Exception):
    pass


class TestBatch(CNN):
    def __init__(self, max_captcha,image_width, image_height,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
        self.image_height = image_height
        self.image_width = image_width
        self.max_captcha = max_captcha

        # 初始化变量
        super(TestBatch, self).__init__(image_height, image_width, max_captcha, char_set, model_save_dir)
        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))

    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]
        img_file = os.path.join(self.img_path, img_name)
        captcha_image = Image.open(img_file)
        captcha_array = self.img2input(captcha_image)  # 图片转输入向量
        return label, captcha_array
    
    def img2input(self,img):
        img = np.array(img)
        test_image = 0.3 * img[:, :, 0] + 0.6 * img[:, :, 1] + 0.1 * img[:, :, 2]
        tmpe_array = test_image.flatten() / 255
        input_array = np.expand_dims(tmpe_array, axis=0)
        return input_array

    def test_batch(self):
        y_predict = self.model()
        total = self.total
        right = 0

        saver = tf.compat.v1.train.Saver()
        with tf.compat.v1.Session() as sess:
            saver.restore(sess, self.model_save_dir)
            s = time.time()
            for i in range(total):
                test_text, test_image = self.gen_captcha_text_image()  # 随机

                self.Y = tf.argmax(input=tf.reshape(y_predict, [-1, self.max_captcha, self.char_set_len]), axis=2)
                text_list = sess.run(self.Y, feed_dict={self.X: test_image})
                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 * 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))

def main():
    with open("config.json", "r") as f:
        sample_conf = json.load(f)

    test_image_dir = sample_conf["test_image_dir"]
    model_save_dir = sample_conf["model_save_dir"]
    image_width = sample_conf['image_width']
    image_height = sample_conf['image_height']
    max_captcha = sample_conf['max_captcha']
    char_set = sample_conf["char_set"]

    total = 100
    tb = TestBatch(max_captcha,image_width, image_height,test_image_dir, char_set, model_save_dir, total)
    tb.test_batch()


if __name__ == '__main__':
    main()