# -*- coding:utf-8 -*-
import io
import json
import os
import time

import numpy as np

import tensorflow as tf
from PIL import Image
import base64

model_path = "model_quantized.tflite" #模型文件地址
chars = '23456789abcdefghjkmpqrstuvwxy' #验证码字符，顺序要与config.json里的一致

# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()

# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

#将验证码数据转换成模型输入格式
def img2input(img, width, height):
    tmpe_array = []
    for i in range(height):
        for j in range(width):
            pixel = img.getpixel((j, i))
            tmpe_array.append((0.3*pixel[0]+0.6*pixel[1]+0.1*pixel[2])/255)
    tmpe_array = np.array(tmpe_array).astype('float32')
    input_array = np.expand_dims(tmpe_array, axis=0)
    return input_array

#识别验证码
def predict(base64Image):
    captcha_image = Image.open(io.BytesIO(base64.b64decode(base64Image)))
    image_np_expanded = img2input(captcha_image, 100, 50)
    interpreter.set_tensor(input_details[0]['index'], image_np_expanded)
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_details[0]['index'])
    codes = ''
    for i in output_data[0]:
        codes += chars[i]
    return codes


# api网关响应集成
def apiReply(reply, txt=False, content_type='application/json', code=200):
    return {
        "isBase64Encoded": False,
        "statusCode": code,
        "headers": {'Content-Type': content_type},
        "body": json.dumps(reply, ensure_ascii=False) if not txt else str(reply)
    }

#云函数入口
def main_handler(event, context):
    return apiReply(
        {
            "ok": False if not 'base64Image' in event.keys() else True,
            "message": "请求参数无效" if not 'base64Image' in event.keys() else predict(event['queryString']['base64Image'])
        }
    )