import os
import sys
sys.path.append('../')

# Import necessary packages
from modules import json_parser
from modules import Client

# Initialise Client object
client = Client(api_key='996c5ec910a35bc0813ba5f12f4ba667ba10af41ecd4c9dd0ff9aa5cbfc0439a',
                project_id='5cd04ee51afd94639a492b8e', user_ID='5ca1c6991afd941dd93a4052',
                project_type='app', source_file_path='handler.py',
                silent=True)

# Make run/train/predict command alias for further use
run = client.run
train = client.train
predict = client.predict

# Make controller alias for further use
controller = client.controller


import os

GPUID='0'  ##调用GPU序号
os.environ["CUDA_VISIBLE_DEVICES"] = GPUID

import torch

from apphelper.image import xy_rotate_box,box_rotate,solve

import model


import time
from PIL import Image


import cv2
import numpy as np

def plot_box(img,boxes):
    blue = (0, 0, 0) #18
    tmp = np.copy(img)
    for box in boxes:
         cv2.rectangle(tmp, (int(box[0]),int(box[1])), (int(box[2]), int(box[3])), blue, 1) #19

    return Image.fromarray(tmp) 

def plot_boxes(img,angle, result,color=(0,0,0)):
    tmp = np.array(img)
    c = color
    h,w = img.shape[:2]
    thick = int((h + w) / 300)
    i = 0
    if angle in [90,270]:
        imgW,imgH = img.shape[:2]

    else:
        imgH,imgW= img.shape[:2]

    for line in result:
        cx =line['cx']
        cy = line['cy']
        degree =line['degree']
        w  = line['w']
        h = line['h']

        x1,y1,x2,y2,x3,y3,x4,y4 = xy_rotate_box(cx, cy, w, h, degree/180*np.pi)

        x1,y1,x2,y2,x3,y3,x4,y4 = box_rotate([x1,y1,x2,y2,x3,y3,x4,y4],angle=(360-angle)%360,imgH=imgH,imgW=imgW)
        cx  =np.mean([x1,x2,x3,x4])
        cy  = np.mean([y1,y2,y3,y4])
        cv2.line(tmp,(int(x1),int(y1)),(int(x2),int(y2)),c,1)
        cv2.line(tmp,(int(x2),int(y2)),(int(x3),int(y3)),c,1)
        cv2.line(tmp,(int(x3),int(y3)),(int(x4),int(y4)),c,1)
        cv2.line(tmp,(int(x4),int(y4)),(int(x1),int(y1)),c,1)
        mess=str(i)
        cv2.putText(tmp, mess, (int(cx), int(cy)),0, 1e-3 * h, c, thick // 2)
        i+=1
    return Image.fromarray(tmp).convert('RGB')


import re
import base64
from io import BytesIO
from keras.preprocessing import image


def image_to_base64(img):

    buffered = BytesIO()
    resize_img = image.array_to_img(img)

    resize_img.save(buffered, format="JPEG")
    img_str = base64.b64encode(buffered.getvalue())
    return img_str.decode("utf-8")


# def base64_to_image(base64_str, grayscale):

#     base64_data = re.sub('^data:image/.+;base64,', '', base64_str)
#     byte_data = base64.b64decode(base64_data)
#     image_data = BytesIO(byte_data)
#     img = image.load_img(image_data, grayscale)
# #     img.save('hahhaha.jpg')

#     return image.img_to_array(img)

def base64_to_image(base64_code):
    base64_code = re.sub('^data:image/.+;base64,', '', base64_code)
    # base64解码
    img_data = base64.b64decode(base64_code)
    # 转换为np数组
    img_array = np.fromstring(img_data, np.uint8)
    # 转换成opencv可用格式
    img = cv2.imdecode(img_array, cv2.COLOR_RGB2BGR)
 
    return img



def handle(conf): 
    """
    该方法是部署之后，其他人调用你的服务时候的处理方法。
    请按规范填写参数结构，这样我们就能替你自动生成配置文件，方便其他人的调用。
    范例：
    params['key'] = value # value_type: str # description: some description
    参数请放到params字典中，我们会自动解析该变量。
    """

    import time
    from PIL import Image


    
    base64_str = conf['img']  # value_type: str # description: some description
    
    img = base64_to_image(base64_str)
   

    h,w = img.shape[:2]
    timeTake = time.time()
    _,result,angle= model.model(img,
                                        detectAngle=True,##是否进行文字方向检测
                                        config=dict(MAX_HORIZONTAL_GAP=50,##字符之间的最大间隔，用于文本行的合并
                                        MIN_V_OVERLAPS=0.6,
                                        MIN_SIZE_SIM=0.6,
                                        TEXT_PROPOSALS_MIN_SCORE=0.1,
                                        TEXT_PROPOSALS_NMS_THRESH=0.3,
                                        TEXT_LINE_NMS_THRESH = 0.7,##文本行之间测iou值

                    ),
                                        leftAdjust=True, ##对检测的文本行进行向左延伸
                                        rightAdjust=True, ##对检测的文本行进行向右延伸
                                        alph=0.01, ##对检测的文本行进行向右、左延伸的倍数

                                    )

    timeTake = time.time()-timeTake
    # print('It take:{}s'.format(timeTake))
    take_time = '耗时：'+ str(timeTake) + 's'
    output = ''
    for line in result:
        # print(line['text'])
        output += line['text']

    return {'take_time': take_time, 'output': output}

# conf={}
# handle(conf)