#!/usr/bin/env python
# -*- coding:utf-8 -*-
import logging
import math

import cv2
from PIL import Image, ImageStat

from plugins import base
from onvif import ONVIFCamera
import datetime
import zeep
import uuid
import upload


def zeep_pythonvalue(self, xmlvalue):
    return xmlvalue


zeep.xsd.simple.AnySimpleType.pythonvalue = zeep_pythonvalue


class IPC(base.Base):
    def __init__(self, *args, **kwargs):
        super(IPC, self).__init__(*args, **kwargs)

        if kwargs.__contains__('name'):
            self.name = kwargs['name']
        else:
            self.name = 'ipc' + uuid.uuid1().hex[0:10]

        self.host = kwargs['host']
        self.port = kwargs['port']
        self.user = kwargs['user']
        self.password = kwargs['password']
        self.image_dir = kwargs['image_dir']
        self.wsdl_dir = 'resource/wsdl'
        if kwargs.__contains__('wsdl_dir') and kwargs['wsdl_dir'] != "":
            self.wsdl_dir = kwargs['wsdl_dir']
        self.kpi = kwargs['kpi']

    def name(self):
        return self.name

    def config_check(self, *args, **kwargs):
        pass

    def exec(self):
        image_name = self.snapshot()
        analyze_result = self.analyze()
        data = {'data': analyze_result, 'image': image_name}
        # upload.add_task(data)
        return

    def snapshot(self):
        camera = ONVIFCamera(self.host, self.port, self.user, self.password, self.wsdl_dir)
        media = camera.create_media_service()
        media_profile = media.GetProfiles()[0]
        profiles = media.GetProfiles()
        if len(profiles) < 1:
            return

        token = profiles[0]['token']
        snapshot_detail = media.GetSnapshotUri(token)
        snapshot_url = snapshot_detail['url']
        image_name = str(int(datetime.datetime.now().timestamp() * 1000)) + ".png"

        # download image via image url
        # urlretrieve(snapshot_url, path.join(self.image_dir, image_name))
        return image_name

    def analyze(self, path):
        if len(self.kpi) < 1:
            return
        kpi_result = {}
        if self.kpi.__contains__('img_cast'):
            kpi_result['img_cast'] = self.get_img_cast(path)
        if self.kpi.__contains__('img_ybcast'):
            kpi_result['img_ybcast'] = self.get_img_ybcast(path)
        if self.kpi.__contains__('img_light'):
            kpi_result['img_light'] = self.get_img_light(path)
        if self.kpi.__contains__('img_rgcast'):
            kpi_result['img_rgcast'] = self.get_img_rgcast(path)
        if self.kpi.__contains__('img_lightcast'):
            kpi_result['img_lightcast'] = self.get_img_lightcast(path)
        if self.kpi.__contains__('img_clear'):
            kpi_result['img_clear'] = self.get_img_clear(path)
        if self.kpi.__contains__('img_quality'):
            kpi_result['img_quality'] = self.get_img_quality(path)
        return kpi_result

    # https://blog.csdn.net/sparrowwf/article/details/86595162
    @staticmethod
    def get_img_cast(path):
        image = cv2.imread(path)
        img = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
        l_channel, a_channel, b_channel = cv2.split(img)
        h, w, _ = img.shape
        da = a_channel.sum() / (h * w) - 128
        db = b_channel.sum() / (h * w) - 128
        histA = [0] * 256
        histB = [0] * 256
        for i in range(h):
            for j in range(w):
                ta = a_channel[i][j]
                tb = b_channel[i][j]
                histA[ta] += 1
                histB[tb] += 1
        msqA = 0
        msqB = 0
        for y in range(256):
            msqA += float(abs(y - 128 - da)) * histA[y] / (w * h)
            msqB += float(abs(y - 128 - db)) * histB[y] / (w * h)
        import math

        result = math.sqrt(da * da + db * db) / math.sqrt(msqA * msqA + msqB * msqB)
        return result

    @staticmethod
    def get_img_ybcast(path):
        return 1

    # https://stackoverflow.com/questions/3490727/what-are-some-methods-to-analyze-image-brightness-using-python
    @staticmethod
    def get_img_light(path):
        try:
            im = Image.open(path)
            stat = ImageStat.Stat(im)
            r, g, b = stat.mean
            return math.sqrt(0.241 * (r ** 2) + 0.691 * (g ** 2) + 0.068 * (b ** 2))
        except Exception as e:
            logging.error('[IPC] calculate image light with file: %s error: %s', path, e)
        return 0

    @staticmethod
    def get_img_rgcast(path):
        return 1

    @staticmethod
    def get_img_lightcast(path):
        return 1

    # https://blog.csdn.net/luolinll1212/article/details/84107066
    @staticmethod
    def get_img_clear(path):
        image = cv2.imread(path)
        img2gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        result = cv2.Laplacian(img2gray, cv2.CV_64F).var()
        return result

    @staticmethod
    def get_img_quality(path):
        return 1