# coding=utf-8
#author:        4N
#createtime:    2021/3/24
#email:         nheweijun@sina.com

from app.util import *
import traceback
from osgeo import gdal
from osgeo.gdal import *
from numpy import ndarray
import numpy
from flask import Response
import io
import os
from PIL import Image

import time
import cv2
from app.modules.service.image.models import ImageService,Image
from app.models import db,TileScheme
from app.util.component.ApiTemplate import ApiTemplate
from app.util.component.SliceScheme import SliceScheme
from app.util.component.ParameterUtil import ParameterUtil
import json
from kazoo.client import KazooClient
from threading import Thread
from app.modules.service.image.util.ThriftConnect import ThriftConnect,ThriftPool
import gzip
import random
import copy



class Api(ApiTemplate):

    api_name = "切片"

    def __init__(self,guid,level, row, col):
        super().__init__()
        self.guid = guid
        self.level = level
        self.row = row
        self.col = col

    def process(self):

        result = {}
        parameter: dict = self.para

        try:
            if parameter.get("guid"):
                self.guid = parameter.get("guid")

            image_service_info, zoo, servers = self.cache_data()

            # bands = [1, 2, 3]

            # 转换参数
            parameter = ParameterUtil.to_lower(parameter)

            if parameter.get("tilematrix"):
                if parameter.get("tilematrix").__contains__(":"):
                    self.level = int(parameter.get("tilematrix").split(":")[-1])
                else:
                    self.level = int(parameter.get("tilematrix"))
            if parameter.get("tilerow"):
                self.row = int(parameter.get("tilerow"))
            if parameter.get("tilecol"):
                self.col = int(parameter.get("tilecol"))

            image_type = parameter.get("format") if parameter.get("format") else "image/png"
            quality = int(parameter.get("quality")) if parameter.get("quality") else 30
            slice_para = image_service_info["scheme"]
            extent = SliceScheme.get_polygon(slice_para, self.level, self.row, self.col)

            height, width = 256,256

            # 多线程获取分布式数据

            intersect_image = [im for im in image_service_info["images"] if self.determin_intersect(json.loads(im.extent),extent)]

            if len(intersect_image) > 1:

                # 结果矩阵
                pixel_array = numpy.zeros((height, width, 3), dtype=int) + 65536

                thread_list = []

                for image in intersect_image:

                    # 该影像的服务器，随机选取一个
                    image_servers = image.server.split(",")
                    image_servers = [ser for ser in image_servers if ser in servers]
                    if len(image_servers)>0:
                        indx = int(random.random() * len(image_servers))
                        image_server = image_servers[indx]
                    else:
                        image_server = "None"

                    bands = json.loads(image.band_view)

                    thread: MyThread = MyThread(self.get_data,
                                                args=(image_server, image, extent, bands, height, width))
                    thread.start()
                    thread_list.append(thread)

                for thread in thread_list:
                    thread.join()
                    data = thread.get_result()

                    # 掩膜在中央接口生成,合图
                    mask = numpy.zeros((height, width, 3), dtype=int)
                    mask_data = numpy.zeros((height, width, 3), dtype=int)

                    mask[data == 65536] = 1
                    mask[data != 65536] = 0
                    mask_data[data == 65536] = 0
                    mask_data[data != 65536] = 1

                    # # 掩膜计算
                    pixel_array = pixel_array * mask + data * mask_data

                # opencv 颜色排序为GBR
                d1 = copy.copy(pixel_array[:,:,0])
                pixel_array[:, :, 0] = pixel_array[:,:,2]
                pixel_array[:, :, 2] = d1


            elif len(intersect_image) == 1:
                # 该影像的服务器，随机选取一个
                image = intersect_image[0]
                image_servers = image.server.split(",")
                #判断可用服务器
                image_servers = [ser for ser in image_servers if ser in servers]
                if len(image_servers) > 0:
                    indx = int(random.random() * len(image_servers))
                    image_server = image_servers[indx]
                else:
                    image_server = "None"
                # image_server = image_servers[0]
                bands = json.loads(image.band_view)
                pixel_array_t:numpy.ndarray = self.get_data(image_server, image, extent, bands, height, width)
                pixel_array = numpy.zeros((height, width, 3), dtype=int)

                for ii in [0, 1, 2]:
                    # opencv 颜色排序为GBR
                    pixel_array[:, :, 2 - ii] = pixel_array_t[:, :, ii]

            else:
                # 结果矩阵
                pixel_array = numpy.zeros((height, width, 3), dtype=int) + 65536


            # 将图片生成在内存中,然后直接返回response
            im_data = self.create_by_opencv(image_type, pixel_array, quality)
            return Response(im_data, mimetype=image_type.lower())


        except Exception as e:
            print(traceback.format_exc())
            result["state"] = -1
            result["message"] = e.__str__()
            return result

    def cache_data(self):

        from app import GLOBAL_DIC

        # 缓存zookeeper
        zoo = GLOBAL_DIC.get("zookeeper")
        if zoo is None:
            zoo: KazooClient = KazooClient(hosts=configure.zookeeper, timeout=100)
            zoo.start()
            GLOBAL_DIC["zookeeper"] = zoo
        else:
            if not zoo.connected:
                zoo.start()

        # 缓存数据服务器
        servers = GLOBAL_DIC.get("servers")
        if servers is None:
            servers = zoo.get_children("/rpc")
            servers.append("本地服务器")
            GLOBAL_DIC["servers"] = servers
            GLOBAL_DIC["servers_updatetime"] = time.time()
        else:
            servers = GLOBAL_DIC.get("servers")

        # 更新缓存
        if time.time() - GLOBAL_DIC["servers_updatetime"] > 10:
            servers = zoo.get_children("/rpc")
            servers.append("本地服务器")
            GLOBAL_DIC["servers"] = servers
            GLOBAL_DIC["servers_updatetime"] = time.time()


        # 缓存服务信息
        image_service_info = GLOBAL_DIC.get(self.guid)
        if image_service_info is None or time.time() - GLOBAL_DIC.get("service_updatetime") > 20:
            image_service: ImageService = ImageService.query.filter_by(guid=self.guid).one_or_none()
            images = image_service.images.all()
            scheme: TileScheme = TileScheme.query.filter_by(guid=image_service.scheme_guid).one_or_none()
            GLOBAL_DIC[self.guid] = {"service": image_service, "images": images, "scheme": json.loads(scheme.parameter)}
            GLOBAL_DIC["service_updatetime"] = time.time()
            image_service_info = GLOBAL_DIC[self.guid]

        else:
            image_service_info = GLOBAL_DIC[self.guid]

        return image_service_info,zoo,servers

    def determine_level(self, xysize, origin_extent, extent, max_level):
        '''
        根据范围判断调用金字塔的哪一层
        :param xysize:
        :param origin_extent:
        :param extent:
        :param max_level:
        :return:
        '''
        x = xysize[0]
        y = xysize[1]
        level = -1
        pixel = x * y * (((extent[2] - extent[0]) * (extent[3] - extent[1])) / (
                (origin_extent[2] - origin_extent[0]) * (origin_extent[3] - origin_extent[1])))
        while pixel > 100000 and level < max_level - 1:
            level += 1
            x = x / 2
            y = y / 2
            pixel = x * y * (((extent[2] - extent[0]) * (extent[3] - extent[1])) / (
                    (origin_extent[2] - origin_extent[0]) * (origin_extent[3] - origin_extent[1])))
        return level


    def create_by_opencv(self, image_type, pixel_array, quality):

        if image_type.__eq__("image/jpeg") or image_type.__eq__("image/jpg"):
            r, buf = cv2.imencode(".jpg", pixel_array, [cv2.IMWRITE_JPEG_QUALITY, quality])
            # r, buf = cv2.imencode(".jpg", pixel_array)
            image_out = buf.tobytes()
        else:
            height, width = pixel_array[:, :, 0].shape
            four = numpy.zeros((height, width), dtype=int) + 255
            four[pixel_array[:, :, 0] == 65536] = 0
            r, buf = cv2.imencode(".png", numpy.dstack((pixel_array, four)))
            image_out = buf.tobytes()
        return image_out

    def get_data(self,image_server, image, extent, bands, height, width):

        if image_server.__eq__("本地服务器"):
            data = self.get_local_wms_data2(image, extent, bands, height, width)
        elif image_server.__eq__("None"):
            data = numpy.zeros((height, width, 3), dtype=int) + 65536
        else:
            data = self.get_remote_wms_data(image_server,image, extent, bands, height, width)
        return data

    def get_remote_wms_data(self, image_server,image, extent, bands, height, width):
        '''
        通过RPC获取远程数据
        :param image:
        :param extent:
        :param bands:
        :return:
        '''

        #需要做thrift连接的缓存，连接池
        thrift_connect = ThriftConnect(image_server)
        image_extent = image.extent

        data = thrift_connect.client.getData(image.path, extent, json.loads(image_extent), bands, width, height)

        thrift_connect.close()

        data = gzip.decompress(data)
        data = numpy.frombuffer(data, dtype='int64')
        data = data.reshape((height, width, 3))

        return data


    def get_remote_wms_data_cpp(self, image_server,image, extent, bands, height, width):
        '''
        通过RPC获取远程数据
        :param image:
        :param extent:
        :param bands:
        :return:
        '''

        #需要做thrift连接的缓存，连接池
        thrift_connect = ThriftConnect(image_server)
        image_extent = image.extent

        data = thrift_connect.client.getData(image.path, extent, json.loads(image_extent), bands, width, height)

        thrift_connect.close()

        return data

    def get_remote_wms_data_client(self,image_server,image, extent, bands, height, width):
        '''
        通过RPC获取远程数据
        :param image:
        :param extent:
        :param bands:
        :return:
        '''
        from app import GLOBAL_DIC

        # 缓存thrift_pool
        thrift_pool = GLOBAL_DIC.get(image_server)
        if thrift_pool is None:
            thrift_pool = ThriftPool(image_server)
            GLOBAL_DIC["image_server"] = thrift_pool
        image_extent = image.extent

        client,transport = thrift_pool.get_client()
        transport.open()
        data = client.getData(image.path, extent, json.loads(image_extent), bands, width, height)
        transport.close()
        data = gzip.decompress(data)
        data = numpy.frombuffer(data, dtype='int64')
        data = data.reshape((height, width, 3))

        return data

    # def get_remote_wms_data_c(self, image_server,image, extent, bands, height, width):
    #     '''
    #     通过RPC获取远程数据
    #     :param image:
    #     :param extent:
    #     :param bands:
    #     :return:
    #     '''
    #
    #     #需要做thrift连接的缓存，连接池
    #     thrift_connect = ThriftConnect_C(image_server)
    #     image_extent = image.extent
    #
    #     data = thrift_connect.client.getData(image.path, extent, json.loads(image_extent), bands, width, height)
    #
    #     thrift_connect.close()
    #
    #     data = gzip.decompress(data)
    #     data = numpy.frombuffer(data, dtype='int64')
    #
    #
    #     data = data.reshape((height, width, 3))
    #
    #     return data


    def get_local_wms_data(self, image, extent, bands, height, width):
        '''
        获取本地数据
        :param image:
        :param extent:
        :param bands:
        :return:
        '''
        pixel_array = numpy.zeros((height, width, 3), dtype=int)
        ceng = 0
        img: Dataset = gdal.Open(image.path, 0)



        for band in bands:

            # 自决定金字塔等级
            xysize = [img.RasterXSize, img.RasterYSize]

            origin_extent = json.loads(image.extent)
            band_data: Band = img.GetRasterBand(band)

            max_level = band_data.GetOverviewCount()

            # 超出空间范围
            if extent[2] < origin_extent[0] or extent[0] > origin_extent[2] or extent[1] > origin_extent[3] or extent[3] < origin_extent[1]:
                empty = numpy.zeros((height, width), dtype=int) + 65536
            # 空间范围相交
            else:
                image_level = self.determine_level(xysize, origin_extent, extent, max_level)

                if image_level == -1:
                    overview = band_data
                else:
                    try:
                        overview: Band = band_data.GetOverview(image_level)
                    except:
                        raise Exception("该影像不存在该级别的金字塔数据！")
                ox = overview.XSize
                oy = overview.YSize

                # 网格大小
                grid_x = (origin_extent[2] - origin_extent[0]) / (ox * 1.0)
                grid_y = (origin_extent[3] - origin_extent[1]) / (oy * 1.0)

                # 完全在影像范围内
                if extent[0] > origin_extent[0] and extent[1] > origin_extent[1] and extent[2] < \
                        origin_extent[2] and extent[3] < origin_extent[3]:

                    # 网格偏移量
                    off_x = math.floor((extent[0] - origin_extent[0]) / grid_x)
                    off_y = math.floor((origin_extent[3] - extent[3]) / grid_y)

                    # 截取后网格个数
                    x_g = math.ceil((extent[2] - extent[0]) / grid_x)

                    y_g = math.ceil((extent[3] - extent[1]) / grid_y)

                    empty = overview.ReadAsArray(off_x, off_y, x_g, y_g, width, height)


                # 部分相交
                else:

                    inter_extent = [0, 0, 0, 0]
                    inter_extent[0] = origin_extent[0] if origin_extent[0] > extent[0] else extent[0]
                    inter_extent[1] = origin_extent[1] if origin_extent[1] > extent[1] else extent[1]
                    inter_extent[2] = origin_extent[2] if origin_extent[2] < extent[2] else extent[2]
                    inter_extent[3] = origin_extent[3] if origin_extent[3] < extent[3] else extent[3]

                    # 网格偏移量
                    off_x = math.floor((inter_extent[0] - origin_extent[0]) / grid_x)
                    off_y = math.floor((origin_extent[3] - inter_extent[3]) / grid_y)

                    # 截取后网格个数
                    x_g = math.floor((inter_extent[2] - inter_extent[0]) / grid_x)
                    y_g = math.floor((inter_extent[3] - inter_extent[1]) / grid_y)

                    # 相对于出图的偏移量

                    # 出图的网格大小
                    out_grid_x = (extent[2] - extent[0]) / (width * 1.0)
                    out_grid_y = (extent[3] - extent[1]) / (height * 1.0)

                    out_off_x = int(math.ceil((inter_extent[0] - extent[0]) / out_grid_x))
                    out_off_y = int(math.ceil((extent[3] - inter_extent[3]) / out_grid_y))

                    out_x_g = int(math.floor((inter_extent[2] - inter_extent[0]) / out_grid_x))
                    out_y_g = int(math.floor((inter_extent[3] - inter_extent[1]) / out_grid_y))

                    # 相交部分在出图的哪个位置

                    overview_raster: ndarray = overview.ReadAsArray(off_x, off_y, x_g, y_g, out_x_g,
                                                                    out_y_g)

                    dat = numpy.zeros((height, width), dtype=int) + 65536
                    dat[out_off_y:out_off_y + out_y_g, out_off_x:out_off_x + out_x_g] = overview_raster

                    empty = dat

            pixel_array[:, :, ceng] = empty
            ceng += 1
        return pixel_array

    def get_local_wms_data2(self, image, extent, bands, height, width):
        '''
        获取本地数据
        :param image:
        :param extent:
        :param bands:
        :return:
        '''
        pixel_array = numpy.zeros((height, width, 3), dtype=int)
        ceng = 0
        img: Dataset = gdal.Open(image.path, 0)

        origin_extent = json.loads(image.extent)

        # 超出空间范围
        if extent[2] < origin_extent[0] or extent[0] > origin_extent[2] or extent[1] > origin_extent[3] or extent[
            3] < origin_extent[1]:
            empty = numpy.zeros((height, width,3), dtype=int) + 65536
        # 空间范围相交
        else:

            ox = img.RasterXSize
            oy = img.RasterYSize

            # 网格大小
            grid_x = (origin_extent[2] - origin_extent[0]) / (ox * 1.0)
            grid_y = (origin_extent[3] - origin_extent[1]) / (oy * 1.0)

            # 完全在影像范围内
            if extent[0] > origin_extent[0] and extent[1] > origin_extent[1] and extent[2] < \
                    origin_extent[2] and extent[3] < origin_extent[3]:

                # 网格偏移量
                off_x = math.floor((extent[0] - origin_extent[0]) / grid_x)
                off_y = math.floor((origin_extent[3] - extent[3]) / grid_y)

                # 截取后网格个数
                x_g = math.ceil((extent[2] - extent[0]) / grid_x)

                y_g = math.ceil((extent[3] - extent[1]) / grid_y)

                empty = img.ReadRaster(off_x, off_y, x_g, y_g,256,256,band_list=[1,2,3])
                img.ReadAsArray()
            # 部分相交
            else:

                inter_extent = [0, 0, 0, 0]
                inter_extent[0] = origin_extent[0] if origin_extent[0] > extent[0] else extent[0]
                inter_extent[1] = origin_extent[1] if origin_extent[1] > extent[1] else extent[1]
                inter_extent[2] = origin_extent[2] if origin_extent[2] < extent[2] else extent[2]
                inter_extent[3] = origin_extent[3] if origin_extent[3] < extent[3] else extent[3]

                # 网格偏移量
                off_x = math.floor((inter_extent[0] - origin_extent[0]) / grid_x)
                off_y = math.floor((origin_extent[3] - inter_extent[3]) / grid_y)

                # 截取后网格个数
                x_g = math.floor((inter_extent[2] - inter_extent[0]) / grid_x)
                y_g = math.floor((inter_extent[3] - inter_extent[1]) / grid_y)

                # 相对于出图的偏移量

                # 出图的网格大小
                out_grid_x = (extent[2] - extent[0]) / (width * 1.0)
                out_grid_y = (extent[3] - extent[1]) / (height * 1.0)

                out_off_x = int(math.ceil((inter_extent[0] - extent[0]) / out_grid_x))
                out_off_y = int(math.ceil((extent[3] - inter_extent[3]) / out_grid_y))

                out_x_g = int(math.floor((inter_extent[2] - inter_extent[0]) / out_grid_x))
                out_y_g = int(math.floor((inter_extent[3] - inter_extent[1]) / out_grid_y))

                # 相交部分在出图的哪个位置

                overview_raster: ndarray = img.ReadAsArray(off_x, off_y, x_g, y_g, out_x_g,
                                                                out_y_g)

                dat = numpy.zeros((height, width,3), dtype=int) + 65536
                dat[out_off_y:out_off_y + out_y_g, out_off_x:out_off_x + out_x_g] = overview_raster
                empty = dat

        return empty

    def determin_intersect(self, extent1, extent2):
        if extent2[2] < extent1[0] or extent2[0] > extent1[2] or extent2[1] > extent1[
            3] or extent2[3] < extent1[1]:
            return False
        else:
            return True


    api_doc = {
        "tags": ["影像接口"],
        "parameters": [
            {"name": "guid",
             "in": "formData",
             "type": "string"},
            {"name": "tilematrix",
             "in": "formData",
             "type": "string"},
            {"name": "tilerow",
             "in": "formData",
             "type": "string"},
            {"name": "tilecol",
             "in": "formData",
             "type": "string"},
            {"name": "format",
             "in": "formData",
             "type": "string"},
            {"name": "quality",
             "in": "formData",
             "type": "string"}

        ],
        "responses": {
            200: {
                "schema": {
                    "properties": {
                    }
                }
            }
        }
    }

class MyThread(Thread):
    def __init__(self,func,args=()):
        super(MyThread,self).__init__()
        self.func = func
        self.args = args
    def run(self):
        self.result = self.func(*self.args)
    def get_result(self):
        try:
            return self.result
        except Exception:
            return None