[
  {
    "path": ".gitignore",
    "content": "# OSX\n.DS_Store\n\nserver/.ipynb_checkpoints\nserver/__pycache__/*\n\n\nclient/node_modules/*"
  },
  {
    "path": "LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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  },
  {
    "path": "README.md",
    "content": "# 参赛作品名\n[【隔空取人】【AI创造营】【mixlab】](https://mp.weixin.qq.com/s/UvYNdSBOmSSuCouWZ4MvwQ)\n\nAn AR+ML prototype\n\n![粘贴到桌面](./demo/01.gif \"01.gif\")\n![粘贴到微信](./demo/02.gif \"02.gif\")\n\n### 作品简介\n\n手机对准某人，确认要“收集”的他/她，然后对准电脑桌面对某个位置，即可把他/她贴到电脑桌面上！～～～\n\n\n### 使用方式\n\n1 需要安装nodejs、python的开发环境，还有electron\n\n2 启动服务端\n```\ncd server\npip install -r requirements.txt\npython main.py\n```\n\n3 启动客户端\n```\ncd client\nnpm i\nnpm start\n```\n\n** 由于新增了自动化库robotjs，需要运行下以下：\n```\nnpm i robotjs\nnpm i -D electron-rebuild\nnpm install -g node-gyp\nnpx electron-rebuild -f -t prod,optional,dev -w robotjs\n```\n\n4 首次运行在终端中输入本机密码，生成https证书。\n\n手机跟电脑需在同一个局域网里，手机打开网站\n```\n   🔒    ❨https❩ Creating server at localhost with locally-trusted certificates.\n   📜    ❨auto-encrypt-localhost❩ Local development TLS certificate exists.\n   🔒    ❨https❩ Created HTTPS server.\n   ✨    ❨auto-encrypt-localhost❩ HTTP server is listening on port 80.\n 🚀 🎉 Server running at https://192.168.1.3\n```\n\n### TODO\n- 尝试让图片动起来~\n\n### 感谢\n\n[百度飞桨官方](https://www.paddlepaddle.org.cn)\n```\n#人脸检测\nhub install pyramidbox_lite_server_mask==1.3.1\n#人像分割模型\nhub install humanseg_server==1.2.1\n```\n\n[ar-cutpaste提供的灵感](https://github.com/cyrildiagne/ar-cutpaste)\n\n"
  },
  {
    "path": "client/index.html",
    "content": "<!DOCTYPE html>\n<html lang=\"zh\">\n<head>\n    <meta charset=\"UTF-8\">\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n    <title>DEMO-by-shadow</title>\n</head>\n\n<body>\n    <!-- <h2>HELLOOOOOOO</h2> -->\n    <script>\n        const {\n            ipcRenderer\n        } = require(\"electron\");\n        ipcRenderer.on(\"new-image\", (event, arg) => {\n            console.log(arg)\n            draw(arg);\n        });\n\n        ipcRenderer.on(\"clear-all\",(event,arg)=>{\n            document.body.innerHTML=\"\";\n        })\n\n        // let c = document.createElement('canvas')\n        // let canvas = new fabric.Canvas(c, {\n        //     width: window.innerWidth,\n        //     height: window.innerHeight,\n        //     backgroundColor: 'transparent'\n        // });\n\n        function draw(arg) {\n            let img = new Image();\n            img.src = 'data:image/png;base64,' + arg.img;\n            img.onload = () => {\n                // console.log({\n                //     left: window.innerWidth * arg.x - img.naturalWidth * 0.5,\n                //     top: window.innerHeight * arg.y - img.naturalHeight * 0.5\n                // })\n                // let imgInstance = new fabric.Image(img, {\n                //     left: window.innerWidth * arg.x - img.naturalWidth * 0.5,\n                //     top: window.innerHeight * arg.y - img.naturalHeight * 0.5\n                // });\n                // canvas.add(img);\n                // imgInstance.selectable = true;\n\n                img.style.left = (window.innerWidth * arg.x - img.naturalWidth * 0.5) + 'px';\n                img.style.top = (window.innerHeight * arg.y - img.naturalHeight * 0.5) + 'px';\n                img.style.position = 'fixed';\n                document.body.appendChild(img);\n            }\n\n        }\n\n        function postData(url, data) {\n            // Default options are marked with *\n            return fetch(url, {\n                body: JSON.stringify(data), // must match 'Content-Type' header\n                cache: 'no-cache', // *default, no-cache, reload, force-cache, only-if-cached\n                credentials: 'same-origin', // include, same-origin, *omit\n                headers: {\n                    'user-agent': 'Mozilla/4.0 MDN Example',\n                    'content-type': 'application/json'\n                },\n                method: 'POST', // *GET, POST, PUT, DELETE, etc.\n                mode: 'cors', // no-cors, cors, *same-origin\n                redirect: 'follow', // manual, *follow, error\n                referrer: 'no-referrer', // *client, no-referrer\n            })\n                .then(response => response.json()) // parses response to JSON\n        }\n\n    </script>\n</body>\n\n</html>"
  },
  {
    "path": "client/main.js",
    "content": "const { app, BrowserWindow, screen, Tray, Menu, nativeImage, clipboard } = require('electron')\nconst path = require('path')\nconst https = require('@small-tech/https')\nconst fs = require('fs');\nconst fetch = require('node-fetch');\nconst robot = require(\"robotjs\");\n\nlet mainWindow, width, height, appIcon;\n\nlet pasteMode = 0;\n\nfunction createWindow(width, height) {\n    mainWindow = new BrowserWindow({\n        width: width,\n        height: height,\n        show: true,\n        focus: false,\n        transparent: true,\n        frame: false,\n        alwaysOnTop: true,\n        skipTaskbar: true,\n        webPreferences: {\n            //开启nodejs支持\n            nodeIntegration: true,\n            //开启AI功能\n            experimentalFeatures: true,\n            //开启渲染进程调用remote\n            enableRemoteModule: true,\n            webviewTag: true,\n            devTools: true,\n            contextIsolation: false\n        }\n    });\n    mainWindow.loadFile('index.html')\n    mainWindow.setIgnoreMouseEvents(true);\n    mainWindow.webContents.on(\"devtools-opened\", () => {\n        // mainWindow.setIgnoreMouseEvents(false);\n        // mainWindow.webContents.closeDevTools();\n        // mainWindow.webContents.openDevTools({\n        //     mode: 'detach',\n        //     activate: true\n        // });\n    });\n}\n\nclass AppIcon {\n    constructor() {\n        this.tray = new Tray(path.join(__dirname, \"assets/appIcon.png\"));\n        this.init();\n    }\n    init() {\n        const contextMenu = Menu.buildFromTemplate([{\n            label: '欢迎使用ARLAB'\n        }]);\n        this.tray.setContextMenu(contextMenu);\n        this.tray.setToolTip('AR LAB');\n        // this.tray.popUpContextMenu();\n    }\n    changeAppIcon(items = []) {\n        if (items.length == 0) return;\n        let contextMenu = Menu.buildFromTemplate(items);\n        this.tray.setContextMenu(contextMenu);\n        // this.tray.popUpContextMenu();\n    }\n    pop() {\n        this.tray.popUpContextMenu();\n    }\n    setImage(base64) {\n        this.im = nativeImage.createFromDataURL(base64);\n        let resizeImg = this.im.resize({ height: 18 });\n        this.tray.setImage(resizeImg);\n    }\n}\n\n\n\napp.whenReady().then(() => {\n    appIcon = new AppIcon();\n    let size = screen.getPrimaryDisplay().bounds;\n    width = parseInt(size.width);\n    height = parseInt(size.height);\n\n    createWindow(width, height);\n\n    serverStart();\n\n    app.on('activate', function () {\n        if (BrowserWindow.getAllWindows().length === 0) createWindow()\n    })\n})\n\napp.on('window-all-closed', function () {\n    if (process.platform !== 'darwin') app.quit()\n})\n\n\n\n// https服务\nfunction serverStart() {\n\n    const internalIp = require('internal-ip');\n    const server = https.createServer(async (req, res) => {\n\n        console.log(req.url)\n        if (req.url === '/') {\n            const html = fs.readFileSync(path.join(__dirname, './mobile.html'), 'utf8');\n            res.writeHead(200, { 'Content-type': 'text/html' });\n            res.end(html);\n        } else if (req.url === '/clear') {\n            mainWindow.webContents.send('clear-all');\n            res.writeHead(200, { 'Content-type': 'application/json' });\n            res.end(JSON.stringify({ status: 0 }));\n        } else if (req.url === '/face') {\n\n            res.writeHead(200, { 'Content-type': 'application/json' });\n            try {\n                let face = await getBody(req);\n                res.end(JSON.stringify(face.result));\n            } catch (error) {\n                console.log(error)\n                res.end(JSON.stringify({ count: 0 }));\n            }\n        } else if (req.url === '/segment') {\n\n            res.writeHead(200, { 'Content-type': 'application/json' });\n            try {\n                let segment = await getBody(req);\n                // console.log(segment.origin.base64)\n                appIcon.setImage('data:image/png;base64,' + segment.result.base64);\n                res.end(JSON.stringify(segment.result));\n            } catch (error) {\n                console.log(error)\n                res.end(JSON.stringify({ base64: \"\" }));\n            }\n        } else if (req.url === '/project') {\n\n            res.writeHead(200, { 'Content-type': 'application/json' });\n            let project = await getBody(req);\n            let { x, y } = project.result;\n            res.end(JSON.stringify({\n                x: parseFloat(x),\n                y: parseFloat(y)\n            }));\n\n            appIcon.setImage('data:image/png;base64,' + project.origin.result);\n\n            if (pasteMode === 0) mainWindow.webContents.send('new-image', {\n                img: project.origin.result,\n                size: project.origin.size,\n                x: parseFloat(x),\n                y: parseFloat(y)\n            });\n\n            if (pasteMode === 1) {\n                clipboard.writeImage(appIcon.im)\n                robot.setMouseDelay(2);\n                robot.moveMouse(width * parseFloat(x), height * parseFloat(y));\n                robot.mouseClick();\n\n                if(process.platform !== 'darwin'){\n                    robot.keyTap(\"v\",\"control\");\n                }else{\n                    robot.keyTap(\"v\",\"command\");\n                }\n                \n            };\n\n            let items = [\n                {\n                    label: '拷贝',\n                    click: () => clipboard.writeImage(appIcon.im)\n\n                }];\n            if (pasteMode === 0) {\n                items.push({\n                    label: '\b清空',\n                    click: () => mainWindow.webContents.send('clear-all')\n                });\n                items.push({\n                    label: '粘贴模式',\n                    click: () => {\n                        pasteMode = 1;\n                    }\n                });\n            };\n            if(pasteMode===1){\n                items.push({\n                    label: '悬浮模式',\n                    click: () => {\n                        pasteMode = 0;\n                    }\n                });\n            };\n\n            appIcon.changeAppIcon(items);\n\n        }\n\n        // else if(req.url==='/fabric.js'){\n        //     res.writeHead(200, {'Content-Type': 'application/javascript'});\n        //     res.end(fs.readFileSync(path.join(__dirname,'./node_modules/fabric-pure-browser/dist/fabric.min.js')));\n        // }\n\n    });\n\n\n    async function getBody(req) {\n        let body = [];\n        return new Promise((resolve, reject) => {\n            req.on('data', chunk => {\n                body.push(chunk);\n            }).on('end', () => {\n                body = JSON.parse(Buffer.concat(body).toString());\n                // console.log(JSON.stringify(body))\n                fetch('http://0.0.0.0:8891' + req.url, {\n                    method: 'post',\n                    body: JSON.stringify(body),\n                    headers: { 'Content-Type': 'application/json' },\n                })\n                    .then(s => s.json())\n                    .then(json => resolve({\n                        origin: body,\n                        result: json\n                    }));\n            });\n        })\n    }\n\n\n    const url = `https://${internalIp.v4.sync()}`;\n\n    server.listen(443, () => {\n        appIcon.changeAppIcon([{\n            label: `🚀 🎉 Server running at ${url}`\n        }]);\n        appIcon.pop();\n        console.log(` 🚀 🎉 Server running at ${url}`)\n    });\n}"
  },
  {
    "path": "client/mobile.html",
    "content": "<!DOCTYPE html>\n<html lang=\"zh\">\n\n<head>\n    <meta charset=\"UTF-8\">\n    <meta name=\"viewport\"\n        content=\"width=device-width,target-densitydpi=high-dpi,initial-scale=1.0, minimum-scale=1.0, maximum-scale=1.0, user-scalable=no\" />\n    <title>Document</title>\n    <style>\n        body {\n            overflow: hidden;\n            margin: 0;\n            width: 100%;\n            height: 100vh;\n        }\n\n        .main {\n            height: 100%;\n            background: #eee;\n        }\n\n        .main .controls {\n            text-align: center;\n\n        }\n\n        .main video {\n            width: 100%;\n            position: fixed;\n            top: 0;\n            left: 0;\n            z-index: 99;\n            height: 100vh;\n            background: black;\n            display: none;\n        }\n\n        .main img {\n            width: 0;\n            height: 0;\n        }\n\n        .main button {\n            width: 56px;\n            height: 56px;\n            text-align: center;\n            padding: 0;\n            font-size: 10px;\n            background: black;\n            color: white;\n            border: none;\n            outline: none;\n            border-radius: 12px;\n            user-select: none;\n        }\n\n        .main #select {\n            margin: 24px;\n            height: 32px;\n            user-select: none;\n            outline: none;\n        }\n\n        .main #detect {\n            width: 50%;\n            height: 50%;\n            display: none;\n            z-index: 99999;\n            position: fixed;\n            top: 0;\n            left: 0;\n            grid-template-columns: repeat(3, 1fr);\n            grid-template-rows: repeat(3, 1fr);\n            grid-gap: 10px;\n            padding: 50% 25%;\n        }\n\n        .play {\n            background: #e9fffa40;\n        }\n\n        .main #detect div {\n            width: 100%;\n            height: 100%;\n            display: flex;\n            justify-content: center;\n            align-items: center;\n        }\n\n        .main #detect div div {\n            background-color: #c5ffec69;\n            width: 12px;\n            height: 12px;\n            border-radius: 100%;\n        }\n\n        .animation-opacity {\n            animation-name: opacity;\n            animation-duration: 1s;\n            animation-iteration-count: infinite;\n        }\n        .animation-border {\n            animation-name: border;\n            animation-duration: 1s;\n            animation-iteration-count: infinite;\n        }\n        .animation-filter {\n            animation-name: filter;\n            animation-duration: 1s;\n            animation-iteration-count: infinite;\n        }\n        \n\n        @keyframes opacity {\n            20% {\n                background-color: #cddc39;\n            }\n\n            60%{\n                background-color: #00e2ff;\n            }\n\n            100% {\n                background-color: #03a9f4;\n            }\n        }\n\n        @keyframes border{\n            from{\n                border: 4px solid #00e2ff;\n            }\n            to{\n                border: 4px solid #ffee00;\n            }\n        }\n\n        @keyframes filter{\n            from{\n                filter:contrast(0.9)\n            }\n            to{\n                filter:contrast(1) drop-shadow(2px 4px 4px #00e2ff)\n            }\n        }\n        \n\n        #segment-result {\n            position: fixed;\n            z-index: 99999999;\n            top: 10%;\n            left: 40%;\n            display: none;\n            width: 20%;\n            /* filter: opacity(0.8) drop-shadow(2px 4px 4px black); */\n        }\n\n        #shot {\n            z-index: 99999999999;\n            position: fixed;\n            bottom: 24px;\n            left: calc(50% - 28px);\n        }\n    </style>\n    \n    <script>\n        /**\n * Check pixels transparency\n * @param {Number} tolerance - tolerance level\n * @returns {Function}\n */\n        const checkOpacityLevel = tolerance => (pixels) => {\n            let transparent = true;\n            for (let i = 3, l = pixels.length; i < l && transparent; i += 4) {\n                transparent = transparent && pixels[i] === 255 * tolerance;\n            }\n            return transparent;\n        };\n\n        const defaultOptions = {\n            tolerance: 0,\n        };\n\n        /**\n         * @typedef {Object} Options\n         * @prop {Number} tolerance - Level of tolerance for the transparency between 0 and 1 (0 mean no tolerance, 1 mean everything is treated as transparent)\n         */\n        /**\n         * Smartly detect edges of an image\n         * @param {HTMLCanvasElement} canvas - Tainted canvas element\n         * @param {Options} options - Some options\n         * @returns {{top: number, left: number, bottom: number, right: number}}\n         */\n        function detectEdges(canvas, options) {\n            const { tolerance } = {\n                ...defaultOptions,\n                ...options,\n            };\n\n            const isTransparent = checkOpacityLevel(tolerance);\n\n            const context = canvas.getContext(\"2d\");\n            const { width, height } = canvas;\n            let pixels;\n\n            let top = -1;\n            do {\n                ++top;\n                pixels = context.getImageData(0, top, width, 1).data;\n            } while (isTransparent(pixels));\n\n            if (top === height) {\n                throw new Error(\"Can't detect edges.\");\n            }\n\n            // Left\n            let left = -1;\n            do {\n                ++left;\n                pixels = context.getImageData(left, top, 1, height - top).data;\n            } while (isTransparent(pixels));\n\n            // Bottom\n            let bottom = -1;\n            do {\n                ++bottom;\n                pixels = context.getImageData(left, height - bottom - 1, width - left, 1).data;\n            } while (isTransparent(pixels));\n\n            // Right\n            let right = -1;\n            do {\n                ++right;\n                pixels = context.getImageData(width - right - 1, top, 1, height - (top + bottom)).data;\n            } while (isTransparent(pixels));\n\n            return {\n                top,\n                right,\n                bottom,\n                left,\n            };\n        };\n    </script>\n</head>\n\n<body>\n\n    <!-- <canvas id=\"result\"></canvas> -->\n\n    <main class=\"main\">\n        <div id=\"detect\">\n            <div>\n                <div></div>\n            </div>\n            <div>\n                <div></div>\n            </div>\n            <div>\n                <div></div>\n            </div>\n            <div>\n                <div></div>\n            </div>\n            <div>\n                <div></div>\n            </div>\n            <div>\n                <div></div>\n            </div>\n            <div>\n                <div></div>\n            </div>\n            <div>\n                <div></div>\n            </div>\n            <div>\n                <div></div>\n            </div>\n\n        </div>\n        <div class=\"controls\">\n            <button id=\"button\">Camera</button>\n            <select id=\"select\"></select>\n            <button id=\"clear\">CLEAR</button>\n        </div>\n\n        <video id=\"video\" autoplay playsinline></video>\n        <canvas id=\"segment-result\" width=\"180\" height=\"320\" class='animation-filter'></canvas>\n        <img id=\"img\" src=\"\" />\n        <button id=\"shot\">GET</button>\n    </main>\n\n    <script>\n\n        //navigator.mediaDevices.getUserMedia在ios上只支持11版本以上\n        if (navigator.mediaDevices === undefined) {\n            navigator.mediaDevices = {};\n        }\n        if (navigator.mediaDevices.getUserMedia === undefined) {\n            navigator.mediaDevices.getUserMedia = function (constraints) {\n                var getUserMedia = navigator.getUserMedia || navigator.webkitGetUserMedia || navigator.mozGetUserMedia || navigator.msGetUserMedia || navigator.oGetUserMedia;\n                if (!getUserMedia) {\n                    return Promise.reject(new Error('getUserMedia is not implemented in this browser'));\n                }\n                return new Promise(function (resolve, reject) {\n                    getUserMedia.call(navigator, constraints, resolve, reject);\n                });\n            }\n        }\n\n\n        const video = document.querySelector(\".main #video\");\n        const button = document.querySelector(\".main #button\");\n        const select = document.querySelector(\".main #select\");\n        const canvas = document.createElement('canvas');\n        const result = document.getElementById('segment-result');\n        const img = document.querySelector(\".main #img\");\n        const detect = document.querySelector(\".main #detect\");\n        const shot = document.getElementById('shot');\n\n        let currentStream;\n\n        button.addEventListener(\"click\", (event) => {\n            if (typeof currentStream !== \"undefined\") {\n                stopMediaTracks(currentStream);\n            }\n            const videoConstraints = {};\n\n            videoConstraints.width = window.innerHeight;\n            videoConstraints.height = window.innerWidth;\n            video.style.display = 'flex';\n            detect.style.display = 'grid';\n\n            //console.log(select.value)\n            if (!select.value) {\n                videoConstraints.facingMode = \"environment\";\n            } else {\n                videoConstraints.deviceId = {\n                    exact: select.value\n                };\n            }\n            const constraints = {\n                video: videoConstraints,\n                audio: false,\n            };\n            navigator.mediaDevices\n                .getUserMedia(constraints)\n                .then((stream) => {\n                    currentStream = stream;\n                    video.srcObject = stream;\n\n                    video.oncanplay = () => {\n                        video.height = video.videoHeight;\n                        video.width = video.videoWidth;\n                        video.oncanplay = null;\n\n                        result.style.display = 'block';\n                        result.width = video.videoWidth;\n                        result.height = video.videoHeight;\n                        canvas.width = video.videoWidth;\n                        canvas.height = video.videoHeight;\n\n                        //window._t = setInterval(_detect, 5000);\n                    };\n\n                    return navigator.mediaDevices.enumerateDevices();\n                })\n                .then(gotDevices)\n                .catch((error) => {\n                    console.error(error);\n                });\n        });\n\n        shot.addEventListener(\"click\", startDetect);\n        result.addEventListener(\"click\",project);\n        document.getElementById('clear').addEventListener('click',clearAll);\n\n        async function startDetect() {\n\n            //if(window._t) window.clearInterval(window._t);\n\n            let ds = document.querySelectorAll('.main #detect div div');\n            Array.from(ds, d => d.classList.add('animation-opacity'));\n            await segment();\n            stopDetect();\n            // await project();\n        }\n\n        function stopDetect() {\n            let ds = document.querySelectorAll('.main #detect div div');\n            Array.from(ds, d => d.classList.remove('animation-opacity'));\n        }\n\n        async function project() {\n            let ds = document.querySelectorAll('.main #detect div div');\n            Array.from(ds, d => d.classList.add('animation-opacity'));\n\n            canvas.getContext(\"2d\").globalCompositeOperation = \"source-over\";\n            canvas.getContext(\"2d\").drawImage(video, 0, 0, canvas.width, canvas.height);\n            let base64 = canvas.toDataURL(\"image/jpeg\",0.5).replace(/^data:image\\/\\w+;base64,/, \"\");\n\n            let resultBase64 = result.toDataURL().replace(/^data:image\\/\\w+;base64,/, \"\");\n            await postData('/project', {\n                view:base64,\n                result:resultBase64,\n                size:JSON.parse(result.getAttribute('size'))\n            });\n            stopDetect();\n            result.getContext(\"2d\").clearRect(0,0,result.width,result.height);\n            shot.style.display='block';\n            detect.classList.remove('play');\n            //window._t = setInterval(_detect, 5000);\n        }\n\n        function stopMediaTracks(stream) {\n            stream.getTracks().forEach((track) => {\n                track.stop();\n            });\n        }\n\n        function gotDevices(mediaDevices) {\n            select.innerHTML = \"\";\n            //select.appendChild(document.createElement(\"option\"));\n            let count = 1;\n            // postData('/test', mediaDevices)\n\n            mediaDevices.forEach((mediaDevice) => {\n                if (mediaDevice.kind === \"videoinput\") {\n                    const option = document.createElement(\"option\");\n                    option.value = mediaDevice.deviceId;\n                    const label = mediaDevice.label || `Camera ${count++}`;\n                    const textNode = document.createTextNode(label);\n                    option.appendChild(textNode);\n                    select.appendChild(option);\n                }\n            });\n        }\n\n        function clearAll(){\n            postData('/clear');\n        }\n\n        async function _detect() {\n            let c =document.createElement('canvas');\n            let ctx=c.getContext('2d');\n            c.width=canvas.width;\n            c.height=canvas.height;\n            ctx.globalCompositeOperation = \"source-over\";\n            ctx.drawImage(video, 0, 0, c.width, c.height);\n            let base64 = c.toDataURL(\"image/jpeg\",0.75);\n            base64 = base64.replace(/^data:image\\/\\w+;base64,/, \"\");\n            postData('/face', {\n                    base64\n                }).then(res => {\n                    let count=res.count;\n                    if(count===1){\n                        shot.classList.add('animation-border')\n                    }else{\n                        shot.classList.remove('animation-border')\n                    }\n                });\n        }\n\n        async function segment() {\n            shot.style.display = \"none\";\n            detect.classList.add('play');\n\n            // let c = document.createElement(\"canvas\");\n            canvas.getContext(\"2d\").globalCompositeOperation = \"source-over\";\n            canvas.getContext(\"2d\").drawImage(video, 0, 0, canvas.width, canvas.height);\n            let base64 = canvas.toDataURL(\"image/jpeg\",0.9);\n            base64 = base64.replace(/^data:image\\/\\w+;base64,/, \"\");\n\n            return new Promise((resolve, reject) => {\n                postData('/segment', {\n                    base64\n                }).then(res => {\n                    let mask = res.base64;\n                    let img = new Image();\n                    img.src = 'data:image/png;base64,' + mask;\n                    img.onload = () => {\n                        canvas.getContext(\"2d\").globalCompositeOperation = \"destination-in\";\n                        canvas.getContext(\"2d\").drawImage(img, 0, 0, canvas.width, canvas.height);\n\n                        const { top, left, right, bottom } = detectEdges(canvas);\n                        // 裁切\n                        let w = canvas.width - left - right,\n                            h = canvas.height - top - bottom;\n                        result.width = w;\n                        result.height = h;\n\n                        result.getContext(\"2d\").clearRect(0, 0, w, h);\n                        result.getContext(\"2d\").drawImage(canvas, left, top, w, h, 0, 0, w, h);\n                        \n                        let width= parseInt(window.innerWidth * 0.5),\n                            height=parseInt(window.innerWidth * 0.5 * h / w),\n                            x=parseInt(0.25 * window.innerWidth),\n                            y=parseInt((window.innerHeight-height)/2);\n                        result.style.width = width + 'px';\n                        result.style.height = height + 'px';\n                        result.style.left = x+ 'px';\n                        result.style.top = y+'px';\n                        result.setAttribute('size',JSON.stringify({\n                            w,h\n                        }));\n                        resolve({\n                            w,h\n                        })\n                    }\n                });\n            })\n        }\n\n        navigator.mediaDevices.enumerateDevices().then(gotDevices);\n\n\n\n        function postData(url, data) {\n            // Default options are marked with *\n            return fetch(url, {\n                body: JSON.stringify(data), // must match 'Content-Type' header\n                cache: 'no-cache', // *default, no-cache, reload, force-cache, only-if-cached\n                credentials: 'same-origin', // include, same-origin, *omit\n                headers: {\n                    'user-agent': 'Mozilla/4.0 MDN Example',\n                    'content-type': 'application/json'\n                },\n                method: 'POST', // *GET, POST, PUT, DELETE, etc.\n                mode: 'cors', // no-cors, cors, *same-origin\n                redirect: 'follow', // manual, *follow, error\n                referrer: 'no-referrer', // *client, no-referrer\n            })\n                .then(response => response.json()) // parses response to JSON\n        }\n\n\n        /* const ws = new WebSocket('wss://' + window.location.hostname + ':443');\n        ws.onmessage = (ms) => {\n            console.log(ms)\n        }; */\n\n    </script>\n\n\n</body>\n\n</html>"
  },
  {
    "path": "client/package.json",
    "content": "{\n    \"name\": \"arlab\",\n    \"author\": {\n        \"name\": \"shadow\",\n        \"email\": \"389570357@qq.com\"\n    },\n    \"homepage\": \"https://github.com/shadowcz007/AR-lab\",\n    \"version\": \"0.1.0\",\n    \"description\": \"AR lab\",\n    \"license\": \"MIT\",\n    \"repository\": \"\",\n    \"dependencies\": {\n        \"@small-tech/https\": \"^2.1.2\",\n        \"internal-ip\": \"^6.2.0\",\n        \"node-fetch\": \"^2.6.1\",\n        \"robotjs\": \"^0.6.0\"\n    },\n    \"devDependencies\": {\n        \"electron\": \"^12.0.0\",\n        \"electron-rebuild\": \"^2.3.5\"\n    },\n    \"main\": \"main.js\",\n    \"scripts\": {\n        \"start\": \"electron .\"\n    }\n}\n"
  },
  {
    "path": "server/camera.py",
    "content": "import asyncio\nimport cv2\nimport paddlehub as hub\nimport numpy as np \n# 模型加载\n# use_gpu：是否使用GPU进行预测\nmodel = hub.Module(name='MiDaS_Small', use_gpu=False)\n\n# 模型预测\ndef predict(images):\n    return model.depth_estimation(images=images)\n\ndef get_depth_map(depth, bits=1):\n    depth_min = depth.min()\n    depth_max = depth.max()\n\n    max_val = (2**(8 * bits)) - 1\n\n    if depth_max - depth_min > np.finfo(\"float\").eps:\n        out = max_val * (depth - depth_min) / (depth_max - depth_min)\n    else:\n        out = np.zeros(depth.shape, dtype=depth.type)\n    if bits == 1:\n        return out.astype(\"uint8\")\n    elif bits == 2:\n        return out.astype(\"uint16\")\n\ndef run(im):\n    height, width = im.shape[:2]\n    # 缩小图像  \n    size = (int(width*0.3), int(height*0.5))  \n    shrink = cv2.resize(im, size, interpolation=cv2.INTER_AREA) \n\n    img=predict([shrink])[0]\n    img=get_depth_map(img)\n    img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)\n\n    # 放大图像  \n    size = (width, height)  \n    img = cv2.resize(img, size, interpolation=cv2.INTER_AREA) \n\n    return img\n  \n\n\nclass Camera():\n    \"\"\"Using Camera as stream\"\"\"\n\n    def __init__(self):\n        self.video_source = 0\n        self.depth_estimation=[]\n        self.depth_estimation_file='depth_estimation.jpg'\n\n    async def frames(self):\n        camera = cv2.VideoCapture(self.video_source)\n        if not camera.isOpened():\n            raise RuntimeError('Could not start camera.')\n\n        while True:\n            _, img = camera.read()\n            \n            if len(self.depth_estimation)<5:\n                dp=run(img)\n                self.depth_estimation.append(dp)\n                cv2.imwrite(self.depth_estimation_file,dp)\n\n            yield cv2.imencode('.jpg', img)[1].tobytes()\n            await asyncio.sleep(1/120)\n\n    async def stream(self, rsp):\n        async for frame in self.frames():\n            # frame=run(frame)\n            await rsp.write(\n                b'--frame\\r\\n'\n                b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n'\n            )\n\n"
  },
  {
    "path": "server/main.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: sanic in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (20.9.0)\\n\",\n      \"Requirement already satisfied: httptools>=0.0.10 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from sanic) (0.1.1)\\n\",\n      \"Requirement already satisfied: websockets<9.0,>=8.1 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from sanic) (8.1)\\n\",\n      \"Requirement already satisfied: multidict<5.0,>=4.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from sanic) (4.7.6)\\n\",\n      \"Requirement already satisfied: httpx==0.15.4 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from sanic) (0.15.4)\\n\",\n      \"Requirement already satisfied: ujson>=1.35 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from sanic) (1.35)\\n\",\n      \"Requirement already satisfied: aiofiles>=0.3.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from sanic) (0.6.0)\\n\",\n      \"Requirement already satisfied: uvloop>=0.5.3 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from sanic) (0.14.0)\\n\",\n      \"Requirement already satisfied: certifi in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from httpx==0.15.4->sanic) (2020.12.5)\\n\",\n      \"Requirement already satisfied: rfc3986[idna2008]<2,>=1.3 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from httpx==0.15.4->sanic) (1.4.0)\\n\",\n      \"Requirement already satisfied: sniffio in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from httpx==0.15.4->sanic) (1.2.0)\\n\",\n      \"Requirement already satisfied: httpcore==0.11.* in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from httpx==0.15.4->sanic) (0.11.1)\\n\",\n      \"Requirement already satisfied: h11<0.10,>=0.8 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from httpcore==0.11.*->httpx==0.15.4->sanic) (0.9.0)\\n\",\n      \"Requirement already satisfied: idna in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from rfc3986[idna2008]<2,>=1.3->httpx==0.15.4->sanic) (2.10)\\n\",\n      \"WARNING: You are using pip version 20.3.3; however, version 21.0.1 is available.\\n\",\n      \"You should consider upgrading via the '/Users/shadow/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install sanic\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"import cv2\\n\",\n    \"import paddlehub as hub\\n\",\n    \"import numpy as np \\n\",\n    \"\\n\",\n    \"# 模型加载\\n\",\n    \"# use_gpu：是否使用GPU进行预测\\n\",\n    \"model = hub.Module(name='MiDaS_Small', use_gpu=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 模型预测\\n\",\n    \"def predict(images):\\n\",\n    \"    return model.depth_estimation(images=images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_depth_map(depth, bits=1):\\n\",\n    \"    depth_min = depth.min()\\n\",\n    \"    depth_max = depth.max()\\n\",\n    \"\\n\",\n    \"    max_val = (2**(8 * bits)) - 1\\n\",\n    \"\\n\",\n    \"    if depth_max - depth_min > np.finfo(\\\"float\\\").eps:\\n\",\n    \"        out = max_val * (depth - depth_min) / (depth_max - depth_min)\\n\",\n    \"    else:\\n\",\n    \"        out = np.zeros(depth.shape, dtype=depth.type)\\n\",\n    \"    if bits == 1:\\n\",\n    \"        return out.astype(\\\"uint8\\\")\\n\",\n    \"    elif bits == 2:\\n\",\n    \"        return out.astype(\\\"uint16\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def show(_im):\\n\",\n    \"    plt.figure(figsize=(4,4))\\n\",\n    \"    _img = _im[:,:,::-1] # 必须为 ::-1\\n\",\n    \"    plt.imshow(_img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 288x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 288x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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kJNkNlWwUXIgWlppHgA2lMhq45sdRtQ8JnVHxOFXpZoLVYeKcFEcX5jiKOKg5xiuSE14IP0XbLkgldYNEFFtE8KQ/0QZgy5OrJBHZTYagZyQXVypzFcacTuvENgm/p5FQRHFpd+8z7j9PMz2mG492hhv3t6K0oSqmeUmFU8+ycCsNkGYKsFiY4EXzXUcuEdx5xjjwVnPe4ltLFK1WEMSeSNlMm5vk6ypFHcTLRLawwz20+t1oB31Ke6me3wLy8GVhtHpClKYRSlG+MOHxIjAMIOWUmpwcgck7PzQ/w9OfDwzwg0+1bBEIUclacRFSFAESpfOJhR9cF7q86fvqtLV95NjBJTxLP02mytGRWsnicKkkdb2yFfRk5u55YUPjY5dLi9iI83SemhadkYTtMvPJwwWXvgWPOOniHD7bIRWyiOt/y78UWHKrUUqkNnc6pMk6F2NnOlfPRLZ53CRXQ4LjZJ4abysOzyKrzQCEV5clmYpcd20m5HhIFZ6HIbs96EbjsejR2lDQSF0v2m4ltVsYps+rMcKm3bFBF28IQqA0HP11AUkE9FKOVVcnM7q5DLQZ2AS0ZLZkogGZqSQTfs4gBFcGrULXSBc92qJRS6aNHxSO1MOWCIBTaYuCYtnSuAgUnHdGHlh0WSgMS382TODUG85w6XM4HGIk7IQjHWF2rcRyqX+LrhG/4i3qhilCJoAUpI3hPv1jgBXKaKJMyAaqOgIA7eiekOVtjniY0A6VqAK/WA/amLRypaqQ9x0zSEsR7m1/UBs5WtOFJ5Rt4UvAhMQ7aLjilDMy7L6RGHLJd2BHajTuASS3VM+9qWu39MXiKZhQlqRLEburHLjwvrjy/4mNrvvZ05O++vWVbA198Z8A5z7pTxlrRYoj8vgrDrtJNAYrntduRl89HPvfokmfXG1JSkgrPbhJVlc+8sGYRzY2rTlBxeAxA0rajlGrEqTQZC9G12F5EyY3ANGTFjVPLYwNS8U4MXFRhmApPbgee7TL7McOLjpfvRdCKw7GdhC+9s+GdnWNfM6ves/I22WqyvLhzji4IY8oMqXC9V7L05FwZSkv7OpoxACMNNVagzqG+mnFAcHMMrB5BKSVDIxup8zi/QPJIjI7VYkUIgc4bJ4BaKQgu9tSkqLPQMedKlI7qW9gA1FwsLdgmtiUKlEUXmXIB8QTnLJlaajMORy9N9S5PQjnyKjhc66m3fTQSB3sh7QaItHsRUG/egK8FakGxzIoPHl+bMfILiippGhAtBi47oYvRvEqnzLRWDe3Zz8aABixW5Q6XUlwDbFvqG8tKgGFEuYGRMhtJNU8d75D6jRCHD4txwHK2HQKSQSshBKSh1RZ7Wwqva2AktNBtzhmX0liGHF0uZ2AOHC80do6LDr6977nXTeCWfOaB4wtvT7y1FaIK6iPeVXIpVPHmogOb6vi7V4XNtCVKZpSeXipZCvsaeHq75RMP1pbyq8ZOpBZc8yLylJnS3jwKbKfMuZKngW61tF2kQCm+IevCPlf6KAQxsHCXlevtQKmBmgrBRW52xid443ZkKMowKTlGJs34zuLRkQ6PsiyB6kGrsFPhzdtbknZ0ZIpWdrkQotCJI6niXbVFhzN3vWUHLE1YcRrmBwG1EsVSZKoVDQGcP+xiXbckukIgs+wMvBxSoQuRTGLc7S1tiLJedGzGwpAbEatzLMVTvGdESbWSCyCFdbeipEQWoVTz/JzBBag2wK5NDMN3jinl2QbcZUOczM220PTumw7pY20/ewDXkTXhq9Cp0gePUT+ERGTKRu5zzln6NgQiltkwfgK4xrBUNcDZe8+UCqU6e92sRwOBmzeKO+ApLRF89JZopQQiBxDeHpeakfsHzXP4ex9KLgmqJ1YQr5R0ZHlJW/R6Qhs9GIgT1pyqorOVPkG3bcyGpiICnSgv3V8xpsrnxfPVZ45JKxdRiR46H7hwgae3e7yPBq5VRXzgtlS0Qt4VXjnvWLjKVDwuBlIudN7jWgy4nxKKx6fMsL2h6wNOLNuQi1IQpqlwmzwZYdVFqnpUC16UVBOxGnhU8aTsGMeK75RFD0PpuUrK5lniq5vE8yGz9oGUK+IdUzOsIsK2wG6XSaVw2S8ITsEtoSovXZ7xZLfnJleid/QxkBWCwM24Y8qZKgFVmfE9ROXg4luYZx6ecwG0Gptyvvv2SBoCL+x3A77rQALPrnf4OtIvO2Lv8T6SU2EZhEVntN9aHXkSRiojBZoHUAQ202TuvZO2exqa751vsXabC84jZZ5xRkNmxrb05H3zrJyxFVrY/77hCXfer67D+4qWkamMBjK7QBGFklh2noJjTGaaLAMEkguII3vAe0JQilZSyRSEfIIx0DyKY2hkC/0IXArvDo9ExMKS+VLFEgDfwDZ8OIyDc47gPVpAq2v4i1KlkqU2S1tRrZRsOfzFYtHwibsPbB4zYebwNwEnJ+6kOFQqPgp+Cnzygee1myeEfoVTx9oXepdYXvZcDxN97KipIMExlokJ4fE204vwsUVAU+GdXeB2P3L/fM1+2HHWezwQpJKmAVVhGo3z3vcLog/NQCQePxt557ayunA4V7i/cKydsJ+UrNB7x9iKp/rlAhQWXc9b1yM3k7mVQxGmEtiNCk7ZVmHIHqeZhU7UovRhwSgwjgMXi2ApRQriM/dXgbIVhlLoXOV+73mwDLy9iVxtE2MVElgKGBpw1twGwfCQ+f6rO/HDDTB2eWIqil/0OCkGpk07eq/ERaQq5KzshoncFr8UJRfPmAtZjdU6u9YWcgaqM9fbTqsxRVsY1UcDA1NKlnHBiusMvjlNbTag9SRNesdreNd4N3X7iINZujb52M5EcM4TysgqWm3GLmUDHhuGYEa1IJrpvGFHQ3VUPMqM+xxRSamnhkDRdiSd+QCVg0G7w+kQuXNM6j8E2QrnhPOzJWnMBiY1wKeUSmnAy4HzUGjx+cjZGmIMJ+h5i7VqZa67lNl1VBA/AzoW73sniCrLdeHbxXF73fHW5FnFwqOlo3NK7IXNXtnsJnLsuC2JLkaud5XBCU/HzMcvz6l5z3ZKPBsn3twJm/3A2cLxsYsF92IhiOWXHYIk6PuezXZgM45I6Lm4PGObNjx+fsvoA3q2IE9GLd5nS7mWnCzH7iyPHcXTBxjGwm5TmZjAebIPZghzQWs0wo1XOipnXeRqGrgtnuuxsvJC7DtupwQKfe+ZxsxENb6BKl3sePHSczPB89FwE5rrfsgHtNj+EN83z8J+No6DKxPS9xSEqtA7JXRCUcvil1pJw44QOjofKeLJKozZQojgHV1QNEujW3vLIM1eiQAzdbiCc56xKDWnA04iAk4qwXmiQFTjbuxKZSpqzNKGZRyqT/Ukyhead3p3Ds8ByrwfK4K4aMzIkggYdyGnwqQBiQsQJeTRKNlimIPxRZxBObWRzoRmAJqBcMfFb2tF7p6Hc0fUtq0DrccznNeD/jyCDR8K4yBioIyTI+hWa6P+qhXkHMlQVqCiU2ZHZbVeEnwjWMuxKAnlkOtVVasVaHRUK5qrB0DK4TmL8Ms+fsb51cjZoqOXiYuzM6Rm/EXPflKG4nhzV3h8M8Cip0vKhVOYtrx8bwmu8vxmIhWF0FPxPLvJuFVg5ZUoDu+NR18ECoHtfiLXgYv1kkeXPeuznnc2A8+2mX3uCdFzO1WGVIliHsUmw9IrH+/hzC85jwP3H0XGKZAmC8OCZK698LQqSQJVwPeO5AL7KkzZyDbOBXQqbMcWn/qZzejY5oIbMU8qBhTIquyyGVfVFrsehnJnCr7L5S6iODFew7pbonlLSgWVQM6JUgr9YkFKmSog0VPFUV1CgmEX3lu1KMUWTq3zrmhHtbNxxieplQPD0gmRSnDQeUfnYeUFR2SXHFPKlAYKm1t+ugCPlyINqH3POEkfwunCtVIALYGp5EPVZAiCqy0cFqULzjCkvgPxVNcyCiilVvPE5sOcYh9wWOUHKQKx83BKK1rUBibL0YNo7//QhxVCCy1CACmk3PLlTi3NEyw9eMQf7JJyFoZdol8oIbpDPv6UenwIL9S8kNlozBPHynUj0RVevnQ8vLhks9uz8D0uQC2eGBznPaSSeHG9YnhwwdvXE7sxcb5esZDCsq8sushLq0vEBfZT4ctvXVHxLLueVQgIE0Ey4ioljzZpUfKUGLqO0HUsZeJT91e8/mzirefXaIjcVGUqsO4Du5zYFce5r7y4dLz8grJ7O1PxXHaB54yoeLrg8LkStLKrlaSVlB2bXBhyw19EGFXIxVK3oEipoAY8qg8kbOdcSOR8EdiPtxQREo4iDpEjOc3wiONEpYFeWlvNSLcg9FZmnKY9ORdiXCGa8V3A1cAwFlJWqs9o3hBjx7KzcwRp4KRHc7FiM2mwtBgU59q/YIVw5lrbf70zDCV6pfMQvDCmwqYoU61Um4jMu+vPl9Y8hB16l38zv0cxz31SQALOR3AFr4laJkSFEiIxCHmaCCESYseYCrkWw5kE8x7eh7Jxeh6nm6KqhXHSUsnv5nLMn6n1XdXC7xofCuMwD+ecofIoUqHkxh/HiB9WZxAOtetUIWVFh4lOHSF0s9N0eFhwvP46x156ekMbidTbjXU5c76MCJVUlaEmFAPYui6gNdOXiY/fB4/HO2XSgDrFVaWPymqRcefKWXfBs13hvFMcjkpvk7cMyJQICud9oe8iV6mQ9wU/JdZrz2df6Fh2gZ947QaW54jAVCrDFDjrHUjitc3EKy7y0vmCSuXRYsnTfeDt24kxF2Lnub8IFIVdVp7vCleAz2q1EjimVkchnYGstaVDjbCrjC0LdDVOvLDynK16yLAZUzMi7R4L5FxB3IGXwZyhd7bcYrfAOUhpgDwhccHkA6QE0sI98fjgWHaWk+86YbFcMSVhnywtl3M1bEqkGYOT2FrqMV3XUHrv3NFoeIc6GKoyqZByJTHXjFhtgiK4eneHnQHt9w5tobt5SrXpbQg2r8SVQ5gCSnDeGJ7FMeY9zgVKLsTQUf2CbYJcxZiM89xVDHNopzQb3tPFPl+/m40lhk0od43b6c9u5q98wPhQGId33/LDwg6AFgNfmvWneQQWhdnFF3UMI4Q6EUMAEYLKocR3TqVZcv7Ii7BQzxnooxanBi8mrIGHKoxJKImWwbB6jpyBkolL0ytYOcNIdmNiEW0BRCc8ugxcrjKgDCmTqyNNntgv8VqIFfo+EGMH14W/+3jgLDjcdk9c9bx40fGxM3j1dsvUrXAZsma6Ah/vHS+uejqvrGIwspcrLC8jqyhcD4WEA8l0zXDdLgNv7EAkkRH2YyIDxbWpLKA4COZpmR01YldWeGc3WT7eF/qoLKQwZjksDgmOrPXwvMzbN1JQcEKHicKMQHFGbqpTMrESBOfARYjB0wdFq2fSyDu3Q8MCbF5Eb5wLFU8QK0hLtVBFrS5ELIa39doWeFXEzfCdb9kvR2ml1DhlhsKZjQwcwlBbRL4ZxHKYtdqOV8W8eycerTNxrRWktXkdveCxsEqcecS1VopEJjEpAgubnIVVQhPvUWqdPaL3wxBbelNmgZv5vGe8bd4U9RBazOH2LyisEJE/Avxm4LGq/iPttQfAfwh8Bvgq8FtV9bmYKfte4DcBO+B3qOqP/3zHmIe2k4+NXlwRtNUZiMwEKFpthe0ss0Wf6y8ObEowkKd97506vne5Y7NAhtGcjVWpYmIdl2ehPXArBqrVdqLl0lJWvoUypRRW0RG9FeeYvkMmNvR6HQPjBBRYr3qCM52Jopbn/vj9yM0+sR8LGcdUC0wDH7+/RmXPW2ng6diz6h0vnwnf/uI5Z16psbJwysJZReeYYdFF7k2Bx1eFTRK6VWAZPXk7chHgE5dLQNjlnre2e3ZFLYYXMxBVrBpwzqMXOcantTZkvJFvnG+oNwYs+5P7ehoCFlUGIqDU4KlpMjzHC9RC33nUd9ScKLVwMxQL9zrHmc+knPDdCpwjiFJxpNJqPA5T3FmlrhzDDMxJIoaI99XEc2wWmOdUCmXWwJgXH7SS9xZ2uraHz1oVLWKCIx5RsTnm21tnT2EmfSNCVkduRxDRhnsB0tn9dKb9MKfNS20gpDvkhOaFcpi/x/kshz/pYU20Y83vaV7z6Xr7hYYVfxT4PuCPn7z2e4C/oKq/X6wvxe8B/nfAbwQ+3/77x4H/e/v3mxoihqpa5aqjOpjEOAEGUDq6EPHBm3CGSCNIHS/2ILelgDtxvRog45w7FHGdfNDitMZZFwUnig/N0qMkKjUYmlwnx+Y2sVx6lp1VOwbvWfXBUnaN46BUq5Go5uX0nUMw4ZVDDr5UdlMi58zDbmDnIyEuQJXXn12RJPL5F8/4lXHBX/r6c7IsCS7y+tbi1vurYLTtDlYO+s4RFPoInfc8voVtzhSvnHeBVCt1SNyM2cAy5wgVfGelwaWo6Ti03eWAbJ/MqqLmYcz3zHa4IzlnxnTeE5sfUHwrZCNPOEkslhFVZZSAC+BLoV+sERdh3FMxJSvqjuh7Bu2sSrRJzJncm5v39tMZBVhZfq2VRDmEloKVjoTg8HqCkTCDqScl3c0LoQnv1CrNCbVFjJpgURUjOR5CnOMEay5/o9c38ZUgl9AyMrTPmOelTUMiHHQtxLezU9O+mDN0NEgdONE4PdaNHMhO83M4WS+nRuX9xs9rHFT1L4vIZ9718m8BfkP7+Y8BfxEzDr8F+ONqd/Wvicg9EfmYqr758x2ntHJe33akqpXdbs8wJcRZVV8uhaFkOu0a96EcbowZQXMXa4FUC0Xqoc5dm1TbnOacUdt5ssxeSNGMqMOLpTmrzso9FeexEm4Xca6j5AGN9tCtrLbh5YdJ1SYRDdmXStdZtWLFCDi1Vm5uNyyi5+F5zzYFYjQFphcuztmPhUjBdZl/4lP3+OKTiS88vSLEJecVHp3Bxy8dN5K4t+4562HhlSDKKlYWvbIrsNkXS61FqyFZ+RW728HQBWlCuqHgkgOdrx3AFJWcFmo14RyRcphTh+yEb7v2ybNQacm32a3l6BojQog9UhzjOLAMjoVksibiwpiBeUqkmsFB9J4QA8MwUQQIEfEe8c5Kopll1uQ9cepsmhDfFqgBdkEwhadq3scsqntgG6oZcWnBiHLMFijaqj9N38Kp6WTSyEZCq5Q8Kf7SVis95zOc76laCW1uzoENXg9p4IOMTXsmOnsAs3fAnLBv72yh3GEamht0NHTu6Dkf5ukHjL9fzOGlkwX/FvBS+/mDZOnfYxzkRH36k596xR6gJaFBhN1mz5SMU1BKMTWkWqmiDMNA13W2uBoAdChYaTfNUlIVKU0IwxuoI4cFC+gc0x2r+GpVJsnE6uijWfqZcCNY4ZQLptKUkxJDOIrDMj9MO6f53s+TSuEApipmxBShi0twjrjo6H1hTEoIHedd5SwK4zSx309U8XzbpXBzq3z5ast+sWbcwqgjLy8FGIkuNCDKvJToE+eLwPNt4dlQmJzj9duJSkZih6uJUNtJinlnrmUrDrtMdVamLZy43q1QrLawD1Owsmuk6SHMMbzcuRkGApuupAue6gVfJ4JataZGqwotpeK6JSWPTLkyqoAEqo5oxUhMqk2r0c7qgOS9z/DzE2rx9+wR+M526JRSW4qz7qXVH0jDpsBRckY1m5KTRLSJuUTnEYXxkAoVxDtkBs/B+AmqVvvQBFwM7Wn36rCRC++uKNWTBV6qzbO58ncuzpImI+c5LTuQw/M5Guk26vvhF8fxCwYkVVXlrjLmN/u5g/r0r/41v7LZhhafnqg6p5Tw3rNerw8lsdM0MQwDMSysOKk9gBnI9N4zTckmrprUmndyyPUaQ64yG+FaTOFZW6VaVjXB0CoGcNJSVQJJC0EKfScsYncoHzYuRT28T1sRlV1Xk4CvpgKVc6FooJRMiB7Cgie3IxdiwOd2qmx2W/I0sV6vWazOGaZMCIEVO37Vyyuiz/zMzS21u8e2Op7uRu6vzqjiGaopTteaqI2pOCHcZGVwjqsaGWrG14S3MjFTBcoBwRB7amYVrdhtGDK5CZ6C1XiAGQLv76oJHcAwBZWjCz/fo3kErB2AePAh0KOWfRLHPiVKybgQTXi2Cbf0wdvz7JZsdxNaBroYUe8bkcod6NLvl4Z0rZT+1HqYArYZa3cSk3TO42oTBHImZhMdrJYR5yPjVFE6cqmUkswMts1n9kLmY89aEVVA5pC3zlW2evA2Z9zT7l8LR+sMjFbmZSaNnTUjaY0KZpiIysFwvzt1+R7j8J5UwN3x92sc3p7DBRH5GPC4vf5NydK/Z5wALLXFRQdGZK2s1mu89+yHPeJN4dn7wG430HcRsB3ZgEpHKZVUCrnMN6PdtHaznJdGxnHtQTRJ71IpClN1OFFKmei7ShcDseWQq5gOoGuxIe6I1heqGRna9CvHSZKbIIcTA8GGMeM8pDqRiMY/uB558bzDqdUODNVze5tYrQIXiw7JIyVlzlc9n7oPvfds9rfUtET6jjevJ3aT46J3RN88sH3lJgmvbZSng5KYrN5COmihg85pPMQ+VwvLZSC6ipAZvfB0D6qBIPVAV54BrTnWtcrYGQl3dxyG42M+7maKEgQWPhA8aHHsx0wp5jlNJaOpsAiOi/UCN6srFeHewlPKBNGTXGSsjqyeAyGgWX6dwwyx6lznrLrxQH5gjsVbPUarLjVBYiVE33QPImCh6mZyzRglvBOCMyzHNCDdwa0X5UT8tVVGtltwV0+CtuvXQ2q2Wt1dq3adx0k40D4H4NoPUcy7oRlJPXUL9JjWPBoI5dRQvnv8/RqHHwL+ZeD3t3//k5PX/xUR+ZMYEHn9zeANh9OUOX5tcVSthwsy8VXPbrtjtYKu65nCxOzLKs7kyNTKhc2i1wPZqcLBKvvqic4wZpE5hWcq11Vp3oMwiWccK32ZOF9CcOBrq/RUoWDkpDqB90rnjEyUq9I5E+xQlCxws8u4qqyXC4p6pjLi1eLezifOY+Cd20x0E+e9515nHIS39/DO1cCZNymyEBd8NkZWknhpZbv8ImRWq8h+qryzE966GfjYRUS98Pr1xK5GxipkrIS9F0+VSpHZr3T4DJ9cw7ffW0LKSHA8HjJf38F5Z5N4XytVpIncmqGsHFF+EJg9CdV29XJwXWvDXlQLvTiWIRzCtbEIOWfEeVzowTkuQqaLE4uuYxGNc5Ij9OLQrLhuCV1HLdomsjZEcsaQOJ4XgFrKsXVPYhaKVJTgwwEvmUMf7yreZ6QKWh04z1Qq4iu9WFbNS9PyLB5XHZ03yfpUDFRUXEuzCv4QzliIqxwJStp6gWgDGW2dmzc7X4EZlhmIbKn6GRCd11E7/8qJx9aucsYjDqbBvZcGfjq+mVTmn8DAx4ci8hrwr2FG4T8Skd8JfA34re3tfxZLY76KpTL/Jz/f97djHK+sgTmLxYL9ZoOIsB8GFotFe4vJuXsf6Lr+EOOmNJkClPcHFPmUBTlrCYoIIdpDM+NTmEU9UWdinthEyBXGVMnF4QQWQYgRk5iXVv6rkPJEVY+PQlZlzAX1Nulr05LofGDMidvBqjSdj5bG04rWQt91FJR39sq+KpfdiuH2FkfEozy/2ZF9z2ZSnly/xaPze9w/v4C0QSjk3WiTIARGPDdZcBq4TYnROcZcSFhfhSBNPg1jHAZVHi07XjmDe10mxkoWuE3QaSVUzyoIQSKTwkBmln8TZsD1xEA07GHO0szPN4hr7xWcmNYRLWWXnKOISaX7Phw0MXXMpDJRtDIpFL+EVKgqxG7ZcJBWrn+cUXdc6pOZdue301TgKR3IEpaubUz2qnfKwhW6KKhbkqZMKYUo0HvBmz6LLUy17xAcVDWvqB3f+nvM3qUcsYS5kGM+hxZCvJt5efIOO2OnuNkrqHW2BAejS3s+pvQ9A8SzM/ULDCtU9V/4gD/90+/zXgX+5z/fd77f6Jr7Nbuci0WPlsJmGMg5c3t7C7TF3XaccRpRheB7RIRhGFiv14cUz3xjTbzVrPWBUo2FBKdMyaPX1gxFUYpazDtW0FypBAKK90c3LUQhTZWdenZZUQLTZJTgXZpY4em9ib/ss4AWppRYLXqbE1UoJbMMsCuVq6Gy8J71+ozr64nbzQaJgeys90V1a75+M3G134JmWCyoPlDzSC4T2S94azMyakZ8x6WHVee4TsLrYyEI1lxmVgMSLJ4G1sETPOyzcq8XPnnueWdQRMwTGycLvZw33YHasjzzPTelIQMuranXcUG6JjQ7B9dZ5ZgNUtM1NBzDvC6cp+8uSGMijXu8CwSNiC+49YIudkwKVTKluAbEfbA03Mk8tbl0kuqWOf5v1xGkGrNSZ1k26xQFiqZC8J5+0ZuATylQM2H2o2ptXBeH+oorLfsj7iCUq83bneepqvEe3m3UTina85iVocyTmD0LW/yzi+z1JDPU6OvSPAxLmhzX2geNDwVDEsAKVvQg+YYIy/WauFgwTiMpmbJTjJGu70GVxaJnu92R6shq2SNUUhpZLK2ce7bKpZV7H3jmYBWah+FajCqod2iRE4JNZaomgzZqYaweGUZCgC50LIKQdCLVaIu8CtOU6IOnTMrz7UhdnuOdEmJAxgRYh6QUjEMhoqw7z7SspK2lz3apIGGBuIQ6Rx8CqWTSMCE+kMVxVRyfvPcA7x1Prm65t7JMxTDuka5jk6CvmU8tA5++6Hk+FaYniW0NpIa5oLa772smE6kFhlpJai0EF8H4AebiWmcuX7SRfo503dkzmxWjURrQecpQ5bA7Vox5WOfJ3MIPa2TUShzacwuLyKqb6DE+SLdYmXKUVFyy+5OxdXGaApwTfe8G3maH+1gmdvQQBGPJRjkSvmTWCHGd4UteKWK9IqqaOnfwDq9iKfAukrJRz5MXgo/4at+X1ejqFQPFVVubPLXajmP4MJ+NnedsBOxvcvjZbPvBL0H9Edhodh+tMkdb5j3MvV+0fiPb8GExDi3WOnUN2w/RtW5A7bVDIaoqEeXi/Iz9fk8ug3V0uk14p8SuA9wJUHnMVsz6e/ZFxlQ8UIDFsQ6BlJWpVIr03OwKm31lUjWthWVHrMqwFwYPyTk2+8ouFfoonLV+kFSP4nnrdkPqPPcuIhcrT5qEKfaWIWiS4U4LQ1V22XO5DNYkOO1QERYX50w74ySEriNPmVXw7GXJm7cJr5nOC/fPL5huJ1ayZxgTl36B80INjk2aWC87Pn7R89ZWeV4nRINhKFIpDp5OhVUHrir7krlJcDspK+eRIAQP11g5t1VgGJ3cnsdRum1+VqZq1DChJkhyGM1wz0ba0VB4ivFaxLOIwjp4fDHlbJ0ycXGB7xfmqdXCsotQhLEqjnpQ8L5Lc3v/JXDqY1S1bmOuLUTFhGS8uNZi8Zhp0KaoNOMmSqY0tuukmVITPgRLbzqMKu+Mi5GqMBahiGfmv5gOhqMlNVpYfHRlZ57lgbHZKO1aZ0znXXUfwgEEdWB9R+DYEEgxb2LeFD9gfEiMg43TCzy4e602/Vj8UkkpMY6jUZZXK+7du8d2s2WaJs7PzxERYojti6wd3Bz7Ht3fE6yj3Wg7nlnW6GCByXmtfOA6Ck83hZthR3A9684RyCQc4gIihbd3kMvE/YVj2Tk6JyZ6GiYmCbz+bGQZYdVFaxJcTMtvVJMQe74dyLJgUM9QM7XVAowq5C4wDgOOSlh0lAIhCLtBWQk8uDgjp0wedjzohE2qFB1QFmwSjFPlPFV65/j0heC2lecJqhcCJmD+LCW6QViHaFWcAvjKygUqhUkrqxjofGAq1YBJf8zIOIVyskubm9+eLXKgOasqQVro1v6KGF8hiCeI7XSL4Oi8suwU0Z7ad3jpTRWpD2xzIah9b++VlSiJQG5p5VN9ibnwapYcVGblh3aurSVCkEZFavUybmZ+ioVFDkt5+qYPWqoZ9VEridZpqtVQ9Fo4xwRwvSj7ohTxLKIj10KSo4tvWMARK5m1Fg7ufzO0ZmybXP68Z747Tam8K2S2n/0hOyPvSTO/3/hQGIc7Jzi7/W0hG/3zSDKaxsZxiJHVanVgQJ6dn7HfDaSUWK1WBO/N0ou0jkL15HsbC7OWwzHl9GF4T8CxGyekCuteCCGz6oSb3Yp9doyjsqWwqyNSPSKO84Xnajvx1q4wbRL3IpxH6JaOZ5uJKRnyf1kzKxepOHZDZpcTax/o+8i0L9yOhUmlcXIVEY86WC7WLHViPxWSE3ZpawrL3YJtEWrZc/+yh5IZwpKr3UDajKwWnrjoudkmXj7zvLCEvS7YbiaKOHzTDSB6drVCNmOxL5niPHsM9CoYUOhalaXUfJxg7k5lQtvhOPHohXxiHOZ8/Ozcq68EoBPoEBYx4Cl4KVAgSaSaAoqxOktlocIigkShEKg1cZOs94M/eIqzMvMshNsA5znKaVup0EhZmJFK1RaT9w5XjbnrWy4yO0WKpYEHrWRn1KMOoasTSy90Uuh95Tx4hurY4xjUwg+t2eZ1UwKzymmh1Qe3e1SZNzG7p3JYG7NxOPAo9AQUPnldRY9p5hlnUQ6eRW1r64PGh8I4AKBzC4/jjq5qsd3MOxdg2S9ZdAvbCeQ0dwtdZ7Tq3W7Hcrmki5FUCrvtln7ZW3dl2q0WWkUmx8IhcYfvcwLni+7w4LwXohQ657mdhKuh8GyrXE0GXi27jpUv/JKH50QPJedDB2XvCh2e1MG6FxwTk1b2kzAVRaRj1MJ64XA+8HxXcFSmDNb7sOBoEnXiGGu2IrAM3eoMdcI47bhcRLxGXrveMHglZY/rhGm8QWRFjZFdKizEo6WwdMLQmvW6CkJgW6yuwhrcClPOJGaVBlP3TgqptnoDV9tfja0YDqC7KXDPuzSKMTHb33BzLH3ymjcyT8JqWFZSrbGOFxZqXl10jcmKssaR1YRyhzQRvXIZA6UanyFYV16mCpM6htpa/M0LhHljOi6QCiaVH6QRxAwDQS3FbZWdlaE1/Q0SCOoIUgmuEkVZi9ID0Vu5vEnlO3oPlEx1/ggQOhpTUg5VxofitvkeqhnnLEKZ10KrXZHD/D/xGk6BSMXA+EbVr05mO4PXkzD+fcaHwjjYJDktVDlFae33mUDiRU7ed8yhj+PAOExcXFwcelzkUti0dOhut2e5VPq+P356pkjObkP7/ZDtELGqQxG09boMVVmFzPIMXlguuB6Et263XA237PBsBhMRXUaHaOZmO3DvYs39Xln1ncmla4+6Sq2Op5PFxxedZ5/gdixsSqWI9U50wMJ5UjWUOtWI+EiMhRhMZm0/ZqZcWKunq4VJI4XeXGWtrNdr0rjHkaix57pkMp1lbw5usxWkqXPs1QqwKrSiID1MSq+mSuSctArJBvDpMS13mKciB8Ou0KjGcxXAuyeBMLsaS+dYO1hJwLuKUKycu2WZxmqGSFDUOSQX7veeRWtZn9Wj3rNJmX2BjLaaEJOaey9E2eZbmwsGsp7IrMnRAzGPyAxZ0MoyQECxqpPCSiIdJiv/NCU2TukRlk4pLpDEuCm1Widx5wzwpBrEO9O2i7NeI6laN+4qBVeNqDbzdk4ZAO+XlZxJaCIWeuTamCdzSC0fTDWHD4lxOB2nD272FmaFG5nf8D4j53xIXYYmCLPdbk1AJlrj0XEcqbWyWCyOYUv77rsYxN2faeXh3sN6EThbBqiFopkX7zm+4+VLUlKq5mZnWtWlC+zTGqcZxbo5JQnspsx2skayXSxcTcr1FFl4h+scOSVyzYgWvA+HsEoA0WiaE1KIUulzgax4v2QzjlTN1NDj44JcNgRVUirErkfKhDjHBCStIJ7gA06tF+V8zxOCeG8Nr0pBqgPvSFroRXASuJmyMQibcTjwQuU4UY8pN/v3iLHNxLPjbmdhsrL2kRWmFRFbG7lciilLO8c+F26qskA5i551ybzUm1BuVmF0jk2pbBX2eFKjyjtnNGNbSCcKrPO4k2adEwKzB0vjBGgDTR0Bx4LKioJvtSRVAqlatmdAmCTygI4kE4MkCkIfHNSCSkHF5AJDdCyr6YcMUhlJWHsG811Kq5GoDScoyIGqfTTGLa2swEnmT2uhlETJrbEvR85P9f0vjAT1D27oHet3cP/hSNaQufrs6FXMHkbOmWW/PLhZ+92OUgrr9ZphGFrbckdOmV21sOMgKnNiGOYiq/fubrNqVG67oMPPmW+XiKZwZzuMCJYmmkxqHSvA0a61Wq/WeOZ2LDzemcTbbj+SBLpl14qfPAFzO1MtzCoE8/z17cllH5sKdyZN1nrPLwJj2aMUk4kXz3YYuVyvKCVQqrnu9nV2p2cdgSNeUIm10gVhiSeLYzNlghgAGaSxDR2HnYyWsjw+rmN4aOHbEVU3nM26ahm2ZoVbXeOjTE0AplSlEmyjaPf3gQiXHtY+4rtA1onbCtfVOkxvamWLoM7TN9Ef5+wcq+Ui7XzeFZaehhtzRalrwEQVrElRq450QF+VhfOISus7oZbVUYdvejDXTHhRvAR6sO7ZUonRMC/bNpqhFOVSHb2z80sY8SuLZ1LHCCQcmVn6kBOPjKYzeXDbrKGyJpzmph9ScTWZwA0O52eq1/uPD5FxOMVGjn7S4ZnJDMgcTd0pM887ZzGmVob9npzzwTCUUui6jpSsLXtVCzdmQNMwhlk5aI4u7gQvB1Wp+fCHRQV2rq0e5OCpmX1AaybTcIzG/nQoZ71y1gsvnZ9RVNhnYZsdTzcT57vE4+s9e+3JPiLOaMuhmopQFTDZciFrxQdBNaDR4TSR0t52nn6J+EgaBmK/YPKd8QAchxy4iFJVWnNaM4ABoUeIqkRRLmMhqXIvRoasXE2VZfBM6nFSqDSNh9ntm42C6CFa0Ll687D4rFxamqvhFRyO6oRRLM02Nm0P8RairQQumurVWajsS2GYlKkqows4IDjhso+sqwUGeS7RR/GiVOdICFOth6aylpVoxCRv5x5bizor2K7NOBumElAippehwJStsK86sVChmpfj2i7tUEKj6lPNsAa1hsSIGazoTd9SMCGjrEp0iq8m4OPFQeWQ4pzToFUbZ0QtNESPOE7FxG1CM+Aej+sdUVZUdfSbJ4zP3vnA9fihMA6noMrs6h9krE5zuPPqPXzu+MtqvQZgO+wZxoHlYkmt9cimHMcDKNP3HSknNpsN5+fnBwGY02KY06q2U5rtfL7z67MJeS9dt5F65Ch8cigmm2NXZ7RsR6XvhPtd4ZUlZF2zTedcjZWvPd3wtU2hug68QX/Be1ItLfV1jKElesQLoZhgafSOaRroosf3HcXNXpe09nJtdxRMcau5p52rnDth4RzqKoN6Uk7cXwYWwcDK2yqU4tvyodUKAK6e3Ju53N1Ba1U/jyLG9pRiBqA0inFp2gYOw3sEYdFIRmfB0SGkUrgulYQVSi1CT5QjfyWVcgyTXEVDJKuQk5JFqU7xzmofgko7lmmK1OYxLKuJDRVVJgeLKpyJkIP10VCUpNWMTxCCM+BPHRRfyY20tK4WhpQKUa0zfO+KpUzFM42TaTo0UaCCNmEXk/ZbSTRSnIgR6Q60/Xbeqi1de+JNnwi6eBGq2HX5boEAY1ZW4zV//Y/+H0jb5x+4Lj8UxkF5L4f89N9DPbyA+KPC9Dzmn6s2aS1n8ur7/Z4QwsFzmP81a10PVOvSulvPxzs1EHer2O6OeeG/+zxUlVoKrrWCO/2biBj4dGI0aiPHeNsaUCksQ+YsVD5+vuTlq8wX3tizVY/DI2qTS4WDq+5aWBCcI3oLTbRMyNLRr3qGouyKHMISK/xslYJNq8FSZMYxCF6I3sKn/WT3FO8RrSyioAWiL4ylNZLFHeXTpDEfm6wfqo2Ic3xmc3W0esMs4sktrmIxtYXQ1tcCETalkMVEWsETnKWph5rpZsl6EVwQtBmtUoR9wUhKzsqzF05Zqukf4G0TKFi1bWnmszppnoVjgSCi7CqQGsLnGknKC0sSvRSK622hSiWJWJjoC1IzwRvY6BGCehzK5PQwB6RCSkauqkDBQ/BMGtgT2DJjOObN2eYzl8Y3AloL72qLlp3z5G72ZpToIKTMqu557S/+AOHJ26fu+nvGh8I4gJJrNgRcmkegM0lFjoaDo6F4vwXrnGO9WrHf7xkHK+dOkzWN9SJICDjvGPZ71osVfYgM+4FhGlmuVnRdd8fo2IMz9Hq++Sde8x0jNZ/PLEHng59RrUPHroMXJCeVitXq9L1Y6DIXfyHBSENa+cxl5KXzFTdD5ktPtzwbIMQlKVvnrVlWxQpwTDqkc0KMHevlEhHYOztOUnNZ1dtW3ZQGcFoPE9Ph2JVCBnpMgj5RuRkz3glTFSq1aTMGIz9VJXjTaChYCXzSo17MHBMfvD2pjRZ5nNgzgbJAq/mw+ZA1GCfTCZmCa+FIaYvbuw7jGFpDXmn3IAPZ28LoqXRCc/EhOg9VGV1lcpBr08BszywH86y8WMiWRFFn2ru+WnbCSyU6odeAVCFjHsPsRQVVFlrpRAkH1NtIVFUrNWHhqFimwondo6SeyZm3k9RRRVlg08k31Sp1mFwAQq3C5ECbZ6LMIr8OCYF1uqWTjObMtl8i+8h0u2G/37+Px3scHwrjoArT1JrYOOPM+7brzPvyafNc4D2LEjiUty6XSyYZmcaRGONBMMaLcLvb0ncdXd8xjAPDONL1Hfv9voUc/Z1QQNCGFtOQ+GN1JyfnMi/8O13A9RgunYZJgnV3uvNgTkIU24GPYQ4KSyaWC7j36JxXr0devd2QXTwKmiKIGrodtXAZPMsIy1BZeCtH32RlrNajYcyZqWAl5u0+VyxliQpZornZjfdQXGSoCdF61F520kIbK2d3zGteQPxR64IZQmqAAwqzcpfaQjl9jo01cbjPgrnHvqHwrdTrkEUIWPvd6AN9u2e11GYIHdrCKWm4iIpQRRjEVLWrYpLxzh2IW95qRu3+VmWhilTzkYI3L81c+nLQCglO6HMkIuAKaGrVng40YGavScfVCjnjUyKiJCckFyzUwnptigp96PCho4qQam2qV7NepwKVsZvoqqcvAVcdtW1mVSAuIisR2A+UPOHGkakMbPNbDCHeoZC/e3wzJdufxMRlX2pz4vtV9XvlF1GBulbIyWSvijO32qnQx1bWzKz9aA/3m3H3+4X1iBiG4SDyuh/3xBhZLBYMaWI3mJJQLsVKxPf7Q4bj6K0cvxNATryA0/FuzGQ2DKd/P33faTbkFFd5dxhyep0igu8Ln3ux5/4y8MbNxOMxkaql0ZAmlycB1wVCNDn0RYAOuI9nrMpYK7lUpgKTesYKQ8mMqdhuLI5BC6lUpIq5seIOVZMHkAMDNE/vRK1Wjj3Lq5dajkCuzqCktEU6l0cdgd72rRzQS07CH4TS8PWgQmhZCC+llTK59q+9Lip48RRnqdvcMiypFlum1eEwgNMK/8wjUqBv6YsqJvMSq+K8eXhz9WVt6lNFrKOaU6i+kkWJUvANwKx5wjS26kE5azeOxGol4J0II46d8+SaWVclFiVVhZrQMlFjRziwNS3jIdXYkSFFxHly5xnEmeZEi+1iFUJYQqz4qnSl8CP/5Z/n2d/+Sfy0Id5V5L0zvhnPIQP/a1X9cRE5B35MRP488Dv4RVKgFgHnjalnxBnDFdJcwiqnC+2YZvwg4wAGysQYrXHpdkdOO8R7VoslOWd2ux1931NLoZTKNE0sl0vGceS2ZTLcyUI9TXU615R2OIYYs3aEpRWx+V3vGoPZpRYxd7WU+q5ro8XsMxJdD+rOFrpXfPUEEi+vMg/XkZQdlcDtkHlzVN7cTCQCz/fKZoSlU9Z9ZOFgHY0WnFUAj5fKmVPOnKLBUzrTjkxa2NdCEevLkXNhXwpjNmCr4pm7kda2yA+n7yqupUIFCzNcIx55E1+wVKq0OgyxmzXbG21h5dz+DaC6RiXGyGh206QZgYpIbvPB0QSUEDHcoIoadd4pHQEvHu8iudoiVqH1LKmt9Z55J762rJCY4VCxoAXRRliC3Hp8NjNCQFiEQhCh0whayDpxr3f0OrIQo0pdj4nsrPOZOLEO3FlwKSM544vigsfXipPO2ukWa4vg1IwVKlSniGZee/3rvP7lV3ny7C2k73hw/wVWPvD0zbfZ3DznxUcvMNxccf3kHZ6++TpPHn+dfhmJckEpv4BshZqS05vt51sR+WlMNPa3AL+hve2P8QtQoBYn1uJsXkgt1x1jOChAz8o2UvXQOBWOu+5ce3EoYqHxykVYr1ZM08RisaSUzG6zIXYdwXn2+xG8w4fIMNp7pmlks9mwXq0J/uQWqQFBtQl5OhHmQPnYCfwkw9I2P9MDkJOvsZBjdplN5epofGboyTkjO+nsiosgvnWRFtslOzE68fm55+GZ5zPLyUKoYCrXaOWtnfBzN4m8S0Z6ch5q5swry1Z/EJzSOYvne/WsaqCgZArqlHPxPBkd26abIG2RmjhONYOGNINSLDTUY9xu/8HYekVanYIeCox8bT0zHIAtgtnyNijqUBU5G4AiRpxSAuVgjgwLkWqeaOeVMxfwYiQpUWGoGXUVX+eaHaV4DgYPNR2Hkq0TOM61FLXV68w9Lnpv2hgei8s8BoRXVZwmyHuWIbJ00PtA501Kv2udwTbZeqVUEUQLSwoRZaqFNBq4fG+1oOsdY50Ys6lvOTzOe57+3Jf44T/67/HGV77I20+uqMU6ZzmnfOZTL+ElMNXC4y6Sc6Lmwn6/5/LyHtebLb0EvpH+xd8T5iAmUf+rgL/OL4IC9WHcSe25pghdyTlbzOeOLqZwDC3MU9U7gJ+VZp/0B1TrItQvlxSU290WnLVkH/cDQRyr5ZIxJaZpIudMjJFpStS65fzsjPnA0uJeN9OtOVbDnf4HR2/gEH4IbbLJybXO13T3s/Y5+/46awm0481/NGBWQI8ZkQUTy2Vhro0s4nE1s1g7Xlh0XI3C9VTYlkyvwhKrJxlFuKoZwdNpJVajGfe+coYwOcekwio6dsV2U6M1V3ChnWxrFY/l+b166xp18jxySzV2MVhmqWkY2M7bCpFnrQJTcENRVKLZRhTEeqgecR6PU6VDiWJNiUxOHsCMdS4wZaXW5mGIlYqbnP28udh9LVNiGke0lqY4DaojXYgsfSRUcJKte5VgZkmUsTrGYhkQaaFEJwUTDjBp/ZyLIRkSqFopatgH4imaTSTGe5Yu4HNl0XvOe6ULiWWBwTsGhZocZe34qf/sz3L12qvcO1/zzrNrUCFGR84JxDGlPeodzkVEHPthx70HL+B8x/lS2G+2hzn4fuObNg4icgb8APC/VNWb05hb9e9dgVpOpek/+Yo9yloP8vGzYpNre6m2v6ONhgutP4AN700aXFVNUacZFK3aKgZtoVmoYerUWirOO1KyMKPWStf1DEOiFlgsOlIph07F3nlLox2MmRwEQE+ByFNMojbhWoWWbjylvdp7fVO4fneYNL9nbiLc7tt7sIzZ2BRnLXiQWeRGcD6AKueuch7h42oxaa5C5z2lmubkdfZsaqAT5dwZsh7Uca6eTgpnrtD3lQ7hJjnUCxlHLfWQZXKAFCU32b3ZRbfiM8MinDqkGninHFOH3rW0biNlMdcyIDgS3kHnA3Mk0gSpm0xbIDZh16wwpImpGKu0YElN54QFlllJ0or8WgiozTPVnNBpS6+1gakGvTqFWArBVRZdh5VkKdLmRVUzgMEJFChTMq+u9VuZUHQqSDEvYcqFKRsXoyL4EKnFMeXCynsWMXAZzEMJYliBo1KyotWRpbJ98zVe/bmf4uL+BTm1JtOqDNMeVau36SKcnS/R6tluJ6asPL26gaJEiWQtB4/1/cY3ZRxEJGKG4f+jqj/YXv4FKVDriTT9r/rV36UHIdi2I5onrQfX3Rz6Vtwzg1uHngk2HMcaBCsdcCZnhhW5iDjCcokqxBgZ/UhJid1uBwpn6zU5ZwOF1mvTIdWmJ9S+N+fcjJcclLJnY1EbtqBKO28aMN+Ya86KweYTnnEELeYo12KhQ9VqvR0PoOThOTAbkfnnecdTSmNkziQrU1GqYmGENmTeo/RS6ZoKFc7xyrnnQXVsUsE7S6eNObEv8Dx5KoG1CiuU+0HoRdlWZRLbtaNYsZACu6Y7kJsRdKqGO6gxUxHbRR0WHoTmOYRWPqgUy7gITdlZOHed9SltndKV1i5RjAyUq7Iriakqaa7bCAFXK7FUghO8KGMjWtUGljqt9nytgQjjtMepeatVhd4HQrAMQs2ZnAZqULyPzUeKaFVybqHl7NbWiqhnqpZxyyXTB8dCrA9G5wMlVXov7IuSckG9R51nSoneKa6znqGzgpMgdKJQR376R/48f+ZP/gl8VC5XC97ZD8Qg9Ks1lcp2t2Mcd3i/4p3HV4haR3rw3F7fcu/iHtM0UcRSzh80vplshQB/GPhpVf2ekz/9EL9ICtRaK8Mw2GTy/iDnDaDOH9w/Mxq1hd/O3MMTVP+AS4iJwswNbbS5pEcKtrVIW62WjKNtQTnbBCk5c36+tvJunSmpx/CgNuJKLrOo6WyapP1vljVvdQbo8QGUuV09R2OH2M6j0iTT1Nq7tR4RFl0YLmDPQ9t/1gpQ0QMW0vZurK7D2JI6FxSp7bLz98/GGCB45YzMWStYLQiuL1TneJ4d1xvYZeEGBd/4AOJZVWH0xTgqOssXVtZi6cVQhSKOnUDy5jUUMbKUNFqxeVK260en9D7QR8fCC52z1nhOLa8/YXH/lISpFKZqxexZIYmjehNksQ5dGFYQWpq2HXPWURSthJpbhWJj1GjrcI01ulkvluaFqtW8eHXUaaCPrQt5tdSv62ZpvKbNIEal3qY8W8XmYSmu5iaEXJHSPkNhnDLUSlWbW8NktGovx1Bte/UO/4/v+x6un7yJFuXp81uuguDikkcPH9L1Hc+eXnGxPielwltvPqXrOz7x0kM229EaPXc9tRa2+y1V1PCnDxjfjOfw64HfDvykiPyt9tr/nl9EBWoRoeu6Nlnvus2HBWO/gR4puDKTpk6yCbNLP+MQJwexpXqAxfWQXajFjMA0TazXZ8TObssxD6+HrInUY7+LGfM44AvONVGR43nMu/yxOcm8qGfeA6D5EHLY1d7FYMQZuUqreQT+RP9SWh+n+ZPiTaDEvBjrIYla3l/80YWc79nxfsnBc8vqUY14rVy6wr1LIz7d7JV98eyKMElh8EquAXGhlUQbk1C0mrcQRzyFvjgqlb03fkdp97WpJaBUigiJgtRCLELwAV+V6ExKL1XILa7fZ9OayMzek7ZWA2YInJiniAg0wLZoJdTaRJ7NS8HXhiNV0rinpA1dt2QUS/1GgVIzZRoRtX4pWYPd8VbjoiLkaiCoVyM4aZxDTU/RiriIlspYjaKVW3OjsRrVe8gjRaWJ3Ca2dWJKjuCgC/b8FiL8hR/6U+TrJwZ+dpGXP/YyuVRutwPPnz+zeaVmuBDo+sBnP/tppt2Gs7MzbndP+Py3fY7X33idWisPHt7j5nb3gevym8lW/JXD3Hvv+EVToD4uDIvN7yhHt83PiUP0uOjngppTbsC7yUmnwKA0937OatCOVUo5pDIXiwXaCEiH82kLhxlBb9iGCHeP7U2x+hR/mI3DqbGyz/hDGhTR1rZdDsajVKN0Z82HGn6gleCWQ8dx9Mi2FGeZjgMwK655RA2DKMd7csruPE2xOufoTKqbmi03LwpePBIqq5C5RMjFaM0uTFhvU5O9m6oy4BirZ9AeT6GTSpDCfSm44NlTGKoHQstECEtR+uBZhMCiyawVqWRaj07x5KpMuaKtwjZ437wOPWC1ddZ7rNr8slZ/6tRkrWdvBehQ3nj1i/xHf/yP8PjnvsbmasPDT32G3/Tb/yV+6ee/E6+VLkYT10mJ0AOhb8oNjuyEXDJRaAVZtvD7zoh0uWSKZjwWRkxayJrJJTHWyVKtXhF16FSahmzruyIG0CYH3nmmYceXvvgzDBni4hzIpJpJCZ4/v6IUO24uuWFnjnv3z3jrrbe4d7Ziu7ul73u+/trXEREuLy/pXMc3SFZ8OBiS5vbaBD10/DnZ9c0FBnWm0DSDliLHeozT/1xLJ53u6q5Wqw04MSAz/2Cx7ImdNbCl5au1ubIKMCv1VIUamLsaHNb2bISsiWFjCDbswD4OTpoi9uwVNZ6fWH2ACbBiYElLsc1UbJFwWMyCcQ9KbsQgzQdjVLKBm4Y7FLw313Ru0Xfcqef8/dFTmVN6IoYDVC22C1ZFs6BSCMEAYgPsMKBVnOlYiMMHk5RPKjxPlZ1EEt7+a2pXVnzkWDmL0YMmLpYdK1/xofUTUUilkitksYY8+5QZtUJ0+BpbteTJ/GnYRdPmadVkepBFs9SzbQY//lf+Iq/+1E8SqfzcF36SL7/6Kn23ok4jP/0jf42/+3d+gv/tv/X7+dS3//J2dwoSAzUGtFb2qeCioLUw7bb4LiJdZ9iJj+YxlIoSEYmoKxQpTOrJCVwXTAMUh0RHpONCCzdXz9g9v2YaEw9ffsD5xX10TFASaZrY325wi8jMrnAENvsNiKXVo/cggo/CvbMlN9cbguu43YwsVitSA92dg/v3L9CSjxvU+4wPiXE4aUBjAf4ddP5uXNR2UH1/j+GDmIbC0eC82+UXkQN5qVar9TUuhRxMgLQHYHpqlTnNqFXuHP80NTQXc80Lb57Mpx6NtvSeZVNmj8UdvBWrsCsnKVth7gv6boBy9ibMMHLo+DVneWY8pp2o/dO+57RuZSZ5zaGUc1axSHNbtZqxaYkgaziLudgFjxflhV55pCMjwg2OnQpeA0GsFFpE8H7umWkS7aVaR6nUUP2kjgSHFoOhdaq60+9yvrcHo3ssvzfjy9FbJNIB+dlzfuD7/wPOVit+za/85axXS6akvPP0Cd55rp8+4S/88A/yP/1ffBZ3fg9F6LsO8cHISNNAKZW+XxCXS4Yx4RfRNraSkJoQ8eAdWjI6DnRFcCGSO+uY5taRDsdnBLq+4//1vf86X/vC36TvVvzVH/lJ4uUZv/Qf+ZX8S7/9d3Bxfs4XfurHbbMppmStM+5WDcpaLRd8/nOf5p2nT7h3/wHvvPMOw5AYdrcsVkv240hKiRgjDx8+IHaR/W7fMjLvPz4kxsHGAZxyx4V+ij84dwoA3jUQ707vnRrEeWHPMfYHpQWhmZ6mZWhKOpZMK7ktHDlhLFY97swnx5rP4TQ0mrkZp+d9aADs3SEjAuYZzYtTvC2KObxSxdShDvfnaGCq0nbxk+8SOcjmcZoBEe7I9p/e75zzwbDZwmqp4dp6f7QTccFZz9BmeVTnfpEer5ksxoq8Xwv31DFgBmvGUUSh6yLOBSYKqSardRDzqyaUImYoPa61uOeg6gfHzMy8e4YGIM/GWGeWlQjZFyYcv+Gf+2eZpj3/znd/Nz/1M1+kVOXtt5+hKgxlAFX+sx/4Qb74hS/we7/ne/n0577jEKYUVdaLyCZn6jCxXq7Y6Y5aMz521ivTmYDxfrNHhz2LaMcvwxan4Lxn1XmWi8iKwPD8Mfsnr9N34EOl6z3b58/5r/7zP8eP/eX/kkePLnlwfsY0DQS3ZBwHalWmKZOr8vGPv8RLjx5w//KMF198wM986We5vdmzHQcevfAC4oTNsKdfLOhiZJom3nnymDTpjJG/7/jwGAdpC1y1xdFN+betRNsI7lKnj6nP9w5tu4Xtmg3cc+446Tku0sMibrhCaT/nWpkm2zG76MlFKdnqD7pFRxcCLugxpXqKR7hjeDQbqtP2fKeeCzScxTewss6Gi4YV1AMWQfMwDrdNBBFvgCXNO8BYmbWe3KsT9uZ8nnNYM4dpdY7TdX4khqPUUuweytzhqqVjlYORy9nQduc9RFscUa34KkuiamZVPerNgFU1LKQMiYrDi31/J2LqVbSKQ5RRgCAHr6yopWULJqjiLf4xsLOlmtt0OXp8QK+VVDJFHL/xf/xbeUjhB3/wT/LopU/yn77x/7UMkwgpZZx63v7yV/h//sE/wL/1Pd+LFAsDa5rwUVhKz25IjNMNq7NFkwOAokY4qtXA7qnCfkyIQOcdZ97hVRmniWkceFPAXT9n+/yafr1ke72lC8p1VhY+8is+/22s14HN7S37orhsHotd4MjFcsnF/TMeP3nMq6++ynd+x+fY7faowmq1ItfMfr/jYx9/hWdPn9HFwG63J8YOXP2HwHOQo6soLV6f045V9UCbFXGofy/QCO/vRZhArD85zBGdhyNxqaoRjWaORclHFz2lgtZMDAv2UyarZ5MKm6dX3D9f8Ojekk5airUZgkPmZOY9wAEwO4KFJ8AgJ4VkyrEi9aT3gmGhMzvglCE4X5MZ0lNimJttQLNfBx/JbIyh+Cj1SL0woyzSFpyis3R6k2Y3NmfzJBR248A4jszufp0mZBjo+x4fjA+gavxHObTWzcTWmrDVHqNqXa1NJ3GyIq92PwwDOrmvCEkCyYWG3BRySVAL3neHZ+taiaWqpQjHAloqwziw95Vf98s+w/Mfvc/bw8jHXnzIVC28fPb8Odc3tzgcr37xS4y7LXmcCCHQdR2xZUDu957dfsvtkw1n5xdImauHDd9YLDo6ucfm6jlpGgjRo94k+vbVqluH58/4c3/iD6NqG5GrjuA7XBj4zCdfIQTHW4+fsugC9+9fUhWmNHF2tkJcYbEMDPuRnCr9YsH17cBmN1FqZrVc0i976iaz2W7wTnj+9Am5CuvzNWWaTPD4A8aHwzjcCR/kkLKrVclVCd7SOqIZbW6Z9/7OTgwn8fT8Te8Tcrw75TmP2bMYxwmw7x7HkTFlloue7ZC42Sc0VSatdCGQsvDkauDFiyXO13Yd7ZKwBfW+2YF3hTOnGhXHtOfMFLWFccAOGmh4cpXH71E9tDp7N4371GvQhu7PBlgaYHdgZLa8/B1At92fCmQtaIZhGBj2Y+OSNPZq6w9RCsRFOIjz0M6ti9BH2wimVJhLrqSFOamVxHunJ+GfNoNp99irGZhQHaIB12oaCMI03xyplKqMUz402VVDJAmLDsQzPXyR/TjyxuMbnj2/Iqvw6NEjHj58aPbTK7/pn/sfsljfY5CtzTmEYVB8LKwWylIifgPbpzdsnm7x6wWLtRXw9V1P3/eERUfOmSEbhyF4AzN/6gs/yl/9wR9gf/2Y+4/usR+2fPqVV/ji197k/v1LHj58QHDKo0cP8Ci3txv6bslqfU7VwtnZku1+j0rHo0cP+frXX+PJkyeklNhut9auj0rJjqc3Vzy4d0YMnmnIlJL55d/xWZ5e33zgsvxwGIeTHd0mrqXgcs5MWYCJvhOWCytNnXf5Q0ftk4V3CjrOExruApV3U4rHcnCw3he12GdKKTBNbLdb9qmyyY5pGNmkyWS61BOoxE/c59HDpeWX2xdZz0hbjNZTURuOcMQKZjDx7rlo29UPN4fZAPiWips9h1rrQTgXse5LpdgCsms6cRcaDuObYQX7fKn1EBIdU8Sgs6IWLdtS60EjQJ2jlMx+GtBCU71ydF13aDKUUiINA/3CpMlKtc8GH1j1rTV9nqz/5lxs5dyh4MpxfHal5gbMGv4jEvGHjFFBmtdGK5We50OoyjLMFO7K0HAkUcWXSn7wMv/110d+5s13WKzOuN1s+OrXvkbwnl/9j/2j/Ob/0T/Lf/c3/jPsh0x/dsE0TqQpUYvgXSIkz/5mS3WBTOVmTEQK2/2uMWnh3oNLVmdnBN8xDCO1ZNLwnL/9X/0X/Bc//EN88hMPoATOVivGPJJabcfHPv1JtMnPilij5vOz++RcqQVW6wuePXtGTkqVxJMnT+hiIMaOruvMCAV71rvdjq5bknJinEbAtCveePzU5ssHjA+HcYC7WEA1dV4fAlFB60xXBjlZ9F4iIgVT3qvUWqjFQKi5A7Q0l1vEUm9VlZysffxBK0LLwdCc7vQhBPoucn27ZbtP7DMMqbIfM7nSctjKm882LILn4rwzBag2GcepIM4zjZmr7Q5xjovlkjCv9wNGUVvHZxrmoAev47ARnmAOs3EAA/bmrkbWFboZF7GqFDd3ZBGxBJgquVRmdFPR5qkdyV3QKOo0YLYd1nAfq42oubAInqFY6LNYLYnBk/MIElgsV5ScKXlg0fcsYqTvHE4MccxYIVYqlSCODqMsq1pGqKLULBhvo6IuYuJvNEEa7JnPhlTmJFKBcCwRF61UVwheWWIFX+oEHyDWnq2LPHrxITfXt+TrLaVASpm/9Jf/Cj/+Ez/O7wvCP/ObfzNDLuzEEV2k1EItnt2+sh+VxTpw7yxyvobdMPB8P7FaL+ld5snrXyf4QJoSP/pX/xrb66f89I/+ZT79YMULF1YM6Lul3eskxOWavu944WzNzdUVVYTdfiD4nu6iZ0hb1sv7vPn6U958/CaPXnwB76D3Ha5bMgxbYvSs3ZqzZc/ZakF0jmfXW3ZD5uGDF9jtB6b9jq+985xpSh+4Jj8UxkGrklK6s4uGEPDOJN1n1aB3o/zq/QGsE2eFVyHYBCu1sduKAWgH4LEYWFhKpaS5oOmYwssp4X2HiO1+3jmWi54qDj8VFqGwDBEXegPwaqHzyn4a8QMs+t5wDEy7cNqNlAJXmxFVZRwyF6sFyz4SgtV9iDiUaoQUVbwPBwN19IpmYtUMLGpD3I5A50zuOc1ilJa9mI2DyFxyfpcjAifcEnEtxsdChQOwaXJmIkL0gZwMNEylsBDBBU8onnEYKS5ztl5SK/S91UlAJgTXnp8a5NhCldtizWs655HiTUeSSgyV4Bw7a/8F3ujpAaWnNZ8VPXiKqcJYcvN4BJWAqvWmFKcHL8g5z263Z7Fa8uS1p+x2O84vzjg/P2O/H9gPI/vNwO/7V7+b/c0t//Rv/I1o1zPlDNgOXXQk9B4fonlMtRKo3Fv17K4f84e+599gf/WM2EWurq5YxBW7/Z5f8tmPc//+JT/3+pv4YoVcu+0erZVXv/wVHj58gZoS0zgxbwab7Q3iCucXlzx5dsWQRl589BCPsN3sWC5XjOOGi8szbnd7xAdiFJ4/e4pzgQfryPXtyHZMrC/WnIc1wxuP8eMvjD79D2Ccpivd+2ojnMa+2gDEYUzk3NKJYruS90KMHhfCoblNbU1BWjiMc0KMnCw+f+LCVsZxf3CPQwice8eyj+RcrD1crocOSq4tvIyw3SeGIR+uaMiZ7WaHqiHUXRdZLSKxD3dYlBb7t7idY5VnKTO/YWZjNp5B2+XLAYeYuRR2/u/GG4BDWHAMX8rdv3O8H2YATunn7mhAmt5hyWpt5pMVjd1udmhdEkXYTyNdFxC3oIsBqExpRJlb/DlKLabVkRLDNDBWuHfmuHd2jzLBpgnMnHU9C6cMaaTgSEVRzc2IOmouOH8U2VWtlCyEzlFrJmsB8UbBThOd90Qc//Vf/Et8/x/6D3j1p7+IBk/fdQTvefbsGdM04cVzvl6x3wz8a//qd/On//QP8z/73b+b/9av/cfYZ2EqmWkaLZzTyjQmbjcbnA94L+zefp1LVxgFlosFen7O/fML3nhr4KUXX0BKYRj23H/xBWoqLGPH1fU1KVUePHjAerng6uqKV175JPv9wNXNDQ/u3+f58+f0vcP7nrPlGV/9ytdYrVZcX19zdn7G48dP2KfE7W7Dg8tXuHwQubne0y09H7u8z8+99pwnT9+ht7zpL07J9rdyqMI4pjZxy7Fc28khHND2xjntpop1ZRJBsxpRpiZjIWIU1DmeKqXgXUcIVkxlGgkWhszo8rxouq4zAZEWVtAWbN9F5uamIKSiVrmYEvv9hJdwKN21c4d76yX3z9Z4H63oR9QCePOW264/JyCVlHIjYx2By9koiNTmPh8XvnPS+hjMHoEesjMGLM4GFrtGjtiGc3LwCMB4AQdBwZP0ygEbadd9lHRzhG5Br5lhHMm58Oz6xjo+U8gCbHd4Z+BtcB27Yc84jCyXS7w3Ty56wfeOlcILFx3BDRQ1E5lT5vq6oKtwQnyy6tUM5JopRZHs27MeSXkiTUIMsFg0xacygQt4b7vw933v/5U//YP/MYiwHwbOLy+4vbkmTRO3mw191zGksV2x41Of+jS37zzn//Ld/wa/57t/L7/mv/ffsQ5n6/MWkhW2m41xNuKS4faGH/6BPwVpRFxBpLI+W3K7H6jiSGXiLEbQyrMnTwCP3LvH6vyC+/2Sq2dXPNvv+PgnPs5iueStt59wfn6PUgux95x3Hcvlmi9+6cv4vqOifOpTn+DLX/4qfb/Ae2XRdbzxxmPunZ8zTUtef/s5l/cK0cHHHj0iJ1PHvnb7D1yXHwrjAFZSOi+GObY+TTnWegTxjnlBi+mdeHyw8CCXSiq2oMwLaROqVlJKzRgouWRUCzF6QjBNh9pIPtLSaPmQM7cwZRwn8Er0nhgCK9+xWvRwXm33yo2mUWtrNuLn5Gxrf9YsgtFp5pSGAWoqZs25u+OrYqnGFkLMVz+Xdc/jkM7kWEp+wCuQRiw7FlfdOQbNLrTMiCnc1nYwPQiWWpghzFR3ccJiEfEe0jSRiwPMxe77DnWVMY9Muz2LrljXrKGw2e/w0eODsOo7+mhk6FyUPCmleMb9RM2FIUbKPtF3AalWl2mqEkIfBbfwqFoIiAgrv+C8D6Qxcb3LiHgWvXmBuVb+zk/8LX7oB/80m80tn/z0p/nYSy9xvlpTUiKVwt/6yb9tJfxN2v7hw4esVmubD1Pi//bv/nt892c+xSuf/JRR3oNjyhlX99w8/hp/80f/Bl/74t/hrZ/7GT77Sz7Ji4sepWOz3zKME/fvXUDJaPB8/vPfxuMnz3jjrXdYnl/y5PkNi7CnTImnm2dUL3z166/z4MELEJQ333rCOCZiFM6WO4I4pI9c315ztvN4b4JC+3HHxXLN42dP8X6N1j0vPbrHbr9nfX6P51fPWC4u6Hx8j/d4Oj4UxkFVsVp+DmkvaDsn/rDDnaYe57i7aobGB3DMXIAjoUmactNMwZ5j0xgic1/vlAppypRaCCGYeOcJF2EORXwwifapVHMpVU3KzkFQxzAkRAy1FwSqufm1GEpeW77+FFRUVaKzOv93k7JSSsbBaAbKta7Vs/eCmpGbG/eI1rvewEmK1P7lXb+3f4FyJHnafW4dsIwW9d57b2+0/2IXCV1sret8w1mtzDyEHucD2/0epONsfY/bmw27fSEuenbDxGKxIDjY5wGnjs1uZDOO+K5nsQwUr9SaCB5Krkw5E1yhdxElMiUj8jgnnAXHQkA8LLrAmA1zcgLrGPhzP/xnuLm54v6D+9w7v+C1114jXSYe3X/Awnt+2Xd+J8+ePzdSF7Berw/3dJxGbm9u+Td/7/+JP/hv/35e+dhDVCtf+NKX+P5//3uYdldW7Okj67MznO9Zrjpub8eWBlZW6zWbmy01Vr7y5pucX1zy8sdeZnt9xdmy5/W336IAr7/1BhI8L14+ZBkX3I47xjGx3e5Z9AvqOCA4bjbXPHjhks9/+6f4xCdGvvyzryGrMz79sU9wtX3OdtgQo6fD47rK649fZ9mf88Kjlxi3E1o/mATlPvAvx4myEJG/ISI/ISJ/R0S+u73+WRH56yLyqoj8hyLStdf79vur7e+f+fmOYQ/W/hOBECyTIC3F5pwjxtgWwTHm7hcdXReInSdGT+w8zoMP1n7d+4jV9zlyzneowvMi8N70Ipybm5+5wzG7rsM5WgFTRqmMU2az3TOVCuLJLaQpRUE8N5sdN7dbUq4M09i6L1Ujm+SCqxVXCpoT5ERsPStmjOOwOAW8tyyLYQ6FWpMJnqAGzAn4aK7zmBNTySQtprSsx7oKo0kbVXrGMe7gHTIrRxXrp4BRrVKFPHsaJ2I8d7wOmaXOBO+cibSoIiUx7Qc2mx1TVRZna5RMqRMXl2c8uL/m3nlP13ummrja7yk+UnxgeXHBoxcf8eDyklXX4X1oAKUnBs+j855FB9e7kWGwXpSixSRgGlNyP2W2U2YzZYaiTAqvfe2r/Mhf+//xqU+9wnd916/g9ddeI+fMdr/j6uaar371K4zjyIMHD7h3754ZrWA6i/v9nnEcmcaJt778df7A7/s/MwwTadjxh//dP8i0ubY+KVn48mtvE5YXdHHJvctLci70Xc+L9+5z8/SKSuAn/+5XkG7JZr9vdr7y1jtv89bjt3j9zdf43Gc+wyc/8QkkeN54/DZP3nluoTeenPf4UEh5IqfMyx97xEsv3ePq5m2Qkd3+hle//CVCBNdB1omrzRXXtxu8W3Hv/ktsdlueb6/vlP+/e3wznsMI/FOquhFThPorIvLngP8V8O+o6p8UkT8E/E5Mafp3As9V9XMi8s8DfwD4bd/oAAqkYu3dgrdiknHKqAjOK84HUq6MYyInk13ruo6QMs5XUq7sd5mzZUcIHnGVGKyCzurti3Hj1YRMihYCleVyQc6V/ZAJIeK9UnWiVEWLkEo0jKO52g5hGQMjypgMSV418U4hsE+Z3Vi43U2c1UoM5tKKzqoLlf1uPCoHeUiTqQYtl+dMJVO0kgcj7gRnTLtIgMaaLDJ7VOZJpJzovKkqm0T93N/DypWDWmemKrYTHIyC2sJugtAHefbDsjdFlINik6pVdTZCYxP9nWX8zEuwnEplnEbGYcTHwPqst2ciymrdsZsKpQ4slkuc77jf94AVWImqiavAAVtxbs44GHtS1LqD4SI+JKOcl2zl26pMubBLleebkevJenxers7ofc+f+aEfIqXEP/mP/3p+9Md/jJvba1bLNcvY8ez5M0Ls2A8DKWcW/cIk9kNkc7sha+He2SXdYkVOAz/6Yz/OH/5j/29++ec/y357zfJ8wX4ayNVISykPPHn2Ds+eFIIPBBGGlNHq2O0y9+69gCp8/e23+LnXX+elyxd44+tv0a+XLIDd1S1PNND3K26uN1zd3rBer1mfrehCYXN7Y/JwUviJn/wJvv71n2XYJ4ZhxHXKkBJJBTdNFKvAp6rw4v1P8GC15vHzt1n2kT7Gv3/joLZNbNqvsf2nwD8F/Ivt9T8G/OuYcfgt7WeAPwV8n4iIfqPgBqC4RrGt4DJ9FyynLhmwKrew8EyBhglUtDpKgnEsbPaJrz+5ZpoGSs1crlc8vHfBxfmCGBy7IZOHTFK4GSf2+4mnT58ax1wCpVYWS8MQeif0fWS9XrZelspqsWS16OmC3ekpWV+H7ZDY73f4EHntnRuePDNhDX+95/7FmhcfrFg4YZOVNx8/oVa4ud0ypsJyuQCpPLpY8bI3HclcCtc3iRgDTiv9IhLjrEQtiLfsylydFMRERXIxsDBgRChxjhK6Ro9tRKeTsEJbD4rSiEGqc/HXLF5ULQwSwfnWC1qVnGuTgrW+jU1JD1El1UrxDr+ILDtPiCavOte5aIhEB2ka2O22rJeKOCPs5GlL3/et7NgMT20VqWbqKrVaw95cjRYfGpaw3RVyMs3MZWeT3UXPw5XDSSHimG6e8+N/46/znd/5Hfztn/wCV1fXpFQYyeTVxDDsubdcHTyrcRhNebyB5f/or/91vPnlr/D222+xOj/jhfV9/vyf+XO89rlPExt+snRLNCfEeTbDjhw9n/vMx3n29IphmFiuI/3yHpvNlr73vPrVr4ITPvfZb+Nysebll17mb37hC1yen3FxtubFF1/kzTffxovj4cUDxGU8A5vtQK6FSmGYBjQ43nz8FO8jXeiZUmFKyjhmVjEgYJIE3vHKx+8x7Hf0wc65foNl+c1qSHrgx4DPAf8+8LPAlarm9pZZYRpO1KdVNYvINfAC8OSDvn8cJ15//DYxBkpWrm/3PH1+xW6YGHJhux1IU+Htx0+53kx00fpehi6geJ48uybXQvCRUqqxK6uRWaIvrJcdqYzGuouRfcpItYIc7z04xzDaMWoFHzp8dJydLSllAq1IVZaLBZf3zxmGkeAi67Nzhmlku9vZDjM5UsnG2ahKF5SPPXpIkMLz6w3bYWS9XpKyeRiII4aOdae8/OCc5WJB9J77l2suzlbcu1jz6MEF0SuXZyvWi44+WOv2aRrsGD4wpZ21gG8ZHu8DTpSujrhg3bhrrg3UbfiNJmPQiSELrrRCtlIZsvH+RTBwMitZQV0wbEgHQuxMSk9s8YpAzUZoMnkzh9di4RAG9I4pUYc9Z+s1g3RkNTm5cdyjmtjvRtxiaboaAs6bt6RYRy3mLJVYC7gyZXDGWSjZFKOW0VkPTABt3AgtfN8f/LfpnKK1sN0PTGnkn/i138WP/9iXeHZ1xWq9OoSdJZsoy3l/zjRNIPA7f/fvYgX8y7/tt/H6a1cslytrOTDe8ks/+5Dzexe89fZTUpoIMbBc9vggdF1kt9+xXp3hfGS73dP1jikllusFn/nEK0hruPTs+paui3zq46/YHNkNXF8/Z312Rk6J7/zlv5S//ZM/xvXtNSnD+fkaHx1DTlxcLhmHwtXVhpQqeMfF5ZqLZc9u2IIIjx6+QK47Fmc9q2nFch1JX/4FFl6pagF+pYjcA/5j4Du+mc99oyEn6tPdYs3/8d/8E4RoOWMXo3EYFBZeWPQLcmrOq9dm3SuFRK3OQDBfuVwumaZMLRCjZ7kUfNeTS6HrFyy7BcOUWPSBLhgFuOTMlArBdeCVsIjgK1PKPHnnBteZ9Lrmws12x8+9fYMXT5QIPAd/BDlTynSdYRohLMjR8ZXXrkkl48uAq5lptyOXCYkrC5Oy8sRFfvZrVzigDxHpPLUkpnGHj5HQ9Sw7x9miY71YIaJ0veP+/XMuztYs+8DDB2ecr3vSYODXcrHgcrXiCz/7Fb7+5lM+/6lX+HW/6jsP8fPPvPYmbz99xma7I00TL927xHsjJF9cXPLowQXf+W2fQPLIWDxSjbdhxiXyfA+72wSNd7Hd3rI+WyB+PHAocv3/U/dnsZal6Xkm9vzDmvZ85pgzI7Myq7KmrGKxqkhqINUa7JZlSwZkWYDbbhv2lW3AgGHY3beGDbTv3Jd9qW7AkLoBt9tQq2VRFNmUKBbFYlXlPEfGeOaz572mf/LFv/aJqGIVSYkUUL2ARMZwYp+Ic/b61vd/3/s+LyitUTp6VWzdkmuNsIHKC7TwCLOgSBUyyWiahovNmlE/Z38yuFaQyuAJnVpTSIHEYUPACRW9L0XOOIvCtqu6xQpB2Tpyqch0LDLnsyU3jw55570PWa8r8l7KcBxIU4HpFF+r1YrgPTpJ6Bd9lJK0jWO8O+bw7m2mj5/QK4YEUSMyRd0aXv3y64R6xmSyw/HJlCRJyXsZKo1r8KdPzjk+vuT11/d4/PgZuzs7hCCZz1d89fUvUq03ZIOCh4+fonXKS3fvspwvqNuWxtR4HFW7orUVb7//NihJ0e+RBxk3bsIzHvcoyxJn4xYNAkJBazbM6pIgA4PhkP3dIx49eMrNezep6wqPi6Swn3H9a20rQghzIcRvAr8MTIQQuuseXiRMb+nTT4UQGhgDVz/lta7p0/3xQbBB0dYepdNurhBBs0kWq601HqUVeZ4ilSZJJZBd8xSyLCXXmmQSV5N5kbNar/GeKFoSkk1jOk9GQtMEijQnTR39XgR5OBtxcc5LsKBShQkOaz3BeHQGWS/thnwG64CgadoWAmgVWQvBC5yxNLWhtku8kGRS01YVvV6K1CnN0iNVpPoY40FoWmup2xrqGLeeJzlaK6SQLFaGTQnWNp3bFKSa0bSGECzDsWJQaLzt9BAhkKWwuzOgKRsuZmv+q3/2Q0bjgs1mw97eEGcaMp3TtJaHJ5t4bEDz9OwRi+Waf+cvf5esp7jcBILQHZg2QOcStc7FnMlE0+vlKFV1kmxHmhdIHDI0uBDwOkGYFYmKdCShNG3d4KyPiHjrsN4hkwRj5hz2BoRMIRTcnqQgUqo6ZnJUdcOiFQSpUSKQaUmhowQ7QxAw1K1lsawoyyW5FmRFTpFItM4RoqauyxisHLqwnTo6SweDQUxDy1MipNihb93n1394xvf/wd9DSkVv0GdVrvnCF79IuZhzenLOxdWcy+mMfjFCJwmjcY5ta9oGghT89vd+l73xHlmesq4qTi/n3H050B8OSSWMiwwTAjJYLq/OCNKz2ixpvEElkjxPWM3WpDLBCUHbWsqyoSgU1WYDQTHo9SjXDpRBZbHpywpBYz29tIcpBU0Lo2GPea6YzqofY5H8axcHIcQBYLrCUAB/lThk/E3gbwN/nz9Mn/73gd/tfv+f/XHzhhBgUxvKTUmiU4qihw+WLJPM5msCjl4vw1rLcuVQWqHqOOTSOhYDIRWr1RprGhARoLK3f8BsusAHQWO6UBHvado5Wmu0VKRaU5lp91SS4OP2wFqPFBpEIMs11rdUrcDWBmMsgYBWCWCvmY5pllHVLkI4bBMHrIkgySCEJq7lvKfaGMo2rkADLmY6SIdSsttcJHHFJCTWRMCpFEkHw42Vvm1ber04T/EhifOC4JkMC7IswZgWpTOcd9igCUbQ2oRPH5wxGIyYrtf0eylpUpPlBYtVS5oINq1juq4gjPknv/4eX3/zBrt5QoWilAOMSLbR3N15NTIO1q0hlYJUpGyMIytACUOWCNIsoSkbymUgSSDPNVI62saA0rSbFhUEOk0j5zJIzlYlzUoisz6nK9DKErTHCYewUVSW2BXN8gJvW7AOvEdrgTU1wUlSmdPUa6SQrMuWp48+49GzM/YPDvibf+1vUa6vqJsalaYEIdjb2yoQI4a7KSsq0/Lq/Vf54W/9Nh9874ds6g1WBfpHd/kL/72/wW/9/f8EUExGA4w1rFYVrkm4Oi2RacpsumB3Mubll+5hGkNTt8ggGA/7nJ08I0sSTFXx+eMnCK3YGUVdQtnWBOkjSg5F3YKQCbWJ2p6mtmSZpj9IMcZgjabctBjjSHOFFzbOTLzk9ZfvMuoXmLZFSMf52YzgM6QqO1n7v2FxAG4Cf6+bO0jgPw8h/EMhxPvA3xdC/N+AHxLx9XT//8+EEJ8CU+Dv/nGfwPvAclWTJBlSa+q6RmuFc5Kq6RiJVUSy+RBiQAhRAoxoEbKhba5IdEaWx/DdgGe6uIg3udRUTYnzccvRGwwxztK2nuWqJgTfhdOEbr/troeeCkFwgrTIWK7L7oaIK0DT2m7/HtV3zgfaVtA00TwkZOfx2Jg4avU2evoJpEl7XRy838rFtyDa2FG3jUWIEDsYYzDG4V0U/PR6RVylWY+znrpylKmg6oMQJVrHuUIgsKki2m6zsYx3xvhgqWpB1dYM+gn11RRjJeNxymIdCMaRypLWWD559wEv3RhgaFnXEpvuIIp+nOYrhdBpLJRaYICrpoIkJdss4r+BeHwY92N3IFXL7PIEgiLrD+PXsW6prSX1OSpJcELTpim5sYRNiR22FOUGNb2iNZZSd91au0EFaFvHZlVjXORmHu0PubG3hxCaBR4jEnZuvsJ7/+pfYZxnVa64d/sW//w3PsZ6x854QL835OLyPDIbsoyqrpn0+pTOsHr6iLd+8PfRiaQMNZOiz7fffInf+q/+AVk/o5cUjAZ96qbBOcnrr94BBCezDb07fby1pEJEb0eacHy85N7dmygBn336gLJsGPb7DAYx4NkqQWtiwrkKCab2oCMsJlWaxrXs7Ixp25ambgkoTAveG7JckGSCtjstfOtbv8BOP6MsrxA7Ocfnmtu3b/Lo0eOIx//TdA4hhLeJEXg/+esPgO/8lF+vgf/JH/e6L14+hOhZcJ6qqQneIYXC+xX9QZ+ybPDrNk7QhUcLhVZpPNt251ACWNdSNg0CgSPeYdHRGcVSQsYOY7lYEfAooSP1p3MAbjMqwlb8BLH9bUPsHMqWLI/TcOscSiqU1BF9JhV1ZfE+rr9aUyFloK0dIiiMJxqtlIzBNhJCF2IjpcKGjjrtXPymiICVIU6UG4dWOa0JVFWDd46yrOn3eygt0UpjnWe9MQQERU9Ft6J16CShcW13FIhDx9ZW5PmA2WyDFIqm9uRZFh19socLCXUQOCmxJuHdpysmOwWpzjBVTWg9JmmwPiCkpj/oEVKFSAIkAVzJ0f6QgQaUxHiDs4Kns8Dq+IqdfgauZbU6oRiO6Pf7pKmkrBp86xgNCwbmDPP0U8qyIegMLx2oltq2+Dahqh3BelYrT+1El6qVsPE1D0/OSeTnjHs591++hZKS1WyGMZYiS9mUJe+8+ylt6/jaN76CaSyz2YI7d+6QdREJAVjPFyznC8r33sKYOHvpJxnf+vKX+OFv/hNsVjAqCnZvjzAucHG5YLw3Qaeai7MF5aYGKRDO0RvneKkwLuBMfCBaY7h5+y4XF+dkCvq9Hk8/v4zSditIdR+cjFuOLEOniiLTTDdzFuuKzbqKszfvCU4wGCRonbJcVnhgb/cQ28DnFw/49ne/xo/e+ohbt/b5/PMHKKXpFX2C/0Mn/uvr50IhGa/OaRjNtgQh0amiaSzGdJZqiDwH7wmmJjoNA6p7yktBd7NuzUMdjr7zJQhiYpW3rtM8mGhIEtu4u4So3d8yDiQuGJyJmQS9PEMmkrqusdYhEtkxFGzcBNTxX6J19AK4NsJposoyELo3R0DjrEV2Z/hoO47RdcF7vImCKy8cOokr3tJXWL9lHEhM27Kwa7JMx+OIUoBiNi2pKk1eKAZ9RVW2DPIBm03coEyvVkgZyHKHqT3ztqLoZThnyPsZ1bzBGxFFGN35HSmYXtUoZRiNRoRgcQGckAjnWK8sSoFKJTrXSOd5+HnFeJjR60tccFSVw7WCrNdjEyKoO0kDpvWcL8/YSy25TJltVsgwYn72kL4MaO+5mi4pEnj17gHj3ojlYsVotIfzcHIxoxEC4wTzac28dkhZoFQCVvL+h08RPcXO8IiD23dYXzyj8Z4npxdQlzx45yN80kMWBetyw3gwZHeyw6cPH7BcRhBKFjyNMYz6mq999RUWyykh6dNPe5SbDb1+jx+9/SFlY3j5tfvY4FAqwzQLNmXD7mRECIJ1VZPIeBS8/8rLnD1+ytnVnFRp1ssrWmO4sXdEs6mZ20UMPU4F3tfMyw1uHUi8ZLw75tbBgMflU3SSIpP4UAp4PIbhMMe5wP7OmOXigm9+42uspxWu1Bi14t5LI87PGga9fdL09GfekT8XxUEpiTMNQiedw1JHYGfnstuG1+iOceC3rMXOvnyNcCdmJ7CNRPNbd2EMOxUETGuhS0X6SbUkdK4NFaWa3nuUjOG5QsSCYYzHmhB3+KFLPXLRfCM6+/QW9AEepVJCcF13tJUzcx2kEzXeErZodyFQSUZw0VS0LTiImCNpXdsVlvhnm8bQ6/Vo20756KGVgrpqSOiRqD5npys8EpIQB6IiZ7OoSHWCcZ7lfM1onHJ12SJCAYGfyMaI5jTnBbPLOUmWgE5Iih5JlgIi6gysoKk6pqMPrFuLnEeqU3/QY9m0FEnJzkCDDhSpxa43HIzGuOWcrCcYDyvYzPHB0BjFnf1d8mTB4aTH3f0RV/M1MijK5YLz+Rp0im0N85XlYmaQSqKygBQOoR2DPiRKMsn7pC99EWFKlsdPefutt/mlN77Aa3deZqMVddc1vnTvHp9/9gBjLYPRiCTRLFczbtyc8K2vfY2zZ8foIuVrX/kKH37wEet1ydOnZ6RpRpCKRAnWywXWKLSAl+/dpq4brmYLggZEzVfffI3d3TGfffAOF5dnaKE4unmE1inzaYnxFpQiKEtQgSA8rTXs7u3wF7/1Xeqm4Wo2ZTzsk2YJl+cXPDk+R2iBFJ7JICe4DB08v/LnvsbXXvsS/+//4r/m7PwC3dMoOeDmjSO0Tn7+XZmEQL/I0EpTd51C8AHjG1BdDDoR4BKsiuExoqMwh+dBswHZHQ2eg2iFEJ3HwMU4tu2EXWXX0mIpO9en8EgV0WbxyLJlHESOpXOeujWA7OzTrpNcq6296lpirBONs3Euorq2/1qsHmJQCUQ+o9aRMg0G5wMmyKiM1BJv4to24LsuZVtUYOvEauo2FgsRaUhRJh0oG03b1DEQxhqKXGBdjW0DTVWSF4peUWCNZTmvyHJNvy9pXcDUUWq9tb1fF9LgcSYW18pvEJKO26iiFLeLcwNBqB2IhMY5MDN+9dUR5eyKzaLm0emKuqfwVcNV0/Daq7dJM03TNNSN52B/n3pT8uj4gkFPc7A74Z0PP2dpNdg1v/rtX2BTPuLxxZI0y2mNoQkeu24pHCCbqIxFkGUe65Ykk5fI96ck56esl0v+1TsfMZxM6GcjBr0eidbM5wt6vR6T3R3qusZ7z/2X7rIzTKjrJUEKgvWoAM9OT+lPRsyulgz7fYx3aCmYjEfM24qrq3M+fPAplXE4ov3/zS+/zuXVJb/xm79N3oe6FRRpj8dnlwgUpqpx2qIySRCO/nCHs+MZWhVsypo/eP8HOBPY299l3S5oVnHLcvPWGK0MRaG5uqiwjWBl53grePDpM3xW8MVf+Bq3Dsc8evgE7+Lmzdj2Z96WPx/FgUCaStq2QacJJoDwFilcdwOKjpDaPXmNQgSFV5FuHLzvYtCIN0eIKj5nA1JHQU5EjTmcd8QsTh8f2HTqwG7O4J1HigRnWwIGLxKkTtjb3+Pk9Fln9JLRRdop96LXIx4VYvyZxPiAElFhJ4UkS7Z4u1j4VKIiOETFo4n0giTRkWblA9Y7QtJ1PcFdzwtk5+IMjuu1LF7G+YdOMK6NRycfWC6XOBugS95eL2PKuLVlzLQAElcjpcc0HmdASk+WGJwJNDZgGk+iVHRldoIxHwKKgG9qys6sVvT6eCGpg0WiSWSGcwpJw8sjx/1Bw9MPH5EWKciCVVVSmZTX7/S4sXOP3/7BJ0wyyc27e1SNwQ0Vi3KNHiiOdguOj58wGg6RjeV0annrk0c8PptxuajYH1m+eHePO1VJkaS0XtI4yUcPz7iYW4QMFH1Hr8gYHn2Vu5sNpjxDF0Ni8AwkHa1pOp1yeXnBcj2/DiG6cTjEe6jWJXlPYYzjw88+RaUpwsFivmA07DMYaMrVnLrY5Qfvv8VqXdMf9tlcLSjrll9886uo4Hn3/Y8AKBtPoiT9LEN4qNqKpJfhSbHG4YPk8mSGUtGOHlrJxdkZgpTNZo5zNd4pkkSQqITSBhZLS1k6lPRszIZ3P3zIeLRDfzxhtrji5NhQpBm3bo6pqnPS5E/nrfi3fnkfKDdN18Y6lIqyYREcTdNhzHhOQRYiQDDR7ei2MwJHmmcd4DQmPsmOdQjEIwBA2K4KuSYiBZ4LQUJweGsibUh3A0fr0ToSe5SK0Bit1fVNuGVfapUQXAzRjZkWkXMZP+45q08IEbmLXcaB79D2IUTUmzEWgsMZFwvaCy7VqICMlvKtGzN0rAjbqTPhORRHStkBcWLqdtzexM/pvWC9qkiz9Po1V8sSEWB3NGG1qRFSobWgMdGDIkW0+YaO3OW7QbK3jrSXMtzpI4Mg0QHTNnzj7oCDcMb3Pr2grSx2VtKYKfvjPkkItNWahUoY91q+8pVX+ae//Yi9XY+ygcuypbdTcL6oGBU92nLD0c4O41yxLh3Vpu3+rTCdzTDGo4d9FssltWm5f/uQcbHpqE8rCiHZ6Jxw83VeciOW5SWz6RpE4PjZGfP5MgqhQny/NV3n8PEnn/K1N75Ar1/QGwxomoZlumYyHLJar3HWsNxUFD3F0/MrPnr0kOlmw0t37/Otr32J3/iN3+bNN17iza98mV//9V/HWA9SIxz88i99h7PjZ3z+2VOkTkgTEV2u0hGQGFfFLlmkCKGoWgM0yEQhlaTc1OShx6Y1aJnR2gZPwJiWmzcP2Rnv8+EHn/DFL71Os6l59Y274C3PHj/h1u3DaBj8GdfPRXEAcE7incATuQdSJbRNNDR1ThwIUeO/PQdLJaOPIETWQ9u2KKWu9fHw/IYS6GuwC93ZfQuN2aowtu2zUKELTpGdSUrw9OkzQuiOLMFhXdvlYCTX3IeYfqWui0UEyvw4Wfqa8ITAhsi8lELgQmfR9r7zOIQuYeo5HSqE6GVItOgYFjEhyhpP3URG5dZ2HjsQ0XVbKg5I3XOKtRAQgkaKhKq0UY/Rz6hKh7OBspxFN6kU5GlKIKaVv/jvidi9+FrGWkzbkihFv1DkiePNl3O+Ml7zw0/mvHzQp9Cai4sF492dKKu2Ga0tQSleunWXTz674HJVsbvbR0nN55+dcePOkDKHwaDHzqDH2XxJtdyQZkOE1IyGGVIFPAmVs9RXFwyHA4LyNNWGpl6RFymJVhhnUErRpn0+fetdgnJcnM8jz6ON1n0Z4vHN89yBKoXm8OgQ7aOJaTIakuqUR4++R9EfMJ1XmNCQa83p1RznLZVpuZhN+Re/83vcvX0HSeCf/dZvMV9tUCjw8Zjx+MFDnj09JssGUcauQOmANYLNpkRKRZpJXNNSGUs2UOR50vlOJNlAILQnyxTKeYSRNCZw4+Yt7ty5haktX3jlVbQIvHT3CNOUXJ6dkSUJDz5/HI+7P+P6uSgO3ges7W7ojndQushclIrrvX8EuwBCx5u2g5L4LnghuPh07E4X1w5EBwjlu8FhfNLEVWKnyyeeoVU3lPLego9PdyESlOyizoimJ63B2TZqHkR8eobOb26djQ5Jazo6cgdtDXEr4XwscFpGoVPwvgt45frYECvW1h+wpSrEwa3Wcb6htpg5b0HETY8gxHTrrthAQKu4vdkOcLc29aivsFGtSCRnrVcbovM+Grm8jNKWugaEi14H757/1TwIKbHUoHOCC0wvZri+Jt9J+HP3dji+9PTyEVoGamvZ2d2nqjfMlpcMhjdYLNYEf0k+6DFbldx/tYdrS2p63L13E+lLxkXOwe4h7z14RH/UAwObyzPK2jHQOVUDV7OSxkRr+WwZcxw8GuMt67rBOYFTjlHtyVNPfyC5WtqYxu06SriLgrYQ3zzXBX2zLnnw2WO+8MpthPc0dcVL915iNHwXG+iEeI7W1jjXkvdSbLAsZjN2bt9lsnvAH/z+DxhMxnzjW99kPd0wHg54/OwJpyeX7O4c4QJU9QpjDWVtUDJHoEmkpJckNK6hbEraWiJkQ5KlBC9IdIpxcV2+s7vHerFBCsvuZEy53nDzlQOMqVlvrhgMRyznc9IsZTkv2dR/OPHtxevnojjEQZ69/maEEHBhi1HXSAnGmggM7aAtjojfctubQ3Zpy84htSa4gPDP6Ufd7C5+tmu0miKELUAFroNpvACSyFFIaoI0NEYCGqkEwitEyCA4fHCEzkYcQoRneOsQznbrVBUHdiJ2CrazkSeyiJ4BF9/QW6ZC/Pe7a1t27Fsihcn5FuPSOLR03aBUOryL8XUiBJSKdGQQKBFItMLZNq5bu6f984/ZovIEtiuUEKJ1fYuDlzLOXYSMsx3rr0nhgYBXEKQnmJL9oSCRgVw35B5OLsdcXKz57NElo50hQkkeP3rInbs3CSrl5PKCqg6M+ppN3ZLnirq2OGLOgnOWvb19bNuwuFjx9Limt3bsjfsYERjtFigpqMv4Z4R09HsD1pslUiY4GxOtQUSPh0spreFeP3BmJLaxBO9ZVnXM6Ix6+K3NlGtcvwu8++6nZFnB7YMhddtSVhVSxePUnbs36Gdwfrlms7IYKwk+5dvf+DZaWJz1fPFLX6ZpKsajnDwtyHPFl3a/wOvuHkXS4913P2W5nHHz6ICd8YiT8yuccfSznOV6QeMbxoMe1noyl3N1OifNE4IJhJCgc83l+Yz7d19CAJcXxxzdvM3TkyckGnYmA+azJYulwbqKcmPRcsiW6vXTrp+T4sD1OhLiUeA5lIQXqltMl/aACw7pO8sw0TK8zdGU0J3Tu+QqYJtMsSU8+dAVjq7FligcjiCj4kxJjfUW7TTCAqFmMMwosgA+YbmKbw5rBdZKgmywROuwUkk0JNFFyAkR1YrCRVVkEDRtFZ/2out+rivXdfPU/bu3uDxPouN8REgRI+VcTMLehgBv08Iixj90c4W47VBKETrYS11v96M+ouJFdFUGH28Ka208fb0Q6ffitZ2leO/jytWm/NLrA/LBhgefX1AZiVAZ/+K9zznav810U3NZVhA8WmsePTujNi3rsiFJUioL40Kzt7tHub5ksnfIfHHJwaRPYzdUjWG2qFmXnqbZMMhS0ixBJ9H+XfQTil6CIMeULa+8dIv5bMNyY/AGvHUM8wzftuzuG240S575FosnKXJMWWJtVLDG7kgSQlSnShlJYtZ7Pv70Ia45ZGc04nd/93vxeOkNv/LnfoXE1bzz1lsMXxvz2ePH3Lx5h8mwx8PPn3Lv3i18MCidsrM7xraKg8NdrAsYc8VmucK0Kyb9HOU87WZJu96AULg0Y7SzS2r7qBDT4uebJVJmlBu4fXiTJPEEHF/92pep1jVBBCaHQ54cX+CdZ2+yy8X5EusqZgvLrdsHBLFgf2dI9tHPLgE/N8Vhez1HmW3PttuhU2Q7XncCxKNDoru4cx+PE1LKaOXtOALbN7YLvhv6dTeDTNhu52zwKAIKT6+QZDI6H4t+ys29IbujATrRtG3DfH7JprRQN+zsDwhWkIiEr375Lov1AmMFdWVZlw2NJ/o5LNQh4eRyFrM3pIIQb9pYz0L3X9fRvADoklJ0N3BH7AgBa9vuaOCjddm1pGmG1hrjuiOakEjoZNf2WiYrrjsU1cFdQlcgxfbTA+G6a9iauFw3QIUYJuu977T7mta33D5I+OxsTVA5uMiDPFvUrKtnONfipcJ5gcXgHLRWkmc5g15GaRu8FCw2a5I85ezqgiwJTMYDnp5d0styStdwdNgnF9GSL4TGtJZBP6dtNyRJGt2WWcJ0NY3bIi3ZSQShPGdz8giVGI6GL/PBo2PKxpL3emxm8zjw0zGp+o03XuetH35EXbfX7zVP9DOs1iV5b4SQnqrakOd9RpM+uVBcXk05vHmDV166x87uAVqnpLIrOFKwmC8JOJ48eUxVegINvUHBsD9hNNT8L/79v8Qn753zL7/3EefLNd4H8n7BvClZXW4YFgOkFsyXczwe6y39NGUzu2DZRmfqH/zgPc6upowmA1576R5p0Az3eyRaURSGNE/Y29vj5Pic3b0dPvnsjMXqvwOA2QgiiaxHHzrQ6/WTvRumEbpzcnf7KEnrXIQWoQlexfg1XwEO5yNmbX+kmYwH9Hp5tCy3lrOrDQTLzd0BiQoUhaCpG/Z3D5kvTrl1+5CqrCnbFc9OTskSRetSqo3lanpOlifYOrBeLJkMB5wfVwxHu+RZRiEMv/DFe5xdTWPLbjRJXnCx3OOTp5c8u1jSuk6ZKWIupSAOVuE55zlGrAeCEiipY95CN4h0ziOR14KuYAN12+BD6CjbFiEkpt36OjxC6y5olk770RGsu1ZlK/QKgFDd8SYQATwhpp8/H/RGSboD9gaK882Mxaom1RmN9axnDeOdEcZ5VJZimxbXeloBRZ6wP0q4ub/DdLEkoxcDaoXHC0OiPYlKubxaMu73qU3LqipxjacWCh36GGcY9RRtG9Otm9aSFRnOBwgKa1tGek158gFfvH+LD68anDc8OXnGfLliNBwy39RkSncdl6dIeygf+Mrr93jv/c9ou4i/a1y/FFwtppy5isF4yJdf/yrPnp1wcvIULQRH+zeYLSrOzy7Z3z/kfLZgMBhQ1RuE0PR6fZrKolVK2xouPzunNxxxcCDw/TU7hwW/8Itf4/Gjz3n8+Jxnl1OKNKf1llW5Yt0YZCp4/e4dDicDfvFL9zi5XHB6tY4+liA4u1zx9Te+xnpxSdO0fOXuIVfTNaPJLtY2NBvB+dWcsqzoZT0S/XO+yowzhziV11IRg3+3KsjO1h+22Q3ieeKVFFgnowRCBIrMsjtS3D68gbNzpFLsDCckfoPsjfj8yQlPjk9IE81+kZH2CkS4whrN4/Mr9vYOODtbMFssUGnCYrGh1x+x2RiGR3ts5i1tvWJn3Ofoxj5X0wtGk4zhcMjlfE2QObPljLayvP3WQ4ys+M53f4mPP3nEfLVhXVnWVWzpX3vlJuOdI46PV5zPKmx31AndcjWeEJ4fp5IkIe22M9ba6wi62FHFb7C13Tr4GrnvkZIuQSseOdz2DU/8WmqdIoSgbU33Ws9XvNfiue0E0ttOYBZJTAKBcJYb+wnnV1OMUQyGcQ4kdUJVV7StuT7KkSpSJcgTyzdfvcnOYMg71Zq2adnYCp324jwogLGBZ+dThoOcIBKs9bx2/wBnNlgDyyrDiwZrLDpEj0yWKaqqpfWSez3Lm7f7+MMvEBz8zuk5Pnf4HUmW9pjP1pRtGyndAEgOdnZQwfPS3dtcnU95ejGN/1ah8Hj6Q41KK0xVo5Ie//J73+PunbvUdUWepEgEJ8fH7Ewm1FXNcrmiKNL4fVKCp89OGPZ77I56hHLDSzduMrpZEVrFbLZPKjwvjwzf+He+zR/86BH/n9/8Ht54UBmrquSLX7jH3/4f/1W0rvCyws08V082fPGLd2mairc+/IRbLw15dPI5Az1kcrCL9QbvAw8fTLl14wjChtt3d5kMcxYz898FhWQ0X0nx46EsW4jK9s31IplZdmIcgkK4hvEYbhxIFEusueJLL9+j6O/y/Q+e8OnnT+lnklu3bnD3zhGpkNw8uMUHDx6zv3eTi8s5g+ER+wc3eP+999mdFNi6RTpJaBsInsePj1nNSm7c2uXWrducnh6T5z3yIqduArN1y2Y1x3qByuDw3hGPnx3zu7/3Lov5msP9AQdDza+8+WWctXz42QnPZuf0RwPuHBUsS8FiU+OCQJBcD8OEjCtb0QkhtsE9SukO1CGutQ1J8iJqPCo40zTpIt4cwRuU2OZZOJAKKcG6qMNIkiTOfrbHr+0353p7Ev0b0RMS5w39Xst4JFnMQSfREKeUxznDumyxNnTrZYtQgqKXk2QZn53NcCcr1rUl+AYdUlarClPX3DoYs3tQUNZdtqXXuEnGeFTHI07b49mjR4gio+j3YutvPCKNnedBZvn2KyN29AY9PuCf/tPfIYhAPx8hyZjN16wWaw4ODzg/u7g2WiUi0M8U89kV/VRSpHmkhsWRD4eHe1i3QQTL8bNn+CBYVVMODl4iy3LOzs4YFINIkmrXTCYJs/mKxcMFWaY5OhijkVSrObffuEtlWpZXBTvjEaO0YZhLnp0840fvfs7Zk3OyPGUyHFK5ir/2P/wrfOubX2A9v+SHf3DByfwx1aVlRcu7H35OVZYsaoPVihu7E26+PKLoGR58Gr0WN28MsO2Gq4tLRnsZmUpZTFf8EZvMP3lx6Czb3weehRD+hhDiPpHlsEdEyP3PQwitECID/lPgW0TIy/80hPDwj37x7k1I6ByZcYOQJFGZFyPUu4Gbj9gvvEWlml6WMM5zZJgimgKkxISctz5a4Clxrub2ZMLluuHJ03P6meDrX/0q8/WGVbnk/OwCPBwc7vHwwcd88xuvM7885/DwgM0aWluDrNnbH5AlU2bLK6aLFVqnqBKqKqrf0IHdviXNhlxcrplONxwc7nF+OotSaSkQCTw6eUpbG3aO+hg/4JNPL7Bmxu5uj6NJD68UZQXlpo3qPSmw3kDrkXkKIiC1IDiP0pLWxCFa3FRET4pWEUNmnENahxKQaoGXCU5KkuAxLmBsQ1VHdafYAmxdp/HourQAcZfcDTsRAudbvIdUW+7fHyBo6fVGzJYLTk7L6zDiurXd+jkGI+dKsd40FEoQfItSmoNRzs3xHh9+esLRzSF5r0cqFKvVjF6e0LQW31QoIak2UXdh3SV/5S+9yuVVxUcPrwhqBMDsak2aa+5NLA8+foevfukV2rpFZmNWpefW3SNmyxVJqskyzXq9pmmabo3pGY4G9IqcYZLw0s1DHv/Dfx7fg94hguTRwxP2D3r00mjRz/sanYG3lmKoqDcOrQsuLh+RaEPRFwxyzelyQZbtIayj3Mwpqw3/9T/+DaYrS280iKI65TE0CJlSLjc0NVReI1dX/G//N3+XYab5+L2PuZiVLFYb3nj9Bt87+YjL9RwvDWQSEwx5mvLdb7+BtgbXJuztgXcG10pu37zP6bMZV+crHi1OWJXherb3pyoOwP8B+AAYdT//f/BnRJ8WQqCkJ8tizmWMx4zaZmctUglSJUi1pshz9kZ9xv0MqZso1mkrJqM9nJHU5ZrJ5JAnpzOapmSxvGR3Z8zt/V1290a01vHWOw9Beo4O9jBjg7M1ezsDYMj0ak1Vad764CE+aDarNTdu7VObNdNVQ/CBPEvwVrJYbEAR98vrFc/OV+S5p20d3nkWiwrnDYNhj9PTBVkqMbZmf++A8rjk+PQKgydNUspFgwyOJPXspwWTo0OWtWGxNrRWY5uWqrT0+gWuG0jiA1Y4ci1JAGMbVCJxvkZrTZI6dg92UDqgRODqquLWOOf1u4ecreDBk0ssGWXjKTcNtnXdlkUQROgYFyB1FHrRibYccSbwrW/doyof07SS9eoK0wR6g2ild8ESfFwVBinxwdM4Ra4Ef+5rL5GHhvlszsHRmGfn53z3q68yHPRZ1Ja3PvmM3ijB+4o8E/RGBWXraRtPIlKskBAkb37hJW7u7fLWR+e4tMSHlEIsaGdrnjx5ipYZeZrx+z/8iDu3bpMoSaYVh3ePOAnHnE+XqDQBG8Nvjq+u6PdzvnhnwsHRHrfu7vHsdIbrwoqclVycNezugEg99bphf6/PcNhjs7rg6nKJ8zMaM2W1qBkOJV965SucHi+oypJRr8eiLJHKslrX6IHCiQZjA2UpY+hMkTAcHODDFblL+Kt/6etkYsPNW19h0zbM5k+5nK0ZnWXcfumIs49WJLmn9pKRzBlkOYurDZtZyXA8ot8LbNqKeV3z7PQUKQKDvE8QjtrbF3w6/4bFQQhxB/gfAP934P8o4m7tz4w+rYTki6/co9/POL+aczndRMUfgkGvRz/XjHNNlgkaV+LNks1asqnW9AdDrJdczmrWqxIhM45nF2zWK3qFQNAyGmkKavJkzPHZFQc3dnE2cPzskk11zqv3biCcifJblVAMM0Kyy+nZnCByjo8XyCTFGE1ZSwib6C8Qnlx7dAj0s7yDtwjSLCV4T12tyLTGmIrRuMdocJOnT09ZVWu8lxweTRgNNK+/epe2bhgNxzRNQz9T9McF5/Ml61XFjYMhxhlOp5p3Pz0nK3okeUpZNyRComXAuZYiS7Euin+CShEhZXO5YjJMEFpye1Lw2l7B9Pycdz8853K2RqiEoj8kEYrRaEJdG2qpaVzAWQsI0qKPELJLsuqR93qMBwYlFwQnKeuWNE0Bj7ElqZZkOhC0Y+9gTNHLqFuNaB0HLsetz/n08oR7t24zu3zKS7fv8vDBHNOeMB4XfPml25xczpB5gdBz6noBQjPsjynCmHJd8fj8nL5S3BnuMHnDUdUl7739Pq/eu8WoOGRvOOaTT5+yWC+xwdLvpbz52sssNy3f//7bBLeVrvuuQ/JkGm7e2KdxnuXiiu/84jf5x7/+O8hgsC46Yb3zbDaBnkj45je+zsHuhPXqktlswetf+goEjQ0tHz94yI/efo98d8kv/9q3yFVKkihaa7icLlk1DdPLNbuDHRKv2TSedCdheKQ4eXqJ94LeqOVyPeXhZcH7n/82jx8/4OzsirVx1HbKoN9nMu6xKlds1i1aaxpvaTYtN44mGOdZLEuObo/4wQ8e0NQpd26NOT0/wQnBcNhHyD+9COr/CfyfgWH38z3+DOnTSgbsZsZ8HZgMhqT7BcYmKCmxpmV2dcysrckzzd7hDlJKTi/nmLYFMrxQLBZTejJB5pLVasq4nyNlIE1HrBYl2d4hHz+84OnxKTo5w9mGo6N9+sNdTs9n9AvDwf4R55dXXMzW6KSP0ClNtcEaQxI0xjq01CAMQjqauqGxOcfnM5xzVG1JlhUUeY+6Lgk+gmdCkNSNp2mvaK1HlDAaQL1ZsDfZ58nTZwQS3nnvAXt7+0wGilE95Mnxio8+esbuJGPQK1hVhvWqpen1Ge5NkBIS0eCtAqGpWnu94hNiw9deGfKr33ydIoMHT2b8g//vD/ihEZBmCJ+h6CNDoF4uo8bDNFFNKQUqKciHh8h0iO4lJGmCEDKyILIeh4eWZnWF0EsO9jOyDKyVNFYjRcZstub+/j6bcg5CsD/WvDQYIUqNbQPf/dYvsJrOWRvFe+8eczK7JMgeycyR6Q2LjePhszN+9VfusbvTp7QLnF1RG8v9O/cYrCTLasVytuTNL97mne8vGBUT3n7/M0AjSKhry9OzU4JIqCvLk9Nz0rzHK6/c5fjxCW7Wbb6URHrHN772JU5OnnJ46w57MufjD3/AZJwzsy29rIexBicEtROMR7sc7u4wPT+hqlYMdg+4Wq5i5zre48/98tehDbzxyg6rzRN+7501X37jJYIAXaR85y98i0wGpuczUlXw8Pgp+b7A2QqdNwSV0diW3/nex7y3+4jpZUmRC2SI8uhQSc6nK3ZHAw76O1SrKb4OoGFvd8RkmNFazcNHF3zy4ASEZrG6Arnim29+mafHU+4c3eQP3v7037w4CCH+BnAeQvgDIcSv/XEf/ye9fow+nRU4AvP5iovpAmNbhOxmDd5gbMPu7g6TyYTp5RxjDEppsizHekPdrNAqAAmb1Rwha2SiaRuLFgm2Frz70UnUD/iE4Czj0YheUlDbBi9TrlYNZ7OHrFclvf4O1aoiCN89PQOhaRAiWqBlZ3ZKswGNMbS2jdLkoMB5qnKNUpLeaJeqqgke6spiTYxZaxpPVSl2J2POL0uctzQmUFeGi+VjgpPkRcFytY5bmWKAVZ5Xv3jAnbpiumhZtGt2b04wdQ/RSBbrGmRGIlru3nC8cmvCywcDerZksbLsTHZ444uv8Lt/8AjtNmR5wOJRSCKQKqUsa65jCesG11pu3XsFmWa41AGaRCWkOsXYPla/zHp+RipKZOJxjefgXoLNNvSPMobCQNXD2x7nxwt+9Mk5m9KzmC350v0Vs8s1Tg04Pn3C4XgPLwOL5oT+cI/Pn62p65Tf+G8f89Uv7XP//i5CtOwe9RGy5P4IzsqGg/19fudf/CZvv/sJO7sTjvbv8Pa7H+Ntxf17r5CpjH6eMRgUDIYDLi7mvHz3iIvTc/JMEeVDmra1LKYrjh+dsy4zHvEQmQrW1QKdZgTXyduVQIfAsyfHnN7b43BviJhK3nj9ZQgGFUZkWY6zNSI0vPvOBUFJgk754NPHXF6dMhwXXFwAreJiekqSS3wSWMwavBWM+kMaLE2j6Y8FVeOxTuBCSpok6EwgkSQyIRE504s5wniUVLxx/z69JGNZGh4+ekwx6FO3LV4Yfum7X+XW/iHvfPA+T8+vePf9T6LJ72dcf5LO4c8B/yMhxF8HcuLM4T/mz5A+neb98PT4BGMc1tmt6AEpFP1+wv5kSF5kTKdXrJYleZ5DCAgS2iZg6oBzBiMqev0xCT28iLv6qqoRHTxmd3eHahWt28p71qs1PhmyXJdYa5AioTcYI9iKV2QnV4wDQB+amMAlJMZEYVCaxg2BtZZEZZ1/IZqnNmX1HEZjAs5GyXfnV8LTcOvmLlmqWS7WNI2lNSVBQNu0HOwV5L0eXnrWteN3fvd9vvTGLlW9ZnmRktiGm/uKV17ZwTLmd3/4Dt/97ldJ/BqzPmdYHHC5cVRlhbma8stfv0tVrnj/k6e0zYZef0C5aWC7tuwYFTHIRtCWM55+9iPGBwfc/sK3qX0MDFapxMnAcHKEzkaY+UNeOurx8mEPpw0fPFjyyfFTNhNHtVgR6kA/ydnNM5ZXC2RS8N5nTwleUTfnfP3r9zgaH/L4yZqL2ZqL2QaVALLBq5R3P1xRmoZvfifn48crlo/P+cadhIfnTwi3X6NQksloD28Mb/3oPTwp/d6Qs/MrjA0IaahNCVKyWiyQ9w4pen0mY0NzNactG5COe/dv0h9InpxcMdrZZZRCkJKLszXTqyX9QZ+2aQjC4lzCxx8/JP/663gRaOoS07Q8fXzBarOgrGv29m+iipLFfMXqYsmw12M4yMn6gePzxygv6Q1zVpsW00iq0iODQoQWY6M/ZP+wD14yGEdZf4YkUYrGGJSSnC9mOGvwMvIZPj9+xqOTZxhnyHTBF9/Y5wsv3eT84pLF5YKr40s+ffKE0tlrUPK/cXEIIfyHwH8I0HUO/6cQwv9MCPFf8GdGn46T8ziI9LgQrcFplkTMfF6wWKwwTWwDbUdCWi6XkWYjBUma8fWvfpl33v0Y4x1t05Alvfi0by3eWxaLGQqPloK86DEc7/D4tKEsLVrFYtI2Bu/ba4nw9nrRzeiFwYfIazTGX+sNYqcTB6g+eALiBaKS7yzW3bNKS7ytqKs5RbpDnihu7O9yfnFJAOZNzaC/g6HhYrbAGJAq4/33F+yONdquuH9jD2SFF4a2cgx0j+/91lvs7Sa89uoh5xdLPn56gdYJt/b2uTyfMe5rhv2cddUisPSKmFLdGIPtSFVSCISwFGlK0xqmT4/ZHX+fwztfoTZDnIwW+TA7489/YcR6OOL46TEXF5qzueWzp0uK3YLWLDgcTXjl1T1u7I1ZLQOz+buM84zaSpwJDPKbfPTBMQ/EkuW6Ye/GhOmjOdoM0CLDWUD2ePi4wQN3X8lJ9YCDnQN++NZjnto5TjjWixLTrtFKgbeYumY6r8iKHKEk09mUtz/2jGRKVVZcXs4YTcacTxekaUa7gX/yG7/Ht7/1Bl/72ldobYszhnF/yN0bgfc++IzZdElHAAFgNi15950HTCYFV9MLtEhpTGBdzWldw6PTC+7eGrGY1ezt7FKVCyY7A1q5joTopgViFEOSws5+gZYaUxus1LTGkxUBUSu0U7SlI0skyiuc8DjdaVJ0QrOqqRZrkiRjZ9jj/r17ZGmPNBVcXpxycTbnarGkMQ21iYPIRGngZ8NexB+XUvdjH/y8OPwNIcQrXWHYJdKn/70QQiOEyIH/jAilnQJ/t4PR/swryXphfPO1a/t0kiXkWYpUkKWRvWBMFO+0dUWW5zhrGQwKjI2DGIGgXFcEoZBJlF97K/EuKgYdcV/dy7NoqW0srkxwUuBpydIMULRNPEZ476+tz9bZbnbQCbJw+BCJUFJlnebAILDkRUqex5uqqR1N23SAFokUgjRVDAY9vG/R0tPvRdVl3Tbs7U2oSkuWx6TmsqoYjTOgx3I5Q0jBemH5xjfu88VXXmc5P2a1OUOww7Onl8zrlqfH54x6KXs7KTePdjBtS+l6vPvxMb2kR1MajDT0ehl5nuNcRNkZ57AuUpzpjGqq2xiNdjKKJHY7ezfvke69xOH+Lq8OrvjqzR6DQY9NE/gXv/8uVgfS3oDz+Rnz6RWDYsx8uqDoFXz+7JKd0S79TLBaVXgraQ2czS1FPyGRmvGg4OqyZl217B/uYFzJZm0Z9cdkWUuaJPzaN26wyzHff+8RSdJnurhktqxRVNSloZ+k3Lt/lw8+Ou5s5pbJXsHJfMlrR7d4/f4N/vm/fJf+oM/VYs2TkzN8UEzGPb7yxisxLSzVCA9aCIrhgMn+If/wH/4T2tpFe7011wlZf/4vfIe93R4PPnlA0wRe/cJ9pospRVGQoRDKUbanvPX2Y27fO2K+mGIajXMVKuvYoUHgvEN4SJOcXj/BBzi8MaKtBA8fHSMU9NMUYaIyNC0SSlMieznreUm1bhjlBV/5wn28MVzOF5Rty2pR4oOkMi112yBlgtKaYALL9Qbr3E9VQv1rFYd/W5fOemFy8zWE8AxGfZI0pvY45/A2kpRiSnR0DiY6ugqzJMFbG9V9StHY+AbPshStY0gNHSZOp5JEZ6zXFd4JmqYlhBiJJ2UM0hUiwfv41Lc2Rts71zEWrh2cz92j1wnVHaylM4FGAZPrMgekJwQLQpFlOXgbXZYyRUjbyZgF/X7KeDJktVxz79YQJSLsdndvzDtvP6Y3kOztD7g8W/O3/tZ/n7PjY1aLBYOB5sEnJ9Tes2oFZ+crqrokTyW//I1XeffjC65WkUGYZQlN1SAISJHjQk2vnwApEo1ONwyHfdoWyk1NXTfkecZkX9DUnjTJENQc7I3pFzkpnq+/+gr4lqv1htPZjFdfucOz4xOObtzk6clTJnu7vP32Yw6Phtw5OuDsYs3p5Yws7WHrGus8T04qEp0xHqfs7h7yyadPmK8aXn15h5fvjdgb7+Lrhh+98y94dqr51jde4/7gAuMGPD0/4/Gzp8xXDakQ9IuEctOS533WmwYXPL1eik6gtQ2DrMcvfONVPvnwKbOl4WI6Z7kpEUrxy99+k7u3dzk+PWUxXXB0eESv6HFxdcnh0Q0uL2a8+96H1MYQgDzLaBpHEPDmm6/S05qdnT3u37tDWS7QRcLJw1M+f3xMHeZMFxvqGqomdO/lQNFXMUjJCEaDMa1raJ0hURqlPcEHyk2LVNCamKGaZBqJJ8GTZwlVkBQ5iCBIdMb8agVGsTceoBVcrlpaL6iqkhCfbnGWhWA6X9O25ue4OKS9sHP7dfI8Qacq5j24mE0RfEzdFiKamLROcS6Sm51x1645Yy1160mzOKeIwa8RrNI0LcFHPwJ0iVA8JyqlOo/sBikQElrTRDNX8NFXLyJ7MWAjfIbn7s4XbeZeRIfkcDikaeJAz9oIGPFAkmiCMzgTQTOjSY41Mc9RJ5EfoFVBP1eYNipFe/2Mum7YPdC0taKtA3jJcnbJl79yn7PzGd988xaDwW3+4N1HXM7meC84P7vgcK/Ps/OSvNhBdpi91hqCoEsKg6aNQJGiSHjpXs7hpCDJczaVxVkYjvroRNBUlsnuEOFryo3l6bOaq1lLaFvuf63PK7fHvLx7l0enT/n82Sle9inXJXkeOZLjUcY4mzBbzFksPUlecHQ4oq4rzuclzSbgTUpVleSDMY+PT/nyl/a4e2uXQgXef+v7fPrZx7S+z1/8le+wI2asKsW8WnFyckZTNdzY2cEiyPIen37ygKKXY70jTXLSVPPS3X3Wm0tcaFlcpiyXK+rWULUG4yy/9he+TZ7AalOSqJTgI+fCGEuv10dKRdO0/PCt9zsnqyRJc8q24taNCf/e3/7rKNPw5PPHmNbR+sA7H39OZRzLeknVGLzTtDbggovBPFkU942HY9qmoTENSZ6gUoVONZ5A2xjaukbruE72HStUOB8ZJCTY1tCWgbSIXMhhPuL+3dtob3nvk8/oD8eIAPP1KiaLKQUByrKlLKufWhx+PuTTIgIzkiRGkDnbJUkDprUopWmaSF5q6nhjGd8SG4OAd6bjIRCpy0n8JhoTsyWimCcyI72LORFSim6O4HC+6chPkQ8YLdRxiitE5wjdCsk6K/jW7bkFrwohUEIgfMDUDQJHoCaCaKOdSifRFt7LC7I0QUpLcJ4sz0BahExZLxuWK4MIkjSLcXwEx9VVoFzXcZaSSm7c3MOLlrxf8f57J5xePEAVIy4up13Rk5xdrdnZ7TPqB8Z5n/PzJbN1wIscTzQDaZnhgiEvNDpTTNdLwqrm8rIhTXqcXy1j+Akpx6cz8lSzmFmendaINEUGxScfrKhWhg/DMf3RmHUbuLy6Ap9y61bBsD9genXJ49WatvFMdvqYcs7xswbvJG2ATbOhbVYsZi16Y0FULGYGFc65ePYR09PHtCVkg5p6ecxp3fLsYoUXFb1iiEBzOV/g0ezt51RNQ97PCQ6W1Yb9gz5tsJQ1rFcl1aomSVKsD9iyQqXRcXp5OaW1gZ2dgsV6QZ5laJ13syVHv5/zzTe/yqeffhqTsYSiqUtOTqb8/37jX/KLr7/Mcl1SlQ3BgvKOVEIuUwbDAet1TTAVIhhMCGidsru3Ry9NmV6UqDwSwQulmc1XqCJBqEDWS1FKdinn8SHkCJRVy2gQu+Qs7yIRUIz7Y1TQLOZr7t29S13VVKsa7cEHgbGWV+6/wscfffYzb8ufj+IAQOQabsUmUsX4uqYxgOmgsJG8vDUJWefielIQkWrdoGjd2VADIUJinEBpcNYTQkSG4+lWogoXIkiGEIEoQoDWsetwocE70clMRUeSigUhdl0++kKIDkoXAgSH1LC7N2S1LAlBUhQpPkRKlJCeqq3xxjDspRSpw/pAUfRZXl1EyI2WBOEp6xotFKvZGqE1IDg6GPLlr9znh299xI3dfZJ+wcXsCb0iByRN4+K2oQ3I9Zq9oUDpwO5BIB0oLi5WMT0LAIuSnvlsjZCGG7fGWGsx3tNWbRz2JhmbdYv3AoHFuwQhM5SIdublZeBUGXZvpizPV2RZQiId66rh7MzxpDrn6GiCEZ7pvKE/0uwfHPDpR0/xXlC1gaI/YDhJ6Q0Mu+Mh3g65PGl5On3I1eVnZBomu3106vj80WN8C3t7++RFj8ViTr8/4sl0gRaG84vL+GT0liTNqJoNIdQ8enrMm1/5BmdPHrIIK1onWWwi9etg94A7tw8Qt0Y8eHhBVmSwUAgBdd2SZZHi5Tee3b0d3uy/wWefPeb4+AytEqzIePuDJ9waT2hbg8oSNs2CG7fGXE1XFOkoamRqj0saRFJQO4eWnmq1YO3AO0+iYNwbQPD0dIJtHSpNQUmyLGFl1tgmELQlTTK8cWzWjkwnDAYDMp1iraWuFzx7NsMn0KwNPZUzGBa0wWKqGnxguZxe+3J+2vVzUxyc9yxXm7hmVPF83/kTOzhL3OO6zowkpESp51zGGHvvu4+Nr7E9W8lOCRfdjTGDwntPknZUKQvRwRituVsadeQ9Pv/iSSmvqc/xWBE5CwI6nF2cqFpvmPRGkSDc4dnaJs4aQoDGG4QSYCSj0Q69wtG2jqpu6fcTGhvIi2isamz8OvSLDJkmKK0YFhl2vWJnMODpsyvG4wFf+fJ9Hp/MUUriHd2cJWW1WnEc4M6dXZrGkOsRr7404YNPnmJti5Qd78JLZpeOcn3K0Y3DeJySCaZ1bBoTNSM6oW5tt80QeCdx0qGKjNms5OjGhCSvWcxXndoU8JrhYER/MGC6uGQ0GrBZN6xXV7RO0bQ1AkWaBOpVS7CGdJJgvOcX3rzJ2eeXFOEmZbVBSMf92y+RpwWfPTomzwWPnx0TnCNPHHXTkOmEal3SHwzZUrql0CgKbt7YZTk7pTUOoVKyRLGpzlA6BQLlpibVntdeu8PV5Rnf+OqraOU5O1szXcS4xavLNUEEdsYjxuMx02WJ0grZNtR1zapcUzcrppsFRjiykKMzgSlLRJ7R2++TkXBxecloIAhBsb+/Q2sFbetIlUX7jHwQlaUffvQBykfxnVlXmI1DK0GepJTlhkGR0dQGlSZUTUVTtSBAakWepazrDeXK4HTCcj3DEo/ik0GfYNuIWPwZ189HcQg8pyarGC+3RaT54CPfoTMG6U7nvyVF/STdefvz7SR5WxheBM4+Xy9uKUuu+7/suAXh+uO2V/y5vwaxbC3k29+7hpHKeC5sGkOaqO7PWdom5lcIKZFCE2xDlqcsViumM8Pezi4ysaQhp93UJElCVdXs7uyyXqwxTjDQiryQFP2MPEv5xldeY9o0/N733uJiZjg7nxNk1sXjVXRJHsyXNeHZFbcO99GkDMdDdCK6LinyMyLjQWDNgJOnseVWOlDXkXilkwRrDM46vAwkOouGJGdjByIynjxd8/KdPklSdjDbCKLRvZzNskSjWazX9Ac589ka5wXeR9Xl7KrEWc94mNO0kqvLS167l/HZx+8z7g95+eZt5qsFpgzoxnJ1tmAxr7DBkUpJWRqa1mAc3L93h81ySa+f0S9SlgtPogX9ouBqesmmrNnZucm7779PqhNab+kNM4ajQ1zbcjWbooqbbJzCtw07hwPSPCUExauvjrm4PKfarFEqkrNmizlf/fJr3Ll9yKefPMAESx08i02JXBoS6bh1OGFu1qybwLCnObw5Ju0FnIuMykBA6gSZOIwpse2Kyqcc3e3R1i2mCgx7Qwibzrci0SonSSHLC5rG0rQNoFBoemiGOxOODg6YzqL1vDWOxXKBEApr2hgB+Ufclj8XxSHQiYi0xrsYDLvlRzpnr8/1EGiahmvoqoh25ec3+XObd+hmA9vB4nNMu/1DHydk/L0464iCoBcJ1tsZQ5QPR2HUlvW4/YgtVRoEbdvGCXQW066KIrZ6Wom48hQCraJdujGWtjY4f4UXHmsV1huSNA5Q57MZAsm6dgjRcri3y+VsxuV0gRSXfPz5Ba2xOFvGJ2DY0rJAeoVAI5RjsTYUyYpvf+MOv/P7byNVQpoG2tYTvEQo8FhilIaOgi8j0TojKzzBR0u4VNGD4rxBqxTXuugSlVCWng/eP2P3QDDo95kvKqRwNE1JIvsIb8kzEZOtE410EmM840nBarmkl+X0eppqc8IvfeuL2HJGkJqi14spT/MV0+U6Wum1JEiLDI7VsqFfjOMmSymORgVqlLCsavb2dsEG8sGQTz4/5t7tI1LV46OPP8H5QJGllJsV1sOzk6fsjEbMZw3jsWH3MOPVL+xjrOWj9xzLeUWSDuj1Cpp6w8HRLiLJubpcMF+s+OzhQ9AFv/qrf54vffHlGBV4teHs8hnf/+HvsWkrylIgPTRN20kmNFmuKIrY0Q7GmvlyTZZmrJaWprKMxznZRGFCS39PEESPoq9w1lGXcZDdGE8vL0jShO9+69vcGO/y7Nkxm+WKlYLpesFwPObuzg1EyDk/u6Bcr7sN3E+/fi62FSotQr5zrwtcsSBkvHm6bcJW9BTnuuqaLRnv0w4O+0KB2B5Lgue6u/DB/ES38Pz3CDEQx/vIeIxPtO0EMqZe080athF2ge1xo0OFyBiVLoTAeYOUkRZdFAVSSqqqQkqJBrI0ZV01JEnMNxTYGMXuBHXrydLtUckRQrQT4wLjYcLBpAcBJrv7XM1m9MZ9Ts8X2LalV4w5O19jrceHaKuWQtCauuNKWiajPqNeSln7jkcpWa1aEMnzf7OQz6fxWnK4n5NkOcvVBqEhBMlmBcZC0BnKgrUVKpFYU7N/kJKkCtE6dkcDhoOU1lgeP5ny+qsv8eTkhNZr5vOS8WTMzVs7EYNvWm4dTJhPz1BZjgqWQ2a8/+AZuVZIFRPYz2dLLtcb7h7t05QbGhtAJsyXK4o05d7hmK+8cosnF3M+PT5DKc/R4SEXsxVf+8KrfPbpIx6dnjPq96kaw3K94c1f+DpHuz0SYdFZxt17hyitIEiurhZcXKw4ODhASsE773xMr5dhrWXQn3B6coLSiqZt2T8c45xh2B9y8+YtXr73Mjf2Dzg/fcz3fv9HnFxOuZydoRKFlhprLYEGlRLVkT4wXZakWUbwMUJhUBQI6yFz0XI/0CipaFaGcmXJiyF7kwH9nmI6WxCEZDVfs9k0aAqctySpZlVu2Ns74I1XvwS24fLykh9+8AlN2/4cbyu6p/oWmy6VQinVPV0VxlrSNMUY2yHsu0Dc8HxTsF0nXv+c+DSL5KQWqZ7//osF0bvYEQTiFsM515GZ47VFTTzvRjzbwN+tYvL6qNK5+5SCLEvJsqzzUkT1ZwS9yu5YExHoSgnytAfeY7xHKYHr5iPOxgRlnWjSTDMa9zmfL5gMx8xnV7x045CD/SHtek2T9Dk5u8K62HEkacTmheC6c2UAIVnXhl6Rcjjps9pUtEGRFTEjZPs9cNdfnxgkdHZWc/Ow4PbBHs4uKfpDHtgly40luJpEF3grMLbFCsfZrKbIe2gpSUOCDBmNgzAYcbKp2QhJOuhhqpbLesPFZ0uMSPEB3jrZoBKNEw2v7OXc2Ykis8VqxWDQ72ZNqhMLZRjZsLMzZLFao5Tgxo0xxgV+9PEx1ln6WUbrLA+fXJBqiWks09nsOn7Ruxgnd/vWLt/55ht88vEnXM02XF3VZFmP9XqNUgmLxQqtM+bzJUU2wJqGtvVcbC7Z2d3hC1/4Au+88w6He7c4OTnls0+O+fTjZ/xw8A5KS1QSQ2eyomUvVThTcHU+p21D18UGgjdIocnzAUoH0jRlterS1KiRYUC9qSgKiTcBnQhuvzzEuRZjr1iWARS0raU3kchUo1XCamnJCk1lFIvFgj/44Q8Z9wdYa/4QPPjF6+ejONBx+sLzAaP3ntbHp6rq8hpj7mS84YsiJQTR4cGya6Lyj88K4gZjyyV4kZoMz2/4RHdhNT7OHKKkNFy/gpTqefERtsvRUF1iVfy4eAyS3d8tQ2tFXde0bUuv16OuG9IkRRLTiPrDAtsdobyTKCkpCoU0LW3TxJzKRMdpqYiY+025QGvJbDEjSzSN8fzBe0/xqaZtPSodoDE4Z2jqhqKYYEzVdScBkDTGc3wxo7ibsaoaWgeii33b+kCUjgo6iIi5IBKenU0Jcp9eUXBxtcG0kaytBKyDo3f7DkUmsbaBoKibisq0bKY14XIZNxsezjZ1XPNupriuqGvVJ0s1KI3QGSiFkoo0NXz95T0ePj1ntV5jPAwGQ8zljIOd3Rgm1AUK13VN8AGJYme/z/nFU9I0oakFvWyACBXDfo/ReI+dyYSqKamblta0MePTeT79+AGQ0Bg4PZ0hmOO8oygyjDFs1jV7O0cM+wV1s6ZpDFXVMJ3P+IMf/IC6qlitp1hXkmaws7NL02zY3xsgdMlqWTFfzqnaBqk8eZ6iRFQ7ah2oKoOTGtsahNQsyhU60SxWNUIodgaCvWFGFKpIXBvYLFoMtjNiaTYrh3UKpRLm0xVJtkZpRd206ESTpQnr9Zrj6Ypg+TODvfxbvbYF4cW5gHMuOi+tpa4rtNbX/8WPi2rICEelw5E9nzFcpzIRrmcQSaLjsFIKEh2Hfk3ToHWCdx5jLFLzwpwjAla3mPhYbFSs5i6G/G4p197H4BfTKTUjCzNqM7wPtG3MtUi0jiAYF/fcvjNkuVDH77sTpDqjNS3OG4peRiIkd25OaBqPCymbynA+XWFaQYJAB0FZReeoVipSoAldF7SF1nZbXyQPnp2SpT10orCmxvsYYENHo3bWXh+7hHIInfLsas0g9xzs77KpLhEIkjzj6P7rpDdvAQ7pDG1jSKolwgYU4NoG21Q4U9F6UHSdjBRd4c0IWhNEnPkoneDQ6MyQFYGdnTEnZ1es1yVZnjMejqLatVxhrUMay3pTkmUpSZIigmKQ7iK0YFOuONyd8Mbr3+Sf/dbv8sO33iZ026yyrkjSDOU9yies5jWt97z00isc7e/w5PFDPvvsM/IsYX//kOVizZP5Y3qFRkuLkorVxmJtzb27dzBtoNwsOdzdJ5grNqsS6yy3b92ktedcXZ7HVe4wZW8ndj14qEuFa8B6x2g8oNlsqCpH0R/RNCUgUKmjsRusy6LmAoGtDc5ZQpZibUOeWfI8IzQNWgtGk4zV3LBZNqhEkyQS5ypGkx7GejYrQ6h/7oN0ub6R/Qszhq1hCehAI7FNDyFgjMFaR1HI68h7a+Mc4PmNLa71ERAHB23bdF4MiEVFotMECFjTgAwomV93ATFtOx49dBIVa03TABatFFqn15sW38FIWxPXm/jY5ag8IU01xrQkaUbdNJ3SUnSfowPK0kXlBYFtIwk6K3pUZUPeT9gZDvhkdsnJ+bQL8u2MXI3uQoI9SgWcjxkeXraAQ0ndUe0DXb5X3Ex0sl3vPUpG/mJM8jZIH4swPtBYg5SONE1pFjUzO8WHBqTGqz6T4TACbwkEIVFpQsYQYS1KCJxKSNIcLwZkzsXOLcQgom33tj0ihg7dL4NGekOSpWQaelmMuHfWUGQ5INBCEWRHrkIgvAcnmM0W5HnK+fkSi+Lp2Rmnx+e0DkzTEELMQc2Lgk0d5zDWVijt+ZVf+Q4fvPcpv/fRRwyGBf18wGcfP6To9cmLhCQBYysklv6gzzKU+NBwcXZKXTa8+oVDTk6ecvP2Aa1b8+DzUz57+hZ1O6eqAloP6E8CVxdLSifwNORpgaVh525GsynZvavZrAR1vcEZh1aKZuMwtSRNa5RKuZpumOymFL2csrQUWUpeJFR1XA23jSdJBDf3h6zXjk1tKLIMZ2o2C0NZN2ihf+wh+JPXz0VxiNEI4Xr9uD0abB2Y26ssy+t5wVZr0DTNdVYlcH1u3hab7ccrpTAmnquTJKFtDW3r0ColyxOapo7dhidqLESC1hHdHruWOCz1bMnOXH++bbfjQ8Dy/LgStxmetq2vZxNt216nN29FX1orCILWGNI0jRbcEFBS0tSWEAzDnT6fP75iuYrBOVIq2taQZRlpUnSJVpbtTETJpNv0wDYv9MWvUzSS/bhOROvt1y6uJ62LK0zdDWuDNzTGUc1nNDj6k3sMX/4CZEm8MUO4VpZqIUFKXOdD2KZpoeLqMh7JtoTxEFkY3d/Jv7BdUlpjbE1VlRT9MU3jSDtitlKKtg3MZrMYQ5innF1e0O8NmM0X9IcD7t7f5Ztvvs4P/tWnfPrZQ/pFSlW1qCARwRNsS6+Y4AO8fPclMpXy5LOHmBCLY1U2pDpjPp0yGPZYrxfcuT0mSQSnF49phEWne0wXCwZ9xZPTE4wXPDr/nIObipsv59TrNcrn1OWKptlA2EUVjlFw5APJfFlTzQypKzjcn6DSwKa6ZLCbMJZ9Ls+WDJM+61WNacGomqNb+yyXM+aLDWmSkWY5Z8drRuOCuqlxDkQvo2kqEILxKGdQ9EnkmE1VIiYK3zg+XT/jZ10/F8UBXtgqdHqBbZFIkudT9BDCNSFZdUPLrUfiRa1DfPpyzVpw3dNq+4arqhrn4g3kRUC25jrEVoqYdtQ0UZItZXT1tR0Uw8N18Ym+jeaFecTzX4dAqqOmQSeaum6ub8Jo+g3dViWi6SIbInYCQjmEj0rQ4bjAWcViaRj00m6AFecbUtoYkWfrbkUaMKZhG+a7FX45H1Wc26/hi5LvbSemZNJ5SAw+OEQI3RA1dh5KCtqqpLIVWvfoFUeoYoxSPTDbWMDnxjQA7yzb3IdYOOg+RkTqsYuJXBHT5q7/fs57Aoq6ip9rb2+HJD3B+0DTGPpFn81mEwVhowEXiyVSSKqmRicp66rmxs09/s7f+eu0mzkPPnpAqiw3bwxZLtYoqTncP6KsKk4vrjja3+OLr97l5OQJjz77nNHkgLOLU1brhkRn7O/t0Cty5ospo0Gfq8UFqU7wOpANM2YXS5LUUhvNYm7Zv5WR9DyzVaBZWqrKk6YZNlfs7WRcnF1xcDjk4qwmhIzySrA72OUXv/UK/+r777DaGNJcRzGfhGYFy3pJCIHRuMdgJ2U6vWQwzBnv9sDH92lejK7Dd5vac/JsQZomJMojC8nGCLI8Jc3jajgvkmu170+7fi6Kg4DO6xB1A9ubPYR4Tqf7tWgo297kqnNquuedBjH6TEpJmibRS9G9weG5FuE658G1hNDifDy+XHcdOHyX7+BDhK0q0YmGiGlQUkqElt1rS2KQrYoy7O7rHWGsDiEDWZoghGcyGRGXIVHctdk0FIXGWEVVNWSZAh9Rb1kvp21a9iZjisKxvzPi5OQJSiusMzHcxnpMqLqVr0GpFCkUxrQxlo+OzyBfXM/yY8XSOY8PDsk2Ai5ucaJTNQ4ZW+cJokfev0uSDUBlkOR4WxNM+txr0gUZB2JxCHSxfM51sxuAbbBv9/0X23LZ/d2CRVpL2nOkypMlmizpU7tI1L6aToHAZDLCdd9vLwMGCTrhO9/6KpNxzqfvvcvV6SXvffApjXM4b9GJYm9vl8osma/X9AvNp59/Tm8QuHfzABkanp2cIkODs5pFeY53CtcK8oFGZy0q0VhD1HE0LaPxCK0C9Qb6k5TBJM6UvBFkac58sUQnDWnWoyktWmmu5hU7RxntEpJMMN3M+G/+6Q/xLiEfKKrKUa4blGjJsoS8LzEONqbELRTl2mJaQX8cSFTMMBUhIThJkgZEAns3d2hbh7AeW7bkPY3oGaRVDJOE1jc/1gX/5PUnBcw+BFaAA2wI4ReFELvAPwBeBh4CfyeEMOvgs/8x8NeBEvhfhhB+8Ee9fiC63yKGS113CC9uHmKxiDh278D75/OFF1tlxHbzESXEP00kBc/fmDHx6adPbIUQhG4t6rsnoNwi27fR3/FVAVAqio9C8ASiUGg4GlCWa7IkJ89TvHO0jQNcFwwDm80aREKe5fjQUlcWqRKa2pFoqKoSawJnzx5grCLJYmE0Jh4TjN1Gt8X23bvt12CbQP4cULId1MYA1RgxqJTo3IfNj0nNvQ9kWUaSjkmERPT2IM1RhUIVOUmad3HxTXz1CE+4BrdGdevzSELV0e1DeD4Xer5e5vr7E5WxhrKKmSG2baiqBqcCWZKgZcTFSylobEDKBGdKDg53+Xf/6q8xygIXZye8/dmnTJcLPJ60SFCdE3W1XlA3JqIBAhR9yacPn3D67IKbB0ecX5xz4/YORb9PdbakMbEjGY5HrKs1m3WDs9A2Pr6mFiymAm89PggefDDHtIKiUBT7fZKQcvp4ST4okEpQNy3D3RGrRUWzcMgkY2dvwvRyRn+kkRRUbsNkp4dEE4JhMN7ho49OsE6Qdfkg/UGOSlq0TFit4Ff/4rfJpOfyYsZ8ueJydkWRC3xIMcFRhTicTIMhk4I863H9JPsp179O5/CXQggvQmL/A+A3Qgj/kRDiP+h+/n8B/l3gte6/7xKJ1N/9I185PFcsxii35wrFF4NshFBorWis6WjH4noIuX1jAfR6vdji8/x4Ym1Mot6KqeLriZ9QVP7ElqI7JjxXRYjrNuwn/37exyAXIQRZmtIaAyLHNBLBEGsb0iyhrS3rdU2vl8ajhYwcB2MDq+WGnb0hk0l8k61XJePRgHK5JoQEJ+mk2U0nGHMkOuk0GhJPuH4KbwugtVHY9fym3xpLo4ZEIHHex6yJa9qViLJq77HO0JqKLOsRqMkGY2Q/pxUWFWq8l113sPW1CK6TUl78mr5wrNkWn+sZw0/I2wmSIAKllTStQwQHSlK1JTv9CZvNhqLIcM7Rti1JIrl7+5C/8df+MqpZYhYldrNmPltSOUuWRq9G28TOyNqaosgI1uBcIO0lrE2JDi0nsxKbNCwbRR2qTgka8K7m5GSD9TXGCZRM0CRoJbg8W9FsFILAeEewM+ph22iUenY8pVxbpMzQiUQXMLk5ZrWK2Li9kWY2i0fD8XiAEJLjJyuSDFRPIYRld3fAYrng7p09VpuKECzGGhAJVdmiCsl43OPi6im2meGNJMsH3Lm9w7gY4oxksSy5uJwxr9fM6zVCeSZ7FY4/HUPyZ11/E/i17sd/D/gtYnH4m8B/2qHhvieEmAghboYQTn7WC21vsCRJ4tO+G5wJnheG0D1RnIs3fewItsKieMZGgE40UkmSJKVuYn7DdqgZi0EcMkYFYwzH9T68+HAFnq9At78eu5pOpdmZrmL3sB2OxiOGlIK6qRDCc++l26xXFZt1Q9HrYVrLfBETkoUUZFlKXbesVhus9zgfWC43tFWF9wGlUvLU4fHkuSYTjvUyRtK1rUEgydI+1tmuU4p2dq0F3sS/T7wvPVvpx7Z4SKFI07juVFrTNOvrrioWDlA6Ssnzoo8mR8qccrkhkwKdCZSzeCShs8v7bpj7k4XWd4VUwvXfJ1xvJ+K2JpaLePwJXkSOgZUYLyjyaI0OxAIslSQrcuq2QheSX37j60yGBQcjReEGrKeGmXXcObzB6XzOxWyJI2aSbiMCq6ohV1m8QQ9G1GdnLFYNJvegBZu6pW5bpleba8m8aQNKx0Sytg1kqaIpHZGuZwhOY0qPUZrplUGklv27PVKdsVyVZLrPdDFnkgVu9XssriyNNdSt5eBmn+W8xlnBjXs9Ls9LqtIw6KfM5huWc48Pa2TiGO8MESIw3CkwTmEbT3AbHj5cISUgUxbLKyY7OY/tlIPJIYeTQzQJNw72WWxq2mDYuAYf1j/zBv+TFocA/BMRpYH/SQeHPXrhhj8FjrofX6Ppu2uLrf+ZxWH7bN4OFbfeA6BLvNpGoQu8i2s6OnFSfB/62CVIGS3fNiY+bZ/uW41D6CTQSgmSVOPdVnhFBMpI1Q3O3AvdhIAQC5XwEITDEUh00q0x22gyckRYjIB+P6c/yNmslrSNw1lHuTHs7+8yHMb15minz2a9pGkNECh6GdYYmtojZEIIDusCi2XFaFSwXK4wRkBQ9PuDqIGwjrpZEXBoVSCkxJiGLMvwjes2By9Qq178xmtNUeRUVYV3njTJo6AoBFSS09pOrOSgbRPC7j4+S9FpEhO2nMUGg7QyrjRDJ7MKL+hMAK6//vFr7ImpZoEIn4mXAJV0xd/R6UxpW8fKgTEtEkkvzxCJRMuE73znm9x55Q51vaS6mPLJ2+9yUs05GIwol2su5jMeX05ZNQaHIM81zkcCUxCCNC+o6hZrNsiFRwuPDwad9ajKkro0oCQqTXEmZpP2hwVVFQndWS5J04ByDueiwExJSS9LcJVFyJZ82EeiaVaG1cxhehsGhaSZWVZ1SxAKn8LObp/Li4ogA0XRA+EZHeYoI0gTsFgoBTpoiqyg2rg4fE1Ten3NatVQ1xYBDEaaJNUkOqWeG2SqWa5X7I52mOztkKcpw7rk/OqS/WyHk4c/MzHiT1wc/nwI4ZkQ4hD4dSHEhz92c4cQxPPU1z/R9SKaXkh9/QbaXnHtqDoNwPPV5vYosO0WkiQh4LHWUBRZlCqHmDhl7XNIrNbyWgDlfbRIW7N9c/puM9EVIiFfKCoS2YFn8iInzRxVtWE0KmhqQ9tGW7cUocPKtXgfh3xJkkDn4hyOcqzfkBeKJOmzWm+6p6zotioWIR1aSpRKsDbODpQSDIYFdVUzHA6ZzxeU1SKuBgUINInOcQ6SRNI0tjviRKbDT652t12UMTXBD/A+EqpAozrnqwgRcVc3HqF6DG/cR036GNFig6MRBu0dOt7NUUXZzWViIjfQdQRxi9N5jLqOIZ5uttoTiL9rnxcTIXDC07iWi+WGGzeP+PKXBd//0QfcvX3IX/4rv0pPBep2yerinN/557+HThKW3qBVzifPnvLh5TmVsezvjAk+4fJihsGh06hqtb4lHUCSSdbNBiUlWaavj6nWeNraotNo5lJKUlUGQRI9PSFuTprG4gIo5dDa4HEoYP9wSOkM08sSrQVKaJJU0h8VzC5X5FkPnQhWC8vGlQxGcci4npUIApPdAfNlSV1K2rYB51BakeUZ1XJJnmaUq5rVvAKXojrOiak0dV1hnUeiyb1iIKBczZnbQD8vMM7y2v1XOT09+dMH6YYQnnX/PxdC/JfAd4Cz7XFBCHETOO8+fIum314vYutffM1rNL1M8vCiovH52TgSnNW14/FFlsL2vBoZDGmaYm2LIN6Uljij2O72hYxT8C32Pt68Kd6HrsNwKJURQhc6e62TsAQcWZ7R6+nunA7lpqKpbUeFliSJRGtFCIqyLMm7fXqWJtHaiyMEz2AwYDZb4p2gqhuMCYDCVg2j4bBTNMYBoXMwGBYYs0Ynmsl4hHOBsqy7o0TojmK6e8PGwmCtJc8LhHQ/dqbfQnPjyUKgdEqRC1q7RnQxeEmiadqWTVWii110vo9RAUeJ9R7pQ+woAtgASUfTjnb358Ad/0LXJgAvAoiYfRG6OcXz4hA/fntJ4vcrUXBxNiUZDLhxNORXfum73HvpEEHFb/63/5IfvfsueS/j4mqBUCm39/cwleF4vqCyYL1kWW6AQFCx8GaZJuBQKqo3Td1ya++Q84srTAvOPte/RMGYwppY/NvGo2RCrx81Jt4JmtbjQnwAaK2xoUH6QGM0jfVkhWT/xoSnn02pTltW04Yil2yqCqGis3I4GrCcrvCtoshzkjTQV5J1EvU2sQBLjGkxXnB0NOHyfEG5iu/nPAnkWYoIHkWCMQ2D8YDVbENWCHZ2eizmFZVTFL0CjUJLyRde+QLf+8H7P/O+/5OE2vQBGUJYdT/+a8D/lecI+v+IP4ym/98LIf4+cRC5+KPmDUA0Xm3djWxb4AjCVAGED8iO9CTUc3hL1CCEuKZzgbZrqaPAyZCmGQhP0et1Z1t/PYOIA7j4Bk0SCUKS5UmnR4iT/O2RQwoJwVOWM9KsH3MlTazkSRLPyjIEej1J22Yslw15pknTAmcFm03NbFbStpbLiw1NY9BJQvABrXOcjQWirlu8Ezi3IUkTwHVhuYb5oqappygdE6W00jHjolulem/RWlAUGdb66w4rDkk1g26QF4uII037KKlJihS7NjGPNNFsqg2b2qJ7e4isTzruofoK6y0yCER4PhPYitdUHLzEHkmAC1Gl6UM8AMo4ccaJgMKiEQQX5wxOdosT380ptgXCe3JRs5p7XH+X0HracsU/+sf/iM1mhXMBE8C1lnTQwzaO2XLJPFiaBmQr0UFSGYfQdMNu3a2XQzcrijyJh0/OwMdNj9QaaMhVJHu1LRR5FoeiKZE5WQva1ncrcUmeJgihEUhSHYeEi1UDKnZUi8sV40mGWVvyLMPLQO0qtE3ZbDakSRKBQz4+FDIlWcxaNsuI72s2LUmWolOJkorpdI4PIXpHmpbVyrBcNmip8apCp4LVdEEQgqoWfPbolMlwws54gnM1Pmx4/+Mz9iYTXizQ/9rFgThL+C+7M6sG/l8hhH8shPh94D8XQvyvgUfA3+k+/h8R15ifEleZ/6s/7hMI4sBRStnRmF/YEna/B3FSj3qeb7h9ImqtMT6GoW4HXUoppIhCKKUkbRvb6W1bvZ1vZFmGMQ7TmPhEC1wXhTRNaJs2mqC0IM0TvGtJUknT1GQ6Hm20SMC3KAHBxUGl0gCB8/NpF6wjkUoz6PchlFhvAdkh7VN8kJ1aUqNUgg9xvZumKVfTGUqlnYw73m5pouO8YXsDSkVVlYzHE5wrsbYlEAtimmiEiE/NKJBSXZGsGAwGgGOzWrM0AacSRH+IGIzI+gNEnmCcuRZRxW9I6AaQseNyIjZk0bcRb3bXeTTkdYGNH+OdQREQQeBCt/0UIFzsMJDxhnXWkBdRIn96eUFTe5q25mI6ozZNVJAqhWlqpFIUaYZxFukFzkK/GJJ0/om12cSuTkisMxRZFNZZAqa1WOPRQZKlkrp2SJkgEBRpHgtMU2NNi0SQJSmt8UDEGKapJjiL8xapdAxD1uCCJ80ThA8sr2qcD2QywbU1LphuKLphf7SD2TRkSqAKxezSUG1SlPTYJlCaGmejXqbXy7CNAp92VPQAaFQSqWht1dIKIEhSlaATgdYJVdVSX8xI5yvyXoFzntV6xenxKpLTf8b1Jwm1eQC8+VN+/Qr4yz/l1wPwv/vjXvfH/swLP96eka8LQNT/EryP1FzB9QbD2ujIjOdrFWXDL6gsdZJEXUEbQ2oERKJRR4rSWlNVVVcU4sAySeLxZNueC6HQiWA06pGkitZEJ95oPIyYsKspy+WcIhcMdI+qqlAqZT5bURQ9QoichyRJsJ2iUiUa19pudC8I3iGlJniLlAGCxLWxUCwWK5wVSJl0IieNlLrL1Ig3aTwqBLzOuqehi62zTLoBbKcy7TYrUkims0ucsyRTTWuazlvRQ+ZD0r1ddK/AIbGujU/N+M2NN7/zYB3eWIJwBCmiLFrILlU7VgPVpYZtjyCCgNyqL6XEOs+WNbKdWHkbkM6xnwruH40ZDFuq2qK05OLkguW6IclSAgbnVLyxAecC1boh1QkyUVSmxJuUPFUYJeOMadMyGKRgAacxrkYgydMURadnsQHTelQa6PdTmqoh+HgEFR6UUCRdAvmmtdjGUvRy6rqNnaBKcK5GAK6N4qTgQcoU21XIUW9Ev8hZpjVt3YIVWKGYzTcx4GYQ348JOrJJBfSyHv//9t4z1rIsu+/77b1PvPnel+tVru7qruqe6enhRA5JkaIClSzYEgwqWAIsWzIg2ZRhQBDtLzZgwDBgWJZgw5Ahw4BsWVaEKVOwJZocBomc4aSeztUVuqpe1Uv33RxO3tsf9rmherrHIw05XQRqVT3cd244Z5/97l57hf/6L51bcJ7vVYjnExt8V7YeRgj7PUgzTS40Dd8DqZmnU5SoWGo8p8Dkc/LM1r80m1V6k+Qj1+VTgZCEFeZAl+AiWaYmkZIit8hJx3FsxsKY5WQsKN+KosB11XJRu65LkedkWUYQhuR5vrIaSvj1Isipja1vcD2BLPkli9L1EMolCF2SJCKK7M44HVtLYDy0QUXP8anWPCCj1a6TxFDk0TLIKkuOCvKcLLfX8lx3lfqTGiioVhpE8QhjCstOrRTRPKbZaJIkpgQ9WR9/HiUW1p3MkUKQFwmOE5CmWRn4K6hWWni+S5Zq2u0Nzs5OmM4npLkt/JLCpShAOiHGUYigiqpXwXfIjbakvRocYZVDobWNiWiDi8AVQGb5LwphyI0Nv0pHWaYnLA1+YQQaiZIGaWzn7izPbQDT+iaLGCa+Ejx3rsaFumFydsiwqHD0+Iw8jzg6O8WRIYH0yu+MTxJlGLUIIgsyndNu+cznKa5QSGHQeYZBUXF8fCUYT3PSxOC64HgunuMjSC3EeZZSFIZcS6bTyFL9OQrhCJI0JZcFeaLxXBdXOcgy3atK/EwyzwkqIVqAlD4vvXyNg0ePmM7mBErhYK0o5XoQ5xSiICFFSUmW5hgtGI8nOFLSrtYpjCbJcoo8YQG175+d4QcBgRcynkwwUhKGAbNoTvnnwUhFVGQ40iOa5xR5RjY3yFjSalcZDmKiuSb6LlWZTwcTlOOboL5rDxxFsYb9X8QiFqm4RXrSxh1YcT0YjePIkhRmhZ60SsNdBSZLTMV6QZdE4boOSgnSLCEvDIHr4SpFpVrF8RRJMqPRrNM9HRDNBK5nI9IGhdEFCItsNBqSNGZ7Z5N5NCeeF6SpIc9sVF85ixZ7ZvloeSld24RHJoQVyfZOi+EgZjJI2d2pgXRJ84JZNCWJc5T0Sp5KnySNEAo8t4qSHghNlsXkaUJW5GS6QBiFxAFHYqRACAekQvku0nPB90AohOvh+qGtdFxkbgqbFtZGo4y0roIsLQFtKNIMswhASoN0HbQQqGhm055KkilbAehmM4TyyYVr581o28gXw05LcmM7oOkYJsM+Nc9hMJ7R7Y4ZzIdkusAxHrVqBUfCMJrh+R5SpkynKXnmMM8i2rUqveGIIAxLUuKMQms69RppllCvbTDqTUtry1ZzJvOIRrNJtz8gCENMkVMNPXRumEYxXuARRTGu75JlmrzQ5Jkl+HEdCDwXz3GoVAJe+dQNOu0Nvv2tW+zvbtDe2KQ/GHL7zvvkOibNYwokSlrymDhOUFKRpRlSSIIgIE0SlBTkpTJ1XJ/9/X1OT0+ZT2ZIpag1Gpz1e+R6sdmZsuamLLKTluk8cDwKErZ2WsyHqSVP8iW5NgyHEWmSf2jK4qmxHJZi0TwlWEnayn+zztG4oIFbFTpZOLBVBgvFsFB6rusu37+oiFxYG+sFXSBI4ti2Y5ewsdmgGjrESUKcRNQbVQbDAXGUsbkT0mw1GQzmTCdDoiil3qgQz621kcQFJ0cD4jRFCoc8tz6253nL7l2+F1KYOdqkeK6H4/gkcYLBo1bzGA4nDPoRrgxB2C9itzvCNuJV5EVOGHhUaoob+zc5uH9IFGs836PbPcGgwVHg+jiuQkgPpGfvTym0gzX/bSzRBhuFQgmFyUtouDYUJgdTlIFFscItGEAYcgmFYmkFLKn4AESBUpZpCVNHFBqBiy4UQtpCrEILpM743a9c4XxzTJ5HfO21O5w8PqYe1vHdCrWKBxOD7zrookDrlLiwgdE0T/A8U5ICFwS+JCligoqDlNbdTOYGrQXK8UmnEb14QCWo0G6FuF5AOpkzwlqS7U7TVsnmOY16BV95PD7uUms2EAaSNCVOMoLAo9mqMJ9F1sqVBiMKzoYz3nrrLj/+hQ6Ozrj14IBzacKl/XOk8S7vHzwkSQs8zxbOtdoV4tixdRR+sNz4HM9D6xylBe3OBqdnfR4+PLCFakqRFRbrYAFogrwoltB+IQTVmk+Wp2SZJImtBRdFksTkdLZDJuOUIPQwg/lHLsWnxnIIG3tLlGNWlHyPYAE2+kneRyihzSw4HS0AScqVZWH98FXKc12xrKdDhRAIY7VtEHgWLuu6JaXYHCFcCmPZpI021OoerXaF0XBKFOUgHaJZBkIvEXha5/iBb8lbFylEpahWK0gpbZBTF5zbbxIGFQ4ODmk0q4AhjkvGK+lgjGQ6mtJuhiSZTc2l8QzXUdQbVXa2NpmPJ0znKSdnY3LPB1chFZa/QfpIx8FISt4Da4eJhQ2vNaIoqyWlh/J8hHJQrt0zFtaVRK8FrhZFUkWZagaMQZQoU41GIZAGWmJK059RFHCWNssF5JDmlOXtAo+cm/t1ftdLu/zyl79FbzKkEHOSWYbrBlw9f45qRRFNI6IsYTyfMhlEaBdqNZ95FhOnOaGq4AqH3qSHlhJXCUvnnhu0MZa8OM3xfRdHhbjKRYgYjUM0jqhV60gPosxmdGp+QOApknlKkgu6vYFV7krRn8zQRlOpVijKsnahNXmWEGc20/LqjevkSUTQqON5Ls1GndBR3Hl4QH8yx5cwz2Oms4ggCImmFkwnhbDxjCTGdxxkUbCzs82jk+6yz4rjeownU5u5cxyKzMaqqjWPPDfEUYpyFizpEt+RxElKYQS+Jy0grCjQBibjiDzXH2o5PBXKQSrPhI09wH65yvVu0Y/Csb0qSndgvdJBl2YvYoGZXGQ5RFlQtSpAQtheF2LxOVaoQRtwcoHSpy57EBotcF2PKJ6jC2NdDFeTpYY4yvBDjzTNKLTdMX3XJ03ntFo1EDAazSl0Tq1apVoJCLyAySQiSSK8ADqbLU5Px0TzjJ2dLY6Pjy0LlKsQUpFnNq21tbVBfzgi15qap9ja2mY8jVB+yOPugAyDLqPxdssu8QbSt6jHpYVgK/RkblGCBoESCuV4SNcHITFCotG2XVs5q6JUAuVslX8cXQKWzDJusAAwYQpqRczl5hxJn+EsY5JvowmJlSBDURgXz2Tc2K3hzHr0jruIiiTOZ+RRCplDrVZnu10hysbEUQ6uIqdgPixQIUjHpvgm4wSlLShIqwLpWnr2JEkQSpZENHYaQr+ClB5KKnzf4bh7hiMdfEdRrwc4ro3kO15A1QtxgSgpGEcxlWqFwWhInGlAEscpSij80CcrUhyhiFO7iTRqVeqBRyu0XAtCOTTrIW/evgPKQ8dzKq0mSjkMhiPb8LlEibqOwzyKLJK0yKlVAra3tiwYLk658+CE8Wy2qKBBYHAc25MFbaySUS6F0Uily1SyLVqjUAjlofMI4WuGo4Q0/fBGuk+HW1EObRGM1KWLoI1GGFParIAwy3TewnQ1plhqBSEUZlknYSHYquycpcviK4u2tuXIel3plJBf1/EsIk0IKNOPUjpgCnzfJ4kigrBCkoxs4KpMwUkhSJI5QeDgBYrZbEa9bpmikyRhPp8wG0+J5pZMdKPd5uRxnygq8Hyfo8cnYMBzlW1r64YoX3B+t0Ovd8wnbz5Htzfg0Vmfs6MTMhzMLAVVgo8Kg5R6VWyGQJd0bAKLDZFa2LraorS+lAKpMFKR6wKBreAsTJlFELZ7iDF6qUgRZlEyZSPyWLg0YIOlRiG1xC8GmKKgOy5IcHEDy1epixpZ7lD3NC+ebxCfPuCw2+XGiy/Tbvg8PH7M7Xv3qAYOmpTDszFxmlCrNzAmRjmGvf1NhpMx03lGnkLFdZlNE6RjNwqdZxSmwHHdZaMb33NsStwoazQJzUl3gO+4eI7CdS3itNA2nVnM5ohGQDSbUqmF5EXByVkPI0EXNs0ssOXkRZpQCGsxFpnlbojilCzNMLnBiTJ8P+DxySnT2AKjNIrZNLZcnQY816USBgR+wOHhIZiSh0MIMl3Q6/e4eOEFrl15iQeHv0CjhHLLNQYvtOUX8yTWSpOKOMuIM3A9l8IYdB5TrTqIwiMvMr5b54qnQjkI1qojBcsvePniMmagtUZ8gEV6SbQCywzGsprSmCVTlFQOUsjSxHeW6c6FxbCAZGdZCsIQzVPCMASsSxDUagwGAyphlX5vgAHyIiuVjAX8eL5DtVphNJzguR6D/mgJ6HIcQZ6D54VATu9sTFYiGY2USMddNuEdxxETXSCNZnp6RJ4ZDt6+a4OfMkA4Etu8U5e7tl2sZemSnQtZBnMNUGib8SmssrU09aUSLYO2mFUNhigp9USZNrYEtSu7Yamrtd21WFgOwiCNxjWSIID+dM5srnA9h8AxTOYak+ds1QpeOFfDy2acv3KRK8/vkkYRSllcgOc65FlBnOdUggobrRZSaXZ2NpnPRiRRSjSa4bke0Qy0Z/D9AF3kVPwKhYwQLuSJQekQ5Qo8T0HhMY8i8jzGcVzCMKQWBmAKwsDjbDAgyQvCsMlOo8V4NkE7kmkcEYQhblChNxjiKQdhcpAWSxPnGYVUeGVPVl1WuTqOQ2rs3t7rdtFCkmY5Ws8QUlLoFL+ccyUFeZIwS1NC38UYRQ4kua0NiY3i1u17PH/lIo4v+Q//wl/gvbfe4Zvffpv+2YBZFKGFUyrqAk95JW7IkGc5cWrRvKGSZFGCUr7lfzDRR67Lp0I5LERrjViYwMVao1qzCrQYnmSphlUcYnGOdRj2gjXKaGPjAmVRlzGLOIWgKMDzvJJ1yiFJYxaV4KLsjZFlC+yDKTMUGj9w0OS4rqIocpK4IJ4nCCFJZGoBSsLm+aXyEWX1IVKQCYXxHSJssC93FIUU5PEco6BQCm0swY12LT9lYQQSDUYjF9Z9+W8FGpPlolYlL6OdM7StikTJci0LW8HquBacmGcUueW+UErglHgTdEFR1p7YisoSvUoJby8Kln1EEQgKBENA4yofIUdkheL0xMcPQ65cCGm5mulZl36iabZc9rcUfq3BwzsDLpzfRaiCSb9Hnmi2O1VqoU9apMwmKd1eCgZanRaDSQ/lBviBhRZL6ZImESq0FHdSCKrVgDjLMMZhPo/Y3GqgdcFkHKG1JopjfEcyHE1o1uoEvsJxrfLthFXC2Folnh9w/+ERjhuCznClQgqLSDVCMUs0xuRUqiF5ppcgu+ksXQbXm80GL7/4HINej/uHx1TqtbKzWcZgOMJ1vPL7C4IMx3VwHZc4iShmmk9cvUoFTZFlzCYTdBIjCsOrn3iJX/uNr5GXDaA9V1JkKVudDXwCWqZge6vJ1laLTjPEMYLjbsL7Dw85G48/cj0+NcphAWbKi2LZlHZRKluUBKiGFWcD2Nzyoj/mAgxSFIsMhFyrjyhKC8QpyWKKkspbESdzwmql7C1RkKRl9JlywSe2ma0QgkIXtnmMVweR4IeC4TCxGPksxXMDCqNRysH3fWZJCsbY0uw0RQtBirY7TqnZLfJQlx2nQAuBFOCW5qtZ3GcJGFrogCXYuFQg9jkbZ7FB2hWATAiBdNwl7FQKgSo/b0oto6W07fqKkgxH2DgNYBVRmaGQ0saF1q01sCa50ZrCaCIR0p1rlBQU6ZxWKDm31+Ha/jm63VOk8qhUHBq1kM3dKYNBTJjsUhiFUjMafgB+halOSaI5/bMhhC6FMgRVn353QrvdZp7FzGcWNCaVIMsTfM8DnVNEGUluMKGx7P7aKsnZbIbvOChhaNaq+G5As1kjy2zn7cD3UJWCdKLpbLZ5/LhLu91gHs3Z7DRI4oJUS9uTQyrLR6lcKl6O53uMp/Oy4M8FA65yyKUu4z6a/Z0tlC7oTqYUpqCIc0yu8Ry3LFe3pfRSCaoBDMdzqmEFbQyPjruoVy7T8AWnp2ccPjyiO5ngnB5y88Y13nj3Hq7nWjqDwjCbjvn0J17k1U9dJfQVYVhnc2uLeaT51ut3eOPd+3y3kONToxyyLFv6ynbBl9ZDCYgS5ZdvPV2zKO6xlsFKaay7Ggv3QpXKwxUOUglcx8OYHKUoW5tbNJ+SqgTk2BQRRuKWCzkpYkCT53O8ABzl4uDgKXsutxKSziMKo22RjMIWfwmNqgRlGEMs6efLGJ51BoQtObdgMEupJkt8vynLoRcMTis/8clmPsBa4ND+YsvIy+BrWW2KWFDGGUxuFafBah4pLO7DolIXPot58ryLgQOa1I5HO7hGYyTEhYMfVqhKaDYvcWGvyXQW8/DgDKVmoFNOTsaEQRv//QxPNjntzugen/D8lS2m/SlRAkIESNdnHEXo1ELSx9mcSlhlPo+oVqoUmW09WKtXkWim4zlh6JMmWdmxCoqyFaEoMhphla2NFkeHZxSZJhMp0inQSU4tCMizjFatRSwLbr17iBs4BFWHXt82BxZCEfgh8/mMVGeEQUCRpVQ9hR+4ODRJ8xypDO1WjauXdqnV6owmY9IkIk3OqFQNl85v8vBxFyhotGpcubTDxfPbzOdzptM5G+0GW60dhsMxX/3Gmxz2JmSF5KAXo5TP8PSEmiPYbtQJg5BmrcJ2u8npYIA2BbVGja2NTV5/+x7SU3z+c59gZ+8Coe+T52OuXNzgs6++zM/9s1/8yDX5VCiHRarR+v8FQiza3YmSEdkGDB1HlT5yaR2UCEpgCbEGliCn9VJlKHkopUQXmjiekecWBh2XreoW7NRFrvFcnzzPcKTAZAYc8FVgy42lwfMsVNkNQuLCkClFkmVI37doSCkQZjFOgWVWKBfVkujEuke2s9eT8ZNl4x0hn3CxLEszy+PF4xJZWrJhA6s04wfSwOvMS/YJWFTcaymWuIZlhWWpGayboddcGFNaDDmqSPFUYVOURcj5usNP/vALfOPr36bfPcMIj63dDkVWAW2otub0e0c0p3s064bxdIZwPO7cf0y3OyUtNDdfuIkbOmSnPWp+hXg+x6/UEK5iNotwMsN8lqALw2QyIwwCWrUqWiikG2FMQR5nOErRrNcB8HzFZDpjNo8Iqw7GCEajEfVWA5ELonGCIGA2HzKLEnQUMRjPmExTG74pEsKsIEpSsqJAOA7NapXReIwX+JzbbDGbzxjNJ5zf2+SzL+zz3JV9Ws2Q4aBvmaP6A2rNLUazgu7JYy5dOEfgahpVhyies7nZJqy2+S//q7/PzVdfQkmHei3kqHvG3/n5X0ZJh5du5Lz68jW6X32T2WSKW+Rc3NlkPB2TSIjSKYenBfVqnTffucubb9/iM6++wu7ODp1Wk3gecfi4y3cxHJ6SVKbjmUpjr1z0drcSUuAop8xIlBHyMle/pBpTkqJMqVm+RwBbxr2Kk5XxCSGW5crGGIosQylLRBsnlnRWShvtlsq1nbBNQbUSkmX50uUR0sVQUJgMoRzLhOS6GGWwZctQbsJPpF0Bu+sLaykYVJlksXGPxbjW+Q9E+bqStl9GURY8Cez92G5UpswblLs/lk3Jev/rsRmBWZRVL65D2Ti4nKzF/AlTlAFOezNlvWV5D2aRLUVgMQSiyJHFhP22ZjLLiZMKP/27Psmof8jxcMDmdoMknZVw5yqzKGVn12M2mTI4G9NqNshNhuv4ZMUcIRVZkpHFhkbDYzLLGPWnxFFMnKbUmiFb7Rbj6YDj7gitJc1aFdeRFElEkhtSnVOvVzEZZUNmTW4KpNRUgoA4jtnd22XUmxAGgsF8zu72DvPJjMFoQqXaIkrGDAYReS7Y3GxzcnpKlme4Sto6mbLWJXR9nrt2idGghzIFYSUkLQpMrjnXafLcczv8np/4lCWGKeNOg/6U3mjAzu4WVy6dw6QxaRpT6BzPc1A5/MWf/dsUgSTTHqPxCA3M0oxKUOE/+NO/n7Tb5417j5nOY3zPI5rMGUYJJ/MhOBKRCkJH4iiHzY0NkixjPo8J3ZA4SpgVMYPxhCx7mlOZ5smGNEJYghVbZVkWBy+i6lif3nVt6/TyQ7a/pFkoF6D0v5fnpPSJyxiF69reB2la4LlBiQkqUK5LnGcIYdOe6TRD23peUMLiBoSwACMpbaWhKMpFW0b1jbUWymGUQxKlmV9yWJbVlEvexcUCFPZehSlLxcG2oZESqTVa5khZxgEk5LpUCsvmPawyEaWyWDbNEWXGRy/a460sBkq8gigDixZjUjJDodeQkSuXwmDQMkaiUAbCIiLKYq60GzhKg+ugNTw+OGNjs02BRZtudHZQIkVJwcbGJq7rYDB4QhAlms52g/5gwjBP2azWCHTCIO8jlMSTLo4WJElKXnhIPC5f3ieeTQl9lyxwcaKEipEMhxOywmakKpWQwLGKtloJ2dhsEscxSRxTq1TYrLc4eXxKUdismedJCu2w0W4ym06RIiMIFDrKKXSBLnK8kp2sVg1xhObahT0GkxkPj4/xPZdOrU6Sax4e9hiM5mw2A4o8B6Oo1aqE9RppkfH40Rm10MENBJVGG9/z+cqvv8HRLKJOBW1mbHZabLY73D14hOeHSKPY2L2Md3DMZqNGbx4jHGhXQk7HY6KswNECVxnQksFwjikRr4lISYqMJE354P61Lt8r+3QL+FvAy+VX/d8FbvFbxD4NK9Nff6CE1Fqyq4Y3UqolWSzGskEXJRuRVCui2DAIl/0zXcdFCLXMYGitbW/r1FKn52bReEXY8mRndR67qFTpjJf+vhBPLv7FXVg7niWharlAhRTltrwKJy5MfZsqXKAG7PsXZc9SKbSxaAX04v6dEpNh4cyOEuTaLKALi7+Xva5wyjSxAWkj2WLpCpiVO1K6O6IMYJb+CAubpMyxWIutHAvGUEgXJ3FwTIKvDJ946RVu37vLF27cYBJPUcrwxc99mnmiuXX7FpWKxGk4tJoud+/fY3fnKnFUcHB4SLvVJmh5DE7HaCnZ3m4CY3JVIELFzZvXEVLwyU++wHw2AGV45+1Dkt1dxqMhm502Z72ejTekOUFQYTqLcH3bOjFNUzJd0GrWmU7nJPGURr2OcASPzsa0GwGXLu2CkQyGIyoVD1c1GA3HbG40KbBZHKmwtTdBsETjDkdDotmEaiVkb3uTyxcv8f7DRxx1B1zY7aBmCa+9fouL+y0CCb7r09raYTKck2fg1hrce3DCuf02ukiIVcIscXD8gM986iYbzSrNeoN3b73PRqPJUbfPt998nwt7HXyvji5ypI6pN6pUwir3Ts9ICoHn2phVrerx6kuX6ff6HHanjKOMajUgyaIlU9qHyfdqOfx14P8xxvxxIYQHVID/lN8q9mlWDE8LRON6K7z1gqnFzaRpiuOWxUoLxGPJyOO6FtPgOS5JkmCkJiv0Mh2PFAglEaqs8iyDhMWaS7AI9lFu6osy5CU3hlk5DctsQRnsW8nyTGXkf2lGrO3s9sAYykrUlUNipLAl2KVFIpRcpnUthsFaVcpRZTXeapFb8FLOkpvJGNa3iScCmQbLGC3EExvJkiB2DXjm5HN+7JUrBDpilmRst87T7HjkWhDmObcijQocTh70cRzBoXxEGEqa1Zx5nCFwcBXs7e7y+OiEQX9KrVYlyqbcvZdQqzdptttI4bDR3kQ5kI6nXNg5j1JTnOAUX2ccPJjiuYaZivCbPsePTygyy01Zq9eZjGcEYchkNifPM1zHQWGzCxsbdYbjEa5f4PjWEimKFD9Q9Hpj4iTh8KyHKRRZntFq1EmzjDjOiaOUwPdxHHf5d4zjhCjVJLkmK12aWrXCzs4GnqeQRcI7bx9x7+5D9ps1POVx8aaDH3p85V+8xtHZjO3dLW6Md3nl5au0OlWOH3dJ05SHB4+4sPcp6jVJu13n4dmIwA948937uK6mXttA6xl5UZDnKWmaUQ0rzMcR5/d38AyEgWQ8HvPSiy+wt5fyc7/4q1SCFrmGLPto9un/35iDEKIJvAZcNWtvFkLcAn58jSbul40xLwgh/mb5+9/94Ps+6hpSecarbpY7mfWrlVJ4nocxK6qzheVgjC5x5qtuWKrszCyEIAgCJrMp8SIDUroqlCCrRVZgIWZRx1GKEqUiECvosZAlzMgszP/Fol9oE4FeWArl5rtevwG2OxcsXluNwQiJLjMysIAzsSS/sRT2amWRLE6y7katzR2sHsu/wZpbQHmO73QzF86CBVYVCGEDjNJYboZmDT59rcMrlzYokhlJFuPWKwRunU+8/Bn+0d/7p8RZhFOtMEsU09GI/f0NRqMuriOYRxGXLu0RzVKmsxylfNIkJ4pzms0KZyd9PvP5G/T6p9y91aVad6jX22x0OoyHI/rDPmFVoTODlCGun5MZW4g2n4zxHZ97D06QwmHv3C69sz6OVJz1ztAaRpMZLzx3AU1Kqg2udNCFpn82QmLY2OhQrVTo9nsIV+H7FQ4eHpaNjhU5hlkc4Xn2e6ewKfRcF9TCKvPpFEcK/EpAkiRs1CpcunSBk+MuX/riD3Hw8C5FoTju9ojTlEajxoV2gwcnPY4HE1qVKpVagFCSo6MzzsZTXE9x9dJ5fvKHX6KIcv6vX/oWo+mcQmuSPCIIKtRDn816HT900VnELIbucMr1Szs8ODxBGYs6TrKkrA2Zs7fd4GwY0x+N0PrDayu+F8vhCtAF/hchxCvAN4Cf4beQfXr5BWYRURdLXgYpV/0oF1bFQgFkWVGSnqz6LRR5QRTN0dJSpi9iBIjSvJeL3Xl9QXxgDPZg2YVlsf8/kRRerC9TsmELC1M2a+81T5x33a34wLUX+mXhlpTXXyg+xyKxMLoog43lJRYLGZuuk2oBATal4pBPXEzoVdBTWPDCcr7X859C2F6ZhVGgNe1Q8NKFbb54YxtJxOOTY6phi9GgRjGbUnM8ouhX8JoJFbHHZD7DV4ZRkdLvTwnDTd577w5aQJT0oJBsdDZJoyntdg3HnZMXUy5dOc/Bwy6zWZ9Pfuoy0o2YTlLOho9IY8P583vs7+/x8PCEOJkhRZV0PsWVDttb5xgN+vzeH/8CxyePOHw8YtgdYiSEQYUbN17k69/+BsPxiPk8wgtCKoFLvdHi4pU2r732JmejGdVqSJqlluXJ9TDlwpJKEEcRWZ6R5Tm+51EUOYGQNKo1VGmpGgzzKLLgpkxz58EjpJQcnZ7S6Wyh/BpuEBIowddev81oPmd7Y4vPXLrGW+++SzyaMZ1MqNUbdJoC6brcfXiE4xj+4I99iVo1YBrPSOYxlSDkwsXzvHv7fc56A4LAZaPdxHFcOu0qtWqA8jySeYznCLY2mzw+7JOkmgeHA5v9+y5Bh4/uhbUSB/g08D8aY14FZlgXYvW9Mx+wWb8HEUL8eSHE14UQX19YB4vMwjp3w6K8eh0VuUhVrlPP50VOYbQ1vVVZjLVu4ku7s2tR0qMvI/arFF85Lijp6Ix48rXybtfve/loLfonzyM+OAYW2Qf5hIISBoRZBASfFIlACYk0lNWO2jIz6RyJQRoQ2ljEHoZCZ2idYVj1B9Vaowu9VDZ2TCX8WhiE0AhdQJEjdI7RtmJPFCnbbswf+9wen7veotFo8vD4lB//fZ8mbBoend4mLTS37x1QAI1Ok25/xHg0J4sjJLY/RLfbo9VqU6kEjEYT6vUaZ2c2+HfW7XHv3gOEqCBcjVdLuXh1h1q1ie+GGC3ZP7/DzvYe3ZMpr7/+BpubkosXWpw7V2d3p8N8liCFQ7tT5/X37uDWN9i6cImgXiPPMwqd897tW7z6ykvM4hSpAipeCMamFd967zZxpsm1YDyLSHNNVgimUcI0mpMWOdNoRppnJY2dxbFUwpBapULdD6lXa+S6wPU8GpWQT958gTQ3DIZT6o0a9YpPrVLl+PEhk9EIZTQvX9nhRz57g5sXalzsSD736eu8+Nw+z18+Tyg0N5+7itCawKtw+GjM3/7ff57hYE7V9/nS51/l+tXzzMcDKoFLWK9QbzQwRtBsVvjLf+nf4cbzF9ms+yAMkySlP5oyT1JsJxRJpWq7lX/kGv0e3Ipd4CvGmMvl8Y9ilcNz/Ja5Fa7xqltlylKUBC6mjBqvAkrGGNIsLaHQK9PccRzrNpsVTVxeWOZqUb7PLHqxlR2rFpH5Mkdauhpl52fBUpkIsyK9XeyyYhnrL1OB5VZerCmDBehopRxs4FGW1sxi1heEKosYhxCU5VLl/AMuK0p+IRYLugw0ltaAlJLCFORm0RBIonWJlLSUU2U8YnHi8ioLN0MvIhv2DqXUXNsJ+dJLF6g5BWHoksSa5164wN27d5kMJJPplPZOh1FvzuHRAbN5xubGBvN5jhYFtXodYwyz+QTHUeSZwnccmvUqxyddqjWfg4ePqdQ7bGxtUKmB77u0GnVmkx7ICcN+zIVzL+C6FSbJERevCK5eavErv3DMZusC0yTh9GyELiJ2dj2SucD3djEiRoiMB/dPODnpsbW1xdvvvMtgGtGo19ndaJNmGYenZ2gpEYVN2GZlaz9tHHKdYpmqXZLE8mhKR+IKSZ4XKKm4dukyp4dHVBo19vf3mQz7PH+xw/sPjzkbRbTbbXa3m3zx1ZscPT7h7uNTWu1N3nvvXdqtKmk05+L+BufPbVKpVjjqjjnpJZg05eDREdXOJg8PHrPT9NjZ2ebBYZfz+7tMRkPiPKO9tUGSFByfntHwAkaDAX/yT/4hzu9UmPYjjPL5hV9/jXcfPEYKzXQ8I85yjDZ0Nhs8fHhGnn842cv3hHMQQvwa8O8ZY24JIf5zoFq+1FsLSHaMMX9FCPGHgL+EzVZ8HvgbxpjPfbfzS+WasLFriVrWQEuLR5uNKMuM1SrKL5U13xdFW0K4y4WS5XkZ6CsXkhRLtKBdISujSZZEseXNrlwPUSqCdfsflsrBgA1uLux8sVr0tuX86ryaZSCiVAJr8YC1x/LTwMoSWbzXpk4XMQdTcgda9p8FkEyt9akwZf8MrUtFIFYdzJ+QMq5QGIksEvZbAT/5w9fx9ZjucZ/MaFrVNsPRnM3tbZTUpFmKMA5n3R5BWKXbH5EVGa1GyGQWMxhNuHz5PEWRk+cJSHjwoEvFbyJFShwVZDriR3/0x+iPD9jZOMeVqy3eeeseRSYQMsLzLQz5wfsTHFWls6VoNUOaYZM33jpG6pTmhsv23j7f+uY7uI6kXaswmOVs7XRoVIQtlBvFPDg45aTbJQwr3L59ByUleWarUHVh7C6ALol4fMbjOa7v2rSlLiyJT5KAsZ2utLGFdACep/B9n1C5OA782Gef45//y1v8kX/rD3D8/iMEBUkSU6QFQatKRSruPXjAxfO73Lr3iDRLee7COTp1yUsvP8/pMOf++weMhyNSBFo66HmMSRKOxzMKx6FTq1MNHM5GA7SQuDIgi+Y8f+UCvqe5cuUCw+GERw+Pef6Fq3z9zdt4PvybP/UF3nn3hNfu3Ofeowf0zuZkH1Gy/b0qh09hU5kecA/LKC2Bvw9cpGSfNsb0y1Tmfw/8FCX7tDHm69/t/MrxTLV1zpK9lr76Aiy0DPAvgn1KLqnWrMlcLHd0pdxlutIClhY9NsXKcsBaEou6gfXXFgFGsdaWfFE4tTxeix1YdPX64v5o5bCerlyZ9awdr8/3enrpSY9NlZwUC7dISrvg8zxHldmcJRdnqZCKoljkPz5UOWhtXZOaTHjp8g4bIYQqo9Vs8PBoTKfTIhrP8IIqDx4d4Xk+3bNjLl7aZzSJrLWTZ0gM1TDADX0Oj89oNmrU6yGGguvXX+Sb33ib85d8TK659c6IH/riPjevX+boQcRsHJPqEYUR1Bs1lFRE8ZzJ7IhW22d45pAmhiKXOL7DdJJzenLM5maLS5d2cT3F8eGQPMv55Kee4+7dR8xnM17+xHV8v0Kns0muNePpnH/wD3+OR49OMDkIaSw6VSrbQjGKiOIYHIUnXUxmKEwBUpR9SQxJkRO4DqHnYXLL8Zhj8KWtqiWeMkxtf4laENJoVGk0qozPRhQmZ7tW5e7hIdVqDRXUmEwmpMmEne0OF3c63Ly+R7u1xcHDYx6cHLNz8RL94wGPD445POszj2PQhka1xngyZp4lPH/tEsPpjCyZUXUrDPpTbty8SOAUhNUGb9094u7DQzYbFeIsxw0CxrM53bMRWfZ90MQZY14DPvMhL/2WsE9rbQtilrt6uf6Wuf9VtM6mN5ddpBdm/CpGAaVVsVQywqb6BEsA1EKWNRr2YFnYJPjOAOkHF9UizbmM54lV0fTqPet3KdaOV7v/2tHaHOqljWIWO35Z52C0XFoHltBmHQOyotKz47WfkcrO8YLNaVFFubw/bagqzUvnN6iTcHx4xo0Xn6c7moPvLTuH+b4i0xFnZ0Pa7U1yI9hs1DBGME9S8jyn0WxQkNJstDAalAxsabtSBL7gwe0hu3uamzdqvPLCNQb9mCBMSGPJowcph6d96vUq57a3aXZ8Rn3btaoWeEyLIQkJRko2OxVmM48k1Zx0H9HZrCGUzUo9enDIdmeDWbXB48M+jjwjmox58fpV5sM5X/rcq3SvTbj/4ICjo2PAUK3WODvrIaXk4sULtNoN7t59gFaCZDZDAkUBRZFTCW315CyOadcaaBMjlSD0Q0azCa52CGoOF/b2yAtrZZ2e9Qk8nzyKmEdznErAhYsXePu999G6QBtFtbVBLjT93pCg2qDZqXHVPcfd+3cIKk1e+uQniL75OlnvlMl8RlU1cJ2AhudzZbdDrXmNd957n1G/z/PXLnPw4IT9i9u0ghrndzucO3+er3z9DVrtBjIrUHrFxfFh8r0EJH8g8sTCM8vIIWb5j9KKEEu6b1HGC+zCk7ZRTLnglONYunTXQbkuynHscwslsHAzymIrKaQN/C2yE+WGLZ7Y7bFZifLX5ZiWYj9kUQiyPNblT44xOSXbit2ul/dr73n1Y8prGxupMBqJRpQ/Wudlitci9cCCwXSubVDRZDhG2mto28jTWSRfSrSjMCW1W9mybq/pcW3DpVarUg0CpHGgEARSs7nZZHdvhzhLCGs+rUbdUuubgnrDYZ5M2NzaRCnHskrrDE9JlOPguj7nd/ZIZymzKGYSRbz/UFIJt3jjjfvcf/iYQj7i4fF9Yp3S3tmgEIbe6JTx5IxatU4USTKTkSUCPfMZduecHI5I54JOs8XFSx08P0HrlK2tLaQTcvvuffIs4969ByivSq8/5O7799nZ2uKlG9fZ6NT5U3/yj/Fn/vS/zY0XnkOYgtBzERomgzFnZ10C3+OP/pHfz+7uBhZVq/B8hxevXeIzL98AIZjOYxrNGgJDUqQkaUxiLDT/vTu3Ob+3iTQZF3a3MemcZrPOyWyKVB4Hj44oKHB8xVarzcH9A648u12LuAAAFhlJREFU9yJ3DqfsX73BNCk4Ou2ztbnNhZ0Onoh58fo1Xrj+ItevXsNRku5oxGA84fbDI9566y00isE8IScl0Tm9/oRbdx9y1J3w2mtvs1UPqJiEV67v4zgOy94AHyJPB3y6lJWC+GDKb5HFKKP8JdjIlDu/Ka0Dx3HJhe3GpDFIx1LCF2X6UyllacOMsUplEVdYnHMNQrA8KAOV669QFoYtTI1l3GB1J6WSWikAs7yPhTJbL6Cyn9UL5KOwvpQpFYQQpU9sDKbsIWELy0oFIqWt5BQaicBoyPQMR7gIIUq3oiziWsyNWdkmCMFgPCaJPQ6OhrQabc6d36A+8+j1Mo6PT5BGguswHsZ4KqRe82m1FVE2wwkD7ty7hycV7Y06geejipROp02cxgz7PQpjSLOcc+cuUK25TGNJUBHUq01ck7O96RCEWyB95tEAI8bMR5poaqHMORFe4DM4GeA2GrZRkEoxWtAIrxJWBOnkAFUUTKMxlWadXj/CcQKSfEqtUicr4PT0iDyHLIl48/VvUxSGP/gHfi8HBw95553b3Hr3DqPxFCdwyZKCX/nyr5EmMa5ne4+kmeDWe/epew6uK9hqNXnlE5/gzr33GYzG5HFCXmToLKMwhl/81d+gXgmpVaqYQhN4PjdevE6R5rz17nso16VZa3Bxe4fDsyO+/utfJ0lTjg8OyOKIvb0tBDAe9qjWfF64fgnnABpjSXtepaBgNJrz1t3H7Gw0mE26dBoNfvRTL9HbP+Kth12SLKWII5pVF9dzUcLj1755h+5g+mSzog+ux6eh8EpI1zhhZ3FU+utWjFr5yaWRvCwsotz9jbEMUGDNPnsaC1BZBCgX3AnSUXYfX7RfEyv6+48Y3Af8gwV+oKxBKPWG7Ztgzf2FLllM/DoaUS7uw0hWQKjFPX5n/MG+ts6SvYZVgCUOxBLlSBztoNOCVKS4TsmZqXVZ9i5IiwKNKesrWGY9XJ3zuSsbVJVha2+PWiXi0uWL/G9/75c4t71D4ArmmeHkbMJsOiOsSrZ2qtT9BrMYbr93lxeuXSWoePiuQzSLCRxJUAk57J4xnSfUmx4OLi/erPL1b5ywu3OeeN6lWa9wbq9JoSUnpyNcJ6QoMlqtFoPBDN+XzJI5Z6dDsnlBlCVUqgHVus/F8+dp1B1cTyF0yHQ856h7TNhokecejbokTk+YTjO2m1uQxPhBldEsI0oyGs0GnXabWr3KuXPniKKEL//SL/ONb73BYDTGcQIMBbV6hcl4WjJHB7RDh4gMmdmvRKVWZzqP2Nho4riKJImZzSIQHqHnUgl9ev0eOxttNpq2yxi+jyjg+PEptdBn5+IO+Tzm7KzLKy9d4qUbLzCfzYmSjNFkSKvp0tzYAy15cP8hk/GMOEqQ1Q53D46YT2dEcczjo2PO73b4iR/+FPcePGJ3c4fJYEglDBAmJU1y/sXrD5gZyXH39PsCQf1AZFVqXLInlc8bodFiBQySZf8DKPEKZfqP0v9eWBOO4zyhGIwxZeMRrCuhViCj73T67cNCIcF62fPiDeu+Ren6LEy0BVfCml+/jIwsrZNFTGARA9Dlub/z77SAkaxjKWzg1qYyl/iFPKdZm+PVJL25Q16wjEcAJYdm+Xm9xjdpIBMeD7pTnmt7vP3OO/z7f+4neefWe2SFptcb8fnPfYKvffMNprM5nc0W0+mMwZlgkJ+CctGFZjQaMhxLNjsbzCZzdLOJkgGFdPBCietIXGH4xCs1To76dE9O0AgenXTJiwr1Wg3ltHGVQ6A0D++fECea8/u76DxH+YZavUp0VLB/8TKdTpv+aRdpKsxmEwbj+/T6Nm1ameY4ns+FCzdp5h0OHn+DTtPh5GRAkp5RCIcsTTnr9Rm0hrTbbe7eeZ/NzQ1+6g/8Pn7iJ7/ESfeE115/j6/95rfJs5xPfOImd+/dIy1gZ2+HR6ddkjgi1xqRZ7iBh+NI0mhOkqQEruQzn7rBb3ztW+Tk+NUqJ6c9prMUvxpw9eoWNeUwH41pNZs8fHhEq9HgeDDmWpzz6OiUzkYHnWZU6lWKIuPxwQl+tc65a89zdnpINunT7/fZ36iRNqo8fnifrVad94+HyNfu86M/dJM8zonzHhUv4PRozFYzpFEPGPYm37XL9lNhOVj2aQuwNEJYmHNp5q/XKy2zCcsMAGVQ0KwyEVgrwojVzq2UQjprqUogL7EDRZ7b/P6y34JArFkVq2ssPrpyfYSQLOM5YlVFuYiR6CfGzuqNy+dXEU2xFvRcVkqyckfs/1VR19IqWfv7OUXB81sFx33DXLgUhVliK/KyKc3i+qK816WyQeKYjM9c3uT6fsjehSv84r/8NmGlwvHBCY2WQ6MZcPxoQlgL2dzdwkPQ653Z5jFKkOcZRruMR3Na7Ro/+sXPIZXk0eEJX/3Kt6k3XM7t1/HDhNAz3Hqrj+Pt0mwFfO2bb7O50WZ7s0azHvDat9+j0gj5wuevcvRgxGgUc/2lLTyvxutvPOK561cJvBoHj+4QhnD7Vpe4ANdJmceSTsfh1Vcu0Qx3GA893rj7Hs+db5PGCe/du4eRBc1Gk8Crk+UxnWadC+fPUxQFcZKQ6ZydzU12t3b4l7/y6wwnEz77w59ld3ebr/z6V+kendIfTzjq9ag1WpydnnD94h5ZWiBdn8OTE6QUVP2AvYsXeefOXXY6DUwSoyodOoFDFsdsbTYYzwvyImM4HHHaH9BptHBkzo/96OeJ4ynXr1+kFrr0e0N2L15mnhRMxjN2dnY5OT5i3O9ihOL0rM9wNKU3njKaRAzPxiRINpoVXn7uPOe2N/jNb7xN1VeMJzPeuveYqeWmeHoth0U8YLnIy3QcPNkDeB11uPpSl4tk7TyW9Umj1AovYYzBcco0X2lRCGGp1KQRJeGJKIFC63NVLt6FobC0KuxrYhF3XL63jJs+GcBYgZoEa9nJDyiBJxCbq3EsUpeLzjEGVgFNsVI8OXAyNkxyiXYksuSnLIoSLYlZXv+JawlrcZlMkekZV557mTffeUi1WqFS8UmzBCkrTCYRFy9f5uT0hNANeHD/fXa2t8mLmCiace3adUbDGaenXRynwdd+81vM5zMm0znxPKVe8+l35xgD5y/B9n6H6SzFdX2CqovOJZ2mz41PdqhtvUI01ly90GA2G1A4AulkTKY9Ll7eJo5nZGlGo+6yv1enHtS5de8xmxses3mdaihRhcOd9+5RGJfL+23u3LlHrRbiegnKFfR6fba3fAyShwfH1KptJpMJYeizu9NmPBxzdHSKX6vTe3zE7XsH7O2d4zNf+CF6Jz36pz3eu32H/mhGdfccm80mSZwQVEK2OzWKIqPeqHHvwTHNSoOap3AE9OZDpoWHrzz6vR4nJ2O+8COf5c35jFqrzjROOL/Z5PR0xIODR7x76wE/8SOfQhlIZjPCSo1BGtM9OSHPDW5zk412k0q9SvewRxoVNHZrdMKQ+4+6jIZTfuk3Xmdvs0OtWmEwmbC9vYN8cGzbAHzUunwaLAfH9U21dQ6wloNYY5s28B2FUkt34Ak8wup3KW2kPNfFMhZhxKozt5AKlFpS0ynplE1KbdBPr51PG7OGe1gHRK0UxGJ5CuTydwMUHwA6rcsKKi1Kxu3Fe57ENQjK7A0LyDdPvL5iZhIUaHwDmTK2bZ2QJYhHrwKiPKlw7Jk10mj2aiE//Ye/xHTY56Q/ZjJPOLh/gCxb1yfpFMepooRhs9OmNx6ghGBzq0qlUmEyTilyzdb2JkVu0EZy78Fd3MAliQ3xLCHNIi5dvEo0nRGGAa6Xs9V20ECzusf2Tgc/yNk7d5nj4yN8NcDzp0jX5fCw4NHRCcptcPCgx2g4Y3tri1dfvcR0Pubg6JTAiblzO+XmjYs0mnXee/+Is8ERsgjQecLz1y6TxFPGkwHzpKBSreC5FUKvjpQOo9HIci34Lu1Wg4sX9pjOphglabcbdKo1ssJglIPJYXByxrffeJs337lFq14hCEKuXL6Aowy7220cV/CVr73J4fGYrbDg5YtNvv7+mFTVaG82SKZ9+mczdnY2ePTokKtXL1LEMWmS44YVHh6fEoQBV89vc+ncBtW6x/mLF5HCIY4y4sQyX9drVYLA59u/+TWG0ymTuODkuMdoNGU0j0gMJHFCmgs8afBcl7zIGU+nFL8TYg6roCNgDI5SK9PcLHL3Kxo0xIqBGkpz3qx+FophEYwsCr2kisvSDAlL/kmlJEKXbfjWFriSNsK/eMasLfR1DqbF8RK7IECW6cz10pPlp5euy+peMB/gjlq6HItMilkgH5bWiSG3QVptw6QajYMF9iwa367SsdgGNaXl5bsOjpDkWcr1izs8v98hGk7p9yaMJhPyQpEmmqoriWJbc1CrhuRpxPFJl1gnNGtVxuOIKM5oVpvUtkOUYzBa8N47d6g4LmjJcDriwpXnaLfbCOEwHY94/tpzDPv3mfbP2N5sU6kokmRONIU7b32VatOnUtXsbUpuv3WH+w99VCDJswFSVvA8gfJctEiZTkdsb1xkOpjx4vWQ7d2MWmPKcJBydX+XzlZOf9jm0cGUva2LFKnC8fpoMubR2JY/Bx1unnuR+/cfMpmlKDUjnk/ReYIiIJA+nVab4XhEIQXNThtDzucan6a+1eC92/e4+fIrRJMxr7zyMkk0oXt2zPXrzzGP32UwT3m7pzjsTVEyweiEv/ozf54v/9+/yi9+6zWiwuXOg2Oqvo9C067W2NjsIIRgc2OXd27d49LFLVqNKXEJ527Ua4SBSxzPORkMuXT1EvtZzp2DE6azjFmc0vQV3f4IRwr7tykKKGyPlvX180F5KpTDMs245i5Q8jZoJdYKoASYtVLnRTpu8TmhV4QuRttS40XGQlj+SSGtyyGMVQxou8RWEGxZdr/Wq47eq4HyHR7HE7K2uA1Qph2XLsgTN73IOpQVDQtlsQxirq4Ja8FTUY63THPaKdB4MkMh8HxDJWhzMhigF+nSxfWFQBnISpBVnqW4rsNuM+TV5y8w6J0w9XxmcYqSLv3BmMFkQq0W2Px9WtA9PSPwJDkp+xd32N8+x1l/Qm94xpXLdTZ3A779rQdMRwWu6yOEQ380I0k1gQftZh0lfSSGo+NHNCoVxpM5nU4VgSSPNffu32c2y6hMfS5da9KbJRyf1JGOw6A3QkmHk94hl69UqVQ1R49HVP0WCmXN7lAzGxiOjg7Z2dqifzqmWzSYRRNqlQ2uXLtMtRJy3DtgMOjRafuM+hNOH01I912mo4jMZExGE+Jozu5Om2qoeHxwTBRFBDWfIs+p+SF7O1v0Dg85v9Hkkzf/KLPZnGK7zTSZ02g0eXFnh26vz5d/9etcvXKR/nhCrd2hSBLiPOetd97m9r33iXVKGPic29ni0fEp+zubVCohh4cnhGHAP//yl6k1WzzunnHn3gEX9vepNwIKHTEcGKTrkRfgiipZkrLdbjGdzhkMBmy2O+S5odsdohyFKTfLLP7onhXwlCgHWGUX1pGIQghyWWIaFkjFRYJhLU24fFzPVAjrjiz6VgAlfb1e9q4UwtLLSbViiLKKStj+FAszfq0/nyh11BOhg7X7+BCNsRZwWI193dV40rVby3qU13zi1UWWYnF9oxAmZ6PjkE8G+G6l7MzkPvEZjUGVyk6jLdhKucRxzHMvXqZ3dEhQCznpDzHG8OjRI6LETnZ/OEQDcZZipGCzucsP/dBFNpst7t49oz+d8COf+xzjZMpXv/oeVb/GlYsbdDpN3n7nDkrFNOp1hoM+cRQxnc6oVOtUKzWKOOb0ZMSNa8/jexWGkzGzVBMLhScF3bMhSZwzHoX0JgeMxymtZkin1eGVlzcxMuGtN0bsbtYJ/IQ4LRAqx8t8Luzd4OpzG/zT979C1jeEboejwxN0MkcFPp3NK6Rxxln3EegQ5eQcnr6D4/o4fgslXE5OB4xGYzY62xSFYXM6Z2u7TafdYDadonVBrVpnXjzm+PYdPvOpTyId8AOXyXjC6dmAOM04f/Eio+mEs24Pz/XZ2KgzizN+7ue/zOPuGO1phGc4fHyE6ynSPKUoNKbI0WnG/tYWaVHQHUWEQZW3b73PzladwdDl2tXnmc3mBL6PKAmaz3qn+L7LlWuXmE0i2rU60XROf2qRyHmek+dF2f7xw+WpiDkIISZY2rnfSbIJnH3cg/hXkN9p44VnY/5ByCVjzNaHvfCUWA7cMsZ8WO3GUyuWh+J3zph/p40Xno3545anprbimTyTZ/J0yTPl8EyeyTP5UHlalMP/9HEP4F9DfqeN+XfaeOHZmD9WeSoCks/kmTyTp0+eFsvhmTyTZ/KUyceuHIQQPyWEuCWEuFNyUX7sIoS4IIT4shDibSHEW0KInymf7wghfkEIcbt8bJfPCyHE3yjv4XUhxKc/xrErIcS3hBA/Xx5fEUJ8tRzb3xO2KRFCCL88vlO+fvljGGtLCPEPhRDvCiHeEUJ88WmfYyHEf1x+J94UQvxdIUTwNM/x9yMfq3IQlvHkf8B2yboJ/AkhxM2Pc0yl5MB/Yoy5CXwB+IvluP4qtsvX88AvsqLoX+/y9eexXb4+LvkZ4J214/8a+GvGmOeAAfDnyuf/HDAon/9r5ft+0PLXsZ3UXgRewY77qZ1jIcQ+8B8BnzHGvIztVPTTPN1z/K8v67UIP+gf4IvAP1s7/lngZz/OMX3EOH8O+L1YoNZe+dweFp8B8DeBP7H2/uX7fsDjPI9dUL8b+HksJvMMcD4438A/A75Y/u6U7xM/wLE2gfc/eM2neY5ZNWzqlHP288Dvf1rn+Pv9+bjdio/qjvXUSGkKvgp8lX/1Ll8/aPnvgL/CqtJ9AxgaS175wXEtx1y+Pirf/4OSK6w6qX1LCPG3hBBVnuI5NsY8Bv4b4CG2g9sI2wHuaZ3j70s+buXwVIsQogb8I+AvG2PG668Zux08NakeIcQfBk6NMd/4uMfyPcpvSye1304p4x9/FKvYzmH7t/zUxzqo30b5uJXDY+DC2vH58rmPXYQQLlYx/B1jzD8unz4RtrsX5eNp+fzTcB9fAv4NIcR94P/AuhZ/HWgJIRYw+fVxLcdcvt4Eej/A8T4CHhljvloe/0Ossnia5/j3AO8bY7rGmAz4x9h5f1rn+PuSj1s5fA14voz2etjgzj/5mMeEsCWT/zPwjjHmv1176Z8Af7b8/c9iYxGL5/9MGVH/AjAy36X932+HGGN+1hhz3ti2hT8N/JIx5k8BXwb++EeMeXEvf7x8/w9slzbGHAMHQogXyqd+Enibp3iOse7EF4QQlfI7shjzUznH37d83EEPbNu894C7wH/2cY+nHNOPYM3Z14HXyp8/iPUXfxG4Dfy/2BaAYAN//0N5D29go9kf5/h/HPj58verwG8Cd4B/APjl80F5fKd8/erHMM5PAV8v5/n/BNpP+xwD/wXwLvAm8L8C/tM8x9/PzzOE5DN5Js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     \"text/plain\": [\n       \"<Figure size 288x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 288x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for fp in ['test/test.jpeg','test/t2.jpeg']:\\n\",\n    \"    im=cv2.imread(fp)\\n\",\n    \"    show(im)\\n\",\n    \"    img=predict([im])[0]\\n\",\n    \"    img=get_depth_map(img)\\n\",\n    \"    img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)\\n\",\n    \"#     cv2.imwrite('depth_estimation.png',img)\\n\",\n    \"    show(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2021-03-11 15:30:15,635] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"human_seg = hub.Module(name='humanseg_server')\\n\",\n    \"import base64\\n\",\n    \"def cv2_to_base64(image):\\n\",\n    \"    data = cv2.imencode('.jpg', image)[1]\\n\",\n    \"    return base64.b64encode(data.tostring()).decode('utf8')\\n\",\n    \"def base64_to_cv2(b64str):\\n\",\n    \"    data = base64.b64decode(b64str.encode('utf8'))\\n\",\n    \"    data = np.fromstring(data, np.uint8)\\n\",\n    \"    data = cv2.imdecode(data, cv2.IMREAD_COLOR)\\n\",\n    \"    return data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 288x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"im = cv2.imread('test/t2.jpeg')\\n\",\n    \"#visualization=True可以用于查看超分图片效果，可设置为False提升运行速度。\\n\",\n    \"res = human_seg.segment(images=[im],visualization=False)\\n\",\n    \"img = cv2.cvtColor(res[0]['data'],cv2.COLOR_GRAY2RGB)\\n\",\n    \"show(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       ...,\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"res[0]['data']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 288x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"b_channel, g_channel, r_channel = cv2.split(img) \\n\",\n    \"\\n\",\n    \"# alpha_channel = np.ones(b_channel.shape, dtype=b_channel.dtype) * 1 #creating a dummy alpha channel image. \\n\",\n    \"\\n\",\n    \"img_BGRA = cv2.merge((b_channel, g_channel, r_channel, res[0]['data'])) \\n\",\n    \"show(img_BGRA)\\n\",\n    \"cv2.imwrite('h.png',img_BGRA)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       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     ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cv2_to_base64(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 288x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"show(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/shadow/opt/anaconda3/lib/python3.7/site-packages/pip/_vendor/packaging/version.py:130: DeprecationWarning: Creating a LegacyVersion has been deprecated and will be removed in the next major release\\n\",\n      \"  DeprecationWarning,\\n\",\n      \"/Users/shadow/opt/anaconda3/lib/python3.7/site-packages/pip/_vendor/packaging/version.py:130: DeprecationWarning: Creating a LegacyVersion has been deprecated and will be removed in the next major release\\n\",\n      \"  DeprecationWarning,\\n\",\n      \"I0309 12:11:52.601193 114978240 analysis_predictor.cc:155] Profiler is deactivated, and no profiling report will be generated.\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_graph_build_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_graph_clean_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_analysis_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [simplify_with_basic_ops_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [attention_lstm_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [seqconv_eltadd_relu_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [seqpool_cvm_concat_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [mul_lstm_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [fc_gru_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [mul_gru_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [seq_concat_fc_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [squeeze2_matmul_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [reshape2_matmul_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [flatten2_matmul_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [map_matmul_to_mul_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [fc_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [repeated_fc_relu_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [squared_mat_sub_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [conv_bn_fuse_pass]\\u001b[0m\\n\",\n      \"I0309 12:11:53.378603 114978240 graph_pattern_detector.cc:101] ---  detected 72 subgraphs\\n\",\n      \"\\u001b[32m--- Running IR pass [conv_eltwiseadd_bn_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [conv_transpose_bn_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [conv_transpose_eltwiseadd_bn_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [is_test_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [runtime_context_cache_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_params_sync_among_devices_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [adjust_cudnn_workspace_size_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [inference_op_replace_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_graph_to_program_pass]\\u001b[0m\\n\",\n      \"I0309 12:11:53.547896 114978240 analysis_predictor.cc:598] ======= optimize end =======\\n\",\n      \"I0309 12:11:53.548173 114978240 naive_executor.cc:107] ---  skip [feed], feed -> x2paddle_0\\n\",\n      \"I0309 12:11:53.552616 114978240 naive_executor.cc:107] ---  skip [save_infer_model/scale_0.tmp_0], fetch -> fetch\\n\",\n      \"I0309 12:11:53.693938 114978240 analysis_predictor.cc:155] Profiler is deactivated, and no profiling report will be generated.\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_graph_build_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_graph_clean_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_analysis_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [simplify_with_basic_ops_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [attention_lstm_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [seqconv_eltadd_relu_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [seqpool_cvm_concat_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [mul_lstm_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [fc_gru_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [mul_gru_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [seq_concat_fc_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [squeeze2_matmul_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [reshape2_matmul_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [flatten2_matmul_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [map_matmul_to_mul_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [fc_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [repeated_fc_relu_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [squared_mat_sub_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [conv_bn_fuse_pass]\\u001b[0m\\n\",\n      \"I0309 12:11:54.324568 114978240 graph_pattern_detector.cc:101] ---  detected 72 subgraphs\\n\",\n      \"\\u001b[32m--- Running IR pass [conv_eltwiseadd_bn_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [conv_transpose_bn_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [conv_transpose_eltwiseadd_bn_fuse_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [is_test_pass]\\u001b[0m\\n\",\n      \"\\u001b[32m--- Running IR pass [runtime_context_cache_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_params_sync_among_devices_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [adjust_cudnn_workspace_size_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [inference_op_replace_pass]\\u001b[0m\\n\",\n      \"\\u001b[1m\\u001b[35m--- Running analysis [ir_graph_to_program_pass]\\u001b[0m\\n\",\n      \"I0309 12:11:54.487866 114978240 analysis_predictor.cc:598] ======= optimize end =======\\n\",\n      \"I0309 12:11:54.488152 114978240 naive_executor.cc:107] ---  skip [feed], feed -> x2paddle_0\\n\",\n      \"I0309 12:11:54.493052 114978240 naive_executor.cc:107] ---  skip [save_infer_model/scale_0.tmp_0], fetch -> fetch\\n\",\n      \"[2021-03-09 12:11:54 +0800] [76707] [INFO] Goin' Fast @ http://0.0.0.0:8891\\n\",\n      \"2021-03-09 12:11:54,495 - INFO - Goin' Fast @ http://0.0.0.0:8891\\n\",\n      \"[2021-03-09 12:11:54 +0800] [76707] [INFO] Starting worker [76707]\\n\",\n      \"2021-03-09 12:11:54,497 - INFO - Starting worker [76707]\\n\",\n      \"[2021-03-09 12:11:59 +0800] - (sanic.access)[INFO][127.0.0.1:58103]: GET http://0.0.0.0:8891/  200 45\\n\",\n      \"2021-03-09 12:11:59,556 - INFO - \\n\",\n      \"/Users/shadow/Documents/code/EF/camera.py:26: RuntimeWarning: coroutine 'StreamingHTTPResponse.write' was never awaited\\n\",\n      \"  b'Content-Type: image/jpeg\\\\r\\\\n\\\\r\\\\n' + frame + b'\\\\r\\\\n'\\n\",\n      \"RuntimeWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"[2021-03-09 12:12:18 +0800] - (sanic.access)[INFO][127.0.0.1:58104]: GET http://0.0.0.0:8891/  200 45\\n\",\n      \"2021-03-09 12:12:18,926 - INFO - \\n\",\n      \"[2021-03-09 12:12:18 +0800] [76707] [ERROR] Transport closed @ ('127.0.0.1', 58103) and exception experienced during error handling\\n\",\n      \"2021-03-09 12:12:18,931 - ERROR - Transport closed @ ('127.0.0.1', 58103) and exception experienced during error handling\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!python main.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import screenpoint\\n\",\n    \"import utils\\n\",\n    \"import cv2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"screen = cv2.imread('test/screen.png')\\n\",\n    \"view=cv2.imread('test/view.jpg')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"AttributeError\",\n     \"evalue\": \"'NoneType' object has no attribute 'shape'\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mAttributeError\\u001b[0m                            Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-3-517940b41078>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mh\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mw\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mscreen\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshape\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[0;31mAttributeError\\u001b[0m: 'NoneType' object has no attribute 'shape'\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"h, w = screen.shape[0:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"AttributeError\",\n     \"evalue\": \"'NoneType' object has no attribute 'shape'\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mAttributeError\\u001b[0m                            Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-4-20ac3adddaa7>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mscreen\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mutils\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mresize\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mscreen\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m      2\\u001b[0m \\u001b[0mview\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mutils\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mresize\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mview\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/Documents/0002-coding/PK-AI-mixlab/server/utils.py\\u001b[0m in \\u001b[0;36mresize\\u001b[0;34m(image)\\u001b[0m\\n\\u001b[1;32m      5\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      6\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mresize\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mimage\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 7\\u001b[0;31m     \\u001b[0mx\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mimage\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshape\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m      8\\u001b[0m     \\u001b[0mim\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcv2\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mresize\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mimage\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0my\\u001b[0m \\u001b[0;34m/\\u001b[0m \\u001b[0;36m2\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m \\u001b[0;34m/\\u001b[0m \\u001b[0;36m2\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      9\\u001b[0m     \\u001b[0;32mreturn\\u001b[0m \\u001b[0mim\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mAttributeError\\u001b[0m: 'NoneType' object has no attribute 'shape'\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"screen=utils.resize(screen)\\n\",\n    \"view=utils.resize(view)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(2872, 1414, (707, 1436, 3))\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"w, h,screen.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"view=utils.bgr2gray(view)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x, y,r = screenpoint.project(view, screen,True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(778, 674)\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x*2,y*2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 288x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"show(r)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'0'\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"k=0\\n\",\n    \"str(k)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#视频补帧\\n\",\n    \"im=cv2.imread('test/test.jpeg')\\n\",\n    \"fps = 12 #视频每秒24帧\\n\",\n    \"h,w,_=im.shape\\n\",\n    \"size = (w, h) \\n\",\n    \"video = cv2.VideoWriter(\\\"test/test.avi\\\", cv2.VideoWriter_fourcc('I', '4', '2', '0'),fps, size)\\n\",\n    \"for i in range(fps):\\n\",\n    \"    mat_translation=np.float32([[1,0,10],[0,1,10]])\\n\",\n    \"    im=cv2.warpAffine(im,mat_translation,(w,h))\\n\",\n    \"    video.write(im)\\n\",\n    \"video.release()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2021-03-12 10:43:33,183] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object\\n\",\n      \"[2021-03-12 10:43:33,188] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object\\n\"\n     ]\n    },\n    {\n     \"ename\": \"TypeError\",\n     \"evalue\": \"load_inference_model() missing 1 required positional argument: 'path_prefix'\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mTypeError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-27-9d96a47d10e9>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mmodel\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mhub\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mModule\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'video_restoration'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m      2\\u001b[0m \\u001b[0;31m#model.predict('test/test.avi')\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      3\\u001b[0m \\u001b[0;31m# model.predict('/Users/shadow/Documents/code/-PK-AI-mixlab-/server/test/test.avi')\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/paddlehub/module/module.py\\u001b[0m in \\u001b[0;36m__new__\\u001b[0;34m(cls, name, directory, version, source, update, branch, **kwargs)\\u001b[0m\\n\\u001b[1;32m    170\\u001b[0m             \\u001b[0;32mif\\u001b[0m \\u001b[0mname\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    171\\u001b[0m                 module = cls.init_with_name(\\n\\u001b[0;32m--> 172\\u001b[0;31m                     name=name, version=version, source=source, update=update, branch=branch, **kwargs)\\n\\u001b[0m\\u001b[1;32m    173\\u001b[0m                 \\u001b[0mCacheUpdater\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"update_cache\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmodule\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mversion\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mversion\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstart\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    174\\u001b[0m             \\u001b[0;32melif\\u001b[0m \\u001b[0mdirectory\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/paddlehub/module/module.py\\u001b[0m in \\u001b[0;36minit_with_name\\u001b[0;34m(cls, name, version, source, update, branch, **kwargs)\\u001b[0m\\n\\u001b[1;32m    272\\u001b[0m             )\\n\\u001b[1;32m    273\\u001b[0m             \\u001b[0muser_module\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0muser_module_cls\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdirectory\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mdirectory\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 274\\u001b[0;31m             \\u001b[0muser_module\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_initialize\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    275\\u001b[0m             \\u001b[0;32mreturn\\u001b[0m \\u001b[0muser_module\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    276\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/paddlehub/compat/paddle_utils.py\\u001b[0m in \\u001b[0;36mrunner\\u001b[0;34m(*args, **kwargs)\\u001b[0m\\n\\u001b[1;32m    218\\u001b[0m     \\u001b[0;32mdef\\u001b[0m \\u001b[0mrunner\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    219\\u001b[0m         \\u001b[0;32mwith\\u001b[0m \\u001b[0mstatic_mode_guard\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 220\\u001b[0;31m             \\u001b[0;32mreturn\\u001b[0m \\u001b[0mfunc\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    221\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    222\\u001b[0m     \\u001b[0;32mreturn\\u001b[0m \\u001b[0mrunner\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/.paddlehub/modules/video_restoration/module.py\\u001b[0m in \\u001b[0;36m_initialize\\u001b[0;34m(self, output_path)\\u001b[0m\\n\\u001b[1;32m     40\\u001b[0m         \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0moutput_path\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0moutput_path\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     41\\u001b[0m         \\u001b[0mpaddle\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0menable_static\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 42\\u001b[0;31m         \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdain\\u001b[0m \\u001b[0;34m=\\u001b[0m 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44\\u001b[0m         \\u001b[0mpaddle\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdisable_static\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/paddlehub/module/module.py\\u001b[0m in \\u001b[0;36m__new__\\u001b[0;34m(cls, name, directory, version, source, update, branch, **kwargs)\\u001b[0m\\n\\u001b[1;32m    170\\u001b[0m             \\u001b[0;32mif\\u001b[0m \\u001b[0mname\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    171\\u001b[0m                 module = cls.init_with_name(\\n\\u001b[0;32m--> 172\\u001b[0;31m                     name=name, version=version, source=source, update=update, branch=branch, **kwargs)\\n\\u001b[0m\\u001b[1;32m    173\\u001b[0m                 \\u001b[0mCacheUpdater\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"update_cache\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmodule\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mversion\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mversion\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstart\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    174\\u001b[0m             \\u001b[0;32melif\\u001b[0m \\u001b[0mdirectory\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/paddlehub/module/module.py\\u001b[0m in \\u001b[0;36minit_with_name\\u001b[0;34m(cls, name, version, source, update, branch, **kwargs)\\u001b[0m\\n\\u001b[1;32m    272\\u001b[0m             )\\n\\u001b[1;32m    273\\u001b[0m             \\u001b[0muser_module\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0muser_module_cls\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdirectory\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mdirectory\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 274\\u001b[0;31m             \\u001b[0muser_module\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_initialize\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    275\\u001b[0m             \\u001b[0;32mreturn\\u001b[0m \\u001b[0muser_module\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    276\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/paddlehub/compat/paddle_utils.py\\u001b[0m in \\u001b[0;36mrunner\\u001b[0;34m(*args, **kwargs)\\u001b[0m\\n\\u001b[1;32m    218\\u001b[0m     \\u001b[0;32mdef\\u001b[0m \\u001b[0mrunner\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    219\\u001b[0m         \\u001b[0;32mwith\\u001b[0m \\u001b[0mstatic_mode_guard\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 220\\u001b[0;31m             \\u001b[0;32mreturn\\u001b[0m \\u001b[0mfunc\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    221\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    222\\u001b[0m     \\u001b[0;32mreturn\\u001b[0m \\u001b[0mrunner\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/.paddlehub/modules/dain/module.py\\u001b[0m in \\u001b[0;36m_initialize\\u001b[0;34m(self, output_path, weight_path, time_step, use_gpu, key_frame_thread, remove_duplicates)\\u001b[0m\\n\\u001b[1;32m     55\\u001b[0m         \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mremove_duplicates\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mremove_duplicates\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     56\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 57\\u001b[0;31m         \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mbuild_inference_model\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m     58\\u001b[0m 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\\u001b[0mstatic_mode_guard\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 220\\u001b[0;31m             \\u001b[0;32mreturn\\u001b[0m \\u001b[0mfunc\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    221\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    222\\u001b[0m     \\u001b[0;32mreturn\\u001b[0m \\u001b[0mrunner\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/.paddlehub/modules/dain/module.py\\u001b[0m in \\u001b[0;36mbuild_inference_model\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m     75\\u001b[0m                 \\u001b[0mexecutor\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexe\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     76\\u001b[0m                 \\u001b[0mmodel_filename\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mmodel_file\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 77\\u001b[0;31m                 params_filename=param_file)\\n\\u001b[0m\\u001b[1;32m     78\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     79\\u001b[0m     \\u001b[0;32mdef\\u001b[0m \\u001b[0mbase_forward\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minputs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mTypeError\\u001b[0m: load_inference_model() missing 1 required positional argument: 'path_prefix'\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = hub.Module(name='video_restoration')\\n\",\n    \"#model.predict('test/test.avi')\\n\",\n    \"# model.predict('/Users/shadow/Documents/code/-PK-AI-mixlab-/server/test/test.avi')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2021-03-12 09:51:32,796] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object\\n\",\n      \"[2021-03-12 09:51:32,892] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"mask_detector = hub.Module(name=\\\"pyramidbox_lite_server_mask\\\")\\n\",\n    \"result = mask_detector.face_detection(images=[cv2.imread('test/test.jpg')])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(result[0]['data'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "server/main.py",
    "content": "from sanic import Sanic\nfrom sanic import response\nfrom sanic.exceptions import NotFound\nfrom sanic_cors import CORS\n\n\nimport segment,project\n  \napp = Sanic(name=__name__)\nCORS(app)\n# camera = Camera()\n\n# @app.route('/')\n# def handle_request(request):\n#     return response.html('<p>Hello world!</p><img src=\"/camera-stream/\">')\n\n\n@app.route('/face', methods=['POST'])\nasync def handle_face_count(request):\n    # print(request.json)\n    json=request.json\n    res=0\n    if json:\n        res=segment.face_count(json['base64'])\n    return response.json({'count': res})\n\n\n@app.route('/segment', methods=['POST'])\nasync def handle_segment(request):\n    # print(request.json)\n    json=request.json\n    res=''\n    if json:\n        res=segment.run(json['base64'])\n    return response.json({'base64': res})\n\n@app.route('/project', methods=['POST'])\nasync def handle_project(request):\n    json=request.json\n    res={\"x\":'0.5',\"y\":'0.5'}\n    try:\n        res=project.run(json['view'])\n        res['x']=str(res['x'])\n        res['y']=str(res['y'])\n    except:\n        print(\"-\")\n    # sanic的json dumps int有bug，需提前转为str\n    return response.json(res)\n\n\n# @app.route('/depth_estimation')\n# async def handle_depth_estimation(request):\n#     return await response.file_stream(camera.depth_estimation_file)\n\n\n\n# @app.route('/camera-stream/')\n# async def camera_stream(request):\n#     return response.stream(\n#         camera.stream,\n#         content_type='multipart/x-mixed-replace; boundary=frame'\n#     )\n\n\n\n\n\nif __name__ == '__main__':\n    app.error_handler.add(\n        NotFound,\n        lambda r, e: response.empty(status=404)\n    )\n    app.run(host='0.0.0.0', port=8891,workers=1)\n    #workers须==1，不然无法运行，原因待查，可能是飞浆hub不支持"
  },
  {
    "path": "server/project.py",
    "content": "import screenpoint\nimport utils\nimport cv2\n\ndef run(viewBase64):\n    \n    # Load input images.\n    screen = utils.screenshot()\n\n    screen=utils.bgr2gray(screen)\n    view = utils.base64_to_cv2(viewBase64)\n    view=utils.bgr2gray(view)\n\n    screen=utils.resize(screen)\n    view=utils.resize(view)\n\n    h, w = screen.shape[0:2]\n    # print(view.shape)\n    # Project view centroid to screen space.\n    # x and y are the coordinate of the `view` centroid in `screen` space.\n    try:\n        x, y,im = screenpoint.project(view, screen,True)\n        cv2.imwrite('screenpoint.jpg',im)\n        return {\n            \"x\":x/w,\n            \"y\":y/h\n        }\n    except IOError:\n        print(IOError)\n        return None\n    else:\n        print('error')\n        return None\n    \n\n\n"
  },
  {
    "path": "server/requirements.txt",
    "content": "numpy\nopencv-python\nscreenpoint\nopencv-contrib-python\npaddlehub\npaddlepaddle\nsanic\nsanic-cors\npyautogui"
  },
  {
    "path": "server/segment.py",
    "content": "import paddlehub as hub\nimport cv2\nimport utils\n\nmask_detector = hub.Module(name=\"pyramidbox_lite_server_mask\")\nhuman_seg = hub.Module(name='humanseg_server')\n\n\n#人脸数量\ndef face_count(b64str):\n    im = utils.base64_to_cv2(b64str)\n    cv2.imwrite('face_detection.png',im)\n    result = mask_detector.face_detection(images=[im])\n    return len(result[0]['data'])\n\n#人像分割\ndef run(b64str):\n    im = utils.base64_to_cv2(b64str)\n    cv2.imwrite('human_seg.png',im)\n    res = human_seg.segment(images=[im],visualization=False)\n    img = utils.gray2rgb(res[0]['data'])\n    \n    b_channel, g_channel, r_channel = cv2.split(img) \n\n    alpha_channel = res[0]['data']\n\n    img_BGRA = cv2.merge((b_channel, g_channel, r_channel, alpha_channel)) \n\n    return utils.cv2_to_base64(img_BGRA)"
  },
  {
    "path": "server/utils.py",
    "content": "import cv2\nimport numpy as np \nimport base64\nimport pyautogui\n\ndef resize(image):\n    x, y = image.shape[0:2]\n    im = cv2.resize(image, (int(y / 2), int(x / 2)))\n    return im\n    \ndef cv2_to_base64(image):\n    data = cv2.imencode('.png', image)[1]\n    return base64.b64encode(data.tostring()).decode('utf8')\n\ndef base64_to_cv2(b64str):\n    data = base64.b64decode(b64str.encode('utf8'))\n    data = np.frombuffer(data, np.uint8)\n    data = cv2.imdecode(data, cv2.IMREAD_COLOR)\n    return data\n\ndef gray2rgb(im):\n    return cv2.cvtColor(im,cv2.COLOR_GRAY2RGB)\n\ndef bgr2gray(im):\n    return cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n\ndef screenshot():\n    im= pyautogui.screenshot()\n    img = cv2.cvtColor(np.asarray(im),cv2.COLOR_RGB2BGR)\n    return img\n\n"
  }
]