From 5dfd1d344c3bf1d5e499ba71be9a40ad20817cd0 Mon Sep 17 00:00:00 2001
From: Supan <supanshah14@gmail.com>
Date: Thu, 29 Apr 2021 23:19:48 +0530
Subject: [PATCH] Updated ipynb by Supan Shah

---
 .../Face_Recognition_Challenge.ipynb          | 4861 +++++++++--------
 1 file changed, 2436 insertions(+), 2425 deletions(-)

diff --git a/Assignment 2 & 3/Bhumiti - Assignment 2 & 3/Face_Recognition_Challenge.ipynb b/Assignment 2 & 3/Bhumiti - Assignment 2 & 3/Face_Recognition_Challenge.ipynb
index 0531a8a..20467df 100644
--- a/Assignment 2 & 3/Bhumiti - Assignment 2 & 3/Face_Recognition_Challenge.ipynb	
+++ b/Assignment 2 & 3/Bhumiti - Assignment 2 & 3/Face_Recognition_Challenge.ipynb	
@@ -1,2469 +1,2480 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_DWiFEBR_rf-"
+   },
+   "source": [
+    "# CSP 502 - Assignment 2 & 3  [FACE RECOGNITION USING SIFT ALGORITHM]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gXLOW_aM_Wom"
+   },
+   "source": [
+    "# Team Name: Bit Coders"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dHOoSvyD_bd8"
+   },
+   "source": [
+    "## Name: Supan Shah - AU 2044011"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pKI1zYjA_eb5"
+   },
+   "source": [
+    "## Name: Bhumiti Gohel - AU 1841051"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AvQMTmTjgSCM"
+   },
+   "source": [
+    "# Mount your drive if you want to use our face database.\n",
+    "\n",
+    "Ask supan.s2@ahduni.edu.in OR bhumiti.g@ahduni.edu.in for access to our face database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
-      "name": "Face_Recognition_Challenge.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "njZqHzB6iRhk",
+    "outputId": "bb8adeff-d8ff-4d7c-ad7c-1f3fd2c3a45a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mounted at /gdrive\n"
+     ]
     }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/gdrive')"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_DWiFEBR_rf-"
-      },
-      "source": [
-        "## CSP 502 - Assignment 2 & 3  [FACE RECOGNITION USING SIFT ALGORITHM]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gXLOW_aM_Wom"
-      },
-      "source": [
-        "# Team Name: Bit Coders"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dHOoSvyD_bd8"
-      },
-      "source": [
-        "## Name: Supan Shah - AU 2044011"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "pKI1zYjA_eb5"
-      },
-      "source": [
-        "## Name: Bhumiti Gohel - AU 1841051"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AvQMTmTjgSCM"
-      },
-      "source": [
-        "# Mount your drive if you want to use our face database.\n",
-        "\n",
-        "Ask supan.s2@ahduni.edu.in OR bhumiti.g@ahduni.edu.in for access to our face database."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "njZqHzB6iRhk",
-        "outputId": "bb8adeff-d8ff-4d7c-ad7c-1f3fd2c3a45a"
-      },
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/gdrive')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Mounted at /gdrive\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "60f7H9hcNKe6"
-      },
-      "source": [
-        "# Imports and helper functions"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ITZUwJMEE9vn"
-      },
-      "source": [
-        "## Libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Xyi9aHVDvGOS",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "97e097f1-c1c5-4d23-e304-884287ddf9d3"
-      },
-      "source": [
-        "!pip install opencv-contrib-python==3.4.2.16\n",
-        "from IPython.display import display, Javascript, Image\n",
-        "from google.colab.output import eval_js\n",
-        "from base64 import b64decode, b64encode\n",
-        "from pathlib import Path\n",
-        "import cv2\n",
-        "import numpy as np\n",
-        "import PIL\n",
-        "import io\n",
-        "import html\n",
-        "import time\n",
-        "import pandas as pd\n",
-        "import cv2 as cv\n",
-        "import matplotlib.pyplot as plt\n",
-        "import os\n",
-        "from skimage import io\n",
-        "from IPython.display import clear_output\n",
-        "from google.colab.patches import cv2_imshow\n",
-        "import cv2"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Collecting opencv-contrib-python==3.4.2.16\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/f1/66330f4042c4fb3b2d77a159db8e8916d9cdecc29bc8c1f56bc7f8a9bec9/opencv_contrib_python-3.4.2.16-cp36-cp36m-manylinux1_x86_64.whl (30.6MB)\n",
-            "\u001b[K     |████████████████████████████████| 30.6MB 142kB/s \n",
-            "\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from opencv-contrib-python==3.4.2.16) (1.19.5)\n",
-            "Installing collected packages: opencv-contrib-python\n",
-            "  Found existing installation: opencv-contrib-python 4.1.2.30\n",
-            "    Uninstalling opencv-contrib-python-4.1.2.30:\n",
-            "      Successfully uninstalled opencv-contrib-python-4.1.2.30\n",
-            "Successfully installed opencv-contrib-python-3.4.2.16\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ubtLtOpwE_Y-"
-      },
-      "source": [
-        "## SIFT algorithm"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Qry-dY-vEN87"
-      },
-      "source": [
-        "# SIFT implementation\n",
-        "\n",
-        "# MIN_MATCH_COUNT = 10\n",
-        "\n",
-        "def sift_match(test, image, plot=False, MIN_MATCH_COUNT=10):\n",
-        "    isMatch = False\n",
-        "    # Initiate SIFT detector\n",
-        "    sift = cv2.xfeatures2d.SIFT_create()\n",
-        "    # img1 = cv.imread(test,cv.IMREAD_GRAYSCALE)# queryImage\n",
-        "    img1 = test\n",
-        "    img2 = cv.imread(image,cv.IMREAD_GRAYSCALE) # trainImage\n",
-        "    # find the keypoints and descriptors with SIFT\n",
-        "    kp1, des1 = sift.detectAndCompute(img1,None)\n",
-        "    kp2, des2 = sift.detectAndCompute(img2,None)\n",
-        "\n",
-        "    FLANN_INDEX_KDTREE = 0\n",
-        "    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\n",
-        "    search_params = dict(checks = 50)\n",
-        "\n",
-        "    flann = cv2.FlannBasedMatcher(index_params, search_params)\n",
-        "\n",
-        "    matches = flann.knnMatch(des1,des2,k=2)\n",
-        "\n",
-        "    # store all the good matches as per Lowe's ratio test.\n",
-        "    good = []\n",
-        "    for m,n in matches:\n",
-        "        if m.distance < 0.7*n.distance:\n",
-        "            good.append(m)\n",
-        "      \n",
-        "    if len(good)>MIN_MATCH_COUNT:\n",
-        "        src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)\n",
-        "        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)\n",
-        "\n",
-        "        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)\n",
-        "        matchesMask = mask.ravel().tolist()\n",
-        "\n",
-        "        h,w = img1.shape\n",
-        "        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)\n",
-        "        dst = cv2.perspectiveTransform(pts,M)\n",
-        "\n",
-        "        img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)\n",
-        "        # print(\"MATCH FOUND\")\n",
-        "        isMatch = True\n",
-        "    else:\n",
-        "        # print(\"Not enough matches are found - %d/%d\" % (len(good),MIN_MATCH_COUNT))\n",
-        "        isMatch = False\n",
-        "    if plot:\n",
-        "        matchesMask = None\n",
-        "        draw_params = dict(matchColor = (0,255,0), # draw matches in green color\n",
-        "                          singlePointColor = None,\n",
-        "                          matchesMask = matchesMask, # draw only inliers\n",
-        "                          flags = 2)\n",
-        "\n",
-        "        img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)\n",
-        "\n",
-        "        plt.imshow(img3, 'gray'),plt.show()\n",
-        "    \n",
-        "    return (isMatch, len(good))"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qHx9V64CIijQ"
-      },
-      "source": [
-        "## SURF algorithm"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "-ubUw2AXYlAs"
-      },
-      "source": [
-        "# SURF implementation\n",
-        "\n",
-        "# MIN_MATCH_COUNT = 10\n",
-        "\n",
-        "def surf_match(test, image, plot=False, MIN_MATCH_COUNT=10):\n",
-        "    isMatch = False\n",
-        "    # Initiate SIFT detector\n",
-        "    surf = cv2.xfeatures2d.SURF_create(400)\n",
-        "    # img1 = cv.imread(test,cv.IMREAD_GRAYSCALE)# queryImage\n",
-        "    img1 = test\n",
-        "    img2 = cv.imread(image,cv.IMREAD_GRAYSCALE) # trainImage\n",
-        "    # find the keypoints and descriptors with SIFT\n",
-        "    kp1, des1 = surf.detectAndCompute(img1,None)\n",
-        "    kp2, des2 = surf.detectAndCompute(img2,None)\n",
-        "\n",
-        "    FLANN_INDEX_KDTREE = 0\n",
-        "    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\n",
-        "    search_params = dict(checks = 50)\n",
-        "\n",
-        "    flann = cv2.FlannBasedMatcher(index_params, search_params)\n",
-        "\n",
-        "    matches = flann.knnMatch(des1,des2,k=2)\n",
-        "\n",
-        "    # store all the good matches as per Lowe's ratio test.\n",
-        "    good = []\n",
-        "    for m,n in matches:\n",
-        "        if m.distance < 0.7*n.distance:\n",
-        "            good.append(m)\n",
-        "      \n",
-        "    if len(good)>MIN_MATCH_COUNT:\n",
-        "        src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)\n",
-        "        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)\n",
-        "\n",
-        "        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)\n",
-        "        matchesMask = mask.ravel().tolist()\n",
-        "\n",
-        "        h,w = img1.shape\n",
-        "        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)\n",
-        "        dst = cv2.perspectiveTransform(pts,M)\n",
-        "\n",
-        "        img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)\n",
-        "        # print(\"MATCH FOUND\")\n",
-        "        isMatch = True\n",
-        "    else:\n",
-        "        # print(\"Not enough matches are found - %d/%d\" % (len(good),MIN_MATCH_COUNT))\n",
-        "        isMatch = False\n",
-        "    if plot:\n",
-        "        matchesMask = None\n",
-        "        draw_params = dict(matchColor = (0,255,0), # draw matches in green color\n",
-        "                          singlePointColor = None,\n",
-        "                          matchesMask = matchesMask, # draw only inliers\n",
-        "                          flags = 2)\n",
-        "\n",
-        "        img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)\n",
-        "\n",
-        "        plt.imshow(img3, 'gray'),plt.show()\n",
-        "    \n",
-        "    return (isMatch, len(good))"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sI184cZsFDrz"
-      },
-      "source": [
-        "## Image helper functions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "o7x4vBOW_Bxe"
-      },
-      "source": [
-        "def take_photo(filename='photo.jpg', quality=0.8):\n",
-        "  js = Javascript('''\n",
-        "    async function takePhoto(quality) {\n",
-        "      const div = document.createElement('div');\n",
-        "      const capture = document.createElement('button');\n",
-        "      capture.textContent = 'Capture';\n",
-        "      div.appendChild(capture);\n",
-        "\n",
-        "      const video = document.createElement('video');\n",
-        "      video.style.display = 'block';\n",
-        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
-        "\n",
-        "      document.body.appendChild(div);\n",
-        "      div.appendChild(video);\n",
-        "      video.srcObject = stream;\n",
-        "      await video.play();\n",
-        "\n",
-        "      // Resize the output to fit the video element.\n",
-        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
-        "\n",
-        "      // Wait for Capture to be clicked.\n",
-        "      await new Promise((resolve) => capture.onclick = resolve);\n",
-        "\n",
-        "      const canvas = document.createElement('canvas');\n",
-        "      canvas.width = video.videoWidth;\n",
-        "      canvas.height = video.videoHeight;\n",
-        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
-        "      stream.getVideoTracks()[0].stop();\n",
-        "      div.remove();\n",
-        "      return canvas.toDataURL('image/jpeg', quality);\n",
-        "    }\n",
-        "    ''')\n",
-        "  display(js)\n",
-        "  data = eval_js('takePhoto({})'.format(quality))\n",
-        "  binary = b64decode(data.split(',')[1])\n",
-        "  with open(filename, 'wb') as f:\n",
-        "    f.write(binary)\n",
-        "  return filename"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Z6cGBpLtFhVj"
-      },
-      "source": [
-        "## Video helper functions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "U74F7ehhAUyT"
-      },
-      "source": [
-        "# function to convert the JavaScript object into an OpenCV image\n",
-        "def js_to_image(js_reply):\n",
-        "  \"\"\"\n",
-        "  Params:\n",
-        "          js_reply: JavaScript object containing image from webcam\n",
-        "  Returns:\n",
-        "          img: OpenCV BGR image\n",
-        "  \"\"\"\n",
-        "  # decode base64 image\n",
-        "  image_bytes = b64decode(js_reply.split(',')[1])\n",
-        "  # convert bytes to numpy array\n",
-        "  jpg_as_np = np.frombuffer(image_bytes, dtype=np.uint8)\n",
-        "  # decode numpy array into OpenCV BGR image\n",
-        "  img = cv2.imdecode(jpg_as_np, flags=1)\n",
-        "  img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
-        "  return img\n",
-        "\n",
-        "# function to convert OpenCV Rectangle bounding box image into base64 byte string to be overlayed on video stream\n",
-        "def bbox_to_bytes(bbox_array):\n",
-        "  \"\"\"\n",
-        "  Params:\n",
-        "          bbox_array: Numpy array (pixels) containing rectangle to overlay on video stream.\n",
-        "  Returns:\n",
-        "        bytes: Base64 image byte string\n",
-        "  \"\"\"\n",
-        "  # convert array into PIL image\n",
-        "  bbox_PIL = PIL.Image.fromarray(bbox_array, 'RGBA')\n",
-        "  iobuf = io.BytesIO()\n",
-        "  # format bbox into png for return\n",
-        "  bbox_PIL.save(iobuf, format='png')\n",
-        "  # format return string\n",
-        "  bbox_bytes = 'data:image/png;base64,{}'.format((str(b64encode(iobuf.getvalue()), 'utf-8')))\n",
-        "\n",
-        "  return bbox_bytes"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FOQcw4lI-BaN"
-      },
-      "source": [
-        "# JavaScript to properly create our live video stream using our webcam as input\n",
-        "def video_stream():\n",
-        "  js = Javascript('''\n",
-        "    var video;\n",
-        "    var div = null;\n",
-        "    var stream;\n",
-        "    var captureCanvas;\n",
-        "    var imgElement;\n",
-        "    var labelElement;\n",
-        "    \n",
-        "    var pendingResolve = null;\n",
-        "    var shutdown = false;\n",
-        "    \n",
-        "    function removeDom() {\n",
-        "       stream.getVideoTracks()[0].stop();\n",
-        "       video.remove();\n",
-        "       div.remove();\n",
-        "       video = null;\n",
-        "       div = null;\n",
-        "       stream = null;\n",
-        "       imgElement = null;\n",
-        "       captureCanvas = null;\n",
-        "       labelElement = null;\n",
-        "    }\n",
-        "    \n",
-        "    function onAnimationFrame() {\n",
-        "      if (!shutdown) {\n",
-        "        window.requestAnimationFrame(onAnimationFrame);\n",
-        "      }\n",
-        "      if (pendingResolve) {\n",
-        "        var result = \"\";\n",
-        "        if (!shutdown) {\n",
-        "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
-        "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
-        "        }\n",
-        "        var lp = pendingResolve;\n",
-        "        pendingResolve = null;\n",
-        "        lp(result);\n",
-        "      }\n",
-        "    }\n",
-        "    \n",
-        "    async function createDom() {\n",
-        "      if (div !== null) {\n",
-        "        return stream;\n",
-        "      }\n",
-        "\n",
-        "      div = document.createElement('div');\n",
-        "      div.style.border = '2px solid black';\n",
-        "      div.style.padding = '3px';\n",
-        "      div.style.width = '100%';\n",
-        "      div.style.maxWidth = '600px';\n",
-        "      document.body.appendChild(div);\n",
-        "      \n",
-        "      const modelOut = document.createElement('div');\n",
-        "      modelOut.innerHTML = \"<span>Status:</span>\";\n",
-        "      labelElement = document.createElement('span');\n",
-        "      labelElement.innerText = 'No data';\n",
-        "      labelElement.style.fontWeight = 'bold';\n",
-        "      modelOut.appendChild(labelElement);\n",
-        "      div.appendChild(modelOut);\n",
-        "           \n",
-        "      video = document.createElement('video');\n",
-        "      video.style.display = 'block';\n",
-        "      video.width = div.clientWidth - 6;\n",
-        "      video.setAttribute('playsinline', '');\n",
-        "      video.onclick = () => { shutdown = true; };\n",
-        "      stream = await navigator.mediaDevices.getUserMedia(\n",
-        "          {video: { facingMode: \"environment\"}});\n",
-        "      div.appendChild(video);\n",
-        "\n",
-        "      imgElement = document.createElement('img');\n",
-        "      imgElement.style.position = 'absolute';\n",
-        "      imgElement.style.zIndex = 1;\n",
-        "      imgElement.onclick = () => { shutdown = true; };\n",
-        "      div.appendChild(imgElement);\n",
-        "      \n",
-        "      const instruction = document.createElement('div');\n",
-        "      instruction.innerHTML = \n",
-        "          '<span style=\"color: red; font-weight: bold;\">' +\n",
-        "          'When finished, click here or on the video to stop this demo</span>';\n",
-        "      div.appendChild(instruction);\n",
-        "      instruction.onclick = () => { shutdown = true; };\n",
-        "      \n",
-        "      video.srcObject = stream;\n",
-        "      await video.play();\n",
-        "\n",
-        "      captureCanvas = document.createElement('canvas');\n",
-        "      captureCanvas.width = 640; //video.videoWidth;\n",
-        "      captureCanvas.height = 480; //video.videoHeight;\n",
-        "      window.requestAnimationFrame(onAnimationFrame);\n",
-        "      \n",
-        "      return stream;\n",
-        "    }\n",
-        "    async function stream_frame(label, imgData) {\n",
-        "      if (shutdown) {\n",
-        "        removeDom();\n",
-        "        shutdown = false;\n",
-        "        return '';\n",
-        "      }\n",
-        "\n",
-        "      var preCreate = Date.now();\n",
-        "      stream = await createDom();\n",
-        "      \n",
-        "      var preShow = Date.now();\n",
-        "      if (label != \"\") {\n",
-        "        labelElement.innerHTML = label;\n",
-        "      }\n",
-        "            \n",
-        "      if (imgData != \"\") {\n",
-        "        var videoRect = video.getClientRects()[0];\n",
-        "        imgElement.style.top = videoRect.top + \"px\";\n",
-        "        imgElement.style.left = videoRect.left + \"px\";\n",
-        "        imgElement.style.width = videoRect.width + \"px\";\n",
-        "        imgElement.style.height = videoRect.height + \"px\";\n",
-        "        imgElement.src = imgData;\n",
-        "      }\n",
-        "      \n",
-        "      var preCapture = Date.now();\n",
-        "      var result = await new Promise(function(resolve, reject) {\n",
-        "        pendingResolve = resolve;\n",
-        "      });\n",
-        "      shutdown = false;\n",
-        "      \n",
-        "      return {'create': preShow - preCreate, \n",
-        "              'show': preCapture - preShow, \n",
-        "              'capture': Date.now() - preCapture,\n",
-        "              'img': result};\n",
-        "    }\n",
-        "    ''')\n",
-        "\n",
-        "  display(js)\n",
-        "  \n",
-        "def video_frame(label, bbox):\n",
-        "  data = eval_js('stream_frame(\"{}\", \"{}\")'.format(label, bbox))\n",
-        "  return data"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ubZBdbm8Azq6"
-      },
-      "source": [
-        "# Choose your face data directory path"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JS3-qNVXmbqA"
-      },
-      "source": [
-        "Our directory will be loaded at '/gdrive/MyDrive/BitCoders/Assignment2/'.\n",
-        "Feel free to load a blank directory and create your own database."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "BFRBWvcBA4Hv"
-      },
-      "source": [
-        "main_dir = '/gdrive/MyDrive/BitCoders/Assignment2/'"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ihqdgHwVQ4N2"
-      },
-      "source": [
-        "## Load the database"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "t7uBqBVmSabL"
-      },
-      "source": [
-        "image_database = {}\n",
-        "pathlist = Path(str(main_dir)).rglob('*.jpg')\n",
-        "for path in pathlist:\n",
-        "  path_in_str = str(path)\n",
-        "  image_database[path_in_str] = path_in_str.split('/')[-2]"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MK4Ku-hhNMck"
-      },
-      "source": [
-        "# Add yourself to the face database"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "v7XWivaI_rAA",
-        "cellView": "form"
-      },
-      "source": [
-        "#@title Your Information:\n",
-        "#@markdown This info will help us to analyse your face and identify you in future.\n",
-        "# Name = 'Insert Name here (eg. Supan / Bhumiti)'\n",
-        "Name = 'supan'  #@param {type: \"string\"}\n",
-        "#@markdown More number of images = better face recognition !!\n",
-        "#@markdown So, please consider clicking atleast around 20 images.\n",
-        "Number_of_images = 25  #@param {type: \"slider\", min: 1, max: 40}\n",
-        "#@markdown ---\n",
-        "\n",
-        "class NameError(Exception):\n",
-        "  pass\n",
-        "\n",
-        "if Name == 'Insert Name here (eg. Supan / Bhumiti)':\n",
-        "  raise NameError(\"Enter name please!\")\n",
-        "\n",
-        "elif len(Name)<2:\n",
-        "  raise NameError(\"Enter name atleast bigger than 2 characters!\")\n",
-        "\n",
-        "elif os.path.exists(main_dir):\n",
-        "  raise NameError(\"Person already exists. Try a different Name\")\n",
-        "\n",
-        "else:\n",
-        "  print(\"Hi, \", Name)\n",
-        "  print(\"Let's begin taking your photos\")\n",
-        "\n",
-        "os.chdir(main_dir)\n",
-        "\n",
-        "main = os.path.join(main_dir, '{}'.format(Name))\n",
-        "\n",
-        "try:\n",
-        "  os.mkdir('{}'.format(Name))\n",
-        "except:\n",
-        "  raise NameError(\"Couldn't create your folder somehow. Try again!\")\n",
-        "  \n",
-        "main = os.path.join(main_dir, '{}'.format(Name))\n",
-        "os.chdir(main)\n",
-        "\n",
-        "for i in range(1, Number_of_images+1):\n",
-        "  print(\"Click image number {}\".format(i))\n",
-        "  try:\n",
-        "    filename = take_photo('{}.jpg'.format(i))\n",
-        "    print('Saved image number {}'.format(i))\n",
-        "    clear_output()\n",
-        "  except Exception as err:\n",
-        "    print(str(err))\n",
-        "print(\"Thank you!\")\n",
-        "print(\"Your face is now registered with us!\")"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zlW531UBSDrL"
-      },
-      "source": [
-        "# Face Detection"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OqOp733-Q9sU"
-      },
-      "source": [
-        "## Face Recognition"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5qzOoZ_goj5E"
-      },
-      "source": [
-        "Run the below cell to recognise your face against our face database!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "zSoYUIJz-r7y"
-      },
-      "source": [
-        "# start streaming video from webcam\n",
-        "video_stream()\n",
-        "# label for video\n",
-        "label_html = 'Detecting...'\n",
-        "# initialze bounding box to empty\n",
-        "bbox = ''\n",
-        "count = 0 \n",
-        "detected = False\n",
-        "while not detected:\n",
-        "    js_reply = video_frame(label_html, bbox)\n",
-        "    if not js_reply:\n",
-        "        break\n",
-        "\n",
-        "    img = js_to_image(js_reply[\"img\"])\n",
-        "    # gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
-        "    # gray = cv.imread(test,cv.IMREAD_GRAYSCALE)\n",
-        "    best_match = ''\n",
-        "    highest = 0\n",
-        "    for key, value in image_database.items():\n",
-        "        try:\n",
-        "            match, num_matches =  sift_match(img, key)\n",
-        "            if match:\n",
-        "                # label_html = 'Hello, {}'.format(value)\n",
-        "                if num_matches > highest:\n",
-        "                    highest = num_matches\n",
-        "                    best_match = value\n",
-        "        except:\n",
-        "            pass\n",
-        "    if len(best_match)>0:\n",
-        "        label_html = 'Hello, {}'.format(best_match)\n",
-        "        detected = True\n",
-        "    else:\n",
-        "        label_html = 'You seem to be new here...'\n",
-        "clear_output()\n",
-        "print(label_html)\n",
-        "final_frame = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)\n",
-        "plt.imshow(final_frame)\n",
-        "plt.title(label_html)\n",
-        "plt.axis('off')\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xzxNryw9kQ6V"
-      },
-      "source": [
-        "# Measuring Accuracy on current database"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "pKOVb_ZJZQVO"
-      },
-      "source": [
-        "## SIFT"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 198
-        },
-        "id": "XaMZuIrykVCA",
-        "outputId": "54809fad-f5df-4b68-c1eb-dacfff5ad85f"
-      },
-      "source": [
-        "image_sources = list(image_database.keys())\n",
-        "labels = list(image_database.values())\n",
-        "data = pd.DataFrame()\n",
-        "data['Image_Sources'] = image_sources\n",
-        "data['Labels'] = labels\n",
-        "data.head()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Image_Sources</th>\n",
-              "      <th>Labels</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/1...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/2...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/3...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/4...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/5...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                       Image_Sources  Labels\n",
-              "0  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/1...  Greeva\n",
-              "1  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/2...  Greeva\n",
-              "2  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/3...  Greeva\n",
-              "3  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/4...  Greeva\n",
-              "4  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/5...  Greeva"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 20
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "kD89xMhhrnwW"
-      },
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "X = data['Image_Sources']\n",
-        "y = data['Labels']\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5, random_state = 0, stratify=y)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "NmmDk-NusDc0"
-      },
-      "source": [
-        "y_cap = []\n",
-        "for test_image_path in X_test:\n",
-        "  test_image = cv.imread(test_image_path,cv.IMREAD_GRAYSCALE)\n",
-        "  best_match = ''\n",
-        "  highest_matches = 0\n",
-        "  for train_image, label in zip(X_train, y_train):\n",
-        "    try:\n",
-        "      match, num_matches =  sift_match(test_image, train_image)\n",
-        "      if match and num_matches > highest_matches:\n",
-        "        highest_matches = num_matches\n",
-        "        best_match = label\n",
-        "    except:\n",
-        "      pass\n",
-        "      # print('Error')\n",
-        "  if len(best_match)<1:\n",
-        "    y_cap.append('Unknown')\n",
-        "  else:\n",
-        "    y_cap.append(best_match)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "oczK35jO5Vyv"
-      },
-      "source": [
-        "classes = list(set(y_test))\n",
-        "classes.append('Unknown')"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Yt_xdOa6-xaz"
-      },
-      "source": [
-        "### Overall Accuracy : 95.71%"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "EhxY1jf67_VN",
-        "outputId": "c8c6bf70-42eb-4701-979f-3dd3f4402f30"
-      },
-      "source": [
-        "from sklearn.metrics import accuracy_score\n",
-        "print(\"Accuracy is:\", np.round(accuracy_score(y_test, y_cap)*100,3),\"%\")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Accuracy is: 95.714 %\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "li6a8pj1-zl9"
-      },
-      "source": [
-        "### Overall weighted F1 score: 97.48%"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HwmZgvt_7_X3",
-        "outputId": "4160090d-043b-483a-ed9f-fe92996398fa"
-      },
-      "source": [
-        "from sklearn.metrics import f1_score\n",
-        "f1 = f1_score(y_test, y_cap, average='weighted')\n",
-        "print(\"F1 Score is:\",np.round(f1*100, 3), \"%\")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "F1 Score is: 97.479 %\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "K5HLd_nT-8Gc"
-      },
-      "source": [
-        "### Confusion Matrix"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "6A301ECLqpMs"
-      },
-      "source": [
-        "from sklearn.metrics import confusion_matrix\n",
-        "cf_matrix = confusion_matrix(y_test, y_cap, labels=classes) "
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        },
-        "id": "4MEvJuFt3kIB",
-        "outputId": "e8550acc-9be1-4803-eab8-47e95c0cd1d5"
-      },
-      "source": [
-        "import seaborn as sns\n",
-        "ax= plt.subplot()\n",
-        "sns.heatmap(cf_matrix, annot=True, ax = ax);\n",
-        "# sns.heatmap(cf_matrix, annot=True)\n",
-        "ax.set_xlabel('Predicted labels');\n",
-        "ax.set_ylabel('True labels');\n",
-        "ax.set_title('Confusion Matrix');\n",
-        "ax.xaxis.set_ticklabels(classes);\n",
-        "ax.yaxis.set_ticklabels(classes);\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Xx8E3dQ6HmIe"
-      },
-      "source": [
-        "## SVM"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KNU30bwMH04x"
-      },
-      "source": [
-        "### Splitting our database for SVM training"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "-HQqp7rDtXFh",
-        "outputId": "ca557113-15a6-4d7c-95e5-5b7e1457fba5"
-      },
-      "source": [
-        "image_paths = []\n",
-        "image_classes = []\n",
-        "class_id = 0\n",
-        "\n",
-        "main_dir = '/gdrive/MyDrive/BitCoders/Assignment2SVM'\n",
-        "train_path = os.path.join(main_dir, 'Train')\n",
-        "people = os.listdir(train_path)\n",
-        "\n",
-        "def imageList(path):\n",
-        "  return [os.path.join(path, file) for file in os.listdir(path)]\n",
-        "\n",
-        "for training_name in people:\n",
-        "  dir = os.path.join(train_path, training_name)\n",
-        "  class_path = imageList(dir)\n",
-        "  image_paths += class_path\n",
-        "  image_classes += [class_id]*len(class_path)\n",
-        "  class_id += 1\n",
-        "\n",
-        "des_list = []\n",
-        "\n",
-        "for image_path in image_paths:\n",
-        "  kpt, desc = sift(image_path)\n",
-        "  des_list.append((image_path, desc))\n",
-        "\n",
-        "descriptors = des_list[0][1]\n",
-        "for image_path, descriptor in des_list[1:]:\n",
-        "  descriptors = np.vstack((descriptors, descriptor))\n",
-        "\n",
-        "descriptors_float = descriptors.astype(float)\n",
-        "\n",
-        "from scipy.cluster.vq import kmeans, vq\n",
-        "\n",
-        "k=200\n",
-        "voc, variance = kmeans(descriptors_float, k, 1)\n",
-        "\n",
-        "image_features = np.zeros((len(image_paths), k), \"float32\")\n",
-        "for i in range(len(image_paths)):\n",
-        "  words, distance = vq(des_list[i][1], voc)\n",
-        "  for word in words:\n",
-        "    image_features[i][word] += 1\n",
-        "\n",
-        "#TF-IDF\n",
-        "occurences = np.sum((image_features>0)*1, axis=0)\n",
-        "idf = np.array(np.log((1.0*len(image_paths)+1) / (1.0*occurences+1)), \"float32\")\n",
-        "\n",
-        "from sklearn.preprocessing import StandardScaler\n",
-        "scaler = StandardScaler().fit(image_features)\n",
-        "image_features = scaler.transform(image_features)\n",
-        "\n",
-        "from sklearn.svm import LinearSVC\n",
-        "clf = LinearSVC(max_iter=10000)\n",
-        "clf.fit(image_features, np.array(image_classes))\n",
-        "\n",
-        "from sklearn.externals import joblib\n",
-        "joblib.dump((clf, people, scaler, k, voc), '/gdrive/MyDrive/BitCoders/trainedSVM.pkl', compress=3)\n"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "['/gdrive/MyDrive/BitCoders/trainedSVM.pkl']"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 35
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "G856Z82vH76w"
-      },
-      "source": [
-        "### Fitting SVM Classifier and plotting Confusion Matrix"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 281
-        },
-        "id": "2LRAiqb1TA0m",
-        "outputId": "b4c2d9b2-5101-44a1-ae18-aa1f15ec100d"
-      },
-      "source": [
-        "from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier\n",
-        "from sklearn.svm import SVC\n",
-        "model = SVC()\n",
-        "ovo = OneVsOneClassifier(model)\n",
-        "ovo.fit(image_features, np.array(image_classes))\n",
-        "\n",
-        "y_train =  [people[i] for i in image_classes]\n",
-        "yhat = [people[i] for i in ovo.predict(image_features)]\n",
-        "\n",
-        "accuracy = accuracy_score(y_train, yhat)\n",
-        "print (\"accuracy = \", accuracy)\n",
-        "cm1 = confusion_matrix(y_train, yhat)\n",
-        "plt.matshow(cm1)\n",
-        "plt.title('Confusion matrix')\n",
-        "plt.colorbar()\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "accuracy =  0.9855072463768116\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD2CAYAAAD720p7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUM0lEQVR4nO3dfbBdVXnH8e+PJOQFCBgTKCRoUJM4ykhARYRREUUjUvGPdooCo1aNWrVYcCygU/Ctta21OJXahhdhhKIUUSlSIVWog5VIAhEhQaCgEggmUHk1kuTep3/sfZmT5N571jl3nbP32ff3mdmTe+7Ze+3nZO5z1tprrb2XIgIza6bdqg7AzHrHCW7WYE5wswZzgps1mBPcrMGc4GYN5gTPRNJMSf8h6XFJ/z6Bck6SdH3O2Koi6TWSflF1HJOZJts4uKR3AqcBLwaeBNYCn4+ImyZY7inAR4EjI2L7hAOtOUkBLIqIe6uOxcY2qWpwSacB5wJ/DewHPA/4Z+CEDMU/H7h7MiR3CklTq47BgIiYFBuwN/AU8Mfj7DOd4gvgoXI7F5hevnc0sAE4HdgEbATeU773aWArsK08x3uBc4BLW8peCAQwtXz9buA+ilbE/cBJLb+/qeW4I4FbgMfLf49see9G4LPAj8tyrgfmjvHZRuL/REv8bweOA+4G/g84q2X/w4GfAI+V+34F2L1870flZ3m6/Lx/0lL+XwIPA18f+V15zAvLcxxWvj4A2AwcXfXfRo7tTUfPipe/bHrSBny/b3/3Vf/H9O2DwjJg+0iCjbHPZ4CbgX2BecD/AJ8t3zu6PP4zwLQyMX4HPKd8f+eEHjPBgT2AJ4Al5Xv7Ay8tf342wYE5wG+BU8rj3lG+fm75/o3A/wKLgZnl6y+M8dlG4v+rMv73lwn2b8BewEuBLcBB5f4vB44oz7sQWA98rKW8AF40Svl/S/FFObM1wct93g+sA2YB1wFfrPrvItd22Mumx7aNL0zagNX9imsyNdGfCzwS4zehTwI+ExGbImIzRc18Ssv728r3t0XEtRS115Iu4xkGDpY0MyI2RsSdo+zzVuCeiPh6RGyPiMuBu4A/bNnnaxFxd0RsAa4Alo5zzm0U/Q3bgG8Ac4EvR8ST5fnXAYcARMSaiLi5PO8vgX8FXpfwmc6OiGfKeHYQEecD9wKrKL7UPtmmvAESDMVw0tZPkynBHwXmtrk2PAD4VcvrX5W/e7aMnb4gfgfs2WkgEfE0RbP2g8BGSd+T9OKEeEZimt/y+uEO4nk0IobKn0cS8Dct728ZOV7SYknXSHpY0hMU/RZzxykbYHNE/L7NPucDBwP/FBHPtNl3YAQwTCRt/TSZEvwnwDMU151jeYiis2zE88rfdeNpiqboiD9ofTMirouIYylqsrso/vDbxTMS04NdxtSJr1LEtSgiZgNnAWpzzLh/vZL2pOjXuBA4R9KcHIHWQRBsi6GkrZ9qmeCSlkn6haR7JZ2Ro8yIeJzi+vM8SW+XNEvSNElvkfR35W6XA5+SNE/S3HL/xyRtAr7W4SnXAq+V9DxJewNnjrwhaT9JJ0jag+JL5ymK5u3OrgUWS3qnpKmS/gx4JXCmpDvZsSbPbS+KfoKnytbFh3Z6/zfAEkk/lfQziv+f2W3K/DLF9ef7gO8B/5I5ZgAkTZF0m6RrelH+WFyDJ5A0BTgPeAvwEuAdkl6So+yI+AeKMfBPUXQwPQB8BPhOucvngNXA7cDPgVuBj1N00HV6rpXAN8uy1gCtf2y7lXE8RNGz/Dp2TSAi4lHgeIqe+0eBDwAfiIglFB1g8ylaAL3wceCdFL3z55efpdU5wAUUHXyfB94HTJd0xGiFSTqB4v9x5HOeBhwm6aTskcOpFJ2CfRPAEJG09VPtJrpIejVwTkS8uXx9JkBE/E2FMS0EromIg6uKYTSSvgt8pfwyqTqWWcBNwIciYlWFcSwALqH40jktIo7vx3mXHrJ7rPzPeUn77jv/oTUR8YoehwTUsAanqJUeaHm9gd42RQdS+aVzKEWPdJVxTJG0lmJsfWWVyV06l2Ksv6/d1QEMRSRt/VTHBLc2ys6qb1GMSz9RZSwRMRQRS4EFwOGSKmvlSDoe2BQRa6o4/3DilkLSRZI2Sbpjp99/VNJdku5s6TsaUx0T/EHgwJbXC+hPr/FAkDSNIrkvi4irqo5nREQ8BtxAF/0VGR0FvE3SLynG+Y+RdGk/ThyJ198dXINfzE7/l5JeTzGt+pCIeCnwxXaF1DHBbwEWSTpI0u7AicDVFcdUC5JEMcS0PiK+VIN45knap/x5JnAsxdBaJSLizIhYEBELKf5ufhgRJ/fn3LAtcUsrL35E0QHb6kMUMxWfKffZ1K6c2iV4OZHkIxRTGdcDV4wxy6svJF1OMYa+RNIGSe+tKhaKGuoUipppbbkdV2E8+wM3SLqd4ot5ZUT0dWiqPsRQ4jYBi4HXSFol6b8lvbLdAbW846ecBnpt1XEARMQ7qo5hRBS3tE7oLySniLidoqOvdiLiRoq5+f05HzCc3n82V9LqltcrImJFwnFTKe5POIJiPsQVkl4Q4wyF1TLBzQZRB7XzI10Ok20ArioT+qeShimmD28e64DaNdHNBlEx0aXnTfTvAK+H4l4BYHfgkfEOcA1ulslw5Lt6Kvt+jqZozm8AzgYuAi4qh862Au8ar3kOTnCzLEZq8Gzljd3309GogBPcLINAbIspVYexi9peg0taXnUMrRzP+OoUTxWx9OkavGO1TXCgNn8wJcczvjrFU0EsYih2S9r6yU10swyKJ7rUr77sSYJP2WuPmDpvn4mVMXdvpr9gfpZbb6bfv8vjwTo2g1nM1pza3FvreMaWK5bf8zRb45nkNnW/m98pepLgU+ftwwGf+3Aviu7Ki06+reoQbACtih8k7xuhvje/U7iJbpbJ8GSpwc0mm0BsjfqlU/0iMhtAk6qTzWwyGso4VTUXJ7hZBoEYcg1u1lzD7kU3a6ZiqqoT3KyR6nqziRPcLIMIPNHFrLnkiS5mTVWsbFK/Grx+EZkNqCF2S9pSjLWySfne6ZKiXAF3XEln68VyvmZNEojhSNsSXcwoq8RIOhB4E/DrlELaJngvl/M1a5KcNfgYK5sA/CPF4opJt8OmXIMfDtwbEfcBSPoGxfpI65IiNZsE+jFMVq6x/mBE/KxYxaq9lAQfbTnfV3UenllzFSubJHdpdbyySbn++lkUzfNk2XrRywfdLYfiaSxmk02PVzZ5IXAQMFJ7LwBulXR4RDw81kEpCZ60nG/5DbQCyPaoJbNBEaGezkWPiJ8D+468LpdIfkVEjLuySUpEXs7XLEHOp6rmWtW2bQ0eEdsljSznOwW4qMrlfM3qqHjgQ19WNhl5f2FKOUnX4HVaztesnvzQRbPGCvDdZGZNNTKTrW6c4GaZ+KGLZg1V3A/uGtyssdxEN2uo4hrcTXSzxpo0iw+aTTaB2D7sYTKzxvIz2cwayr3oZg3nTjazhppUM9mm37+FF518Wy+K7sqH77m76hB2cN6ixVWHYD3ga3Czhioe2eQEN2um8DCZWWPlfuBDLvXr9jMbUDkXPhhtZRNJfy/pLkm3S/q2pH3aleMEN8tg5Bq8xyubrAQOjoiXAXcDZ7YrxAlulknOBB9tZZOIuD4itpcvb6Z4wvG4fA1ulkEF4+B/Cnyz3U5OcLMcArb3cGWTVpI+CWwHLmu3rxPcLIMOx8G7WdkEAEnvBo4H3hARbRcYcYKbZdLrJrqkZRQri74uIn6XcowT3CyD3Nfg5comR1M05zcAZ1P0mk8HVpbrk90cER8crxwnuFkmkTHBx1jZ5MJOy3GCm2VSx5lsTnCzDCLqebNJ23790abMmdnOxNDwbklbP6Wc7WJ2nTJnZjuJUNLWTynLB/9I0sLeh2I2uBp/P7ik5cBygBnMylWs2WCI4jq8brIleDnVbgXAbM2p4Uc16y33ops1VJB3HDwXJ7hZFvV8qmrKMNnlwE+AJZI2SHpv78MyGzzDw0ra+imlF320KXNm1iLCTXSzRqtjE90JbpZJo4fJzCY7N9HNGiro/zTUFE5ws0xq2EJ3gptlERB9HgJL4QQ3y6SOTXQvfGCWSUTalmKMpYvmSFop6Z7y3+e0K8cJbpbByFz0jPeDX8yuz2E4A/hBRCwCflC+HpcT3CyHAEJpW0pxoyxdBJwAXFL+fAnw9nbl+BrcLJM+THTZLyI2lj8/DOzX7gAnuFku6Qk+oaWLACIiJHllE4DzFi2uOoQdfPieu6sO4Vl1+78ZXOpkmKzbpYt+I2n/iNgoaX9gU7sDfA1ulkP05aGLVwPvKn9+F/Dddgc4wc1yicQtwRjPYfgCcKyke4A3lq/HNSma6Gb90fOliwDe0Ek5TnCzXGo4Gd0JbpaLE9ysoXyziVnDuQY3a7Aa3k3mBDfLpP28sv5zgpvl0MEYdz85wc2ySL9TrJ+c4Ga5uAY3a7DhqgPYVcraZAdKukHSOkl3Sjq1H4GZDZTMD3zIJaUG3w6cHhG3StoLWCNpZUSs63FsZgOljr3obWvwiNgYEbeWPz8JrAfm9zows4GT8W6yXDq6XVTSQuBQYFUvgjGzvJI72STtCXwL+FhEPDHK+8uB5QAzmJUtQLNBUccmelKCS5pGkdyXRcRVo+1TPlNqBcBszanhRzXrsUEcB5ck4EJgfUR8qfchmQ2gYDCHyYCjgFOAYyStLbfjehyX2cBRpG1JZUl/UQ5L3yHpckkzuompbQ0eETeR81k0Zk2V6cJU0nzgz4GXRMQWSVcAJ1KsdtIRz2QzyyVvz9NUYKakbcAs4KFuCvFTVc0ySG2epzTRI+JB4IvAr4GNwOMRcX03cTnBzXJJn6o6V9Lqlm15azHlqqEnAAcBBwB7SDq5m5DcRDfLJb2J3m5lkzcC90fEZgBJVwFHApd2GpIT3CwT5Rsm+zVwhKRZwBaKZ6GvHv+Q0TnBzXLoYAisbVERqyRdCdxKcbPXbZSTyDrlBDfLJWMvekScDZw90XKc4Ga51HCCthPcLJM63mziYTKzBnMNbpZLDWtwJ7hZDpF1mCwbJ3gFzlu0uOoQnnXdQ2urDmEHbz5gadUhdM81uFkziXp2sjnBzXJxgps1VMaZbDk5wc1ycYKbNZd70c2azDW4WUN5fXCzZnMnm1mTOcHNmss1uFmT1TDBfbuoWQY5H5sMIGkfSVdKukvSekmv7iYu1+BmueStwb8MfD8i/kjS7tDdkr1OcLNMcl2DS9obeC3wboCI2Aps7aastk10STMk/VTSz8rF0D7dzYnMGi8St/YOAjYDX5N0m6QLJO3RTUgp1+DPAMdExCHAUmCZpCO6OZlZo6Un+Lgrm1C0rA8DvhoRhwJPA2d0E1LK6qIBPFW+nFZuNewvNKtQZ3eTtVvZZAOwISJWla+vpMsET+pFlzRF0lpgE7Cy5cRmNiJTEz0iHgYekLSk/NUbgHXdhJTUyRYRQ8BSSfsA35Z0cETc0bpP2cxYDjCjuw4/s4GW+W6yjwKXlT3o9wHv6aaQjnrRI+IxSTcAy4A7dnpvBeXyKrM1x014m3RyzmSLiLXAeM34JCm96PPKmhtJM4FjgbsmemKzRkltnve56kupwfcHLpE0heIL4YqIuKa3YZkNoBq2W1N60W8HDu1DLGYDy09VNWs6J7hZcynql+FOcLMcvHSRWcPVrwJ3gpvl4k42syZzgps1lJcuMms4J7hZM3mii1nDabh+Ge4EN8vBSxeZNdukmeiiqVOZMmdeL4ruytDmzVWHUFtvPmBp1SHsYNN3X1x1CM/aftqPOzvANbhZc9Wxk80rm5jlEEBE2paofBbibZK6fv6Ca3CzTHpwDX4qsB6Y3W0BrsHNMhgZB8+4NtkC4K3ABROJyzW4WQ4dNr8TnAt8AthrIoW4BjfLpIMafNyVTSQdD2yKiDUTjck1uFku+VY2OQp4m6TjgBnAbEmXRsTJnYbkGtwsk1zX4BFxZkQsiIiFwInAD7tJbnANbpZHAJ6LbtZcvZiqGhE3Ajd2e7wT3CwXP1XVrLnqOFXVCW6Wg28XNWuuYiZb/TI8eZgsx8R3s0YbTtz6qJMafMIT382abGBr8FwT380aK6IYB0/Z+ii1Bs8y8d2syerYi962Bk+d+C5p+cjk+a3DW7IFaDYwMj/wIYeUGjxp4ntErABWAOw9bd8afpeZ9VBNVxdtW4PnnPhu1mgDWoObWYoatls7SvCJTnw3a7I6DpO5BjfLIYAhJ7hZI4lwDW7WaE5wswZzgps1VND3G0lS+KGLZpkoImlrW450oKQbJK2TdKekU7uNyTW4WS75mujbgdMj4lZJewFrJK2MiHWdFuQEN8shAobztNEjYiOwsfz5SUnrgfmAE9ysMj24Bpe0EDgUWNXN8U5ws0w6GAefK2l1y+sV5c1aO5Yn7Ql8C/hYRDzRTUxOcLNc0hO83dJFSJpGkdyXRcRV3YbkBDfLIePKJpIEXAisj4gvTaSsniT4E9s3P3Ldpq/+aoLFzAUeyRFPJo5nfHniedvEAyHf/83z03fNeivoUcApwM8lrS1/d1ZEXNtpQT1J8IiYN9EyJK1u14zpJ8czvjrFU1ksmRI8Im6ieBLzhLmJbpZDAEP1m8rmBDfLIiCc4J3YZdigYo5nfHWKp5pYfLNJutHGBavkeMZXp3gqicXrg5s1nGtwswZzgps1VAQMDVUdxS6c4Ga5uAY3azAnuFlT9X/l0BROcLMcAsITXcwazDW4WYP5GtysoTxMZtZskemhizk5wc2y6P/a3ymc4GY51PRmE69sYpZLDKdtCSQtk/QLSfdKOqPbkFyDm2UQQOR76OIU4DzgWGADcIukq7tZ2cQ1uFkOETlr8MOBeyPivojYCnwDOKGbsFyDm2US+YbJ5gMPtLzeALyqm4Kc4GYZPMlvr/uvuHJu4u4zUlY2ycEJbpZBRCzLWNyDwIEtrxeUv+uYr8HN6ucWYJGkgyTtDpwIXN1NQa7BzWomIrZL+ghwHTAFuCgi7uymLEUNZ9+YWR5uops1mBPcrMGc4GYN5gQ3azAnuFmDOcHNGswJbtZgTnCzBvt/Jyx9halSNVIAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 288x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "ztQCJ7SaQOyK"
-      },
-      "source": [
-        "y_train =  [people[i] for i in image_classes]\n",
-        "y_train_pred =  [people[i] for i in clf.predict(image_features)]"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 281
-        },
-        "id": "UL5-iFnMLAC1",
-        "outputId": "eaf9b09b-8f18-4d96-8d1f-4fc1cb7be0c3"
-      },
-      "source": [
-        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
-        "\n",
-        "clf1, class_names1, stdScalar1, k1, voc1 = joblib.load('/gdrive/MyDrive/BitCoders/trainedSVM.pkl')\n",
-        "test_path = os.path.join(main_dir, 'Test')\n",
-        "test_classes = os.listdir(test_path)\n",
-        "\n",
-        "test_image_paths = []\n",
-        "test_image_classes = []\n",
-        "test_class_id = 0\n",
-        "\n",
-        "for test_class in test_classes:\n",
-        "    dir = os.path.join(test_path, test_class)\n",
-        "    class_path = imageList(dir)\n",
-        "    test_image_paths+=class_path\n",
-        "    test_image_classes+=[test_class_id]*len(class_path)\n",
-        "    test_class_id+=1\n",
-        "\n",
-        "test_des_list = []\n",
-        "for image_path in test_image_paths:\n",
-        "  kpt, desc = sift(image_path)\n",
-        "  test_des_list.append((image_path, desc))\n",
-        "\n",
-        "test_descriptors = test_des_list[0][1]\n",
-        "for image_path, descriptor in test_des_list[0:]:\n",
-        "  test_descriptors = np.vstack((test_descriptors, descriptor)) \n",
-        "\n",
-        "test_features = np.zeros((len(test_image_paths), k1), \"float32\")\n",
-        "for i in range(len(test_image_paths)):\n",
-        "    words, distance = vq(test_des_list[i][1],voc1)\n",
-        "    for w in words:\n",
-        "        test_features[i][w] += 1\n",
-        "\n",
-        "nbr_occurences = np.sum( (test_features > 0) * 1, axis = 0)\n",
-        "idf = np.array(np.log((1.0*len(test_image_paths)+1) / (1.0*nbr_occurences + 1)), 'float32')\n",
-        "\n",
-        "test_features = stdScalar1.transform(test_features)\n",
-        "y_test =  [class_names1[i] for i in test_image_classes]\n",
-        "y_test_pred =  [class_names1[i] for i in clf1.predict(test_features)]\n",
-        "\n",
-        "accuracy = accuracy_score(y_test, y_test_pred)\n",
-        "print (\"accuracy = \", accuracy)\n",
-        "cm = confusion_matrix(y_test, y_test_pred)\n",
-        "plt.matshow(cm)\n",
-        "plt.title('Confusion matrix')\n",
-        "plt.colorbar()\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "accuracy =  0.9714285714285714\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD2CAYAAAD720p7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUQElEQVR4nO3df7BcZX3H8fcnCfkFCRgTKSRoQCGOZiQgIsKoqKARqfhHO0WBqlWjtlosOBbQKfirta21OJXaRkEYoShFVIpUCAp1aCWQQERIECmoBIIJVCAgJrn3fvvHOctsknvvPrv32d2z535eM2dy9+45z/lu5n73ec7zPOc8igjMrJ6m9DsAM+seJ7hZjTnBzWrMCW5WY05wsxpzgpvVmBM8E0mzJP2HpCck/fsEyjlF0vU5Y+sXSa+W9LN+xzGZabKNg0t6B3AG8GJgK7AO+GxE3DzBck8DPgwcHRFDEw604iQFcHBE3NfvWGxsk6oGl3QGcD7w18C+wPOBfwZOylD8C4B7J0Nyp5A0rd8xGBARk2ID9gaeAv5wnH1mUHwBPFxu5wMzyveOBTYCZwKbgU3Au8v3PglsB3aU53gPcB5waVPZi4EAppWv3wXcT9GKeAA4pen3NzcddzRwG/BE+e/RTe/dBHwa+O+ynOuB+WN8tkb8H2uK/23ACcC9wP8B5zTtfyTwY+Dxct8vAdPL935Ufpany8/7R03l/yXwCPD1xu/KY15YnuPw8vX+wBbg2H7/beTY3njs7Hj5y2YkbcD3e/Z33+//mJ59UFgODDUSbIx9PgXcAjwPWAD8D/Dp8r1jy+M/BexRJsZvgeeU7++a0GMmOLAn8CSwpHxvP+Cl5c/PJjgwD/gNcFp53NvL188t378J+F/gEGBW+fpzY3y2Rvx/Vcb/vjLB/g2YA7wUeAY4sNz/5cBR5XkXAxuAjzSVF8CLRin/bym+KGc1J3i5z/uA9cBs4Drg8/3+u8i1Hf6yGbFj0wuTNmBNr+KaTE305wKPxvhN6FOAT0XE5ojYQlEzn9b0/o7y/R0RcS1F7bWkw3hGgKWSZkXEpoi4e5R93gL8PCK+HhFDEXE5cA/w+037fC0i7o2IZ4ArgGXjnHMHRX/DDuAbwHzgixGxtTz/euBQgIhYGxG3lOf9BfCvwGsTPtO5EbGtjGcnEfEV4D5gNcWX2sdblDdAguEYSdp6aTIl+GPA/BbXhvsDv2x6/cvyd8+WscsXxG+BvdoNJCKepmjWfgDYJOl7kl6cEE8jpoVNrx9pI57HImK4/LmRgL9uev+ZxvGSDpF0jaRHJD1J0W8xf5yyAbZExO9a7PMVYCnwTxGxrcW+AyOAESJp66XJlOA/BrZRXHeO5WGKzrKG55e/68TTFE3Rht9rfjMirouI4ylqsnso/vBbxdOI6aEOY2rHlyniOjgi5gLnAGpxzLh/vZL2oujXuBA4T9K8HIFWQRDsiOGkrZcqmeCSlkv6maT7JJ2Vo8yIeILi+vMCSW+TNFvSHpLeLOnvyt0uBz4haYGk+eX+j0vaDHytzVOuA14j6fmS9gbObrwhaV9JJ0nak+JL5ymK5u2urgUOkfQOSdMk/SnwCuBsSXezc02e2xyKfoKnytbFB3d5/9fAEkm3SvoJxf/P3BZlfpHi+vO9wPeAf8kcMwCSpkq6Q9I13Sh/LK7BE0iaClwAvBl4CfB2SS/JUXZE/APFGPgnKDqYHgQ+BHyn3OUzwBrgTuCnwO3ARyk66No91yrgm2VZa4HmP7YpZRwPU/Qsv5bdE4iIeAw4kaLn/jHg/cD7I2IJRQfYQooWQDd8FHgHRe/8V8rP0uw84KsUHXyfBd4LzJB01GiFSTqJ4v+x8TnPAA6XdEr2yOF0ik7BnglgmEjaeqlyE10kvQo4LyLeVL4+GyAi/qaPMS0GromIpf2KYTSSvgt8qfwy6Xcss4GbgQ9GxOo+xrEIuITiS+eMiDixF+ddduj0WPWfC5L2fd7Ch9dGxBFdDgmoYA1OUSs92PR6I91tig6k8kvnMIoe6X7GMVXSOoqx9VX9TO7S+RRj/T3trg5gOCJp66UqJri1UHZWfYtiXPrJfsYSEcMRsQxYBBwpqW+tHEknApsjYm0/zj+SuKWQdJGkzZLu2uX3H5Z0j6S7m/qOxlTFBH8IOKDp9SJ602s8ECTtQZHcl0XEVf2OpyEiHgdupIP+ioyOAd4q6RcU4/yvl3RpL04cidffbVyDX8wu/5eSXkcxrfrQiHgp8PlWhVQxwW8DDpZ0oKTpwMnA1X2OqRIkiWKIaUNEfKEC8SyQtE/58yzgeIqhtb6IiLMjYlFELKb4u/lhRJzam3PDjsQtrbz4EUUHbLMPUsxU3Fbus7lVOZVL8HIiyYcopjJuAK4YY5ZXT0i6nGIMfYmkjZLe069YKGqo0yhqpnXldkIf49kPuFHSnRRfzKsioqdDU9UhhhO3CTgEeLWk1ZL+S9IrWh1QyTt+ymmg1/Y7DoCIeHu/Y2iI4pbWCf2F5BQRd1J09FVORNxEMTe/N+cDRtL7z+ZLWtP0emVErEw4bhrF/QlHUcyHuELSQTHOUFglE9xsELVROz/a4TDZRuCqMqFvlTRCMX14y1gHVK6JbjaIiokuXW+ifwd4HRT3CgDTgUfHO8A1uFkmI5Hv6qns+zmWojm/ETgXuAi4qBw62w68c7zmOTjBzbJo1ODZyhu776etUQEnuFkGgdgRU/sdxm4qew0uaUW/Y2jmeMZXpXj6EUuPrsHbVtkEByrzB1NyPOOrUjx9iEUMx5SkrZfcRDfLoHiiS/Xqy64k+NQ5e8a0BftMrIz5ezPjoIVZbr2Z8cBujwdr20xmM1fzKnNvreMZW65YfsfTbI9tyW3qXje/U3Qlwact2If9P/Nn3Si6Iy869Y5+h2ADaHX8IHnfCPW8+Z3CTXSzTEYmSw1uNtkEYntUL52qF5HZAJpUnWxmk9FwxqmquTjBzTIIxLBrcLP6GnEvulk9FVNVneBmtVTVm02c4GYZROCJLmb1JU90MaurYmWT6tXg1YvIbEANMyVpSzHWyible2dKinIF3HElna0by/ma1UkgRiJtS3Qxo6wSI+kA4I3Ar1IKaZng3VzO16xOctbgY6xsAvCPFIsrJt0Om3INfiRwX0TcDyDpGxTrI61PitRsEujFMFm5xvpDEfGTYhWr1lISfLTlfF/Zfnhm9VWsbJLcpdX2yibl+uvnUDTPk2XrRS8fdLcCiqexmE02XV7Z5IXAgUCj9l4E3C7pyIh4ZKyDUhI8aTnf8htoJZDtUUtmgyJCXZ2LHhE/BZ7XeF0ukXxERIy7sklKRF7O1yxBzqeq5lrVtmUNHhFDkhrL+U4FLurncr5mVVQ88KEnK5s03l+cUk7SNXiVlvM1qyY/dNGstgJ8N5lZXTVmslWNE9wsEz900aymivvBXYOb1Zab6GY1VVyDu4luVluTZvFBs8kmEEMjHiYzqy0/k82sptyLblZz7mQzq6lJNZNtxgPP8KJT7+hG0R057q6t/Q5hJze+7qB+h/Cs4S1b+h1Cbfga3Kymikc2OcHN6ik8TGZWW7kf+JBL9br9zAZUzoUPRlvZRNLfS7pH0p2Svi1pn1blOMHNMmhcg3d5ZZNVwNKIeBlwL3B2q0Kc4GaZ5Ezw0VY2iYjrI2KofHkLxROOx+VrcLMM+jAO/ifAN1vt5AQ3yyFgqIsrmzST9HFgCLis1b5OcLMM2hwH72RlEwAkvQs4EXhDRLRcYMQJbpZJt5vokpZTrCz62oj4bcoxTnCzDHJfg5crmxxL0ZzfCJxL0Ws+A1hVrk92S0R8YLxynOBmmUTGBB9jZZML2y3HCW6WSRVnsjnBzTKIqObNJi379UebMmdmuxLDI1OStl5KOdvF7D5lzsx2EaGkrZdSlg/+kaTF3Q/FbHDV/n5wSSuAFQAzmZ2rWLPBEMV1eNVkS/Byqt1KgLmaV8GPatZd7kU3q6kg7zh4Lk5wsyyq+VTVlGGyy4EfA0skbZT0nu6HZTZ4RkaUtPVSSi/6aFPmzKxJhJvoZrVWxSa6E9wsk1oPk5lNdm6im9VU0PtpqCmc4GaZVLCF7gQ3yyIgejwElsIJbpZJFZvoXvjALJOItC3FGEsXzZO0StLPy3+f06ocJ7hZBo256BnvB7+Y3Z/DcBbwg4g4GPhB+XpcTnCzHAIIpW0pxY2ydBFwEnBJ+fMlwNtaleNrcLNMejDRZd+I2FT+/Aiwb6sDnOBmuaQn+ISWLgKIiJDUn5VNNG0aU+ct6EbRHblhab8j2Nlxd93f7xCedcPSOf0OoSbUzjBZp0sX/VrSfhGxSdJ+wOZWB/ga3CyH6MlDF68G3ln+/E7gu60OcIKb5RKJW4IxnsPwOeB4ST8Hjitfj8vX4GbZdH3pIoA3tFOOE9wslwpORneCm+XiBDerKd9sYlZzrsHNaqyCd5M5wc0yaT2vrPec4GY5tDHG3UtOcLMs0u8U6yUnuFkursHNamyk3wHsLmVtsgMk3ShpvaS7JZ3ei8DMBkrmBz7kklKDDwFnRsTtkuYAayWtioj1XY7NbKBUsRe9ZQ0eEZsi4vby563ABmBhtwMzGzgZ7ybLpa3bRSUtBg4DVncjGDPLK7mTTdJewLeAj0TEk6O8vwJYATBzyl7ZAjQbFAPZRAeQtAdFcl8WEVeNtk9ErIyIIyLiiOlTZuWM0WwwDGInmyQBFwIbIuIL3Q/JbAAFgzlMBhwDnAa8XtK6cjuhy3GZDRxF2pZUlvQX5bD0XZIulzSzk5ha1uARcTM5n0VjVleZrsElLQT+HHhJRDwj6QrgZIrVTtrimWxmueTtZJsGzJK0A5gNPNxJIX6qqlkGqc3zlCZ6RDwEfB74FbAJeCIiru8kLie4WS7pvejzJa1p2lY0F1OuGnoScCCwP7CnpFM7CclNdLNc0pvorVY2OQ54ICK2AEi6CjgauLTdkJzgZpko3zDZr4CjJM0GnqF4Fvqa8Q8ZnRPcLIc2hsBaFhWxWtKVwO0UN3vdAbS1OGGDE9wsl4y96BFxLnDuRMtxgpvlUsG56E5ws0wG9mYTMxtMrsHNcqlgDe4EN8shsg6TZdOVBI+hIYa3bOlG0bVww9I5/Q7hWdc9vK7fIezkTfsv63cInXMNblZPopqdbE5ws1yc4GY1lXEmW05OcLNcnOBm9TVpetHNJiXX4GY15fXBzerNnWxmdeYEN6sv1+BmdVbBBPftomYZ5HxsMoCkfSRdKekeSRskvaqTuFyDm+WStwb/IvD9iPgDSdMpFj9omxPcLJNc1+CS9gZeA7wLICK2A9s7KatlE13STEm3SvpJuRjaJzs5kVntReLW2oHAFuBrku6Q9FVJe3YSUso1+Dbg9RFxKLAMWC7pqE5OZlZr6Qk+7somFC3rw4EvR8RhwNPAWZ2ElLK6aABPlS/3KLcK9hea9VF7d5O1WtlkI7AxIlaXr6+kwwRP6kWXNFXSOmAzsKrpxGbWkKmJHhGPAA9KWlL+6g3A+k5CSupki4hhYJmkfYBvS1oaEXc171M2M1YAzOysw89soGW+m+zDwGVlD/r9wLs7KaStXvSIeFzSjcBy4K5d3ltJubzKXM1zE94mnZwz2SJiHTBeMz5JSi/6grLmRtIs4Hjgnome2KxWUpvnPa76Umrw/YBLJE2l+EK4IiKu6W5YZgOogu3WlF70O4HDehCL2cDyU1XN6s4JblZfiupluBPcLIfJtHSR2aRUvQrcCW6WizvZzOrMCW5WU166yKzmnOBm9eSJLmY1p5HqZbgT3CwHL11kVm+e6GKV86b9l/U7hJ0svnVWv0N41ro/VnsHuAY3q68qdrJ5ZROzHAKISNsSlc9CvENSx89fcA1ulkkXrsFPBzYAczstwDW4WQaNcfCMa5MtAt4CfHUicbkGN8uhzeZ3gvOBjwFzJlKIa3CzTNqowcdd2UTSicDmiFg70Zhcg5vlkm9lk2OAt0o6AZgJzJV0aUSc2m5IrsHNMsl1DR4RZ0fEoohYDJwM/LCT5AbX4GZ5BOC56Gb11Y2pqhFxE3BTp8c7wc1y8VNVzeqrilNVneBmOfh2UbP6KmayVS/Dk4fJckx8N6u1kcSth9qpwSc88d2szga2Bs818d2stiKKcfCUrYdSa/AsE9/N6qyKvegta/DUie+SVjQmz+9gW7YAzQZG5gc+5JBSgydNfI+IlcBKgLmaV8HvMrMuqujqoi1r8JwT381qbUBrcDNLUcF2a1sJPtGJ72Z1VsVhMtfgZjkEMOwEN6slEa7BzWrNCW5WY05ws5oKen4jSQo/dNEsE0UkbS3LkQ6QdKOk9ZLulnR6pzG5BjfLJV8TfQg4MyJulzQHWCtpVUSsb7cgJ7hZDhEwkqeNHhGbgE3lz1slbQAWAk5ws77pwjW4pMXAYcDqTo53gptl0sY4+HxJa5peryxv1tq5PGkv4FvARyLiyU5icoKb5ZKe4K2WLkLSHhTJfVlEXNVpSE5wsxwyrmwiScCFwIaI+MJEyupKgm/lN4/eEFf+coLFzAcezRFPJo5nfHniecXEAyHf/80L0nfNeivoMcBpwE8lrSt/d05EXNtuQV1J8IhYMNEyJK1p1YzpJcczvirF07dYMiV4RNxM8STmCXMT3SyHAIarN5XNCW6WRUA4wdux27BBnzme8VUpnv7E4ptN0o02LthPjmd8VYqnL7F4fXCzmnMNblZjTnCzmoqA4eF+R7EbJ7hZLq7BzWrMCW5WV71fOTSFE9wsh4DwRBezGnMNblZjvgY3qykPk5nVW2R66GJOTnCzLHq/9ncKJ7hZDhW92cQrm5jlEiNpWwJJyyX9TNJ9ks7qNCTX4GYZBBD5Hro4FbgAOB7YCNwm6epOVjZxDW6WQ0TOGvxI4L6IuD8itgPfAE7qJCzX4GaZRL5hsoXAg02vNwKv7KQgJ7hZBlv5zXU3xJXzE3efmbKySQ5OcLMMImJ5xuIeAg5oer2o/F3bfA1uVj23AQdLOlDSdOBk4OpOCnINblYxETEk6UPAdcBU4KKIuLuTshQVnH1jZnm4iW5WY05wsxpzgpvVmBPcrMac4GY15gQ3qzEnuFmNOcHNauz/Aae8jYTooEATAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<Figure size 288x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "f-Izp6mpV_Cn"
-      },
-      "source": [
-        "from sklearn.preprocessing import label_binarize\n",
-        "\n",
-        "y = label_binarize(y, classes=people)\n",
-        "n_classes = y.shape[1]"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "bQJ1Qz6MWzMf"
-      },
-      "source": [
-        "y_test_bin = label_binarize(y_test, classes=people)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "K-zxA40rVfjx"
-      },
-      "source": [
-        "from sklearn import svm\n",
-        "random_state = np.random.RandomState(0)\n",
-        "\n",
-        "classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,\n",
-        "                                 random_state=random_state))\n",
-        "y_score = classifier.fit(image_features, y_train).decision_function(test_features)\n",
-        "\n",
-        "# Compute ROC curve and ROC area for each class\n",
-        "fpr = dict()\n",
-        "tpr = dict()\n",
-        "roc_auc = dict()\n",
-        "for i in range(n_classes):\n",
-        "    fpr[i], tpr[i], _ = roc_curve(y_test_bin[:, i], y_score[:, i])\n",
-        "    roc_auc[i] = auc(fpr[i], tpr[i])"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wfiU1GLvIFdx"
-      },
-      "source": [
-        "### ROC Curve"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 677
-        },
-        "id": "41zvagluVfc_",
-        "outputId": "a149959f-cfa0-4a1d-d3c6-f5f75d099e7d"
-      },
-      "source": [
-        "from scipy import interp\n",
-        "from itertools import cycle\n",
-        "\n",
-        "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test_bin.ravel(), y_score.ravel())\n",
-        "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
-        "\n",
-        "# First aggregate all false positive rates\n",
-        "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
-        "\n",
-        "# Then interpolate all ROC curves at this points\n",
-        "mean_tpr = np.zeros_like(all_fpr)\n",
-        "for i in range(n_classes):\n",
-        "    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
-        "\n",
-        "# Finally average it and compute AUC\n",
-        "mean_tpr /= n_classes\n",
-        "\n",
-        "fpr[\"macro\"] = all_fpr\n",
-        "tpr[\"macro\"] = mean_tpr\n",
-        "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
-        "\n",
-        "# Plot all ROC curves\n",
-        "plt.figure(figsize=(10,10))\n",
-        "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
-        "         label='micro-average ROC curve (area = {0:0.2f})'\n",
-        "               ''.format(roc_auc[\"micro\"]),\n",
-        "         color='deeppink', linestyle=':', linewidth=4)\n",
-        "\n",
-        "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
-        "         label='macro-average ROC curve (area = {0:0.2f})'\n",
-        "               ''.format(roc_auc[\"macro\"]),\n",
-        "         color='navy', linestyle=':', linewidth=4)\n",
-        "\n",
-        "colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n",
-        "for i, color in zip(range(n_classes), colors):\n",
-        "    plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
-        "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
-        "             ''.format(i, roc_auc[i]))\n",
-        "\n",
-        "plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
-        "plt.xlim([0.0, 1.0])\n",
-        "plt.ylim([0.0, 1.05])\n",
-        "plt.xlabel('False Positive Rate')\n",
-        "plt.ylabel('True Positive Rate')\n",
-        "plt.title('ROC Curve')\n",
-        "plt.legend(loc=\"lower right\")\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n",
-            "  del sys.path[0]\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 720x720 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "S8lj6Ca5ZTyP"
-      },
-      "source": [
-        "## SURF"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 198
-        },
-        "id": "Cr9ONgNFZaDK",
-        "outputId": "0d7aff34-9a66-4a67-a874-13b03e393cac"
-      },
-      "source": [
-        "image_sources = list(image_database.keys())\n",
-        "labels = list(image_database.values())\n",
-        "data = pd.DataFrame()\n",
-        "data['Image_Sources'] = image_sources\n",
-        "data['Labels'] = labels\n",
-        "data.head()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Image_Sources</th>\n",
-              "      <th>Labels</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/1...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/2...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/3...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/4...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/5...</td>\n",
-              "      <td>Greeva</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                       Image_Sources  Labels\n",
-              "0  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/1...  Greeva\n",
-              "1  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/2...  Greeva\n",
-              "2  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/3...  Greeva\n",
-              "3  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/4...  Greeva\n",
-              "4  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/5...  Greeva"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "_AOlXYnHZaDN"
-      },
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "X = data['Image_Sources']\n",
-        "y = data['Labels']\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5, random_state = 0, stratify=y)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "AkQZJ3xkZaDN"
-      },
-      "source": [
-        "y_cap = []\n",
-        "for test_image_path in X_test:\n",
-        "  test_image = cv.imread(test_image_path,cv.IMREAD_GRAYSCALE)\n",
-        "  best_match = ''\n",
-        "  highest_matches = 0\n",
-        "  for train_image, label in zip(X_train, y_train):\n",
-        "    try:\n",
-        "      match, num_matches =  surf_match(test_image, train_image)\n",
-        "      if match and num_matches > highest_matches:\n",
-        "        highest_matches = num_matches\n",
-        "        best_match = label\n",
-        "    except:\n",
-        "      pass\n",
-        "      # print('Error')\n",
-        "  if len(best_match)<1:\n",
-        "    y_cap.append('Unknown')\n",
-        "  else:\n",
-        "    y_cap.append(best_match)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "q6uASEn9ZaDO"
-      },
-      "source": [
-        "classes = list(set(y_test))\n",
-        "classes.append('Unknown')"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eijGPFWzZaDP"
-      },
-      "source": [
-        "### Overall Accuracy : 98.57%"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "lHrbyVtbZaDP",
-        "outputId": "c34be713-969f-456b-d8ab-824fc615f831"
-      },
-      "source": [
-        "from sklearn.metrics import accuracy_score\n",
-        "print(\"Accuracy is:\", np.round(accuracy_score(y_test, y_cap)*100,3),\"%\")"
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "60f7H9hcNKe6"
+   },
+   "source": [
+    "# Imports and helper functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ITZUwJMEE9vn"
+   },
+   "source": [
+    "## Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Xyi9aHVDvGOS",
+    "outputId": "97e097f1-c1c5-4d23-e304-884287ddf9d3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting opencv-contrib-python==3.4.2.16\n",
+      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/f1/66330f4042c4fb3b2d77a159db8e8916d9cdecc29bc8c1f56bc7f8a9bec9/opencv_contrib_python-3.4.2.16-cp36-cp36m-manylinux1_x86_64.whl (30.6MB)\n",
+      "\u001b[K     |████████████████████████████████| 30.6MB 142kB/s \n",
+      "\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from opencv-contrib-python==3.4.2.16) (1.19.5)\n",
+      "Installing collected packages: opencv-contrib-python\n",
+      "  Found existing installation: opencv-contrib-python 4.1.2.30\n",
+      "    Uninstalling opencv-contrib-python-4.1.2.30:\n",
+      "      Successfully uninstalled opencv-contrib-python-4.1.2.30\n",
+      "Successfully installed opencv-contrib-python-3.4.2.16\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install opencv-contrib-python==3.4.2.16\n",
+    "from IPython.display import display, Javascript, Image\n",
+    "from google.colab.output import eval_js\n",
+    "from base64 import b64decode, b64encode\n",
+    "from pathlib import Path\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import PIL\n",
+    "import io\n",
+    "import html\n",
+    "import time\n",
+    "import pandas as pd\n",
+    "import cv2 as cv\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "from skimage import io\n",
+    "from IPython.display import clear_output\n",
+    "from google.colab.patches import cv2_imshow\n",
+    "import cv2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ubtLtOpwE_Y-"
+   },
+   "source": [
+    "## SIFT algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Qry-dY-vEN87"
+   },
+   "outputs": [],
+   "source": [
+    "# SIFT implementation\n",
+    "\n",
+    "# MIN_MATCH_COUNT = 10\n",
+    "\n",
+    "def sift_match(test, image, plot=False, MIN_MATCH_COUNT=10):\n",
+    "    isMatch = False\n",
+    "    # Initiate SIFT detector\n",
+    "    sift = cv2.xfeatures2d.SIFT_create()\n",
+    "    # img1 = cv.imread(test,cv.IMREAD_GRAYSCALE)# queryImage\n",
+    "    img1 = test\n",
+    "    img2 = cv.imread(image,cv.IMREAD_GRAYSCALE) # trainImage\n",
+    "    # find the keypoints and descriptors with SIFT\n",
+    "    kp1, des1 = sift.detectAndCompute(img1,None)\n",
+    "    kp2, des2 = sift.detectAndCompute(img2,None)\n",
+    "\n",
+    "    FLANN_INDEX_KDTREE = 0\n",
+    "    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\n",
+    "    search_params = dict(checks = 50)\n",
+    "\n",
+    "    flann = cv2.FlannBasedMatcher(index_params, search_params)\n",
+    "\n",
+    "    matches = flann.knnMatch(des1,des2,k=2)\n",
+    "\n",
+    "    # store all the good matches as per Lowe's ratio test.\n",
+    "    good = []\n",
+    "    for m,n in matches:\n",
+    "        if m.distance < 0.7*n.distance:\n",
+    "            good.append(m)\n",
+    "      \n",
+    "    if len(good)>MIN_MATCH_COUNT:\n",
+    "        src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)\n",
+    "        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)\n",
+    "\n",
+    "        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)\n",
+    "        matchesMask = mask.ravel().tolist()\n",
+    "\n",
+    "        h,w = img1.shape\n",
+    "        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)\n",
+    "        dst = cv2.perspectiveTransform(pts,M)\n",
+    "\n",
+    "        img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)\n",
+    "        # print(\"MATCH FOUND\")\n",
+    "        isMatch = True\n",
+    "    else:\n",
+    "        # print(\"Not enough matches are found - %d/%d\" % (len(good),MIN_MATCH_COUNT))\n",
+    "        isMatch = False\n",
+    "    if plot:\n",
+    "        matchesMask = None\n",
+    "        draw_params = dict(matchColor = (0,255,0), # draw matches in green color\n",
+    "                          singlePointColor = None,\n",
+    "                          matchesMask = matchesMask, # draw only inliers\n",
+    "                          flags = 2)\n",
+    "\n",
+    "        img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)\n",
+    "\n",
+    "        plt.imshow(img3, 'gray'),plt.show()\n",
+    "    \n",
+    "    return (isMatch, len(good))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qHx9V64CIijQ"
+   },
+   "source": [
+    "## SURF algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-ubUw2AXYlAs"
+   },
+   "outputs": [],
+   "source": [
+    "# SURF implementation\n",
+    "\n",
+    "# MIN_MATCH_COUNT = 10\n",
+    "\n",
+    "def surf_match(test, image, plot=False, MIN_MATCH_COUNT=10):\n",
+    "    isMatch = False\n",
+    "    # Initiate SIFT detector\n",
+    "    surf = cv2.xfeatures2d.SURF_create(400)\n",
+    "    # img1 = cv.imread(test,cv.IMREAD_GRAYSCALE)# queryImage\n",
+    "    img1 = test\n",
+    "    img2 = cv.imread(image,cv.IMREAD_GRAYSCALE) # trainImage\n",
+    "    # find the keypoints and descriptors with SIFT\n",
+    "    kp1, des1 = surf.detectAndCompute(img1,None)\n",
+    "    kp2, des2 = surf.detectAndCompute(img2,None)\n",
+    "\n",
+    "    FLANN_INDEX_KDTREE = 0\n",
+    "    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\n",
+    "    search_params = dict(checks = 50)\n",
+    "\n",
+    "    flann = cv2.FlannBasedMatcher(index_params, search_params)\n",
+    "\n",
+    "    matches = flann.knnMatch(des1,des2,k=2)\n",
+    "\n",
+    "    # store all the good matches as per Lowe's ratio test.\n",
+    "    good = []\n",
+    "    for m,n in matches:\n",
+    "        if m.distance < 0.7*n.distance:\n",
+    "            good.append(m)\n",
+    "      \n",
+    "    if len(good)>MIN_MATCH_COUNT:\n",
+    "        src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)\n",
+    "        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)\n",
+    "\n",
+    "        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)\n",
+    "        matchesMask = mask.ravel().tolist()\n",
+    "\n",
+    "        h,w = img1.shape\n",
+    "        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)\n",
+    "        dst = cv2.perspectiveTransform(pts,M)\n",
+    "\n",
+    "        img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)\n",
+    "        # print(\"MATCH FOUND\")\n",
+    "        isMatch = True\n",
+    "    else:\n",
+    "        # print(\"Not enough matches are found - %d/%d\" % (len(good),MIN_MATCH_COUNT))\n",
+    "        isMatch = False\n",
+    "    if plot:\n",
+    "        matchesMask = None\n",
+    "        draw_params = dict(matchColor = (0,255,0), # draw matches in green color\n",
+    "                          singlePointColor = None,\n",
+    "                          matchesMask = matchesMask, # draw only inliers\n",
+    "                          flags = 2)\n",
+    "\n",
+    "        img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)\n",
+    "\n",
+    "        plt.imshow(img3, 'gray'),plt.show()\n",
+    "    \n",
+    "    return (isMatch, len(good))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sI184cZsFDrz"
+   },
+   "source": [
+    "## Image helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "o7x4vBOW_Bxe"
+   },
+   "outputs": [],
+   "source": [
+    "def take_photo(filename='photo.jpg', quality=0.8):\n",
+    "  js = Javascript('''\n",
+    "    async function takePhoto(quality) {\n",
+    "      const div = document.createElement('div');\n",
+    "      const capture = document.createElement('button');\n",
+    "      capture.textContent = 'Capture';\n",
+    "      div.appendChild(capture);\n",
+    "\n",
+    "      const video = document.createElement('video');\n",
+    "      video.style.display = 'block';\n",
+    "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
+    "\n",
+    "      document.body.appendChild(div);\n",
+    "      div.appendChild(video);\n",
+    "      video.srcObject = stream;\n",
+    "      await video.play();\n",
+    "\n",
+    "      // Resize the output to fit the video element.\n",
+    "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
+    "\n",
+    "      // Wait for Capture to be clicked.\n",
+    "      await new Promise((resolve) => capture.onclick = resolve);\n",
+    "\n",
+    "      const canvas = document.createElement('canvas');\n",
+    "      canvas.width = video.videoWidth;\n",
+    "      canvas.height = video.videoHeight;\n",
+    "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
+    "      stream.getVideoTracks()[0].stop();\n",
+    "      div.remove();\n",
+    "      return canvas.toDataURL('image/jpeg', quality);\n",
+    "    }\n",
+    "    ''')\n",
+    "  display(js)\n",
+    "  data = eval_js('takePhoto({})'.format(quality))\n",
+    "  binary = b64decode(data.split(',')[1])\n",
+    "  with open(filename, 'wb') as f:\n",
+    "    f.write(binary)\n",
+    "  return filename"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Z6cGBpLtFhVj"
+   },
+   "source": [
+    "## Video helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "U74F7ehhAUyT"
+   },
+   "outputs": [],
+   "source": [
+    "# function to convert the JavaScript object into an OpenCV image\n",
+    "def js_to_image(js_reply):\n",
+    "  \"\"\"\n",
+    "  Params:\n",
+    "          js_reply: JavaScript object containing image from webcam\n",
+    "  Returns:\n",
+    "          img: OpenCV BGR image\n",
+    "  \"\"\"\n",
+    "  # decode base64 image\n",
+    "  image_bytes = b64decode(js_reply.split(',')[1])\n",
+    "  # convert bytes to numpy array\n",
+    "  jpg_as_np = np.frombuffer(image_bytes, dtype=np.uint8)\n",
+    "  # decode numpy array into OpenCV BGR image\n",
+    "  img = cv2.imdecode(jpg_as_np, flags=1)\n",
+    "  img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+    "  return img\n",
+    "\n",
+    "# function to convert OpenCV Rectangle bounding box image into base64 byte string to be overlayed on video stream\n",
+    "def bbox_to_bytes(bbox_array):\n",
+    "  \"\"\"\n",
+    "  Params:\n",
+    "          bbox_array: Numpy array (pixels) containing rectangle to overlay on video stream.\n",
+    "  Returns:\n",
+    "        bytes: Base64 image byte string\n",
+    "  \"\"\"\n",
+    "  # convert array into PIL image\n",
+    "  bbox_PIL = PIL.Image.fromarray(bbox_array, 'RGBA')\n",
+    "  iobuf = io.BytesIO()\n",
+    "  # format bbox into png for return\n",
+    "  bbox_PIL.save(iobuf, format='png')\n",
+    "  # format return string\n",
+    "  bbox_bytes = 'data:image/png;base64,{}'.format((str(b64encode(iobuf.getvalue()), 'utf-8')))\n",
+    "\n",
+    "  return bbox_bytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "FOQcw4lI-BaN"
+   },
+   "outputs": [],
+   "source": [
+    "# JavaScript to properly create our live video stream using our webcam as input\n",
+    "def video_stream():\n",
+    "  js = Javascript('''\n",
+    "    var video;\n",
+    "    var div = null;\n",
+    "    var stream;\n",
+    "    var captureCanvas;\n",
+    "    var imgElement;\n",
+    "    var labelElement;\n",
+    "    \n",
+    "    var pendingResolve = null;\n",
+    "    var shutdown = false;\n",
+    "    \n",
+    "    function removeDom() {\n",
+    "       stream.getVideoTracks()[0].stop();\n",
+    "       video.remove();\n",
+    "       div.remove();\n",
+    "       video = null;\n",
+    "       div = null;\n",
+    "       stream = null;\n",
+    "       imgElement = null;\n",
+    "       captureCanvas = null;\n",
+    "       labelElement = null;\n",
+    "    }\n",
+    "    \n",
+    "    function onAnimationFrame() {\n",
+    "      if (!shutdown) {\n",
+    "        window.requestAnimationFrame(onAnimationFrame);\n",
+    "      }\n",
+    "      if (pendingResolve) {\n",
+    "        var result = \"\";\n",
+    "        if (!shutdown) {\n",
+    "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
+    "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
+    "        }\n",
+    "        var lp = pendingResolve;\n",
+    "        pendingResolve = null;\n",
+    "        lp(result);\n",
+    "      }\n",
+    "    }\n",
+    "    \n",
+    "    async function createDom() {\n",
+    "      if (div !== null) {\n",
+    "        return stream;\n",
+    "      }\n",
+    "\n",
+    "      div = document.createElement('div');\n",
+    "      div.style.border = '2px solid black';\n",
+    "      div.style.padding = '3px';\n",
+    "      div.style.width = '100%';\n",
+    "      div.style.maxWidth = '600px';\n",
+    "      document.body.appendChild(div);\n",
+    "      \n",
+    "      const modelOut = document.createElement('div');\n",
+    "      modelOut.innerHTML = \"<span>Status:</span>\";\n",
+    "      labelElement = document.createElement('span');\n",
+    "      labelElement.innerText = 'No data';\n",
+    "      labelElement.style.fontWeight = 'bold';\n",
+    "      modelOut.appendChild(labelElement);\n",
+    "      div.appendChild(modelOut);\n",
+    "           \n",
+    "      video = document.createElement('video');\n",
+    "      video.style.display = 'block';\n",
+    "      video.width = div.clientWidth - 6;\n",
+    "      video.setAttribute('playsinline', '');\n",
+    "      video.onclick = () => { shutdown = true; };\n",
+    "      stream = await navigator.mediaDevices.getUserMedia(\n",
+    "          {video: { facingMode: \"environment\"}});\n",
+    "      div.appendChild(video);\n",
+    "\n",
+    "      imgElement = document.createElement('img');\n",
+    "      imgElement.style.position = 'absolute';\n",
+    "      imgElement.style.zIndex = 1;\n",
+    "      imgElement.onclick = () => { shutdown = true; };\n",
+    "      div.appendChild(imgElement);\n",
+    "      \n",
+    "      const instruction = document.createElement('div');\n",
+    "      instruction.innerHTML = \n",
+    "          '<span style=\"color: red; font-weight: bold;\">' +\n",
+    "          'When finished, click here or on the video to stop this demo</span>';\n",
+    "      div.appendChild(instruction);\n",
+    "      instruction.onclick = () => { shutdown = true; };\n",
+    "      \n",
+    "      video.srcObject = stream;\n",
+    "      await video.play();\n",
+    "\n",
+    "      captureCanvas = document.createElement('canvas');\n",
+    "      captureCanvas.width = 640; //video.videoWidth;\n",
+    "      captureCanvas.height = 480; //video.videoHeight;\n",
+    "      window.requestAnimationFrame(onAnimationFrame);\n",
+    "      \n",
+    "      return stream;\n",
+    "    }\n",
+    "    async function stream_frame(label, imgData) {\n",
+    "      if (shutdown) {\n",
+    "        removeDom();\n",
+    "        shutdown = false;\n",
+    "        return '';\n",
+    "      }\n",
+    "\n",
+    "      var preCreate = Date.now();\n",
+    "      stream = await createDom();\n",
+    "      \n",
+    "      var preShow = Date.now();\n",
+    "      if (label != \"\") {\n",
+    "        labelElement.innerHTML = label;\n",
+    "      }\n",
+    "            \n",
+    "      if (imgData != \"\") {\n",
+    "        var videoRect = video.getClientRects()[0];\n",
+    "        imgElement.style.top = videoRect.top + \"px\";\n",
+    "        imgElement.style.left = videoRect.left + \"px\";\n",
+    "        imgElement.style.width = videoRect.width + \"px\";\n",
+    "        imgElement.style.height = videoRect.height + \"px\";\n",
+    "        imgElement.src = imgData;\n",
+    "      }\n",
+    "      \n",
+    "      var preCapture = Date.now();\n",
+    "      var result = await new Promise(function(resolve, reject) {\n",
+    "        pendingResolve = resolve;\n",
+    "      });\n",
+    "      shutdown = false;\n",
+    "      \n",
+    "      return {'create': preShow - preCreate, \n",
+    "              'show': preCapture - preShow, \n",
+    "              'capture': Date.now() - preCapture,\n",
+    "              'img': result};\n",
+    "    }\n",
+    "    ''')\n",
+    "\n",
+    "  display(js)\n",
+    "  \n",
+    "def video_frame(label, bbox):\n",
+    "  data = eval_js('stream_frame(\"{}\", \"{}\")'.format(label, bbox))\n",
+    "  return data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ubZBdbm8Azq6"
+   },
+   "source": [
+    "# Choose your face data directory path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JS3-qNVXmbqA"
+   },
+   "source": [
+    "Our directory will be loaded at '/gdrive/MyDrive/BitCoders/Assignment2/'.\n",
+    "Feel free to load a blank directory and create your own database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BFRBWvcBA4Hv"
+   },
+   "outputs": [],
+   "source": [
+    "main_dir = '/gdrive/MyDrive/BitCoders/Assignment2/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ihqdgHwVQ4N2"
+   },
+   "source": [
+    "## Load the database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "t7uBqBVmSabL"
+   },
+   "outputs": [],
+   "source": [
+    "image_database = {}\n",
+    "pathlist = Path(str(main_dir)).rglob('*.jpg')\n",
+    "for path in pathlist:\n",
+    "  path_in_str = str(path)\n",
+    "  image_database[path_in_str] = path_in_str.split('/')[-2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MK4Ku-hhNMck"
+   },
+   "source": [
+    "# Add yourself to the face database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "v7XWivaI_rAA"
+   },
+   "outputs": [],
+   "source": [
+    "#@title Your Information:\n",
+    "#@markdown This info will help us to analyse your face and identify you in future.\n",
+    "# Name = 'Insert Name here (eg. Supan / Bhumiti)'\n",
+    "Name = 'supan'  #@param {type: \"string\"}\n",
+    "#@markdown More number of images = better face recognition !!\n",
+    "#@markdown So, please consider clicking atleast around 20 images.\n",
+    "Number_of_images = 25  #@param {type: \"slider\", min: 1, max: 40}\n",
+    "#@markdown ---\n",
+    "\n",
+    "class NameError(Exception):\n",
+    "  pass\n",
+    "\n",
+    "if Name == 'Insert Name here (eg. Supan / Bhumiti)':\n",
+    "  raise NameError(\"Enter name please!\")\n",
+    "\n",
+    "elif len(Name)<2:\n",
+    "  raise NameError(\"Enter name atleast bigger than 2 characters!\")\n",
+    "\n",
+    "elif os.path.exists(main_dir):\n",
+    "  raise NameError(\"Person already exists. Try a different Name\")\n",
+    "\n",
+    "else:\n",
+    "  print(\"Hi, \", Name)\n",
+    "  print(\"Let's begin taking your photos\")\n",
+    "\n",
+    "os.chdir(main_dir)\n",
+    "\n",
+    "main = os.path.join(main_dir, '{}'.format(Name))\n",
+    "\n",
+    "try:\n",
+    "  os.mkdir('{}'.format(Name))\n",
+    "except:\n",
+    "  raise NameError(\"Couldn't create your folder somehow. Try again!\")\n",
+    "  \n",
+    "main = os.path.join(main_dir, '{}'.format(Name))\n",
+    "os.chdir(main)\n",
+    "\n",
+    "for i in range(1, Number_of_images+1):\n",
+    "  print(\"Click image number {}\".format(i))\n",
+    "  try:\n",
+    "    filename = take_photo('{}.jpg'.format(i))\n",
+    "    print('Saved image number {}'.format(i))\n",
+    "    clear_output()\n",
+    "  except Exception as err:\n",
+    "    print(str(err))\n",
+    "print(\"Thank you!\")\n",
+    "print(\"Your face is now registered with us!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zlW531UBSDrL"
+   },
+   "source": [
+    "# Face Detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OqOp733-Q9sU"
+   },
+   "source": [
+    "## Face Recognition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5qzOoZ_goj5E"
+   },
+   "source": [
+    "Run the below cell to recognise your face against our face database!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "zSoYUIJz-r7y"
+   },
+   "outputs": [],
+   "source": [
+    "# start streaming video from webcam\n",
+    "video_stream()\n",
+    "# label for video\n",
+    "label_html = 'Detecting...'\n",
+    "# initialze bounding box to empty\n",
+    "bbox = ''\n",
+    "count = 0 \n",
+    "detected = False\n",
+    "while not detected:\n",
+    "    js_reply = video_frame(label_html, bbox)\n",
+    "    if not js_reply:\n",
+    "        break\n",
+    "\n",
+    "    img = js_to_image(js_reply[\"img\"])\n",
+    "    # gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
+    "    # gray = cv.imread(test,cv.IMREAD_GRAYSCALE)\n",
+    "    best_match = ''\n",
+    "    highest = 0\n",
+    "    for key, value in image_database.items():\n",
+    "        try:\n",
+    "            match, num_matches =  sift_match(img, key)\n",
+    "            if match:\n",
+    "                # label_html = 'Hello, {}'.format(value)\n",
+    "                if num_matches > highest:\n",
+    "                    highest = num_matches\n",
+    "                    best_match = value\n",
+    "        except:\n",
+    "            pass\n",
+    "    if len(best_match)>0:\n",
+    "        label_html = 'Hello, {}'.format(best_match)\n",
+    "        detected = True\n",
+    "    else:\n",
+    "        label_html = 'You seem to be new here...'\n",
+    "clear_output()\n",
+    "print(label_html)\n",
+    "final_frame = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)\n",
+    "plt.imshow(final_frame)\n",
+    "plt.title(label_html)\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xzxNryw9kQ6V"
+   },
+   "source": [
+    "# Measuring Accuracy on current database"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pKOVb_ZJZQVO"
+   },
+   "source": [
+    "## SIFT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 198
+    },
+    "id": "XaMZuIrykVCA",
+    "outputId": "54809fad-f5df-4b68-c1eb-dacfff5ad85f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Image_Sources</th>\n",
+       "      <th>Labels</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/1...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/2...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/3...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/4...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/5...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Accuracy is: 98.571 %\n"
-          ],
-          "name": "stdout"
-        }
+      "text/plain": [
+       "                                       Image_Sources  Labels\n",
+       "0  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/1...  Greeva\n",
+       "1  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/2...  Greeva\n",
+       "2  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/3...  Greeva\n",
+       "3  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/4...  Greeva\n",
+       "4  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/5...  Greeva"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MHDHi9-xZaDQ"
-      },
-      "source": [
-        "### Overall weighted F1 score: 99.5%"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ElIJ77sPZaDQ",
-        "outputId": "b8424a40-bf7a-423b-f5c3-7df7f75e531f"
-      },
-      "source": [
-        "from sklearn.metrics import f1_score\n",
-        "f1 = f1_score(y_test, y_cap, average='weighted')\n",
-        "print(\"F1 Score is:\",np.round(f1*100, 3), \"%\")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "F1 Score is: 99.248 %\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "execution_count": 20,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_sources = list(image_database.keys())\n",
+    "labels = list(image_database.values())\n",
+    "data = pd.DataFrame()\n",
+    "data['Image_Sources'] = image_sources\n",
+    "data['Labels'] = labels\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "kD89xMhhrnwW"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X = data['Image_Sources']\n",
+    "y = data['Labels']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5, random_state = 0, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NmmDk-NusDc0"
+   },
+   "outputs": [],
+   "source": [
+    "y_cap = []\n",
+    "for test_image_path in X_test:\n",
+    "  test_image = cv.imread(test_image_path,cv.IMREAD_GRAYSCALE)\n",
+    "  best_match = ''\n",
+    "  highest_matches = 0\n",
+    "  for train_image, label in zip(X_train, y_train):\n",
+    "    try:\n",
+    "      match, num_matches =  sift_match(test_image, train_image)\n",
+    "      if match and num_matches > highest_matches:\n",
+    "        highest_matches = num_matches\n",
+    "        best_match = label\n",
+    "    except:\n",
+    "      pass\n",
+    "      # print('Error')\n",
+    "  if len(best_match)<1:\n",
+    "    y_cap.append('Unknown')\n",
+    "  else:\n",
+    "    y_cap.append(best_match)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "oczK35jO5Vyv"
+   },
+   "outputs": [],
+   "source": [
+    "classes = list(set(y_test))\n",
+    "classes.append('Unknown')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Yt_xdOa6-xaz"
+   },
+   "source": [
+    "### Overall Accuracy : 95.71%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "EhxY1jf67_VN",
+    "outputId": "c8c6bf70-42eb-4701-979f-3dd3f4402f30"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy is: 95.714 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "print(\"Accuracy is:\", np.round(accuracy_score(y_test, y_cap)*100,3),\"%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "li6a8pj1-zl9"
+   },
+   "source": [
+    "### Overall weighted F1 score: 97.48%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HwmZgvt_7_X3",
+    "outputId": "4160090d-043b-483a-ed9f-fe92996398fa"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Score is: 97.479 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score\n",
+    "f1 = f1_score(y_test, y_cap, average='weighted')\n",
+    "print(\"F1 Score is:\",np.round(f1*100, 3), \"%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "K5HLd_nT-8Gc"
+   },
+   "source": [
+    "### Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6A301ECLqpMs"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "cf_matrix = confusion_matrix(y_test, y_cap, labels=classes) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "id": "4MEvJuFt3kIB",
+    "outputId": "e8550acc-9be1-4803-eab8-47e95c0cd1d5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "ax= plt.subplot()\n",
+    "sns.heatmap(cf_matrix, annot=True, ax = ax);\n",
+    "# sns.heatmap(cf_matrix, annot=True)\n",
+    "ax.set_xlabel('Predicted labels');\n",
+    "ax.set_ylabel('True labels');\n",
+    "ax.set_title('Confusion Matrix');\n",
+    "ax.xaxis.set_ticklabels(classes);\n",
+    "ax.yaxis.set_ticklabels(classes);\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Xx8E3dQ6HmIe"
+   },
+   "source": [
+    "## SVM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KNU30bwMH04x"
+   },
+   "source": [
+    "### Splitting our database for SVM training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "-HQqp7rDtXFh",
+    "outputId": "ca557113-15a6-4d7c-95e5-5b7e1457fba5"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        },
-        "id": "I_Zm-eHgfTL6",
-        "outputId": "4d3b5292-03cd-45a0-a7f8-68fd11b99193"
-      },
-      "source": [
-        "from sklearn.metrics import confusion_matrix\n",
-        "cf_matrix = confusion_matrix(y_test, y_cap, labels=classes)\n",
-        "import seaborn as sns\n",
-        "ax= plt.subplot()\n",
-        "sns.heatmap(cf_matrix, annot=True, ax = ax);\n",
-        "# sns.heatmap(cf_matrix, annot=True)\n",
-        "ax.set_xlabel('Predicted labels');\n",
-        "ax.set_ylabel('True labels');\n",
-        "ax.set_title('Confusion Matrix');\n",
-        "ax.xaxis.set_ticklabels(classes);\n",
-        "ax.yaxis.set_ticklabels(classes);\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+      "  warnings.warn(msg, category=FutureWarning)\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Wi2XkZpvZaDR"
-      },
-      "source": [
-        "### Confusion Matrix"
+     "data": {
+      "text/plain": [
+       "['/gdrive/MyDrive/BitCoders/trainedSVM.pkl']"
       ]
+     },
+     "execution_count": 35,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_paths = []\n",
+    "image_classes = []\n",
+    "class_id = 0\n",
+    "\n",
+    "main_dir = '/gdrive/MyDrive/BitCoders/Assignment2SVM'\n",
+    "train_path = os.path.join(main_dir, 'Train')\n",
+    "people = os.listdir(train_path)\n",
+    "\n",
+    "def imageList(path):\n",
+    "  return [os.path.join(path, file) for file in os.listdir(path)]\n",
+    "\n",
+    "for training_name in people:\n",
+    "  dir = os.path.join(train_path, training_name)\n",
+    "  class_path = imageList(dir)\n",
+    "  image_paths += class_path\n",
+    "  image_classes += [class_id]*len(class_path)\n",
+    "  class_id += 1\n",
+    "\n",
+    "des_list = []\n",
+    "\n",
+    "for image_path in image_paths:\n",
+    "  kpt, desc = sift(image_path)\n",
+    "  des_list.append((image_path, desc))\n",
+    "\n",
+    "descriptors = des_list[0][1]\n",
+    "for image_path, descriptor in des_list[1:]:\n",
+    "  descriptors = np.vstack((descriptors, descriptor))\n",
+    "\n",
+    "descriptors_float = descriptors.astype(float)\n",
+    "\n",
+    "from scipy.cluster.vq import kmeans, vq\n",
+    "\n",
+    "k=200\n",
+    "voc, variance = kmeans(descriptors_float, k, 1)\n",
+    "\n",
+    "image_features = np.zeros((len(image_paths), k), \"float32\")\n",
+    "for i in range(len(image_paths)):\n",
+    "  words, distance = vq(des_list[i][1], voc)\n",
+    "  for word in words:\n",
+    "    image_features[i][word] += 1\n",
+    "\n",
+    "#TF-IDF\n",
+    "occurences = np.sum((image_features>0)*1, axis=0)\n",
+    "idf = np.array(np.log((1.0*len(image_paths)+1) / (1.0*occurences+1)), \"float32\")\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler().fit(image_features)\n",
+    "image_features = scaler.transform(image_features)\n",
+    "\n",
+    "from sklearn.svm import LinearSVC\n",
+    "clf = LinearSVC(max_iter=10000)\n",
+    "clf.fit(image_features, np.array(image_classes))\n",
+    "\n",
+    "from sklearn.externals import joblib\n",
+    "joblib.dump((clf, people, scaler, k, voc), '/gdrive/MyDrive/BitCoders/trainedSVM.pkl', compress=3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "G856Z82vH76w"
+   },
+   "source": [
+    "### Fitting SVM Classifier and plotting Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 281
     },
+    "id": "2LRAiqb1TA0m",
+    "outputId": "b4c2d9b2-5101-44a1-ae18-aa1f15ec100d"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "TADP0GkUZaDR"
-      },
-      "source": [
-        "from sklearn.metrics import confusion_matrix\n",
-        "cf_matrix = confusion_matrix(y_test, y_cap, labels=classes) "
-      ],
-      "execution_count": null,
-      "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy =  0.9855072463768116\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        },
-        "id": "pcCTstL1ZaDS",
-        "outputId": "6c8640a7-9418-445e-c117-ae88dccdddb1"
-      },
-      "source": [
-        "import seaborn as sns\n",
-        "ax= plt.subplot()\n",
-        "sns.heatmap(cf_matrix, annot=True, ax = ax);\n",
-        "# sns.heatmap(cf_matrix, annot=True)\n",
-        "ax.set_xlabel('Predicted labels');\n",
-        "ax.set_ylabel('True labels');\n",
-        "ax.set_title('Confusion Matrix');\n",
-        "ax.xaxis.set_ticklabels(classes);\n",
-        "ax.yaxis.set_ticklabels(classes);\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "model = SVC()\n",
+    "ovo = OneVsOneClassifier(model)\n",
+    "ovo.fit(image_features, np.array(image_classes))\n",
+    "\n",
+    "y_train =  [people[i] for i in image_classes]\n",
+    "yhat = [people[i] for i in ovo.predict(image_features)]\n",
+    "\n",
+    "accuracy = accuracy_score(y_train, yhat)\n",
+    "print (\"accuracy = \", accuracy)\n",
+    "cm1 = confusion_matrix(y_train, yhat)\n",
+    "plt.matshow(cm1)\n",
+    "plt.title('Confusion matrix')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ztQCJ7SaQOyK"
+   },
+   "outputs": [],
+   "source": [
+    "y_train =  [people[i] for i in image_classes]\n",
+    "y_train_pred =  [people[i] for i in clf.predict(image_features)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 281
     },
+    "id": "UL5-iFnMLAC1",
+    "outputId": "eaf9b09b-8f18-4d96-8d1f-4fc1cb7be0c3"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vP9Beqr3BqAK"
-      },
-      "source": [
-        "# Sample Outputs of our SIFT algorithm"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy =  0.9714285714285714\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 177
-        },
-        "id": "NfzFWropB-sv",
-        "outputId": "dc16b5cf-c06f-4fc2-ffbd-cdcf6330a6c3"
-      },
-      "source": [
-        "test_image = cv.imread(X_test[131],cv.IMREAD_GRAYSCALE)\n",
-        "match, num_matches =  sift_match(test_image, X_train[120], plot=True)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD2CAYAAAD720p7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUQElEQVR4nO3df7BcZX3H8fcnCfkFCRgTKSRoQCGOZiQgIsKoqKARqfhHO0WBqlWjtlosOBbQKfirta21OJXaRkEYoShFVIpUCAp1aCWQQERIECmoBIIJVCAgJrn3fvvHOctsknvvPrv32d2z535eM2dy9+45z/lu5n73ec7zPOc8igjMrJ6m9DsAM+seJ7hZjTnBzWrMCW5WY05wsxpzgpvVmBM8E0mzJP2HpCck/fsEyjlF0vU5Y+sXSa+W9LN+xzGZabKNg0t6B3AG8GJgK7AO+GxE3DzBck8DPgwcHRFDEw604iQFcHBE3NfvWGxsk6oGl3QGcD7w18C+wPOBfwZOylD8C4B7J0Nyp5A0rd8xGBARk2ID9gaeAv5wnH1mUHwBPFxu5wMzyveOBTYCZwKbgU3Au8v3PglsB3aU53gPcB5waVPZi4EAppWv3wXcT9GKeAA4pen3NzcddzRwG/BE+e/RTe/dBHwa+O+ynOuB+WN8tkb8H2uK/23ACcC9wP8B5zTtfyTwY+Dxct8vAdPL935Ufpany8/7R03l/yXwCPD1xu/KY15YnuPw8vX+wBbg2H7/beTY3njs7Hj5y2YkbcD3e/Z33+//mJ59UFgODDUSbIx9PgXcAjwPWAD8D/Dp8r1jy+M/BexRJsZvgeeU7++a0GMmOLAn8CSwpHxvP+Cl5c/PJjgwD/gNcFp53NvL188t378J+F/gEGBW+fpzY3y2Rvx/Vcb/vjLB/g2YA7wUeAY4sNz/5cBR5XkXAxuAjzSVF8CLRin/bym+KGc1J3i5z/uA9cBs4Drg8/3+u8i1Hf6yGbFj0wuTNmBNr+KaTE305wKPxvhN6FOAT0XE5ojYQlEzn9b0/o7y/R0RcS1F7bWkw3hGgKWSZkXEpoi4e5R93gL8PCK+HhFDEXE5cA/w+037fC0i7o2IZ4ArgGXjnHMHRX/DDuAbwHzgixGxtTz/euBQgIhYGxG3lOf9BfCvwGsTPtO5EbGtjGcnEfEV4D5gNcWX2sdblDdAguEYSdp6aTIl+GPA/BbXhvsDv2x6/cvyd8+WscsXxG+BvdoNJCKepmjWfgDYJOl7kl6cEE8jpoVNrx9pI57HImK4/LmRgL9uev+ZxvGSDpF0jaRHJD1J0W8xf5yyAbZExO9a7PMVYCnwTxGxrcW+AyOAESJp66XJlOA/BrZRXHeO5WGKzrKG55e/68TTFE3Rht9rfjMirouI4ylqsnso/vBbxdOI6aEOY2rHlyniOjgi5gLnAGpxzLh/vZL2oujXuBA4T9K8HIFWQRDsiOGkrZcqmeCSlkv6maT7JJ2Vo8yIeILi+vMCSW+TNFvSHpLeLOnvyt0uBz4haYGk+eX+j0vaDHytzVOuA14j6fmS9gbObrwhaV9JJ0nak+JL5ymK5u2urgUOkfQOSdMk/SnwCuBsSXezc02e2xyKfoKnytbFB3d5/9fAEkm3SvoJxf/P3BZlfpHi+vO9wPeAf8kcMwCSpkq6Q9I13Sh/LK7BE0iaClwAvBl4CfB2SS/JUXZE/APFGPgnKDqYHgQ+BHyn3OUzwBrgTuCnwO3ARyk66No91yrgm2VZa4HmP7YpZRwPU/Qsv5bdE4iIeAw4kaLn/jHg/cD7I2IJRQfYQooWQDd8FHgHRe/8V8rP0uw84KsUHXyfBd4LzJB01GiFSTqJ4v+x8TnPAA6XdEr2yOF0ik7BnglgmEjaeqlyE10kvQo4LyLeVL4+GyAi/qaPMS0GromIpf2KYTSSvgt8qfwy6Xcss4GbgQ9GxOo+xrEIuITiS+eMiDixF+ddduj0WPWfC5L2fd7Ch9dGxBFdDgmoYA1OUSs92PR6I91tig6k8kvnMIoe6X7GMVXSOoqx9VX9TO7S+RRj/T3trg5gOCJp66UqJri1UHZWfYtiXPrJfsYSEcMRsQxYBBwpqW+tHEknApsjYm0/zj+SuKWQdJGkzZLu2uX3H5Z0j6S7m/qOxlTFBH8IOKDp9SJ602s8ECTtQZHcl0XEVf2OpyEiHgdupIP+ioyOAd4q6RcU4/yvl3RpL04cidffbVyDX8wu/5eSXkcxrfrQiHgp8PlWhVQxwW8DDpZ0oKTpwMnA1X2OqRIkiWKIaUNEfKEC8SyQtE/58yzgeIqhtb6IiLMjYlFELKb4u/lhRJzam3PDjsQtrbz4EUUHbLMPUsxU3Fbus7lVOZVL8HIiyYcopjJuAK4YY5ZXT0i6nGIMfYmkjZLe069YKGqo0yhqpnXldkIf49kPuFHSnRRfzKsioqdDU9UhhhO3CTgEeLWk1ZL+S9IrWh1QyTt+ymmg1/Y7DoCIeHu/Y2iI4pbWCf2F5BQRd1J09FVORNxEMTe/N+cDRtL7z+ZLWtP0emVErEw4bhrF/QlHUcyHuELSQTHOUFglE9xsELVROz/a4TDZRuCqMqFvlTRCMX14y1gHVK6JbjaIiokuXW+ifwd4HRT3CgDTgUfHO8A1uFkmI5Hv6qns+zmWojm/ETgXuAi4qBw62w68c7zmOTjBzbJo1ODZyhu776etUQEnuFkGgdgRU/sdxm4qew0uaUW/Y2jmeMZXpXj6EUuPrsHbVtkEByrzB1NyPOOrUjx9iEUMx5SkrZfcRDfLoHiiS/Xqy64k+NQ5e8a0BftMrIz5ezPjoIVZbr2Z8cBujwdr20xmM1fzKnNvreMZW65YfsfTbI9tyW3qXje/U3Qlwact2If9P/Nn3Si6Iy869Y5+h2ADaHX8IHnfCPW8+Z3CTXSzTEYmSw1uNtkEYntUL52qF5HZAJpUnWxmk9FwxqmquTjBzTIIxLBrcLP6GnEvulk9FVNVneBmtVTVm02c4GYZROCJLmb1JU90MaurYmWT6tXg1YvIbEANMyVpSzHWyible2dKinIF3HElna0by/ma1UkgRiJtS3Qxo6wSI+kA4I3Ar1IKaZng3VzO16xOctbgY6xsAvCPFIsrJt0Om3INfiRwX0TcDyDpGxTrI61PitRsEujFMFm5xvpDEfGTYhWr1lISfLTlfF/Zfnhm9VWsbJLcpdX2yibl+uvnUDTPk2XrRS8fdLcCiqexmE02XV7Z5IXAgUCj9l4E3C7pyIh4ZKyDUhI8aTnf8htoJZDtUUtmgyJCXZ2LHhE/BZ7XeF0ukXxERIy7sklKRF7O1yxBzqeq5lrVtmUNHhFDkhrL+U4FLurncr5mVVQ88KEnK5s03l+cUk7SNXiVlvM1qyY/dNGstgJ8N5lZXTVmslWNE9wsEz900aymivvBXYOb1Zab6GY1VVyDu4luVluTZvFBs8kmEEMjHiYzqy0/k82sptyLblZz7mQzq6lJNZNtxgPP8KJT7+hG0R057q6t/Q5hJze+7qB+h/Cs4S1b+h1Cbfga3Kymikc2OcHN6ik8TGZWW7kf+JBL9br9zAZUzoUPRlvZRNLfS7pH0p2Svi1pn1blOMHNMmhcg3d5ZZNVwNKIeBlwL3B2q0Kc4GaZ5Ezw0VY2iYjrI2KofHkLxROOx+VrcLMM+jAO/ifAN1vt5AQ3yyFgqIsrmzST9HFgCLis1b5OcLMM2hwH72RlEwAkvQs4EXhDRLRcYMQJbpZJt5vokpZTrCz62oj4bcoxTnCzDHJfg5crmxxL0ZzfCJxL0Ws+A1hVrk92S0R8YLxynOBmmUTGBB9jZZML2y3HCW6WSRVnsjnBzTKIqObNJi379UebMmdmuxLDI1OStl5KOdvF7D5lzsx2EaGkrZdSlg/+kaTF3Q/FbHDV/n5wSSuAFQAzmZ2rWLPBEMV1eNVkS/Byqt1KgLmaV8GPatZd7kU3q6kg7zh4Lk5wsyyq+VTVlGGyy4EfA0skbZT0nu6HZTZ4RkaUtPVSSi/6aFPmzKxJhJvoZrVWxSa6E9wsk1oPk5lNdm6im9VU0PtpqCmc4GaZVLCF7gQ3yyIgejwElsIJbpZJFZvoXvjALJOItC3FGEsXzZO0StLPy3+f06ocJ7hZBo256BnvB7+Y3Z/DcBbwg4g4GPhB+XpcTnCzHAIIpW0pxY2ydBFwEnBJ+fMlwNtaleNrcLNMejDRZd+I2FT+/Aiwb6sDnOBmuaQn+ISWLgKIiJDUn5VNNG0aU+ct6EbRHblhab8j2Nlxd93f7xCedcPSOf0OoSbUzjBZp0sX/VrSfhGxSdJ+wOZWB/ga3CyH6MlDF68G3ln+/E7gu60OcIKb5RKJW4IxnsPwOeB4ST8Hjitfj8vX4GbZdH3pIoA3tFOOE9wslwpORneCm+XiBDerKd9sYlZzrsHNaqyCd5M5wc0yaT2vrPec4GY5tDHG3UtOcLMs0u8U6yUnuFkursHNamyk3wHsLmVtsgMk3ShpvaS7JZ3ei8DMBkrmBz7kklKDDwFnRsTtkuYAayWtioj1XY7NbKBUsRe9ZQ0eEZsi4vby563ABmBhtwMzGzgZ7ybLpa3bRSUtBg4DVncjGDPLK7mTTdJewLeAj0TEk6O8vwJYATBzyl7ZAjQbFAPZRAeQtAdFcl8WEVeNtk9ErIyIIyLiiOlTZuWM0WwwDGInmyQBFwIbIuIL3Q/JbAAFgzlMBhwDnAa8XtK6cjuhy3GZDRxF2pZUlvQX5bD0XZIulzSzk5ha1uARcTM5n0VjVleZrsElLQT+HHhJRDwj6QrgZIrVTtrimWxmueTtZJsGzJK0A5gNPNxJIX6qqlkGqc3zlCZ6RDwEfB74FbAJeCIiru8kLie4WS7pvejzJa1p2lY0F1OuGnoScCCwP7CnpFM7CclNdLNc0pvorVY2OQ54ICK2AEi6CjgauLTdkJzgZpko3zDZr4CjJM0GnqF4Fvqa8Q8ZnRPcLIc2hsBaFhWxWtKVwO0UN3vdAbS1OGGDE9wsl4y96BFxLnDuRMtxgpvlUsG56E5ws0wG9mYTMxtMrsHNcqlgDe4EN8shsg6TZdOVBI+hIYa3bOlG0bVww9I5/Q7hWdc9vK7fIezkTfsv63cInXMNblZPopqdbE5ws1yc4GY1lXEmW05OcLNcnOBm9TVpetHNJiXX4GY15fXBzerNnWxmdeYEN6sv1+BmdVbBBPftomYZ5HxsMoCkfSRdKekeSRskvaqTuFyDm+WStwb/IvD9iPgDSdMpFj9omxPcLJNc1+CS9gZeA7wLICK2A9s7KatlE13STEm3SvpJuRjaJzs5kVntReLW2oHAFuBrku6Q9FVJe3YSUso1+Dbg9RFxKLAMWC7pqE5OZlZr6Qk+7somFC3rw4EvR8RhwNPAWZ2ElLK6aABPlS/3KLcK9hea9VF7d5O1WtlkI7AxIlaXr6+kwwRP6kWXNFXSOmAzsKrpxGbWkKmJHhGPAA9KWlL+6g3A+k5CSupki4hhYJmkfYBvS1oaEXc171M2M1YAzOysw89soGW+m+zDwGVlD/r9wLs7KaStXvSIeFzSjcBy4K5d3ltJubzKXM1zE94mnZwz2SJiHTBeMz5JSi/6grLmRtIs4Hjgnome2KxWUpvnPa76Umrw/YBLJE2l+EK4IiKu6W5YZgOogu3WlF70O4HDehCL2cDyU1XN6s4JblZfiupluBPcLIfJtHSR2aRUvQrcCW6WizvZzOrMCW5WU166yKzmnOBm9eSJLmY1p5HqZbgT3CwHL11kVm+e6GKV86b9l/U7hJ0svnVWv0N41ro/VnsHuAY3q68qdrJ5ZROzHAKISNsSlc9CvENSx89fcA1ulkkXrsFPBzYAczstwDW4WQaNcfCMa5MtAt4CfHUicbkGN8uhzeZ3gvOBjwFzJlKIa3CzTNqowcdd2UTSicDmiFg70Zhcg5vlkm9lk2OAt0o6AZgJzJV0aUSc2m5IrsHNMsl1DR4RZ0fEoohYDJwM/LCT5AbX4GZ5BOC56Gb11Y2pqhFxE3BTp8c7wc1y8VNVzeqrilNVneBmOfh2UbP6KmayVS/Dk4fJckx8N6u1kcSth9qpwSc88d2szga2Bs818d2stiKKcfCUrYdSa/AsE9/N6qyKvegta/DUie+SVjQmz+9gW7YAzQZG5gc+5JBSgydNfI+IlcBKgLmaV8HvMrMuqujqoi1r8JwT381qbUBrcDNLUcF2a1sJPtGJ72Z1VsVhMtfgZjkEMOwEN6slEa7BzWrNCW5WY05ws5oKen4jSQo/dNEsE0UkbS3LkQ6QdKOk9ZLulnR6pzG5BjfLJV8TfQg4MyJulzQHWCtpVUSsb7cgJ7hZDhEwkqeNHhGbgE3lz1slbQAWAk5ws77pwjW4pMXAYcDqTo53gptl0sY4+HxJa5peryxv1tq5PGkv4FvARyLiyU5icoKb5ZKe4K2WLkLSHhTJfVlEXNVpSE5wsxwyrmwiScCFwIaI+MJEyupKgm/lN4/eEFf+coLFzAcezRFPJo5nfHniecXEAyHf/80L0nfNeivoMcBpwE8lrSt/d05EXNtuQV1J8IhYMNEyJK1p1YzpJcczvirF07dYMiV4RNxM8STmCXMT3SyHAIarN5XNCW6WRUA4wdux27BBnzme8VUpnv7E4ptN0o02LthPjmd8VYqnL7F4fXCzmnMNblZjTnCzmoqA4eF+R7EbJ7hZLq7BzWrMCW5WV71fOTSFE9wsh4DwRBezGnMNblZjvgY3qykPk5nVW2R66GJOTnCzLHq/9ncKJ7hZDhW92cQrm5jlEiNpWwJJyyX9TNJ9ks7qNCTX4GYZBBD5Hro4FbgAOB7YCNwm6epOVjZxDW6WQ0TOGvxI4L6IuD8itgPfAE7qJCzX4GaZRL5hsoXAg02vNwKv7KQgJ7hZBlv5zXU3xJXzE3efmbKySQ5OcLMMImJ5xuIeAg5oer2o/F3bfA1uVj23AQdLOlDSdOBk4OpOCnINblYxETEk6UPAdcBU4KKIuLuTshQVnH1jZnm4iW5WY05wsxpzgpvVmBPcrMac4GY15gQ3qzEnuFmNOcHNauz/Aae8jYTooEATAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 177
-        },
-        "id": "dm_2WLEIDRrA",
-        "outputId": "bc518e18-efde-4ffc-f594-5241ed9d462c"
-      },
-      "source": [
-        "test_image = cv.imread(X_test[20],cv.IMREAD_GRAYSCALE)\n",
-        "match, num_matches =  sift_match(test_image, X_train[33], plot=True)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+    "\n",
+    "clf1, class_names1, stdScalar1, k1, voc1 = joblib.load('/gdrive/MyDrive/BitCoders/trainedSVM.pkl')\n",
+    "test_path = os.path.join(main_dir, 'Test')\n",
+    "test_classes = os.listdir(test_path)\n",
+    "\n",
+    "test_image_paths = []\n",
+    "test_image_classes = []\n",
+    "test_class_id = 0\n",
+    "\n",
+    "for test_class in test_classes:\n",
+    "    dir = os.path.join(test_path, test_class)\n",
+    "    class_path = imageList(dir)\n",
+    "    test_image_paths+=class_path\n",
+    "    test_image_classes+=[test_class_id]*len(class_path)\n",
+    "    test_class_id+=1\n",
+    "\n",
+    "test_des_list = []\n",
+    "for image_path in test_image_paths:\n",
+    "  kpt, desc = sift(image_path)\n",
+    "  test_des_list.append((image_path, desc))\n",
+    "\n",
+    "test_descriptors = test_des_list[0][1]\n",
+    "for image_path, descriptor in test_des_list[0:]:\n",
+    "  test_descriptors = np.vstack((test_descriptors, descriptor)) \n",
+    "\n",
+    "test_features = np.zeros((len(test_image_paths), k1), \"float32\")\n",
+    "for i in range(len(test_image_paths)):\n",
+    "    words, distance = vq(test_des_list[i][1],voc1)\n",
+    "    for w in words:\n",
+    "        test_features[i][w] += 1\n",
+    "\n",
+    "nbr_occurences = np.sum( (test_features > 0) * 1, axis = 0)\n",
+    "idf = np.array(np.log((1.0*len(test_image_paths)+1) / (1.0*nbr_occurences + 1)), 'float32')\n",
+    "\n",
+    "test_features = stdScalar1.transform(test_features)\n",
+    "y_test =  [class_names1[i] for i in test_image_classes]\n",
+    "y_test_pred =  [class_names1[i] for i in clf1.predict(test_features)]\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_test_pred)\n",
+    "print (\"accuracy = \", accuracy)\n",
+    "cm = confusion_matrix(y_test, y_test_pred)\n",
+    "plt.matshow(cm)\n",
+    "plt.title('Confusion matrix')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "f-Izp6mpV_Cn"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import label_binarize\n",
+    "\n",
+    "y = label_binarize(y, classes=people)\n",
+    "n_classes = y.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bQJ1Qz6MWzMf"
+   },
+   "outputs": [],
+   "source": [
+    "y_test_bin = label_binarize(y_test, classes=people)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "K-zxA40rVfjx"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import svm\n",
+    "random_state = np.random.RandomState(0)\n",
+    "\n",
+    "classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,\n",
+    "                                 random_state=random_state))\n",
+    "y_score = classifier.fit(image_features, y_train).decision_function(test_features)\n",
+    "\n",
+    "# Compute ROC curve and ROC area for each class\n",
+    "fpr = dict()\n",
+    "tpr = dict()\n",
+    "roc_auc = dict()\n",
+    "for i in range(n_classes):\n",
+    "    fpr[i], tpr[i], _ = roc_curve(y_test_bin[:, i], y_score[:, i])\n",
+    "    roc_auc[i] = auc(fpr[i], tpr[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wfiU1GLvIFdx"
+   },
+   "source": [
+    "### ROC Curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 677
+    },
+    "id": "41zvagluVfc_",
+    "outputId": "a149959f-cfa0-4a1d-d3c6-f5f75d099e7d"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n",
+      "  del sys.path[0]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAJcCAYAAACixjPMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZyN5f/H8dc1w1jGIAzKXiZCoyIlS7KMvZIiFVLSQhK+0u6rfm1fFdJur+yVPcwkFW0khIQIY8mEMZYx6/X744wxwwwHc8595pz38/G4H851n/vc93vGMh/Xfd3XZay1iIiIiIhvCXI6gIiIiIicSUWaiIiIiA9SkSYiIiLig1SkiYiIiPggFWkiIiIiPkhFmoiIiIgPUpEmIiIi4oNUpImITzDG/G2MSTTGHDXG7DPGTDTGFDvtmJuMMUuNMUeMMYeNMfOMMbVOO6a4MWakMWZnxrn+ymiXyeW6xhjT3xiz3hhzzBgTa4yZaYy52pNfr4jIuahIExFf0tFaWwy4BrgWePrkG8aYhsASYA5wGVANWAusMMZcnnFMCPA1UBtoAxQHGgIHgAa5XHMU8ATQHygFXAnMBtqfb3hjTIHz/YyISG6MVhwQEV9gjPkb6G2tjclovwHUtta2z2h/D/xurX3stM99BcRZa3sYY3oD/wdcYa096sY1I4BNQENr7S+5HLMM+NRaOzajfX9GzsYZbQv0AwYABYBFwDFr7eAs55gDfGutfcsYcxnwDtAUOAq8ba0d7ca3SEQCjHrSRMTnGGMqAm2BrRntosBNwMwcDp8BtMp43RJY5E6BlqEFEJtbgXYebgduAGoBU4GuxhgDYIy5BIgCphljgoB5uHoAK2Rcf4AxpvVFXl9E/JCKNBHxJbONMUeAXcB+4MWM/aVw/Xu1N4fP7AVOjjcrncsxuTnf43PzqrX2oLU2EfgesECTjPfuBH601u4BrgfCrbXDrbXJ1tptwMfA3XmQQUT8jIo0EfElt1trw4BmQE1OFV+HgHTg0hw+cynwb8brA7kck5vzPT43u06+sK4xJNOAbhm77gE+y3hdBbjMGBN/cgOeAcrlQQYR8TMq0kTE51hrvwUmAiMy2seAH4G7cji8C66HBQBigNbGmFA3L/U1UNEYU/8sxxwDimZpl88p8mntqcCdxpgquG6Dfp6xfxew3VpbMssWZq1t52ZeEQkgKtJExFeNBFoZY+pmtIcCPTOmywgzxlxijHkZ19Ob/8045hNchdDnxpiaxpggY0xpY8wzxpgzCiFr7RbgPWCqMaaZMSbEGFPYGHO3MWZoxmFrgDuMMUWNMdWBB88V3Fr7G67evbHAYmttfMZbvwBHjDFPGWOKGGOCjTF1jDHXX8g3SET8m4o0EfFJ1to4YDLwQkZ7OdAauAPXOLIduKbpaJxRbGGtTcL18MAmIBpIwFUYlQF+zuVS/YExwLtAPPAX0AnXAH+At4Fk4B9gEqduXZ7LlIwsU7J8TWlAB1xTjGznVCFXws1zikgA0RQcIiIiIj5IPWkiIiIiPkhFmoiIiIgPUpEmIiIi4oNUpImIiIj4oHy3GHCZMmVs1apVnY4hIiIick6//vrrv9ba8Av5bL4r0qpWrcqqVaucjiEiIiJyTsaYHRf6Wd3uFBEREfFBKtJEREREfJCKNBEREREfpCJNRERExAepSBMRERHxQSrSRERERHyQijQRERERH6QiTURERMQHqUgTERER8UEq0kRERER8kIo0ERERER+kIk1ERETEB6lIExEREfFBKtJEREREfJCKNBEREREfpCJNRERExAepSBMRERHxQSrSRERERHyQijQRERERH6QiTURERMQHqUgTERER8UEeK9KMMeONMfuNMetzed8YY0YbY7YaY9YZY67zVBYRERGR/KaAB889ERgDTM7l/bZARMZ2A/B+xq8iIiIizlq2k6SB31BpzY7MXSHGEJv2fI6Hf/zAPJ6d+Ftmu3f9irzyywMXFcFYay/qBGc9uTFVgfnW2jo5vPchsMxaOzWj/SfQzFq792znrF/J2FUDPBBWREREJI+Zwfxqra1/IZ91ckxaBWBXlnZsxr4zGGP6GGNWGWNWeSWZiIiIyAU6eBz6zISEExd3Hk/e7swz1tqPgI/A1ZPGIM/1/on4lTeN61f9nRE/l/EnnUD+k67vwYW56aZxWAt1uncC4OPHSmW+l5SUSuHC/5fZDgGS7Is5nmf0fbN54rO1QALwKbCfxIj7Ml5fGCeLtN1ApSztihn7RERERDxu5crd/PhjLAB1urv2JSQkUbx4IQBCQoL555/Bbp2r90ftubH/Fdx1163s3LmfGjVq8uqrr/Lpp/mzSJsL9DPGTMP1wMDhc41HExEREckro0b9fMa+kwUagDGGsmVD3TrX1q1/cNttrdm3bx/XX389CxcupEyZMheVz2NFmjFmKtAMKGOMiQVeBAoCWGs/ABYC7YCtwHGgl6eyiIiIiGSVnm5JSEjKk3P98MMPtG/fnvj4eJo3b87s2bMJCwu76PN6rEiz1nY7x/sW6Oup64uIiIjkJijIMHduNzZt+pd33vmZ5JNvLNsJg5fBjgT4z/Uw5Nyzg02bNo34+Hg6derElClTKFy4cN5kzJOziIiIiORDNWuW4d1325/acbJAK1cUyofC5PWu7SzefvttPvjgA2bMmJFnBRqoSBMRERE5ZUeC69d/jsOgZa5t9OozDps2bRoHDx4EIDg4mIcffpgCBfL2BqWKNBEREZGTutdybSdVKQ4jmmU2rbW89tprdOvWjQ4dOpCamuqxKPlinjQRERERr3irefZfs7DWMmTIEEaMGIExhh49euR571lW6kkTERGRgBETs43ExJTz/lxqaiq9e/dmxIgRFChQgKlTp/LII494IOEpKtJEREQkIOzadZg2bT6lYsW3efrpGHbtOuzW55KSkujatSvjx4+nSJEizJs3j65du3o4rYo0ERERCRDvvbeStDTLwYOJvPbaCjp2nOrW58aNG8cXX3xBiRIliI6Opk2bNh5O6qIxaSIiIuL3jh9P4aOPsj+l+dhj17terN1PZkkUPgYiw+HrUz1ljzzyCJs3b6ZXr17UrVvXS4nVkyYiIiIBIDk5jd69r+WSS1zzmF1ySWHuuy8y1+N3795NXFwcAEFBQYwcOdKrBRqoJ01EREQCQMmShXn99Va88MLNfPbZ7yQlpVK0aMEcj91yfC+tGjWiTJkyLF26lOLFi3s5rYuKNBEREQkYoaEh9OlTL9f316TG0vrXD9ifcpjy5ct7dB60c1GRJiIiIoFj7f7s7bplXduKg+zb+hPN7AccTjlMy5Yt+fLLLylWrJgzOVGRJiIiIoGk5Yzs7bh+AOxaH03M2F6kpSRyxx13MGXKFAoVKuRAwFP04ICIiIgEtDVr1rDkw/tIS0nkwQcfZPr06Y4XaKAiTURERPzU0aPJDBy4mM2bD5z1uLp163Lljd24uuXjfPzxxx5d6ul8+EYKERERkTw2adIa3n77J95++yfatq3OkCGNaBYZDrjW4TyadoIwwBhD425vYYKCMMY4GzoL9aSJiIiI30lPt0yevC6z/dVXW/n6623wdVfSo+9iYN2faXToLQ4dOgSACfK9ksj3EomIiIicRVpaOtu3H2Lx4q2MGfMLP/8ce8YxcXHH+PLLrplzoRUoEMSjj15PamoqDzzwACNHjmTTpk2sXLnS2/HdptudIiIi4nOstbneenz++W949dXlme1nnmnMDTdUzHZMuXKuqTOqFw1h3fEUulQvTanXVnDnzg+YM2cORYsW5YsvviAqKspzX8RFUpEmIiIijktKSuX5579hy5aDbNlygP37j/HPP4NzLNQiIkpla2/ZcjDX80YkpLAVeHJvAu0+6M83KVsoWbIkCxcupGHDhnn9ZeQpFWkiIiLiMdZa/vnnGFu2HGDLloNcfXVZrr++whnHhYQE8/77qzh6NDlz3759R7n00rAzjo2IKJ2tfbYirX+RwgwrGkyvo++yKnUn5cuXZ8mSJVx99dUX8VV5h4o0ERER8Zjhw79l2LBvM9tPPdUoxyLNGMOVV5Zm9eq9mfu2bDmYS5FWivDwokRElCYiohSRkeVyvX7TggWxtgA3FqjKwfTjRK9YweWXX36RX5V3qEgTERGR8/Lzz7EsXLgl49bkQXbvTiA2diBBQWfemqxW7ZJs7bPemowola1I2779EE2bVjnjuHLlirF//3/OmdNai3mzGQYYlX4zw44nUDqfFGigIk1ERETOQ0JCEl26zGLnzsPZ9u/Zc4SKFYufcfzp48fONrHsQw9dR4cOVxIRUYqIiNKUKlXkgnOuXr2aJ598klmzZhEeHk4QUPqcn/ItKtJERETEbcWLF+LHHx+kQ4cp/Pbbvsz9W7YcyKVIK02xYiFUr16KiIhSXH112VzP3aJF3vRyfffdd3Ts2JGEhAReffVV3nrrrTw5r7epSBMREZHzctllYXz3XS/uumsmixZtBVy3MW+5pdoZx5YuXYSEhKFem8l/folHuCthPCdIoUvItbz22mteua4nqEgTERGR8xYSEkzfvtczZMhNRESU5rLLzhzgD3h1maXPPvuMngkfk0Y6fQo34r3QLgSHhHjt+nlNRZqIiIict5CQYDp0uNLpGJneeecd+vfvD8DTRVrxf0U7+tQ6nBdCy0KJiEi+0R4wOWwn5fReoGyekJaWzvvvryQlJc1DV8g7u3btAuB/RW/nldBb832BBupJExGRfGSh0wF8XLs8Pt/gwUsYOfJnvvhiE7Nm3UWJEoXz+AoXaOBS+GTjqfabzXj99dfp0KEDTZs2dS5XHlNPmoiI5Dv2tC23/YG2Lbiwb2eOxoz5hZEjfwYgJmYbjRqNP2PaDSel2jSGHpvD3nRXJmOMXxVooJ40EREROc2JE6mMHv1ztn2HDp3IcbJaJySmJnH3kXHMTf6d71L+YoW91WO3fJ2knjQRERHJpnDhAqxY8QA33lgRgNDQgsyf3y3HedC8LSEhgbZfPs3c5N+5xBTl7dA7/GL8WU7UkyYiInmmPd4ZN+afP5J9S3h4KEuX9qBXrzl07x7Jtdde6nQk4uLiaNOmDatj13LZZZexZMkSateu7XQsj1GRJiIiecbJgf15PWheoEiRgkybdqfTMQDYuXMnUVFR/Pnnn1xxxRVER0dTrdqZk+f6E93uFBGRPOepgfFnO39eDpoPFNZafvoplv79v2Lv3iNOxzmrmTNn8ueffxIZGcny5cv9vkAD9aSJiIgEpI8//pXXXlvBtm2HALh8/HoGvNcWetTJ+QPhY7K34/rlfNzk9TBo2al291rwVvOcj20xHdbFnWrHdIG6OaztuXY/A18NoWBoZ3qUaEfJe7+Fr7vmfE4/op40ERGRAJSQkJRZoAF8lpTkYJqcfffdd5mT1Bpj6F+kGSX/OJ69sPNjKtJERET81KFDiaSlpef43t131yHrQ5GrUtPY/NIKLyU7t7lz5xIVFUVUVBSHEuKzv1nF+adMvUFFmoiIiB9JTExh5swNdOo0nfLl3+Tbb3fkeFyFCsVp1qwqhYDOISF8EVaMKoeSvRs2F5MnT+aOO+4gKSmJZs2aUTw0y+LtVYrDiGaOZfMmjUkTERHxI/36LWT8+DWZ7c8+W0fz5jkPsv/ggw6UG/UrJQq5UQ7kNgbtdD3q5D6u7XQ5jCsbNWoUAwYMAODZZ5/lpZdecs2D5u71/YiKNBERkXzgm2+289tv+9i9O4HY2CMMGtSQBg0qnHHcXXfVzlakff75H7z7bnsKFz7zR/6VV5aGd6M8mttd1lqGDRvG8OHDAXjzzTcZOHCgw6mcpSJNRETEIQkJSfz44y527z5CbGwCJUoU4oknbszx2LFjf2PKlN8z261bX5Fjkday5eWEhxclLu444JqU9u+/46lZs4xnvog8EhMTw/DhwwkKCmLs2LH06tXL6UiOU5EmIiKSx/buPcLOnYeJjU1g9+4j3HVXLS69NOyM47ZuPUibNp9ltmvXDs+1SKtQIfvnd+9OyPG4AgWC6Nv3euLjT3DPPVdTv/5l+WLZpJYtW/LMM89Qv359OnXq5HQcn6AiTURExE1Hjyaze7er8CpSpAANG1bK8bhu3T7PNmC/Ro3SORZpZxZeuU8oez7Hvvhis1zfY+1+aDnjVDsy3LE5xxITE/n333+pVKkSxhj+7//+z5EcvkpFmoiIBLz0dEtc3DF27z5C7drhFMphIP2CBZvp0GFqZjsq6goWL74vx/NVqJB9iojcCqrw8FAKFgwiJcU1TUZ8/AmOHUsmNDTkjGMbNKhA377XU6FCGBUqFCcyspzbX58vOnz4MB07dmTPnj0sX76c8uXLOx3J56hIExERv3biRConTqRSsmThHN+/5ZZJrFixM7NQWr/+UWrXPnPW+9N7wnK73QhQsaJ7xwYFGTp1uoqCBYMyi6/cNGxYKdeeu/zmn3/+oU2bNqxZs4YKFSoQHx+vIi0HKtJERMTvLFiwmWefXUpsbAIHDiTywAPXMG7cbTkea63NLNAAYmMTcizSzud245VXlqZu3XJUrFicChXCuO66S3M9dvp031jA3Ft27NhBq1at2LJlCxEREURHR1OlShWnY/kkFWkiIuJ30tIsa9f+k9mOjT3bWC/3b00WKxZCmTJFM3u9UlPTKVDgzHnhH3zwOh588LoLTO9hdcs6NufYxo0biYqKYvfu3VxzzTUsWrSIcuXy921bT1KRJiIifsfdJyHh1K3JEiUKUbFicQoVCs7xuKAgQ0LC0HzxpKQv2r9/P02bNuXAgQM0btyY+fPnU6JECadj+TQVaSIiki8tXryVpk2rUKRIwTPey9o7FhRksDb38zzzTBNeeOHmHAfrn04F2oUrW7Ysffv2ZdWqVcycOZOiRYs6HcnnqUgTEZF8Z/nynbRvP4Xatcsya9ZdRESUzvZ+2bKh/PxzbypUCKNcuWI53pI8qUSJnB8o8BvLdsLgZbAjAaKqwpAGrv11zxx35wknTpygcGHX93jYsGGkpaVRoIDKD3dogXUREcl3unadRVqaZd26f6hX7yNmz96U7f2gIEODBhWoUKH4WQu0gHCyQANY8rdrjrSs86R50MSJE6lduzaxsbGAqydSBZr7AvxProiI5Ed79pwa3H/kSDIFC+rHWa5KFHJNWBsZfmpfldyn+sgrb7/9Nr169WLbtm3MmTPH49fzR/pTLSIi+U6XLrUzXw8d2oj27a90MI2P+7qra2td1dWuUhxGNPPY5ay1PPfcc5mLo48cOZK+fft67Hr+TH2OIiKS70yb1pmmTSszZ86fvPRSc6fj5A9DbnBtHpSenk6/fv14//33CQ4OZvz48fTo0cOj1/RnKtJERCTfMcbQt28DHnvsej1x6SOstdx3331MnTqVQoUKMWPGDG699VanY+Vrut0pIiL5lgo032GMITIykrCwMBYtWqQCLQ+oSBMREZ+1f/8xNm8+4HQMcdNTTz3Fxo0badasmdNR/IKKNBER8Unx8Sdo3fpTGjcez5o1+5yO4zvCx2TfcjN5/alj3vjZI1H27dtHu3bt2L59O+DqTatYsaJHrhWIVKSJiIjPOXYsmfbtp7BmzT7i4o7TrNlEfvxxl9Ox8q99x2DgUteWR/7++2+aNGnCV199Rb9+zqwF6u/04ICIiPicsWNX88MPp4qyw4eTGDHiR/i8koOp8rFPNrp+zaP50TZu3EirVq3Ys2cP1157LRMmTMiT80p2KtJERLygPbDQ6RBedNHD+Z+40bVl8cXFnjPQ5dH8aL/88gtt27bl4MGDNG3alLlz52qhdA9RkSYi4gWBVKB5WjunAzgtzs1biz3quLY8FBMTw+23386xY8fo2LEj06dPp0iRInl6DTlFRZqIiBdZpwN42MketLz8Ok+cSKVwYf248gUbN27k2LFjdO/enXHjxlGwYEGnI/k1/akXERGfFvAF2uT12dt53Dt2Pvr370/16tVp06YNQUF69tDTAvxPvoiIOO2zz9ZRr95l1KxZxrsXPn36itxuI05eD4OWnWp3rwVv5bIUVYvpsC7uVDumC9Qte+Zxa/dDyxmn2pHhrvU1c5L12uD1Im3MmDFERUVx5ZWu9VHbtQv4G85eozJYREQc8/nnG+nRYzaNG49n5crdTseRLKy1PPPMMzz++OO0bt2axMREpyMFHBVpIiLiiKVLt3PPPV+Qnm45cCCR5s0nExOzzelYvqd7rVOv82gKjXNJS0vj0Ucf5dVXXyU4OJiXXnpJDwg4QLc7RUTEEXPn/klyclpmOzExhePHU/L2Ij40nuui5dEUGueSnJxM9+7dmTFjBoULF2bGjBl07NjR49eVMxlr89ezRvUrGbtqV/7KLOKYNzOetRukvzNO88RTj77ofL5Oay3PPruUV19dDsDEibfRs+c1eRvI3XFnAsCxY8fo3Lkzixcvpnjx4sybN4+mTZs6HcsrHnrvIAAfP1YqT89rjPnVWlv/Qj6rnjQREXGEMYZXXmlBmTJFsdbmfYEm523x4sUsXryY8PBwFi1axHXXXed0pICmIk1ERBw1cGBDpyNIhjvuuIN3332XFi1aUKNGDafjBDwVaSIifirQlqLKUdZB95Kjbdu2ceLECWrVcn2vHnvsMYcTyUkq0kRE/JRTBdrps2glJaXSu/c8/vOfm4iMLOfdMLnNZyYArF+/nqioKIwx/PDDD1SpUsXpSJKFpuAQEfFz1svbgizXTktLp3v3L/n003XcfPNEli/f6dGvVdz3008/0bRpU/bu3UuNGjUoVSpvB8zLxVORJiIiHmGtpV+/hcycuRGA+PgTtGr1Cd98s93hZLJkyRJatGjBoUOHuO2221i4cCFhYWFOx5LTqEgTERGPsBYKFgzOtq9y5RLUqZPDMkniNTNnzqRDhw4cP36c+++/n1mzZlG4cGGnY0kOVKSJiIhHBAUZRo1qw/DhzQCoUCGM6OjuhIeHOporkG3dupVu3bqRkpLCgAEDGDduHAUKaHi6r9LvjIiIeIwxhuefv5lLLw3jppsqUblyCacjBbTq1aszYsQIjh49yrPPPosx5twfEseoSBMREY/r3duhSVFbTM/e/rqrMzkcZK1l9+7dVKxYEYABAwY4nEjcpdudIiLiv9bFZd8CTFpaGn369KFevXps2bLF6ThynlSkiYjIRbPW8v77KzlyJMnpKNlFVXU6gWOSkpK4++67GTt2LAkJCWzfrqdq8xsVaSIictE+//wPHntsIVdcMZrRo38mKSnV6UguQxqcel2luHM5vOzo0aN07NiRWbNmUaJECaKjo4mKinI6lpwnFWkiInJRUlPTefbZpQDExR3niScWMWDAIodTnaZKcRjRzOkUXnHw4EFatmxJdHQ0ZcuWZdmyZTRu3NjpWHIB9OCAiIhclEmT1rB584HMdlCQ4YknbnQwURZ1y0JcP6dTeE1ycjK33HIL69ato0qVKkRHRxMREeF0LLlA6kkTEZGLctddtXnhhaaEhhYEoFeva6hZs4zDqQJTSEgIjz76KLVq1WLFihUq0PI5FWkiInJRihcvxH//ewvbtj3BE0/cwLBhzZyOFHBSU0+NAXzkkUf49ddfqVChgoOJJC+oSBMRkTxRtmwoI0e2oWJFBwfoL9sJ9SdD+Bi4dz6s3e/a/NiKFSuoWbMmGzZsyNynZZ78g4o0ERHxH4OXwY4E1+slf0PLGa7NTy1atIhWrVrx119/MWbMGKfjSB5TkSYiIufFWktycprTMXJWohBEhru2k/x06o1p06bRsWNHEhMT6dWrF++8847TkSSPqUgTERG3WWsZMiSadu0+IzExxek4Z/q6q2trXdXV9tOpNz744APuueceUlNTGTRokBZK91P6HRUREbekp1ueeOIrxoxZCUDnzjP48suuFCrkgz9Khtzg2vzQ66+/ztChQwF45ZVXGDp0qBZK91PqSRMREbe88caKzAIN4KuvttKnz3wHEwWm8uXLExQUxPvvv8/TTz+tAs2PqUgTERG39O59HXXqlM1sly5dhAED/LO3ypf17NmTTZs28cgjjzgdRTzMo0WaMaaNMeZPY8xWY8zQHN6vbIz5xhjzmzFmnTGmnSfziIjIhStTpigxMd2pUaM0ZcuGsmzZ/Vx77aVOx/J7J06coFevXqxZsyZznyapDQweG0hgjAkG3gVaAbHASmPMXGvtxiyHPQfMsNa+b4ypBSwEqnoqk4iIXJxy5YqxdGlPjhxJokYNH1pVYPJ6GLTM9fo/1/vNeLQjR45w++23s3TpUn788Uc2bNhAcHCw07HESzw52rMBsNVauw3AGDMNuA3IWqRZ4OSz0SWAPR7MI+I/vmgP2xc6nUKA9rj+dxlILrssDAhzOkbu9h2Dga4F33mrubNZLsKBAwdo27YtK1eupFy5csyYMUMFWoDx5O3OCsCuLO3YjH1ZDQPuM8bE4vp37vGcTmSM6WOMWWWMWeWJoCL5zvkUaNU0isCTzqdAyy+/E8ePp/D00zEcP+6DU2y445ONru27WKeTXLDY2FiaNGnCypUrqVatGitWrCAyMtLpWOJlTj833Q2YaK190xjTEPjEGFPHWpue9SBr7UfARwD1KxnrQE4R3zRIfx18hb/8Thw9mkzHjlNZtuxvVq3ay7x53Shc2OkfFRcgH8+PtmXLFlq1asWOHTuoXbs2S5Ys4bLLLnM6ljjAk3/zdgOVsrQrZuzL6kGgDYC19kdjTGGgDODfC62JiPigw4dP0K7dFH74wXUTJCZmW+ZcaCEhPnKbLfy0pY/i+rl+7VHHtfmBtWvXsnPnTm688UYWLFhAqVKlnI4kDvHk7c6VQIQxppoxJgS4G5h72jE7gRYAxpirgMJAnAcziYhILtLT7Rm3OHfvTuDIkSSHEgWmO++8kzlz5hAdHa0CLcB5rEiz1qYC/YDFwB+4nuLcYIwZboy5NeOwQcBDxpi1wFTgfmutv9w1EBHJVy65pAhLltxH7dqudS/r17+MpUt7Urp0UYeT+b+FCxfyyy+/ZLY7duxIsWLFHEwkvsCjAw2stQs5bVyttfaFLK83Ao08mUFERNwXHh5KTEwPnnoqhtGj21CiROGLP+na/dByxgHYybIAACAASURBVKl2ZLhrfc2cDFzqGvR/0pvN/OY2Zm6mTp1Kjx49KF68OGvXrqVixYpORxIfkQ9Hg4qIiCeVL1+MSZNudzpGzk6OQfMT7733Hv369cNaS+/evalQ4fRJECSQaVkoEZEAFBubwNGjyU7HCFjWWl5++WX69u2LtZbXXnuN119/XetwSjYq0kREAsy2bYdo3Hg8HTtOzb9zoeVj6enpDBw4kOeffx5jDB999BFPPfWU07HEB+l2p4hIANm8+QAtWkwmNjaBHTsO06nTdObMuTvv5kJbe9oMSnXLujZ3b1O+1TxfrxLgjtWrVzN69GgKFizIlClTuPPOO52OJD5KRZqISAAZNGgJsbEJme0lS/5izJhfGDz4pry5QNYHBMDvxpDlhfr16zNhwgQuvfRSWrVq5XQc8WG63SkiEkAmTbqdyMhyme377otkwIAbHUwUGI4cOcLq1asz2z169FCBJuekIk1EJICUKlWEmJju1KoVzoMPXsvEibdRoIB+FHjSv//+S/PmzWnevDlr1qxxOo7kI7rdKSISYMLDQ1m+vBclShQmKCiPnyaMDM/b8+Vzu3btIioqik2bNnH55ZdTvHhxpyNJPqIiTUQkAF1ySRHPnDi3SWoD0J9//kmrVq3YtWsXV199NYsXL+bSSy91OpbkI+rjFhHxM9ZaZszY4HSMgLZ69WqaNGnCrl27aNiwId9++60KNDlv6kkTEfEj69b9Q79+C/n++53QpTYAiYkpFClS0DMXHLg0e9vPp89wx5EjR4iKiuLAgQO0bt2azz//nNDQUKdjST6kIk1ExI/Ex59wFWhZdOo0nQUL7iE42AM3T7Kuswkq0oCwsDDGjBnDnDlzmDRpEiEhIU5HClij5x/h9535d8Jm3e4UEfEjTZtW4fbba2bb1759hGcKNMkmLi4u8/Xdd9/NlClTVKA57HwLtKsre6jH+QKpJ01ExM+88UZL5s/fTGpG+/HHb3A0TyAYPXo0zz33HDExMTRo0ABA63D6kI8fK+V0hAuiIk1EJB+x1vLVV1v54INVzJrVhZCQ4DOOiYgozcaNj3GlNwK92cwbV/FZ1lqGDx/OsGHDAPjll18yizSRi6UiTUQkn1i37h8GDVpCTMw2AN5/fyVPPJHzagEREaW9E6pHHe9cxwelp6fz5JNPMnr0aIKCgvjoo4948MEHnY4lfkSDFERE8on331+ZWaAB/Pe/33LwYKKDiQJXSkoKPXv2ZPTo0YSEhDBz5kwVaJLnVKSJiOQTw4Y1o1ixUwPR4+NPsHjxVgcTBa57772XTz/9lNDQUBYsWMAdd9zhdCTxQyrSRETyiXLlivHMM40BaN68Gr/+2odu3a52OFVguueeeyhbtixff/01LVu2dDqO+CmNSRMR8SHffLOdP/88wCOP1M/x/QEDbqRu3fK0bVtdTw96WXp6OkFBrr6N22+/nZYtW1KsWDGHU4k/U0+aiIgP+PPPf7n11qk0bz6ZAQMWsWNHfI7HFSlSkHbtIlSgednOnTupV68ey5cvz9ynAk08TUWaiIjDUlPTiYr6lHnzNgOQlJTG009/7XAqN4WPyb75oU2bNtGoUSPWrFnDc889h7XW6UgSIFSkiYg4rECBIF588eZs+6ZNW8/WrQcdSiQnrVq1iiZNmhAbG0ujRo2YPXu2ejHFa1SkiYj4gJ496xIZWQ6AG2+syIoVD1C9ev6cJd1fLFu2jFtuuYV///2Xtm3bsmTJEkqWLOl0LAkgenBARMRLfvoplrCwEGrXLnvGe8HBQYwZ05a9e49y11211FvjsLlz59KlSxeSkpK4++67tVC6OEJFmoiIh8XFHeOJJxYxdep6mjevRkxM9xyLsCZNqjiQ7iLF9XM6gUcUKFCAtLQ0Hn30Ud555x2Cg89cfkvE01SkiYh40MaNcdxyyyT27z8GwNKl21mwYAsdOnhlZU25QO3atWPVqlVERkaqV1McozFpIuLT2gPGh7dziYgoRcWKxbPt+89/oklLSz/P74R40smF0r/++tRTtXXr1lWBJo5SkSYiPm2h0wHc0O4s7xUsGMzkybdTqJDrdtk115Tn/ffbExysf359RXp6Oo8//jgvvvginTt3Jj4+5znqRLxNtztFJF/IzzNT1a5dltdea8mRI0kMHdqYggX9YHzTsp0weBnsSIA3m0GPOk4nuiApKSncf//9TJkyhZCQECZOnKgnOMVnqEgTEckD33+/A2MMjRtXzvH9AQNu9HIiDztZoOVjx48fp0uXLixYsIBixYoxZ84cmjdv7nQskUzqbxcRuQgnTqQyePASbr55Ivfd9wUJCUlOR/KOrAXaG784l+MCxcfH07p1axYsWEDp0qVZunSpCjTxOSrSREQu0J49R6hf/yPefPNHrIUdOw4zaNBip2N53z/HnU5w3jZs2MAvv/xChQoV+P7777n++uudjiRyBt3uFBG5QC+99C0bNsRl2zdu3G8MHNiQq64KdyiVl3Sv5XSCi9KoUSO+/PJLateuTZUq+XB+OgkIKtJERC7Q/fdfQ6NGlfn33+PExiYwefJapk270/8LNIC38t+twY0bNxIbG0tUVBTgmgtNxJepSBMRuUA33FCRG26oCMCWLQd44YWbKV68kMOpJCcrV66kTZs2JCYm8v3331OvXj2nI4mck8akiYjkgYiI0irQfNTJhwIOHjxI8+bNueqqq5yOJOIWFWkiIuK3Zs+eTdu2bTl69Cj33HMPX375JUWLFnU6lohbdLtTRETc12J69vbXXZ3J4YYJEybQu3dv0tPT6devH6NGjSIoSH0Tkn+oSBMRuQDWutZACLi1HdfFnfsYH7Bv3z769etHeno6L774Ii+++GLg/V5Jvqf/UoiIXICZMzdy880T2bBhv9NRJAfly5dn5syZjBo1imHDhqlAk3xJPWkiIucpISGJAQMWsXfvUa655kMGDWrI8883JTQ0xOloAS0tLY1169Zx7bXXAppiw5NGzz/C7ztTnI7h99STJiJynl544Rv27j0KQGpqOm+99SO7duXvdSzdFtMl++YjkpOTuffee7nxxhv5+uuvnY7j9/JTgXZ15YJOR7hg6kkTETkP6emWbdsOZdv3n//cRM2aZRxK5GV1yzqd4AzHjx+nc+fOLFq0iLCwMIKDg52OFDA+fqyU0xH8mnrSRETOQ1CQYc6cu5k16y4qVAijWrWSPPtsU6djBaz4+HiioqJYtGgRZcqU4ZtvvqFZs2ZOxxLJE+pJExE5T8YYOneuRVTUFfz9dzxFi+bf2yn52b59+2jTpg1r166lUqVKLFmyhJo1azodSyTPqCdNROQChYUV4uqryzkdIyClp6fTrl071q5dS40aNVi+fLkKNPE7KtJERCTfCQoK4n//+x833XQT33//PZUrV3Y6kkie0+1OEZFzsNbmOs9We2Chd+M4a+1p88J5+UGCw4cPU6JECQBatGhB8+bNNQea+C31pImInEVSUiqNG09g7NjVpKfbM9739QItz2cKazkj++ZFMTExVKtWjUWLFmXuU4Em/kxFmojIWYwY8QM//LCLhx6aR5MmE1i37p8cj7M+ui3Is++Esz7//HPat2/PoUOHmD17ttNxRLxCRZqISC62bTvEyy9/n9n+4YddvPvuLw4mCkzjxo2jS5cuJCcn079/f9577z2nI4l4hcakiYjkYs2afWS9m1amTFFefbWlc4F8QWS4Vy/3v//9jyFDhgDw3//+l+eff163OCVgqEgTEcnFHXdcxcaNfenf/yvmzdvMiBGtKFWqiNOxnPV1V69davjw4bz44osAvPPOO/Tr189r1xbxBbrdKSJyFlWrlmTu3G58801PevSo63ScgHLLLbcQFhbGp59+qgJNApJ60kRE3NCsWVWnIwSErNOdNGnShO3bt1O6dGmHU4k4Qz1pIiIZcppiQ7zn2LFjdOjQIdvTmyrQJJCpJ01EBNi06V/uvnsWY8feSv36lzkdx/cMXJq9/VbzPD39wYMH6dChAz/++CO///47bdq0oXDhwnl6DZH8RkWaiAS8+fM3c++9X5CQkESnTtNZufIhypcv5nQs3/LJxuztPCzS9u7dS1RUFOvXr6dy5cpER0erQBNBtztFJMBt2LCfW2+dSkJCEgCxsQl07jyDpKRUh5P5mHJFPXLabdu20bhxY9avX0/NmjVZsWIFV155pUeuJZLfqEgTkYBWu3ZZBgy4Mdu+K68sjdXwtOyGNDj1ukrxPDnl77//TuPGjdm2bRv169fn+++/p2LFinlybhF/oCJNRALeG2+0olWrywkONowa1Ybx42+lcGGNBslRleIwolmenCoxMZGEhASaN2/O0qVLKVOmTJ6cV8Rf6F8hEQl4BQoEMW3anaxb94+m2shNjzquLQ81aNCA7777jlq1amkMmkgOVKSJiAClShVRgeYFM2fOJD09na5dXSsXXHfddQ4nEvFdKtJERMQrPv74Yx5++GGCg4OJjIzkqquucjqSiE9TkSYiAaNPn3kcOZJMsWIFKVYshOeea0rp0p55alGye/311xk6dCgAw4YNo2bNmg4nEvF9KtJEJGDMm7eZffuOZrb/859GDqZxU4vpsC7uVDumC9Qte+Zxa/dDyxmn2pHhuS+GPnBp9nnP3myW+3iz8DHZ23Hnt4amtZahQ4fyxhtvYIzh3Xff5dFHHz2vc4gEKhVpIhIwjh5NztYuVizEoSSBIS0tjUceeYSxY8dSoEABJk+eTLdu3ZyOJZJvqEgTkYBgreXYsexFWmhoQYfS5FPnOT/a9u3bmTFjBkWKFGHWrFm0a9fOQ8FE/JOKNBEJCNbC7Nl3c/RoMkePJnP8eArBwZoq0m0XMD9a9erVmT9/PsYYGjdu7JlcIn7M2Hw2rXb9Ssau2pW/MovkuTeN69dB/v93IeMrxVe/0jzL5+EFzL3lwIED/PDDD3Ts2NHpKOJBD713EICPHyvlcBLfZ4z51Vpb/0I+q540EfFrJ29xhob6+PgzDy5g7i179uwhKiqKTZs2MXfuXN3eFLlI6usXEb+zZ88RPvxwFe3bT6F06TeYOnW905H83tatW2nUqBEbNmygRo0a1K1b1+lIIvmeetJExO+MG7eaF15YltmeN28zvXs7PLP9RU5l4cvWrVtHVFQU//zzDw0aNGDhwoWULl3a6Vgi+Z6KNBHJl5KSUilYMJigIHPGe7feWiNbkRYd/RfHj6dQtKgPP835ZjOnE1yQFStW0L59ew4fPkyLFi2YPXs2xYoVczqWiF9QkSYi+UZc3DEWLNjCvHmbWbx4K9HR3WnYsNIZx0VGlqNSpeLs2pUAQLlyxdi+/RC1a+cwCayvyOPFy70hKSmJbt26cfjwYe644w6mTJlCoUKFnI4l4jdUpIlIvvH4418xffqGzPbcuX/mWKQZY3jyyRtJTEzl1ltrULt2OMac2eMmF6dQoULMmjWLTz75hLfffpsCBfQjRSQv6W+UiOQbt95aI1uRNm/eZl59tWWOxz75ZENvxXKPH41B++OPPzIXR2/QoAENGjRwOJGIf9LTnSKSb7RtW53g4FM9YocOneDQoUQHEwUWay2vvPIKderUYfr06U7HEfF76kkTkXzjkkuK0Lv3dZQrF0rHjjW47rpLc3xwwCdMPm3aj3w45iwray2DBw/mrbfewhjD4cOHnY4k4vdUpImIT4mLO8avv+6lTZvqOb7/wQcdvJzoAg1alr2dj4u01NRU+vTpw4QJEyhYsCCffPIJXbt2dTqWiN/T7U4R8RnWWu6/fw5t237GoEGLSU5OczpSwDtx4gR33XUXEyZMoGjRosybN08FmoiXqEgTEZ8xcuRPLFy4BYC33vqJRo3GO5xIevXqxezZsylZsiTR0dG0bt3a6UgiAUO3O0XEJ8TFHePZZ7MvMl6wYD7+f2T3Wk4nyBNDhgzht99+Y8aMGURGRjodRySgqEgTEZ8QHh5KdHR3unX7nF27EihZsjBTp3amqtPBLlQ+XCD9pMTERIoUKQLAtddey4YNGwgODnY4lUjgycf/TRURf9OoUWXWrHmETp1qMm7crVSpUtLpSAFny5Yt1KpVi0mTJmXuU4Em4gwVaSLiU0qVKsLnn3fhjjuucjpKwFmzZg2NGzfm77//ZuzYsaSnpzsdSSSgqUgTEZ+jJZy87/vvv+fmm29m//79REVFsWjRIoKC9CNCxEkakyYijrDWeqYYCx+TvZ3bckyT12efy6x7rdzHkbWYDuviTrVjukDdHBZrX7sfWs441Y4Mh699f7qKBQsWcOedd2ZOt/HJJ59ooXQRH6D/JomII/r1W8iQIdGaC81hs2bN4vbbb+fEiRM89NBDTJ06VQWaiI9QT5qIeN3nn2/kvfdWAfDttzuYOrUzl19+icOpAtNVV11FWFgYffr04dVXX9WtZhEfoiJNRLwqLu4Y3bt/mdn+5Zfd3HXXTFatekgFggNq167N+vXrueyyy5yOIiKnUZEmIl4VHh5Kjx51+fDDXwHXhLUfftgh7wq03Magna5HHffX03R3XFndsu5f3yHp6ekMGjSImjVr8vDDDwOoQBPxUW6PSTPGFPVkEBEJHP/7XyuqVXPNgfbGG62oX19FgjekpqbSq1cvRo4cyYABA9izZ4/TkUTkLM7Zk2aMuQkYCxQDKhtj6gIPW2sfc+OzbYBRQDAw1lr7Wg7HdAGGARZYa62957y+AhHJd8LCCjFx4u2sWrWHJ5644eJONnl99ra7vWMB5sSJE3Tt2pW5c+cSGhrKl19+qR40ER/nzu3Ot4HWwFwAa+1aY0zTc33IGBMMvAu0AmKBlcaYudbajVmOiQCeBhpZaw8ZY3J4pl1E8qOYmG1s23aIPn3q5fh+06ZVaNq0ysVfKOs0GqAiLQcJCQncdtttLFu2jEsuuYSFCxdy4403Oh1LRM7BrTFp1tpdp40XceeZ+QbAVmvtNgBjzDTgNmBjlmMeAt611h7KuM5+d/KI+Jwv2sP2hU6n8AnHjiUzZEg07723ipCQYBo2rMjVV5c747j2gL5jnhcXF0fbtm359ddfufTSS1myZAl16qiQFckP3BmTtivjlqc1xhQ0xgwG/nDjcxWAXVnasRn7sroSuNIYs8IY81PG7dEzGGP6GGNWGWNWuXFdEe9zokCr1s771zwHay0tW36SOb1GcnIaPXvOznEutPP5jvneV5p/xMfHs3PnTq644gpWrFihAk0kH3GnJ+0RXOPKKgC7gSXAOcejncf1I4BmQEXgO2PM1dba+KwHWWs/Aj4CqF/J2Dy6tkjeGxTYfzyNMQwa1JC77pqZuW/Nmn0sW/Y3UVFX5PiZi/6Oda91sWfwaxEREcTExFC2bFnKly/vdBwROQ/uFGk1rLX3Zt1hjGkErDjH53YDlbK0K2bsyyoW+NlamwJsN8ZsxlW0rXQjl4j4oDvvrEW3bnWYOnU9V1xxCZMm3U6jRpU9d8HclnIKYKtXr2blypWZU2xERkY6nEhELoQ7Rdo7wHVu7DvdSiDCGFMNV3F2N3D6k5uzgW7ABGNMGVy3P7e5kUlEfNiYMe2oXLkEzz/flNDQEKfjBJRvv/2Wjh07cuTIEapWrUrr1q2djiQiFyjXIs0Y0xC4CQg3xgzM8lZxXFNqnJW1NtUY0w9YnHH8eGvtBmPMcGCVtXZuxntRxpiNuB5G+I+19sCFfzki4i1HjiQRFpbzGo+lShXhtddaejmRzJ07ly5dupCUlETXrl255ZZbnI4kIhfhbD1pIbjmRisAhGXZnwDc6c7JrbULOW18sLX2hSyvLTAwYxORfOL773fQufMM3n+/PZ07a0yYL/jkk0/o1asXaWlpPPzww7z77rsEB5/z/9Mi2Yyef4Tfd6Y4HUMy5FqkWWu/Bb41xky01u7wYiYR8WGTJq3hoYfmkZKSTvfuX1K1aknq1cuDSVGX7YTBy2BHwql9MV1cSy2dbu1+aDnjVDsy3P2lm/zQ6NGjeeKJJwB45plnePnll7UOqlyQ8ynQrq5c0INJBNwbk3bcGPM/oDZQ+OROa61G64oEmO3bD2UWaACJial07DiVtWsfITw89OJOfnqBJm6Jj4/ntddci7mMGDGCQYMGOZxI/MHHj5VyOoLg3jxpnwGbgGrAf4G/0dOXIgGpWrVLeO+99tn2PfxwPcqUyYOlfUsUcvWIRYZf/LkCSMmSJVmyZAmTJk1SgSbiZ9wp0kpba8cBKdbab621DwDqRRMJUL17X8egQQ0pVCiYqVM78+KLzfLm1trXXV1b66oXfy4/l5KSwvz58zPbderUoUePHg4mEhFPcOd258kb1HuNMe2BPYD6QUUC2Ouvt6RXr2uoXdsDy+0OucG1nU3dshDXL++vnQ8kJibSpUsX5s+fz8cff0zv3r2djiQiHuJOT9rLxpgSwCBgMDAWGODRVCLiqMTEFCZOXJPr+8HBQZ4p0OSsDh8+TJs2bZg/fz6lSpXSJLUifu6cPWnW2pN96oeBWyBzxQER8UPbth3izjtn8Ntv+0hNTad373PNWy3esH//ftq0acNvv/1GhQoVWLJkCbVqafoTEX+Wa0+aMSbYGNPNGDPYGFMnY18HY8wPwBivJRQRr4mJ2Ua9eh/x22/7AOjXbyG//rrH4VSyY8cOmjRpwm+//Ub16tVZvny5CjSRAHC2253jgN5AaWC0MeZTYATwhrX2Wm+EExHvKl++GMnJaZntpKQ0nn12qYOJxFrLvffey+bNm7nmmmtYvnw5VatWdTqWiHjB2W531gcirbXpxpjCwD7gCi3bJOK/6tQpy8cfd+Tee78A4I47rmLChNu8c/GBpxWDWjgdAGMM48aN4+mnn2b8+PGULFnS6Ugi4iVnK9KSrbXpANbaE8aYbSrQRPzfPfdczcqVu6lYsTgDBzb03sz1n2zM3g7wIm3Hjh1UqVIFgBo1avDFF184nEhEvO1stztrGmPWZWy/Z2n/boxZ562AIpL3rLWkpKTl+v7bb7dh0KCbtLSQQ+bMmUONGjUYNWqU01FExEFn60m7ymspRMRrjh1L5uGH5xMUZJg06XYVYj5m0qRJPPjgg6SlpbFlyxastfo9EglQZ1tgXYuqi/iZzZsP0LnzDNav3w9Ao0aVePjh+g6nyvBmM6cTOG7kyJE8+eSTADz33HMMHz5cBZpIAHNnxQER8QPWWnr0+DKzQAPo338R1113KddfX+HMD6zdDy1nnGpHhruWbcrJwKXZx5S92Qx61Mn52PCMGXxOXzEgt+MDgLWWF154gZdffhmAt99+mwEDNGe4SKBzZ8UBEfEDxhgmTLiN0NCCmfuqVy9FWFghB1MJwP/93//x8ssvExwczMSJE1WgiQjgZpFmjClijKnh6TAi4llXXRXO+PGuKTW6davDzz/3pmbNMg6nknvvvZdq1arx+eef07NnT6fjiIiPOOftTmNMR1yT2IYA1Ywx1wDDrbW3ejqciOS9Ll1qc+mlxWjcuLLGOzkoOTmZkJAQAKpVq8amTZsy2yIi4N6YtGFAA2AZgLV2jTGmmgcziYiHNWlS5dwH1S175rix3LzV3P15zdw9px+Lj4+nY8eOdOjQgaeeegpABZqInMGd250p1trDp+2znggjInlj2rT12R4QcMva/dk38Yh//vmHZs2asXz5csaMGcPhw6f/8yoi4uJOT9oGY8w9QLAxJgLoD/zg2VgicqHi40/w0EPzOHo0mdatr2DQoIa0bHn5uW9tZn2SE9Tj5QF///03rVq1YuvWrURERBAdHU2JEiWcjiUiPsqdnrTHgdpAEjAFOAzo0SMRH/XRR79y9GgyAIsX/0WPHrOzLZouzti4cSONGzdm69atmQuln1z2SUQkJ+70pNW01j4LPOvpMCJynk7OOZYhZc+jjB79c7Z9/fpdT6Hpm2DQslM7u9cK+LUxvWn16tW0atWKgwcP0qRJE+bNm6ceNBE5J3d60t40xvxhjHnJGBO4s02K5ANBQYZRo9rQsGFFAIoUKcAjj7i5okBkePZN8kx4eDihoaF06NCBxYsXq0ATEbecsyfNWnuLMaY80AX40BhTHJhurX3Z4+lE5LwEBwfRuXMtOneuxY8/7uKPP/6ldOmi7n04t9UE5KJVqlSJFStWUL58eQoWLHjuD4iI4OZkttbafdba0cAjwBrgBY+mEpGL1rBhJR544FqnY+Rb7QHjxpabCRMmMHz48Mx2pUqVVKCJyHlxZzLbq4CuQGfgADAdGOThXCLiDnefwOxRJ6DXxrwQC8/j2Hantd98800GDx4MQOvWrbnhhhvyLJeIBA53HhwYj6swa22t3ePhPCIiPuV8JoW01vLcc8/xyiuvADBq1CgVaCJywdwZk9bQG0FEJP9rz/n1QPmTtLQ0+vbty4cffkhwcDATJkyge/fuTscSkXws1yLNGDPDWtvFGPM72f8zaQBrrY30eDoROdPk9dma+9tUo3PnGfTtez133lmLAgXcGmrqEedboJ1+mzC/Sk5OpkePHkyfPp1ChQoxc+ZMOnbs6HQsEcnnztaT9kTGrx28EURE3JR1vjPgvW3/snz5TpYv38lTT8UwfHgzeva8xpFoJwXaunHx8fGsXLmSsLAw5s2bx8033+x0pHxt9Pwj/L4zxekYIo7LtUiz1u7NePmYtfaprO8ZY14HnjrzUyLiTYnW8u67KzPbO3ce5tChEw4mCkxly5YlOjqaQ4cOUa9ePafj5Hsq0Jx1dWU9hewr3HlwoBVnFmRtc9gnIl72TUoK/yYcz2yXKFGIBx/UtBvesG/fPmbMmEH//v0BuPzyyx1O5H8+fqyU0xFEHHW2MWmPAo8Blxtj1mV5KwxY4elgIpKL7rUyX7YD/ugTyciRPzFp0lr69KlHWFgh57IFiO3bt9OqVSv++usvChcuTJ8+fZyOJCJ+6Gw9aVOAr4BXgaFZ9h+x1h70aCoRyd1pa27WBD74oAMvvXQLzt6i8QAAIABJREFUxpxtelXJC+vXrycqKoq9e/dSr149OnXq5HQkEfFTZyvSrLX2b2NM39PfMMaUUqEm4lvCw0OdjuD3fvrpJ9q1a8ehQ4do1qwZc+bMoXjx4k7HEhE/da6etA7Ar7ge1sr6X3QLaACGiASM6OhoOnXqxLFjx7j11luZPn06hQsXdjqWiPixsz3d2SHj12reiyMSQFpMh3Vxp9oxXaBu2TOPW7sfWs441Y4M12LoXpaWlsbgwYM5duwYPXv2ZOzYsRQo4M5zVyIiF+6cs14aYxoZY0IzXt9njHnLGFPZ89FEJCfxKWkcO5bsdIyAEhwczPz58xk+fDjjx49XgSYiXuHO1OTvA8eNMXVxLaz+F/CJR1OJSK5G7DxI5cojefbZr9m794jTcfxbTAzWuqbmrVSpEs8//zxBQc6t6CAigcWdf21SretfqduAMdbad3FNwyEiDph/4CgHDybyyivLqVp1FN98s93pSH7HWgtDh0KrVvz3v/91Oo6IBCh3+uyPGGOeBroDTYwxQYCmIxa5WO6OK6tbFuL6AbB37xHWXvZW5lspKWlERpbzRLqAlZaWxqOPPgoffwzBwURERDgdSUQClDs9aV2BJOABa+0+oCLwP4+mEpEc/fzz7mzt66+vQOnSRR1K43+Sk/+fvfuOr/n6Hzj++mSQ2ESMoqREyJQQo3YiVohZMWrUaouq2tWqttpS64dSRatobUqVIqLU+ipBzFaN2hFJSCIiss7vj1u3riSE3Jub8X4+Hp+HnM89n3PeuTLeOZ/zOSeRHj16sHjxYrCxgZ9/plevXuYOSwiRTz1zJE0pdUvTtBWAt6Zp7YDDSqnlpg9NCPGkjh1rcPPmSIKCLrJ9+0Xq1Clv7pDyjPv379O5c2eCgoIoVqwYsVu2QOPG5g5LCJGPPTNJ0zStG7qRsz3o1kr7StO0MUqp9SaOTYi8ZflpGLVH9/EYbxhb74WaKV++KH371qJv31rGi00wfPhwgoKCKFOmDNu3b8fLU/ZAFUKYV2bmpH0AeCulbgNommYPBAOSpIn84Sd/+OdX47Z56z6M/E338RPbPAnz+Oyzz7h8+TILFiygevXq5g5HCCEylaRZPErQ/hVF5uayCZE3ZDZBc2ib+TZ/OKv7t7JsKWRO4eHhlClTBk3TKF++PLt27TJ3SEIIoZeZJG27pmk7gFX/lgMBIw8rCJELjFLGba9yMZjRzLhtikw7deoULVu2pG/fvkydOtXc4QghRBqZeXBgjKZpnYFG/55apJTaaNqwhMiD+rjqDmF2Bw8exN/fn+joaA4fPkxiYiIFChQwd1hCCGEgwyRN0zRHYAZQFTgFjFZK3ciovhDCdLZvv8DatWdo3boaLVq8QqlStuYOKdfasWMHnTt3Jj4+no4dO7Jq1SpJ0IQQOdLT5pYtAbYAXYCjwFfZEpEQIo0NG87y/fehBAaux95+OrNnHzJ3SLnS2rVrad++PfHx8fTr149169ZhY2Nj7rCEECJdT7vdWVQptfjfj89pmnYsOwISQhhSSrF9+0V9OTVV4eJib8aIcqcNGzbQvXt3lFKMHDmS6dOnyz6cQogc7WlJmo2maZ7o1kYDsH28rJSSpE2IzLCfZ1j+d4unzDp7NoLr12P1ZVtbKxo3rmyMyPKVJk2aUL16dfr06cP777+PpmnPvkgIIczoaUlaGDDrsfKtx8oKkMWdhMgGjo527NrVh+3bL7Bjx0UqViyGjU1mHswWSimUUlhYWGBvb8/Ro0cpXLiwucMSQohMyfAnvVKqeXYGIoRIX4EClvj4OODj48C0aX4kJqaYO6RcISUlhTfffJPChQsze/ZsNE2TBE0IkavIhAwhcpkCBSzNHUKO9/DhQwIDA/nuu+9YvHgxFy5cMHdIQgjx3OSeiRCm9pxz0ETWxMXF0alTJ4KDgylevDhbt27F0dHR3GEJIcRzkyRNCJFnREVF0bZtWw4fPkzZsmXZsWMHHh4e5g5LCCFeyDNvd2o6r2ua9tG/5Zc1Tatr+tCEECLzbt68SZMmTTh8+DBVqlRh//79kqAJIXK1zMxJ+xpoAPT4t3wPmG+yiIQQAFy/HsvMmQc5c+Y2Shl539A8yMbGBgsLC5ydndm/fz/VqlUzd0hCCJElmUnS6imlhgIJAEqpu4DsoSKEic2d+wejR+/E1XUBL788m5kzD5o7pBytVKlS7Ny5k71791KhQgVzhyOEEFmWmSQtSdM0S3Rro6Fpmj2QatKohMjnzp+PMtj66fr1WCwsZPHVJx04cIBRo0bpRxrLlSuHnZ2dmaMSQgjjyMyDA3OBjUAZTdM+B7oCH5o0KiHykuWnDct9XNNU8Qd+ffyEox0kTjSoM/LfQ+hs27aNLl268ODBAzw9PXn99dfNHZIQQhjVM5M0pdQKTdOOAr7otoTqqJT60+SRCZFXjNpjWE4nSfs1zZncq2029LFq1Sr69OlDcnIyAwYMoEePHs++SAghcpnMPN35MhAP/AJsBu7/e04IYWTqiSNVKXYGXyJVqTSv5dRjqynemMcsWLCAXr16kZyczNixY1m8eDGWlrLArxAi78nM7c6t6H72aoAN4ACcA1xMGJcQAtA0jRYtXjF3GDmCUoovvviCDz/UzbaYOnUq48aNe+520txaFkKIHCoztzvdHi9rmuYFDDFZRELkVnuuwug9ULwg7Ar873xvZ7OFlJc8fPiQn376CU3T+Oabbxg8ePALtfM8CVp23LoVQoiMPPeOA0qpY5qm1TNFMELkaqP3wJVYcLc3PD/Lxyzh5DU2NjZs376dQ4cO0b59+yy3JyvPCSFyuszMSRv52DFa07SVwM1siE2I3OVK7H8fT/vDfHHkIQkJCcybN4/UVN2qP/b29kZJ0IQQIjfIzEha0cc+TkY3R22DacIRIg84GaE7xj7/gHOvXj9RrVpJHB3t8PIqj7Oz/bMvyqPu3btHhw4d2L17Nzdu3GDKlCnmDkkIIbLVU5O0fxexLaqUGp1N8QiRr61ceUr/8Vtv1WbBgnZmjMZ8IiMjadOmDSEhIZQrV46ePXuaOyQhhMh2GSZpmqZZKaWSNU1rmJ0BCZFrBXczanOOjvlz5fzr16/j5+fHX3/9hYODAzt37qRq1armDksIIbLd00bSDgNeQKimaZuBdcD9Ry8qpX4ycWxC5C4eZYzaXLVqpYzaXm7w999/4+fnx9WrV3F1dWXHjh289NJL5g5LCCHMIjNz0myAKMCH/9ZLU4AkaUIY2aJF7Th//g4XLtzBxSX/zUcbO3YsV69epX79+mzdupVSpfJfoiqEEI88LUkro2naSOA0/yVnj8jT6yJvWn7acBun3s5QIYO6vmt0Dwg8Etwty6NpgwbVztL1ud2SJUv46KOP+PLLLylcuLC5wxFCCLN62hIclkCRf4+ij3386BBCiCw7cuQIKSkpAJQqVYp58+ZJgiaEEDx9JC1MKfVptkUihMh3VqxYQd++fenfvz8LFy5E07RnXySEEPnE00bS5KelEMJk5s2bx+uvv05KSgp2dvnzSVYhhHiap42k+WZbFELkFH1cdcfjZmZQ9/H9OUWmKaWYPHkykyZNAmDatGmMGTPGzFEJIUTOk2GSppS6k52BCCF0CUxevuWXmprKe++9x9y5c7GwsGDhwoUMHDjQ3GEJIUSO9My9O4UQ2adkyS+pU2cR16/HPrtyLjRjxgzmzp1LgQIFWLt2rSRoQgjxFJKkCWFmDx8m6z+OiXnI0aNh2NnZmjEi03nzzTdp0qQJW7dupUuXLuYORwghcrTMLGYrRN5nP8+wHDEs27q+fDkanErryxUrFsPW1jrb+je1e/fuYWNjg7W1NcWLF2fPnj15+pauEEIYi4ykCWFmcXGJBuW8tB1UREQEzZo1Y8CAAaSmpgJIgiaEEJkkI2lCmFnt2v/tTXnjxsg0SVtude3aNfz8/Dh37hwxMTFERkZSpoxx9zcVQoi8TJI0IXKQl14qau4QjOLcuXP4+flx7do13N3d2bFjhyRoQgjxnCRJEwKydQ5aXnfs2DFatWpFZGQkDRs2ZMuWLZQoUcLcYQkhRK4jc9KEEEZz9OhRmjVrRmRkJG3atCEoKEgSNCGEeEEykiaEMBonJyecnZ1xcHBg2bJlFChQwNwhCSFEriVJmhBmlJSUgpWVBeTyJx4f7ZRQpEgRgoKCKFy4MJaWluYOSwghcjVJ0kT+tvy0YfnJfTtNbMKEXVy9GgtrugJw6lQ4bm5lszWGrJo7dy4HDx5kxYoVWFpaUqxYMXOHJIQQeYIkaSJ/G7XHsJyNSdrmzeeYMeN/usK/Sdq6dWdzTZKmlOKTTz7hk08+AWDAgAH4+fmZOSohhMg7JEkTwgySk1N5770dac6PHNnADNE8v9TUVN59913mzZuHhYUF3377rSRoQghhZPJ0pxBmYGVlQXBwb+rVq2BwvkQJGzNFlHlJSUn06dOHefPmUaBAAdavX88bb7xh7rCEECLPkZE0kb/1djZb1w4OJdm37w0mTtzNl2aL4vk8ePCA1157ja1bt1KkSBE2bdqEr6+vucMSQog8yaRJmqZprYE5gCXwrVJqagb1ugDrAW+lVIgpYxLCwCwfs3ZvbW3J1Kktck2SBroN00uVKsW2bduoW7euucMRQog8y2RJmqZplsB8wA+4DhzRNG2zUursE/WKAu8Cf5gqFiGEcdja2rJ582bCwsKoUaOGucMRQog8zZQjaXWBC0qpSwCapq0GOgBnn6g3GfgSGGPCWIRI30/+8M+vJu9GKQWAlgvXQ7ty5QozZ85k1qxZWFlZUbx4cYoXL27usIQQIs8z5YMDFYBrj5Wv/3tOT9M0L6CSUmrr0xrSNG2wpmkhmqbJrVBhXJlN0BzaZqmb2bMP0anTGu7ceZCldrLb2bNnadiwIV999RWfffaZucMRQoh8xWwPDmiaZgHMAvo9q65SahGwCKBOJU2ZNjKRL40y3ZdVSMhNxo0LJikplVq1vmHlyi40avSyyfozliNHjtCmTRuioqJo1KgRI0aMMHdIQgiRr5hyJO0GUOmxcsV/zz1SFHAF9miadhmoD2zWNK2OCWMSIlvFxj6ke/f1JCWlAnDtWiwdO64mLi7RzJE93e7du/Hx8SEqKoq2bduyY8cO2ShdCCGymSmTtCOAo6ZpDpqmFQC6A5sfvaiUilFKlVZKVVFKVQEOAQHydKcwC981usPIYmMfUqqUrcG5RYvaU6RIzt14fNOmTbRp04a4uDh69uzJpk2bKFSokLnDEkKIfMdkSZpSKhkYBuwA/gTWKqXOaJr2qaZpAabqV4gXcjJCdxhZxYrF2L+/P6NH63YSGDKkDp071zR6P8ailGLRokU8fPiQoUOH8sMPP2BtbW3usIQQIl8y6Zw0pdSvwK9PnPsog7rNTBmLEOZSoIAl06e3pE0bRxo0qGjucJ5K0zTWrl3LypUrGTRoUK58GlUIIfIK2RZKiGzi4+OArW3OG5VSSrFs2TISE3Xz5IoUKcLgwYMlQRNCCDOTJE2IRyoXM3cE2S41NZWhQ4fSr1+/TO2/6Q9oufwQQojcQpI0IUCXoM1oluVmDhy4yt27uWMttMTERHr16sWCBQsoWLAggYGBz7zG9Mv+Zo+srXonhBDZQzZYF/nLidvpnw/pk+WmL1+Oxt9/JcWL27BqVRdefbXSsy8yk/j4eLp27cq2bdsoWrQomzdvplmzZpm+XhYrFEII05MkTeQvLdYalscbp9mkpBR69NhATMxDYmIe0qTJ90yZ4suYMQ2N04ERRUdH0759e/bv30/p0qXZvn07tWvXNndYQgghniBJmhBGsGBBCIcOXdeXU1IUVlY5czbB5MmT2b9/PxUrVmTnzp2yUboQQuRQOfO3iBC5zFtv1eG99+rry/7+jowYUf8pV5jPZ599xhtvvMGBAwckQRNCiBxMRtJE3rLnKozeA1dideXezjDL57/X3e1N0m2BApbMmtUKHx8HPvjgN77/vkOOWsLi/PnzVKpUCRsbG2xtbVmyZIm5QxJCCPEMkqSJvOXxBC09u554gnFmd6N2365dddq2dcTCIuckaIcPH6ZNmzY0atSIDRs2YGUl3/ZCCJEbyO1Okbc8LUHLJjkpQQsODsbHx4c7d+6QmppKcnKyuUMSQgiRSZKkibxlZjPdUdZ0G4LfvfuAEydumax9Y/npp5/w9/fn/v37vP766/z000/Y2NiYOywhhBCZJPc9RN7Sx9XwXyN78CCJDh1Wc/z4LX76qRt+flVN0k9WLVmyhEGDBpGamso777zD7NmzsbCQv8mEECI3kZ/aQmRSSkoqvXr9xL59V4mLS6Rt25WsWHHS3GGlsXnzZgYMGEBqaioff/wxc+bMkQRNCCFyIfnJLUQmbd58jo0b/9KXk5NTWbToGKmpOWv9/VatWtGyZUvmzp3LpEmTctRTpkIIITJPkjSRN+y5CnWWg/08k3XRqVNNZs1qqS+7uNizaVNgjnhQICUlhQcPdHuGFixYkG3btvHOO++YOSohhBBZIUmayBuetfSGkbz3XgNWrepC1aol2b79dUqWtDV5n8+SmJhIz5496dSpE4mJiQBye1MIIfIA+Uku8obHE7Rpf5i0q+7dXTlzZggVKxYzaT+Zcf/+fTp06MDatWs5ePAg586dM3dIQgghjESe7hR5z/QjMLaeSbsoWND83zp3796lXbt2HDx4EHt7e7Zv346bm5u5wxL5zNwt9zh1NcncYQiRJ5n/N40Q4rmFhYXRqlUrTp06RaVKldi5cydOTk7mDkvkQ6ZK0NxetjZJu0LkJpKkibwhYphRm7txI5bevTfy+ec+NGhQyahtZ9XNmzdp0qQJFy9epEaNGgQFBVGpUs6KUeQ/i4eUMncIQuQ5MidNiHS8//4udu++zKuvLqFXr5+4di3G3CHp2dvbU6NGDerUqcO+ffskQRNCiDxKkjQhnnD48A1++OG/RWpXrjzF1q3nzRiRIWtra9atW8euXbsoXbq0ucMRQghhIpKkCfGErVv/Nii7uZVh4EAvM0Wjs3PnTtq3b09CQgIAtra2FCtm/qdLhRBCmI4kaUI84ZNPmrN7d188PMoC8H//1worK/N9q6xfvx5/f3+2bNnCt99+a7Y4hBBCZC95cECIdDRrVoWjRwezc+clfH1fMVscixcv5q233iI1NZURI0YwZMgQs8UihBAie0mSJvKG5acNy31cs9ykpaUFrVtXy3I7L+rLL79k/PjxAEyePJkPPvhA9uEUQoh8RJI0kTeM2mNYNkKSZi5KKcaPH8+0adPQNI158+bJCJoQQuRDkqQJkcOkpqZy8eJFrKysWLZsGT179jR3SEIIIcxAkjQhgISEZGxscsa3g6WlJStWrCAkJISGDRuaOxwhhBBmkjN+KwmRVb2ds3R58eJTqVu3Ak2avIyv7yv4+DgYKbBMiouDTz/l/qRJFC5cmIIFC0qCJoQQ+ZwkaSJvmOWTpcsTE1PYv/8q+/dfZd++q9mapN25cwf8/eHQId6+dYvly5dnW99CCCFyLlknTYgnNG1aOdv6unnzJk2bNoVDh6ByZSZOnJhtfQshhMjZZCRNiCc0aZI9SdrFixfx8/Pjn3/+AWdnCArCsUKFbOlbCCFEzidJmhDA1asj2Lv3Cr//foUGDUy/YfnJkydp1aoVt27dwtvbmyPbtoGdncn7FUIIkXtIkiZyN981huVdgS/UTKVKxenVy51evdyNENSzLViwgFu3buHj48OmTZsoVrRotvQrhBAi95AkTeRuJyPMHcELmTt3LlWqVOHdd9/FxsbG3OEIIYTIgeTBASGyybZt27h37x4A1tbWjBs3ThI0IYQQGZIkTYhssHDhQvz9/enQoQNJSUnmDkcIIUQuILc7Re4W3M3cETyVUoqpU6cyYcIEAPz8/LCykm87IYQQzya/LUTu5lHmhS+dPv0AY/4dSw4Lu0f58sadvK+UYsyYMcycORNN0/j666956623jNqHEEKIvEtud4p8SSnFjBn/05dfemkWO3ZcMFr7ycnJDBw4kJkzZ2JlZcWqVaskQRNCCPFcZCRN5Evr1p3l9u37Bufq1HnJaO1/++23LFmyBFtbW3766Sdat25ttLaFEELkD5KkiXypa1dnWreupi83aFARO7tCRmt/4MCBHDlyhP79+8tG6UIIIV6IJGkiX7Kw0Pjhh06wTFf++mv/LLcZFRWFpaUlJUqUwMrKiu+++y7LbQohhMi/JEkTuduJ24bl53iQoHTp/0bOatUql6Uwrl+/TsuWLSlZsiRBQUEULlw4S+0JIYQQkqSJ3K3FWsNyxDCD4oULdwgLu0fjxqbbNP38+fP4+flx5coVXFxcuHfvniRpQgghskye7hR51saNf1K79iI6dVrDtWsxJukjNDSURo0aceXKFerVq8fevXspVy5ro3JCCCEESJIm8qhx43bSufNaYmMfEhX1gG7d1pOYmGLUPvbv30+zZs24ffs2LVq0IDg4mFKlShm1DyGEEPmXJGkid3O3Nzz+Vby44Z6Yhw5dZ8WKk0br9tSpU7Rs2ZKYmBi6dOnCli1bKFKkiNHaF0IIIWROmsjddgWme3r8+EYcOHCNX389j5WVBTNntqRfv1pG69bFxYVOnTpha2vLwoULsbS0NFrbQgghBEiSJvKoR0tsdOiwmmnTWtCgQSWjtPvw4UMKFiyIhYUFy5Ytw9LSEk3TjNK2EEII8Ti53SlytYcPkzN8rVQpW/bu7WeUBE0pxWeffUbjxo25d+8eAFZWVpKgCSGEMBlJ0kSu9ccf13Fymse2beczrGOMJCo1NZWRI0cyceJEQkJC2LNnT5bbFEIIIZ5FkjSR6yil+OqrP2jc+HuuXInh9dc3cvWqaZbYSE5Opn///syePRtra2vWrFlD+/btTdKXEEII8ThJ0kSuc+ZMBCNG7CApKRWAO3ce8Npr64y+xEZCQgJdu3Zl2bJlFCpUiC1btvDaa68ZtQ8hhBAiI5KkiVzH1bUMn3/uY3CuYTJoY3YbrY/4+Hjatm3Lzz//TMmSJQkODqZly5ZGa18IIYR4FknSRK40dmxD2rWrTlFNY33RIsy6moD1yr+M1r6trS0ODg6UL1+evXv30qBBA6O1LYQQQmSGLMEhciULC43lyzsSWXURjiZYo0zTNBYtWsStW7eoUKGC0dsXQgghnkVG0kSOdenSXRYuDMnw9ZIlbY2aoP39998EBAQQHR0NgKWlpSRoQgghzEZG0kSOtHnzOfr23UR0dAIVKxbD3796+hVnNjNKf8eOHaN169ZERETw0UcfMXfuXKO0K4QQQrwoSdJEjvPll/sZP36Xvty790aOH3+TypVLpK3cxzXL/e29CO0/bU5sbCwtW7ZkypQpWW5TCCGEyCq53SlynAYNKmFp+d8itHfvJrB48TGT9LXlLLRaDLGxsXTr1o1ffvmFwoULm6QvIYQQ4nlIkiZynCZNKjNlii8AVlYWzJrVksmTmxu9nxUrVtBxKSQkw+DBg7m3ciUFCxRAg2w/hBBCiCfJ7U6RI40e/SqXLt3l9dfdadjwZZP0cejQIVJS4X0f+Pybb7Aw8z6cbc3auxBCiJxGkjSRI2maxoIF7Uzax5w5c2gVP492zsBjCZoyaa9CCCFE5sjtTpFvpKamMnXqVCIjIwGwsLDQJWhCCCFEDiQjaSJn2nMVRu+BK7G6ZTYyeorTfp5hOWJYutWSkpIYMGAAP/zwA1u3bmXv3r1oZr69KYQQQjyNJGkiZ3qUoBnBgwcPCAwM1D+5OWnSJEnQhBBC5HiSpIkcITk5lW7d1uHuXpbKlYtT5XwUja2tsNI0mHY4c+uhVS6W5lRMTAwdOnTg999/p1SpUvz666/Uq1fPBJ+BEEIIYVySpAmzS01V9O//Mxs3/sXGjbpN0i2ABLtSugrh8c9upHIxmNHM4NTt27dp3bo1x48f56WXXiIoKAgXFxfjBi+EEEKYiCRpwuwOHrzGjz+eNDjXqZod1u0zMas/gzloAEuXLuX48eNUrVqV4OBgqlSpksVIhRBCiOwjSZowu0aNXmblyi707r2R5ORUKlcuzuzdfaBi2tuXz2PMmDE8fPiQQYMGUa5cOSNFK4QQQmQPWYJD5Ajdu7uyaVMgDg4lCA7uQ8UXTNCOHz9OeHg4oFtrbeLEiZKgCSGEyJUkSRM5hr9/df76axjVqpV6oev37NlD06ZNadWqFTExMUaOTgghhMhekqSJHKVAAcsXum7z5s20bt2ae/fuUbNmTWxtbY0cmRBCCJG9JEkT2Wrr1r/ZsOGsUdtcvnw5nTt35uHDh7z99tv8+OOPFChQwKh9CCGEENlNkjSRbX7//TJdu66jW7f1LF0aapQ258yZQ9++fUlJSeHDDz9k/vz5WFq+2GicEEIIkZPI050iW4SE3KR9+1UkJCQD8MYbPxMb+5DhwzNYWNZ3jWF5V2CaKr/99hsjRowAYNasWbz33ntGjVkIIYQwJ0nSRLY4fz6K+/eTDM49df7ZyYhnttm8eXOGDx+Op6cn/fr1y2KEQgghRM4iSZrIFj16uGFjY0X37htITExh6lRf3nqrznO3k5SUxN27dylTpgyapjFnzhwTRCuEEEKYnyRpItt06lSTLVt6cODANcYF3YAZ8/57MbgbeJR56vXx8fF069aNf/75h71792JnZ2fiiM3DH/jV3EEIIYQwO0nSRLby86uKn1/VtHPOnhTczaAYHR1N+/bt2b9/P3Z2dly7di3PJmk5PUFra+4AhBAin5AkTRidUgpN07LWyGOjauHh4bRu3pzQ0FAqVKhAUFAQzs6Z2Nczl1PmDkAIIYRZyRIcwqgePEiiZcsfWbnylFHau3LlCo0bNyY0NBRHR0cOHDiQLxI0IYQQQkbShNEkJaXw2mvrCA6+xK5dl4iNfZjxwwHpLKnxpMjISBo2bMiNGzeoVasW27evPhlxAAAgAElEQVRvp2zZskaOWgghhMiZZCRNGM0bb/zM1q3nAVAK3n57Kz/8cOKF27OzsyMwMJDGjRuzZ88eSdCEEELkKzKSJoymXbvqrFlzhuTkVADc3Mrg719d9+LI3wwrz/LJsJ3k5GSsrKzQNI0ZM2bw8OFDbGxsTBW2EEIIkSPJSJowmu7dXdm0KRAbGyuqVi1JUFBvSpX6d6PzH84aHhnYtGkTnp6ehIeHA6BpmiRoQggh8iUZSROZdvJkOOHhcURGxhMZGU///p4ULmy4kbm/f3V27Hidl18uTrlyRQwbKFsIwuMzbP/7779n4MCBpKamsnz5csaMGWOKT0OIXCMpKYnr16+TkJBg7lAy1LeObuT8zz/DzRyJEOZlY2NDxYoVsba2NlqbkqTlcydO3GL37stERsYTFRVPs2ZVCAx0Tbduhw6ruXw5Wl9u08aRatVKpanXpEnltBeP8YZyhWHUnnTbnjVrFqNGjQLgo48+YvTo0c//yQiRx1y/fp2iRYtSpUqVrC9rYyKXb+v2461SRn6diPxLKUVUVBTXr1/HwcHBaO3Kd1UulZqqSElJxdo67f6XiYkpDB26laioB0RFPeD+/URCQgan287evVd4770d+rKmaRkmaXZ2tgZJWmRkfLpJWrrG1oPlp3UfVy6mP62UYuLEiXz++ecAzJ49m3fffTdzbQqRxyUkJOToBE0IoaNpGnZ2dkREPHvf6echSVoOkJycyp07D4iKiicpKRV39/SfYpwyZR/Llp0gKuoBd+484Ouv2/Lmm2mXuLC2tmDp0hP6CfwACQnJ2Nik/e+2sytkUI6MzPh2ZOnShnWjojKum6HKxWBGM0CXoA0ZMoRvvvkGS0tLlixZQp8+fZ6/TSHyMEnQhMgdTPG9KkmaiVy4cIfr12OJitLN32rXrjoVKhRLU+/PPyNwdv5aX3ZysuOvv4al22Z0dALnzkXpy1FRD9Ktp8vobQkPv/9Y3fh0+7ezszUoZ9QmgJdXeRITUyhduhB2dra89FLRDOumq4+r7jCI046CBQuydu1aAgICnq89IYQQIg+TJO05XLkSzdat5/WJV7VqpXjnnXrp1h069FeCgi7qy1u3Fk83SdI//fivp41kPc+ol51dIYMkLTIy/STN0dGOoUO99YnX025ffvGFb4avvajJkyfTu3dvnJycjN62ECJ77Nz+C1E3zzF+/Hhzh2JWS5cuZcyYMVSoUIGEhATefPNN3nvvPf3rixYtYtasWQAUK1aMWbNm0ahRI0D3kMjEiRPZsGEDRYsWpWDBgnz00Ue0adPGLJ9LRkaMGEHnzp1p0qSJuUNJ19GjR+nXrx8PHjygbdu2zJkzJ80I1549e+jQoYN+7ljnzp356KOPOHfuHIGB/y20funSJT799FNGjBjB6NGjadu2LT4+GS8fZQomTdI0TWsNzAEsgW+VUlOfeH0kMBBIBiKA/kqpK8aOQylFSorCyir9FUc++GAXFy/eJSrqAZGR8fz6a0/Kl087SnTuXBRDh/63/bWPj0OGSdqTtwYzSqieTNLu3HlASkoqlpZpY32yzbi4xHTbBPj8cx+DUS8np9Lp1nvllZLMm5d9W2ZHR0czZMgQpk2bRsWKFdE0TRI0ITLLfp5hOSL9UXeWnzZ8SKe381PXJswqv9btqVKmU6bqKqVQSmFhYZ4VoB6tw2gqgYGBzJs3j6ioKJycnOjatSuVKlViy5YtLFy4kP3791O6dGmOHTtGx44dOXz4MOXKlWPixImEhYVx+vRpChYsSHh4OL///rtRY0tJScHSMu085syKiori0KFDzJ49O9PXmPr9ftLbb7/N4sWLqVevHm3btmX79u3pJrqNGzdmy5YtBuecnJwIDQ0FdO9VhQoV6NRJ93X9zjvvMGjQoGxP0kz2XaJpmiUwH2gDOAM9NE17ctPF40AdpZQ7sB6Ylpm2fXyW6Y+YmPQfTV+9+jTu7guoUGEWNjafM27czgzb27TpHGvWnCE4+BKhobe4fft+uvXS3hp82khW5upaW1tSooQNJUvaUK1aKerVq0h8fFK6dTt0cOLUqbcJCxvFw4cfsmhR+wz779ixBt26ueDj44CHR7l056NlC/t5+uOW3Rc0a9aMVatWMXDgQPPEI4TItMuXL1OjRg369etH9erV6dWrF8HBwTRs2BBHR0cOHz4MwPrVyxg2TJcwhoeH06lTJzw8PPDw8ODgwYNcvnwZJycn+vTpg6urK9euXWPMmDG4urri5ubGmjVr0u3/8OHDNGjQAE9PT1599VXOnTsHQP369Tlz5oy+XrNmzQgJCeH+/fv079+funXr4unpyc8//wzoRrgCAgLw8fHB19eXuLg4fH198fLyws3NTV8PdKP7Tk5ONGrUiB49ejBjxgwALl68SOvWralduzaNGzfmr7/+eup7Z2dnR7Vq1QgLCwPgyy+/ZPr06ZQurfuD2cvLi759+zJ//nzi4+NZvHgxX331FQULFgSgbNmydOvWLU27R44c4dVXX8XDw4O6dety7949li5dqn//Adq1a8eePXsAKFKkCKNGjcLDw4MpU6bw2muv6evt2bOHdu3aARAUFESDBg3w8vLitddeIy4uLk3fGzZsoHXr1vryp59+ire3N66urgwePBillP7/Y8SIEdSpU4c5c+Zw9OhRmjZtSu3atWnVqpX+PVm8eDHe3t54eHjQpUsX4uNfYJ7zY8LCwoiNjaV+/fpomkafPn3YtGnTC7W1a9cuqlatSuXKutUKKleuTFRUFLdu3cpSjM/LlL+56wIXlFKXADRNWw10APQrmSqldj9W/xDwemYa3r37sv7jxyfHPy4+PolTp27ry5GRGc+1SjshPv26z3O7sWbN0jRsWEk/klWzpn2GdSMjx6Q7cpZe/0/GkFv8kxKJX8x8Lt6JpHr16ixatCj9ij/5wz+/pv+aECLbXbhwgXXr1rFkyRK8vb1ZuXIl+/fvZ/PmzXzxxRfMXrTeoP7w4cNp2rQpGzduJCUlhbi4OO7evcv58+dZtmwZ9evXZ8OGDYSGhnLixAkiIyPx9vamSZMmlC9f3qCtGjVqsG/fPqysrAgODmbChAls2LCBwMBA1q5dyyeffEJYWBhhYWHUqVOHCRMm4OPjw5IlS4iOjqZu3bq0aNECgGPHjnHy5ElKlSpFcnIyGzdupFixYkRGRlK/fn0CAgIICQlhw4YNnDhxgqSkJLy8vKhduzYAgwcP5ptvvsHR0ZE//viDIUOG8NtvT+yk8pirV6+SkJCAu7s7AGfOnNG39UidOnVYtmwZFy5c4OWXX6ZYsbRTUh6XmJhIYGAga9aswdvbm9jYWGxtbZ96zf3796lXrx4zZ84kOTmZV155hfv371O4cGHWrFlD9+7diYyM5LPPPiM4OJjChQvz5ZdfMmvWLD766CODtg4cOEDXrl315WHDhunr9O7dmy1bttC+fXt9rCEhISQlJdG0aVN+/vln7O3tWbNmDR988AFLliyhc+fODBo0CIAPP/yQ7777jnfeecegz927dxvcMn6kUKFCHDx40ODcjRs3qFixor5csWJFbty4ke778r///Q8PDw9eeuklZsyYgYuLi8Hrq1evpkePHgbnvLy8OHDgAF26dEm3TVMwZZJWAbj2WPk6kP69QZ0BwLb0XtA0bTAwGKB2xfRqpPXkSNbT529lbtSrTJnCDBrkpU+80izW+pi33/bm7be9MxVrZhK03OxMchgtY+dzMzUGLy8vtm3bRpkyZdKvbI4EzSH7bvcKkds4ODjg5uYGgIuLC76+vmiahpubG5cvX05T/7fffmP58uUAWFpaUrx4ce7evUvlypWpX78+APv376dHjx5YWlpStmxZmjZtypEjR9I8PBQTE0Pfvn05f/48mqaRlKS7y9CtWzdatmzJJ598wtq1a/WJQ1BQEJs3b9aPfiUkJHD16lUA/Pz8KFVKN+dWKcWECRPYu3cvFhYW3Lhxg/DwcA4cOECHDh2wsbHBxsZGn3DExcVx8OBBg1Gohw8fpvt+rVmzhr179/LXX38xb948o+6Ycu7cOcqXL4+3t+53y7OSOtD9HzxKKqysrGjdujW//PILXbt2ZevWrUybNo3ff/+ds2fP0rBhQ0CXYDVo0CBNW2FhYdjb/zfgsHv3bqZNm0Z8fDx37tzBxcVF/549mtt17tw5Tp8+jZ+fH6C7jfgoGT99+jQffvgh0dHRxMXF0apVqzR9Nm/eXH8L0li8vLy4cuUKRYoU4ddff6Vjx46cP39e/3piYiKbN29mypQpBteVKVOGmzdvGjWWZ8kRDw5omvY6UAdomt7rSqlFwCKAOpU0lZk2nxwdi41N/xsKYMgQbzp3romdnS2lSxeienW7dOsVKmT91FuMIq0/ki7TNnYBd1Q8Ta2qsXn37kz9YGFUpv6bhchfMpqD9qQnnqTOike33wAsLCz0ZQsLC5KTkzPdTuHChZ9ZZ/78+SxevBiAX3/9lYkTJ9K8eXM2btzI5cuXadasGQAVKlTAzs6OkydPsmbNGr755htAl3xt2LAhzTzXP/74w6D/FStWEBERwdGjR7G2tqZKlSpP3dUhNTWVEiVKZCpZeDQnLSQkhJYtWxIQEEC5cuVwdnbm6NGjBnOajh49iouLC9WqVePq1avExsZm7ufjE6ysrEhNfXzJpf8+FxsbG4N5aN27d2fevHmUKlWKOnXqULRoUZRS+Pn5sWrVqqf2Y2trq287ISGBIUOGEBISQqVKlfj4448N+n30fiulcHFx4X//+1+a9vr168emTZvw8PBg6dKl+lu0j3uekbQKFSpw/fp1ffn69etUqFAhzbWPv8dt27ZlyJAhREZG6m9Fb9u2DS8vL8qWNVwOKyEh4Zkjl8ZmyiGcG0Clx8oV/z1nQNO0FsAHQIBSKuNM6jG7dvXRH8WKFUy3jodHOY4eHcyVKyO4f38C+/a9kWF7LVq8wuuvu9OmjSPe3hUoXlz2ijSW3yeU4Y6KJyAggG2xJ1/oB5AQIvfw9fVlwYIFgG7UJCYmJk2dxo0bs2bNGlJSUoiIiGDv3r3UrVuXoUOHEhoaSmhoKC+99BIxMTH6X7JLly41aCMwMJBp06YRExOjv6XYqlUrvvrqK/3cqOPHj6cbY0xMDGXKlMHa2prdu3dz5YruebWGDRvyyy+/kJCQQFxcnH5iebFixXBwcGDdunWALvE4ceLEU9+HOnXq0Lt3b+bMmQPA2LFjGTduHFFRumWUQkNDWbp0KUOGDKFQoUIMGDCAd999l8RE3QNhERER+v4ecXJyIiwsjCNHjgBw7949kpOTqVKlCqGhoaSmpnLt2jX9XMH0NG3alGPHjrF48WK6d+8O6Ob4HThwgAsXLgC6W6R///13mmtr1qypr/MoIStdujRxcXGsX78+Tf1HMUdEROiTtKSkJP18wnv37lG+fHmSkpJYsWJFutc/Gkl78ngyQQMoX748xYoV49ChQyilWL58OR06dEhT79atW/qvkcOHD5Oamoqd3X+DM6tWrUpzqxPg77//xtXVOH/8ZJYpk7QjgKOmaQ6aphUAugObH6+gaZonsBBdgnY7nTbS5ePjoD/SW3EfoEiRAnh5lefll4tTqJDx9tESz2fMmDGsWrWKDRs2ZPtfIEKI7Ddnzhx2796Nm5sbtWvX5uzZs2nqdOrUCXd3dzw8PPDx8WHatGmUK1cuTb2xY8fy/vvv4+npmWbUrmvXrqxevdpgcv3EiRNJSkrC3d0dFxcXJk6cmG6MvXr1IiQkBDc3N5YvX06NGjUA8Pb2JiAgAHd3d9q0aYObmxvFixcHdKNv3333HR4eHri4uBg8bJCRcePG8f3333Pv3j0CAgLo378/r776KjVq1GDQoEH8+OOP+lt/n332Gfb29jg7O+Pq6kq7du3S/FFboEAB1qxZwzvvvIOHhwd+fn4kJCTQsGFDHBwccHZ2Zvjw4Xh5eWUYk6WlJe3atWPbtm36hwbs7e1ZunQpPXr0wN3dnQYNGqT7YIS/v79+tKtEiRIMGjQIV1dXWrVqpb8F+6QCBQqwfv16xo0bh4eHB7Vq1dInWJMnT6ZevXo0bNhQ/3+QVV9//TUDBw6kWrVqVK1aVf9k5zfffKMfcV2/fj2urq54eHgwfPhwVq9erV+m4/79++zcuZPOnTsbtJuUlMSFCxeoUyftAvKmpD3KJk3SuKa1BWajW4JjiVLqc03TPgVClFKbNU0LBtyAsH8vuaqUeuqKpnUqaSrkmtwKy8l+/PFHGjVqRJUqVZ7/4pn/rmdjhtudj1bSMfdXV06JQ5jfn3/+Sc2aNc0dxlPltb074+LiKFKkCPHx8TRp0oRFixY9NenJbxo1asSWLVsoUaKEuUPJVhs3buTYsWNMnjz5qfXS+57VNO2oUuqFsjuTflcppX4Ffn3i3EePfdzClP2L7Dd9+nTGjh1LtWrVCA0NzdQ8FCGEyCkGDx7M2bNnSUhIoG/fvpKgPWHmzJlcvXo13yVpycnJjBo1Ktv7zRt/+ghDz7OQpe8aOPnYhrDB3cAjnScvT9yGFmv/K7vbw67/VmZ+9LTU1Km69YqHh9WicJXvdS/ObGa0ScxCCGFKK1euNHcIOVq9ek9bpCHvevzJ3uwkSZrIspSUFIYOHcrChQuxtLRkqd8YXg9J+0SNEEIIITIvby/QJUwuMTGRnj17snDhQmxsbNi4cSOv1/Qzd1hCCCFEridJmsiSX375hbVr11KsWDF27NihX8hQCCGEEFlj0qc7TUGe7sx5vvzyS/z8/IwzwVae7swxcQjzk6c7hchdjP10p4ykiecWFhamX9AQdGsByRNQQgjxn8uXL2Nra0utWrVwdnamT58++m2tQLc1Vt26dalRowY1atRIs5/x8uXL9RvQe3p66re6ykk2bdrEp59+au4wMnTnzh38/PxwdHTEz8+Pu3fvplvv6tWrtGzZkpo1a+Ls7Kzf7qxXr144OTnh6upK//799f9/W7ZsSbOvqalIkiaey6VLl2jUqBEtWrTIcONaIYRpaNonBkdGFi06alBv8OBfsjHK55OSkmK2vpVSBtspGVvVqlUJDQ3l1KlTXL9+nbVrdU/I37p1i549e/LNN9/w119/sX//fhYuXMjWrVsB3bZEs2fPJigoiFOnTnHo0CH9orrG8jxbemVk2rRpDBkyJFv7fB5Tp07F19eX8+fP4+vrq1994El9+vRhzJgx/Pnnnxw+fFi/t3SvXr3466+/OHXqFA8ePODbb78FdIv6/vLLL8THZ7wnuLFIkpZX2M8zPEzg1KlTNGrUiEuXLmFvb2+wp58QIu+5fPkyNWrUoF+/flSvXp1evXoRHBxMw4YNcXR01G8/FHrsMA0aNMDT05NXX32Vc+fOAboEbPTo0bi6uuLu7s5XX30FQJUqVfQj8OvWrWPVqlW4ubnh6urKuHHj0o0lLi4OX19fvLy8cHNz06/4P378eObPn6+v9/HHH+tHnaZPn463tzfu7u5MmjRJ/zk5OTnRp08fXF1duXbtGm+//TZ16tTBxcVFXw90+4fWqFGD2rVrM3z4cP0K/ffv36d///7UrVsXT0/PZ+4+YGlpSd26dfV/2M6fP59+/frp70CULl2aadOm6ZOIKVOmMGPGDF566SVAt3/qoEGD0rQbHh5Op06d8PDwwMPDg4MHD3L58mWDrYtmzJjBxx9/DECzZs0YMWIEderU4fPPP6dy5cr6JPX+/ftUqlSJpKQkLl68SOvWralduzaNGzdOd/eBv//+m4IFC+r3u/zll1+oV68enp6etGjRgvDwcP3/R+/evWnYsCG9e/cmIiKCLl264O3tjbe3NwcOHAB02zOl9zWUFT///DN9+/YFoG/fvmzatClNnbNnz5KcnKzfAL5IkSIUKqTb+7tt27ZomoamadStW1e/L6imaTRr1ky/bZhJKaVy1VG7Ikqko/RXhoeRHTx4UJUoUUIBqnnz5io2NtbofSillJqB7jCDR19k5pZT4hDmd/bsWYMyfGxwZGThwhCDeoMGbX6h/v/55x9laWmpTp48qVJSUpSXl5d64403VGpqqtq0aZPq0KGD+ic8SZ28EKWSkpKUUkrt3LlTde7cWSml1Ndff626dOmify0qKkoppVTlypXVl19+qZRS6saNG6pSpUrq9u3bKikpSTVv3lxt3LgxTSxJSUkqJiZGKaVURESEqlq1qkpNTVXHjh1TTZo00derWbOmunr1qtqxY4caNGiQSk1NVSkpKcrf31/9/vvv6p9//lGapqn//e9/+msexZWcnKyaNm2qTpw4oR48eKAqVqyoLl26pJRSqnv37srf318ppdT777+vfvjhB6WUUnfv3lWOjo4qLi4uzXvn4uKilFLqwYMHqlmzZurEiRNKKaU6deqkNm3aZFA/OjpalSxZUimlVMmSJVV0dPQz/3+6deum/u///k8fe3R0tEG/Sik1ffp0NWnSJKWUUk2bNlVvv/22/rWAgAD122+/KaWUWr16tRowYIBSSikfHx/1999/K6WUOnTokGrevHmavpcsWaJGjhypL9+5c0elpqYqpZRavHix/rVJkyYpLy8vFR8fr5RSqkePHmrfvn1KKaWuXLmiatSooZRSKiYmJt2vocfFxsYqDw+PdI8zZ86kqV+8eHH9x6mpqQblRzZu3Kj8/f1Vp06dVK1atdTo0aNVcnKyQZ3ExETl6emp9u7dqz/3448/qmHDhqVp78nvWaWUQrfL0gvlPDLTUzxTUFAQnTp1Ij4+ng4dOrB69WpsbGQTeiHyAwcHB9zc3ABwcXHB19cXTdNwc3PTz925FxvDa68N4Pz582iapp+7ExwczFtvvYWVle5XTalSpfTtBgbqFsM+cuQIzZo1w97eHtDdYtq7dy8dO3Y0iEP9u2D23r17sbCw4MaNG4SHh+Pp6cnt27e5efMmERERlCxZkkqVKjFnzhyCgoLw9PQEdCNx58+f5+WXX6Zy5crUr19f3/batWtZtGgRycnJhIWFcfbsWVJTU3nllVdwcHAAoEePHvp5Y0FBQWzevFk/YpeQkMDVq1fTTBi/ePEitWrV4p9//sHf31+/Ebyx/PbbbyxfvhzQjdYVL148w3lXjzx63x99vGbNGpo3b87q1asZMmQIcXFxHDx40GDx1ocPH6ZpJywsTP9/BnD9+nUCAwMJCwsjMTFR/74BBAQE6PduDg4ONtjPNTY2lri4OGJiYujbt2+ar6HHFS1alNDQ0Ge9Lel6NCL2pOTkZPbt28fx48d5+eWXCQwMZOnSpQwYMEBfZ8iQITRp0oTGjRvrz5UpU4abN2++UCzPQ5I08VSXLl2iXbt2JCUl0bdvX7799lv9D1whRPZSatKzKwGDB9dm8ODaRunz8WkNFhYW+rKFhYV+jtGsLyfRvHlzNm7cyOXLl2nWrNkz233WlnF//PEHb775JgCffvopd+7cISIigqNHj2JtbU2VKlVISEgAdKvBr1+/nlu3bumTEKUU77//vr6NRy5fvmzQ9z///MOMGTM4cuQIJUuWpF+/fvp2M6KUYsOGDTg5OT213qM5aZGRkTRs2JDNmzcTEBCAs7MzR48epUOHDvq6R48excXFBdAlw0ePHsXHJ4OdYp7CysrKYJ7dk5/L4597QEAAEyZM4M6dO/r+7t+/T4kSJZ6ZDNna2hITE6Mvv/POO4wcOZKAgAD27Nmjv8X6ZJ+pqakcOnQozR/6w4YNe+bX0L179wwSpcetXLkSZ2dng3Nly5YlLCyM8uXLExYWpp9r9riKFStSq1YtXnnlFQA6duzIoUOH9EnaJ598QkREBAsXLjS4LiEhQZ94mpLMScsrIoYZHkbyyiuvMGnSJEaMGMGSJUskQRNCpHEvNpYKFXS7jCxdulR/3s/Pj4ULF+qTuTt37qS5tm7duvz+++9ERkaSkpLCqlWraNq0KfXq1SM0NJTQ0FACAgKIiYmhTJkyWFtbs3v3bq5cuaJvIzAwkNWrV7N+/Xr9CFCrVq1YsmQJcXFxANy4cYPbt2+n6T82NpbChQtTvHhxwsPD2bZtGwBOTk5cunRJP1q4Zs0a/TWtWrXiq6++Qv27hNXx48ef+v6ULl2aqVOnMmXKFACGDh3K0qVL9YlQVFQU48aNY+zYsQC8//77jBkzhlu3bgG6RcMfTVp/nK+vLwsWLAB08/9iYmIoW7Yst2/fJioqiocPHz513lSRIkXw9vbm3XffpV27dlhaWlKsWDEcHBxYt24doEtIT5w4kebamjVrGjzlHxMTo/8aWLZsWYZ9tmzZUj83EdC/B49f//jX0OMejaSldzyZoIEuCX0Uy7JlywyS4ke8vb2Jjo4mIkK3PeJvv/2mb+vbb79lx44drFq1CgsLw3Tp77//Npj7ZyqSpIl0RUZG6j+eMGECs2bNSvNFKoQQAG8OHcX777+Pp6enwRN8AwcO5OWXX8bd3R0PD49098UsX748U6dOpXnz5nh4eFC7du10f5n26tWLkJAQ3NzcWL58OTVq1NC/5uLiwr1796hQoQLly5cHdMlAz549adCgAW5ubnTt2pV79+6ladfDwwNPT09q1KhBz549adiwIaAbKfr666/1E+iLFi2qf8Jy4sSJJCUl4e7ujouLCxMnTnzme9SxY0fi4+PZt28f5cuX58cff2TQoEHUqFGDV199lf79++sXA2/bti3Dhg2jRYsWuLi44OXlRWxsbJo258yZw+7du3Fzc6N27dqcPXsWa2trPvroI+rWrYufn5/B+5SewMBAfvzxR4PboCtWrOC7777Dw8MDFxeXdB+MaNKkCcePH9cnqh9//DGvvfYatWvX1j9MkJ65c+cSEhKCu7s7zs7OfPPNNwCMHTs23a+hrBg/fjw7d+7E0dGR4OBgxo8fD0BISAgDBw4EdLeJZ8yYga+vL25ubiil9A9pvPXWW4SHh9OgQQNq1aplsNzI7kCJS7UAACAASURBVN278ff3N0qcTyOL2QoDSinGjRvHypUrOXDgAJUrV87eAGQx2xwThzA/WczWvOLi4ihSpAhKKYYOHYqjoyPvvfeeucPKMd59913at29PixYtzB1KtgoPD6dnz57s2rUrzWuymK3QGfmb4ZIby09nucmUlBQGDRrE9OnTCQ8Pf+YQvhBC5GWLFy+mVq1auLi4EBMTk2Z+W343YcKEbFkrLKe5evUqM2fOzJa+8t6fPuKFPHz4kF69erFhwwZsbW1Zv349bdu2NXdYQghhNu+9956MnD1F2bJlCQgIMHcY2c7b2zvb+pIkTRAXF0enTp0IDg6mePHibNmyhUaNGpk7LCGEECJfkyQtn0tKSsLPz49Dhw5RtmxZduzYgYeHh7nDEkIIIfI9SdJyq1k+uiOLrK2tee2117h16xY7d+6kWrVqRghOCCGEEFklDw7kU48/1Tty5EhOnDghCZoQQgiRg0iSllv4rjE8suDkyZN4enpy/vx5/blixYplNUIhRB5kaWlJrVq1cHV1pX379kRHR+tfO3PmDD07++HzqguOjo5MnjzZ4A/Abdu2UadOHZydnfH09GTUqFHm+BReSI8ePXB3d+f//u//MlW/SJEiJolDKcXw4cOpVq0a7u7uHDt2LN16Dx48oGnTpqSkpJgkDmOYMmUK1apVw8nJiR07dqRbp1+/fjg4OFCrVi1q1aplsNht+/bt9Wu3ff/99wBERETQunXrbPscspskabnFyQjD4wUdPHiQpk2bcuLECb744gsjBiiEyItsbW0JDQ3l9OnTlCpVivnz5wO6pCAgIIC3ho/lt4NnOHHiBAcPHuTrr78G4PTp0wwbNowff/yRs2fPEhISYvTRemMtevqkW7duceTIEU6ePGn2pzu3bdvG+fPnOX/+PIsWLeLtt99Ot96SJUvo3LkzlpaWmWpXKWWwfZSpnT17ltWrV3PmzBm2b9/OkCFDMkwop0+frt9JoFatWgDMnz8fZ2dnTpw4wZ49exg1ahSJiYnY29tTvnx5Dhw4kG2fS3aSJC03aVklS5dv376dFi1aEB0dTefOnfUrPQshcj7NRMfzaNCgATdu3AB0eyU2bNiQJs38AChUqBDz5s1j6tSpAEybNo0PPvhAv+K9paVluglGXFwcb7zxBm5ubri7u7NhwwbAcGRq/fr19OvXD9CNtLz11lvUq1ePsWPHUqVKFYPRPUdHR8LDw4mIiKBLly54e3vj7e2d7i/xhIQEfd+enp7s3r0b0O1WcOPGDWrVqsW+ffsMrgkPD6dTp054eHjg4eHBwYMH03w+vr6+eHl54ebmpl+t//79+/j7++Ph4YGrq6t+m6nx48fj7OyMu7s7o0ePThPjzz//TJ8+fdA0jfr16xMdHU1YWFiaeitWrNDv1JBRDJcvX8bJyYk+ffrg6urKtWvXmD59Ot7e3ri7uzNp0n97w3bs2JHatWvj4uKi31g+K37++We6d+9OwYIFcXBwoFq1ahw+fDjT12uaxr1791BKERcXR6lSpfTbFHbs2JEVK1ZkOcacSB4cyC3GeEMrBwi6/EKXr1mzht69e5OUlET//v1ZuHCh7MMphMi0lJQUdu3apd94+syZM9SubbiJe9WqVYmLiyM2NpbTp09n6vbm5MmTKV68OKdOnQLg7t27z7zm+vXrHDx4EEvL/2/v3uOiqtbHj3+WoCAZpqYcL6XmBbnMAAoqKWgRaEh4wUTFTDI7aWpHDbWkPKnlpRTDyPJbHjNN7NhB/OVRCcWv4R04KioEHG8wKiL5BVHQgP37Y2DHyHDRuOp6v17zkpm99t7PzObyuNba6zGhqKiIiIgIAgMDOXr0KJ07d8bKyorx48cza9YsBg4cyKVLlxgyZAhJSUkGxwkLC0MIQWJiIsnJyXh5eZGSksKOHTvw8fExWmR85syZDBo0iIiICIqKitTaoKXMzc2JiIjA0tKS69ev079/f3x9fdm9ezcdOnRg586dgH74Ljs7m4iICJKTkxFCGCSbpXQ6HU899ZT6vFOnTuh0OrX8Fehre547d44uXbpUGgNAamoq3377Lf379ycqKorU1FSOHTuGoij4+vpy4MAB3N3dWb9+Pa1btyY/Px8XFxf8/Pxo06aNQWyzZs1SE9uyxo4dq5ZgKvs++vfvX+59GLNgwQIWLVqEh4cHy5Ytw8zMjOnTp+Pr60uHDh24efMmW7duVUsVOjs7ExwcbPRYjZ38K91YzO0HJ0uKA3e+v/ljX331FVOnTkVRFN555x1WrFiBEPf7f2hJkupTfZUJy8/Px9HREZ1Oh42NDZ6enjV6/OjoaMLDw9XnrVq1qnKfl19+WR3W8/f3Z9GiRQQGBhIeHq7WoIyOjubs2bPqPrm5uWqZp1KxsbHMmDEDgF69etG5c2dSUlIqnaO7b98+Nm7cCOh7B0vreZZSFIX33nuPAwcO0KRJE3Q6HZmZmWg0GubMmcO8efPw8fHBzc2NwsJCzM3NmTx5Mj4+Pvj4+FT53o25fv06TzzxRJUxAHTu3FlNlqKiooiKisLJyQnQ98Clpqbi7u5OaGgoERERAKSnp5OamlouSavufL37sXTpUv7yl79w9+5d3njjDZYvX84HH3zAnj17cHR0ZN++ffz3v//F09MTNzc3LC0tadeuHZcvX67xWBoCOdzZ2HS2hE8H39cuxcXFKIrC0qVLZYImSdJ9KZ2TdvHiRRRFUeek2draEh8fb9D23LlztGjRAktLS+zs7Mptvx9lf08VFBQYbHvsscfUr11dXUlLSyMrK4vt27czatQoQP9778iRI+rcJp1OV2uT+8vavHkzWVlZxMfHc+LECaysrCgoKKBnz54kJCSg0WgIDg5m0aJFmJqacuzYMUaPHs1PP/1kdAJ8x44dSU9PV59nZGTQsWNHgzbNmzc3+IwqigEMPztFUXj33XfVzygtLY3Jkyezf/9+oqOjOXz4MCdPnsTJyancNQB9T1rpBP+yj9Ih7/t9HwDt27dHCIGZmRmBgYHqkOg//vEPRo0ahRCC7t2707VrV5KTkwH990fz5s2NX5BGTiZpjYlDO4ibCIOfvq/dpk6dSkJCAvPnz5cJmiRJD8TCwoLQ0FBWrlxJYWEhAQEBxMbGEvu/+iLT+fn5zJw5k7lz5wIQFBTExx9/TEpKCqBPmozNg/X09FQTP/hjuNPKyoqkpCSKi4vVHh1jhBCMHDmS2bNnY2Njo/b2eHl5sWbNGrWdsaFLNzc3dS5TSkoKly5dwtrautLPwcPDg7Vr1wL6IeCcnByD7Tk5ObRr146mTZsSExPDxYsXAbh8+TIWFhZMmDCBoKAgEhISyMvLIycnB29vb0JCQjh58mS58/n6+rJx40YUReHIkSO0bNnSYKgT9L2PRUVFaiJVUQz3GjJkCOvXr1eHbHU6HdeuXSMnJ4dWrVphYWFBcnIyR44cMbp/SEiImuCVfdw71Fn6PsLDw7lz5w7nz58nNTWVvn37lmtXOt9OURS2b9+Ovb09AE8//bRa0DwzM5Nff/2VZ555BtBfu9J2DxuZpD2ECgsLmTVrlsH8i9LubEmSpAfl5OSEVqtly5YtNG/enMjISD5f/THPP2uHRqPBxcWF6dOnA6DValm9ejXjxo3DxsYGe3t7zp07V+6YwcHB3LhxA3t7exwcHNQ5TsuWLcPHx4dnn322XFJyL39/fzZt2qQOdQKEhoYSFxeHVqvF1tbWaII4bdo0iouL0Wg0+Pv7s2HDBszMzCo912effUZMTAwajYY+ffoYDKkCBAQEEBcXh0ajYePGjeqNE4mJifTt2xdHR0c+/PBDgoODuXnzJj4+Pmi1WgYOHMiqVavKnc/b25tnnnmG7t27M2XKFPXu2Xt5eXkRGxtbaQzG9hk/fjyurq5oNBpGjx7NzZs3GTp0KIWFhdjY2DB//nyDuWQPys7OjjFjxmBra8vQoUMJCwtTh6y9vb3V4cqAgAA0Gg0ajYbr16+rc83ef/99Dh06hEajwcPDg+XLl/Pkk08CEBMTw7Bhw/50jA2RKLumTWPg/JRQ4tIbV8x/Suk8tFIO7SptXlBQwPjx44mIiKBnz56cOXOmcd0gsFLf0yfm1N81ru/vrtK+zvqOQ6p/SUlJ2NjY1HcYlbpwTb8MRpd2jej3zEMoISGBkJAQvvvuu/oOpc65u7sTGRlZrfmMtc3Yz6wQIl5RFOcHOZ78qapr+y/BO/vhYu4fr71iW3GJpxd+MHyeNb3CQ9+8eZMRI0awb98+nnjiCf7xj380rgStAfCu7wAkSZIeQO/evXnuuecoKiqq9lppD4OsrCxmz57dIBK02iD/gte1exO0GpKdnc2LL77I8ePHsbKyIioqCq1WW+PnqUuyJ0mSJKn6XnvttfoOoc61bduWESNG1HcYtUYmaXVtZu8/vl5xDDJvV95e2/aPCgMVLL2RkZGBl5cXSUlJdO3alZ9//plu3brVUMCSJEmSJNUHeeNAXZtor39cvVV1glZWJUtv/PLLLyQlJWFnZ0dsbKxM0CRJkiTpISB70urL3H76R1X2+lfZZNy4cSiKwtChQ2ndunUNBCdJkiRJUn2TSVojFRsby+OPP46DgwMA48ePr+eIJEmSJEmqSXK4sxHauXMnnp6eDBkyhIyMjPoOR5Kkh5iJiQmOjo7Y29vz0ksvGdSXPHPmDONHefL8s3b06NGDxYsXU3ZZp127duHs7IytrS1OTk7VquXZUIwbNw6tVlvt0ke1Vc0gOTkZV1dXzMzM+PTTTytspygKzz//PLm5NX9jWk0ZOnQoTzzxRKXlr+7cuYO/vz/du3enX79+XLhwQd22dOlSunfvjrW1NXv27AH0dUvd3d0pLCys7fDrhexJa2S+//57Xn31VQoLC5kwYUKVizxKkmQo9KebJF76vb7DqJZXnYvVdcjqi7l5c7ZHxQEwZ0YgS5avYfqsdynIz8d7mC+LV3yO+2BP2rW4i5+fH1988QVvvfUWp0+fZvr06ezcuZNevXpRVFTEunXrajS2wsLCWllm6OrVqxw/fpy0tLQaP/b9at26NaGhoWzfvr3Sdv/+979xcHCotO7ovep6uY6goCBu377NV199VWGbb775hlatWpGWlkZ4eDjz5s1j69atnD17lvDwcM6cOcPly5d54YUXSElJoVmzZnh4eLB161YCAgLq7L3UFdmTVlP2XwLnjbDxdMVt2n6uf6w4+kCnCAsLY8KECRQWFjJv3jzWrVv3SK2HI0k1obEkaPfq8l3TWnncj97O/cm8qgMg8l9b6NPXFffBnjRvJrCwsODzzz9X6zauWLGCBQsWqKvdm5iYMHXq1HLHzMvLIzAwEI1Gg1ar5ccffwQMe6a2bdvGpEmTAJg0aRJvvvkm/fr1Y+7cuXTp0sWgd69Hjx5kZmaSlZWFn58fLi4uuLi4cPDgwXLnLigoUM/t5OSkVjvw8vJCp9Ph6OjIL7/8YrBPZmYmI0eOxMHBAQcHBw4dOlTu/Xh4eNC7d280Gg2RkZEA3Lp1i2HDhuHg4IC9vT1bt24FYP78+dja2qLVannnnXfKxdiuXTtcXFxo2rTya7V582aGDx+uPh8xYgR9+vTBzs7OIDlu0aIFc+bMwcHBgcOHD7Np0ya1EsJf//pXioqKAH05QWdnZ+zs7Fi4cGGl564uDw8PHn/88UrbREZG8uqrrwIwevRo9u7di6IoREZGMnbsWMzMzOjatSvdu3dX63qOGDFCLe/1sJE9aTXlftY/++S4/gGVLk5bSlEUlixZwgcffADA8uXL1fp4kiQ9mP+Z1vBvsklKyqz1lfyrOr4Q+jZFRUWcOLafyZMn06WdKZnpyQwa4GKwf7du3cjLyyM3N5fTp09Xa3hz8eLFtGzZksTEROCP2p2VycjI4NChQ5iYmFBUVERERASBgYEcPXqUzp07Y2Vlxfjx45k1axYDBw7k0qVLDBkyxKBUHuj/4yuEIDExkeTkZLy8vEhJSWHHjh34+PgYrfc5c+ZMBg0aREREBEVFRWrdy1Lm5uZERERgaWnJ9evX6d+/P76+vuzevZsOHTqwc+dOQF9fMzs7m4iICJKTkxFCGCSb9+vgwYMGPVTr16+ndevW5Ofn4+Ligp+fH23atOHWrVv069ePlStXkpSUxPLlyzl48CBNmzZl2rRpbN68mYkTJ/LRRx/RunVrioqK8PDw4NSpU+XW3vzkk0+MJkfu7u6EhoY+0PvQ6XQ89dRTAJiamtKyZUuys7PR6XQG5ak6deqETqf/D4O9vT3Hjx9/oPM1dDJJqyllE7QVR6t352YF657dKyEhgYULF9KkSRO++uorXn/99QcMUpKkRqueSqXl5+fj6OiITqfDxsYGT0/PGj1+dHQ04eHh6vPqrBz/8ssvq6MI/v7+LFq0iMDAQMLDw9X6ndHR0QZ1NXNzc8nLyzPooYuNjWXGjBkA9OrVi86dO5OSklLpkOG+ffvYuHEjoO8dbNmypcF2RVF47733OHDgAE2aNEGn05GZmYlGo2HOnDnMmzcPHx8f3NzcKCwsxNzcnMmTJ+Pj41PpXK2q/Pbbbwa9VKGhoWph+vT0dFJTU2nTpg0mJib4+fkBsHfvXuLj43FxcQH017pdO33pwR9++IF169ZRWFjIlStXOHv2bLkkLSgoiKCgoAeOuaaYmJjQrFkzbt68WWVPXWMjk7SaVrpAbVVJWiXrnt2rT58+hIWF0bZtW0aPHv3nY5QkSaqm5s2bc+LECW7fvs2QIUMICwtj5syZ2NracuDAAYO2586do0WLFlhaWmJnZ0d8fLx6B/r9EkKoXxcUFBhse+yxx9SvXV1dSUtLIysri+3bt6sFuYuLizly5Ajm5uYPdP4HtXnzZrKysoiPj6dp06Z06dKFgoICevbsSUJCAv/+978JDg7Gw8ODDz74gGPHjrF37162bdvG559/zr59+x7ovKamphQXF9OkSRP2799PdHQ0hw8fxsLCgsGDB6ufobm5uZrgKorCq6++ytKlSw2Odf78eT799FOOHz9Oq1atmDRpUrlrALXTk9axY0fS09Pp1KkThYWF5OTk0KZNG/X1UhkZGXTs2FF9fufOnTq/1nVBzkmraZUtUJs1/Y9H3EQY/HSFTQsKCgy65qdOnSoTNEmS6o2FhQWhoaGsXLmSwsJCAgICiI2NJTo6GtD3wsycOVOdihEUFMTHH39MSkoKoE+avvzyy3LH9fT0JCwsTH1eOtxpZWVFUlISxcXFao+QMUIIRo4cyezZs7GxsaFNmzaAfl7ZmjVr1HbGhi7d3NzUJCMlJYVLly5hbW1d6efg4eHB2rVrAf3E+5ycHIPtOTk5tGvXjqZNmxITE8PFixcBuHz5MhYWFkyYMIGgoCASEhLIy8sjJycHb29vQkJCOHnyZKXnroy1tTXnzp1TY2jVqhUWFhYkJydz5MiRCt/Ltm3buHbtGqDvjbt48SK5ubk89thjtGzZkszMTHbt2mV0/6CgIE6cOFHu8aAJGoCvry/ffvstoJ+L+PzzzyOEwNfXl/DwcO7cucP58+dJTU2lb9++gL4s4pNPPlnlvL3GSCZpNeUVW8PHn5Cbm4u3tzdubm7l5lBIkiTVFycnJ7RaLVu2bKF58+ZERkayZMkSrK2t0Wg0uLi4MH26fp6tVqtl9erVjBs3DhsbG+zt7dUkoqzg4GBu3LiBvb09Dg4O6uT9ZcuW4ePjw7PPPlvlXez+/v5s2rRJHeoE/XBfXFwcWq0WW1tbownitGnTKC4uRqPR4O/vz4YNGzAzM6v0XJ999hkxMTFoNBr69OljMKQKEBAQQFxcHBqNho0bN6o3TiQmJqoT9D/88EOCg4O5efMmPj4+aLVaBg4cyKpVq8qd7+rVq3Tq1IlVq1axZMkSOnXqZHSZjWHDhrF//35Av9RFYWEhNjY2zJ8/32AuV1m2trYsWbIELy8vtFotnp6eXLlyBQcHB5ycnOjVqxfjx49nwIABlX4m1eXm5sbLL7/M3r176dSpk7qMxgcffMCOHTsAmDx5MtnZ2XTv3p1Vq1apN6LY2dkxZswYbG1tGTp0KGFhYWqPYExMDMOGDauRGBsaUXZNm8bA+SmhxKU3rpjvR1ZWFi+++CLx8fG0b9+eqKgo7O3t6zusurNSP8Qh5iiPbIH10kGeR/X917YpX/wGNJYbB5KwsbGp7zCkRuDKlStMnDiRn3/+ub5DqXOjRo1i2bJl9OzZs75DMfozK4SIVxTF+UGOJ3vSGpD09HTc3d2Jj4+nW7duHDx48NFK0CRJkqQH0r59e6ZMmdKgF7OtDXfv3mXEiBENIkGrDfLGgQbi119/xdPTk/T0dDQaDXv27JEL1UqSJEnVNmbMmPoOoc41a9aMiRMn1ncYtUYmaQ1AXl4egwcP5urVq7i6urJz585q3YYuSZIkSdLDSw53NgAtWrTgo48+YujQofz8888yQZMkSZIkSSZp9enmzZvq16+99ho7d+40WP9HkiRJkqRHl0zSaorHVsNHFTZt2sQzzzxjsC5OkybyckiSJEmSpCezgppyKsvwUYnQ0FBeeeUVrl+/XuEigZIkSQ2BiYkJjo6O2Nvb89JLLxnUlzxz5gzPP/881tbW9OjRg8WLF1N2Waddu3bh7OyMra0tTk5O1arl2VCMGzcOrVZLSEhItdqXLTdVkzZv3oxWq0Wj0fDss89WuOCtoig8//zzDfruzm+//ZYePXrQo0cPdcHaewUFBdGrVy+0Wi0jR45Uv98uXLhA8+bNcXR0xNHRkTfffFPd54UXXqhWzdfGSCZpdUhRFP7+97/z9ttvA/qSGvPnz6/nqCRJkipWWhbq9OnTtG7dWq0OkJ+fj6+vL/Pnz+fXX3/l5MmTHDp0iC+++AKA06dPM336dDZt2sTZs2eJi4uje/fuNRpbYWFhjR6v1NWrVzl+/DinTp1i1qxZtXKO6uratSv/+7//S2JiIu+//z5vvPGG0Xb//ve/cXBwqLTu6L2KiopqKswq/fbbb3z44YccPXqUY8eO8eGHHxpNrDw9PTl9+jSnTp2iZ8+eBiWrunXrplY1KLs48SuvvKJ+3z1s5N2ddaS4uJi//e1vrFmzhiZNmrBu3TomT55c32FJktRIlC7CW9PuZ1FfV1dXTp06BcD333/PgAED8PLyAvRloz7//HMGDx7MW2+9xYoVK1iwYIG64r6JiQlTp04td8y8vDxmzJhBXFwcQggWLlyIn58fLVq0IC8vD9CXB/rpp5/YsGEDkyZNwtzcnP/85z8MGDCAf/3rX5w4cYInnngCgB49ehAbG0uTJk148803uXTpEgCrV68ut3J+QUEBU6dOJS4uDlNTU1atWsVzzz2Hl5cXOp0OR0dH1qxZg5ubm7pPZmYmb775plo9Ye3atTz77LMG72f48OHcuHGD33//nSVLljB8+HBu3brFmDFjyMjIoKioiPfffx9/f3/mz5/Pjh07MDU1xcvLi08//dQgxrLH7t+/PxkZGUavzebNmw0SuBEjRpCenk5BQQFvv/22uq1Fixb89a9/JTo6mrCwMC5cuEBoaCh3796lX79+fPHFF+q1On78OPn5+YwePZoPP/zQ+DdFNe3ZswdPT09at9Z/v3l6erJ7927GjRtn0K70+6n0/W7btq3KY/v6+uLm5saCBQv+VIwNkUzSakp05evTTJkyhfXr19OsWTO2bNnCqFGj6igwSZKkP6+oqIi9e/eq/7k8c+YMffr0MWjTrVs38vLyyM3N5fTp09Ua3ly8eDEtW7YkMTERoFrDVhkZGRw6dAgTExOKioqIiIggMDCQo0eP0rlzZ6ysrBg/fjyzZs1i4MCBXLp0iSFDhpQrsxcWFoYQgsTERJKTk/Hy8iIlJYUdO3bg4+NjtN7nzJkzGTRoEBERERQVFamJZClzc3MiIiKwtLTk+vXr9O/fH19fX3bv3k2HDh3YuXMnoK+vmZ2dTUREBMnJyQghDIaSjfnmm2948cUXjW47ePAgX331lfp8/fr1tG7dmvz8fFxcXPDz86NNmzbcunWLfv36sXLlSpKSkli+fDkHDx6kadOmTJs2jc2bNzNx4kQ++ugjWrduTVFRER4eHpw6dQqtVmtwzvspsK7T6XjqqafU5506dUKn01X6ftevX29Q6uv8+fM4OTlhaWnJkiVL1OS5VatW3Llzh+zsbLV268NCJmk1xaFdpZs9PDz45z//yb/+9S9eeOGFOgpKkqSHRX2VscrPz8fR0RGdToeNjQ2enp41evzo6GjCw8PV59VZgujll19W6zb6+/uzaNEiAgMDCQ8PV/+oR0dHG9TVzM3NJS8vz2DuWGxsLDNmzACgV69edO7cmZSUlEqHDPft28fGjRsBfe9gy5YtDbYrisJ7773HgQMHaNKkCTqdjszMTDQaDXPmzGHevHn4+Pjg5uZGYWEh5ubmTJ48GR8fH3x8fCo8b0xMDN988w2xsbFGt//22288/vjj6vPQ0FC1MH16ejqpqam0adMGExMT/Pz8ANi7dy/x8fG4uLgA+mvdrp3+b9kPP/zAunXrKCws5MqVK5w9e7ZckhYUFERQUFCFMf8ZH330EaampgQEBAD6igqXLl2iTZs2xMfHM2LECM6cOaNeq3bt2nH58uWHLkmTc9JqUdkJtOPHj+fcuXMyQZMkqVEpnZN28eJFFEVR56TZ2toSHx9v0PbcuXO0aNECS0tL7Ozsym2/H0II9euCggKDbWWXKnJ1dSUtLY2srCy2b9+ujlIUFxdz5MgRdQ6TTqertcn9ZW3evJmsrCzi4+M5ceIEVlZWFBQU0LNnTxISEtBoNAQHB7No0SJMTU05duwYo0eP5qeffmLo0KFGj3nq1Clef/11IiMjK0xCTE1NKS4uBmD//v1ER0dz+PBhTp48iZOTk/oZmpubqwmuoii8+uqr6mf066+/8ve//53z58/z6aefsnfvXk6dUEy5dwAAIABJREFUOsWwYcPKXQPQ96SVTuQv+5g5c2a5th07diQ9PV19npGRQceOHY2+lw0bNvDTTz+xefNm9fvAzMxMfe99+vShW7dupKSkqPsUFBTQvHlzo8drzGSSVkuuXbvG4MGDiYuLU1978skn6zEiSZKkB2dhYUFoaCgrV66ksLCQgIAAYmNjiY6OBvS9MDNnzmTu3LmAvpfl448/Vv+QFhcXG0z2LuXp6akmfvDHcKeVlRVJSUkUFxerPULGCCEYOXIks2fPxsbGRv1D7uXlxZo1a9R2xoYu3dzc1OG6lJQULl26hLW1daWfg4eHB2vXrgX0Q8A5OTkG23NycmjXrh1NmzYlJiaGixcvAnD58mUsLCyYMGECQUFBJCQkkJeXR05ODt7e3oSEhBi9c/PSpUuMGjWK7777rtL6lNbW1uo8uZycHFq1aoWFhQXJyckcOXKkwveybds2rl27Buh74y5evEhubi6PPfYYLVu2JDMzs8JVCIKCgtQEr+zj3qFOgCFDhhAVFcWNGze4ceMGUVFRDBkypFy73bt3s2LFCnbs2IGFhYX6elZWlnqjw7lz50hNTeWZZ54B9Mnm1atX6dKlS4WfT2Mlk7RacPHiRdzc3Dhw4AAzZ8406FGTJElqrJycnNBqtWzZsoXmzZsTGRnJkiVLsLa2RqPR4OLiwvTp0wHQarWsXr2acePGYWNjg729vZpElBUcHMyNGzewt7fHwcGBmJgYAJYtW4aPjw/PPvtslXWM/f392bRpk8H8pdDQUOLi4tBqtdja2hpNEKdNm0ZxcTEajQZ/f382bNiAmZlZpef67LPPiImJQaPR0KdPH4MhVYCAgADi4uLQaDRs3LhRvXEiMTGRvn374ujoyIcffkhwcDA3b97Ex8cHrVbLwIEDWbVqVbnzLVq0iOzsbKZNm4ajoyPOzs5G4xo2bBj79+8HYOjQoRQWFmJjY8P8+fPp37+/0X1sbW1ZsmQJXl5eaLVaPD09uXLlCg4ODjg5OdGrVy/Gjx9f7oaLB9G6dWvef/99XFxccHFx4YMPPlBvInj99dfVDo3p06dz8+ZNPD09DZbaOHDgAFqtFkdHR0aPHs2XX36p7h8fH0///v0xNX34ZnCJxpZAOD8llLj0hhtzcnIynp6eZGRk4ODgwJ49e7CysqrvsBqPlfqubTFHoeFe5dpVOsjzqL7/2lZ6l2R9zfG6H0lJSdjY2NR3GFIjcOXKFSZOnMjPP/9c36HUubfffhtfX188PDzqOxSjP7NCiHhFUYxn11WQPWk15eQ14r6Pwu3ZgWRkZDBgwAD2798vEzRJkiSp1rVv354pU6Y06MVsa4u9vX2DSNBqw8PXN1hPYtyX4HtzHXnKHV5sasu2qCiD8XRJkiRJqk1jxlS+FNTDasqUKfUdQq2RPWk15IZym9vKXcaZ9WG75RSZoEmSJEmS9KfInrQaMsrMkQNN/oaraReaiEcr9x0G/LuGjiXnYUmSJEmSnkzS/oQ1a9bQu3dv/Z0v2rYMoG19h1QvaipBK8u7Fo4pSZIkSY2JTNIegKIoLFy4kMWLF9OqVSvS0tJovde/6h0fcjXZC7azBo8lSZIkSY3RozUuVwOKi4uZMWMGixcvxsTEhJCQEHWtFkmSpIeNiYkJjo6O2Nvb89JLLxnUlzxz5gzPP/881tbW9OjRg8WLFxusC7lr1y6cnZ2xtbXFycmpWrU8G4px48ah1WoJCQmpVvvaqmYQGRmprg/m7OxcYVmo/Px8Bg0apC742hAtXbqU7t27Y21tzZ49e4y2mTRpEl27dlWrF5QuQpycnIyrqytmZmYGRejv3r2Lu7s7hYWFdfIe6prsSbsPv//+O5MmTeL777/HzMyMrVu3Mnz48PoOS3rIhf50k8RLv9d3GNIjqrQsFMCrr75KWFgYCxYsID8/H19fX9auXYuXlxe3b9/Gz8+PL774grfeeovTp08zffp0du7cSa9evSgqKmLdunU1GlthYWGtLGB69epVjh8/TlpaWo0f+355eHjg6+uLEIJTp04xZswYkpOTy7Vbv349o0aNUks+VUVRFBRFoUmTuumrOXv2LOHh4Zw5c4bLly/zwgsvkJKSYjTeTz75hNGjRxu81rp1a0JDQ9m+fbvB682aNcPDw4OtW7eqdT4fJrInrZpu377NyJEj+f7772nRogW7du2SCZpUJ2SCVvM0Tzet7xDum6ilx/1wdXVFp9MB8P333zNgwAC8vLwAfdmozz//nGXLlgGwYsUKFixYoK64b2JiwtSpU8sdMy8vj8DAQDQaDVqtlh9//BEw7Jnatm0bkyZNAvQ9LW+++Sb9+vVj7ty5dOnSxaB3r0ePHmRmZpKVlYWfn5+6wv3BgwfLnbugoEA9t5OTk1rtwMvLC51Oh6OjI7/88ovBPpmZmYwcORIHBwccHBw4dOhQuffj4eFB79690Wg0REZGAnDr1i2GDRuGg4MD9vb2bN26FYD58+dja2uLVqvlnXfeKRdjixYt1PqVt27dMqhpWtbmzZvVv0kVxXDhwgWsra2ZOHEi9vb2pKen88knn+Di4oJWq2XhwoXq8UaMGEGfPn2ws7OrkeQ6MjKSsWPHYmZmRteuXenevTvHjh2r9v7t2rXDxcWFpk3L/+yOGDFCLe/1sJE9adUUFxfH7t27adOmDbt37/6jNMfsfYYNVz1f98FJj4TGsEK+9PAqKipi7969TJ48GdAPdfbp08egTbdu3cjLyyM3N5fTp09Xa3hz8eLFtGzZksTEROCP2p2VycjI4NChQ5iYmFBUVERERASBgYEcPXqUzp07Y2Vlxfjx45k1axYDBw7k0qVLDBkyhKSkJIPjhIWFIYQgMTGR5ORkvLy8SElJYceOHfj4+Bit9zlz5kwGDRpEREQERUVF5OXlGWw3NzcnIiICS0tLrl+/Tv/+/fH19WX37t106NCBnTv1M25zcnLIzs4mIiKC5ORkhBAGyWZZERERvPvuu1y7dk3dv6y7d+9y7tw5tXZlRTEApKam8u2339K/f3+ioqJITU3l2LFjKIqCr68vBw4cwN3dnfXr19O6dWvy8/NxcXHBz8+vXHH3WbNmqYltWWPHjmX+/PkGr+l0OoPyVJ06dVIT/nstWLCARYsW4eHhwbJly6os1WVvb8/x48crbdNYySStmtzd3QkPD8fOzs6w5MN3hnXbZJImSVJtqK/lafLz83F0dESn02FjY4Onp2eNHj86Oprw8HD1eatWrarc5+WXX1aHyfz9/Vm0aBGBgYGEh4er9Tujo6MN6mrm5uaSl5dn0EMXGxvLjBkzAOjVqxedO3cmJSUFS0vLCs+9b98+Nm7cCOh7B1u2bGmwXVEU3nvvPQ4cOECTJk3Q6XRkZmai0WiYM2cO8+bNw8fHBzc3NwoLCzE3N2fy5Mn4+Pjg4+Nj9JwjR45k5MiRHDhwgPfff18tal/q+vXrPPHEE1XGANC5c2c1WYqKiiIqKgonJydA3wOXmpqKu7s7oaGhamH79PR0UlNTyyVp1Z2vdz+WLl3KX/7yF+7evcsbb7zB8uXL+eCDDyrdx8TEhGbNmnHz5k0ef/zxGo+pPsnhzkpcvHjRYJLm6NGjZR09SZIeKaVz0i5evIiiKISFhQH64tzx8fEGbc+dO0eLFi2wtLTEzs6u3Pb7UXZYr6CgwGDbY489pn7t6upKWloaWVlZbN++nVGjRgH6m7yOHDnCiRMnOHHiBDqdrtYm95e1efNmsrKyiI+P58SJE1hZWVFQUEDPnj1JSEhAo9EQHBzMokWLMDU15dixY4wePZqffvqJoUOHVnpsd3d3zp07x/Xr1w1eb968ucFnVFEMYPjZKYrCu+++q35GaWlpTJ48mf379xMdHc3hw4c5efIkTk5O5a4B6HvSSif4l32UDnmX1bFjR9LT09XnGRkZdOzYsVy79u3bI4TAzMyMwMDAag+J3rlzB3Nz82q1bUxkklaBs2fPMmDAAF588UWjXd6SJEmPEgsLC0JDQ1m5ciWFhYUEBAQQGxur9urk5+czc+ZM5s6dC0BQUBAff/wxKSkpgD5p+vLLL8sd19PTU0384I/hTisrK5KSkiguLlZ7dIwRQjBy5Ehmz56NjY2N2tvj5eXFmjVr1HbGfo+7ubmpc5lSUlK4dOkS1tbWlX4OHh4erF27FtAPAefk5Bhsz8nJoV27djRt2pSYmBguXrwIwOXLl7GwsGDChAkEBQWRkJBAXl4eOTk5eHt7ExISwsmTJ8udLy0tTb1jNiEhgTt37pTr0WrVqhVFRUVqIlVRDPcaMmQI69evV4dsdTod165dIycnh1atWmFhYUFycjJHjhwxun9ISIia4JV93DvUCeDr60t4eDh37tzh/PnzpKam0rdv33Ltrly5AugTyO3bt2Nvb2/03GVlZ2fz5JNPGp2v1tjJ4U4jjh07hre3N9nZ2bi5udG1a9eKG68cXGdxSZIk1ScnJye0Wi1btmzhlVdeITIykhkzZvDWW29RVFTEK6+8wvTp0wHQarWsXr2acePGcfv2bYQQRofzgoODeeutt7C3t8fExISFCxcyatQoli1bho+PD23btsXZ2bnc3K+y/P39cXFxYcOGDeproaGhvPXWW2i1WgoLC3F3dy+XJE6bNo2pU6ei0WgwNTVlw4YNVc5/+uyzz3jjjTf45ptvMDExYe3atbi6uqrbAwICeOmll9BoNDg7O6s3TiQmJhIUFESTJk1o2rQpa9eu5ebNmwwfPpyCggIURWHVqlXlzvfjjz+yceNGmjZtSvPmzdm6davRmwe8vLyIjY3lhRdeqDAGY/skJSWp8bdo0YJNmzYxdOhQvvzyS2xsbLC2tjaYS/ag7OzsGDNmDLa2tpiamhIWFqYOWXt7e/P111/ToUMHAgICyMrKQlEUHB0d1Wt29epVnJ2dyc3NpUmTJqxevZqzZ89iaWlJTEwMw4YN+9MxNkSi7Jo2jYHzU0KJS6+9mPfu3cvw4cO5desWPj4+/PDDDzRv3rzWzvcwKP11USNXZWXJ0eY0ru/LmnTv5znli98AeePAoygpKUlOsZCqJSEhgZCQEL777rv6DqXOlSb1PXv2rO9QjP7MCiHiFUVxfpDjyeHOMiIiIvD29ubWrVsEBATwr3/9SyZokiRJUoPXu3dvnnvuuQa9mG1tuHv3LiNGjGgQCVptkElaiczMTAICArh79y4zZsxQu5clSZIkqTF47bXXqr2Y7cOiWbNmTJw4sb7DqDVyTloJKysrvvvuOxITE1m4cGGFCwZKkiRJkiTVhUc6SVMUhdTUVLWb1M/PDz8/v3qOSpIkSZIk6REe7iwqKmLq1Kk4OTmVK+shSZIkSZJU3x7JnrS7d+8yceJEtm7dipmZGdnZ2Q9+sLafGz7Pmv7ngpMkSZIkSeIR7Em7ffs2w4cPZ+vWrTz++OPs3r2bl156qb7DkiRJapBMTExwdHTE3t6el156yaC+5JkzZ3j++eextramR48eLF68mLLLOu3atQtnZ2dsbW1xcnKqVi3PhmLcuHFotdpqlz6q7WoGx48fx9TUlG3bthndnp+fz6BBgxr03Z1Lly6le/fuWFtbs2fPHqNt3Nzc1MoFHTp0YMSIEYB+etLMmTPp3r07Wq2WhIQEALKysqqs1NCYPVJJ2o0bN/D09GT37t08+eSTxMTEMHjw4PoOS5IkqcEqLQt1+vRpWrdurVYHyM/Px9fXl/nz5/Prr79y8uRJDh06xBdffAHA6dOnmT59Ops2beLs2bPExcXRvXv3Go2tsLCwRo9X6urVqxw/fpxTp04xa9asWjnH/SgqKmLevHl4eXlV2Gb9+vWMGjWq2nd3KopCcXFxTYVYpbNnzxIeHs6ZM2fYvXs306ZNM5pQ/vLLL2rlAldXV7XM165du0hNTSU1NZV169YxdepUANq2bUv79u05ePBgnb2XuvTIJGmKouDt7c2hQ4d46qmn+OWXX+jTp099hyVJklQ9K0XtPO6Dq6srOp0OgO+//54BAwaoiYOFhQWff/65WrdxxYoVLFiwQF3t3sTERP3DWlZeXh6BgYFoNBq0Wi0//vgjYNgztW3bNiZNmgTApEmTePPNN+nXrx9z586lS5cuBr17PXr0IDMzk6ysLPz8/HBxccHFxcXoH/GCggL13E5OTsTExAD6lfh1Oh2Ojo788ssvBvtkZmYycuRIHBwccHBwKDenOS8vDw8PD3r37o1GoyEyMhKAW7duMWzYMBwcHLC3t2fr1q0AzJ8/H1tbW7RaLe+8847Rz33NmjX4+fnRrl07o9tBX69z+PDhlcZw4cIFrK2tmThxIvb29qSnp/PJJ5/g4uKCVqtl4cKF6vFGjBhBnz59sLOzY926dRWet7oiIyMZO3YsZmZmdO3ale7du1dalzM3N5d9+/apPWmRkZFMnDgRIQT9+/fn//7v/9QSUiNGjFDLez1sHpk5aUIIgoODmT9/Pjt37uTpp5++vwNsPG34fGJJPTE5B02SpEdAUVERe/fuZfLkyYB+qPPe/+h269aNvLw8cnNzOX36dLWGNxcvXkzLli1JTEwE/qjdWZmMjAwOHTqEiYkJRUVFREREEBgYyNGjR+ncuTNWVlaMHz+eWbNmMXDgQC5dusSQIUNISkoyOE5YWBhCCBITE0lOTsbLy4uUlBR27NiBj4+P0XqfM2fOZNCgQURERFBUVFSuXJW5uTkRERFYWlpy/fp1+vfvj6+vL7t376ZDhw7s3LkT0NfXzM7OJiIiguTkZIQQBslmKZ1OR0REBDExMRw/ftzo53H37l3OnTtHly5dKo0BIDU1lW+//Zb+/fsTFRVFamoqx44dQ1EUfH19OXDgAO7u7qxfv57WrVuTn5+Pi4sLfn5+5WqGzpo1S01syxo7dmy5+p06nc6gvFSnTp3UhN+Y7du34+HhgaWlpbr/U089VW7/9u3b4+zsTHBwcIXHaswe+iStoKAAc3NzAIYNG8aQIUMwNX2Atz1nv+HziVUXfZUkSaox9VQqLT8/H0dHR3Q6HTY2Nnh6etbo8aOjowkPD1eft2rVqsp9Xn75ZXVYz9/fn0WLFhEYGEh4eDj+/v7qcc+ePavuk5ubS15enkEPXWxsLDNmzACgV69edO7cmZSUFDUxMGbfvn1s3LgR0PcOtmzZ0mC7oii89957HDhwgCZNmqDT6cjMzESj0TBnzhzmzZuHj48Pbm5uFBYWYm5uzuTJk/Hx8TFa2/Rvf/sby5cvp0mTige+rl+/zhNPPFFlDACdO3dWk6WoqCiioqJwcnIC9D1wqampuLu7Exoaqha2T09PJzU1tVySVt35eg9iy5YtvP7669Vq265dOy5fvlxrsdSnh3q488iRI3Tr1o39+/errz1QgiZJkvSIKp2TdvHiRRRFUeek2draEh8fb9D23LlztGjRAktLS+zs7Mptvx9lFxQvKCgw2PbYY4+pX7u6upKWlkZWVhbbt29X5zAVFxdz5MgRdX6TTqer9cn9oB92zMrKIj4+nhMnTmBlZUVBQQE9e/YkISEBjUZDcHAwixYtwtTUlGPHjjF69Gh++uknoxPg4+LiGDt2LF26dGHbtm1MmzaN7du3G7Rp3ry5wWdUUQxg+NkpisK7776rfkZpaWlMnjyZ/fv3Ex0dzeHDhzl58iROTk7lrgHoe9JKJ/mXfZQOeZfVsWNH0tPT1ecZGRl07NjR6Gd4/fp1jh07ZlA0vbL9CwoKHtoSjg9tkvbzzz/zwgsvcPnyZb7++uv6DkeSJKlRs7CwIDQ0lJUrV1JYWEhAQACxsbFER0cD+h63mTNnMnfuXACCgoL4+OOPSUlJAfRJ05dfflnuuJ6enmriB38Md1pZWZGUlERxcbHao2OMEIKRI0cye/ZsbGxs1N4eLy8v1qxZo7YzNnTp5uamzmVKSUnh0qVLWFtbV/o5eHh4sHbtWkA/BJyTk2OwPScnh3bt2tG0aVNiYmK4ePEiAJcvX8bCwoIJEyYQFBREQkICeXl55OTk4O3tTUhICCdPnix3vvPnz3PhwgUuXLjA6NGj+eKLL9R5WqVatWpFUVGRmkhVFMO9hgwZwvr169UhW51Ox7Vr18jJyaFVq1ZYWFiQnJzMkSNHjO4fEhKiJnhlH/cOdQL4+voSHh7OnTt3OH/+PKmpqfTt29focbdt24aPj486Cla6/8aNG1EUhSNHjtCyZUvat28P6K+dvf3DObr1UCZp27ZtY9iwYdy6dYuJEyeyYcOGP3/QV2wNH5IkSY8YJycntFotW7ZsoXnz5kRGRrJkyRKsra3RaDS4uLgwfbp+nq5Wq2X16tWMGzcOGxsb7O3tOXfuXLljBgcHc+PGDezt7XFwcFDnOC1btgwfHx+effZZ9Y9xRfz9/dm0aZM61AkQGhpKXFwcWq0WW1tbownitGnTKC4uRqPR4O/vz4YNGzAzM6v0XJ999hkxMTFoNBr69OljMKQKEBAQQFxcHBqNho0bN6o3TiQmJtK3b18cHR358MMPCQ4O5ubNm/j4+KDVahk4cCCrVq2q9NyV8fLyIjY2ttIYjO0zfvx4XF1d0Wg0jB49mps3bzJ06FAKCwuxsbFh/vz5BnPJHpSdnR1jxozB1taWoUOHEhYWpg5Ze3t7GwxXhoeHM27cOIP9vb29eeaZZ+jevTtTpkxR7yIGiImJMeh1e5iIsmvaNAbOTwklLr3imL/++mv++te/UlxczNtvv82qVasqHcuX/rzSQYka+U4qvdusnubfNAT3fp5TvvgNgP+Z1rpe4pHqT1JSEjY2NvUdhtQIJCQkEBISwnfffVffodQ5d3d3IiMjqzWfsbYZ+5kVQsQriuL8IMd7qLKX1atXM2XKFIqLi1m0aBEhISEyQZMkSZIeer179+a5555r0IvZ1oasrCxmz57dIBK02vBQzaK3s7PDzMyMTz/9VO1yf2AeWw2f7/U33k6SJEmSGoDXXnutvkOoc23bti03R+9h8lAlaZ6enqSlpdGpU6c/f7BTWX/+GJIkSX+SoigGdzpKktQw1cb0sUY9Fnj37l0mTJhgUAOsRhI0SZKkBsDc3Jzs7Oxa+eUvSVLNURSF7OxsgztSa0Kj7Um7desWo0aNIioqin379vHf//73oV0nRZKkR1OnTp3IyMggK0v27EtSQ2dubl7jHUWNMkn77bff8PHx4fDhw7Rt25adO3fWfIIWPaZmjydJknSfmjZtSteuXes7DEmS6kmtJmlCiKHAZ4AJ8LWiKMvu2W4GbAT6ANmAv6IoFyo75u9FMGjQIE6fPs3TTz/Nzz//TM+ePWs+eIeKC9lKkiRJkiTVtlpL0oQQJkAY4AlkAMeFEDsURSm78t9k4IaiKN2FEGOB5UClt1EmX4O7V07Ty8SKn2++TqeKErSNpw3rbb5iC6ueN97WY6vhjQLRY2SSJkmSJElSvaq1xWyFEK7A3xVFGVLy/F0ARVGWlmmzp6TNYSGEKXAVaKtUElQTIZQ+nWDXFHjysYpaSY2deIQXsy31eskitqXkYraSJEmNz59ZzLY2k7TRwFBFUV4vef4K0E9RlOll2pwuaZNR8vy/JW2u33OsN4A3Sp7aA6drJWipLjwJXK+yldQQyWvXuMnr13jJa9e4WSuK8viD7NgobhxQFGUdsA5ACBH3oBmpVP/k9Wu85LVr3OT1a7zktWvchBBxD7pvba6TpgOeKvO8U8lrRtuUDHe2RH8DgSRJkiRJ0iOtNpO040APIURXIUQzYCyw4542O4BXS74eDeyrbD6aJEmSJEnSo6LWhjsVRSkUQkwH9qBfgmO9oihnhBCLgDhFUXYA3wDfCSHSgN/QJ3JVWVdbMUt1Ql6/xkteu8ZNXr/GS167xu2Br1+t3TggSZIkSZIkPbhGXbtTkiRJkiTpYSWTNEmSJEmSpAaowSZpQoihQohfhRBpQoj5RrabCSG2lmw/KoToUvdRSsZU49rNFkKcFUKcEkLsFUJ0ro84JeOqun5l2vkJIRQhhFwaoAGpzvUTQowp+Rk8I4T4vq5jlIyrxu/Op4UQMUKI/5T8/vSujzil8oQQ64UQ10rWfzW2XQghQkuu7SkhRO/qHLdBJmllSkq9CNgC44QQtvc0U0tKASHoS0pJ9aya1+4/gLOiKFpgG7CibqOUKlLN64cQ4nHgbeBo3UYoVaY6108I0QN4FxigKIod8Lc6D1Qqp5o/e8HAD4qiOKG/0e6Luo1SqsQGYGgl218EepQ83gDWVuegDTJJA/oCaYqinFMU5S4QDgy/p81w4NuSr7cBHkIIUYcxSsZVee0URYlRFOV2ydMj6NfQkxqG6vzsASxG/x+jgroMTqpSda7fFCBMUZQbAIqiXKvjGCXjqnPtFMCy5OuWwOU6jE+qhKIoB9CvUlGR4cBGRe8I8IQQon1Vx22oSVpHIL3M84yS14y2URSlEMgB2tRJdFJlqnPtypoM7KrViKT7UeX1K+mmf0pRlJ11GZhULdX5+esJ9BRCHBRCHBFCVPa/f6nuVOfa/R2YIITIAP4NzKib0KQacL9/G4FGUhZKejgJISYAzsCg+o5Fqh4hRBNgFTCpnkORHpwp+iGXweh7sQ8IITSKovxfvUYlVcc4YIOiKCuFEK7o1xm1VxSluL4Dk2pHQ+1JkyWlGq/qXDuEEC8ACwBfRVHu1FFsUtWqun6PA/bAfiHEBaA/sEPePNBgVOfnLwPYoSjK74qinAdS0CdtUv2qzrWbDPwAoCjKYcAcffF1qeGr1t/GezXUJE2WlGq8qrx2Qggn4Cv0CZqcD9OwVHr9FEXJURTlSUVRuiiK0gX9nEJfRVEeuICwVKOq87tzO/peNIQQT6If/jxXl0FKRlXn2l0CPACEEDbok7SsOo1SelA7gIkld3n2B3IURblS1U4NcrizFktKSbWsmtfuE6AF8M+Sez0uKYriW29BS6pqXj+pgarm9dsDeAkhzgJFQJAF55NeAAAE7ElEQVSiKHIUop5V89rNAf5HCDEL/U0Ek2TnRMMghNiC/j8/T5bMGVwINAVQFOVL9HMIvYE04DYQWK3jyusrSZIkSZLU8DTU4U5JkiRJkqRHmkzSJEmSJEmSGiCZpEmSJEmSJDVAMkmTJEmSJElqgGSSJkmSJEmS1ADJJE2SpBonhCgSQpwo8+hSSdu8GjjfBiHE+ZJzJZSsxn6/x/i6tKC1EOK9e7Yd+rMxlhyn9HM5LYT4f0KIJ6po7yiE8K6Jc0uS1PjIJTgkSapxQog8RVFa1HTbSo6xAfhJUZRtQggv4FNFUbR/4nh/OqaqjiuE+BZIURTlo0raTwKcFUWZXtOxSJLU8MmeNEmSap0QooUQYm9JL1eiEGK4kTbthRAHyvQ0uZW87iWEOFyy7z+FEFUlTweA7iX7zi451mkhxN9KXntMCLFTCHGy5HX/ktf3CyGchRDLgOYlcWwu2ZZX8m+4EGJYmZg3CCFGCyFMhBCfCCGOCyFOCSH+Wo2P5TAlBZaFEH1L3uN/hBCHhBDWJavOLwL8S2LxL4l9vRDiWEnbcp+jJEkPjwZZcUCSpEavuRDiRMnX54GXgZGKouSWlCI6IoTYcc9q6eOBPYqifCSEMAEsStoGAy8oinJLCDEPmI0+eanIS0CiEKIP+lW9+wECOCqE+F/gGeCyoijDAIQQLcvurCjKfCHEdEVRHI0ceyswBthZkkR5AFPR11TMURTFRQhhBhwUQkSV1MYsp+T9eaCvnAKQDLiVrDr/AvCxoih+QogPKNOTJoT4GH0JvNdKhkqPCSGiFUW5VcnnIUlSIyWTNEmSakN+2SRHCNEU+FgI4Q4Uo+9BsgKultnnOLC+pO12RVFOCCEGAbbokx6AZuh7oIz5RAgRjL6W4WT0SVBEaQIjhPgX4AbsBlYKIZajHyL95T7e1y7gs5JEbChwQFGU/JIhVq0QYnRJu5boi5bfm6SVJq8dgSTg5zLtvxVC9EBf7qdpBef3AnyFEO+UPDcHni45liRJDxmZpEmSVBcCgLZAH0VRfhdCXECfYKgURTlQksQNAzYIIVYBN4CfFUUZV41zBCmKsq30iRDCw1gjRVFShBC90dfRWyKE2KsoSmU9c2X3LRBC7AeGAP5AeOnpgBmKouyp4hD5iqI4CiEs0NdofAsIBRYDMYqijCy5yWJ/BfsLwE9RlF+rE68kSY2bnJMmSVJdaAlcK0nQngM639tACNEZyFQU5X+Ar4HewBFggBCidI7ZY0KIntU85y/ACCGEhRDiMWAk8IsQogNwW1GUTcAnJee51+8lPXrGbEU/jFraKwf6hGtq6T5CiJ4l5zRKUZTbwExgjhDCFP3noyvZPKlM05vA42We7wFmiJJuRSGEU0XnkCSp8ZNJmiRJdWEz4CyESAQmop+Dda/BwEkhxH/Q91J9pihKFvqkZYsQ4hT6oc5e1TmhoigJwAbgGHAU+FpRlP8AGvRzuU4AC4ElRnZfB5wqvXHgHlHAICBaUZS7Ja99DZwFEoQQp4GvqGKkoiSWU8A4YAWwtOS9l90vBrAtvXEAfY9b05LYzpQ8lyTpISWX4JAkSZIkSWqAZE+aJEmSJElSAySTNEmSJEmSpAZIJmmSJEmSJEkNkEzSJEmSJEmSGiCZpEmSJEmSJDVAMkmTJEmSJElqgGSSJkmSJEmS1AD9f5KE9OVXYoKwAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 177
-        },
-        "id": "hcgGhs0bClR6",
-        "outputId": "3c86a5fb-dc1b-453a-fff0-0578095b01f4"
-      },
-      "source": [
-        "test_image = cv.imread(X_test[0],cv.IMREAD_GRAYSCALE)\n",
-        "match, num_matches =  sift_match(test_image, X_train[18], plot=True)"
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy import interp\n",
+    "from itertools import cycle\n",
+    "\n",
+    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test_bin.ravel(), y_score.ravel())\n",
+    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
+    "\n",
+    "# First aggregate all false positive rates\n",
+    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
+    "\n",
+    "# Then interpolate all ROC curves at this points\n",
+    "mean_tpr = np.zeros_like(all_fpr)\n",
+    "for i in range(n_classes):\n",
+    "    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
+    "\n",
+    "# Finally average it and compute AUC\n",
+    "mean_tpr /= n_classes\n",
+    "\n",
+    "fpr[\"macro\"] = all_fpr\n",
+    "tpr[\"macro\"] = mean_tpr\n",
+    "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
+    "\n",
+    "# Plot all ROC curves\n",
+    "plt.figure(figsize=(10,10))\n",
+    "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
+    "         label='micro-average ROC curve (area = {0:0.2f})'\n",
+    "               ''.format(roc_auc[\"micro\"]),\n",
+    "         color='deeppink', linestyle=':', linewidth=4)\n",
+    "\n",
+    "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
+    "         label='macro-average ROC curve (area = {0:0.2f})'\n",
+    "               ''.format(roc_auc[\"macro\"]),\n",
+    "         color='navy', linestyle=':', linewidth=4)\n",
+    "\n",
+    "colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n",
+    "for i, color in zip(range(n_classes), colors):\n",
+    "    plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
+    "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
+    "             ''.format(i, roc_auc[i]))\n",
+    "\n",
+    "plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.05])\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "S8lj6Ca5ZTyP"
+   },
+   "source": [
+    "## SURF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 198
+    },
+    "id": "Cr9ONgNFZaDK",
+    "outputId": "0d7aff34-9a66-4a67-a874-13b03e393cac"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Image_Sources</th>\n",
+       "      <th>Labels</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/1...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/2...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/3...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/4...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2/Greeva/5...</td>\n",
+       "      <td>Greeva</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
+      "text/plain": [
+       "                                       Image_Sources  Labels\n",
+       "0  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/1...  Greeva\n",
+       "1  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/2...  Greeva\n",
+       "2  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/3...  Greeva\n",
+       "3  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/4...  Greeva\n",
+       "4  /gdrive/MyDrive/BitCoders/Assignment2/Greeva/5...  Greeva"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 177
-        },
-        "id": "F2fjmXO0C129",
-        "outputId": "a486f6a2-371e-485f-dce8-ded06f937cbc"
-      },
-      "source": [
-        "test_image = cv.imread(X_test[16],cv.IMREAD_GRAYSCALE)\n",
-        "match, num_matches =  sift_match(test_image, X_train[18], plot=True)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
+     },
+     "execution_count": 12,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_sources = list(image_database.keys())\n",
+    "labels = list(image_database.values())\n",
+    "data = pd.DataFrame()\n",
+    "data['Image_Sources'] = image_sources\n",
+    "data['Labels'] = labels\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_AOlXYnHZaDN"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X = data['Image_Sources']\n",
+    "y = data['Labels']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5, random_state = 0, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "AkQZJ3xkZaDN"
+   },
+   "outputs": [],
+   "source": [
+    "y_cap = []\n",
+    "for test_image_path in X_test:\n",
+    "  test_image = cv.imread(test_image_path,cv.IMREAD_GRAYSCALE)\n",
+    "  best_match = ''\n",
+    "  highest_matches = 0\n",
+    "  for train_image, label in zip(X_train, y_train):\n",
+    "    try:\n",
+    "      match, num_matches =  surf_match(test_image, train_image)\n",
+    "      if match and num_matches > highest_matches:\n",
+    "        highest_matches = num_matches\n",
+    "        best_match = label\n",
+    "    except:\n",
+    "      pass\n",
+    "      # print('Error')\n",
+    "  if len(best_match)<1:\n",
+    "    y_cap.append('Unknown')\n",
+    "  else:\n",
+    "    y_cap.append(best_match)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "q6uASEn9ZaDO"
+   },
+   "outputs": [],
+   "source": [
+    "classes = list(set(y_test))\n",
+    "classes.append('Unknown')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eijGPFWzZaDP"
+   },
+   "source": [
+    "### Overall Accuracy : 98.57%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "lHrbyVtbZaDP",
+    "outputId": "c34be713-969f-456b-d8ab-824fc615f831"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy is: 98.571 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "print(\"Accuracy is:\", np.round(accuracy_score(y_test, y_cap)*100,3),\"%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MHDHi9-xZaDQ"
+   },
+   "source": [
+    "### Overall weighted F1 score: 99.5%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ElIJ77sPZaDQ",
+    "outputId": "b8424a40-bf7a-423b-f5c3-7df7f75e531f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Score is: 99.248 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score\n",
+    "f1 = f1_score(y_test, y_cap, average='weighted')\n",
+    "print(\"F1 Score is:\",np.round(f1*100, 3), \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "id": "I_Zm-eHgfTL6",
+    "outputId": "4d3b5292-03cd-45a0-a7f8-68fd11b99193"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEWCAYAAABLzQ1kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd5xU1f3/8dd7KYIKKDZqBIMmMfZC7EETwQpGDWpsMSYkUZPYY4w/u/km3yQmtpgvUYNdMVYUFBMLYgUVlRYIRakWDHZh2f38/jh3YFx3Z+7Mzp251/08fdzH7ty5c85nh/HMmTPnfI7MDOecc+lTV+sAnHPONc8baOecSylvoJ1zLqW8gXbOuZTyBto551LKG2jnnEspb6Bdq0nqLGmMpPck3dWKco6WNL6SsdWCpHGSjq91HC77vIFuQyR9T9JkSR9KWhI1JHtUoOjDgU2ADczsu+UWYma3mtngCsTzGZIGSTJJ9zY5v210/omY5Vwo6ZZi15nZ/mZ2Y5nhOreaN9BthKTTgT8DvyE0pl8C/gIMq0DxmwKzzGxVBcpKytvArpI2yDt3PDCrUhUo8P+nXMX4i6kNkNQNuBg42czuMbOPzKzezMaY2VnRNWtJ+rOkxdHxZ0lrRfcNkrRQ0hmS3op63ydE910EnA8cEfXMT2za05TUL+qpto9uf1/SXEkfSJon6ei88xPzHrebpEnR0MkkSbvl3feEpEskPR2VM17ShgWehpXAfcCR0ePbAUcAtzZ5rq6QtEDS+5JelLRndH4/4Ny8v/OVvDguk/Q08DGwWXTuh9H910q6O6/830n6lyTF/gd0bZY30G3DrkAn4N4C1/wa2AXYDtgWGAicl3d/D6Ab0Bs4EbhG0vpmdgGhV36nma1rZtcXCkTSOsCVwP5m1gXYDZjSzHXdgYeiazcALgceatID/h5wArAx0BE4s1DdwE3AcdHvQ4CpwOIm10wiPAfdgduAuyR1MrOHm/yd2+Y95lhgBNAFeL1JeWcAW0dvPnsSnrvjzXMsuBi8gW4bNgDeKTIEcTRwsZm9ZWZvAxcRGp6c+uj+ejMbC3wIfKXMeBqBrSR1NrMlZjatmWsOBGab2c1mtsrMbgdmAgfnXfN3M5tlZp8AowkNa4vM7Bmgu6SvEBrqm5q55hYzWxbV+UdgLYr/naPMbFr0mPom5X1MeB4vB24BfmZmC4uU5xzgDXRbsQzYMDfE0IJefLb393p0bnUZTRr4j4F1Sw3EzD4iDC38BFgi6SFJX40RTy6m3nm3l5YRz83AKcDeNPOJQtKZkmZEwyrLCZ8aCg2dACwodKeZPQ/MBUR4I3EuFm+g24ZngRXAIQWuWUz4si/nS3z+439cHwFr593ukX+nmT1iZvsCPQm94r/FiCcX06IyY8q5GTgJGBv1bleLhiDOBoYD65vZesB7hIYVoKVhiYLDFZJOJvTEF0flOxeLN9BtgJm9R/gi7xpJh0haW1IHSftL+t/ostuB8yRtFH3Zdj7hI3k5pgB7SfpS9AXlr3J3SNpE0rBoLHoFYaiksZkyxgJbRFMD20s6AtgSeLDMmAAws3nANwlj7k11AVYRZny0l3Q+0DXv/jeBfqXM1JC0BXApcAxhqONsSQWHYpzL8Qa6jYjGU08nfPH3NuFj+SmEmQ0QGpHJwKvAa8BL0bly6noUuDMq60U+26jWRXEsBt4lNJY/baaMZcBBhC/ZlhF6ngeZ2TvlxNSk7Ilm1tyng0eAhwlT714HPuWzwxe5RTjLJL1UrJ5oSOkW4Hdm9oqZzSbMBLk5N0PGuULkXyY751w6eQ/aOedSyhto55xLKW+gnXMupbyBds65lCq0cKGmPnn8ukx9e9llyAW1DsG5NmvVykWtzm1S/87c2G1Ohw03q0ouFe9BO+dcSqW2B+2cc1XV2FDrCD7HG2jnnANoSF86c2+gnXMOMGsu40BteQPtnHMAjd5AO+dcOnkP2jnnUsq/JHTOuZTyHrRzzqWTpXAWhy9Ucc45CF8Sxj2KkHSDpLckTW1y/meSZkqalrdZRou8B+2cc1DpIY5RwNXkbUwsaW9gGLCtma2QtHGxQryBds45qOiXhGY2QVK/Jqd/CvzWzFZE17xVrBwf4nDOOQg96JiHpBGSJucdI2LUsAWwp6TnJT0paediD/hCN9AX3DSOvc+6hsMu/vvqc9eOeZp9f3ktwy8dxfBLR/HUa3NrGGFxQwYPYtrUCcycPpGzzzq51uEUlbV4wWOuhkzE27Aq9mFmI81sp7xjZIwa2gPdgV2As4DRkgpmxftCD3EM3XUrjhy0A+eNGvuZ88d8a0eOHzywRlHFV1dXx5VXXMZ+BxzFwoVLeO7ZsYx5cDwzZsyudWjNylq84DFXQ2biTX4l4ULgHgsbwb4gqRHYkLCJc7O+0D3oHTfvS9e1O9U6jLIN3Hl75syZz7x5b1BfX8/o0fcz9OAhtQ6rRVmLFzzmashKvGYNsY8y3QfsDSBpC6AjUHCX+i90A92SO554me9e8ncuuGkc73/0aa3DaVGv3j1YsHDx6tsLFy2hV68eNYyosKzFCx5zNWQm3hLGoIuRdDvwLPAVSQslnQjcAGwWTb27Azg+6k23KNEGWtIukiZJ+lDSSkkNkt4vcP3qgffrH5yQSEzDv7kdD176I+789ffZsOu6/PHuxxOpxzmXMRWcB21mR5lZTzPrYGZ9zOx6M1tpZseY2VZmtoOZPVasnKR70FcDRwGzgc7AD4FrWro4f+D9xIP2SiSgDbquQ7u6OurqxKF7bMPU+UsTqacSFi9aSt8+vVbf7tO7J4sXe7yV5DEnLzPxVrAHXSmJD3GY2X+AdmbWYGZ/B/ZLus5C3n7vw9W/PzZlNgN6bVjDaAqbNHkKAwb0p1+/vnTo0IHhw4cx5sHxtQ6rRVmLFzzmashMvA318Y8qSXoWx8eSOgJTomWNS6jiuPc5141h8qwFLP/wEwafcy0/PXh3Js9awL8XvIUEvTboxnlHD65WOCVraGjgF6eex9iHbqNdXR2jbryT6dNn1TqsFmUtXvCYqyEz8aYwH7SKjFG3rnBpU+BNwreVpwHdgL9EveqCfFdv51xcldjV+9Nnb4/d5nTa9aiq7OqddA96R+AhM3sfuCjhupxzrnwp7EEnPdxwMDBL0s2SDpL0hV4Y45zLsArO4qiURBtoMzsBGADcRZjNMUfSdUnW6Zxz5bCG+thHtSTeozWzeknjACNMtTuEMN3OOefSI4U7qiS9UGV/SaMI86APA64DUriEyDnX5qVwiCPpHvRxwJ3Aj3M5UJ1zLpXaWg/azI4CXgb2BJDUWVKXJOt0zrmypLAHnfQQx4+AfwD/F53qQ8jo5Jxz6ZLCpd5JD3GcDAwEngcws9lx9uFyzrmqW5W+Xb2TbqBXmNnK3KYB0TzoTK0QdM61ESkcg066gX5S0rlAZ0n7AicBYxKu0znnStcGVxKeQ9jO5TXgx8BY4LyE63TOudK1tTFoM2uUdAswwcz+nWRdzjnXKinsQSfaQEsaCvyekM2uv6TtgIvNbGixx2YtO9z7vz2g1iGUrOs5Y4tf5FxbUcGesaQbgIOAt8xsqyb3nQH8AdjIzGq6J+EFhFkcywHMbArQP+E6nXOudKtWxT+KG0Uzm5NI6gsMBt6IU0jSDXS9mb3X5JzP4nDOpY9Z/KNoUTYBeLeZu/4EnE3MdjDpBnqapO8B7SRtLukq4JmE63TOudKVsJIwf4Pr6BhRrHhJw4BFZvZK3JCSnmb3M+DXwArgNuAR4NKE63TOudKV8CWhmY0ERsa9XtLawLmE4Y3YEmugJbUj7KayN6GRds659Ep2+tyXCd+/vRIt3OsDvCRpoJm1uMV5Yg20mTVIapTUrZlxaOecS5eGhsSKNrPXgNVpLiTNB3YqNosj6SGOD4HXJD0KfJQ7aWY/T7he55wrTQXnQUu6HRgEbChpIXCBmV1fajlJN9D3RAes+dayKrvhOudcSSrYQEeplgvd3y9OOYk00NG3lX3M7Jro9gvARoRG+pdJ1Omcc63ShpIlnQ0cmXe7I7AjsC7wd8Imss45lxrWmL4lGkk10B3NbEHe7Ylm9i7wrqR1EqrTOefK14Zycayff8PMTsm7uVFCdTrnXPkSnMVRrqRWEj4fbXf1GZJ+DLyQUJ3OOVe+FO5JmFQP+jTgvmiZ90vRuR2BtYBDEqrTOefKl8IhjkR60Gb2lpntBlwCzI+Oi81sVzN7M4k64xgyeBDTpk5g5vSJnH3WybUKo6COg4+n80/+SKfjLlx9rsNuw+h07AV0OuZ81jr0VLROt9oFWEQWnuOmPObkZSLeCiZLqhRZFSsrRfuOvSsaWF1dHTOmPcV+BxzFwoVLeO7ZsRxz7EnMmDG7IuVXKh90Xe/NsfoVrLXfD/j0pgvDyY6dYOWnALTffh/UvRf1/7ql1XVVOh900s9xEjzm5FUj3lUrF7V6fcXHl/8odpuz9ul/q8p6jqSz2aXGwJ23Z86c+cyb9wb19fWMHn0/Qw8eUuuwPqdx0Wz49KPPnowaZwDar0VaM7Zm5TnO5zEnLzPxNlr8o0qS3lFlLeAwoF9+XWZ2cZL1NqdX7x4sWLh49e2Fi5YwcOftqx1G2TrsfgjtttwVVnzCp3f9odbhNCuLz7HHnLzMxNuGZnHk3A8MA1YRcnHkjmbl51htbGzxsjap/un7+PRvv2TVjOfpsN0+tQ7HuS8ca2yMfVRL0rk4+pjZ57Z9aUl+jtVKj0EvXrSUvn16rQmsd08WL24xy19qNcx8nrW+83Pqn32g1qF8ThafY485eZmJN4UrCZPuQT8jaeuE64hl0uQpDBjQn379+tKhQweGDx/GmAfH1zqsWLTe6iyFtPvydjS+m8IXN9l8jj3m5GUmXmuMf1RJ0j3oPYDvS5pH2FVFgJnZNgnX+zkNDQ384tTzGPvQbbSrq2PUjXcyffqsaodRVMcDfkS7PltA53Xp9KP/pf7ZB2jXfyvq1u8BZtj7y1hZgRkcScjKc5zPY05eZuJNYQ860Wl2kjZt7ryZvV7ssZUe4khapabZVVOlp9k5VyuVmGb30flHxm5z1rn4jqpMs0u0B51riCVtDHRKsi7nnGuVFKYbTXQMWtJQSbOBecCThBWF45Ks0znnylLBedCSbpD0lqSpeed+L2mmpFcl3StpvWLlJP0l4SXALsAsM+sPfAt4LuE6nXOuZBWeZjcKaDqD7VFgq+g7uFnAr4oVknQDXW9my4A6SXVm9jiwU8J1Oudc6SrYgzazCcC7Tc6NN7NV0c3nCDt7F5T0LI7lktYFngJulfQWBRaqOOdczZQwi0PSCGBE3qmR0TqOuH4A3FnsoqQb6GHAp8CpwNFAN6Dqy7ydc66oEpZ65y+qK5WkXxNWV99a7NqkZ3F8JKkHMJDQ3X8kGvJwzrlUqcaehJK+DxwEfMtizHFOehbHDwk7qBwKHA48J+kHSdbpnHNlSTibnaT9CBtqDzWzj+M8JukhjrOA7XO9ZkkbAM8ANyRcr3POlaaCSZAk3Q4MAjaUtBC4gDBrYy3gUUkAz5nZTwqVk3QDvQz4IO/2B9E555xLlwoOcZjZUc2cvr7UcpJuoP9D2ED2fkKW+WHAq5JOBzCzyxOu3znn4klhLo6kG+g50ZFzf/SzS8L1OudcSawhfUu9k57FcVGS5adJFhMPvdp3u1qHULJtFkypdQhfeBuu3bXWIdRGW+tBS3qcZjbQMzPfEsQ5lyrVmGZXqqSHOM7M+70TYX/CVS1c65xztdPWGmgze7HJqaclvZBknc45V5b0DUEnPsTRPe9mHSFRUrck63TOuXLYqvS10EkPcbzImjHoVYR80CcmXKdzzpUufe1zMg20pJ2BBVEOaCQdTxh/ng9MT6JO55xrjTR+SZhULo7/A1YCSNoL+B/gRuA9yswA5ZxziWos4aiSpIY42plZLln1EYRcqXcDd0vyiazOudRpSz3odpJyjf+3gMfy7kt63Ns550rXhnrQtwNPSnoH+ISwowqSBhCGOZxzLlUshSs0EmmgzewySf8CegLj8xJT1wE/S6JO55xrDcv6LA5J6wN9zezVYtea2ed27zazWaXU55xzVZPFBlrSE8DQ6NoXgbckPW1mpyccm3POVU0ae9BxviTsZmbvE7atusnMvgF8O9mwnHOuuqwx/lEtcRro9pJ6AsOBBxOOJ1FDBg9i2tQJzJw+kbPPOrnW4cSStZi7f38om427hs3G/YXu3x9W63BiydpzDNmL+U9XX8rU2RN54pkHah1Ki6xBsY9iJN0g6S1JU/POdZf0qKTZ0c/1i5UTp4G+GHgE+I+ZTZK0GTA7xuNSpa6ujiuvuIyDDj6GrbfdmyOOOISvfW3zWodVUNZiXmuLTVnviCHM+87pzD3oFNbdZyAdNu1Z67AKytpzDNmM+c7b7uOow0fUOoyCKtyDHgXs1+TcOcC/zGxz4F/R7YKKNtBmdpeZbWNmJ0W355rZYbFCTJGBO2/PnDnzmTfvDerr6xk9+n6GHjyk1mEVlLWYO365L59MmYV9ugIaGvn4hdfoOmS3WodVUNaeY8hmzM89M5nl/11e6zAKskbFPoqWZTYBeLfJ6WGEFdVEPw8pVk6LXxJKuopmku3nBfDzolGmSK/ePViwcPHq2wsXLWHgztvXMKLishbzilmvs/EZx9FuvS40frqSdb+5E59O/U+twyooa88xZDPmLChlbFnSCCD/I8FIMyuWxmITM1sS/b4U2KRYPYVmcUwu9uCWSJpoZntI+oDPNvICzMya3VMn/49Wu27U1a1TbgiuBlbOWcCy//sHX7rxUho//pRPZ8zFGhpqHZZzsZgV7xmvudZG0oq8QmZmkoquLW+xgTazG/NvS1rbzD6OWfke0c+SNofN/6Pbd+xd0YXxixctpW+fXqtv9+ndk8WLl1ayiorLYszL7xrP8rvGA7DxGcdRv3RZjSMqLIvPcRZjzoIqzM54U1JPM1sSTbx4q9gDio5BS9pV0nRgZnR7W0l/iRONpJvjnKuGSZOnMGBAf/r160uHDh0YPnwYYx4cX4tQYstizO02CPsxtO+5EV2G7MZ7DzxR24CKyOJznMWYs6CxQbGPMj0AHB/9fjxwf7EHxFlJ+GdgSFQ4ZvZKlEI0jq/n34gSKO0Y87EV1dDQwC9OPY+xD91Gu7o6Rt14J9Onp3thYxZj7nvNubRbryu2ahVLL7yWxg8+qnVIBWXxOc5izNde9wd222Mg3TdYj5emPc7vf3s1t998d63D+ow4X/7FJel2YBCwoaSFwAXAb4HRkk4EXidMXS5czpo0GS1W9LyZfUPSy2a2fXTuFTPbtsBjfgWcC3QGcsMiIuSIHmlmvyoWWKWHONznvdp3u1qHULJtFni22qRtuHazXxGl2tLlM1rdus7fbt/YbU6/KY9WrjUvIM486AWSdgNMUgdJZwIzCj3AzP4nGn/+vZl1jY4uZrZBnMbZOeeqzSz+US1xhjh+AlwB9AYWExatFFy6JOmrZjYTuEvSDk3vN7OXyojVOecSU8khjkop2kCb2TvA0SWWezphutwfmysS2KfE8pxzLlGlTLOrljjZ7DYj9KB3ITSuzwKnmdnclh5jZiOin3tXKE7nnEtUQ/mzMxITZ4jjNuAa4DvR7SMJO6Z8o9gDJbUDDgT65ddlZpeXGqhzziUpkz1oYG0zy5+7fIuks2KWPwb4FHiNVKbDds65IFNj0JK6R7+Ok3QOcAdhiOMIYGzM8vuY2TatC9E555JXzdkZcRXqQb9IaJBzbys/zrvPgDjT5cZJGmxmvszJOZdqmepBm1n/CpT/HHCvpDqgniLJkpxzrlYaGuMsC6muWJvGStoK2BLolDtnZjfFeOjlwK7Aa1ZsyaJzztVQGluoONPsLiCsKd+SMPa8PzARiNNALwCmeuPsnEu7xozO4jgc2BZ42cxOkLQJcEvM8ucCT0gaB6zInfRpds65tMnqNLtPzKxR0ipJXQk5TPvGLH9edHSMDuecS6U0fs6P00BPlrQe8DfCzI4PCasJizKzi1oRm0tYFjPDffBItl5SXYZcUOsQSvbOx+/XOoSayOQQR26zWOCvkh4GuprZq3EKl/Q4zexraGaei8M5lyqZmsXRXBa6/PtiZqQ7M+/3TsBhwKr44TnnXHWkcISjYA+6uUx0ObEy0pnZi01OPS3phTiBOedcNVVyiEPSacAPCW3la8AJZvZpqeUUWqjS6kx0ecvFIWwOsCPQrbXlOudcpVVqFoek3sDPgS3N7BNJowlJ5kaVWlashSqtkL9cfBVhRseJCdfpnHMlq3A2t/ZAZ0n1wNqEzU7KKiQxFVou7pxziTPi96AljSBsSpIz0sxGApjZIkl/AN4APgHGl5uPKOkeNNF+hv34bD7oOKsQnXOualaVMMQRNcYjm7tP0vrAMKA/sJyw9d8xZhZ3gd9qcZZ6i7Dl1WZmdrGkLwE9zKzol32Sbga+DEwBGqLTRrxl4s45VzWl9KCL+DYwz8zeBpB0D7Ab8VdgrxanB/0XwvDMPsDFwAfA3cDOMR67E2GgPI0zWJxzbrUKjkG/AewiaW3CEMe3gMnlFBRnZvY3zOxkws4omNl/ib9seyrQo5zAnHOumgzFPgqWY/Y88A/gJcIUuzpaGA4pJk4Puj7aW9AAJG1EkTcbSWOi67sA06O5z/nJkoaWE6xzziWlkrM4zOwCoNXr/OM00FcC9wIbS7qMkN3uvCKP+UNrA3POuWpqqNwYdMXEycVxq6QXCeMoAg4xsxlFHvNk03OSNgSW+Xi0cy6NUrjjVfEx6GjWxseEHbofAD6KzhV6zC6SnpB0j6TtJU0ljEe/KWm/SgRejiGDBzFt6gRmTp/I2WedXKswSpK1mLMQ7wU3jWPvs67hsIv/vvrctWOeZt9fXsvwS0cx/NJRPPXa3BpGWFwWnud8WYi3EcU+qiXOEMdDrFkN2Ikwt+/fwNcLPOZq4FzCsu7HgP3N7DlJXwVuBx5uTdDlqKur48orLmO/A45i4cIlPPfsWMY8OJ4ZM2ZXO5TYshZzVuIduutWHDloB84b9dnN6Y/51o4cP3hgjaKKLyvPc05W4k3jR/uiPWgz29rMtol+bg4MpHg+6PZmNt7M7gKWmtlzUVkzWx9yeQbuvD1z5sxn3rw3qK+vZ/To+xl68JBahRNL1mLOSrw7bt6Xrmt3Kn5hSmXlec7JSryNJRzVUnIC1CjN6DeKXNYIIOlQoul5+UWUWmcl9OrdgwUL1yyHX7hoCb16pXsGYNZizlq8Td3xxMt895K/c8FN43j/o5ITj1VN1p7nrMTbKMU+qiXOSsLT827WATtQPPHHtpLeBzoD7SWtJCRLaiBvZ/Bm6lq9vl3tulFXt06x8JyriOHf3I4RB+6KENc8MJE/3v04Fx23f63DclXUUPySqovTg+6Sd6xFGJMeVugBZtbOzLqaWQfCopbDCVP13gVuLPC4kWa2k5ntVOnGefGipfTt02v17T69e7J48dKK1lFpWYs5a/Hm26DrOrSrq6OuThy6xzZMnZ/euLP2PGcl3kbFP6qlYAMdLVDpYmYXRcdlZnZrKYmnzaweGAfcQUg/ekirIi7TpMlTGDCgP/369aVDhw4MHz6MMQ+WlWCqarIWc9bizff2ex+u/v2xKbMZ0GvDGkZTWNae56zEm6lZHJLam9kqSbuXW7ik/YEjgEHAE8B1wPByy2uNhoYGfnHqeYx96Dba1dUx6sY7mT59Vi1CiS1rMWcl3nOuG8PkWQtY/uEnDD7nWn568O5MnrWAfy94Cwl6bdCN844eXOswW5SV5zknK/GmcRaHWlo3IuklM9tB0rVAb+Au4KPc/WZ2T9HCpduBO4FxZrai2PX52nfsncbny9WY7+rtmrNq5aJWd2tv6n1M7DbnuEW3VKUbHWcedCdgGSGbXW4+tAFFG2gzO0rSpsCewD8ldSZMwfug/JCdc67yqjl9Lq5CDfTG0QyOqaxpmHNivdNI+hFhVkZ3Ql7oPsBfCcvGnXMuNRpSuNS7UAPdDlgXmh0Rj/tR4GTCwpbnAcxstqSNS4rQOeeqIGs96CVmdnEry19hZisVTeyW1J50jsU759q4NDbQhabZVaLD/6Skcwm72+5L+KJxTAXKdc65ijLFP6qlUANdiXHic4C3CbsK/BgYS/Fc0s45V3VpzMXR4hCHmb3b2sLNrFHSfcB9uQ0UnXMujSq51FvSeoR1H1sRhnV/YGbFksx9TsnJkuJQcKGkdwipSf8t6W1J5ydRn3POtVaFl3pfATxsZl8FtgUKbnLSkkQaaOA0YHdgZzPrbmbdCRnwdpd0WkJ1Oudc2So1xCGpG7AXcD2Ama00s+XlxJRUA30scJSZzcudMLO5wDHAcQnV6ZxzZSulgZY0QtLkvGNEXlH9Cd+9/V3Sy5Kuk1RW9rekGugOZvZO05PROHSHhOp0zrmyWSlHXubN6BiZV1R7Qlrma81se0KKjHPKiSmpBnplmfc551xNVHAMeiGw0Myej27/g9BglyxOLo5y5BL2N5Xb19A551KlUrM4zGyppAWSvmJm/yZMWZ5eTlmJNNBm1i6Jcp3LWna4TxY/VesQSta51561DqEmGiu7yPlnwK2SOgJzgRPKKSSpHrRzzmVKJRegmNkUYKfWluMNtHPOkc4kQd5AO+cc6UyW5A20c84Bq5S+PrQ30M45hw9xOOdcavkQh3POpVSFp9lVhDfQzjmHD3E451xq+RCHc86lVEMK+9DeQDvnHN6Dds651DLvQTvnXDqlsQedVD7oVBoyeBDTpk5g5vSJnH3WybUOJ5asxZy1eCEbMZ/3m8vZ68AjOeSYn6w+d8b/+x8OO/5kDjv+ZAYfdjyHHZ/O2CEbz3EjFvuoFpmlr1sP0L5j74oGVldXx4xpT7HfAUexcOESnnt2LMccexIzZsyuZDUVlbWYsxYvJB9zpdKNTp7yGmt37sy5l/yB+2756+fu//1Vf2Pdddbmpz84utV1VTrdaDVeF6tWLoq3lWsBP+03PHabc+380a2uL44204MeuPP2zJkzn3nz3qC+vp7Ro+9n6MFDah1WQVmLOWvxQnZi3mm7renWtUuz95kZDz82gQP2HVTdoCcwDu8AABQGSURBVGLKynO8Cot9VEsiDbSkHQodSdRZTK/ePViwcPHq2wsXLaFXrx61CCW2rMWctXghmzE39eIrU9lg/fXZtG/vWofSrKw8x1bCf9WS1JeEfyxwnwH7NHdHtDPuCAC160ZdXVkb4TrXpox99AkO2PebtQ4j8yr9JaGkdsBkYJGZHVROGUltebV3mY8bCYyEyo9BL160lL59eq2+3ad3TxYvXlrJKiouazFnLV7IZsz5Vq1q4J9PPsPoG66sdSgtyspznEDP+BfADKBruQUkNcSxT/Tz0OaOJOosZtLkKQwY0J9+/frSoUMHhg8fxpgHx9cilNiyFnPW4oVsxpzvuckvs9mmfeix8Ua1DqVFWXmOG0s4ipHUBzgQuK41MSU1xPFN4DHg4GbuM+CehOptUUNDA7849TzGPnQb7erqGHXjnUyfPqvaYZQkazFnLV7ITsxnXfBbJr38KsuXv8+3DjmGk048lsMOHsK4fz7J/t8eVOvwCsrKc9xQ2RltfwbOBpr/ZjemNjPNzrla8F29q6MS0+y+t+l3Yrc5t79x34+Jvi+LjIyGaJF0EHCAmZ0kaRBwZqrGoHMkrQUcBvTLr8vMLk6yXuecK1UpY9D535c1Y3dgqKQDgE5AV0m3mNkxpcaU9Dzo+4FhwCrgo7zDOedSpVJj0Gb2KzPrY2b9gCOBx8ppnCH5XBx9zGy/hOtwzrlWS+OOKkn3oJ+RtHXCdTjnXKslsVDFzJ4od/wZEupBS3qNMFujPXCCpLnACkCAmdk2SdTrnHPlqvAsjopIaoij7HcM55yrhTYzxGFmr5vZ64Q3gKXR7/0JXxi+l0SdzjnXGpVcqFIpSY9B3w00SBpAmJLSF7gt4Tqdc65kbSlZUk6jma2KlndfZWZXSXo54Tqdc65kaRziSLqBrpd0FHAca5Z9d0i4TuecK1kaV1UnPcRxArArcJmZzZPUH7g54Tqdc65kDVjso1oS60FHuVB/bWar9+Axs3nA75Kq0znnytWmhjjMrEHSppI6mtnKpOpxzrlKSOMQR9Jj0HOBpyU9QF4ODjO7POF6nUuFLGaGa6vaVA86Mic66mhlXlTnnEtSNafPxZVoA21mFyVZvnPOVUpbWuoNgKSNCLsKfJ2QFxUAM2t201jnnKuVNA5xJD3N7lZgJmGZ90XAfGBSwnU651zJGrHYR7Uk3UBvYGbXA/Vm9qSZ/QDw3rNzLnXMLPZRLYmvJIx+LpF0ILAY6J5wnc45V7I0DnEk3UBfKqkbcAZwFdAVOC3hOp1zrmRtcRbHg9Gv7wF7J1mXc861RoNVJpGopL7ATcAmhI1LRprZFeWUldSOKldBy29HZvbzJOp1zrlyVXBseRVwhpm9JKkL8KKkR81seqkFJdWDnpz3+0XABQnV45xzFVGpMWgzWwIsiX7/QNIMoDeQjgbazG7M/S7p1PzbzjmXRqWMQUsaAYzIOzXSzEY2c10/YHvg+XJiSvpLQigw1OGcc2nRWMIQR9QYf65BzidpXcKuUqea2fvlxFSNBto551KvkrM4JHUgNM63mtk95ZaT1JeEH7Cm57y2pNy7hwAzs65J1Oucc+Wq4CwOAdcDM1qbuTOpXb27mFnX6Gif93uXWjbOQwYPYtrUCcycPpGzzzq5VmGUJGsxZy1e8JirIQvxNprFPorYHTgW2EfSlOg4oJyYlMYk1QDtO/auaGB1dXXMmPYU+x1wFAsXLuG5Z8dyzLEnMWPG7EpWU1FZizlr8YLHXA3ViHfVykVqbRmbb7Rj7DZn9tsvtrq+OJLOxZEaA3fenjlz5jNv3hvU19czevT9DD14SK3DKihrMWctXvCYqyEr8VawB10xiTbQktaS9D1J50o6P3ckWWdLevXuwYKFi1ffXrhoCb169ahFKLFlLeasxQseczVkJV4r4b9qSXoWx/2EZd4vAiuKXZw/t1DtulFXt06y0TnnXKTBGmodwuck3UD3MbP94l6cP7ew0mPQixctpW+fXmsC692TxYuXVrKKistazFmLFzzmashKvGn8Pi7pMehnJG2dcB2xTJo8hQED+tOvX186dOjA8OHDGPPg+FqHVVDWYs5avOAxV0NW4k1jwv6ke9B7AN+XNI8wxJGbB71NwvV+TkNDA7849TzGPnQb7erqGHXjnUyfPqvaYZQkazFnLV7wmKshK/GmsQed6DQ7SZs2d97MXi/22EoPcTjnvrgqMc2u53pbxm5zliyfXpVpdkn3oH8ITACeMbOPEq7LOefK1uYS9gNzgaOAK6Pl308BE8zs/oTrdc65klRqqXclVWUloaQewHDgTGB9M+tS7DE+xOGci6sSQxwbdt0idpvzzvuzsj/EIek6YEvgTULv+XDgpSTrdM65clRzhWBcSQ9xbAC0A5YD7wLvmNmqhOt0zrmSpXEWR9Kbxn4HQNLXgCHA45LamVmfJOt1zrlSVXN+c1xJD3EcBOwJ7AWsBzxGGOpwzrlUaXM9aGA/QoN8hZktLnaxc87VShpncSQ9xHGKpE2AnSXtALxgZm8lWadzzpUjjV8SJp1u9LvAC8B3CdPsnpd0eJJ1OudcOcws9lEtSQ9xnAfsnOs1S9oI+Cfwj4Trdc65klR409j9gCsIs9iuM7PfllNO0g10XZMhjWW0oV1cnHPZUamesaR2wDXAvsBCYJKkB8xseqllJd1APyzpEeD26PYRwNiE63TOuZJVcAx6IPAfM5sLIOkOYBiQrgbazM6SdBhhl1uAkWZ2b5zHVmLpZkskjYg2B8iErMUL2Ys5a/GCx1xppbQ5+bs/RUbm/V29gQV59y0EvlFOTKnd1TtJkiab2U61jiOurMUL2Ys5a/GCx5xW0USI/czsh9HtY4FvmNkppZaV9CyOQyXNlvSepPclfSDp/STrdM65GlsE9M273Sc6V7Kkv7D7X2ComXUzs65m1sXMuiZcp3PO1dIkYHNJ/SV1BI4EHiinoKS/JHzTzGYkXEc5UjkGVkDW4oXsxZy1eMFjTiUzWyXpFOARwjS7G8xsWjllJb3l1RVAD+A+wp6EAJjZPYlV6pxzXxBJ96C7Ah8Dg/POGeANtHPOFZF0D7q7mb3b5Fx/M5uXWKXOOfcFkfSXhGMkrf5SMMoLPaY1BUraRNJtkuZKelHSs5K+0+pIq0DSryVNk/SqpCmSypobmRRJDVFcr0h6SdJu0flBkh5MqM7rJG0Z/X5uk/ueqWA9ub9tWvT3nSGppqtaJX3Y5Pb3JV1dgXL7SZrawn0XS/p29PsTkj435a25x0u6UNKZBeqsSOzus5Ie4vgNoZE+EPgKcBNwdLmFSRJhPPtGM/tedG5TYGiT69qnbecWSbsCBwE7mNkKSRsCHWscVlOfmNl2AJKGAP8DfDPJCnNzRSPnEl4zuft2q2BV+X/bxsBthCG4CypYR1WV8zo3s/OTisdVXqI9CDN7CPgTMB4YBXzHzKa0osh9gJVm9te8Ol43s6uid/AHJD0G/EvSOpJukPSCpJclDYOwTl7S7yVNinqyP47O3xG9kRDdHiXp8Kg38VTUo1zdqyxDT8KWXyuiuN8xs8WS5keNNZJ2kvRE9PuFkm6OPiHMlvSj6Py6kv4VxfJa3t/VT9IMSX+LeonjJXUuM1YIjdd/826vK+kfkmZKujV6s6RI/DdGz93r0Zz4/41iflhSh+i6J6LH/RboHPVyb43u+0wPs1Ki/DAjgFMUlPSaSCKmpiQdLOn56LX7T4W0vfmvi6eBmyV9PXqNT4li3zwqol1zr4XW/g3Rv9fvojpnSdqzmWsOjF63G0b1XSnpGYVPvYdH1yh6zqdGr4kjovPXSBoa/X6vpBui338g6bIEXueplkgDLemq6B/lSkKj2g2YR/gf4spWFP11Cm86uwNwuJl9E/g18JiZDQT2Bn4vaR3gROA9M9sZ2Bn4kaT+wJ2ElKgozF38FvAQ8Bawr5ntQMglUm7844G+0Yv6L5Li9Ey3ITx/uwLnS+oFfEp4o9sh+rv+mGssgc2Ba8zs64R9IA8rMcZcAzkTuA64JO++7YFTCZsAb8aa5fuFfDmKfyhwC/C4mW0NfAIcmH+hmZ1D1Ms1s7I/ZcUV5UloB2xM6a+JSsk931MkTQEuzrtvIrCLmW0P3AGcnXfflsC3zewo4CeEDTG2A3YiLCuG1r8WCmkf/X91Kk0+gSgMN54DHGBm70SnewJ7ED5B5rK6HQpsB2wLfJvw/2dPwgYfuUa/d/S3Ep2bUIW/LVWSGuKY3OT2i0lUIukawj/8SkL2qEfzvpQcDAzVmnGzTsCXovPb5PUiuhH+wccBV0hai7ATzAQz+0RSN+BqSdsBDcAW5cRqZh9K2pHwQtsbuFPSOUUedr+ZfQJ8IulxQhKWh4DfSNoLaCS8iDeJrp+X9wnlRaBfiWHmDwPsCtwkaavovhfMbGF035So7IlFyhtnZvWSXiM0hg9H518rI7YklfSaqGC9q59vCOO4hEYWwuqzO6NGqyOhg5PzQF4czwK/ltQHuMfMZkfv1615LbQ0cyB3PjcLq2m5+0TxDzaz/BXD95lZIzA990mA8P/t7WbWALwp6UnCm+NTwKkK30tMB9aPnoNdgZ8TNqJu7es8MxJpoM3sxiTKBaaR925pZicrfLzOvSF8lHetgMPM7N/5BUS9zZ+Z2SNNC1f4eD6E0FO+Izp9GvAm4Z2+jtCDLUv0YnwCeCJqtI4HVrHmk0ynpg9p5vbRwEbAjlHjNz/vcSvyrm0Ayv7oZ2bPRs/tRi2UnXvtFIo/N5zTKKne1kwZaiT57z8KkrQZ4e94i/BaKeU1UQ1XAZeb2QOSBgEX5t23+nVuZrdJep7wiWRsNDwzl9a9FpYB6zc51501bxK5svNfBwBzCJ+utuCznbT8WAomJDKzRZLWI3pDjOodDnxoZh9I2oAKvs7TLulcHLtLejT6WD9X0jxJc1tR5GNAJ0k/zTu3dgvXPgL8LPfxX9L2eed/qjVjoFtEQx8QPtKeQOjl5np73YAlUQ/gWEJPsGSSvpI3Pgjh493rwHxgx+hc049qwyR1il6UgwhLSLsBb0WN897ApuXEEyPerxL+1mVFLp1Py/GXqj7375IkhY0j/gpcHb1plPqaqIZurMnfcHxLF0VvNHPN7ErgfsKwWKuY2YfAEkn7RHV0JzSYxT4xvU54Ddwk6etFrn0KOEJh/H8jwsbSL0T3PUcYPpkQXXcmbXSz6aR7MdcTeqAvEt7pWsXMTNIhwJ8knQ28TehN/JLPv4teAvwZeFVhOtU8whjYdYSPRC9FjffbwCHRY8YDNxOGFlZG5/4C3C3pOML/oPm99FKsC1wV9Q5WAf8hfFH1NeB6SZcQetf5XgUeBzYELom+VLyVMDPmNUIvZWaZ8TSnczR8AaGnc7yZNawZ4m7WRQXiL9VIwr/XSwmMQ+f+tg6E5/9m4PLovlJfE9VwIXCXpP8SOib9W7huOHCspHpgKWEWTCXy3RwHXCMp9xxdZGZzirwWMLOZko6OYj+4wKX3EoYtXiF8MjzbzJZG9z1FGCb5j6TXCb3oNtlAJ71Q5XkzS9Vc36yQdCHhY90fah2Lc642ku5BPy7p94QvFfJzcRSaieGcc47ke9CPR7/mKhFhpGKfxCp1zrkviEQaaEmn536NfhphXG+i5+Fwzrl4kprF0SU61o2OLoT5keMkHZlQnc4594VS1T0Jo+k6/4xWwTnnnCugqtm8olV+ie3W7SpDazK/TZV0l6SW5prHKWt17gflZa5r4dpBKiPXifLygcQ53+SakvJ9qEhWN+cqqaoNdLSw4r9FL3S1lsuJsRVhGf1P8u+UVNbsHzP7oZlNL3DJIKCSGeycy7SkkiW9ppBZK/9YCPwOOCmJOl1ingIGRL3bpyQ9QMip0FIGOEm6WtK/Jf2TkIyI6L7V+Ycl7aeQke8Vhex8/QhvBKdFvfc9JW0k6e6ojkmSdo8eu4FCFrNpkq4jxqcySfcp5A+fJmlEk/v+FJ3/V7SqDUlfVsi692L0d3+1mTJ/Lml69PdXcxm4ayOSmgd9UJPbBiwzs3JX4bkaiHrK+7NmifMOwFZmNi9q5N4zs50Vkgk9LWk8IevdVwhZyDYhJLy5oUm5GwF/A/aKyupuZu9K+it5i3Mk3Qb8ycwmSvoSYUn21wgZ1Caa2cUK6UBPjPHn/CCqozMwSdLdZrYMWAeYbGanSTo/KvsUwqrGn0TJh75BWFHadHroOUD/KL/3erGeVOdKkFSypNeTKNdVTf6S76cIS/Z3I2S0y02TbCkD3F6syVK2WCE/d1O7EDLDzYPV300059vAlnnLi7tKWjeq49DosQ9Fy6GL+bnW7LzTN4p1GSFx053R+VuAe6I6diMsV849fq1mynwVuFXSfYSNJJyrqJpmFHOp9Zk0mABRQ9U0W+DnMsBJOqCCcdQRciJ/JoNgsXwQTSlkg/s2sKuZfayQoa5p5r0ci+pd3vQ5aMaBhDeLgwkpP7cudYcT5wqp6Z5sLtNaygA3gTVZynoScl839Rywl0JS/Nz0S4APCHPmc8YDP8vdUMjJTVRHbsuz/fl8asymugH/jRrnrxJ68Dl1QO5TwPcIQyfvA/MkfTeqQ5K2zS9QIQFXXzN7nJCsqxthzr9zFeMNtCvXdYTx5ZcUNhj9P8InsnuB2dF9NxESyn+Gmb1NyOR3j6RXWDPEMAb4Tu5LQkKC9p2iL+Gms2Y2yUWEBn4aYajjjSKxPgy0lzSDsKPHc3n3fQQMjP6GfVizq8nRwIlRfNOAYU3KbAfcopBV8GXgSjNbXiQO50pS1YUqzjnn4vMetHPOpZQ30M45l1LeQDvnXEp5A+2ccynlDbRzzqWUN9DOOZdS3kA751xK/X9mc6yNsgMFXgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nWZ2rjMjPBCk"
-      },
-      "source": [
-        "# Testing other team's data (BuggerDebugger Group)"
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "cf_matrix = confusion_matrix(y_test, y_cap, labels=classes)\n",
+    "import seaborn as sns\n",
+    "ax= plt.subplot()\n",
+    "sns.heatmap(cf_matrix, annot=True, ax = ax);\n",
+    "# sns.heatmap(cf_matrix, annot=True)\n",
+    "ax.set_xlabel('Predicted labels');\n",
+    "ax.set_ylabel('True labels');\n",
+    "ax.set_title('Confusion Matrix');\n",
+    "ax.xaxis.set_ticklabels(classes);\n",
+    "ax.yaxis.set_ticklabels(classes);\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Wi2XkZpvZaDR"
+   },
+   "source": [
+    "### Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "TADP0GkUZaDR"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "cf_matrix = confusion_matrix(y_test, y_cap, labels=classes) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "id": "pcCTstL1ZaDS",
+    "outputId": "6c8640a7-9418-445e-c117-ae88dccdddb1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "8AOQWsTrPvLx"
-      },
-      "source": [
-        "other_team_dir = '/gdrive/MyDrive/BitCoders/Assignment2_other_team_data'"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 198
-        },
-        "id": "hTcEmO2WQMrY",
-        "outputId": "8c669c0a-6018-4f4e-8a35-6eb259d647ee"
-      },
-      "source": [
-        "image_database_1 = {}\n",
-        "pathlist_1 = Path(str(other_team_dir)).rglob('*.jpg')\n",
-        "for path in pathlist_1:\n",
-        "  path_in_str = str(path)\n",
-        "  image_database_1[path_in_str] = path_in_str.split('/')[-2]\n",
-        "\n",
-        "image_sources_1 = list(image_database_1.keys())\n",
-        "labels_1 = list(image_database_1.values())\n",
-        "data_1 = pd.DataFrame()\n",
-        "data_1['Image_Sources'] = image_sources_1\n",
-        "data_1['Labels'] = labels_1\n",
-        "data_1.head()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Image_Sources</th>\n",
-              "      <th>Labels</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
-              "      <td>Rani_Mukherjee</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
-              "      <td>Rani_Mukherjee</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
-              "      <td>Rani_Mukherjee</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
-              "      <td>Rani_Mukherjee</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
-              "      <td>Rani_Mukherjee</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                       Image_Sources          Labels\n",
-              "0  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee\n",
-              "1  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee\n",
-              "2  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee\n",
-              "3  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee\n",
-              "4  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 15
-        }
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "ax= plt.subplot()\n",
+    "sns.heatmap(cf_matrix, annot=True, ax = ax);\n",
+    "# sns.heatmap(cf_matrix, annot=True)\n",
+    "ax.set_xlabel('Predicted labels');\n",
+    "ax.set_ylabel('True labels');\n",
+    "ax.set_title('Confusion Matrix');\n",
+    "ax.xaxis.set_ticklabels(classes);\n",
+    "ax.yaxis.set_ticklabels(classes);\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vP9Beqr3BqAK"
+   },
+   "source": [
+    "# Sample Outputs of our SIFT algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177
+    },
+    "id": "NfzFWropB-sv",
+    "outputId": "dc16b5cf-c06f-4fc2-ffbd-cdcf6330a6c3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "9RNieXwWP6NY"
-      },
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "X_1 = data_1['Image_Sources']\n",
-        "y_1 = data_1['Labels']\n",
-        "X_train_1, X_test_1, y_train_1, y_test_1 = train_test_split(X_1, y_1, test_size = 0.5, random_state = 0, stratify=y_1)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 236
-        },
-        "id": "dihZDZgXSHsb",
-        "outputId": "96e47306-3a82-4e3e-eea3-7b55511f431a"
-      },
-      "source": [
-        "# a1 = cv.imread(X_train_1[0], cv.IMREAD_GRAYSCALE)\n",
-        "# cv2_imshow(a1)\n",
-        "a2 = cv.imread(X_test_1[45], cv.IMREAD_GRAYSCALE)\n",
-        "# cv2_imshow(a2)\n",
-        "sift_match(a2, X_train_1[50], plot=True)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(False, 0)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 44
-        }
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_image = cv.imread(X_test[131],cv.IMREAD_GRAYSCALE)\n",
+    "match, num_matches =  sift_match(test_image, X_train[120], plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177
+    },
+    "id": "dm_2WLEIDRrA",
+    "outputId": "bc518e18-efde-4ffc-f594-5241ed9d462c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "SPfrtT7rTTSX"
-      },
-      "source": [
-        "test_image = cv.imread(X_test_1[131],cv.IMREAD_GRAYSCALE)\n",
-        "match, num_matches =  sift_match(test_image, X_train[120], plot=True)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "QDeBl9ILQj50"
-      },
-      "source": [
-        "y_cap_1 = []\n",
-        "for test_image_path in X_test_1:\n",
-        "  test_image = cv.imread(test_image_path,cv.IMREAD_GRAYSCALE)\n",
-        "  best_match = ''\n",
-        "  highest_matches = 0\n",
-        "  for train_image, label in zip(X_train_1, y_train_1):\n",
-        "    try:\n",
-        "      match, num_matches =  sift_match(test_image, train_image, MIN_MATCH_COUNT=1)\n",
-        "      if match and num_matches > highest_matches:\n",
-        "        highest_matches = num_matches\n",
-        "        best_match = label\n",
-        "    except:\n",
-        "      pass\n",
-        "      # print('Error')\n",
-        "  if len(best_match)<1:\n",
-        "    y_cap_1.append('Unknown')\n",
-        "  else:\n",
-        "    y_cap_1.append(best_match)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "w23SfK-fRgDZ"
-      },
-      "source": [
-        "classes_1 = list(set(y_test_1))\n",
-        "classes_1.append('Unknown')"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "pRasht6hRfvb",
-        "outputId": "81615c9c-0247-4e25-db08-7cf3a06d379d"
-      },
-      "source": [
-        "from sklearn.metrics import accuracy_score\n",
-        "print(\"Accuracy is:\", np.round(accuracy_score(y_test_1, y_cap_1)*100,3),\"%\")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Accuracy is: 32.5 %\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_image = cv.imread(X_test[20],cv.IMREAD_GRAYSCALE)\n",
+    "match, num_matches =  sift_match(test_image, X_train[33], plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177
+    },
+    "id": "hcgGhs0bClR6",
+    "outputId": "3c86a5fb-dc1b-453a-fff0-0578095b01f4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAACgCAYAAAD3jCzdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9W4h123XnN9auy951/27nHB0d6UgCC4LIQ4yhTQiYxqYhnTQoD4ntbjB2Y9BLGxISTJu8dB7y4H6JMXRwEHETK4SojZNgExxC6HTTNKSD2nKwccsJJ0KSjzjf+S71Ve29a1+r9spD1X/Wb/1rzF0lt5Qui5qw2XuvNde8jDnmf/znmJfVtG0bD+EhPISH8BB+uELvX3UBHsJDeAgP4SF8/8MDuD+Eh/AQHsIPYXgA94fwEB7CQ/ghDA/g/hAewkN4CD+E4QHcH8JDeAgP4YcwPID7Q3gID+Eh/BCGHwi4N03zbzdN8383TfNB0zS/8oPI4yE8hIfwEB5CPTTf73XuTdNsRMT/ExF/JSI+jIivRcRfb9v2X3xfM3oID+EhPISHUA0/COb+lyLig7Ztv9m27SIivhoRX/wB5PMQHsJDeAgPoRI2fwBpvhcRf4b/H0bEj3ukpmm+FBFfiojY3t7+sXfeeaeaYNM0d7p213sardw1XT3j9277f5frnu73Ute7pqvf2ShttVqV37eN4ng/i6trtXRq5V33nF/r9/uxsbGxto29fWvluWt9XHZt294qq9vy+/OGTA+/19Dr9aLX68VyuYzFYhEXFxfRNE1sbW3FxsZGRERcXFzExcVFnJ+fx/n5eaxWq7T8WVlq+uY6Wasff98lHvVd6et3r9eLtm1jc3Oz6E2v17tRdv9dk3NNJ24Lnpanf9e0er1ebG9vlzp84xvfeNW27VtZ3B8EuN8ptG375Yj4ckTE+++/3/7yL/9yKTAbSr+ldBHRacC2bctzEZeV17MObt6Argj8r2+lrXRZnuz+urT0W3Gzcuiep+vlrKVPGaoTq8zqoPqsVqvSiSOugZ7xFHgPbZh+VqtV+UREnJ+fl/ir1epG+f15lfXi4qJTho2NjfjMZz4TBwcHN+TEwPJ7J1SZWE7WR7Lg803TxMXFRadM+q/ns86b/Vf6vOadPAMOysn1mnEYz69JHw4PD2M+n8cHH3wQL1++jJ2dnXj33Xdjd3c3VqtVTCaTODk5ifF4HK9evYrhcBjz+fxGmhkgrlarTptQJyKu9ZmyU7qS7/n5eSdO27Zxfn7e0Q3pkQxTr9eLjY2NTr/Z2NiI3d3diIgYDAbx1ltvxcHBQfT7/Rv9Uc+SOFCW6kcZuK8zYpkBWUcuM2JCY9Tv9+O9996Lvb296PV68aM/+qPfvpHIVfhBgPt3I+LT+P+pq2trQ9ZJdd1BgY2skAlKzzCON44LvGbVPX0psb69U1IhPNQYkNfTOzTLS2PDcp+fn3cMgpSf9XZQU9yNjY0bMmH6Wcclo2MnJcAqXYI+n6PhyAyy1zEzBpQhy+1lUiCI677/dp0joHsdasaEeuDpKriB8XpT16kn3n78r2tss7ZtY2trK9q2jW9961txfHwcOzs78c4778Th4WFsb2/H69evYzKZxGQyidPT0xiPx8U4k8AwH+qmGwDWTzKXkfE2W61WsVwuO7ommfV6vc4IwvOUnitN5TWbzWJnZyfOz89jMpnE0dFRR85u0L3s1EcaI/Yv1Unx2UdqGOQg7m1OY0y5qy+xj9fCDwLcvxYRn2+a5nNxCeo/GxF/47aHaNlr1jFjOHpWFjfrbA7g3sEyZqcGWtc4BC52QNZB91ludjgHMwKMswvFy1gE66xnmW/N0LCeXkd/RnVhPqwfZcWOyeec8RL02QaUkRR8Z2enk9Zd2JOPViin2nPsyDTemYFy9slO6qCW5c//mZ56RybzZ3vxt4OQyrO9vR3b29vx/PnzGA6HcXBwELu7u7G3txeDwSBevHgRw+EwRqNRDIfDGI/HsVgsOmXQh/rrQOg6JfmRBGVtJ/2hEdRHBoajOQE6++TFxUUp6/n5eYmzu7sbZ2dnMR6PC8CrbEpbeTs40ziznMpzc3MzbUcH35qOuvHPCGbEZf/b29u7gSm18H0H97Ztz5um+aWI+F8jYiMi/n7btn9y23NsVLfYV+mmwonossqMOdEIOJuQuycbgjloqowUupSHSkUwyYyN8mfdEjl2/hP8VF5+K/+ajDx/T8uf1TUyf8qbHZ5simVyYHXAdUAngHqH39zcLENRdWK1nbc30/SyZYac8pFrgHGdqdM94+VV/TPQ9/bQdTfUWRwaPALkuvornmS1sbERb968ie9+97uxvb0djx49il6vF48ePYoXL17E6elpnJ2dxWg0irOzs5jNZh3i5KCeuUBZVycD6/pDRMTm5mbHkMrfz3yUP/WdchB5INmbz+dFV46Pj2Nrayv6/X6nLK6LyseBNNN1GhnKXek6UXQjSLm5+1fpbW1txeHhYQF3b+ss/EB87m3b/n5E/P73+hwVm53YrbPiOpOX8Ag+7HgOYHxGafIey8X4blnJONxt4aDrrID50EdO1uOuKeWdGT4xFu94DhgOHFRIlj8bNnpcyZlKVwNRB+6MsbOMynd7ezv6/X5H3jX/eA04b/vtBoi6R7D2ZyOigArb2IHfDTvT9lGP15MyY3o+ilNgXqvVKra2tiIi4vnz57G5uRkHBwexXC7jU5/6VLx+/TrevHkT4/E4Tk9Pi1uGcyXyR8s37Xrh+kIdoRGkzm1tbaWuDu/L6kObm5txfn5emHKNKPB5yW+xWMTGxkacnZ3FZDIpdVG4uLiI5XIZ29vbHcDOgJ3tKiPihpfs3g292jgzzpSp0hewa/7gruFf2YSqBwcVKge/M4ZAgTq4UgmpXBE3XUDZc+xUmeLqHkGY6WXuDgYHYI4A/JrLgs9753dZuLxqIOfy4fWMKXh5vF5ZfZ3ZcBRFV4bibWxsdIa+zoC8HsqHndGBJqtTBqAOTtloRNd9YpplyPJwYPSyZW3o9VOeLmsHiNVqFaenp3F+fh6DwSCm02k8ffo0hsNhHB8fx2g0itPT0xiNRrFYLDruGK002dzcLIBLA6T0s/pQh9gH1Z4eBOb6rbqoD6gfcOKTBlb/FV/MW6OAiIjpdBq7u7sdcNfzy+XyRj/PCJL379rcYPYMwT9rL6axtbUVR0dHsbu722H2lE8t3CtwdwBXZdwlofgRXQuo67SaCpxV9zSUV8biXVkzdqsyOuDwOT3rDVIDKp8govVfZ/U9X8ZZxxQckDwtl0kN0PmtQBcG21P1JqjX5EuGRNDIOhLT4Ud5u6F2WdHgkDm7UVI71dxPlGcmU+oC603AdhDIALNWf4LAcrmM09PT6PV6MZ/PYzAYxMbGRrx69SqOj4+LO2Y+n8dyuSzPef9Suu6a29zcLHJR8FEn3XjuUlGg3AXMansBtruKCOquR5TPfD6PXq8Xw+EwBoPBDUJI3SRRc7lmZVfZFMd1i23pJE33fLJ0MBjEwcFB7OzsdNLN8DAL9wbcI7qKQx8jlZRAcxvAZWwmA6qImwJzhczycwvvFt0NFZ91Q0bFWOevldLrm6DHsqsM2URsVj6XW1ZGldPBl22WdVZ2CJaP9WX9GU/Mkek5u2N5qTvOFmkwHdCz/37PwUPMmXGdvXsaNPBikpQrdcgnJl0GlK+no+synKPRKMbjcbTt5cTq4eFhHB8fx6tXr2I8HsdkMon5fN5Zcihwlp7JJUMwdOBXHbgsMWszxc/YLBcp0NDpWyt+uIxXaWkSVW0t0FQ8LbUcj8exvb3dWdJMg0H98f5fI5s+4iMRoZFxMpXJdGtrK/b392Nvby81rqrLunCvwF0hY2c+vFXImJIDKNOtAZozIl7nb29opu35k+VlDMstt65n7hBOLol1KK2tra0OQ3RgygDN6+7fbszcODCeu7to7FQfpuGg5Z2GTJBlpguA8mV+AieWMaufs3dvawIL08qWzSk9gYoz/0y2mbx43fN3vc7ahXokQNvc3IzpdBrD4TBms1k0TRPPnj2L09PT+Oijj2IymcR0Oo3ZbBbL5fIGWKq9CFAuk4w0ZTJS27mf3XXF3RIcSVBP6Fah8WAfWC6XHTnJZz+fzzujOE3maikm54+8XbJ6eh5OLF0W3i/1LTJzdHQUOzs7HVxSoK6tC/cK3GnZKAQHO3aoTOn8/zpfPH97ehlIenkU39kZy6e4NRdMRHQaS75NXVssFmXlQr/fL9c3NjZie3u7dBym6fXKOiXrTwD1OM6MMwOVpesdw9uKqynYrlm6ZIK8z7rV/Nsug5qbhHE9HTF0dV4a0tVq1VmDzcnVmuHI6kggdcDn5K6PYLO6yE1wfn4ew+Ewzs7OIiLi0aNHZdWMGPtisegwdqWrj+8IrumS6pC5N+nG8eeUr7tHdN1941l+DpD+jOKJJMmgaZJytbpcY6+JZ/ZVyV6Ew0kag/p85h5iO5LkEFe2trbi0aNHsbOzc2NeQ/UTifkL4XN3INC1zEpG3FxnqpAxwAyYKHDm7cw0CwR+NXw2nCRYshFYdm7xdnCPuGQd8/k8ptNpYQNbW1sxm82Kz3QwGJQNKJ43lceVXTLJfmdtoP/ZCIVAk11nm7gs+e3P1Fii7tHPSb832z0byWT6dhd5eD78uIxqDD9jdDR8WTmyTuyGz8mQyjkcDuP09DTm83n0+/04PDyM4XAYJycnxRVDY8T0BTa85v3J6xiRT/SxXTjCqAXGzYLkzHK4DjAt9jsZ5slkEoPBoIC2j5S4+Y6jjmyEr0AscFebtzHJYa/XK8tTtSrMMYr9WfHXhXsB7uuGO1QE7yAZG2CarnScyGHIWKCXJQMgLzPBj/EyQF8ul+VsD53v4cAkxs4dghsbG7FYLKLf75cGPj8/L7PpWtkQcc12nXGobGQYNePqYJiBokJmdNnh17nFXG5ZXg5+3mbqnHTdZOXns0zby55968P0OanK9DIQ99+uu54G5ZDpdE0mEZf6MxqNijvm8ePHsVwu4/nz5zEajYobhu4JApOvb8/mn7K6Ztd8xOdxMxmsI1+ZUXXj4WRP8cR6NWIhiDdNU1bMcOUaZcQloiqXG5rMWOi6XEAq28bGRvT7/Tg6OipHI9Tko/y4gqwW7gW4R3QbqwYeukdQ0rO65w1O68gGXFeOzLoyKA0HxuxbzxPM5d+cz+cdYI+4Zu3n5+dF+bzu8/k8tre3C5BrSCmwF/BvbW2VtcQOAlLy7LqDvNcnogtKDoIsb9auDqr6zsrp6fC60nGD7WfS+LNZedf54MkIlWdmFMnssiWKep4jSMrA68v0MzCvEQ4B03K5jNlsFhcXF3F4eBhbW1vx0UcfxcnJSbnu8zQ0Ouo3mVvG5evlZ1lqRpIGP9MJzRuwT9VYM2WaTU7TCAugF4tF2eAkgJe7U221Wq1ic3OzQ1zU1sqHxCzbTKgycbWPyry5uRk7OztxdHRUmPg6UuOunHXh3oE7gbgGPIqX/WfIlIxC80kvZ28Z0KwzQDWAEJCPx+OYzWYxn89L56J139jYKL5b+QazTidXjpRS8fv9fgwGg7i4uCinJ7JsbqxUx4x1ZHJXORzwvR0Jdg7avO6dOmu/GoCxLEzDwYL18zJm7M/LkTFFHxUQIMnindWp/fjNuq77dnlkesE6rVarmM1msVgsomkul9Wdnp7G8fFxGQ0S7Fgu36x0l9GDExuWl/Kg/GU41pECZ+yZYczy0LfvHmW/OT8/LxOuKovakq6u2oiT91xH2Jfa9nr1EI2+FkccHBwUUufehZphy9ycHu4FuNOKR+TDeyqvh1pHua2zuoHg/XUTP67MzNMbWwcW6bwOdiyWV2UhqFNhCRQEMLGG1WoVOzs7HddEZqBoDHWdcs7k4zJ0eWXGtwamfD4zEpmxcLlmZWdZM3+uG3YvR9bWNfl4PmyrzNh4GTLg9vJlupWlwbZknOVyGePxOJbLZezt7cXFxUVZzy79q8mESx89T5ZdYEX50QXIZ/ntRIzXnHApUPdZVm8jN7wuW5VZhm0+n0fTXJ62yLNouLae7eETm+y73taso89V9XqXZyUdHByUeY0MZ5ie+/szLGS4F+CuUJv9zRg0/7MTrGPbteezcyo4q13rZJnVZuOfn5/HaDSKk5OTePPmTSwWi9LIXHPtFrm2xEmNyzJGXLIRHsmqVTRKM2NcNBw1fzE7isunBswZYKjsnketfajEZE0qqw+Rs/zckGRgw3jKq1Zmdk4acraV6xzLmRGTDFidkUbc7Bccmmejz4uLixiPx3F2dlZkqfNieD670lK5Njc3y5nuTqbIHNkemZtkHQCxXfkc9YsjyZrxpRuFxIdAqzR8t7PuL5fLjiy0Sk3PMd2svj6h7/jjukc57u7uxsHBQRkt+BHimZ7QuBEHauHegHvNF+4CJVNb92zGcDzcxi6z67qXpUtQVAd7+fJlWa3g5aQPTt++GzIDQT8To2muz5OZz+fFjydlyNhQjd3y210b6+rL/xmIrzO2zuSV923GmiFz78jAeh4EwhrgEpglK7pesl2R+u/XmDY7vDP4rM3ZiTNdkG+c8pOxv7i4iO3t7VitVjEej2M6nabzEUpbk3TuklEfJLh7+TKSoPuuH/qduTvY39xlpH6fASDnj6jzbny9r8llOhgMOgyaeuIj/Exva0Bb07l1q2mYb2YoXWa1cG/A/S4sjArtE1N3YYW1zsS4HocKdhsDk/KsVquYTqflQCa+7SZjIgJnKvQ6IMjiUC6atJ1Op5030DjbzgyZ/me/s3apGSA+y/IShLKyZErM8mXzAjQ+Xo5a+l4v7p70uPzQv+7szN0GNZ1cZwRZp3XpZN9N092KHxFlE5OW0/qqLOk2J+g5mco8Mj1gP8kIkhMatZ8fr8H0a3FUJjJmypOrWPStD+vDcso9I+PA+vhIhWkpjuMVmb73c9dJ6VptpOS453K4LdwbcFfwynmFPXgnyAAni+eAWWNHZCVsFMb1CcLpdBpv3ryJ4+PjmE6npdPTQFBhMj9hVm5nU7ymCZmLi4uYzWaxublZPrzP52uMO/OJ1gDGAbrGZLK28bhe9wxMfD7G2R87nAMOy5S5JdTZWbasPTx9yX1d2gQL1s91kHpxm5uS6bKO0+k0JpNJ2d05nU6LS5B5CsR18qCW6AlAMkBU8MlWd1s5sDvYyR2R6Yryq8mIZdjc3Cw7WLn6x8/80c5U6mDbtmUVm3bzMk93RXqb1k5BzfRN8SQTTmZnwE/jw3bm57Zwr8DdO/s6FuAC9OHfbczHrzO4gL2R3O/lyjIcDuPNmzcxmUw6bhbucswmZrJyKA9ad+9YvCeXwWQy6Si6drxxVj7z7TvLzYLLucaaKe/MNZSBp+K6EVN8n+fwPGtpOrgocEKaZXV/e8YGCWyetpMBn6BnvdzYsbxqb8bNAFfPzufzGA6HBQx0EBg3KrEPCdh9bXs210SDSvCn/LJRJQG1NoKl3DISoHJQ13RffvR+v1986fTJq53Zd7hhkEZBsnajL1nVSIx/e9t4O8rXz9VCtVGfysMlqT5KzMK9AncGB9aa8rCCWYN4yJiYdyg+7+DiSkvmdH5+HmdnZ+XoVAFtZtW9bgo8DMwBvTZcziapdAiUFPfg4CAGg0Fsb2931r6zwyttV5oMmD3Ql+lslfV1Ni3wqzF7d+nouqexDjCoS7X2YH6ejiblaiDv6dQM9W1kgu3oE2w1PVT5IroT69JH6oCTIwEGNyspfbFhpU+5ZKNQPx89Az62k8ehQaU7Rv+zeTe2vUYCGq2I9LDe7Ksqh2QmsuP9LpO7T/T679q+AO/vkqXOhqoZU25Y8jKtC/cO3B3M9Zv3GKgwXNN6W0fLnuc1ffOs6nVlWK0u1xWfnp6WFwpnYOATUM7u/JQ6H4Y5A9A1d0/wWAP5FPVaNQ1RuXJAAEbAyJgE8+J1thPL5SBIWWRpe5r6n/k5MwaVtTs7E8vlwTudd1DVvfahMfchvRsU3Ve9OMFG8uLy9P9ez+VyWdpT4C7mzrS3trbKa/c0kXhxcVH0VulQXmS/ZJxKT5vnvD4RNw2E9wvu71A8gTSXI3rf9mubm5udtyxFXB8eRrmxbXhWkII2M1HvhAeuDxn5WGfMlf5qteockEd/feYey4jQunAruDdN8/cj4q9FxIu2bf/1q2tPIuIfRMRnI+JbEfHTbdu+aS5z+/WI+HciYhIRv9C27ddvy0MCUbjLEDaLq/vZREeWFwVF8GQn8Pzd+ivIFXJ6elo2KDmzy+rDDsT1xd6JvMOvAw+ly/W8eqbX6xXl97IxzdqQL2PJbAPWZ53P1QE6y4P1cqBhXH7XjG/tf62et3VOPatOn9WbcnQdowvAQUEfH6X5CI1GQs9rV7PAmQanaZoyDyNQ57kqi8WiPMclk3RHUC9ZP8ZVmnIDrnMhKC/6vnnWkmTLkxapbyQ/KpPvyuazqisZP5dD6pr3f4+nuFm7ZaSD92pzdgze9mzj2gjbw12Y+38TEX8vIr6Ca78SEf+wbdtfbZrmV67+/+2I+KsR8fmrz49HxG9cfd8pOOBkHazGhGog4ZNNWaeqlcWFGnGT0ao88/m887Z4Z4tuRNY1qPto6XdnvgR7z0cflWMymZR0tUSu3++Xjsi6uBxq35StAzLr722VtavL2+WTlXEdyGfB0yZDd3eRPydQEGC68XTQcV3zepKRZUx93WSqgw/zI0Dzd9u2ZR27zjGXLqsuWm0igPYJYTJrkheNEvSsZMVzjrJ2UTo6BkDypFxZPo6E3ehxVZiMF0GQ6fH8FhkWLSXWyZAkWMyL32yrWj9hfG8r3qcuOtHMyF2Gdx5uBfe2bf9J0zSftctfjIi/fPX7tyLiH8cluH8xIr7SXub6z5qmedQ0zbtt2350Wz6qDPKtduSIm++Z1DWCeAZWLqB1hiFLw90fujadTuP09PTGskfd93qw4/ssuDNiNrifPOnfKjcBnh28bdvif2/bNnZ2djorF3xnq7dFZvAoh+w6n6+5dLLfLmMquQfeywyKvmuuI0/Hwd7jCDAionNyIp/xDpyxcN4jkETU17j7swo6u0jl5U5oroihf1ztsb29HQcHB50XWAikM6AVKHLRABmxTimlq88DWXq2K5syJPFRudmXWEZOEGukIlLDOjMfuWHUf0l6JKtspMV2WOcxUBurrBpd+HwC3cA146LwLw3ulfAOAPt5RLxz9fu9iPgzxPvw6tqdwN1BhQBYA1l/XoHCZEPSr8U82WAZ+GTuH4Xlchmj0aisJa5ZVWe6ZDechGGjErAy4M1kQ3mok7NcFxcXsbe3V67t7OzcGDGsY8o1xuxAWDMQ6+LUwFFxa4bZDeltLJzGxhlVBuQuazFOgQfdFGLXmf5Ip+m7ZdrZ8jp2cDFlrxuX8hE4uH7fl8dubGzE7u5u7O3tlc1O7h7hShvpjsq6s7MT/X6/HFAmoBaQZqNC/pdsvY/q/tbWVuzs7HTe4Zq1pQiM9ncsFoviftSx2D7yoC6tVpfzZQRXtjlHzB4y/eJ+AV1TXrPZLIbDYYzH43L0gV6+o1U+b731VpGt9xt3Wa8L/9ITqm3btk3TrM8lCU3TfCkivhRx+QIB+rwQJ+2kGXurMTB2IrIAPsdvdv6a75/luri4KCtkCKBkIF4+N0wcolMp+O3K7+WvGRMyIq11dj8p/fCclPZ6M5+sbRw4a4ay9lsh8yVKsR3IybSzZxzIMz1RkG+2xu5pTMi6XGfJugjQuk9w93zIUOnuESAzLTJllUXMUyBN2ckVp8Oqnj17Fk+ePInlchkvX76Mk5OTWCwWJQ+6akQSnDErrsiCRg4kJZksGYcy1AhDowxvJ8lU/n09qzPQdZaTjlrgPo/Nzc1CdDh6kRFcLBYxGAxKOnqWo17VQd+cs9C7Tt99990OOZvP56Xsz58/jw8++CCapokPP/wwVqtVfPKTn4zNzc0y8n/27Fl84QtfKK4l5qn+mmGghz8vuH/cXLlbmqZ5NyJeXF3/bkR8GvE+dXXtRmjb9ssR8eWIiE996lNtjWnVZoez/2Q1Cu4ScRbGDshGq40KHKT5phtOAKmzsVwevLOyQ3hZnGlknYRGKWNMvkNRqwg0jBXjc+ChYrs8PI/avdp9j+t5SZ5Ze/Cey6FWBubjdctAyecOaEwYn+1DBq//Wdtlo1K2H/uEOjWXxFF+Kqd813QPbm1txd7eXjx79iyOjo6iaZo4PDyMn/iJn4iXL1/G1772tTg9PS3GoNYOrKfreK93ecS0EwoGkRh3GyptTfJmrpda3+TOUhkwHb+hzVsq29bWVkcurMdqtSpHaW9vbxeDGdHdrOT5C9jfeuuteP/99+Ob3/zmjTPZJYeNjY3Y39/vuKzm83m8ePEiNjY2OqMHuVKdKNR0PAt/XnD/vYj4+Yj41avv38X1X2qa5qtxOZF62t7R305FpmB8zWvGCPibrhx367CDcRlgZgHZoZi/l0+n75EVZ4xWadEAORCTkbhyZBNzlEnEtSHk8aL0lapsYlfqhAL2pum+QSYDOgcxXaeMa8aA7NvjETD5n/kwrxojdFDlSMXvMy/uTHXXDNPRfXZYxclcWixLZtzUbpmByuRH3fV+oYPCBBy6/v7778f7779fRmhN08QXvvCF+Oijj+IP//APyyscaXBYX5dJZtQoWx1aJxlpf4Wu+aqctr2e+/F9CJmr0GXLdvP3GmiyVkZHK9n0Qg7po65xrbvrgcpDHer1evHuu+/GO++805GdNkdpBKVnZEzkRhI5VJ2049blLQzwUf26cJelkP99XE6ePmua5sOI+DtxCeq/3TTNL0bEtyPip6+i/35cLoP8IC6XQv7NW0twFWpbeQl+V+XpKBIb2Q2Ed5JsCK3napNUGYhI0KvVKs7OzmIymRQFicjdCmQKDjD+cTlk9XB5sH4cLnMZG9cSM311Bk0MZm6grDO5oaNcHEBr3+tYCO+tAzwPLsOaO8ZHQLyu/D2fzFjScFNWGdPTUH/daE1t4HpPlx2fURnG43FncnO1up5cPDs7K+8LffLkSRwfH8cf/dEfldUhk8mk1I/1VP0zfXVG6wZPvmdN0tIFmK0w8R3TypPvd82Ing2URKMAACAASURBVPu25U7a398vu1Y14tEEsBsuNzb0vWekZrVaxfb2drz//vtxdHQUEVH2ubx58ybatu0AuIyG4sjdxX0RXPPuBKqGDevCXVbL/PXKrZ9K4rYR8bduzdWCM+cauGYdSHEy0K6lVQP4deXyPKQgZ2dnZTcoGU5mcHSPzJz+du/cUlwH2IxNUym8Y+pZDvf56r7Nzc0Yj8exv7/fOYLUwYWyyIye19fvu2wYvG18xFYrS9Z+mRHwEZobU5bd02WdlBYNaBaX8qPBUztkozim5wSFbU8wY5uTeSpoYlObjPT7j//4jwvw+NvAIuLGzlbWnavByCYFrCqnXCZkrlzqqLpwkjPTY+oN3TpsK9/Rq5dg7+/vl/wPDw9jMBjE8fFxnJ6eduol2VGver1e59wZH3F/+tOfjr29vWjby3fVfvvb3+5M3nJU17ZtWSqteQ8dDSFjTJ300TP7L/FkXbgXO1QJfN4RdD8iX0bnoKh7FELNWFCIPnlKIVPAVMLZbNZZ1+5s1AGVylmrR8aQnaVR+Qk23hFrQc+q821vb5elnPJZksF5h6oBvcs7KwPLnhmBdeVm3fmsA39mSMgsa8YlkzNBjWXwydTM4Oib+fqoiqCe1d87M9NQ2rondxvlsL29HYvFIj766KN4+vRpfP7zny8nhso9IMMgfeemIjcUrD/rwaWVbdt2Xt7MJZnUUTcgHHnW9JmGxUmOuy0Wi0WcnZ2Vl9gsl8vY3t6OJ0+exGQyKT5u1k0yF9uv6aNGRBcXF/Hhhx/Gmzdv4q233oonT5509FPlXy6X8c1vfjP+9E//NA4PD6PX65XXbXLDmRM573MZLtbCvQD3iHyVjN9bx9Kc1bJjux/2NmNBxqV7WbqLxaJz6iOH5w5iWR7rGKzi+fCVdWLH9M00BI9s4o4sTJNO4/E4Dg8Pb2xgIcgxvYwNq85koP48DUdmfPWfy+4ok8yYU64OxpQl082e9/oqUB4qtyb/stEVO6W70fy6t/M6Yy6WSlnpOTFwB9xXr15F214u0Xvz5k3HBSEWrZGcb8XnXITuSwacsFUd3BBJL7lUVHnrt/zkCkqb+kedo2GhDJQXZawVMHI9qq40GnyWBoXlcd2Wcfrggw9itVrFO++8U/qO9wu16e7ublk6KpnpHpfTutuIekojluEHw70Bd4Uao4u4ya7IgiK6qxQ8DQfILA6DGwMKWsp5dnbWeYl1LU/P39e0szOocVmfLA2yLrmEtCqAa+fJqDS8VIcSc9fQfDabxWg0KqsF2Fm9jbxNvIzOdClXv+Z1q7HXLH+PQ7ZEQ1vLj/+5+oMd2kGdsqaMOMfB9tRv97M7a4+4fk2djzKUrhsSXRMD1LPb29vlUKymaeLo6Cjato3Xr1+nrJmuEukUD5+TLAjW3ObPepP5coLS21gjQi1NdAD3PqF0M5cYv9UOGoVwsYP0Xu+W5ZwURz40SNQf5TOfz+NrX/ta7O/vx9tvv13SY19s2667Z2NjIw4ODgohJIlsmiYePXoUg8GgswvYFziwLDXsKrq09u7/j8E7rINvxtprAO1unSwd/82QsW5elyJMJpOiLLovy3pbHiw7J8F4pnRE3DixT43OfKXM8qeSubP+3GlH/6w2oKhO+/v7sbW1dcMgZvJzZXP3goOUM2rKNmsHN+gKGcAz7dsMLdOrxeM16QOBLjsOgh2W4OO+aR+BuCES8EVcnxRKveZ9gTWZp0BBZ6LwvBcCi/SObFZuGa0NH41GBYClNzrGQHLX9n3KVksPndGr3FoSyDbhiELlvbi4SF0o3HXroyAaVKUt8iIjSFlHdOcZJBcndzR+9OFHRLx586bTzpKNyqBD2XhQm96Y1utdvix7MBh09iiQFHgfuC3cG3CPuMm8/V5E3nF5r3bfOzKF7vmReTGewmp1uSZ2MpmUTR/e+BE33TqcROLQlCDAXat6juX0vNSJCDTsKC4LKZ+GqmIzi8WigPtsNovd3d0bMmPdMqMqYPD7td9ZYLxMkZ21ZEabMveRnOtXBqguN2fYAhZ9fFI0+2Z+bgBpIBX82UxH6WYiKKkOYu9t28a7777bAdMsf66P185OLc3TjkrJRiRE7R0RZZeryu3uDObHPkJQF7BpZNTv92M6ncZsNiuEYzqdRkTE/v5+rFarslPYwVwyU9+YTCZljiwDd65u8d2wLK/y1OhGcmCa6gschXu5Njc349GjR6VPypDR5UpjzTZzEpmFewPuHGI6mEbcZFsefOi7jo05y2Lnus1QRETZbu1nttQYrF939udb0TWk5RBTcamwPLaAjU5F4nDYDUjTNDcmdObzeTlGwetQA1yXc9Z2tf9Zu9KIsE41mToQ+/Nsn8wfmrVfzRjpt0+2kZ3SxZDVk3XRs6qnj/q8bFmZ2/b6NEj6ziOuX5b+2c9+Nl6/fh2j0aiThvLU5KD04vz8vLjouPVfwM88OBHattcHlLE99NuJDH+rPGTMEVGWTmo9ftM0sbe3F0+ePOmwcY4mfKREedIlKRCWi8kPRqM+SQbT6TRGo1G0bVt2tLqPnACfuVZVJrpdZDQWi0W8ePEijo6OOr54jjAyHfNwb8CdgsyU2Ttljb0rrgdnPZ7OOv+9s1WBO1m7W2hn/AQwB3YpPH3xykdpcNKKLEhxXYEy3y+H8lK68Xhc3DIqg+YSuPzLldYnAL0tdS+bKHQjoOu1+zXDlenBOkN9G3hnIO4Avrm5GfP5vOx4pBuMI7NMj9jONVmRoUl27t7JRhV04Qhgd3d3Y7W6fFHL4eFhnJ6elnzFyKV/ep6+d7n/2rYtm4Kk19QrAo8mLuXCoX67IW/bNvb29sqIUbLRCEOjiIjo+Pe1A1Wboeg3V2C+qpfi+4Q6681FCkpHPnv1Qc1Pcdmw65nktL293TkbX21GuSkdMf35fB6vX7+O999/P/b3929g4V/ICdUMdJ3VKZ5fI0NYBzx+3/OuDduVlhphNpuVCScPNVDRUFPLDTnxw07M5zXJ5x8pEAGP/kzWxSf9FLR7z1dPyHDpQCNvlwyEs/aosU4azHUsPEubhoxy8jwz+TMuZZSVn3nxoK2Dg4M4OjoqI5usTVgPgj0NgZYZavTk7URdjohi6AkMlJX84Epfh2atVqt4+vRpAXwBEc9QyUa7AnfqE5m2LwpwJqq4kp2nLXnp5RoCUKZNNjwYDGJra+uG4Ym49t2TIMkQk9Rod6qC9zfNPcgA9Hq98rpKGUP6+73NqTd+LIKu023kIx6SSc6hUDcI7K7LHu4VuEfEjY6hwEZgPP4mu3cQypi9M0bmkXV6CV3+di7XcubPNNSxBez6rfgqOxtQzzsj9yViLC/fOMNVChpOkhkyfQ1Dpdhyzezt7d1wE2QMhIrJtGusOruepc/gBtN1JGP8NCD8VnrrRglODgQWbDu2m1gdOyMBhoBAPeN2c+/s0h35gtX2y+WyuCrkihGDVHtKNv1+Px49ehRNc32s72KxiK2trXj06FG8fPkyIi6ZsUZxBHa2r8rLPkZZRlzvTpX7hvekZyrfxcVFjMfjUnbOLdFF5YZC6fCF12oj13Gmtb29HePxuEpWJDu5adgu6h+uWxFx4/RGGR66VPQh9hD0aYQUz3/TMLg+Z+FegTuBSv+ziiuQEfN53q8xdMV3A+Hgw28B6Gw2K64MZ9AeNMzUCW9i7LT62XOsvwyKsyAyU/oR6bccDAZlxQTZm8tTSq3PZDLp+PZqsqyxYgfHWnvUGDPrnxl6r39N0d2Vs65jZG4fPpcxJpWFTF56RXDjKI0dm0fw6r/S8fkctS3XhWuE9fHHH0fbtuXgsI2Njej3+7G9vV1GYDo5Ua6Gx48fx2AwiO985zsdXZHfV/m6Qc/aLXMTUHe8fZWu3H+DwaCsA9dO2qzNVf/j4+Oyfl/31b+Yj2SmCWLNLdDwKl31Abl6OApxAjIej2MymcTR0VE8fvy44xqSrFVH3/2rdDKyQRavZzN53yXcG3Cnpc4qwvvZvYyxeyBIZMrJ9BTf/YVyW3BNMY0ROwI7GIexzIMbMvSMgm9JFvtzFsDVEmRvBBeez+H1poKL1XFk4gDjI4fMqBKAs2/Jax0we5q3XXNDkAH6bXrhBkpt7CMeBpdhbdToRIHAn+WptF3ecr+oT+goW7kJZVj29vaK+yUiCmgOBoM4PT2N+XxeTot8/vx50Ve2sdJivTM94GjTXYkez0fIMnw7Ozvl1ES+LcplqJGl5gCof3TL+GYvrSqjAVD9yMjpaiXYsv/NZrOybDjTYxp9uXZ8ZMa3ofE54YzrL+W2bpSrcG/AvQYaPqzjtWxYeFvavLbO3+qsXt/yt/u6YndfaBjM4boal0Ne+uvUUekb5XBMaTMfrs3lpBaZh8DJwVhloBFQfebzeSwWi8L2XLHYwZm3l9PBMmPy3j613zWDwVAzQgQg1t91i/H1EZubzWZFvmpLbQoTaCgNrcFWnIjobDCTPH2+haNA36dAOXhfYXkHg0E8fvw4Dg8Py7nmGkFqYnEwGJR7OutkOp2WcqkuT58+jadPn5b/To4EXicnJ2Uivm2vj5H2Pkz9EIjpWADNAY1Go467SxOoenuYPnTLsI8R4Ml8xd53dnY66/XVj2iguHs3Isqr+2RcfFLWGTfz1KSq4kkX2E/ZttIJLckUYWP/ruEWw70Bd4WMgfs1Biq3x8/iRdQZiDOpjHV642YAwpcNaJdexPVhY4ov4FXeYmAZU+Z/1lPKwM6n/2TxlCHBXPFldOTHVUfb29sr6XJCzQProbo6kGdtR9llbebsl8+t++2shvWtMR6yZspZnZ2rNyQ3PsdrKi8ZJIFHhpdtxXYUmHApotqJ7acRpMCo1+uVUwo/+ujytO233367AIIYofRTSyi3t7fLhKPyJ8nQNT0nOWbno1OnaDCdmKgO9HFPp9OOi1H9iSRGukk3JUFRsvZ+7X2AhEsfus60zt51dTAYxLNnz2K1WpXRkXTAJ6EjorPZy/sJ5aB9CT6hrLrpQxmuC/cG3DMmwo6YMcFMcbLhkQOjp+P/s86vTk5r7kMm/VZnFGPL/L6qG321YoFMMwNEsj11vq2trbIxqWma4rvU6//IDrlMUp2HZ2xr8kibVtxtQNmzfs7QsuvrZMw8bmP0GRvk73UjBm+LrA31n6Mzds4sz4yQtG17g4FnLsas03JeRO3m+xZ0/fXr17G1tRWHh4flzCMZBm6N14iMw361O4+cpatJoKXANeLL5bKcIy8A5o5RJyEEUpVJfnQtd4y4JgeaA5pOp3F8fFzaRIZM5SD7VSA4sq258EAyXSwWhWXLVUKQJevf3d29sc+BJIpGlHmT3fs8FvvDo0ePShsQH7K2WRfuBbg7oDtoZ8wt60QK7DzrfPV6jvmuSzfi2g/O3WeMpwbUMMxnwHnOBNlwBuLqcPr21RY8x2Jvb6+sa46I2NvbK0f5Uhk4WUegoCLLgMlXKGWljNzX6bLyOlHJvQ15P5OF7lHB2Rky1p/lwfb2MjpTUh4EdHW2bNSm+xnJ8PKp3ZmXd1jF0e+IKDrl9abPWi+tJuPV5h+xXhny+Xxe7kVE0TUnVMpLO7KlO2K/TJPl4Ugmc0NKf0UmNN/DlUA0pmSxLivlS+KUzaOxj2svQK93uUFKo6WDg4PY29vrkDQZED/6N2sPb3+Cvsdzd6CMterjk7vsg7eFewHu3gEzYfg1hazjeMUzIOLzbmE9PXZyboLQNU6WahMC17HTmmcuErJ8r5e7AbyByQI0nBVAywBwNxs7HJVKvkfuFNTcAp935q702FZuLB00szZ3Ju0jA347QGftmemJG23Pz0cTkiMZF4HfDRVdUfzmu0xdXk4oMkOltByglJbKpVVcevPQ7u5u2ZSmeQO63LTHgYRjd3e3s4ZcOi92K8YcESUtHQ8QEWWeiW4n1kl9QuAqYOe8xv7+fuzs7ETbtmVBAt2VTXP5XlIto6ScXMfYDsqT+tHv9zuLGlQ+7UXR3AknqiUr6gLLkJXJdSJr36zttVEye/Y2gL8X4K7AzkcQrE0oeiBA89lap3FwYRr+W2USa/fJOKXBXYrszNyqrbSb5tq36cDNCVX+Z6fWsi6xJ5ZJpwEyXdaTrF1KT9mvVquy+oJnczubq8ktU/wacNH1wHTXseqsLN4Wbugd0KkbDvx8jqsnVKfMt5qxw2z+huzT2bvSqdXNDZfY83Q67Zx1tFpdvkhmf3+/vFBG6+UF7KoD2bpAWUt3uSBAeioXjMBdLg2f95GO+kiAMuFoSOmenZ3F3t5ecS3u7e2V1/BJNnqZN9eek5xwPosy0x6FjY2NsqSUx3Z7O/tCBNcrJzUqB9uV3x7X3Vf6zf0CmV7x2Vq4N+C+jj0721V8B4AMrNelJdZbAwzm4+XSfymArqnjC+hl8SO66359yRZnwumyUaciW2MnUXrKl0M41iczcDIa7LiKr1UVi8WivNWdzynPjNFn/nQCuH47q8vkW7uXtY+DufJlvFrHzMrLTkQg5PNyYWUuuszvLJmxY7ocKGOWhx8nQtxpzHoLpHXYlgCR7jee5cJt9xp1sD05spOx4MSr90nWVemzbuo/XFrctm0hK9zjwTkmbf+n4ff15A6Gmg+Qb59vQVI5WT+1TTbSpZG+C3GkXrINs37qz2jVDOfaPI8s3OUdqp+OiK9ExDsR0UbEl9u2/fWmaZ5ExD+IiM9GxLci4qfbtn3TXJbo1+PyXaqTiPiFtm2/fod8blTOOx8bKrOM/jyf8W+/lhkWAjjZGq0pG987n9KgUvqIQn4+f/GDL3HkMJeKwkkol9ltAE8Q8jkAMarpdBqHh4c3ZKLgLJOuC6XjebtxZVmyNqi1e81V5+m5XtwlD/qIVU6BAMFODFb3mC5B0zstZeZ18BGUP+8jHRpLTZ7KXzsajcru04ODgxvrp2ngVUc3wvpoBCPQFVD3+/3OMb2si7svtCJM97iaTEBLXeKxBJQv+ySB3Y171re5K5gTo5xHoKGRa9Lz9knwjCASG/we/3OEI726uLiIg4ODG2mz7daFuzD384j4T9q2/XrTNAcR8QdN0/xvEfELEfEP27b91aZpfiUifiUi/nZE/NWI+PzV58cj4jeuvqthHftyhuLsNbuWAZh3YN6n39LLxP/0D3rZHOB0T53e3SrqVJubm7G/v9/Z6n9+fl62SRN4HRCoGCwjFdoZv3dg5cH/6jRaCSHGc1v7uQxcLpn8HaDdX5mBtBvPDJzdeDNIBlkdXGZepiwuAYb64DrFetT+O4h7HoPBoLBlARPnZVRnjiaWy2Wcnp7GwcFBZ1Ka+dGNSCPvbeP/yXZ1j+eq0EdPYJKOiZFqQ5KfoUP5Z24T/fa+S3mqHAw0iPTnc0TDERvTddadyY9lpMz9o8B9M8KYw8PD8ko+6sP3jbm3bftRRHx09XvUNM03IuK9iPhiRPzlq2i/FRH/OC7B/YsR8ZX2Mud/1jTNo6Zp3r1KZ10+NzpVRN3SKbg/3oEmY2iMG9FlWMwnAx2ulsnKkzXganV9opz71bVB4+DgoKNYGq7yzfEOdNxYo7cn3SbjGrhkCte21ysk+NYhxs8MrdqFZc7KkpXB7/F/xo5qbX2bDDIwcB2gPJwkeJ6ZEXJwqgGQQuZzVxl0lvnOzk7ZRyHd0giLZSCzFVnI5KZv+vO5zNABlfXTnI9cQprknM1m0e/346233upMoDZNU+K9fPky5vN57O7uxs7OTtkZy77B8rqhdAKjQJ3zPi4A1aiDbeMvyRFb131NEpNI+X4RtqNjCdslKyuBvWma2NnZiWfPnnVexydgzwhLFr4nn3vTNJ+NiB+NiP8zIt4BYD+PS7dNxCXw/xke+/DqWgfcm6b5UkR8KSLKK8BUgav7NzqSlM0nEjIWz3uupMzHAZ9DQN2nu4VrZB0Y2YmdpShNgjsVn8vTfLmZ/N9kSE3TxHg8jrOzs7JzjsqXycRl44pFv7JYlcqVAa+nx3QzEPX7LKenS6aShaw8mSHPjLaXMStXVk4HuouLizg7O4vpdFra0QFA7ey64Yae7eXy6/V68ZnPfCZ2d3fLTlAtW9QuS60113OcaJzP52XFjPJgoBuCK8HYFt5fCJr0dUsuk8kkjo+P48mTJ0U/5SrSCzMkF67OaZqmM4Hv7eTlIYmgLMm2NScxmUw669qbpinHbOiZxWJRZKz2ERaIjFFHOCrh+n7qI+faqANujFWHwWAQT5486YzmKQcf2dTCncG9aZr9iPgfIuI/att2aCDRNk1zO21CaNv2yxHx5YiI9957r1UFskkoZwvObCrlLULL0tG9bIjjACDAi4iyyoD3NHzjUkdfwsi8uZJACi/3B18AorJpPbLYGlmbGIXK575BGskaeHkdyQg1OpDvzw2rG7hae7iMa/Je9+y6eH7NGZUDZk0e3mEycqBnNdmnt/FoKaGOp22appxVrolMtZdAmRvPqCtu2L7zne9Er9eLd955pwNs5+fnsbe3FxE3/dARl+A7m82Kfrk7gvL339Qbupxc3nzlo+qvPASiSlvLNA8ODjrtMp/Po23bztucKItsxLGu3dnnRI40MSnd3tjY6OzK1aY9rjRTmpz/Yr4qn2+iIgizvyuI9etYBenFYDCIo6Oj4kJjWq7D7mrycCdwb5pmKy6B/b9r2/Z/vLr8cXPlbmma5t2IeHF1/bsR8Wk8/qmra7eGrLNd5Z927KxhHdQj8q3oNaCq5SPWrrcU0ZXjzEadjOyMcXytrE9EqQx0AZHhKF1tT88YApnAOkAmIHg51Km1FpqMlGmTMdYA2K+ThdTK5vJgmmzfWps5a1fczBB5frXhM/NRvpK52lMrMrTb8eLiorwujmWl640GmWUgQLFcTdOUZaqSIVe/0FCTufvIgKtD3H3kJMXLxboKMCOis+FO+kvDIAD3F7xzqWVGTLKQMWBvJxlhlZftprNw6GLZ3t6Op0+flmMF5LIhyMvNQ93h3hI/MVbuK/axXq8X8/m8nCqpkzH1BiYZBfYRySWbz/Fwl9UyTUT8ZkR8o23b/wK3fi8ifj4ifvXq+3dx/ZeapvlqXE6knra3+NtVaH67f1dC4vKnq/KVOFbuWn3SZxjfh6Rk7WIYBInMv6zGzyZSHdRcKRVf7hBNOHGzVER3V6lGNV5mpp/JRM8TDAjUq9WqnPdRO73OZbjOSHveWTx/nnHvcj0b2a3772lmBkgy5KoZBQ3VDw4OOm20s7NTJsyXy2W8efOmbPoR0Ozu7sbh4WEZcXH5LP3EEdF5bjAYlF2VKitlQT3QLlUBGQ0AV6q4fDJ3hxt/AiZ37/Z6vY6/WHWQy4WjBme4bMOs3Vi/dQCn+mtJr8rFOrvLVJuW9vf3y4maPE+nRj40es5IgfKR3CWn0WgU4/G4PD8YDMpLtyk7H1nVDJ2HuzD3fysifi4i/rhpmv/r6tp/Gpeg/ttN0/xiRHw7In766t7vx+UyyA/icink37xLQdZ1dN3nxAzv+fJApqn7ziw9bXZef0YdYjwed96crlBbreCdw8tVk4MUXcvGVHf5/MiKvO41Nux1d2ZIvyt9oWIcOiFS5WPdWKeafJUHZcRysB1YD+azjs2zLLX6M74/y0BDx7hebgHCanW5pE4Hdql95vN5nJyclKG/iIHSEEh/4hOfKAdUjcfjODk5id3d3TLJPp1OYzqdxsuXL4vrRztJ6d5T3TMDLXAneK4zpi7rLC7BTPoqtwfLxf5F9yFdVCRuNdmz3VmuGplRP9RoW2fGuPuR6bHvacOTH5bmcnK9W6dXMrZi9vLt67jjw8PDQuSctVMGt7H2iLutlvmnEVFL6aeS+G1E/K1bc775XPnOmAKvU2n5DOP6Dk9natl/KrFb98Vi0XlzutJ2Bs566ChTNxwR61dOSMGcqVMuBEyCfAaQ/luskB2jxgg0fJZf1Vksy+9tkLUv6+Lx3AB5R3LZuc7UOpWuURey4Xumg+4mcX2SnnFHZ8QlcI9GoxgOh50drjQKApHt7e3Y2dmJpmni7OyspKt16VqXTQCXz57p6bcbXunifD4vSw7dFedtIHkqPZeFvtleLF82j+BAqDXuTJs6xRGS668bYG9TlV+7aCOiM/oljpAwqd/JpSZGLWKpkZTXmyPYGpFQuygvjez6/X4cHBzEwcFBB9j5bDaJehvA35sdqgoqMNfZesf1xnVQvY3h8ZtxMsCVknFY6yyEiqvf2pLd7/fLMkLG9ecyV4KDHRmzry0m+3F2JaX1OlNWrBNdZKqLVvA4uCnUXCG1PP357FrtPplglkfEzZegrGt/ARjTqBkZ1o1toElEMb7d3d0yJ7K9vR1HR0fx5MmT+Pjjj+P09DRGo1GZx5COaDnj9vZ2HB4eljNVtGKJE+qc01mtVmUpLEcTnASV39nBPes/7uP1eus6wV765X2Vm+J0jWV33eCy34xEEOBZpsxoiyXLSPCtVyqvjGvE9VuwNjY2yt4TgTvbv9ZXWW7XPd6TgdDIbX9/vywJ1dJnytpd1DVPhYd7A+5SDFr5iHwm31mY7pF18H8tr9r1zOLKNcGGysBGz0RcH6zEN+dkzLZm6dmR+J0pkAInehVfw0qP68EnVAUAqgdfHuBl9TplBlm/3dAwjcyg10YEGfvP8quF7FkaUL8WcW0I5CJ7//33Y3NzM6bTaQFWvWLt8ePH8bnPfa4w7LZtYzqdxpMnT+L58+fx/PnzAmij0Shev35d5jbUXvrWShu1JV2Bm5ubxXConM56dWbL4eFhAT0nGVmouRaVt8uSH7pkpJdyw7BtPF2u/nK9WNd3M53UqLPX6xUGzn4oGa9W3b0oBHdfa07j5CMf/fe6U55aoCBw393djf39/XIapgyMZENMvCuwR9wjcFfwDk3LnQ3dFKh8WUNnipEBiN/TahE/QY/xPA8yELElsS6uOllXHxoRHyXomcwYeZ194i/rkOrs2WiIpwlmTM/LncnC4/G6vWWa+AAAIABJREFU4pIFalh6mzH0tuIIKDMElHkG/g4ibsT1nDq1GLP2aUg3ptNpjEaj+MxnPlNOF1R7Cfhns1nRE73NiHWfTCadIboAysGOJGdjY6Ms56OPWO3GxQBZO1AuuuaGLhsJuUHktyab6T7UPS7NrBljH1VnYJ/VQ9eUh0BUK3SUHl01Ozs7ndHUYDAoIzHpo4/i2KeUNwHe9U2gLtIksqT5gBpWSVdqrt0s3Dtwd+G5FdR1BwT6BWtD/kxR+M08I6LDWieTSQF3356s5xyMBJpk7wQtPZf5PRXHQdcb3g0U0+AEtK9/p7y5vt7zUfnkmpH7gcYj63iUA5m6A68rscrkdfHfNXDODIjriqfjAJbpEOtMmWqdssu63+/H69evYzgcFjfN22+/HRcXFzEcDuPFixelMw+Hw+LG04hguVyWF0APBoOy7NF1xz/SR5GJpmmKf14GhYbBjaBPuLpO+wiZ17jU0v3LnDj1NmH/dn3Olt/W9Mx1Qflp1LOzs1NcX2LqOzs7HReJViTpxeK9Xq+4vDSqovxIJlQf3+hEfeNyz+VyGbu7uyU9jtR4reYK9Tw83BtwZ4MpZB1RIZsccwaQGYQaQ/BrbdveOCKV5fQ02emcQQpA2VG0Xp1Ddiokl6tx9EKgVt7sBEqLbJEd0eugvHwlh7M/7pxlvrW2YxuyfVzGWRtlIF0bITGOZOSjKxozlYPXa/mRMDBvjcp2d3fjyZMnnQlPvWxiNpvFixcvSofVFvsPP/wwjo+PO28e0moOug2kb2r/5XJZzjinTgm8eeYRXw1H15yOcKYcqLM18kNXAnXNR5QZe+eSRzei+s0J5sx16G5Ab3+VIwN9GRwelSD90Att5vP5DdeLXGCsnx83wLJmE54sC12r9PHrvbacEFcds9+e37pwb8CdISt0jQXqnv/OGK2DgKfPTi93hHaP6o017FwqC9k884m49h9SAbmETb855JYbRH5RlYtLIVUv/80VC85s3OAIGMTKM/DXf76ezUdSklk2nNTzvtLBQcDbPwMCgk92P1uxkD3r92g8CewKHF77nMbZ2VnRj7fffjuOjo5id3e3vDRD8cbjcZyenpYRIA2vDCkNsoBF+qDRVa/Xi9PT0xvvD339+nWphxvg3d3d6Pf7nZe51ADRh/3eJ6jDvpvaiQZHQN4HHZgVhyek6r5cI756LGtbv7dYLGI2m8V0Oi1nwIuNP3r0qNSD8xk0OiofJ2JZZtU1879Tf1Q2uvS0R0GuGhoPlx/rpDLcNod2b8DdgSED4Qyw+dutZsTNlRC8p2f8PndlcnefK5KX2ZdERVyzHg0NvVGctTob0H/fsebMWSzAJysduBxYtcHFX+HmIChDJ0XMys1r7m90pXf2THmw87uB8meyeLxXG8mxDJpQ8+cZTx+uRGnbthh9rYaRn/bx48fx3nvv3ZCh3o/58ccfR9Nc7zJ9++23OyxegHx6elpcCdLLFy9elOV9EdevY6MPW/k1TRP7+/sRcblcU8cBsK6+Oob9iDonQ+MnN0ou+q7tR8lkr7Ko/JPJpAO0YrnO6smomZ6TmbOzs1gsFvHy5cvO2TJHR0cdYNYbn3ROECd+WT/lx7kEupdoLLlaiQaPZdZelmwzoGON8sh0PQv3Btw9sAIZeGiYxFCzdN6xM6Chr1DC1hLI6XTacZcwHVcy5inw9AlJllPMnp3Dt0h7B/LgjL5WX1d8Df+1yYbgyf8yWurYvnuOoximsa5TZwzfFd/j1dpv3SjA77nxc+NOg+3GivXj5LLY5v7+fmc53dOnTztp6N5yuYyXL192fL0Cl93d3Q7A8EyYwWBQdEnb6be2tjrr1dkWTXO5RHNvb6+QC7kgWEcfdRHMOSKUbkrfKEuSDukPda7WTyKujyug61IuQBoL1s1dZt5e6sd6VaTKwTc8sT11XIQTCLUDMYg66v2SZWJ/pC5T1k3TlLkQldsnTtkWNT3Pwr0E9wx8KQwOjWnVvLGdHbh1r1l7Dj3lp8z8ixH56gHe18Yf7fDUjlMqqZdX6Uqx+Z/DQl/uRqZQYzZ8Vi4ZdkTJ0IegbvRUfl/5422YgbXHYZ4EnWwklumHG0wPvM72XddZeC2rm/LjeSq9Xi+Gw2EZrYnVq/Or7dmZ+/1+HB4eliG6QF5ylv/cR0Ic5ZF40J3R7/fj2bNnHYAej8dxeHiYrlRh3di2ZOhcoknwkq7yvCSer+Jsk21ON6jiE+izEYH3Xba10tTeFJW7aZrOqKNt25KnNhT1etfnu6s+3sepU+uwxQkpCZvK0O/3i864rrJNXFe93bJwL8CdBc4YXcbM3IoyZBX3/+osVBaydwEf30sZce17lELrmqfrgEXWS2aTMV9aav0ny6qxJWcSzn7b9vooWDJxTsa5ESC7km9eGy040VUzyN6+zpRZX/33TuTDXcbzEV7WAb2dKROWIzMgmeFX+21sbBR31nA4jD/5kz8pk/BPnz7t7GakrLQiY7VaxWg0itFoVFwoqoNWxuisdl2nMWY5pUN8y5FOFpTe6VV7OhDL5VhLkxvklDb3TRC0NHcQEZ3VZa6X1Gce90s9VV0yl0zWXg6AcnHJMMjFo/vq4zzkzUmjuzW9P9Kget1UP5eT0lqtVmV5Jp93naw9fxuDvxfgHtH1DasDcQKFAvOhdQYwmWX3xndQlcB51K1cFlQoNqB80EqXDJJxCKJk4BHXPlMCWWaVqWRkM7pH5WJgOfQtFu6Ghnnxv0BLuyX9naveFp6/txNl5G4Rj0vD5e3p7ZGVJQN/pnWXjqJy6DcNPifGtKORbhLplJ7lSphXr16VM/m5e1Q6of0VYvA+0sq+m6Yp67rpPppOp7FYLIprImOB3q/0vNiv4pNYRHRHmkqPL3jh6IHtwPJ5vagXxAMSMW836fZyuYzJZJIyZski4vJdrHwphpMyGSzv+y6r7D9lxfJKBjI4MqD+vPd39rXbWHvEPQJ3dp5sSEPr5QDqoQYCnp8Di66LOahjsXzrwCYDCylt01yeFy3Wq8ZlWVhH1tnlozj0hXqnpJGgG0YfgTuPL6YRYDnE9iUPPUOlV/ChflYuxfO2qRml28D3LvcyfakxP3+eboKIawAZjUZxcnISTdPE48eP4+DgoBg9GlIy/ogoZw71epc7IXu9XmfbucqpydnhcHiDKS6Xy85OTq7A0FuQxNY3NjYKsI/H43j16lV84hOfuDGycdkrCFjprmObkMUTmCkz/6aOcJ5JukfiwjZQGpkPn0djT6fTG2+f4gFgAveI641MEVFGYzyR1YGZusGNR2oHJ0kqgxMM5efypyx91MB+ehvA3xtwZ6AQHERdSBnQu/+KDF0KpHR1nRse1Hk1GeN+eObhmyz8t/LT8DpjvO5Lp39RgXUka5J7RGXPJnc4GnFgVx2Vn+TgSqz4Yo702Xo5yaxYlhp41sDZGSmDt3lmvHUtmxfxZ9zI+IjO42xubsbnPve5WK1W5W0+q9UqhsNh9Pv9ck6I0uEywojLlStnZ2cFpMnoFATiERGHh4c3iI2n3TRN2TF7eHgYo9EoIqK8hUgjr1evXsXjx4/LxKqMkbeh192XIjr46B7dMdQpJyQ1X7b3LV9pwn7vgKl7p6enZW4j4hp0+Zani4uLGAwGZZOS0pRx4GjF9StrB9XPRxzEIe+bamNfZUe9Zh3btr3RDrVwb8A9G/Z4o2WNmTGyTDEjcoai6/7REki+Ys7LxOedtWfuGW0z92MA6KJhXX3oq7g0TPqvb66bV104uUcDxgPBaEw4UaeOJdfSdDqNo6OjjiFy2bDurBO//Rk34N72lEumD55XBshqq6xMbojIQLNyrFarMv9wcHBQgLTX65XVVdrhyHTURs+ePYsnT54UAGFb1kaFOmxMbUwSownOw8PDePToUVneKrYecX3W0Ww2i48//jjefvvtjmHJ6s7yUAaud9Rxuka481lEhBOVTiIyPcpYegbuJCynp6fFuLBvUJ5uZJwlU89UV9eJTOeysrorliSuaZrOC3FIIu8C4rVwr8Ddh4KZzykDD4Ih77tgyCr1nDeCwFAbU9jI9D86mDuwe/rKV5O0ZEK+6kXKw0mgiJuHhHkdyTY1POWpgryuTs4OlHUe5aNnuSzURwpunL19WW5e83bNOg47l4NyrVM5+Lve1PSDYOHtKHCdTCYxnU47Z4KoDXRejNanu9Gaz+ext7fXcdG5caFMZFh9rkQAyZU2+/v70bZtWUIpXeNqFC3F1ImEnLdxQ+Zt623iwK8y66NzVJyp09i50aQu0u3ooMv+rnvL5TKGw2FMJpMbbSo5SmY680ltQMxx9wrbg7J3d53rIHU6IxACfK3sibiew9Fv11O6rtaFewPumQVfZ6n53G0KySEX88gUS752dQ51DFc6Pe+swq9LsR0gBQIcGmeTuxHR2bFHI8Y49EPqv45NUKcmKDurYsfhwV2+/p3vy1T+3naUrQM52zozlFlHyAw2Zaw4Pi9Dd1ymU1lg3GxExfJowlMrRNztRn2kW0yTowTx2uiDRkXgFXG9dV31lAtI+itXTNM05QXaGqW1bRuTySQ+/vjjcg6OgNiZuBtLyl86wTKT5eqUw8xoqxzsU95vfRmiAzv7pPR7sVjE8fFxZ4WOdF6y4b4CHecrwyky9NFHH8V7771X+qfrDl1NAluOWC4uLsouZX10sqpGFzI24/E4vv71r3dOpWzbtuiV2pp7Ivb392NnZ2etLt8bcM8amNd8CMY42XCaaRHAMqYRcc3IfPljDXj4n5MiDkpcK6yytO3lzsaIKGeOcCWBFIabLzRc4yQmAZ1gJqURsLMjCZz9dDwBeTZBSybLF2ar07DsDBkLZ3vQ4GVt6kv+nNG6/lAW3s78zsqTEQAvL0HnyZMnZQ8EdxiSvQlYBLpurN1AkYlm9VKaAgFuZIq43pEqEOv1erG7uxvD4bDMrdAwD4fD2NzcjLfffrv4ndUuLmvXYzcANKgR3R3VbhQpGxpuui6Yn8fLdEA6/fLly/J2I7YZyyaD1rZtAUulRxzgXAZXzC0Wi/KKvMlkUj7z+fzGhkfOO8jgZKSMxIpYR4ZPfMgOJ/Nwl3eoDiLin0RE/yr+77Rt+3eapvlcRHw1Ip5GxB9ExM+1bbtomqYfEV+JiB+LiNcR8TNt237rtnwI5BkrdjaUuW28w9cCGZ2el8tEs+ycOFQcZw9ubAgorIvH7fV65WhWMi92COWhcmh4K79ctixM9ecZIjQEtTO8M2US0LMjCsSkwNzRJ7kyPbbrOnbqwOqMUWVQuTKjkY3WanXN2o+/nRS4P7rf78fR0VHVXVCrj9qBR8gqfXb6zMAoHQEPXTICH5EMjdT29vaKTnPSjkD2+vXr6PV68eTJk3LPD6HzkaLP+6i89F37NcqHh5BFXE+YuvxUBuq46w51/+TkJE5OTjpg6IaSO2vFikmc5Ko5Pz+P7373u4UkCcg1l+GLCtzwuF5QFtzc5d8yvjzgjfITuaAu18JdmPs8In6ybdtx0zRbEfFPm6b5XyLiP46IX2vb9qtN0/xXEfGLEfEbV99v2rb9kaZpfjYi/m5E/MxtmWSdjoWn0nPo4uyM6TloudtC4eLiogxj5a8j8HrHdQBhgzobzuqj9HyyVkrpS8u48UhMmTPmrmQO6gR+gjefUd5Kz1dQ6LqYOzczZWDMtvN73j4ZCFMnMgDO2rsG7A7WHgisDghejojLTiqmy/L4mSqeP0EvA36Xk3d6jq4od/WHiCgjMpEBuWOoD9RRHS2sSd7BYFDkQH1QmbOjMagruuZ7VFgvTnRSH9xFQyOUATvdjOPxOF68eNHZQ+AjAW2+EnjrREi9JEWfpmnio48+iufPn3fOHWJbk4jx+A8aRJVVMlRflnuMR5LI2HGEID1UnKwfrQt3eYdqGxHjq79bV582In4yIv7G1fXfioj/LC7B/YtXvyMifici/l7TNE17S2ky5u0d2ePUgB1lv/GbjEHCnkwmcXJyEqPRqLhkOFGikDUy03eWwDqwzFR2+ebECDXc8tUEHOrRF0ngcmPociETJ3BTecgiCRx0O8h1pdUgBKKMvWQM0IF7HXv3evGag3imH4xXC86cPW+Wz2XvbZoxKtaHcxTrjJz+c9RF46zjDKQ3PjekTXheBpKjtr1k0sfHx9G2befwMx+xkKnTUHk/od4yjayNa23jAJ7hgGQwmUzi9evX5agBP3WT8ub+FZ2rpHJyJY8MFEcpEdEZMXndaIxYLzJ9ucacPPK+ZMoP+9ltZEXhTj73pmk24tL18iMR8V9GxP8bESdt22rs8GFEvHf1+72I+LOryp03TXMal66bV5bmlyLiSxFR3mTDQrOT1ZTALb13lOy3QtteujyGw2GcnJwUhsNJJ37YEH4gUE3IypfLzdhADmRUCjEkTboyroMx5UFgIZhTtnpeqzq06oVl4xJIPwFPSyK1QcYng7NRStZ+LLODdNZemWwVmJ/nnZWFYFMrM8E4KztXOWWA7mVjvTIjmNWL7I1AwE5P3ec9EhWvvxip6nZ+fh7Hx8cxn8/j2bNnnfd5Kj3WIftQ11g2H2VTptRjN26cI9AzkoEYsIjZ2dlZB8g5MtWkqHRfZZMbRmxea96zSeXMyNDAsH/4h8DN9qFcOTrzPS7edutcdwx3Ave2bS8i4t9omuZRRPxPEfGv3eW5W9L8ckR8OSLik5/8ZOsdqtZ5GUfX2Ykp+JoCSjmGw2G8evWq+CR9BQmtcQ2c3Yq6dV1Xl8zoZGlQafWdGRQCvfvunfFJwb08CgRduiv0/NnZWfFPNk13a3oGogR+5ZUZqBqLI1hkZXRZuvK7jmSEYV17ZIaC5Xd9YDs4aPnoyPPN9GW1WnUmyJU2Qd3PoPE5Ft3zTUGsk/zLamt3P7kMuWxPZVJa1EEH9l6vV9yS3ofostBIu2maUh8B++npaZycnBQ94JJR5emGeWdnJx49ehSbm5txcHAQR0dHpbx0qzAfzl9xQlr5+YieBlmjK5Eid7+QoFIfaEhc5zj/tC58T6tl2rY9aZrmH0XEvxkRj5qm2Wwv2funIuK7V9G+GxGfjogPm6bZjIijuJxYXZfuDYWOyDec8Hct/jpDEHE5NHvz5k0545nrfzMj40BH0CULI6P2UQWV1OsdEaUjSfGdPciiKz0prhsv3lcezlIzF07EzSNKacwICAKS09PTks7Ozk5npODtpeveRpRhrc3YcTLw53M1Bu86QwDI2sP/Z8ZA/zMi4UCYtb2XM2PyNKhyiSkt+pE14tSxvnyZu9pS39QF1w8u5dNbizjpqHJxMl2jN58HIFi77DRqdJeDvt29wWuz2SxOTk6KK8n7ibePrusEzvfee6+Arvo9lz0703YXpvSY5c0MtfvPhQ80Xmp34oXycSN+m/57uMtqmbciYnkF7DsR8VficpL0H0XEvx+XK2Z+PiJ+9+qR37v6/39c3f/f2zuWiCCwLo6+a2DGaxKmFEgW/8WLF+UlHD7kokL4UMzL4WVyxfbO6mw6KzfBnd8O/Izv4MLhdKaEVBgFgZS7esia1Rl6vV6cnZ2VtNx95IZXaWWG2eXlcnJgJYhQDwiy3mbscFlbesfM2sZ/Z2lko6UMvP05NwqsixgifbUytloGqRU0bXt9HAEBQnkojr9FiYadoEM3hs6OV1DebXvzeOCIuJF/216/pJt6Tb3kHgz2Jd3TckPtHuekKZcesvwqu0YhzIPr/yV3Mm830pS96uWLH9xIMfCau6ZonPicy1FlyPoYw12Y+7sR8VvNpd+9FxG/3bbt/9w0zb+IiK82TfOfR8QfRsRvXsX/zYj4b5um+SAijiPiZ++QRwkOOLzuHcBBk64GD1La0WhUgF2N6r4vCp1l8jLomg+xnCk7C1d59e2dXnG5qYTxmR6XRKqx6eN0A0hFYboZS1AQy2E5ySLfvHkTGxsbsb+/XxhdBuJsM2+nTN41IM3cXt7J1j3LcvkEe1a+rD6eh8vR43HUouvrykcd52Q63QJi1Bxx8mA3gpSnSZ2n4VcZMgOnuRnFubi4KG9N8r6YTTYKnNu2+xpBArOOTHAywj5GNu3XM0NM//t8Po83b94U/aUxy87OyfoR77O9M2Pq4E0Q92skKYyjtHWfK6LWhbuslvmjiPjR5Po3I+IvJddnEfEf3Jbumvw6/x1oatbKAd8BWazh+Pi4TDS5ENdZXOarFSsCcIIpv7MyZtfJPMjMnLErLq8znqe/TiGd/fNeJjsaTpVRQNM01+/wfPz48Y261dpJ385g3BCua/OsE/F5N8TOjpUOZeD5+cggM9Y+umG9WF8+523mriIBIld4tO31qqqNjeu3F+kl3VyNoXLVgIaBRoP33NUiXZArQydcchu/XEUOtHIbEaA5ByRGzXy8Xdk/6brhyMz1Wv10NpsV5u5t4pO+7G8+endjqEDMcGPJkZjK7W3C/s+2cHJBfVwX7t0O1YjuSoZanAwka6AUEWU1wHA47Bzlq+c8HzUGG4np+SiBDejlyeoWcRP41Oh8GS9ZOYHdj4d1BVB5sw/LobSoUFwho3Jpfb1kwv+akI64XIWxt7fXGSmwnq6YSs9ZrRsk6kO2msHjUQZZ8I7oxqRmhLI8vS2zOrFMAkwHjawtFbQbVv5tjaa4IYr+croy2rbtgLT7c10mZMECawdNrrJp22tXXbYzlUFlcID3vkW910cjB8ota2sZO+r8arUqu3ZFnlR36j8JjLtfMrCmHnibcjRPffANStmxv8yXfZRE4Ps6ofqDDuzQ2fC0BpYZw3NQPjs7i+Pj47LDjGwgCxziMx92cgeYdYzdy8eOrt8+rPRRQfZ8Deg8XsYy+L5PdlTF987mbaAOKsY4HA7LK+Lkg+f7Ot01kYEoZeYA7x3JAd87oD+frVxxWWXATTlTfqoT45LBZ8CT5UEDIYNIcsJNSLyfbdFn2zm4qXwsq9qK9zSqJZjTJ++uwohrsOEbpWqhRpiUF/uG+geXG2arRZhOBuwabUgnfeTleqA6uY46aXT2TdwioLM9PE0aAZexy+x7mVy9N+DOgjp43/X5THDqDC9fvuycz54ZEm8YAgw7pzNpKRnBax0jy7ZbM1+f4PVJOv/NsrsMvUzekcgK3FB5JyZj0yQVO/58Po/T09OyJO/g4KCc3cEDmBjcJUMZO8h63d3loGvOGjla4IjLjWbWFm4Q2HYsm7ezAwfz0WgmA3xn+2S3AnrJVIxa5aCuZKzRjTPz8dEe5cF6ZzpC4KceZS4SgiLlQrau/NzYubH2304iXB5yAencKD+Qz3WQRtD1zO+5PH1ilWVlHoqjOjqRYr28Tf7CgLuCFzgTNv97xflfcc7OzmI4HHbWrPqEmfvuIvJGVXwvDw1CNqnL52vxpJRkVF5nX0njQObWPZMjO4lYjufnjFTfYuR8yQFXYGipmoLYnF5ATONI5c6G8Q4MisfOnhngmtLTUPnkq9eX+eq6j5Yo0wxQnJg4ANCwZYZLQROkcn0MBoOOsZS+qC20HJI6qXiss4O0x8km86kvGfHRChql40bdjRjlT1cIXYCus2TE7IsiHCqj7wTlgXnn5+eFxe/s7BSiIhzISJqPhthuTvDWxVW9ONGrQP12o+DEwwmdh3sH7q5kCg6oGZtjXIXlchmvX7/uWFLFySa8PE8qHxuZw7Maa1YgKOh/ZsTELLR70H3f2SiG/j+CTsYGHVgVX4DgQ0HWXwCuE/X6/X4MBoMyeSY/sLMoDYcjojPploGsG2zJvTYxWes8Xv5sJJCVM5PzOmBaRwSYtpeJusPr7oqkOyEiyntRZVR5lLNkyhUsfM+ry0rX2N9cPi5r/fZX4rleqV3pcqwBFfXVz1TKykx5Uo/bti1H4sqgyF3IVXHz+bysMJKx0mjI95mQefsIU+XhnBJlSJBWYPvSpZQZEiesxJy7hnsH7m6RI+JGZ/AGztiG4p+dnZVTHtVJ1FASGL+98eSyoCK74rIsrEfGKLkLjnVzMHY/nAcqna9oUP41A6j/2UsKXA4+seadWPIQm2c+lF9EdNJSoFuIB285wFFWPgF2F4V3kKZ7yfPMnls3RFY5/TkBxLqRRc0QOLAcHByUe03TlPOIqMcR1y966Pf7nd2RGXBT93mPgSMsAi+BmPLhM/qtZ8nCfTTGuSN3HRLE9dHBaIq7sbFRAJqvzdMokpuWCK4C/tlsVo5bEIHRSa3s/2yz2hJTb2PHKMcdyiEibuhNRmAVb124F+DOCnvHJvjVnmOlKZSLi4s4PT3tbMWmgtZ8YkzTd5FF3Fyb7p1FcbwxaNVZVrIPDtduY+9M3xta/lgBKstII6iOorpSsdQJyAA5vBV743I8Dq9pZLm6gz5alkurIVgGjiYImN4ZFBxUKKtsVMKwbs7E5e2dzxm756n2zIxJlq5e6CBCIln3er3OGUi+YkLlltEk48yOkaUuyIA72LLdaiBO/XQ5+4hV7erpcBTqcvG4/hINjST92GzJK+vHEV3yxtVIegmL3reg0Yp0mcCeYVDNBUbMcZeTExm1uesx81kX7gW4R3SVwzuZAhsmA3vF12c6nRZfu1tZpkcQomFwn2imdM66+dvjuLXOLDPTIHt3tp8xaJelyyPLS2lLeb0OZEhUUNWDG2y4TV158HwOdRRNsNKgefvq25eFUpZkYQ4YDB7/tmt8rpYOf2edz2WZ6Zvnx6WMXLO+WCxusFXXES9Lr9crL6HQxiBnxAxqj2yCVOeeK90sDZ4tlKXvMnEjnOl2RupkLNyNyLcVcTTYtteTsyo/9T6iSyIF8iIv0+m0rOPn4gClr5VMXkaRGaXvhlyylsGlXBXf5wGdnGXGj+FegXsGftlwl2wjY1gCnZOTk/I6KzK+rGN4Z3dfsJeT17L1pm5EeE3lcVaqwHLqXgYKNQPHNBzUvTzZb7pflstlGZp6vlQ4X6ssxq+P6ipwEkvySVY3RmK7PomsMnCLn1V1AAAgAElEQVSoznrUjGvWEb0+ktE6ZpSlmRlbb8vMgEZEObpWrFDr2muTxxzhCQjo49XZLcyXfUD5bmxcvqdXhkDXCEbuFnMd8LZYR7wcUBVUPgdB9lPKUscJOCkUsAvQSeqYftY+BFjqoRYJzOfz8mo+uXD6/X5nI1ZG0LJ5xAyLKEcZ1WzXbTZiy8K9AHcqS+aeIZNXqFktNYaW5fkbaPThENMBhQpA/y7jrmNr60YejKcGzEDNgd19npKRW3B2Ak+LHUAKwglOpsklf5KhGyMOYwWGWo0gcPdNNNz2LebCM7IpB7Y/GY6XNWONDkCZntSu1QDe9dINrTNn6kuNJOi3Xrjdtm05a5w7KZvmenQlRi+50o2neL3e5amLMqAqv58qubW1FXt7e51zYwSONf1xebleK23KicaaekufuY/aMqLFcjMP6WztHbYe3LC4EVPbEydkbGezWVllI1nJ0PgogH27Bv5uuCUDxzxn97eFewHuETd9nNlQs+ZDdavctm2Mx+PyKruImxuE6H90YF9n8QlwWXmc0Tv4+v8MFMhSCZ5UON1bxzD5jJTNWaSCOjPTleJOp9MSX8Ctcou5qL4CnbbtviKQMuPqGdXTn6XPXj7P2WzW6YAsq+rnsqMsXCaS9zp3hQNXRgL024EtA70sPif9Bco8fprb+SVX7rAmsyUA6AXalJe+Nb+xt7dXXrriTNgJhuRWG9XWZJrpv/vWMzLkeatc7v5TWbnpSc/KUFLm3p9YH412aCBYd+Un15mWXqqNtra2ygvQfV7E9YCkyImsl9VHgEpvXbgX4E5LllnbWmdyBWTldWpcRNdtwniuULyn+y5YZ44OmF6HjInrm0DJIZmUlB91imzoSNk5oJMxKH2WhYyQHYS7Fvk8r4tFqh5uMFVvld03anAlAtcgN01TlrSJmR8eHsbJyUlMp9Mb7aI19KPRqOSpfAWOWrbJ/NmBCNzrWHamtwo1t4IDlq5fXFzEZDKJs7OzmE6nBXA18pxOpwV0FQjk0l8dzXtxcdGJy7q4T116IB8y5eYjlEyv3a/tDDsDfU/TR+k1pp3l7cBOQuBpyphJv1lGxZPcvW9kOqLrmg8RqEtX5fvX6/z0zmHWx+WhPOhuo/Hx40Bq80sM9wLcI26yYBcAG9WVjkHDpvF4XNL1jsvnMktIMBM4kF2QiWaGR/99uJUBLCcD2fnEBgjuZBQOKkqHflgaJYK3XskmheJQ1pkR02F6UjCBiuqiOBz98BoNifz5Yqly4+iZvb296PV6pQMdHh7GfD7vyFND5b29vTg5Oen49DVK2Nraik984hM3NoWxnahrmW7d5hbMdMsBhGDBl7HTv6412HpPrTq8lplGRJEVN+QobV/umxkltbFkRTcM+1mNSBCUqYsZq/d+52TJycg6+bKd+JtGnO3I8nteEdE55ZLlpOuIukYGTlLHZ9S3tNdAa+95jDHZubuptP6e+BDRdVt5eWrh3oA7AxtewYdm656V/9KXP9X85PpmA/uzzuhcSWvszOvh95km3S18M7t3JOZHf7TSZLn4vPKTD5asTgqmNFUmnsNDRRM40K8rBu4+fWcaAmW+fFgsiOd4a72y3DtPnjwp4EeGMxqN4uDgoLAkHTOh8uzu7lYZdER+Do2Dsg+LnUHWjASvK43FYhGj0aiU00kAl79lbkVNUtOFQMCgbFlHtp/rHQmLAvsNDTr1lmWnrlG/naiRSPg16m/WXrXlmG5oIqLMTZDYKG3+zhg6y+bxMkPPPQU8yVMsXqvEZLSpH6ynys9yMM8sbi3cO3B3tuHgmXUgt66np6edSSa6FtjRFPi8W2U2PBWYHdDjOQOh8iq4X5/p8MPnPJ+Imwf5e1CZBa4C3Iiu8rPTcK2v/NzK2yenlabANGMUzmjdLaJ7XHEwn89jMpkUUJpMJqWMYrqM++jRo/IiFhk7AaBOqVzXMWrtVnuGHc71J6u7wnK5jPF4XN4ApvrNZrPSoXlsL9tNciZr393djaa5PliLgMy8MyLiRkV5uZ6RFTvAZ/V3AkLQzUYDGQmj/JlX1o+c7KicIh8Ec8WhbBzQfQTiIw36wdnGbAf1KzFxTbpqlMRjmRloqN2DQJnViArDvQF3V5SI/DRCf8ZZkYBA9ykkTuY4+2AZWA4vF5Xby+7XMkBhY0npOPlHJkYXhis1FbVWdmcB/L25uVl8tZINO3fE9ZviVWcqP4/4lVIz0FBwWZpWxyiO2sc3WwnwNMogQ4+4HlHIuAnUpTNyXURcT9yyXFmbME5tlMeOVfN9Ui/JvM7Pz2M0GsVkMinl1np2Bz2BEVm45KSRTL/fL9fYjqq/By8/6+kT9ww110VmGHSP6Uh/FRyovD9RDuuAnfXyOtLAaEWRyuZ67kY6y8efYfBRVkT3fHw9I/ci5cxVZCSXWfheAf7egDsDBUKQ9A7DOPqQ1dAfHHFtLNwSZ5ZXSsEgJaWLZx2DoQFhh1BcLqPy4OzE/YeZQeF9fhTkOtEaYYH7+fl59Pv9G8ZH952p0LD4TlTVl8vSFHw1g+SoyVQBXdM0ZWkeXwahLeKDwaDs3tRKiOl0WjaX0G3R610etFUDD7ZX1mYMZIlucPmMuy6kh8PhMCaTyY3OKfCJiHK+CWVD3ztfbEHQJJEhAaltLmI7q/2pZ1l/03UHXAX6jynjzI3BtHjN47Gv1fqA7vlEq8rkbh62DdPl3BH7pBtBHxVxzottyb0G8sWfn5+XNs6MpMrsBtVHjU5AsnBncG8uX7P3zyPiu23b/rWmaT4Xl+9PfRoRfxARP9e27aJpmn5EfCUifiwuX4z9M23bfusO6a+95kCese22bctwl8MerkhhwziD8WFWZljcB5o1jC//y1ZjiJnTt66yuMvH2aF3PG9k7wT8rf+7u7txdnZWNmdoeK+yLhaLGI/HneWcLJczeRpRH22wjTgEV1kE7JSp4mhySp1qOp3G7u5uee78/DwGg0EsFovY2tqK5XIZ0+m0M+LwcmS6RrBj/dwgZ89lOkAdWq0uzzg6Ozu7QQpkwNTpdY8bmNq27YxUHOD5caPhekHjQ9clDXXGfr3+DjAZ0WI6viqs9pzuEfQy1uz5s1/pv7ss3RApDe1GpR5IXu4y4jwSPQuKz2/HJpVBbi7NlzGeyz9zY2WGNQu3r6e5Dv9hRHwD//9uRPxa27Y/EhFvIuIXr67/YkS8ubr+a1fxbg2Zdcp+R9R93KvVKsbj8Q2QditYE34G/ASu72VIyHQdsNRo2uGmHYJ8q07WuAzui9e3T1bxv9LirsTValUMIus8mUzKm2vIzMlKIrrvp+SH9wlK9PN725Gp0YfPo5q5U1CMqGmaMjmpUYDK6NvUHbAoN8qSZfN2dN3Th3VQ0AhIxkZnxmjil29OkiHlqFNuGKUroJRMCfbu3qr5r8mwVSbXHxpFgnJNHpSl+4udMbOv+OSxy066zrXkbKter1eMv8qs+5K5kzW6oIQHrAPlQpKoOJRXZtSzD1eOuSvUZUL8oq65x8GNt4c7gXvTNJ+KiH83Iv7rq/9NRPxkRPzOVZTfioh/7+r3F6/+x9X9n2oyVLyZxw0QpzK4Yuja/9fetYXYdZ3n75+b5syMRjOKakmW7xAKfmpCKDYpJfTiuMa4FBxwMNTphUD70stDGuOnQl/SlpIUSh3TtJSQJmnTtDGBEtrYeXUbk9ZV47hWEjuREnckjeeqmdFcVh/O/ra+882/R1Ii6ZwZ9g/DnLPP2uv2/+v7v/WvtdfWtNwbTGDQaap2vG7ZA3qZghsYsHvRJqsHleb3OvPRQTM0NFSDrJ/t4lM/vd+70wdiU/9qW0ZHR2u2vrGxgeXl5RpENzY2sLq6ilKuPFmqRu5s2NkimQ1Zqe6B50AFrjB2TcN2E7h2dnbq+DmBbmlpqQfEuPiqcX+to86KnJWq/Wj/6HVPrztXKO6QmX57e7tnHztDSWzPzs5O3e90RNoftGXex+M0MnDV9Bpe9DHkoKFPcdM2uI2Pf9ovGevWfvA+VaFuszHkdVV7cQftYO5jgDar5WgI0YmTP/zks1EnZ449bjdNBMB1RQKi4K3taSKGe411yrWGZT4O4CMAeO7oOwAslFI4ms4COFV9PgXg+1WjtyJisUp/QTOMiA8D+DAAHD58uHH654bkjE8ZAF98LWXs2cFWn54/Zzja0RmAZ2xFDZTp1Ag1jgegZ2ugG7tP+dzIVNnaRgcnljs01H06kcBz6dKl2tFwKynv1/yzB6rIOL3dCjBk2j5wfBalaRhr1n3YGnJjmIblc1um7uzI9O3gnYF8NoCzWKjnxyk368dwDOuth3ixH5VFK7Crc+VsxMMBuvNJ682/bH+0p+E6FZ9U1Qd9mKeSH7W9jGxon/r/JhB3B8F+VHbK/vJDuTyURmEoSOPbrmcVB1NNq9e0/Rnh0/Gm7VUs8/ZR/2yTluuzZ+bxY+9zj4hHAcyVUl6OiPddLf21SinlOQDPAcCJEydK0+KANpDfq3r1/M6Brk/r+QB0EHIjzvJWUGQMzge9ArB+zuLm/E+AY4iBTwnq6rmm1/IozhSUnWdOSO/ndHZmZqZmzwsLC+h0Oj2Ayby5IKR9w8GjfeUGqFNqtvvy5cs9fcrrCnjMh4vjGq7gQz5DQ0N1yGJsbKxeQI2IHta2F9PRwUhRPbodOOBpWh/Em5ubNeEgO9eHWajzUgrW19d7nC/z4huV1AG4M3G7VT1kREGvs6/0Pa1sl9tXFqbRfsrCG5nt6Xcfo0p4lGlre7RsLVNDMnSIGQhnOtf+YPm6uysjSypuX1qu/6b9wDHEh/my0JTrVeuxl1wLc38vgMci4hEA4wCmAXwCwExEjJQue78DwLkq/TkAdwI4GxEjAI6gu7DaKG5Qykwyz+7MgINgZWVl1/5RNQD1jGroGg+juAH7wPWBoverN3bnoPkzhsypb8a8szb7dWWAWl+Ww7oogPJap9PBzMwM5ufn6/3XZBVk6mRLOjCy/uXg0h02LEsdgzMa7xPtVzJJXZDjTho+4s3wBh0lRW2qib03OXZPrw7L2695an6MrftZMWwb+4QzFGdoGurzs1T4X52M6t4B3vtebSbiyludtK/cdpocJPNToHXQ5/1ZLN7HiNuZOnzPV+9XoqDvS3bClc289xrrvK59p/Xi/b5xw3XEe1SXukDLcvxJW02rNpLpQuWqMfdSytOllDtKKfcAeALAC6WUJwG8CODxKtlTAL5UfX6++o7q9xfK1VzMlbJSVpB5RQrjsOvr6z0HS2meCuC6AKiG7yvrboS6T9bZUFNH+yBx0GUdeL4Ip+7K1Dw/5qVtcm/ug4Bpfa2BZfD1bQQkTtN1Uc+B0mOgNFoFeWXbOqh0sdXXKTR/Zftk7AQh1ofsl/XV+KyeQ6724APX9eXrHerAVNfuOLTv+RSqHvNAOyIj1APAaINcZFWAZ500zMUyVUdqN17/bMbJuvOpX30aWe1K25otXPJ6Nnad4Ljz8GseO/e1HbW3pj86TLX3jEj4eFFCoSHZzEa0LvysR3ZkdqEOO3MiOoaoM8UVn+VeDVZ/nH3ufwDgcxHxRwC+AeBT1fVPAfh0RJwBMI+uQ9hTtAHK2vX3zOD4fXNzE6urqz0vJFBFZUxRwVUHi7JWLV8V5/XQQc8/NW5Pw/tpPFx042lyGXviPRnbyVgC8/dpvPYFf89YMge6gqMPaGeE/qeAw75lfdTJArtfwafXtXzOcMbHxzE7O4v19XVcuHCh3nlEQNcdOU2Sladty/TMduh92t/qsHXh1LfncpDrMQE7Ozv1dk4uuLnufYab2ZTaoOrWnayCBTcjTExM7AohqrPwvLNQjAO7Ores7swn24HlOlD785mw1kOdpgI888vYu4dStT+9/brjhXX2vlBHoXV3vVAHtG13sl7PDCMzuS5wL6V8DcDXqs/fAfDTSZp1AB+4znx3gc5eaZ25MCTD+GSWVo2FAK5sJutMDS0oCGo+FHcKeq+WrwarMXyyOZ5DofmqEWuZPg32GLC2K2P3HNQrKyv17hg1Vi3fjTxzMlq29q0ashp2tvdZAZJOleXoPRMTE7jrrrvQ6XTwyiuvYHl5eVdMWllSVmfKXgOFddGF5Ijo0RPPYde2c8umzoBYfz39kqCgDlUf5mJ/6fhgercDtQFvWwZwag9DQ0P1e1k1Ty3XY89qj05Isj5me/U3ddgKphqK4f0AdtkD89A6X758Gaurqz19rjNJFbdRxSJfd1JdKCirvWmdMuerZWgeWV9qeYp3tMeryUA9oepeL/PcTMfO5+l6fKRbjTFzBMBuJqKMkvEvZ6EOUG54aqDaBt7DIzvd6HWQ6dSY+98B9BwgRtF66HWP0Wr+ajB0isvLy/Wsx9vjU1LXjcYPM0N0Y1Vw17CSHmeqfeZOngBIIBoa6r5K7s4778S5c+dw4cKFOg+GMAjA6rgyO3NyoX3Hd3Py7HrG+vWsEDpHhlu4sK8OxgFL+41hF7c7ZbHUi4J1Vm91Bu4YHBC1L9bX13Hp0qX6aWXVv4egXKdNB3opyGkdCU4af87qq2nYh96P7ojoUNUhNIVnfK1C66K/6X8nOmpL+rvvVPJZl5IX2gBJROZE2H8+tptkYMA983BAftSqggd3JPhTZsoQPF92rubpe6GVSWgn6qChsjz0kdVbDdAHpnvliMDS0hJ2droHQ/GN7gR5HZgOoBlLoGi8l8DO/eHe1mygav3psHygsRzPjzrhZ/aJApX2lduB9+vKygpeffVVnDp1CmNjYzh+/Hi9gOkhOdedti/rJ5WRkRFMTU3V3w8dOoSZmZl6ITci6pdiLC8vo5RSP/mr+tR9/Fq2niZIZ8l0TkaabJHijNBBMhtfas8AcOnSJUxPT/e8MzUjV543r2t65suZhtdN8/d8PK06FS0rqw93e2WOz9uhfZfl5SE4JZY+cyYWOAHUflF7Z/RAowJaR8cox6Um8kUZKHB34HXvnDEeLqY6QOhhS1qGdoqzIvWs2fanLA6o4KosBeg9sIptyLy+sxSyuKWlJaytrdXHhXY6nZ5XezmzYj08X9ZJnSEByJ0Q83BWpPn7LEWdrYKS/u6zpWwvtfan3sd0+mQht24CwPT0NI4cOYLbbrsNP/jBD3q2QfLpRWVLWq6WrfZFPU1MTGBycrLOc2pqCuPj43V+nDXSdvSBK33CUfeuswwNfbEO/uCNOn+tm4YY1AbdHlT/2q6M0W9vb2NlZQWbm5s1eweQ6orsUfNxfWkfax4+5rRdtH91/Hq/68f1x5BY04xFSQoB2klado9f07WoLPyiYTNtg/eLflcd+SJ51tYmQkIZOHBX49AGOfhz4PjZ7cqotdOYj4p7VAXLvRSrcVwF7CbWlD1Y4zE5ZzbqJBhC4R8PHuLj2AQ9B10ame7OWFtbw9raWs/icwZ4zr6A3hdnO8PR/uc9Gut05+PM1MtXRqn5sCwOxNXVVUQEJicncfz4cVy8eBERUe9P5oFYmm/WXtbD9T40NFTPDI8dO4Zjx47Vr6Pb3NzE+fPn6xfDcP89dxzpw1YO4CxDH1bytR1KpicnQE4cmp5BcFvmvdQF3z08MTHRQ3A0xOAPFun/pv5k/X2W20SgFGydsfo41ny2trZ64u2qS/2s/ZOxeg0XOUH0NTB3VDpDUyfVNOYVO5g/D7vTtZiM/e8lAwPuwO6tRC7sQDaMZ2MrUFH43RdRfMFQ824yOh2czoA0LfNUY9Spmz8ExDS6M0UXx5RxEqC5L55gwNi8nlPD/iGg6+KeMkkHPdariRV43VlXApQyRAcf7z+uL+gah+vdw09q0Nq+4eFhnDx5sgYkHizmkrXXwd7tZWenu4Pl6NGjOHr0KCYnJ7G5uYnFxUW8/fbb9RZWAiTj7exjt0MFFWd33g8KLs7e3RFpOgWJbCwp63bgKqVgeXkZs7Oz9cwzY/oO3E7GeE9GGLJ+J2t3PWX5en31Gu3d7a5ppui2wN/1bKMsPfP2B60UG5zsaX8zX68X63vo0KE6bMMzoGg/tLd9Be5NDFsHtDJFbjXzLWZqfNnUVa+pJ+cZD1nZTd89russSmcROgiVMamidnZ2emLZPrD5lCPL4BnRPKOCbdBDtBx8vf0qzpAUaPR8GDfOJkbFvvV+85BExq6yPJ2p0jHefvvtePPNN+vBMDw8jI2Njfq8lqyOys5cpyxrZGQEs7OzGB7uHiV8/vz52lGWUupXARLYlWjozEPtlqyN+tAZTBaDdSDMZqZMl/Wn95+THXfoa2trWFxcxOTk5K43NGU6aQIarYfWVf9nISX+3uTgvD80DcONbkvuJLyeWicF56yNihv8ruPLyZmSPv3vsy3VDbdysgxduPaF2iYZGHCnkesU3hWnHpPxTp4lowMmA1ydFjG9luEDg+Kgo+Js0w1WQbrpeFYgf9BD89P2UcgayOiHh4frHSR6fG7WHv+sYOQG51NQrZcaKxcYNWSS9VsWdvBBpPkqO3J9sI0rKytYWlrC/Px87YAYe9U2ZTrUmZICD21leHi4DoOpo+SsifbKN0XpwXUZABHAMzDQcoeGrhwHq/VyFu124zbkZMdBU4X3bG9vY2FhAdPT0zh8+HD9oJj2o5MlstRsHDUxduaZ1U+Jkd6nebJMbRMXtTle1HEqfui4pbguvF+0fMcbBXgti7pWhq7g7qSP15lWy3DnczUZKHDXDnFAYBp+ZvzYtz1lSmxi3x4D88Gi5Tu7UfDba+BoGMh/i9i9cOtMwwHSjYfntCgg8I9x+cyICVzeJxlTUlahA0odsva3ig9IDrimEImzLC0/c8KMsZ4+fbpm6WTPfAF3Fn/mn8aMfdpM/fhTr7rdtNPpYHJyEhMTE3jjjTd2LaDqEcdZW/y726XqQcFO+0vz0TdXaTt8Zsv7M8IDdLdFzs3N9Tgw9qNu63UdOsFxXTvh8Hxor34stNqVkw6Wp6eZZu3PxlJG2KgDJVM+PmgbHk5THSmxYBsYTtXZnBIerZsuqurvqou9ZKDAHcgXvBTk2EjdldAECsDuA7zUWHQwZw9o0Dsr+9YyNJ2yPE3nTNN3GaiRchB5PdVrZ7Ma5q3Mj4ap5SkLyhbAMmNxJqOOjn2jawMKSuwT9gUZbZa3z7bYRtUf0+jAI6Oem5vD3XffjYmJiXoXjZIF1xfz0T7J4rLcLaP6V+BnOGx9fR2nT5/u2SHjA9f73IHLGZtec9aostdsVEFHgYMA7Y5H67a4uIipqSkcO3ash71nMzgHTnc8Tmx8HDUBfgbs/p/t8I0C3geelzp5HaP6u+ajtsrrPmvRdYrModMBa34atvGFXGIQIxR6oNrVZGDAXTvYgdqnLaWUni2Q7AAHd4KAx7W9XP9Ow+e9BC49dVAB0pXm+WbGqeLMpkm0f3Tm4M6qaRVe/zLj0Py0bdomZRwAemZOCjIK1nTGGrMHrjBRZSYZoDOttpt6p154aNzs7CyWl5cBoN7Op0yf6xNk5Hx+gE5KT+UcGhqq97izXObp7PLChQu7Dq5z8HPdZgue2hckA64ftxfVl9p6pkNfi+I9GSna2trC+fPnMT4+junp6XQdKHNaFGW32vYsfq561b5rGg9qb8zT33TFdmg7dVbE3912mVZt0ce169bxx52Iz9xd70qEPISs5ISODOjdjpnJwIC7Go17egUu4MqxsVk8iumZTlmWAlvGdhQItTz+7uzeB082NVPF6ADzAe3GmDEfBQkNq3h/qUF6OQq82cBxB6D11oVRTedTfgcgv8/TKlDrYNdrCriaD8vf2NjAwsICTp06haNHj2JxcbFmm+Pj45iamsLo6Cg6nU7PQ2G0jdHR0ToEwZAfwzzKojmgGHZZXV3FxsYGzp49i42NjV3gro6WNqUOy5kjhX2oDjGz2+y/22jmXFS3GThTP2tra5ibm6tf7pI5KeZ3LeFJHVMqmT15/v5Z+4MvcdFZU0YWFZS1rTo2qScNgTKfTOjoM2fqdfYnuh1XvC4R0UOgPJyzlwwEuDuw6jVvBDtSH8DxxUMOKo+paYfSMHQR18FQp65aL1W4sy/34DQQzV//VHzB1FkZ2++DV41XF5Gc/TmzcEP3GYk7Wx0c7vi8zsyPzFrL10GpzlevefiK+fvg1XYvLCzgxIkTOHLkSP1g0dbWFmZmZnD8+PH6YTB9by3ZNwFe+407j3T7GZ/q5fnrdAhk7aWUejuqH17m/9kPWf+pw8xCVrx/LwetjDGrh+fj9kGAW1hYwOjoKE6cOIFOp7MrrZebxbdV9+rQHXidfHkdM/vllmhdQFdy5pjCvBxvtN7MP8MWYPc++Kx/ndVnpIr4AvQuLnscPxtvTc6GMhDgnhmuN0BBjI/PZ4xP06uyfDuSAqfuVdUygd1PnzkA+mB0xsD/alDuGJhGtxXyfzaQtA0KINl0nW3wftbfXRcamvD2+cDLBltWpg5qOlTtR/1dy6Yoi9Wy6Th3drpneK+srNQnGxK8R0ZGMDExgfHx8bpuul7Aezc2NmrQHx8frwnCxsZGz6v9xsbG0Ol0sLPT3bG1tLRUn23E/Bn7dQKidpexO7dDBQjtI6AZYPyaiztN16N/39rawvz8PIaHh3HixIn6zCN11M7MvR3eZubrQOiSORC1KTpeHhyo5anzVELleVBPXk/NwxeRdUz48y1aV8cDDYFRB7TtnZ2dHoLB8rRvlQDui7CMD+YMqClk7Z7eFab/Ne6nK9WahuJMSh9EYXqNB6sCfRGTbctYhw7ErJ1Z+71/lBE4SPoOEW1rBs4KdlqGirPAzKnqPXpEgvaZ7+tmnTMgYv23t7d7jNmdHgfI0tJSHUPXZyF0VxEBn8cIcDFuaOjKO23Jvufn5+s8O51O3R4SgrGxMYyPj/cAlW4JdX2yrar3DNBdP742kelDr/lMUW0tA1G/prqls7p48WLP2TrZkQKZrWldnB27PrUPMvt1IrW9vV07WN1Z4jND76dMNPSlwJ2HhUQAAAaCSURBVM52MD+ON50hM0yqJMudqAK1PqXMsr2+7JORkZGeN8wxvQJ9JgMB7hT3nM4C6KX5OLgqUweEs0aNnznIKIDqLMDj1k3bLd0onT1rnhpj0/TOjLUv3JmoKKBTFDyYj6fJQEW30Olg0Hyz7+pAVSKuLF4CV87x8R1DjGMrq1GAy6bKnoaOiSDNsAnZ9Ntvv43FxcUazHkP8+SRAXzKd319vX55eCmlfphHwzQc4GTyDBPqVkh/klDBqQlk2Ea3fZ/SZwSDeqS9s1+dBarTycaF14f2v7a2hrfeegsRgcOHD9cPi2k6rXfTGOHvWr6338dBFvvmrErf5eDA78zXZz3uYLQ/aavUl+5yUTzQMhzYtS+8PTrT0zU6H9d+jlQppd7mu5fEXp7sVklELAN4rd/1uMFyDPZS8H0ubXsGWw5ae4CD16ab0Z67Syk/kf0wKMz9tVLKe/pdiRspEfH1g9Smtj2DLQetPcDBa9Otbs9V36HaSiuttNLK/pMW3FtppZVWDqAMCrg/1+8K3AQ5aG1q2zPYctDaAxy8Nt3S9gzEgmorrbTSSis3VgaFubfSSiuttHIDpQX3VlpppZUDKH0H94h4OCJei4gzEfHRftfnWiQi7oyIFyPimxHxPxHxO9X1oxHxrxHxevV/troeEfHnVRtfiYh397cFuUTEcER8IyK+XH2/NyJequr9+YgYq64fqr6fqX6/p5/1ziQiZiLiCxHxrYh4NSIePAD6+b3K3k5HxGcjYnw/6Sgi/joi5iLitFy7bp1ExFNV+tcj4ql+tEXqkrXpTyq7eyUi/ikiZuS3p6s2vRYR75frNx4H/bHkW/kHYBjAtwHcB2AMwH8BuL+fdbrGep8E8O7q82EA/wvgfgB/DOCj1fWPAvhY9fkRAP8CIAA8AOClfrehoV2/D+DvAHy5+v73AJ6oPj8L4Leqz78N4Nnq8xMAPt/vuidt+VsAv1l9HgMws5/1A+AUgO8C6IhuPrSfdATgZwG8G8BpuXZdOgFwFMB3qv+z1efZAWvTQwBGqs8fkzbdX2HcIQD3Vtg3fLNwsN/KfhDAV+T70wCe7rcR/gjt+BKAX0T3KduT1bWT6D6cBQCfBPBBSV+nG5Q/AHcA+CqAnwPw5WpQXRAjrXUF4CsAHqw+j1Tpot9tkLYcqYAw7Pp+1s8pAN+vQG2k0tH795uOANxjQHhdOgHwQQCflOs96QahTfbbrwD4TPW5B9+oo5uFg/0Oy9BgKWera/tGqunuuwC8BOB4KeWH1U9vAThefd4P7fw4gI8A4MEj7wCwUErhUYxa57o91e+LVfpBkXsBnAfwN1WY6a8iYhL7WD+llHMA/hTA9wD8EN0+fxn7V0eU69XJwOvK5NfRnYEAt7hN/Qb3fS0RMQXgHwH8billSX8rXRe8L/aZRsSjAOZKKS/3uy43SEbQnSr/ZSnlXQBW0Z3y17Kf9AMAVSz6l9F1XLcDmATwcF8rdYNlv+nkahIRzwDYAvCZfpTfb3A/B+BO+X5HdW3gJSJG0QX2z5RSvlhd/r+IOFn9fhLAXHV90Nv5XgCPRcQbAD6HbmjmEwBmIoLnD2md6/ZUvx8BcPFWVvgqchbA2VLKS9X3L6AL9vtVPwDwCwC+W0o5X0rZBPBFdPW2X3VEuV6d7AddISI+BOBRAE9WTgu4xW3qN7j/B4B3Viv+Y+gu/Dzf5zpdVSIiAHwKwKullD+Tn54HwNX7p9CNxfP6r1Y7AB4AsChT0b5LKeXpUsodpZR70NXBC6WUJwG8CODxKpm3h+18vEo/MIyrlPIWgO9HxE9Wl34ewDexT/VTyfcAPBARE5X9sU37Ukci16uTrwB4KCJmq9nMQ9W1gZGIeBjdEOdjpZRL8tPzAJ6odjLdC+CdAP4dNwsH+7kQUdnaI+juNvk2gGf6XZ9rrPPPoDt9fAXAf1Z/j6Ab0/wqgNcB/BuAo1X6APAXVRv/G8B7+t2GPdr2PlzZLXNfZXxnAPwDgEPV9fHq+5nq9/v6Xe+kHT8F4OuVjv4Z3Z0V+1o/AP4QwLcAnAbwaXR3XewbHQH4LLrrBZvozq5+40fRCbpx7DPV368NYJvOoBtDJzY8K+mfqdr0GoBfkus3HAfb4wdaaaWVVg6g9Dss00orrbTSyk2QFtxbaaWVVg6gtODeSiuttHIApQX3VlpppZUDKC24t9JKK60cQGnBvZVWWmnlAEoL7q200korB1D+H3qjwr/a6VepAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ZDjaAH6jR0ib",
-        "outputId": "a39636a1-924a-4b0c-d921-d4fb8eb2dc8e"
-      },
-      "source": [
-        "from sklearn.metrics import f1_score\n",
-        "f1_1 = f1_score(y_test_1, y_cap_1, average='weighted')\n",
-        "print(\"F1 Score is:\",np.round(f1_1*100, 3), \"%\")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "F1 Score is: 44.286 %\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_image = cv.imread(X_test[0],cv.IMREAD_GRAYSCALE)\n",
+    "match, num_matches =  sift_match(test_image, X_train[18], plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177
+    },
+    "id": "F2fjmXO0C129",
+    "outputId": "a486f6a2-371e-485f-dce8-ded06f937cbc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAACgCAYAAAD3jCzdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9SaxlWXae952+u81ro3kRmZVBsqrYFEkQAlyeWDBkCLBlARxJsDyxDQOcWHNr5qmmBgwY4ECwNbGomQxIgAcWKAsELIlmSawqllSVyozM6OM1993m9J0H560T6+53XmTSYkFpIjbw8O499zR7r732v/619tr7WH3f86F8KB/Kh/Kh/Pkq9r/vCnwoH8qH8qF8KH/25QO4fygfyofyofw5LB/A/UP5UD6UD+XPYfkA7h/Kh/KhfCh/DssHcP9QPpQP5UP5c1g+gPuH8qF8KB/Kn8PycwF3y7L+U8uy/o1lWZ9alvW3fh7P+FA+lA/lQ/lQ7i7Wn3Weu2VZDvBT4C8Dz4F/AfyNvu//5M/0QR/Kh/KhfCgfyp3l58Hc/wPg077vP+v7vgL+HvDbP4fnfCgfyofyoXwodxT353DPR8Az9f058H3zJMuyfgf4HYA4jv/CkydPxt+0N9H3PZZl3To2dZ4u5m/yd9f1dxXLst77/X3H7zr3q57V9z1d11FVFX3fE0URdV1TliVd19F13eR1lmWNf1/1nLva/j6ZTMl06vtd8tb1sm0by7KwbRvf97HtdzxDt8e87k9b5/f9PlUvKZ7nYds2juNg2/Z72/51y5+Flyzj4d+l2LaNbdvUdU1VVbRti2VZeJ6H4zgAtG1L27Y0TUPTNHRdN1n/qbpMjVmz7l9H/94nZ31cnqV1Xz5L37mui+M44zGz7ubnu+SsdeZP05/mvcz7f917mePlJz/5yUXf96dT5/48wP1rlb7vfxf4XYDvfe97/e/93u+NDRZFmvqT3+R/0zS0bXvrnKnz6rqmbVvqugYGBe667lbH6s6VwS3niPKb54nSyHm2bdN13XhMK9pN+/cATY7btk3TNDx79oyrqysePXpE3/d8+umnpGlKlmU0TTOeK0rrui6u644D9H3GZkqRRFZallPKJ79ruZsg0HXd+Lv8iUFyXXcEF9/3iaKI+/fvs1wucRxn/BMFlrZInUSGoifA2MciT9OwdF2H4zjjeVJvuVb6KI5jHj16xMXFBfP5nDiOieOYKIrGNug2a0M7NXinvrdte+uYKecp4NAy0P14l+7qe8sxkftisaAsSz799FPOz8+JooiHDx8SxzFd15FlGdfX1+x2Oy4uLthsNpRleeueU4Ao8tTtkePwzqhr2em+EF3S54i+6X6XcSuGSRtieY7jOMRxDEAYhpyenjKfzwmCYK/++lo9dqb0bgrc32fEpgzI+0iF+ZvWa8uyCIKAR48ekSQJtm3zW7/1W1/cuslN+XmA+wvgI/X98c2x9xYRnCi/NM4cwFPWf4olCrjq+2tlkWfoTpoaCCYbEEFro6CBXd9PH9OKNMUytIJvNhvW6zW+75NlGW/evGG321GW5SgfAUGpp4C7yTLN55usX8vaVGANXHLMcZy9ASXnO46z1zfymyknXScZzLvdbo8pB0FwizHr/6bcpuRoDg6tP1I/KbZtkyQJT548gRDa3Tujfxegm7qkddNkn1oWpkzMPtG6odti9oW+Zsq71P2tjZ7nefR9z9OnT7m6uhqN62KxwPd9Li8vybKMLMtYr9fsdrs9MjHFjnU/mGNJt090V3RWt0faVNf1HjHShlt7EOYzRSflnvKsoiiIooimaciyjOVyuSdnjTlTddd6pI2RHNdtkvO1bk2RR9NI6Gea5M/EIRlnmkjeVX4e4P4vgG9blvWEAdT/C+C/fN8FGnjluxTpKC0k+S7WXF+jGbxmjKZA5X53Wdgp1irXaeWccvVMJm8OeH2O+VvXdaRpCgwK8+bNG7bbLVVV7Q0yYejC2oXpmkZNg7C05S6FkvZoZmQCidkGzSxMNjUlX22sAZqmoSgKiqIY6ynhqDiO99xpuc4M4egBOAWy5u+62LbN8fHx4CF5Pf/k8J/wefg5v/HZb3ByecLx4pggCPYAyPwzdcSUmwkeZj2+irTogayZv5aB/myCkNTH93183+f169dsNpvRO0mShDAMefv2LZvNhu12y2azYbfbjX1hEhVT33U9tD7Id23kpoBM6482gtoTlPbL+VrP9JiXuor3aFmDV5amKbvdbgR4qZvcW49vKfJd96ceG30/hHym+tEE37sYvmn8p8JGMIzNJElGgP+q8mcO7n3fN5Zl/U3g/wAc4O/0ff/jr3Hd+Nm05lqoWgG0ImihmvcV9qXZz5Tlm2IU+pi2pGYn6HtNhWKmzpFrdcnznN1uR9d17HY7siyjLMvRkDmOg+d5I0sPggDf90dGJufo9mt5fZXczTqZgKP7wGRtwuo1ozdBXu5h2/bogldVRVmWRFE0gvt6vabve+bz+dgek9WYhknkqgeirp8GJQnVPHz4kNPTUyzb4sq9YnO94TK75A/rP6RxGx7MH/DJ8hMOqgMW2YJZP8NpHfr2dqhQy2wK9M026PqZ381zRO5T+mQaCrPv27YdicBqteLFixf4vs/BwQG2bXNwcMDbt29Zr9ekacp2uyVNU4qiGOU0BepTnqluq4CQ+Sd1M8es9jz7vh9DfeZ4Avb0TMtBxrkO55VlOerQ1dUVnueNBluu0wRGA7wJpNpzkLZqI6PlLvc1yY9pBLXctF7r+3mex2KxGMHd7Oup8nOJufd9/4+Af/SnvOaWostxU9nFek+5xrpo4YgVFyDUv0vRIK6P6ZCDAItWHvOZWrl1Z8m5U+AHQ+cLS++6jqIoRsYuiiHA7roucRwzm83wPA/Lsqjremyn1MWUnx4QGvjNvpjqBy0TfS8Bat2PJgjpwWDWyTS+0q+73Y4wDHFd9xbQaXlP9b9+nhkbl3otl0uWyyVt2/JZ8Bm/X/0+QRXw14q/xiyaYWPjlz6s4dK95EX8grW9puxLvMZjXs85KA6Yl3O8woMWLN4BkAn8pvchctEGU+uf9oS0cdX3M0NpZvvl3qLzr1+/xnVd5vM5dV3z+PFjLi8vWa1W7HY71uv1GJYRRqt13pyDmgI1/Vnrku4DqdNUqEPOlfYJkLuuS9M0I1M2Wb7WVQ2kbdtSVRWO44zzVtIWKTIX5/v+HmBPAbvuV8EB0/BOeb7mGJoyzlqmmswtFotx/uDrln9vE6pm0UBrMhmzE+9SZpMJaCEJgMC+NTYZiPlM/bsOyWhGb9bHdd3xOn2OBjqzw5umIc9z1us1XdeNbFYmgS3LGidMfd9nuVxydHREkiTUdU1RFLcUT3822ZUpc3NQTk0gaTnDbbdYT6hqudzVV9pAm0a66zrKsiRN0zEeL4Na30cPIl033e+6/Xrg+74PQJqmfPn8S/pPeuKDmKdHT6nsCrd3WfZLTtoTDsoDHuweYLc2TdeQkrLxNlz5V3wRfUFODh3EdcxheciiXDBrZji9Q9/dNpImi5tit2aIYKrtpiHXv2uA6LqO9XpN0zSEYUie5xwfH7PZbLi6umK73bJer0dyocMxetLe1GthyXe1x9R/0Q2zL6VooqC9BAF40UsdqtP6KN/lfBnr4gXA4B3HcbwH7nJ9Xde3xoKWpT4u7Ze6mkbFJDEm4XgfVsl3z/NYLpfEcbyHRVo+d5VvBLiblt1UdFESAQ99jjTUDLuYjE0DyBRgyT1NhdHAYTIyk2ncBeQmuJodLjFFYRUC1hKOkRi77/uEYcjh4SFPnjwhjmOyLCPPc8qyHNPazOwhmJ5gNvtAylRdTcNgMkjdHzqOqZVRxxQ1gOnMGs3KbNumKAqyLGM2m93yJEzDYTJ404ia+tH3PVfrK/5x/o9Zzpb85dd/mZPkBN/zafuWyqnIw5wr/4oX8QvyeU5v9YRlyCyfcVAe8NHVR3zSfELf9ZSU7Nwdq2DFZ7PPyLwMLAjqgFk5Y5kvmZUz/NbH6vYn3kyjqetvEp8pPTNlofvQtoe0x/V6jW3blGVJGIY4jsPFxQVXV1djOEYIhVxnhi/lvtprE+JhMmgNxtpbMVmuqVfyHAFmAXIBbDNUpEH9rnBc3w/hGdu22Ww2hGG4VxeprwnWWp+0Xpp1l7rJOaaO3kVENFaZk6VhGDKfz4miaO++pqd9V/lGgDtwC3hNpTYnkkwBaqHL9XKeCQSm5TdZu/yfiqXqP/N8sz2aGZhW1gQlCUOUZTkCtQCkpAwmScL9+/f5hV/4BRzH4fz8nNevX5Om6V5c3pSRWW8NDnfVyWQHJpjr+2gPR1iT/NfGVYr0pXZ9zdCJGDN9rQnOup5mW6ZkbNbx+fPnvMhekP1HGZ+ffs7mcsPHwcectCfMizlxE3OUH3FcHEMPXd9RUZE6KSt3xfP4OetkTWu1eJ1HnMcs0gWn21MeXT3C6i06qyN3c9b+mtfha9JFSuu0OL0zAv68nBO3MU43sPwp2esyFceVYjJIYdXb7ZbdbkffDxOri8WCq6srLi4uJud2NEPXKarmHJQJ/DLOdFqinmPR7TL7UddZ6mBmz4luSB/q3y3L2iMKGjS1zjVNw263w/f9vYQIbTBMj0/XdQpctZdofteRAdOD0XLW9fB9n8PDQxaLxeixy5gIgmA0Tu8r3xhw10LRAK1ZvRaeBgPzHvK7/qzPN2OGU/9NVqUBzTxnyrKbk01Thspsa57ndF03Li6BAdiTJGG5XPKtb32Lw8NDdrsdL1++HNPWqqraC99I3UyFFSXRCmfGr6VemsVMMWJzYMs1phE2gVtPBslvMuDEfZZBJ6WqKna73a2QmNlHZhxTDyRTv2Tdw+PZY4KnAQ9+9oBlvmQX7HiTvCFNUtq4ZZksOYlOWFZLlsWSuIiJy5gkSzjrzob79DW5nXPtXA9hmuQLCqfA6RySOmFRLJhncw7XhzitAzbUbs0u2LEJN7w8fEnpldjYRHXEQXkwPKuK8ToPq5/OLtFerdlWATTXdcnznM1mQ1EUWJbFyckJ6/WaV69ejZ5fURSjDmmd17I29VjqoOtm6oQckzqZYTsNeqK3ZlhCexIyVrQuyW9Sd93fdV3vyUli9npBoIwN8XBk3Og6aplMtdN8hsnMxWgJQMukbhRF43qKMAyJoogwDAnDkCAIxnpJkfEiZPB95RsD7qKY2nUxXU24DTRmmEafpw2CnpTUHWYODn1cd4rpcpngOKX4pjchv+ssAild142hGJ2lMJvNuHfvHh999BGz2YyrqytWqxWbzWYvjCGKKe2cAnrNjHR9pmR7lyxNpqxlJOdo5dYArhmfZu1934+TyCIbYXvixUjGg8kodX6/Zj/aQJneigaPIi+YZTPatqVsSxwcltaSRb+gp+fxk8ecPjnlyr3idfia7WxLQYHTOsRVzDybM8tm+IXPcX3MUXc0sMe+pbZrUj9lE214u3xL4RVgQdiEzPM5i3zBvfQej5pH2Ni0TksZlWyjLV8uvqTwCzq7I2gDZvmMZbFkXs0Ju3BP/lMkR+TdNA2bzWZMrz04OBizZoSxi5c4BWTStyZDn9Ib6VutJ1oHpU+m9E33kdxDjpux8ann6XtpcDbPk9i7GDSZpBRSJRPPWkf0nJcZjpH5CB02FcAOw5AkSYiiaGTbAuyiq9JXkjUmXu9ut2O1WlHXNXVd3wpfflW8Hb5B4K5ZumYDJnucAn79m8mKzU6fKlPu+xQTkeOiAOZx7ZLKvTT463rB7UVOkn5WVRWWZY0LTB49esRisaAoCtq2JQgCjo+P95aIAyMbLcuSPM/J83xkYyLfruvwfX8ERd3uu+Qv4KzZiXkd7E+SmXMd0jem12R6L2LktNGVQaUXT8mgr6pqb2WuNgCa4WnyIPMbVVVh28OEoUkS5Fy7sVk0C5Iy4fHu8QACXU1Bwdbdculd8ubwDZmV0dLiVz7RLiJJE5IyIdklzLdzHluP6enpnI7CK9iEG97O3pKGQ5gmaANm1YxlueQwPeTh+iEODp3VUfs1a2/NVXzFs6NnNE6D27kkdcKyWLKslsRNPKRoKqPW9/24IK4sS4IgYLFYsNlsuL6+HkMxOnymdVrmevQxkxTp8TPl9Zrjygx73FWmPF7zfjrsIfqj9VffSxM76dssy/aysbTX43neCMriPct6gCRJRvDWqcia8OmxWJYlu92O8/PzcQ5JPCVNyBzHYbFY7K2g1YRQE0zbtsew5V3lGwHuMnClmGA5BQCmcphscwpMtTUXZmiy9rvcLjlmupH6uGYspuU3n68ZpG3bY2hFlMv3fR4+fMjDhw8Jw3BMW3vy5AmfffYZb968GcFeWIdW9tlsRpIke5O0U7n+5kDURkCzCvmsmZGpfFJ3MYACnhpotbehAVV7WFI/8QD05KwGdnFtZTBog6I9tbIsxzqK8RBgk8Ft6qPJBrUuWZ2F3w5MfbldDrLvO2pqMicbwjOzK54dP6O2a+zWJq7igXkXc8Iy5GH5kIfXD7Fsi97qqbyKLMrYRBteL19TORWO5YzXLYoFJ5cnY1in9VpSP2Udrfl89jm5m9PTEzcx82LOsljibl22u+0Yjjk8PKSua16/fs12u93TCa2v5lJ8rStToGuOh6ljMs6mAP0uT0DjgOkFT3mX5viUa8TDk9Th+XzO4eEh9+7d4969eyyXy5Fhy38hQDJmRafEi8zznNVqRZ7nZFnGbrcbPW8Zy8K0TYI3taVJFEXMZrNJwDZlrdv0vvKNAHcpZsdrFqW/699N919PNplKc9fmQabbbk5Wmefo52uF06liUg9x50xjIWAsHS0AJm199OgRjx8/JggCPM/jW9/6FovFgh/84Ae8fft2L6whCmfKTKy74zgjwMvvsrBFF83wRZE1sOvfdSxWmLOWp4CvubeMNuRa1sJipL3aiJvL1uM4Zj6f43kes9mM5XLJarVivV5T1/UYZ3YcZ1wBC4z3lTkNk+mZWUYmkdC6aMaN6RhBPOojTq5OhvOsjtqpyfwBuJ8ePqUMyiGLpgmYV/Mxi+YoPeJ0dzroDx2t25IFGdtwy7OjZ++yb5qARbngoDzgbHNGcB1AD63dUvgFG3/Dy+QlV/EV18k1fdFzzLDSNn82xN/LotwD9in2rQ3p+8B8Cti1LtxlJHUIZ2ruR291IXLW21KIZ+F53t4+QIvFguVyyXw+5+DggPl8vhceEb0X4iLZaVmWjex6t9uNwC2poaKjeiJXy0DqOTXRbIac5LPrukRRxHK5HIF9yvMwx8uUQTXLNxbcTWasGaV5jamYejDKOZqJmKCmlUeKZrVyrWYH+vnaAJgdqcFfP0sPqLsmBwWgl8sleZ7zx3/8x+NeH23bjuCpgUdkpTMGJMTjuu44YaTrYbZHgF17ExrgTQOiN/oyt0VwXXcEkak9aUwvQcBX2i8MSG9JoPuoaZpxk6vNZoPv+3uxVQk7yCSaGFHYD9dM6aK4zqae3RXC0W0QvXAtF7ux8Wufg/TgpoOhd3pKv2QX7ljNVjw/fk7t1ri9y6yeDTH5csGsnHGQH2Bd3wCl05O7OZtgw3lyzhdHX9A4DV7vjYZiWSw5Xh2zXW05vzqn9mrCxyEv7Bc8Xz4nXaR0dYe38wjWAfE2JigCrPYdK9TsUo8PDSpmeM0cR2YIRoOUJgG6PwSsJSwioJ0kCbPZbPwTIBcyoPPZi6Igz/Mxd//zzz9nvV6zXq9ZrVZjdlnXdWMsXIiAEC69SPGuEJHprWsd0WRRPFo96SvjQNIdtTdrztNpL0TuOaWzZvnGgPtUDE6EMcXYTYWRogerKWwdtzJBV67Vz9bHpiyy/D5lGHQ99bmarYhyC0OR+sznc/q+Z71es1gs8DyPH/3oR1RVRZ7nYyaNyEe7fybQSz0EgAVs74qLCgjrXR/vmjfQRkRCKJKqJV6Sbp/0syk3uU9ZlqOXI1k9ApwC7mKg9HPzPOfy8pKmacbsClmQomPqJhjLM2ezGUEQjANbx161Z2jqlHlPk93r9sq1lmUNq1g7e4jJlwkPNg8GmTgWjdsMbD3a8sXRF8MkrH3D1qvFOKn6YPeAs90ZFhY4UDkVu3DHtX/Ny+Qlm27DNt7SJz3H7TGzasbs6YyH5w8Ho0dJGZfki5yLjy9oouadN5HfeBP1DLu6O2ypx6cUvU5BzhEjLyE0CX9I6FBi2BLHFj2VMSrsOs/zcX+Y8/Nzrq+v2W63rFar8bPeOkGYtvaw5bOkF8/nc+7du4fv+3uAqnPrtb6a2V6arJh9reVi6r1t2+PzxevVOjZFInVUwrz/VPnGgLsJSne5HVOZFlJk0Mr94HZ2y5SSTrmROqwyJWiTjev/8jwNoLpTBAQFrLQbattD1svV1RVRHNH1HW/evBknALVrCIwupQZi7fJq78EELFMxzTbpNtwVXpJFL2IMJIwjoZowDIF3Oe0CqjJIJDVNhwiEkevQUlEUI+BLX8OQJrndbsfftcssLN40LABlWbK6Xu2FlTQx0F6eaeC0h6jd8ykCYrJaXUyiYGMTtAFBFgy59TCy/MIt2IZbrqJhUrV2BpYfVRGH5SHLaslBfsBJekJd1VxeXfLq6hWpl2I/tPlZ+DMuP76keFhgFzb+2ifexCzfLjl6foSNjRM49POe6qBic7LhfHZO67R4jUdURMzTOUmeELcxkRcRBuHYxxLPFtAWZi2pfXo/JJFbVVUjy86yjMvLy9EDk0we+U2yyCRUKH+ic9pbE4NghtDM/tSTmfAurKqJnEnG9DjWJEmfo8e5SfzkHhJaFDJhhoun9ETj2xTRNMs3AtynmOYUgGr315zwmrJkGqh1vqjuqCmwNy3nVOqgDm2YxsFUBG31NXvQICxpUGM8cNnyk1/4CflpzoOfPRiZiN6XXgyBjmtLvfTEpMhFMkt06pY8TwaanryUJc9TDEbkIqvnNIuWgdi27V5Or2QPmEZPP8OcMxH5SFaCdt0Bsiwb9xsXhq7ZlV68IvK1bZvSK/nstz7joDjgwfbBeL6cqwenqXPagNzF2Kbc5qkQh9arKcZmWzZWbw2hmmY+sHXLoqMbWP5NLP+Lgy9I3ZTO6rAzm9qqaauWZbVk9nZGvI5JNgllV1J5FfksJz1KWX2yAh8CK2BWz1hkCx5bj7lv3+fIOSJKIsKjEOeeQ3PccGlfsmk2eK7HqX/KmXPGmXvGsltiVRZV+W7CUWLYsvpV9qyRNEQJk+mtfM3/OrQj/aK3D5EiniPsg6BpfEW/5H6ik2KAdB9qYqj1wQyZTEUQTH0w8UXPHWiipftfkyvzuV8F7PANAXcpJuvWLrQWpGZvZiNNAWhg1rFgXUwg1uxNb+dpunbayst/XQ9tzafYsjxLJlcFeLezLf1/3vND+4f8MPwhZ2dndLMON3NxMxcv9QjyACdzoIKqrqjyCvrpzcp0OpsOPYixEBl6nkfTNKOc5Hxz4lGzFc2KJH5ZliWbzWZM6RS33Pf9MbyiAVA/SwawTDRJLFRYlWQ2SHhGFn7JQNA6JLF6kYdk1RRFQRZlVFHF8197TrgOSUjwdt4Qf94FuIU7rBhVOqf1UctD96UJ9FrH9Oe7vD+5zvxunmdj4zc+YRdyXA4sv6en7ms23YZn5TPenrzl6uSKy+CSpmpoL1oO6gN+JfoVPl5+zMn8hOV8yWw5w1/41POat/Zb3nRv2LGj7muiPuLAPuCgPIAVPFw/JMsyrnZXvMxe8kfVH3EdXFPHNXVbQ87oFYRpiFM5WO3tOL0O55gkTfRLr9XQ4GeGFUWeeqMzfT89XrV8xVAL6ze9MpMMmvWbwh/N9E1PzjQKggPmmNXnTPX/12Ht8A0Dd9Mamw3VQpbjcPeydMcZXvyQJMkeAAh4aYARZi/Ko9mtDNogCEZ3UjpEexBybzlH6qYzRzRb16Bg28O+4rZtMzueEd4PqV5U3Fvd43J9yTXXbKINxbygpqalhR7czqXLOtrrFqd08FMfr/CYN3P83Merh1CGizuCvyiaufJPDzhtVLU7a7qmus9kH5wgCDg4OGC73Y4DThtWiYOa95AJWVkMIgAvfSLuvjw/TdO9+kgf6mfKG2ukfjK5epwd873X3yN+FnMvuYc1s9j5O3YHOy4eXlD5FXRQLkuKWcGiWBCXMX7jww1+677XDFG7zlqepsv9Pq9Pg4g+LiEkvYmcxLHFuxE5iBwdz6F1W1aseFG/4Hn1nNfla76sv2TpLznqj7Bf2XR/3FFf1xS7gizP2FU7ftr/lE24YZtsKYKC1mpxKodZPhvCNEXCcXlM13T0Tk8TNmRxxvX8muw0o3ZvUkHzmGSbkGQJfuljY9N3d7NerR+mjKRISM18s5qQBG2EhSCYOKLDnJrImSRMP1N7rlPjwPTodZtkTEn97vIGtPHR+qP/vqp8Y8BdGmJaSV2mJl319VJ0WESUXf9uZgLIuQLCMrA04OvrNLhpYU9NpN3lnosSNk3Der3mwYMHLBYLuq7je7/8Pe6v7/OdZ9+hzEpWq9WQl9zVNFZD2qds2bJzdpRhSeZlbLoNZVKSHWT0Uc91cE3TNViVhdd7BHWAV3rETcysmeFmLlER4dc+Tu3QVR1919N2LV37LrNFDwadkij/TYYi7XIch+VyOYaTBJiEiZshCcuymM/nnJ2dsVgsxr3G9f4ylmWNbDzLsrE+kkrmeR5pmlLX9WhMNNP2fZ/ZbDZmDpnMCqCve6gBiyHe7Ho0XsPr4DW72Y7KqbAai7AMSfKEeT7HS73BeBq6qkHaHIyic2LQBLD10nOJV2vgljCXTg7QIYaiKMaYdZqmrNdrrq6uxp0fZe6mqiuavqEOa9qDlvKgpJpXNH6D4ztEfUTSJiRpwmKz4KA5wMKipaV0S/JZzvX8mlenrwYAb2zCImSeDit2D9YHWM2Qw187NXmckyYpzw6fUfkVVm/hFz5xGjPbzQjyALvaJz2aaetxo2Uo47Lv+3FSXPpfg6O5AE6OT833iG6bBkYTo6lwi/6vfzNJEbCXmaMJgKmPUgSfpkJPd5VvDLibExIwnS0jHWwKGZj8b1rPKfdKjut6aDapLam+r17kcZe1N624ZpFyr77vSdOU169fEwQBH330EU3dMItmdHXH6ekps6Cn6vcAACAASURBVNmM1WqF67pDitx2Nm7PWtc1RTlsWdBYDXZs0yc9uZ9ThiXtoqWNWqqwoj6qWYdrSsoxnutUDlZhERQBdm7jpz5u5hLkAW7mYpc2VAy53IZLqEM4JvsQ5i39KMxey1H3gX7VXtd1eyxU7qlDZToeL4xe0ubEdZcBoFPszMltzahMYpHUA9t8tH1E27X09JRWydbacu1f82Lxgt3hjtqqcVuXMA+H1alZwtJeErsxcbi/d4j+kwwSDdhSHwFsSUuV/WFk1aM5mV2WJev1mvPzc/I8H1NBZR5EjKrI03Vd4jLGu/Jw1jfpf45F4zTkwQDGL49fUkXVsDla7RGlEeEmJFknHL85xu7tYSFW0FPNKrJZxouzF9RBDRb4pU+SJszSGafnp9yv7o85+bmfk85SLk4uyIOcxm6wC5twF5LsEsI0xC1c+vr2+3FNgBWdkWwp0RXtgeo5KR3KkbRIMQAm+ZNijvEp71OeOxWGMyMN0s8ymW9GJOS+WufNOr2vfCPA3WTpGrzNOLz+XZ9nFtPKa0XQA8h0gbX11kxVD/qGhn/p/0sO+0O+23137/qp+ujOMNsmSiXZHhJ6SNOUk5OTsc5nZ2c8ffqU1Wp1K4e8bVsCf2AtAQF91WPV1rgHieu62I6N7djEs5jWHdhXHdUUUUHmZqR+Shu0ZEcZ5UlJ4wypcV3TYVUWbuXiVR5hGeLl3hD+yT2CIsBvfJzOGQY6797IBO/mLYShSChFezYiHy1vYV1meELvLS4po1rhJbZvehmmvpnhO53NY4ZI5L4ug6GZO3Pu2ff2mLQf+Tgzh3pWs423nPfnbNnS2R3YEFkRD6wHLJslTu7QFA1lUY5pe3o3UJnY1oxS6qoBwBzocq32uvQEvJaVhHT0y2vatqUuhvkfriDqIiJuwmJWS+EWlLOSy+Ulr85e0XgNdm8TlsNeOQfVAUdvjjh9cYpjO/TekMufJilXx1ekZymN1eBVHkEWkGwTFusFR2+P6OqOpm/I7Zw0SrmeX7Nb7mi8Bqux8HOfeBsTbSO8zMNuBl3TQC39pN+yBOyFAbUcRXbmewhEFqZ3JECr5W+SAq1fd0UZ5P4yz6bJptxPMEe8O41TptdwV/lKcLcs6+8AfxV42/f9926OHQG/B3wCPAX+et/3K2t42v8I/BUgA/7rvu//6KueIQKRRmsgNdm5nCugeNd95HeZINSTdzp+pVmmhGKks3R6koCpZVm8dl/zE+cn/Nj/Md+vv8+RfcRxd8yRdURSJwRdgIc35CD303nvfd/vLVe2LGtMJwNG1tp1wx4Yst1vURRcX1+Pees6/dB0Y3UbAaIgwsXFqR382scqLPqrfmSHjutQdzV5k1O51TAwvZQqrmiihiIsuPKvaBYNeNC7PQ4OTu/g1R5u6eLnPkEVEBYhYR5y4BzgFi5UENrhyJynQmz6LVIm0zHZkvSl2e8m6GlDLueJTMzQ2dTgPDk54dd//dcnwyGir5pdZ6sM56VDWISkWcom23BVXfFvm3/LJtqQRzmFPQBwWIXMq/mwa2Q9J+xDnH5/yb82QhpQ5JjpCUmqoEzS64VnYhzFAEooSAypeIESLrwVOughXIfEr25edGFDbddUUUV+kLM93tIkDZZrEbQBSZmwyBfMNjOOL4+hhZZ22GohzNjNd1ycXlDYBX3d4+5cgusAb+WRvEiIy5iu78bc//VizZvDN7SPWyzbwi1c4jQmWkdEeYTXedjY40pkDd4aU7Q89OIlkemUR26eJ+dqfTE9P5NEmETSjBboYva96e2a5HWqfB3m/r8A/xPwd9WxvwX8n33f/23Lsv7Wzff/HvjPgG/f/H0f+J9v/n9lMV0Sfcz8roFh6ph8l5Q8M8QyBYQmoMs99G/y19LSHDf8ZvmbxEXMyl/x3HtOGqbUYU3lVfRdj2/7xPUQ455Xc2bdbGC5rTO4ps1gyGThxnq9HgFLWPlsNhvdRtmHW+LYOo9bd7Y5JyCAJPFrzUZ0+1zHpWs7nMrBz33sxsZr3r3Wb7vdklc5eNB5HV3S0S962kVLN+uGQR7n7I52dEGH5Vo4noPTDH9u5pJdZBSvCuydjbt1CcoAq7pJdevbvcVHwLgSV9pnMnWtG/q4DAhzkc2fxq0VmRVFMbT9JoVPNmWTlY5T2zSYg29uzVnay6GejkXrtFRBRRqlXM4veRG+oHZqnH7YJnhZL1mWS5ImwWtviALT4CPHu67bA2j9WQiMToEVI6uJELzztswJYQF9Ob9vbgC/DIk3Me7LwUP0Qo82bKkWFdvDLW+P31J79bAnT+4T72LiXcy9F/eghLIqydqMLMhIZylXZ1cUwTBunczBW3t4Vx7BFwFRGUEPvdcPocaDit3hju7jDjxwW5c4iwnXIe61i7txabt2zwuSrCl4t9mehAxlZ0hN7LRO6f9aT0w8MnVOHzN1Q/pRs3Z93DQecq/3eQbwNcC97/v/y7KsT4zDvw38xzef/1fg9xnA/beBv9sPT/2/Lcs6sCzrYd/3r77Gc26BqnzWlvB98XYT9HWa01SZEp5pVfUkL7wDxkcvHo3nJWlC3Mfct+8Pz6Yf9xTJ3ZzUT7kILsj8jDqp6awOp3MI65B5O+fUOaW6qrByi4+Cj3Bch7Z5t7WnZVlcX1/vvbzDst6lhMnSfy1H7dLJYNVgrkNbclxYnICVXC+yBIadCssOp3Q4tA/xW5/ydfkObKwey7WwoyHu3y96ci9n5+3InIzMysjOMrpwGIyWY+H0Dm7rcs45F+4FJ/4Ji27BvJ8zq2fEXYzf+oM31Ft77riOp0r/mOxa97XISNpm9q94dnLs5cuXXFxcjLqgWa1kRGmwtaz91bX6+RocbNsmaRIOt4dYu5vfbGjchtzP2YZbXhy8IPfyYWK3G7YXWJQLDuoDoibC7fYXlYkHMYYPlSckyQJ65bCML8saFtXN5/O9F1hoYyV6og1BXdfjxPZeGK208fuB2HibwTBj8S6WH6ec3z8nczMaGvq8x17ZQ0rqm4Ht00LVVxR+QbkoyR/mVL9U0fs9VmnhrT2C64DwOmT2aobDMF9Qe8MEcXqUUn+3pvRKmqKhu+qwLiyC6wC7tHEaB7p9EuA4zrjeRE+4i6z0GNP/9Zi6izhofRXCIp/1fU39mzIuUv6dwf2Ocl8B9mvg/s3nR8Azdd7zm2PvBXdttTRgi1LJOeb5+lwNUnpg6c4zLaj8DvusXneegKVmklP3kXvo+zqVQ9IPwC/nW5YFDsMElF+x83a8DF6yOlxRnVSs7q34zP8Mu7eZuTPiMsZyhsUhTdZQ5PtvpJeFRHEcj+mWOjtD3G0d45OBaTIJWWCkWaFOGZVQkCzz12+D0QuknM4ZQjS9T1AH5HmOv/GJ8sF9LqqC3u+xZzYsoJt3VHGFc+xQH9dcLa+4Cq5ogxbHvvE6On9YKVlFBHVAXMXDJl1VRFRFOJWD13l4vTcCm3aBTbA3Xdr3eYBTrrjIVbwa6Q/RY3OhjTmnM7UwTq474IAH+QP67OZ6Byq3IvVTroNrPl9+TuEWdAx7vS+qBbNsNnhATofruXshGXm+1FXCi47jjBPQ4gXq1EAdrxe56MVsMiEsWTpiTMRoaoC0sHAbl6RKSLYJ997cG+YZ2pLMytj6W7J5xur+itqroQU3G8Iux/kx8SomuAoGIxE0lLOS9BdTsiRj5+zoqg5rY2Ff2ENY5zIhciJsy6ayKzbWhuvgmu2TLZv5BhxwS5dwE9KXPVu2HFlH2I59q891+M8sU9EF0QeTcAF72UxlWY6eu2VZY5bP6enpKFszyqD1+OcF7voBvWVZ73/KRLEs63eA3wG4d+/eXiOM8/SzbsVPtatuAr0Wxl2Trzo1Sp9vnmfG18y6yXepmxgB00CNE4w3izvCOiRxEh44D+jp+Y3Zb3A2O+OiuOBN+YYX3QvOrXOu5leUcUkzHzIK3IWLtx0mpub9nOViSRzGtM27VaFdN7wUWRQpiqI9Q6dj3xqYpti+ZhXSVskv1qEsuU5cW2Bv86+mbuibHruz8VoPL/Nwzofr4yTm8PCQk3snuJFLHw5503VSUwQFeZhTBiXrcM3b+Vsap6Gzu2GvltYmaAKCJiCpE6I6IqmG/1EZEbbhEBqyXGzrdpqbBm3RC2Avk0GTD826TIDWrEuHBOV3De6m/uiJNK3DsRNz2B3yuHiMXdlgQdVVZHbGLthxObvkdfCap9bT4ZrUpnvT4V17eFuPmHjvFW3z+ZyTkxOOjo6o63rcq2Vq3yKRk15JKmNKzk2SZG+DNzNspNuoSZPTO4RViL21mb2dDdc5Fk7k0M070llKfj9nFa7onA6/8QmygHAdcnR+xMOXD/dedLK7v2P1eMW5dU5LS1AHBJsA663F4nxB9OkQ1rE9G+ZQH9bsTndUDyuuT66J7IioiFiWN2/eauNb2KHHv56zkNXaDx8+3CNJsmOrbdu8fv2aTz/9FMuyeP78OV3XcXZ2huu6rNdrqqri5OSEX/3VX93zyHVUYyqTZ6r8fwX3NxJusSzrIfD25vgL4CN13uObY7dK3/e/C/wuwHe+851eKn7z263BZ4Zl9HkmkzdDDiIIHQ+F2yEXXeSZOp1uyi0yj5nP1jFKYA/0ZPDICwAsy6KtWuzSJtgEnGQnJHnCaXo6LN8uM2pqCqdgY21Yh2s2iw2v4lc8mw0Ok5VaxG3MiXXCrJ7R2i2N1YyrPbMs2zN+ZqhGT7gdHx/fcjfluqIoOD8/H+8lIGiGgvR7XbWHZjLKvu/HeYimbPAdH6/3sAoLd/eOGff0dH1HZw9hrzqoKbyCXbCjCqph4s1b83bxlsZt6JwbgOyGFZ1BExBVEWEVDuBfRkR1RNiEuI0LzbD6U4p4K1rXdFhLg7ke9Fpm+jftoZoDVz6bc0MiN5GVnOdbPl7nscyX3G/u83D1EOdHDjU1O3vHylmRnqZUTyrCecgu2nFkH2FVFofOId///vfZXmz5w3/+h+N2yVr3TW9Zt1MbN2mLTJZLn5vekejGVGhVQkM6VbVbd8N2xlInp6cLO4qkYDffsb6/pvKqYU+eIiDJEubZnKOLI7q8IysyUielWlTsHu3YfrIlazIoIcxConWEv/IJL0JOXp5wfHpMeBjSLlt2yY6nB0+pwxrP8QjKgCQd7h9X8TAP0r+LCpyenvLxxx/z2Wef7cX0pc9Fl2TPdrmuLEvevn2L4zjj/KDjOONciUkUtNH8eTH3/x34r4C/ffP/H6jjf9OyrL/HMJG67r9GvB1uZypMZVNMAbv+fFdIRQ8uEaoAq8nw4bYLbzIQPWhN46OVWrvGco6O/2smIIptvgZM/qqqoqu7IVxjD3Ho+/39sb5e4NFYDbmVUwQFaZzyNnzLNtzSnDW4jotVWLABd+Pi7TzczMXqrPGlzPJsvVir6zrSNB1fdnB4eIjneeMGT3p5v8n6pR8llimsTuqsY7vSDzIJqPfakUVQZp/Yto2/9QmsgCXLdxtItQOjb+0hfa/wCsqoJPVTCr/gOrym9muaWUO7aOntHrd3cTsXvxyyfeIqHl6qET7ko/gj/MrHrVzs3h4WevX7Wyfo0J3uV1NX9O9a37QuTTFerVt6tbAeM2ma0rc3hqzwWRZLDl4d8Mknn/DR7CPauqWdtewWO2bfnfH3d3+fL6++pHvU4cwdwnXIvJwTVAHU07te6ufpP224zRebS8KAHDOzcvq+H8mHOQGuQ4x93+MWLn7hM7+YD/rVdwNrD0vyeT686OSswnIt3HaYUPUvfE5fnbJcLdmuh9ck1oua6l5F9iijT3q28Za1s+Ye9zhpTzi7OsN+c7MeIHLJvGGnzvOTc1I/pbd6vNYjKRI+8T/h8PiQnyU/42J2wb3+Hq7tjskBGqgFE0TH5TWIoiOy4taUtxj3u8KMU+XrpEL+bwyTpyeWZT0H/gcGUP/7lmX9t8AXwF+/Of0fMaRBfsqQCvnffGUNborJUkygNpmCOVA0W9bHzPQluY9+WYNm5xqc9SB9XzjGZCliQIBxck1vxC8dLdsYwLCUfrFY7O16J3XULFcWXcigEiDt2x6rG1LQ/NRncbUY96nGgrIrWVdrrtortu6W1cmK3M0pnXJYOl4OqWhJnQxpeVXIpthAA0VZjFu/2vawvYC8DMNcgCFApVNO71pMIixUDIQYQpmkM3N9zT7Q32UDMf0GJtOoO53DzJoxt+d0dHRuR+M1VGFFFVa0i5Y8ylkFK9pZSxu2PH3wlH9z+m/oux63dfFrf9j7fGvh7TwOnUMW7YJZPyPqI5zWwbGcMfRjeob6hS7SDrMtIkeTUOg4rkksum54YbKe3BSPynEcsl027ETozPnF+Bfxn/rkf5yTNAnE8Lp6zSbY8OXhl1ReRdd3WLlFkAbEm5ggHVY40zJuG6AXr0m/wjuDJ7FnmaSVfg+CYDLDxExtFnnoVF/ToFmWNcy1ZB5JmnDYHg66ZnUERwFpmLJarkgfpaSkbK+3tOsWd+USX8TMv5zjtR6zwxnzj+a091tenb6inbU0XYNbuxw2hyzyBUfbI87WZ9BB0zf0Sc/htw8pDgr+WfjP+Jn7M/wDH+dPHOzG3gNwIXhFUbBer0eSp8PBOufdxJop4vpV5etky/yNO376TybO7YH/7iufevu6W8zb/E27uabSmyxGu74ykHR8UL+AAbh17ymWLp/vMggmW5NBGEURdV0Tx/FetoEAlgy+PM/3AM5st453a+NhykYmxQ4PDzk8PBy3Tm3qBqdwOGwOmdWzd+GirqVqK3bdbsheWDRsjjZcJpc0TkPTNdi1jZsP2xUkZUK4CnFLF9u18W1/LwyhY8nCMnUoTO86aV6n5aYNgRkGEfnrfpJFQJZlcXJycuuNO1qf9LFRd2qwrqxx+wVs6OyOe9+6x+mTU1IvJXVS1s6a1E5ZRSuyOON58pzevQlLtB5e45G0CXETE5cxs3bGvJ0PoaA2xOu9cTGUtEOD1l0sXhsrzWQ1sMrkp9ZZmdiUnTnl8w9/+MPBO2pa+rJnXs6Z9bPxuqItSK2UNErZJBvyk3yY6OwYdOHmPbFREeE13pCtYr17mbR4gpZl7TFX0W8976MNstkmTdj0WDYB8Jb+NB3tquVkdkKcDWMPG4qu4Lw859w6J32UDpOrPsR2zH33Pveae5y9OWPxdkHTNKRWSrWsuF5c8+LkBa3X4rbDWPjuwXc5do6p8ooiKzg7P+OoOSLxEzqn2/Pq+r4f57/0BnuS2KDHvNZvs99Ng/6+8o1YoSrlrtAK7E8oTIGpCMdk3fqcqWfdVQ/TNdb31iwFbr/xve97vvWtb5EkCev1mqIoxqwC3TYNQMKEbdves/YahLR3YoKTNgBSl8vLywHYjW1Vpc5yvd3ZxE2Mm7mwgfAiJAgHdtXQUFrlENqIS7KjjKvoisqqaKwGt7lZuZqGw0RmGeGWLn25PzktWT2SN64V1rKsMU1P95nuO9PImv0rxjIIAs7Ozm7FPfW9NHiYxEC7wABJlDBP5yy6BfBu0dJmt6GzOsJlSBM0VFFF6qVk3pCvnfs5F/EFjdvQ2MNqX6d38BufqItYtAuSZvCSkiZh1s4ImxCv83B7d8xrNwezqXt67kSHvaStshPnq1evOD4+5tvf/vaYqy9tEcMgoNI0DU09GPXFZsGCm7Z3LbVdUwYlaZxysbygOqvAB6/38AufYB0Q7SLm3XxIN2R/cZoGa3MVrjb4JrBro2x6LXJfM2xRVRVpmhJF0djWyIp4YD+ged5Q/Nti8CwDB3fuEj+JKR4VPD15CvFNhk/usigWHG+O+ej8I9zOHcJAs5L6fs2Pkh/xT71/yuHLQ77vfJ9f8X8F6xf3Q1V9PywU/Oyzz/jX//pfs1gssG1774UiMqZNvTc9FtN7eV/5xoC7dqW1gk6FaEwQ16A7ldJolrquSdN0jzVOLV4ygRDeuZr6PJ0xIfd8+/YtrutydHS0t2eKAFBZluNgEsYlYQ7paD1wNTPTCq3brhmSvJlIDIQZ2jAVxrbtccVr27Y0dTPsqOknPFw8HHdnBGjzljdv37DLdtROTemXVElFvsy5Dq4p3IK2b7E7Gyd3sNYWO2uHu3MJ65B+3Q8LYHjH7k3Q0vWamng0Da+0x0xNNNPRTMCfKlquOsdZy50OAi8gaiOc0hmBTOrZ09Pb/RD68Rty9ybX38vI/IwiLNiEGz53P6e2a7DB7oeXdYRtOGb7xHU8GIA2IekSIivCt3ysflj9rMeLzFOIrkndLy4u6PshRW+1Wu3Fu/X8E9xeiq9JxPAQCIphFem8HeLebuBiRRZ1PGy9cPHwgjezN/R2j9/6RGXELJ0R5zcEohsmzrVe6zEm+q/HvPbodJhSg7yMGa3fVVURhuGefmui0/c9NGDtLGYXMw7bQ2ZXM4IwAB+29pY0TnmzeEMRD7ti+p3PvJlz0pxw/vSc+CDmcfCYT5pPxgyzKWIRx/FI8iScKb9pndVjf4pgypj4Kl3+xoK7gB3sz97rBptCnBKqHNduX1VVXJfX5EnOwe6AOHq3qRMMLv719TV1Ww8baykwj+N4L09Y6qefLyxKp0OKIjqOQxiG5Hm+13GSUyyfpYPNfOO7PBvNRm3bHncFzLIMy7LGPWvkXHiXraMHjxQ96IuiwHXd8XV0bdsOm5hZLk7rEObhMFl7eSPrvqOlpXVacjvnmmvKWcn10TXMoWqHDAc3d7F3NkmR4NYubuPi+i6O6+zJ+K5JJAEgkbv8LlvdmmA+Fc6T42bKrBRz1z7tNovR1qGivXUBONidTZ/3zKwZ9+37785zHWzXprVaGq8hczMyZ0hrzJyMjbvhPD4f9nOxS3qnx+5tfPwB5JuIWTMbNjarE/q2Z+tvqd16WCDnh3vzM8vlkp6ei8sL6LnFms2VtXKtft2iBh5NRKzKIrAC3NIluBxysz3Pww6GHPMyKSkPh/4vvJt1GoVDmIVDNswlBG0wZCn17wBcY4L2zKdIlUlURG/03JUYEjkm+iV9qj2fvhsmTOflsMulez2Ab2d11F7N1tvyBy/+gOd/4Tmf1J9w7+IevufvjSdh7Hoh2Xw+H99BoMmjZVkcHBwQhuFehp3eTE7joKnPU+UbA+5w2xU3AV0zOzMGpYWlWZrppg5f4Kf/4U+5eHzBRz/7iCcXT/Bdn8iNyBc5X977ks31ht3hjl/6f36JqIho63dvCUqS5FY8V+qoJ0iKorhlWKTo/GE5TyZHJSzTdd24vN1k8WYna8Oi3W0BZ1lxqsFKts4FRqMlCqkHVNu24+ILvWe6nC/1GWPb/cBCrdYiaiLqTc3J+mS8b1mXeDOPxm8o4xL7xCY7GLIRrmfXhF5I0AVDzLqekVQJQRXgtcPeISiCL/KQBVjaCJm55OagMEMy+r/WM61jGuj0/U19M9mV6YHYlj3uyxPUAfN22ASN9KYO1g3I2S2t11L5FamdkvkZO3+YH1kFK15Fr8jsjO18y2v/NU3W4FYucR3jpA7uxiUoAup5zdMHTzmdn/Ltp9/ea6/JZoVM6G0nJItKwEq2MZA+0Dt+inwCe9haImqGzb76vqdpG6q+ovRK2sOW9dGa1fGKLuhwLRev8HBWDv61j7fz8FpvmNRX24hIH+hVt3uytfc34JI6yTYS+qXv2rBrgydyMXHIxsYvfRa7Bb/W/Rrf/ePvcu6f01/0/NT+6V4/i2ykDrKNiN6oLYqi8RzZ7llCNbo9pr5+nfKNAXcz7nlXjFUDmQn4Jthr98z8m2dz0iYlCRLOv3POq+gVXdSxO9mxnq15k7wBC378az/m9M0p8UXM4nLBw+YhURjxMH5I1EbYnU2R3WwEFYZjneq65u3bIf1f3jGZZdkICL7vj6EZsfaidGYYRjMrE9y18ZoK18iLh+X+IsOmadhut2Malkz6mlkccj+J20vRhky+6z7U/SMTa2JYPMcj6AKsjUWcx8zKGdZLC8d1ODo6Ij6I6aKOIiwok5Lre9dUfkXjNFjtkOIWldGQrpiF+IWPUzp4vjfuFikLQHS4xzSwcswkBuJpwb5HZc4hyJ85KTr1X8vGTJk03W+4eb0ew178YRuyqBYcdUdYuVqSbkFndaRNyovVC/7wZ3/IdXdNFmb0y54maVgtVxRxwatfeUVrtzhfOpReOfl8vZpZPExJzZOFcDKuZH5E64SscjXluifjHuxmAMiojZjXc+brOZZtQQCpm7ILd6xP1/S/1EMIbdpSvC6Y5TP8tU99WePUDrNkNnrJOgtJY4g2wlmWsdvtxvFmgrvObjHf1qa9BXlm0zRYry2OOCJrMtI+3buvnmvQRkKHEOW9BfInY1zImfzpzCSpz1elQ35jwF3K+1wOM56uFUcXMxYtHSICdGyHb//k2/ziz36Re4t747tCHc9h5+z4o/t/xHF4THKdUFkV66M118fXPPulZ/xg9gPsyMZzPZb1ktPylPnbOUe7I345/GVO21OiNMK+tnFW7974pN+gY1nDkmMBbBlQ6/UaeBcS0cqmJ0V1+6SN+rMGHhN0NBsXIJQBq1+xZwK1DE7dB3rw6n7QCqg3ZZJJLr0sfT6fM5/Px3egBn5A7MR4vYdd2liVhbN1hj3D6WmtlsqtKIKCLBx2Csy8jNXZit1uR2zF3H90nxPrZEhRbGf47bAlscXtHftMPTPbJZNepn6aL/vQ4ZmpvW3Mga1/1yEhfdxkjeb96IGeYWXuJmD2egY7OOBg0DXPpekamMH8bE6+yXn45UPiMmbNemyreHSiE6IL2+12TGMUsNPh0qnMJ63r0gYzZCLf9xYUNsMcTdAObP/QHdZTBFFA7dY82z4jTVJWD1a0v9DixR6t32JdW3SvO4LNMP8REo51MudqRJ769ZICwhJi2tsYzdBtkUGe52y3W/q+H18AP+VNy731WNVkDNgLu4jRqKqKt2/fslwuJ+eP9FqU95VvDLhrN0g6RoOFDjtIMTtNH9fX+8BLygAAIABJREFUw7vl1HJv3/X3XDuxpHEZ05fDsvf0IAUbkiLhl//kl/Fzn1k04/STU376Wz/l8/nnFGHBs8fP2Hgb/kEwrOUK25A4i4l+JeJB9YCT7Qln9RnzzZxlvWTezYfJxtIZ9+UQIJSBpV/CoD+brH0qpiwDVq+slKIHpIQu5N7ym34LvBnn0/Mad8W0pZ+09yH3adt23DNDQihiUORtQ7LoRQ8q3ccxMZTQF8P3pm24uLjgfHWOt/R48p0nlEnJhXvBF+4XVFZF2w8TYSNbrOcj8Lu9uxfq0R6hBnYJ+5RleevlH6JfWn+1LDSw6XCOlq2ArByXQW+Gd8xwo/QLsEcm4mhIAZzHc/5S9pd49vIZ19vrsY9lLxM9VnTsXXSv7/tx6wLRLdMz1vMiYhD0/U3mKm1MkmQMD4psRG+F9AAssgXBxfB813OZLWf0Uc/W25IuUlZnKy7CC1zPJWojkmJ4QUiYhcMcUPtuS2Mzn163W2cPiTwlLCo4IfvmS3aa7jNNfEQWck8pptzkPvqlK5eXl3z88cfMZrPbXt3/nyZUzfCKzpXVrpP8166gaQDkM9xm+jqLQGdBSLxrvpyDBX+x+4ts4g0fNR/xgyc/4If+D/nnf+Gfk5c5R6sjjqNjFv6Cs/KMT55/gr8eVkkePzgm9VPeuG/4aflTnvZP+fLel/zo4x+Rezmd1Q0LJuoZh+khy82S6FWE9cxivppTNzVXzRVBF1A39bsVl2pSSzNLzSymWJ4GaHi3B4wevDoGL/KZuudU2GwK2PVnGSzCCuVFEvInqZFidOU1czozR4c+tGssfS/PcmwHj+FlIg/qB0RlhNPcTJYxvGCloCB1Unbujqvwii/tL8ntHPohTTGqo3F75qQdNnzrix7LvomR30yILZfLPW9EZyJp1i//5XdtCMSQW5Y1ftYgqMEcGDNKzPCilrW+vxjurus4Pj6GDjz33dYXwjilHWYfin5MjUutS3KuyUTlXNMwy/kiL3m5hgCovrfW8zAMx43xLMuir3u6siMhYX5589IW28IJHbqkozvoyI9zrj+6pgs6qqLi+otrujcdzpWDs3XwGg/L3t9XSCcv2LY9btchxlDH+80+1/ovnoE2fKbna3o8msBqL13rhgb2qeiGLt8IcJ8CCtMKmgxFztODYSquNQWEnufx6NGj0ZJbloXt2vyro3/FKl7xV9q/wol3gm3ZPNg94Du77/AH9/+AH4c/ZrFbcMghx8kxaZ7yk+gnlElJHdREcUTohCyaBcvVkt98+Zvc9+4T9RG91VN4BRfBxfDSgegNL5OXXP3qFcVvFbRei9Vb/EPrH3JQHHB4fchsNePg8oDkOmG2Ht4z6ZXesEKwu53rbxbtImqvR4ep4N1+N1oZTTA3mYLJUMxQh4C51E8WGY1xd89jPp+PQCev5JM4r76fVmwpmjnC/ra72iC07bCzpGu5+J3PrJlxr773Tvdsi7qrqexhE66Ns2EVr3jhviC3c3bxjiqrSNqEBYthz57mJie994YYsmXv7TmvB6EGt6n4vF5ubg52kbvejkHCCrLK03yphp6TsSxrfFm5Zb3b1reqKjzP4+DggPPz81EHdrvduAZBZ6to4iTtkX6V/6ORVYkGensJ6TNzwnK32411N+fMpL9NQyH30S+8Fr22exu3cHHOHdzVzWIxqydrM55vnvO2f0v2OKOZNVi+hWd5w+rba48u65g5M+q2xrO8vSQKnVlm4pC5e6OQGh1S0XgkMjMnSrVHpIFdPutxpsf3XeUbAe4awKeskgZ23WAzvjUFPsCekomAfN8nSRKurq4AaLqGV80rXlmv+P3F7xO7MaEdMnNntFXLol7wnT/5Dmefn3Hy5ISoj3hjvcG3fHbejuv+GidzOHAOsHqLrbfl+viaV4tXdG5H5VTD8vXK57g+5tc3v863v/w2+UVO13ZcHV3x4pdeYD2xcHD4/PBzNg82w/YAdofbuMNLmXcJ8/Wc5dWS5dWSxfWC+XZOkicEdYDTOOMboKbkYhoBDTRa/npS0RzMIl85ZrKWrutGMO+6bnzXqwlqkm0hgKzTTHXdzD8NhHe1T84z+14G6kgebl6q4vUeUR1xXB/f3Ghoy+Xqksv0ktqvqWc1m2TD8/g5pVPS/r/Uvcurpkme3/eJ5357b+eamZVZmdXV1dO3QW0hMSOMhMFo4YXQTjtjGYM29sLghYX/Aq0MWhkGvLDAYBtssBdeaDBIg5FmJFwzmu72dFd3V1dmVlbmyXN7L8/9Fl7EiffEiXxOVg02phRwOOe873OJJ56Ib/x+399NDLijKiY962fMhzmLYUEyJoSEOO67bpL2RmWm4NX/m7SkKeToZzL9wrUf99nZGVIqjwwNrCbNpYFer4e2bVmtVkRRxIsXLxBC7NNVmHmAbK16ai7Au7V19X30ejObeV1tg9FOB7qmrDkm5v30819dXe399825axpC98LGKAnHkEW9oD6rVRZKx8ELPEQi6Oc9dVazframPCy5mF0QiYi0TknyhGAb7FMvCNQ7yPOcsipZzBesVqs7LpV6rPUzmo4H5vPYGGdqARrrTIPsX7Z9K8Ad7r5w86GnjKVThgQTzPX/ZtPUANymKPV9f1+T1JUuf/3tX2fz5YZ5M8cPfOqw5tVfVSHHj14+4mX9ki8+/IIyLZkPc4Ix4HH/GIBfiV/xPHuOUzkMzkArWuqoJhszwj5kVs8YhxFndGj8htez17x5/IbisaorKZF0y47vb77Pw+1D3I3LsBto+5bKqdjGW7bzLdfza3bzHa8fvKaKKgZPGaKC9qYQ8TZjdb1idj1jfnED/EVK1EYqk90o9oZFs9mSurnh2hSA/b7s8ddqpeMod1DNp/q+vwdwXb9TA7vmdG1AsCe1TUXZVI1NS03135T2baOwqX3IUeXr8VufRCZEIiLsw1v3VyFVxKbfUAaqDu3r6DWt1zK4A4ETEMlIgf64YCmXzJgRylBx/A5cOpes5AqXu7yt+Vy2BmaWKtSAZvql6zFN03RPvwB70IyiiM1mQ9M0pGnK0dERb9682W8I9ljZQXvmGOn/TarMtG3Z/bc1F9OgG8fxPmuiWS3K3MD1BlZV1d4GYM4R0/hocuF63EzjsJBC+eZfhYRvQ7wXHtksY3GwIFgFtFlLnuScf3iuqqtJSdiHRLuIq/yKi+yCH7z6AQfi4B380X3q+35P7diamZ7zdmS21mrMtWjOZ/23jY12+9aAu/ny4F3DnanOmDubOdFszn3qOp7nkaYpSZJwHV/zs0c/44frHzKLZlwsL7j4/ILiK1XEebfYUec1v/B/QdZmvHr0isiJ+OTqE076E/zOJ4gC2rDlg+oDojrCHVxqv6bsS2QviUJl0On7HulKWqdFOpK2a+nHnqBWaVsDqYx90SLibfSW0i1VNRnRMchBFRWuQ5598Yy4jPFKj67uqEVNFVfky5zdckd+lPObp7+h/lFN76uwd7/1iaqI2XbGbD1jdblivp4zX89J85SgCPB6D2d415/WVh/NiWZPLv0ONG/Ztu0+SZKWUBeLBeOoSp1pDhXYS22m6m0am8yJbYOdBiAbNOykZrrfWsrSx5ubvm2El/I2U6WmmUze0/OUMXbmzlh5K3V/AcIViEDQeA11UHPpX/IqfEUTKk3McRx23o7O7/hh80N+r/q9d9RxO4jPfAZ7DEwgiKKI1WrFfD6nKIq9d4W2b2hpX3+nc51UVbUfE/0eDg8POTw8vFNb2FyvGrzW6zVFUewlfpvqs6kEE8R0yci2bWmaht1ut3//2sNMU3bmc5q0jD1n9Lw1JV/t3qnzPWk7hRBiL4wIFJ/v5i5JkRAOIYccqqCz0KEJGjb+hvOPzhk3I+VRCZt3U32b99RGVT0vgTtR63a8hJ53et3oNWAarW2Ndap9a8DdplhMSWCKgrEniR5I81hzoZpqjo6w/CP3j3g1e8XmOxsOnUPOl+d89vQzHv35I+abOWmR0vx5ww/XP8RtXOR3lQrfez1nh2fIA0lJST+oHCtpk7KQC5LrhPqqJt/ljNHIVmw5b8/popuyX4HKM+5KVU7PxyeZK4m2dmpl3Zeq0EAwBCoT36AMg8Ws4Hp1TU9P6ZaM/QgDhHnI/NWch798SFRHeIMHHjSJSoW6PdqSL3PenL7hi4++oE1aRfcMHmEVku0yZusZy6sli+sFi/WCLFch47ruq5b47cVqjrfm2nX2u7qu914Di8VinzBJRwObaqwGBJPvhekMifa8MeeByXWa97iPXjKvb7o4amDQG5bpvTG16ZmfmX13XWXsDZ2bwCoHpCcZZgPPHz7n7PKMX5a/vMPRm9qNljZNbxv9vNqbxayCtFgsAHj9WmXbPjk52T+jlgg1aGpXW53zx+S3zVQb+jPTnVfTKqYDhE2v2u/AfIf6GbQrYtd1VFV1x1VRA7zpLaY3Adury3yPtvZnCywmYJranUmd6WRr+3k2glM6JCIh/TQlX+Uc58dEye2mYxuhgTvBXibw2+Ogq5zZBmX9bPpnah1MtW8FuE/RADaA6+PMhzY3ARP09fm2SmgD/I+vf8zxwTHPymf80yf/lJ9mP+WvXf01kmcJhV/wNnhL4RRsvS2lXzJ/NWd1vqKVLd8rvseT1RN2Ysfj7WO29ZbNfMN2tuW195qvvK9Yn67xXCVht69b/Cufw/KQqI8Qo6AaK5qoIThW6lnVV0hHqgLU/Yjsb/K/9zC4ClQ6r8MdXCSSrMwY5KByY7iCYTVwfXBN7/eqgLUzIgZBUAfE65iD5wdEpcroiAN90FPMC8pVyfZ4y2654/Xj1zRxw+Df+B3XIUmRsFgrfn91uWK+mTPbzAjLEK/wEJ1g7G6Lk2gg1B4QGtx1wIZeNBqMNF1jSum2RKObLb3rz+w5ZHsomOeY82iKlzfnljlvzMVpU1S6HzaQSCnfkcD1mMzOZ3z/+ffxRo8vxZfvPKNpYDPHxL6f67pcXl7i+/4+bXRVVfuNQds7NHVjGmf1BqE9QsxnN5/BdB80fcTNPE0agM2IUXNjnfL20cZgnW9Fj6/eMMz6BldXV/v3pueO7sfUXLE1e/N59RjqMdXpt02qxARZU+o3o7k5ub2XaYA2/eTNuWm+f1sw0m25XO7fgemVNfVu3te+FeA+tSBMmkU3czB0m+J84V1pT9/HVNtX/Yrjq2OSVULohMyYcZFecNqdMjojy3bJEUdcZpcUsuDt7C2bZMOXyZf82eGf8Sp9Re3W/PjBjzkuj6n8Cndw+f3t7zN7NUNsBamXsqk2/Pbqt2yjLcWy4Hp5TeVXdE5HIAOO42P82ie8DgmLkNPDU8quJBc5W3dL6alEU53fgVTZ+dzRVRI7IdKRShJkYBADfuczDqoI9+goyb6OasqwpPM7pCsZggFncPA6jyRPePirh2RFRtAGuNJlDEaarGG33LFdbdkcbPjtd3/Lz//Kz+l9ZSMI2kClft2kzC/nZOcZyVmC99aje9XR1q2qhekrtTpN032UrvluNCWj35spoep39T6NweRX9XywpbIpQcFcKOacs8POTf7bjDq0qYYpLyPze3Me7hf/IOhl/86CNeki/bnpc2/fV3PWumi1KfGaaTD0ptt1HU3T7L8D9kmvbOlS36ssy/2GrUHRvqbZH3OTnXKE0N4n2hNFe/GYnkD2+jY3CXOs9H3Njdv2rtLH6nerC9Bo25DWlmaz2T7FiNZg9AZialA2wNobvgncU9SU+Z3+3Jz32lPK9P4yN8ava98KcLclJfOzqeOm/rY5eVPSsAda83zDMFAEBS+CFzSbBtlILrwLVuOKpEkUZSFD4jKmoaETHRfhBV3Q8VH/EbKXVH3Fs/YZjdew9tdcR9d89slnvPneG6JGVbZJrhO8lx7Z24zlZsnR5RHe1sMbPJZHS7LTjNfyNW/6Nypv9MkrWr+FFpzaIcojDt4eEFURXucxMKgUvFFDkzS0UUsnOlqvxREOozciOgEjuK3L4A14ePROj9/7yE4iG2UwHBioRMVuvuN8dc6YjCoU3IOoj4iaiOM3x3z4mw+JqghnUAbjOqzZLW6Af7XhzZM3FD8saMMWKSSiFXhrj+AqIDvP2LU7Ffjx2mVZLXF7F6dS73uxWOyNq7pNgfKUtKybuZA1j2xu5LbEby4SU7q054xJC5qL1KYKNRjb1I8Qt7Vmp+gDuw/2c5nXsgFKX0v3S9NhOs+O1oY0t24mzdLeTHZiqyRJ9utD31MDjJl1Uo+z1hK0PUJXFNNgZAtZGvg1uGpg1/2r65osy/b1frXHj5kyQAjlWqvdKN83Z8z3oO9p4oHWIHXfdP+CINhL73qTNIHX1PbsPkz1yZ4TU+936t3rmrZT5/5bQcvoZkpoutkqldmmFr8dAGVedy+9yJZ6qAnGgJ+lP+Plm5c8/+A5bu7yrH4Go0r1ObojvdszuKqsWtM0DO6As3M4254hY0nsxXzqfYovfcZWaQOtUNfPZMbJ5oSiLVgfrDl/eE49rxm8gdEZiauYw+GQ0/wU/ysf97nL6uWKp189ZRktVeSeoyrD77Idm+MNZVoyuKo0XNiEhHXIarMiOUsI2oBhHGi8RuXcjgpKV0n9oz+Cp1y5XFzczgWp6J9WKDfN3ukVZeOD53o4vkPv9GwONlweXjK6I6M/IpE4nSrlNruecfzimHAXMlZKirvoLsiTnOq0on3Y0jxr+M3Rb/hZ9DN6t1ebZhOSblIW+YKPho84rU85yo9Y1AsW7YKEBDEql05b+oN3YyFMg6Gdz2NqQZjgbEt+5rGaGtDNlO6nPLnM87TAos/Tv03p0wT1qec0zzXpSN209FxV1V6y1scWRUGWZRRFsQdyE9jNXEW67xqUdW4e039fS61T9JuWas1NxzSI2+Osn8vUhvR1i6IgTdO9W2SapvsyfHpsdDFv0/fc1IhM47g5Zjotgo6gdRxnn6XRfJ/6PdvGd1sQmBI27fd6HzNhj4X5vRkvMDWvzHPva98acJ+SzGzVytwl7Qlvf28eYxrGKlnx6UefQga/+5vf5fDlIe1xy9/4139DSVlJxy7YUbgFhV/QOR2d3zEwMK/mZChXLS/wGGsF/utkzfn8nAebB2zjLW+zt3R0OLXDi6MXtF1L0ze4jUuyTZSHTOOT+AlJlnDhXrD+cE31OxV1UOO4DpnIbo2cl0sW6wXHb46Jy5igC3CEQx/01FHNbrbj8uCSMi1pvZYR5RcfFgpAj86O8KqbPNr+QJu0NFFDndQ0QYMMpAJdqcr1ebWngn68AOEqKX5E1aqkgt7t1bXcgTzLWS/WjIw0bkNbq9Bsb+fh5R7Hf3rMB599wFF4pAx2/sBwOLCZbbheXNM/6vk3s39DGZQ0XoMjHaI+Yt7MOSwPOSqOOC1OOcwPWRZLFt2CuFPl7OjfBVtT0p4SBsx5ob0pbOlOL2iTjjGvb17HjLi1590U76zntDmv76MQbfXdfj5zo9LgbdJSwB6kq6oCuJNx1Oy7vp8Zdq+1DnNNafDUEr32U7fdDvUzmM+qr28LXhpoTY1I0z/aTqA3Fn0fHf5vUj22P7kNhtoeoLl9swqS7qf5fPrd2EZymyKakrhtLDM3a/Md2te2KRpg7zWj72ULDPe1b1JD9QnwT4BTQAJ/IKX8x0KIA+B/BJ4BXwB/T0p5LVSP/jGqlmoJ/H0p5adfdx/dWXty6MGwB3OKj9dtisfaLwyhAop+/eTXbOoNs3rGIljwq5NfkY4ps2HGvJpz3Bzj1i5iEIzOSCUqzqozullHHdc0YUMf9chAsvE3eJ3Hq0z5xGvuu4gKHBxVhPqmOEUXdQyhooN2wU6Vsut7RCNwa5f5+RxRCg6iAzzfo/VavvjoC5qwoUoqJBJv9EjzlPlmzupqxXKz5ODygMcvH+PXqip7JzryIKdICnaHO+q4po5rdEWgpE7IyozjN8f4lY8zOHSio3RLykhF8I3zkT7s6f2eAcXRO6NDXMXopFWDUM86MMAITdEgpGBkpFt1VKcVZydnvOY1vdMTiIBgCEiqhMdvHvNs+4wZMzzp0TkdZViyyTZcJpecp+d8dvQZ//rxv6Z1WwZnIOojkjbhoDzgoDjgZHvCKl8RnoVs11viJmYQA5JpKcqY1/uFYgODDT4akDUImHNPS7D6O/MeJmja83FKOzX/N+9tn28aWc357jjO3niq+drdbrePPp3NZu/4T2uqwXxGcyMyKSCtwWjQ1UAdhuE7KTJ0s+kL7ZOvv9PUjH42c5PUfL4JtPZ70fc0DZhmMwFen2tGBZveUaYdwdxotAfL1JyYeodmH0xtzP7O/N/UcPS8GoaB2Ww2yVBMPavdvonk3gP/hZTyUyHEDPi/hBB/CPx94P+QUv4jIcQ/BP4h8F8C/wHwyc3P7wH/zc3v9zZbvTEns+mvahrK7Ik0tRB005M4qAIOmgM+4RO++MkXLH++VLmvt8otsUxKyrSkP+j3UjA9hG1Ic97wKHiEs3GIhohABLi+S/Om4Xp5jT/67LId18k1m2BDHudceBds5Iads6PObugR5yYJFZ6q3dkr3+je6akWFeNqZB2s8dIbt7heRbaurlaEbUjYhQgEvdvz+vFrPv/e5zRhQ+d1SmKvQuZXc2ZXM+bnc5LLhOPdMXEX4wsf6UnapKVICoplweWjS+qwVpJ5B6IQZEXG7M2MrMlwB5de9jS+kvbrsKZKKtqwpQs6FYQ1SLpNB2vwG1+lrPUE85M52TajE8qQK0KB8ARlVNItOn6Z/BI8GJxBBQxJn3k35zQ/5XsX31N9Hnx6eqqgYhNuuEwvuYwveTV/xWfHn7ELVYa+fuiJyoh/Ef0Lng5PeTQ84kn7hJP2hOP2mGzIiEeVplkg7mSJNOeQ6WdsZ/Uz56ieaybAmIKJTa/Yc3LqfxvE7XtEUbSXljUwmTSUSU/qz7uuY7PZ7NM92BQC3DVG6vtN0Zv2/6a0q78z86rYkc763hqYtUSqA5LsHDr6PuZvc3z031M0ljkm5qZhPoumhex+6X6btIjNFkxpXzZlY86nqR/dzFwyet7N5/N9ST5zPvx/JrlLKV8Dr2/+3gkh/gL4APi7wL93c9h/B/wzFLj/XeCfSHXnPxZCLIUQD2+u8777vMM56sHXn9m7tLkh6GOmwN0cGCklx7855vzwnIPFAV8++hL3ysXJHEpKcEB4gmiMCNuQJE9I+oQ8zvnF939BJCOSMWHjbhjcQQUgjQFZm5F1Gc/aZ0RlhNu7NF3D281bvrj8gtfta6q0ojvpaFct9WFNESnpXQNzE6pwfSkkzuAgekEkb1KKOiPb+RbpSAZ/UEZTKXAGh6iKSIuUtExxOxenc+i8jusH13z58Ze0QUsbtLiDS1iHzDdzluslq2sVzHT85hi/9NV5KF/8Iito5g2vTlTgTeu1iEHgdz5xHrN8uyQuYtzWpes7tv2W15XKid8mLeNihEx56eBAMAZEvRpT3/Fpx5ZsnuE6LgiovRrhCnqvp/Zr8jDnzewNndupjUz0hH1I0AcsqyWPNo/Imgyv9xiHkcvmkj8++WMuji94Hb/mIrngX7n/aq9Z+FLlkD/sDzlqj3jUPOJR84iHzUNW9YqgDGivWorrgrpUUbXanVDnw9dz6T7O1fw9BU73AZBu77Mt+L4qdhHH8T4bpZ7X2lXQnu96zfR9T57nd/pggrFJbej1pQHWBlTz+XRMgqaEtJFT1ww+Pj6+Y0DVNE/TNJyfn9M0DUmSEMfx3gCuf0z3UXtMzPG1gd22pei/9TiYWof5bnSUqD5eS+v6e20kNuknUyuxpfUpTcxmHMy+msAuhCCOY46Oju6U4zM1za+T2uEvybkLIZ4B/w7wJ8CpAdhvULQNKOB/aZz25c1nd8BdCPEPgH8AcHR0dEcVMgfHlIbg1vf3vl1rarHZk/0ivaAtWsZw5PDykPR1Shu0pEmK9G8GW440olFRhE7H68PXFPOCn0U/Y9WsSPuUeafyhUdDhOM6rL015+45tVvTyY6hHegSBXzuucvh+SHx65h5OCeOYoq+YCd3VFHFhbjgMrxkl+4olgXtvKVbqnwmZND5HaM7KnlTCJVKQKrCFX3YMwQD26Uq2qyLWjijQ1zGynB7cYjburiji3Qlm6MNZx+cUSSFMhiLkaiKSLYJi8sF86s5i6sFD37zgCBXEaxSSPqwp0ordumOtw/fUiQFrWgp65L6vMbZOcQXMf7nKlfLYr4gXIb4xz7iUDAsBkq3ZMgGiqzAD3zc3iXuY+IuVul3UVqMptAGMVC7tUq+5tWcZ+d8NfsKgVD1R0fo6o6mb4iLmHk+x525fJx/jI+vjNL9jp3YUcqSX4y/4E/5U9pYbUICgdu7hHlIsknILjNmVzNm5zM+EB/wJHxC2IbKCG3Ms2EYKIpiX9xcL3ibfzapHBPAplzbTJA1z3/69ClJkuwjQbXboo6y1L7m+jzT0Ng0zd5jRt/DbObaMzVk3V9bkLJB06S09LiUZcnV1RUHBwf7FL6aKtIFM/S4mN45Qoh90I+5fqdoCRsPzLE0pW1tk9BuuKbvf1mWe4zRNJAeY/1+9KaraSQTe0ytxPTvNzFoKqWGOYa2lhZFEQcHB6RpekdK1z/3uWLa7RuDuxAiA/5n4D+XUm4tKUQKId6vI1hNSvkHwB8AfPzxx9JW98zdyVRH9KS1jwfeGVh7QPTuP6YqwMe9dhG54Dw8xxs9om2kiv82PqK/4dq9ChEIjrfHrB6t+MH8B3jCYxTqGm+SNyrE3K3pnR5XukRdRFbfZA7sVBbHIi3YrXYM8UCUqURO5OAUqqhH8DbgZH3CQX5A13aMjuK7h9mA98gjfBJSH9VUi4p6WdPOWtpY0SKDGBicW97SGzyQ4I4ubdzSRi275U75wyNVSoLOx299ZtsZXq1KsUkpGRnZLXdcPrikSirlFy8kQROQ5IkC/csF6XnK4YtDTjentOuWs7MzSlEyzAe6w447T0WEAAAgAElEQVTmqaoOX85L/NgnEQlLuWRZLTl4fcChd8giXTD6quJSG7fk8W0B6dZVlJg7ugRdwLJZ4o8+jnT2z9s5Kptj4zQUQ0Gap/SypzgoKOuSzXqDG7o4rsNYj8hK4jauqjvap2ruOMr7pw96cKCLOy6fXPLq+68YvZEhGIhG9R7nxZzFbsHqekV2mRGfx7R9S+3VqjJQkamEczfpaYUQ+1zl2pBpRuH6vr8v3mwCvrnYdXvx4gWO43B6enoH2Pq+J03T/TowNVxgHzFcFMWdyF997s3aNtf5nTVk0jz3aR7axRJUzIJOVGbaAPS1tZvmbDa7s051VTKzmpM5FlMah90PfQ9TwtVjYFZY0hKy67p3onJ1ojtdd9YEbj12U95P2ihs4dudzcE0BsOtLUKnVdDzIooiFovFnkIzr2Vv+jbVZLdvBO5CCB8F7P+9lPJ/ufn4TNMtQoiHwNubz18BT4zTH9989o2a2Xnz/ynpQf891UwV19zVHcfh98ffp1yXhHm49/VuaVV6gFlD/bBWgUNejegEfuNz3BxzcHnA0/EpoQj3KXxLXx2nc8YA9LJnkAOXySVrd83b9C31UCMQREPEalgR7lRBYUbY+lvyhznt45ZWtIzDqAyshYu7cXFfuqRvUj6MPyT0Q7zQw525MId23rJLd2yzLZtswzbZUsbKKNp5nXKbRDIKReNIJO7o0ntKMq7iam80dgaVHjWoA5zeIVtneI2nMuJJkK6kjmquP7mm/XFLnagAGNlInDOH4Cwg+ioiehuRvcw4fHHISXzCfDbHzVzkUpLHOcVxwWaxoc96HNfBE57KqtjNOd2dknYp/uDvKaJdsGPn78jDnDzIqdyKnh7ZS5zSwakcwk3IqlkhB0l/3iP9W/qqj3uVzM1tGf2RJm0YwoHRHfdcvzu6Ktd+n+F3Pt7GQ0hB4N4UnvA6Bmfgen7Nm9Ub8h/ljHKkRZX/C8uQky9OWG1WnNQnHBaHzPoZV8kV4kvB1eqKh3/xEK+/Vf81KGjJVs/XmzV3B+BN6VJ/r9MG6Lluer+YkqspuduagekdYtNHJoDYtgS9pkzvE7PYiymtaqpBNw3gZp1fKeUdV0uT3zfB3G5TErCJJcDe40b317Qz6Fw4JsUSBAGHh4f7XDb7RGMGyGuax8QoO6rWZgy0+6kpcDZNs88qqTNj6gpMelMwhda9gDphz7HbN/GWEcB/C/yFlPK/Nr7634D/CPhHN7//V+Pz/0wI8T+gDKkb+TV8uzlAX/e/yfXpNgXkptuQOZg6Km3pLBEH05Zr0QhEe+OGJHsGb6CdtxR+QemXrMM1jdfQC+U+GA0R83pOOqT4o08veyoq1mKNl3sM24GqqXD8G/4xkmzjLe1C+bFXQ0U3dEqK37r4174C/gGGbGB7uCVPct6s3hAFEZEbKa8eOWe+m/PB5Qd8PHyMGFVu8t7tacKGMirZZTvlrjhbs4t37JIdbXJjCPVUYrHBu1ngvtoAqrRCjIJiVuyTibm9CyN4nSpiHG9iZl/NaIuWJm8YvEFRQz9WmSvH2YgTOYQyZNbOWO1WrNYrFtcLTnenHG2OWIoloRdCAGVYkkc51/E1LxYvKPxCuWeOniogXc54cPkAzqG6rijKgsZvqKKKKq6UJjNXG7T0VDZHr/dI2oTg+iYxmucow3Wg0jM0UUPv9tThDeUT1IzeyM7bqSr3XofneLieS9AH+J1PUics6yUP24dQQVmXXJ1e4UiHGTOK04LP4s8YwoFyqbJhVk7Fg6sHdEcdH/7iQ8b1DRXkuSRpoja/G1pHg4YGEtOgqxN76cyOOqpSrw+TstB/a/Cs63oPZOYGcF+0rQ3iJpVgC1lmaT4zIMjki/UzaMrF1BpsCVcfb1IPNi0zxXVPYYqmWrT/v+mcYW4GpnTt+z5Zlu0zapr5dMxnN/uk+fkpTl3fR4+7Hqfdbkee5/vzoyhiPp/f0fxMXDKf/5u0byK5/7vAfwj8VAjxZzef/VcoUP+fhBD/CfAc+Hs33/3vKDfIX6NcIf/jr7vB+3g1ezDvM0iYA2F7BJjNnEzwbiCArY7pVKxpl7LqVlAa/stC0okbA2SgKvycx+fUoqahQXZSLfrNjGSdEBOrupZhTxM0VEGlPGH6gG5QUszgDnTzjvwopw97xvCmIHHnkbUZTqWCh7qhYxftKLKCIRqQvpLO3d5VaQFqxbV/8PoDwj6EHpq2UZJm3FMnNUVasJ0piT+f5cqIGqsAqMG5kWzdASnknpcHYKE8fGQnGapBSe69g9M4KqL2LCJ4HRCLmCRLCMKALuo4Oz3ji0++ULnRw45wDEn6hOPymKPiiAfFAw6qA55ePyVqIrzRox5qrvtrvmy/5HP/c/JZTnvU0oe9krhrV6VPKB+yXC+JBxXs0vsqBqCICyXxL3IV3HXjS+/2rrIxNAmLasE4qGRtvae8pBq/oQ1bvIWHM1PBXJ3bUYUVeZQrW8A40A6tCuhqAsbZSFZlxFcxUR8xfjlyFV9xvjqnaRsu0guuf3KNHG/oIVJOghMeLB+QuikH1QHu2mW4HJj7cxazBXKQVFVFVVWcn5/vo0h1JKkGJD0np9T3cRz34G4LQubamlqT5nq0PzfBTAtOmvYw+2VuNPp4DaJmojg72ZvZl/cB3ZTErp9vGAaqqtpHLev+mceZ19Pr36wDaydLs8fJxpL7KCPbG0ZKuef2dbrj+Xx+J/W17W5p9vPr2jfxlvk/gfuu9O9PHC+B//Rr72w0c/JMGZZMKcKUwuHdyazP1b+nBmFKGrHvbb8oe0CFUK50wRgQNAGz5pZDHMaBbugo2oK3+VueF89ZB2vW6RqZqjwwjCALSXAV4G98KFWR4C5Qm4Uf+/Se0hqEEAhf4CUeTaak1SEZkLHE8z0CArI6U9XhuwC3U7TLerXmrf+WwVUeI6ITiEoQlRFxHXP45pDT56eIVjCMA73oaYOWMlTuoPki3//UM5VauIuUZtDT08kOGUmIlQEUgfJ86WsFzGNNJSrCUWW3DAk53hzjji6Jm+B6yrjbuz1fLb7i10e/Jg9y5TaJJG1SonXE7HzGweaA0+qUH9U/IttlhDJEeIL1sGbjbSgOC9bRmpfBS+XCKkeCNiCqIuLrmOV2iV/4jN1IQ0MZllRxRZEUKm5hfqN9iAGv9RT4XqestiuO0iN6R/n6V35F4zbkQU4pSnbujkEMjMFIERbKtnGzGdJBERRklxmrr1b4rY9XKZrLwcGLPMbZyC8e/YIH4wMu+0uWYsl5fE6QBWTzDK/zcGqH4WKgHEuCMFDCwNjtpTsNAvrvKa8x7aWiXQ5NSXIKSE3u2qQCzHVirwlzTU7ZEWwg1D7u5rXNdWe6UZpr3jze1jRsTNFRtMAeNM3n05qHCaZmAjEtUWvNSmtS9nNPBbHZ46rfi75XHMf7MoO6ULwJ7Oa5U0bU/9e0zP9f7T7/W1s1BN6ZkPDuSzYn9tSA2JuBbRAxJQm9GKYs4eY99huFZB/iH1cxy0sFLPsc166gHmq2/Za1XFNEBc1BQxVWjM7IKEdEJfA3PuE2VGCDS7yK8Q48hmxgSAbwwfVd9RO4FGHB5nBDG7UqmMoZ8AaPuI5JioSkTnB65e7YzlsuDy9VYQk5IHuJW7t4uYpszc4yVi9WOJ2DHCWjM9I4jdJOOGeTbKiPa5qDhnbV0s27/YaDAwTQxz2lV1J4qtC4cJV3jzu4zIYZvvRVjMGNETpuY06uThR90EmIIJknNL/TcOae8Sv/VyrHj+yI25h5rYK4ZlczDreHnJyd8CR/gqiUcawUyvawSTYUWUF1WCltyBlxW5egCAg3IYcvDwmrEKdVeXOaqKFNW+p5Tb2oebN6o8ZJDIheELYhy52KGj7MDxUFNPfwUo9u1tGnPWVaUjolTu8gjyTFolALt3CJziL6qme+m6tSju0DrrIrfnzxYw6KAw6vDnncP2Y5LqmGisv2kuf1c5pFw4vZC4ZYbULjOBKPMV7tsWt2FLLA2TnIUkILQ3drBO1kR9VV74D7lIZsc7z2+tKfm2BvuuiZ19LgbYK0adQ116UGPpvOuA/gbcHLBngtJetNwqx6pftrVgQzo2CzLNsnDzMLnpj9N8fFBG/9mf3M+ju9QWh6LcuyvUuozuRpjrXJNJhA/3XtWwPupjXeVhPfR9NM/a1/2xPAvqY+znwJ5o5u83662aqZblMUkrbU65e5j8hrwMkdkjrBbdSE7vpO5XIXA52v/N7bTPnEj9lIM2tUnVYZERcxYR0StAGe4yECwTgb6ZOePlb54kdX9bN3ekpZslvuaE4blU7BHfB6xZ+Ha5WjRrSCgYHdbMf6YK2AEDUpvdpD7ATt65bhzcA8n7PslwztTYSqPyBnEnkkGY4HxGOB99RDPBA0M0VxaCOmRFIGJaMzcpFeMAr1zF7v4fYuaZOSOikH3gEjI7NeBVM92D1g6AbarqUbOvIx56vxK4rTgvZDlVJBuzUmm4TkKmF2NiO9TJk/n3NanBJ0AXKUyp8+qalmFflRrtxjb3LcO4ODl3tEu4gHrx/wZPcEr1UeUrWr4hPyMOciuUA8FLRJS94p7jQbMmbDjLiK+WH3Q469Y6QvqbyKy/GSF+IFbz55w/JqSfinIV7ssepXRFcRz6vn/Hr1a/wDn99mv2UezAm7EGfn0Fw1HJfHrOoVsYiRo6STnaKfghr/wOdt95byg5LaqekHRVt5lYfTOmyfbEmPU/7W7m/dMWJOrTd7rtvClb2GzGvY2rVeT3p9m/Yye8PQzfQvNyX+9/Hs9jPo/7uu2wdGaQncBFoN+uM43tGETHC3fc3NzckWNM3cNuazm+Opk7WZ5QWzLNtnwzQ9a2zN55sCO3yLwH0KiG3VUv+eUsVs1c4+32xTGoJ97NT35r3sY03VVf/YeaPhNqRaewqYqi8SRdf0ElEqX3ZP3qYYjZOY+cGcYBEgZ1IV4lhV9FFPF3Q4OPj4xNuYpEkImgB3dBncgSZoaJOW1m+VzzwjA4OiV0RHvsrpT3q66MaffhQEVUC4DQk2AX3e05YtRVBQP6uRoTLAIpXHjrt2yV5kRNcR2VlG9NOIWTIjm2eISCDmAv+Bz3g60h617BY7dumOXbCj9Etap0VEgiiNGGcjpSjZyA019b4geNzEqixaEUKFyirZSbLrTHkB9S6DoxZXH6hgqM33lCbTxi24yjAcbSPS65TZ2xmzqxnLt0uCbYBXeohRMLgDdVxTZRW7j3f85oPfUAQFvdOrHPc3wW2HZ4c8Xj/mMDpku9lSORVt0vLlgy/ZFlsW6YKH7UNKpyQSit/X0npz3rC6WvGd7jt84n3CbrtjO2zJvVxFSTsljWiovZpyVnL24AzhCAIREIoQv/MJ25C4jsmqjHk+5/SLU+ptTdsozy3pS7qgo8gK+rBnO2wZmBaipoDWlDh1m+KVTdrU/k77hZtzXX9numZOce1mf+w+m2B/n3Bn3kODqF53+nomVRPHMcNwW39WF2s3yxra3L7pfaPvPZW8zGQdzGyYmhLS9gBbwDTvZdM1/1bQMvdJ6eb35gQxH96WyO+TRuxJOCXR37cRmN9N9cumgnTTIG5yemaKVfM6euLYL/WOTWGUiF7glR7hEDLbzG4lBJRLZ+d1tFFLEzfs0h3dTOWyEY7AHVz8wictUtzShU75eTe+Sh3chUoSHJ0R6ch9QNRmtaE9bam8inZUhkR35+KuXZzGoT1qKX+/JH+bq8CqGwk82kX4O5/oSlWGci4djv/0mGRMOIqPOHFOlOtnPNLPe/rDnu60Y3gwUC0qyrSk8AsqKhrZ0MiGAgVUQzIwPLgdx7BUrqXRLsJvffzax22V77kjHZR7v1CeQULSxi3nz8558bsvGIKBPuwVVVMGJOcqkCk7yzjZnvDx848J6xBnUIbVylc1bc+Tc15/+JqX2Usuq0uVX38ISduUyI+YP5+zKTZkY8YiXVA/q3n66inf+Yvv8NMvf8qQDbzN3rIO1lRHFQKBN3qM/siVf8VH5x/xydtPEKng569/Tpd1NFnDkAx7N9y32VsVXNb2XMwu6HYdbuUSViFBruw5yXnCd//Zd/n42cd4Tzxa0U6uBT3f4G5gkwmgU2vP5Lft8/S8Nw2nej2Zwph9LX2MGRBmfm//PbXezUyTvu8Tx/E+kldL6nEc36FItEeSLizuOM4+HbVeyyYFZLps6uexA51M3DDdPbuuI0mSO7YJO1+8afOw8eU+bUu3bwW4wzTN8j7J2wxSMI+zOb/3Se76BenBNAHa5BbhFmRt7xrd7IlmWuv1tczkSvqa5sTW93ufZmJOajNibhzHvVQZtiHZJru9llCePb2vDKZ1UquMkIfNPqWx0zm4tUt6le5V+dEZGaKBNm6pwopiKKj6SrlbSuVL3ke9qi7/OlTSfKDSAjujQ7tsVR6XXlA+LZG+5LP+M/ydT7yLia4jorOI+DImuojwv/DxWg9v9Fg4C1beijEaGWYD3aqjXJUUy4JyUVLPVOBTF6mAL+kpw+zucMcQ3Hj6eANur2wfUR4RVqoYitM4hLkqDjI7nyn6amCfirkPeq4eX/Hmkzd8vvqcf57+c8QoiJqIZbHkcHuoyhFuFvx492OyPOPTy0852B3gRR7VquJz+Tm/Gn/FT49+ikwk+VHOcXZMUiYsL5d88Z0veHr+lI+//Jj5Zk7XdwRxwBiOvJq94sq9oq1b2qIl6pVRed7MEVcKeAY5UMuaNmjJw5xLecnuUqWpHhYDxaGqIDb0A2M3EhKqOI7jBn/n4299Bf61SjvhCOeduW/OSS2UmIBrSrK2lK9/my6PthZgrwPbO0Q3u7iLLQCagpEN+nrDMVMlaOEqTVM8z6NpmneoF53iwXw+O92A2dcpg6fZF9PJw+T4dZpq0yCun3Hqb/t+72vfGnAHJl+WCeLmw9iGnvf9Nift1A6omy0VTBmWzGvfd772QtCRe1LKfeizedx99zapHRPczeRC5kZiG4Ntu8H+80bg1R7ROrr9XqpMmYM7qERgmQrkapMbjlyoxGlu4RKfxzjnDt1W2QZkIumznj7rIUalbnBRsyoAGUslYUcD3rXyDHFrF9EI6qCmPqyRxxJ8wEGBrIPaaAZXeZbkCojiPCa4DkheJyzaBc5wEzPgS/qkp8oqmmVDs2wU8CfKXXIMVCTx4A3ky5z18VpFnvoqy6XbKZomKALCPMStXXXPtU+CSsWQHWQIKai9mpGRN8s3PD96Th7m/GH8h8oVVhY82jzi+7vv08Ud49uRB3/0gNnVDDlI6mXNl7/zJd5Lj5eHL2nDls1ig7NTkpnv+YRuiDM6fLL+hIfXD3FbV2XXvAFWzR1vNptbOkDCTMxoLho+ePsBXd+BC2M40sUdRVwwLhTgd0HHRXRBF3c0h40KAhsc/N4naiPCJmTezEnqREVqtyrFBcOtcdIUWmxqUa8tE/zvo1Xsta6P0cU+TA1CUyOmq6S5Fu17mN+1rUpBXVXVPge8lsaXy+X+OTSYm1K3ScWYhlizz2bVpClHD/2d7ps+Tl8Tbqkac/Owx898Jt2H+wRN3b414G5SFbYUPgXuNq1iTxjzeJMPtwdmykvHPM82tpqbiqmaTrlhmQvBNsLYHKRu2ghr/q+P0RNxauPSRiFzDEypXvfLHAMhlB//ON4Uyi5DRCUQF7fP1Y2dyh3jlKzlmjItaVYNfazC9ZHgli7ehQJit3MZh5ExGhErwZipCFH5m1sQl77ixPuwZ8iUh40MVeAR/Q24j6pObB3XlKuSq/AKGUjcQVFBXuEpQC5DFaDUOQTrgHk+Z+Eu9sFZQzxQRzVNrDxgdB6e0bkxOAtFTRWrgu3Jdl+8HFBeKMOOo+GIuI6Jmgh3cEnrlH7sOR1PCbOQnbfjy5MvOXKP+LMP/4zar0nmCf4DH79UP6vtiuQ6gQ8g8zOOnh9xsj2hW3QEQcDx6bGSLKuGvu9ZuSvCWchms9lTCfr9v337du/ep+ew3gDkKBFSUXByK1mK5b5gdpzGfLD5gM7vWIs1u3BHFaogsDqs2WZb3rqK5nGkAn2v9QjKAHfr4l6riGmRK/rIFJZMydNMFGauVVv6NteK7n9ZlneAVku5tlR/nxePrT0XRUHbtpyfn9/JLbNYLO4As674pPMEmYZf8/lMesUUxGwsEeJuZS67ZKS+rk6BfB/+6d/m5jBFXU+1bwW4T4G3vUNOvbwp3m2K03vfPe+T1O3j7L+nrqObKVlrw4mewK7r3smPY4afmxFs+jr33Vefa/Z7yqpuP5/JE+pm8pr22Lm4hEOILCTtpqXf9oSdojSEJ5SEnkhYwrAcaJKG2q/BU+d6rUd4GeIXPm5zU/0pGOnSji7pGOIBJ3LAV7nhe9EjI8mYjPRpjxQ3z1M6ODsHt3ERvUCMQmW7TFRKAUcoMIqbmLRIibYRcakMsPSq2Ejv9/SR+mnSRuXoSVvqWa3y7PsDgaPARN93TEausiu6pYo/GMW4T9EQ1zGH3SGzZsYPzn9Akibshh3Pqmf8zZ/+TY4ujqj8ijzMOU/PWT9d87MPfkblVoiPlMH8wD3gofeQ/qAnJuZvv/rbBHnAq/QVnutxUB4w5LdGQa0N6nB63/fv+KvrzVu/8yiKSNNUpSrwAoZyIPRDVu2KxbhQ8wOpaDun37uBVkmlgD+qyOc51aKifXRTZq+/SXndBkRlRFRGpFVK0ib4na8k/f6ut8vUXDaB0uTktcStn8UsnqKfzRTY9Jw116LWNOq6vpPS16zwZAKk7/uTaSA00Ov1MyUImpL5nbVjrStbINR/13V9Z5xsw6n5Pu/Dg6n2rQB3G7h1s6Vz+5gpdWxqIOyNwGz3DZr5mXme+WLMvthqpp5cpgFVv3j9o9XDNE339SD1MbZrqL62+Sw2xWRLNvpadv9N7cXUUmytxFZ5tc+v1hL0pHdbF/dSGVjbtqWsStVPX0KKKvox62kOG8ZYUSRiENBCcBUQtRFO5ex57z7p6WYdY6Q0AOlIRm8EH2QolatnPCq+35HKh75VBVHaoWVIBvJFvuf/deSun6u0CfF1zOr1imgXETQBsr0ZX3ek95QraRu3VGlF+klK9CRik24ookLl7glVPqGLgwsuxaVK43Az/p3TEYwBv/6rv+bp26ccbY9YlktWxYrT61Oevn7Kdtiy+9WOclXyo+WPiD6O+MPwD+mHnvP5OfWy5s+zP+dp/pRlsoTrW2FHvwtzUzdTCZh0RhiGHB0d7d+d4zjkec58Pn/HU0VIgd/7+LmPKG7pFYmkpycfcrZiq6T8rKZPe9qsJV/lXHvX4KjNPBgDwibEqzyiPCIqbn66CLdzlWbB3WIg5lrRAGcC/ZRGcB9m6O/G8TYqV0vIQog7+eK1wCWl3AcUOc5tckIN7LaHkI09U3hjbgY2zpi2tjAM2W637wixX4dR9tqfat8KcJ8CbhvkzZ/7ztGAeN/Odp96aO/WthQ/9fLsa+q/p4DdBE7zvr7vkyQJ4zju/Vx1+TCdwtVWd/W5U89l9nFKmrfBeur8qcWj729KO/a1tEeBpp3atlUUy86BHYSE+2MHeWPwDAeGbKBf9oynykd/8AbkIHFaB3fjEn0VKal9dBChYEgHukxJ/IM/qI3CExAq4B/TUWWyTCVO4CjQqZW07+DQZI0Cp48UPSNdufcHD7YB8WVMeB0SXAesvlzxne13+M75dxjlSC97arem8isuvAvyRU51UrHO1rw6foXsJY3fgAOLekHhFJTzks8PPmfwBjq3UzaMAfK/knP8+phFvuAn+U/4O7/5O4he8Kh+hDM4HMwPVObIxt97cGiJdioaUs8Vs8qRziyoOWVdak8nxDLn89R82FOCnSrAshyWLHdL3EtlyHdcR9lqglZV+krVT5/0VIcVF8cXtKJFjqp6WNAHRHWktKsqJakSRauVHtS3gCXlbaEUPYdtwWVqHdoAqNMO6LWjKR4TL9q23UvtOqe8uQZsatbWiu1iJOYzmNrG1Jobx3Hvnmmebx5jruWp797XvhXgDu9WeYG7D2BKlfYgw61v6ZRkrts32e3M+9m7s/5+Smq3r2saVE2A18fqiDS4W3Ve14vUE0ffX3vG2Pk3bDCe6qv+bMouMDU2UwZtHcxhg7uW4M0EVnox3qdBIVWpP7d3EaVAnBv3RhlAZSwZ0oEmbWiPW4bZgAiFktIrgZur/OsiV/yyCISK3E0H5EJCCCIWuIGrpPdgVDltsk7x/YHyWXc7F7/0EZ2gd3q2x1vkI2UTGL2Rs8UZv85+TVzFZDvlTx7nMelFSuEVfPf//i6H20PWizXPT5/z+OwxM2aIWJAHObmfUwSFclENVKnAgoIr54o8y/mT8E/4l+m/VAAkXLxBlVCc1TO8ymO32uEmLofbQ+b+nG7dcdVeKbtD6xGOITSoDXNUNgRNx+g5pce9qiratt1TE1NSoL3+9Pla+tXHO47ysHGlS9AEzLs5Tn6TT8ZV2UU7p6NwVUGaKq6o0oo2alkv1px75+o9DyrPjlup9xlVKkDP23o4Owenu9VEzNQBUwCv150WrMqynJSY9ViAqsVqFsWwtV3tnji11t6HNea60lqXjVt6w7nPGGvb+0yM+Tocg28RuMO7O5XNXU/tbDYPZoPvfVKuSXuYoGYPqA1Q90n6GjxNFdksWmxSMWEYkiTJ3ivABGAt0fu+v1+AeiLoH30vUzKwXbHet/FMTZQp8DfdP83JqK9rGpL0wjftCWY/7PdrSjnm/feG3kGlQvAG73bD8Byko1w6tQTfHymjLBHKl79Xvur+W5XHJXZivMhTgV5pRxvfGFXdAelJ5ULpK7BvD5WHUB/1StLvHBzp4HUqQ+cu2rFO1jQPGi7CCzbhhriI+fjnH3Mkj3h6/pRsVMVb/M5nNs6UK+J4k5SMns7r2MgNuZ/Tpi0sofXVfZus4ergCidymBdzrmfXvDx5ycHZAT/44gcsvAWX5SyrkTEAACAASURBVCWucCniApbKftH7Pc3YMA7K28j3fJbxkoqKoVa5ckQpaJ2Wel1DDQ+Th7ijixiV/cIGFnN+aGA1nQLMNWcWqdCbNyM4rUM6psRjfLuuHVT8RKD4/SJW4J+HOfW8Zn24Vu65UuXm8WtV9CVrMmbNjKRKCIoAt3FVxlKj22bh76qq3qk+ZSYA0+AOt4FMoAQtHdWq+2wDszmfzcAjG1f02jDXmC1UmRGz9jnm/ezNZWpd2+1bA+5mh6d2ZXOQzQc2uTmzvU9tuQ/4pzYC89ip86boGnNDgttNShdnmM1md/hDbWQ1eVCz7Nge8IwqP2YfpzYlu79Tz2AD632TRT+XuelqVdful04ra743s5nai/ljG8LNTWt/vREYVNpht3Dx3hgBH45QNEyISsGQ9VTLiiItENlNlRypvGzS6xQvV4FWGmjatFVc/02E7uCp3DzBg4Au7djNlWeJTiAma0n+OKfqKk7WJziFQxEXtF2LEzqEfojv+gRNQNAGhEWo/OzbkNl6xoIFxcsC11GS7iCUMXr80chsO+Ppr57yzHlG+jRFuIIH1QMCJ6C/7OkuOtI63WtSXdfRdipZWpRELA4WLI+XrKs1FRWVrMjHXKWAPt5xFVyxfrJWhmzJngN3Bkflsh88/MEnaFWaY69X/3uDp7yYpOLNJe+m7gD2dKSWRs15rTlsr/MIqoDZ9YyT8YRhHFTCPdGpFA9BQZEUVHFFkzVcH1zzNnq7z7/v9A5RrWIX0ipVcQx5iFd4DN3AZrPZGyrhFnTNKk/DMBBF0Z5S1H3Um4OprdhYYq4XU0s1UyfYx9lsgJ7jU4GNU9q5/nvKJXSqfWvA3QRG3UzVSDc9aWxqRL8Us9nShwkU73MpmpJ+zX7au6+5GZmgrq/lOCq3dZqm+4mk6Rft7VDX9R1JWEvvQog7gSAm926/dH2u/ez2//YGab+HqTGxNQPbmm+O7ZRmYG/AU2NsHmNvWuaYmwtxPw5SKC+aQRCUAcFFsO+T53n4sY+buHRRpzxkFjVd3NEFKgOlGAVBE5CdZ7i5i6jVMxw9O+KgPqAKKxpP1ZLtZEfZl7RZi+d77J7sOPjlAUEXMAiV+0aMKpMnArqgY+fs6Bc9gz/wxfwLnrRPiAtVWnA2qkIl6ZDyvc33CMYA9yMFos/GZyolxVyy2+328982sHquouzm6ZyD9IB+26tI5jakyzuWLAnKAGerzlt+vuTk9ATHdVRsgst+U+t8RV11bkeVVWqMPKkMzq7K/umiPJ98fMIhVODfeXiNymIpasHYj8haqtTXOLiOi+u4+76aXLpAqHoBUuXg97c+s2F2u3F7DoOvkrpVUUURF9RJTZmUbBYblS9JDNCCzCW7Fzu6Nx3etacMulVE53SEhKpojbylD037lLkGzDWm1x7wjYRJGx/0ffQ7swUkDfBmXn973f5l27cG3G3AhHe9RO6TLs1Ffx9Q2+Bm/z91jv2/DfYmsNpSu5Ry7zubZdkesG03tXFUZb3MivFaAtDH6PJfcDcgwpQQzEk49Z0twZuf22Nifm9n9bPHTXPxdyRsq02dM+X9YT/Hfdd838alpUUzqnEYBkStasqGVUh4Hd4ePw6K5/dHxnSkSRqarKE9aen9Hh6Cl3p4ncdiuyCoAkQlON+eq3qgxzkH6wPmF3MeXT5SfRaSXvQMkfKxL8OSJmpUENH8Qhlr4xU/KX5CF3UUUUHhF2zCDZ2jUir7o7/P6Bk1KsiqLmtaqdI/mC5zWroMw5Asy5BS7svqlWW5FxA01dB1Hefn5/uMhFrz8hwPGoiJ9+NjU2jypkwjDipuIYDRV0bs2qlpPeVaWi0qZUx1VM0DXFX20RkdXFz83lcFz0elHfidj1M7OL0DLXuX072UOoA7qCyrMTErqaoX6UR7raeiqDf+hjf9G9qupX3YUn63ZBftcFyHy/GSpEsIi5C0TMmaTGUCjZVhXfYSx3VoU7WB30ddmmNvplSwE6LBXVuivcb0BmN69sBtsW79tz3fzYIo72vfGnA3QVH/mICmj/m63XOKkjCpAPP6JtDaUr45oe280vf12/wfYLFY7K3+2mqv76O5PZ1/Rtd0NMFpHMd9kiE725wZFGEC5fuaSXdMaRn3aU/m+JhAb7qK2dyhPYnN36bmcR+dZJ9vPrsJ2rbBy9689PF6fO1raNc8t1E++e7FTd+QjMPI0+885cNPPqT2alWJKyrZJTueP31O13XMPp9x/PkxaZmylds7uUFkLglQib5m/Yyu68jCjIvvX/BR8hFxr5J+LcvlbZ9Rrp2901M7qtzjzt9xMb/gMr7kYnlB+VGJ27okMlHFywuPrFfpJrqxo87rfUFoIcS+gLb2HZdSRUyfnZ3tk1bpuWbbgOy54IibTV4KRKeoHKdRn83lfD/WJgCN8iZYTKiyhqM30gqVwK4PVa6eXbyjO+xUERQ5KOrLURK/16tYBbd38RulIfi92hDc1lV1ClpBUiTIXHL9xTUHuwO1CQWoVNRLiXPiMB6OdAcd2+9u2SQbvgq/4mXwkrkzJyxCth9uue6uSQJV8/cYVX/AXhcm1aSf1TTmam28aZr9j67jqv3vtdNFnud8+umnd7JSao1T/wRBsM9/ozdx7ZBxX/vWgDvcXeAmwOpmq+PmIrXVdHODmJJWbcpnClCAd8DPBEZ7s5BS7l+i6WVgqlpmv8yNx/xeP7f2yQ3DcC91mbSIKU3ofpv9nwJMO1JuSorX39tUjL62aXwyaz1O9cWmWux3a/fVNmDZbUqt1dc2P7c3W8dx3pGM7A3hTj+Eg3BVBK/bqDTE8agWU9/3fCg+5MWjF2SvMsI8nJTe9LNoF8VxHPFbn+9/9n0O/5/2vjVGtuysbu16dlX14/Z9+d6ZsfGMsWzND8RLCIsoQk54xLGMIhl5ABGTEFlK/pCgiGDxI0QiP5xEASIQxooTGUQw4ECwLCGUgFGEhByP48SZMHYY8OveuY++t5/16uqq2vlxau1eZ/U+fe/AeLrvVW2p1VWnztmPb3/f+tb+9uO8+VJxTLM45VqtiIHHWfGS81WsojvqYnO2iclkgp3dHdy5cwf9cR/z5hzDxhBH3SMcbh5i2B1i0poUq4Omc8z356gNa9icb+Lg3gGG+0Mc4QjT2fF7P/f3C2d09erVFC5k32nfkKm6juVIFNvjesEy5/M54iSiEzuYD8sv1aG8prPFccQ1FBPoYVo4g3rxashpe4pRZ4T91f3ie7N4ccxsNsP23jYOrh0UR2of1QoHNK4Vp4nutNHZ6WCtsYZLFy5hHuboXOmgebWJw7VD7Lf2i+Ocu3189sJncXD5AN8w/Qa8bfdtxa7r+fEGK74ibzgcpr/Dw0OMRqPSogqdd9Bwsuqx2oifX6M4pfaYO5zM08O8Q3UFwH8H0F7c/7EY4z8PITwN4KMALgH4DIAfjjFOQghtAL8C4FsA3Afwnhjjl04rQ0E4BwQ5RumMOfe7tOEEW9frzvQ0f++MHNOlNx6NRjg8PEz3qYFrJzI//c0dBZ8/Oiq2p+uaX/3vMvTQTk4GPswD8i8S53UfblLR9LRLDv31Xi+H5VedsaPKn1Nwn1NhHiyfbdCYtJY3n89T+Mtjm+6cdHmax/3b7TY2VjbwxPAJxM2IeCGvt1VkQ4+QZVmqZ7n+KTIC5rM5pqPiBMvVsFocU3z/+CC5WqOG4XSIURihcamBfr2PrQtbGGwOiheEz4qzgmr9YnJ5OBxiOpniSuMKurMummiiXiv3jxIad/o6GlXd9GsqHz2EDEBJT2NcTNbGWIRiYr041XMCNGOzpA+KG7PZDPe272F0a1SsAmoGhHYoTjltF5vhDtcPgQsoXqayMURoB/S6PXS7XQBAc9bEtVvX0O13EZ4JeP6p5zH48gCN/9nAoD/AcDjEeDwuTRgrSfO6OT5RFvrybP9PO+d/lQv7pGq/i6eHYe6HAN4eY+yHEJoA/jiE8HsAfhzAz8YYPxpC+CCAHwXwS4v/OzHGrw8hPAfgAwDe86BCtPLq1XyTgAJMDmhzgJ0rS5miX9dr6lw0jMP7j46OUoePRiMcHR2V2C6BnwcWsQNjjCnkwnYqyLNuk8kkbXRiaIf1cMfBOgJIoOttcsbOpHVWFsx6KYjrZJgug2N7eY+vgFEQU+NXAD7NwSpga//kQggsWwFJ26QyrnJ2LiutM5mulu1nqnh7tKwc8Ds5caNXe2BZrsfTyRS1aQ099NC4W7ycffNgE93DYrPMJE6K0y+7xa7h4doQX9z4Il6++DLWLq6h3SheibhyWJw/3x610TpsoT1vox3bxYthrD1qi6oj3la2S52m273bsfaX5sF8KJt+v4+tO1s4Gh0Vq38mAY3DBho43k3dbrexvr6ObrebzmvvdDsIjYDD2SHGszEAoFlvon/Ux9rRGsa3x/j8i58v6SFtlzbge1DU3lhvtoFYwHorsw8hlFb/KaE8jchWpYd5h2oE0F98bS7+IoC3A/jBxfWPAPhpFOD+fYvPAPAxAL8QQgjxYWqDk4zcGapPwingO+M7LX8NG1QxTHcqOcMdDodpKDYcDjEYDBBjTDEyxtvm8/mJoa+/sJiGq22is9AlXDl2roxRWajn50xYr/M5ZyUaW6cyqxETyOnIVKYeZnEnoiDsIyy/hwagjFyB25k4y9Fr/KzHJfM31yMHYw0f8JrKVPunSu9Ylvel/u42oIRA+46OlC968FNDx+MxxuNxqQ5hWpy3X5vUEHeORwqNRgPrF9dx4eoFNNYaxfECvQkONg8wWSnO76k36miHNlamK+hOuugd9dA57KA1aaEe64izgnVTT7StbKePaPR/Tl4Kdu4IKIPhcIj79++nowYYzswROJ7Lw3fKUj7U67V68S7kS8NLeP3916M1baHePXZUOontbVNnpO1Spq82on2lv7PP9U9tWrHrtPRQMfcQQh1F6OXrAfwigD8HsBtj5NjhBoAnF5+fBPDVReOmIYQ9FKGbe5bn+wC8DwAuXbqUhJFjMUyq3AoKkuephkWlq5oczbF9dRbuOTlpxYkRTqDwfl5XcGH8lSxcvbYrP4FxMplgdXX1BEPP1UlloE7AnyMw+GiExpQbeiu4629AoWg8K9tZWFVfajm5/mJ9AJSYKp/zNch6JojLwh2C97metunyVODX5KPKquT9oXVzfXcCoY5NgUCNXvtZfxsOh6XjCrQNPB+IbZtOp9i5v4PJeILLly+j0+lgrSX7MWoBtWYNtW4NsRsx6U3Q7/Zx78K9YlNZfQbMgNa8he5Rt3hv76SL3qyH1qxVLA1FeXOPtj03T6JHEPAZyoAMeDgcYnd3F4PBoKSPSk64H0BHotQfOkbuQeGOUe+fnJNRB8P8c38K3No/as86OtOVXqqLqhOn6Vvq4wfeUWQ8A/CNIYQLAH4HwFsf5rkH5PkhAB8CgDe+8Y1Rrld6dmV/ud/0eWeKVQakeeXig/qfn+fz4ozt/f39ZDzseJ4TQaXks+wwbqxQYGfSEIM+OxqNsLm5idlsVtok5ADmISZtozsDNQKWrcyIdVaHqEzdGe98Pk9H0/K6Dy8dzLx/mNzpKRD4vaoXNBY+o+CloM9nyPA0zKXOg5+dNWqZfE4BVlMOtLQvqnTaRwJ0ntzxzLwV1P0MGn9PKn87bVL98PAwHWI3m81K4ScuZVw5WkGv30ujnxBCWv4ZVyLGrTGGK0PsbO7g9sptTOvFAW/1WbHapd6vo75f7EBuHjaL44NRvFuA9SIQKvFhewjse3t72N3dTX3uZCrnmDudDi5cuIBGo4G1tbV0HDLlov3CcvQce7VrlqdyVH3X0RUxw8MvToJUl1U3XH8cO3LpFa2WiTHuhhA+CeBtAC6EEBqxYO9PAbi5uO0mgNcDuBFCaADYQDGx+qC8S4LR6/p77jcmB/XcPf6sjwA8Dph7bjQaYTgclsCBKzFoFFQU9e4cAnIbtK6R9aGdAhUZMV8J5kzMgUVB00MLVCyXWVVYQl8rVpUf60zHkwMz7ddcuCgHng6AlEdulOOOjtdUjmo0Ct56DIQ7RW1HTp9y9dJ2OVN3/csRDS1P9YfOPYEt16c3GimO22w204ottSef7MyFygiqBCHqHePJWi89HpckoB3awBhoj9q4sH/heD/DYi36tDYtXmzSGKC/2sf22jZGzVGxiSoUB7w1D4s3RDX6jWLSF43iBNHZsSzG4zF2d3exvb19giDk8IPXGXN/8sknE+jSyRN01anoZyadrFfyknPUHj9nGFSdF/tdQZ7l5EbpVXiWSw+zWuYKgKMFsHcAfBeKSdJPAng3ihUz7wXwu4tHPr74/ieL3/8wPkSNcuwOKBs5v2sn5hiPg3LOM7qSV7FDL2s8Hqc1w8AxI9chmAI5gBSC0bbS+HQ5IYFXwZX37+7u4tq1a2i32ymGr3LSUYcCsc9ZuIyqZOZ5VP3O+nFugb/pfVV96CCoycNFCkBV7dZrrHsu5FUFxDkiUaVLnlSvVK5V4O3PeV20rmSIGqsl+242mwCKYx+oizr5quSFDoGAprqvczcKOhrG4G5qJpZNPXa90PLTYXF7dazFNWzUNo7bg2L9+2HtEMPGEP1GHztrOzi6XOyUjTEC8+JtYOEgYH5vjuFgiMPGYfEaRRQviJnNZunE0TAt71HhyY8EcJ4fo+v/KXdl3u6kVfZsu/a5v0fWnY2HVhWTFGv0Oe9H1iFHPjU9DHO/DuAjoYi71wD8ZozxEyGEPwXw0RDCzwD4LIAPL+7/MIBfDSG8BGAbwHMPUcaJVGXMaiy8L2dATDmjU4PiM7nJU1/BcnR0hO3t7RPLEn1pEzcn8QiBnBdPw1nrVG8Dn93b28Pm5iaazWYpDukg5CxekwMLjb1KSTQk4wCkz8xmM/T7/VK91DCUuef6Tj8TlFiuOjvm5QBLGfFeX/uucnEgB8rrrF1GDwrLqKx4f+4+H71UOQkfSaru6IRbjDExap0v8JNI2Reep4KM6q622UmAkhTWR3Vc+zs32cjJzBhjej9pjBFxHhEnRdhm5WgFtUkNa3Gt+B0RcxSbniatYhfq/so+9p7Zw+hNI8xbc9RmNcRJRG2/htH1EUIMWP/jdTR3m6n9Ogre2dkphTh1XsdHVy6H3MiQ13PO1MFb7d2vKVnSe5g3f6dj1p2sufQwq2U+B+CbMtf/AsC3Za6PAXz/g/KtKKv02ZlYrtFqdD604fVcJznzVwPIgcB0OsXW1hZ2dnYQwvHBXjrpydl3dgSFn/PeHO7qNTUuvUZmvL29jStXrpRWeeTYL+vlgO+yI5jp6MONUmWiQK9KyuNVfclaFVN1IPV66r1uLMp6FGjc4eb0yf/TiSgAK8PXdrAcla0zbR8+e6jL+8KdiTJmPsNJRcbbY4xpEpA7mmnoBHayR9WhHNBoUqehv/mSS+1zjij43lM9SM432fA5gjqdMR2WhoRcV2OMqM/r6Bx2UL9fRxgExN2I9qiN6XwKNIC4EnHUPUL/6/qoHdWKl6PPj0cT83mxxZ/M3fvER4oaxsoRzBzWeOhRdUNHYmy/94nG4bUvnFw4hlWlc7ND1ZVKDTmnkDngqsozZ+T62R2E501w3dvbS1u6GW/XYS4Vk4ZOlqKORPPX5xVM9KgCdvpsNsP29nZao+tLIJ1ZVDF3TQ5UzsxzoOt9wIm84XCYrlXJ24FbY5A6mQXk15nn8lInkxsJEUQ1T8pdJxbZH95WNeRc7FMJiNbNh+Ta735s84McIoB0/hDj2IwZ64Yof0EMnYM6cAURr7c6BB0x0Yk4edD8aQ+5namaVKYK8NpnVeEz2kWJ9c5icUrkUQ3NvSY2f3sT884c7WG7eH2glHd4eJiepxzZHy6PXPglB9aqB96nqsOqD75BKXfsr5ZLfdV7fC4gl84FuOcAOPfdmZ4aNXByyK735zqiKh6dYxwHBweloZ0qu79cg0NlBxMFeTJ7NRhVABqLGgDPA3nyySdPABGTGkOOgbKNVQydcvQRgTIbXiej3NvbK63i8WG9O2ktn8nr6oCbG8bqsstcO/yZXFvUAJ3FqzH5vIOyeC8zNwLQ8t05eBnK6jgqVNnxd41za10dZBw4ta6c/NffGINW3fTzgLwPqaO+iS+XcnVl0mfpgAnsvDe3WiT9NquhOW+eaDtHG3raahUpZPnaDyxD+8X700mF2rP2h+epOugydpm9ksnVcwHumpyJ+W8OOp50FYQm97Q6fFThuoBjLC8PY5n6lnSgHNcjo/Ftwrl10eqJ0xvshRGx7lTmnZ0ddDodbG5unmDpDjAOoDmj433ueLT9+rwC62QyQb/fT68EPC2po8sZB5MbElAOGajz4jU1em+bAp0/57LwobPXL8ZYYnNaX5eRt0vL1nCQy9XZvjp3Aj0PjyKjVjn4KMXBIwcM7oRyYOZHRqiTVuBXG3C2z7L0uxMRLc+dnfbXaTqjjtnnCLi8EUAa9Xi7vN9ddrnfXJ4+sap11TLUtlUOqp+5z48cuOcawOs5g2FyQ3WvrOxZX4LBjQtkxboLUAXP8AqAtPGBBkdF4pBU173rhKQOddVQyN41Xl01AUYl2NraSut0cxNX+l3l57JUeTsDzU2COgsaDofY29srDXf5rObFa86IcqMH/V3lom0g86rSoSqg8mtZ5if1dADh9aqRjQNKjpwwqaPLGa8CAidIOZpbWVlJk5jMS9k8l0Oq3vA+lYGDtN/jDJ067sCtcuAKGuaj9dSyvS4aLgGOJzd9VMa89XklI1xmTILEewm4HFFPp9PE4judTtIp6oTXj9dyslNdYjtOu5ftUkLIxNG664DriMqgKp0rcHdBqAH55JN2uAKLAxd/X11dRavVSrHHjY2NNAkUY8TKygru379fmowKIaRljZ4vO4GsstVqlSZECIC5SUgaoRu/t1eVTX8fj8e4desWQgjo9XqpXh4/zU38Ofg4+Hlf5NgtjaTf76dVMjlQ1TxyQOL95YakoKIG7M7D2+RMWGVa1VYtU8s5DZhOY3faNi+L97jxqg4zT+oRgPReVC7p4wSrOktdwUKGmusTl5uHBdzW9LMSEpW524fqeBVQKdnw1/Xl6pwjcewnHomrb6jSs1zm8yLuzrCpEjLdnaqETJ1+rj46GnddUweoMmCf5a57O1XnqshtVTo34J7z5vysYOCd7yCu+THPVquFjY2NNIxlWINsSCerlL1zslB3BupkFYC0g02BSOulzyhg6aQpf+Mzer6EykY7eDAY4M6dO7h69Wp64YKW73Jj3r61Wc+Qd3nngJ2KORgM0g7dHBDkFNGBV+vp9+ZGDa4bKuMYy0N5d1ouF40zuwHSMNlWZ025z86iWI6HPPS/X/N6KLCsra2V5EjCoYYPHB9n3G63S7sjc8CtzNhl632muksgzemXPsPPfFZZuI8alYioLPV+BV7fBFiv1xNA62vzuN9ENy0puBL4x+NxIn8c4fN8KNqw91nVCNv7WNuk/eWyVwzR33IjSd53WjpX4O7fFQz0v/7uz6gRk2VevHgR9Xodg8EAvV4Ply5dQrfbRQgB/X4fd+7cSUsYdbgzm81KhwzpOe28hx2vIOfOKMZYAnNVbt1ZmAMlKhDDQMqGCPDz+Ty9lzW3+sblWwVKOSVSkKUz5EFNBwcHWaBSsFZmyfwUbLwMBWrKI7dMVcMnyoRdzgpiCiIeD1Xg0jAbwww5uWj5ub5zOSsDrBo1MV+OGMnaKf9ardgNTb3xFRPuwNQecsfIqiw5onSwDSGUljqqvrjO6DVe95Gij94U1HPOUJPWR+XHUx51tQ77Vc/XcefBhQ4chbP9fA1mp9NJS0/5rB9tndPnqhCY6p2HnPQ5XlcHltOV09K5AXcgvxSRyRmHxq2ViekkE9eSt1otTCYTtFotXL58GZcuXUqTk9vb2+j3+6VZfpbDXZfj8Tid/OgjBnYAY3gOXs5A6TCUiehvnr96egUglsuwyGQywcbGRjo90kHE2W6VXHnN604DGI/H2N7eTquHeL+HzfS6lqPgmqtDjvlon/qogiMt1QEFC3d0OgGuBILXtIxms5nmaDRfJx4548uNYFS/vWy2RdeP+3HRyla1bP/ONrTbbcR4vKku58iZOLmocmR+fBuQysbz4O8529X7vd0+slCHchqB0yMjWD6ZNtvgIzrW3/Vd5UdbJh6MRqO0jp8T2XR08/m8tEpM68iRAvN3R05Z5/Zn6AhZ5ebkJuf8NJ0bcHeDyf3G8yxqtVopPsZhmAqMnaMnvrFTCOg8ElU3VtBYCGSTyQTj8bh0lG1u2KgeORfmcA+uEz80QAUcXSGhoOkACiBNBo9GI1y8eDG9fstlqmCbCyMosKpRka0Mh0PcvXsX+/v7pdU8OvF2GnvV/HQJHp21l6+yUIXX9ujkNfNyOSngKbPXeuoKFu2TnPFW6auDFst2MpBzupQvWSFDhTq57IDkctEREkOMWq7GkVluvV5Hq9VKjoDXtP/UyTs4a52crFTJjPX3UUCV/tAmVZY8TkBlr86JgK4MW/PP9U8urEvZ8sTXVquVwj8M39AhqGzV2eZsTstVAkc50qkqlqg8HrTGHThH4A6cnFhQ8AbKk4yj0SiBbqfTKb0ViOBJ9nP9+vXUGQwrMBRCFkCwp8D7/X5pIsaHUc4wffjE5AySQJYLOzgAeKzSWSLL57WdnZ0E8KurqyU25k6T9XHWyfs0XjuZTDAYDHDv3j3s7++XQEVBqsopOyvns0dHRyVW5axN5euy8bKoF2xv1TyC95kClcqJDk239ufq5nXOhXmcKeqz/Mz3AlAuBHe9l6EBMnqyUiUlvK9WqyX5Kqv2UyWbzSZ6vV7p3Bg/+dMdhMvR+4Z55wiJjkJVXurcczig5Slz12t+TpPXz/VFP7sTo+5rKIrOloSw0+kkWdHR+CjA9T8H/rlRioZomJzdPyidC3DXjufnVquVdmN2Oh3EGHFwcIDRaJQm8ih8elKgZZPBSAAAIABJREFUWFEwnU6xs7ODZrOZ4uu6k0/j4Hw5MH9fW1tDq9XCpz/96WTgvubcd1M6O1bGyd+1Q3WttE+2KmA40/LYHmWnIM3TKjm/QAWk0jvAs36aFxWTZ9Tv7u4mmass9FkNabgha1n+WcM0OSPj/b6hxOvvzypTc2PziSheo3Pwnau5/s21yYEtB3q5+weDQSITBGX+Ud90lMeXqjMp+dC2tVqtUht0hMURTK/Xw8rKSiJRLlPWVUetHh7UerD9VXFiBXXP3+/1slkvPaVS66qkic/SUarMffJW26PvOXAAVlnSPjgCZx8xlAfgxLyI64HP/6jsvK65EGPVCInpXIA7cBLgDw8P0ev1sLa2htlshoODg/QS2hgj1tfX0Wg0MBqN0O12ceXKFayvrwMogGZ1dRUrKyspTqasRwXMeKJu6d/d3cXOzg7m83liblRKDn+B/AYgtsXByjtCFYn58rqybV2aqY5DlV3lxvZzhML2Mx7pTJOxXOZFpj4ajZLMGZ7QfvIYuzs71jPH4HRVUg4QnNVqG33E4w5L26WArDKjQavBupPxEE3OkPx6VVjBAYvXub9iMBhgNBolwGVYZjQalUaurLv+AUg7pmezWelerbvH1NlexpApu9zoydtB+Whc2xl2DvQ9Tx0Je/4uKy8759ipG54nnRkdgNZR9cXX7nseKk/a72QySaBOW6Kt8ZA/ztdpe1weLEPDbep89Kwed+JV6VyB+3x+fHRso9HA5uYm7t+/j/39/dJJaFevXsV4PMbBwUEaEgHFQfz0qjyzmZOqqogK9lzvOp1O0xvNX3zxxXSsrk5yugd3ANN7gfzqE2XlVChdgaOKrQqojsQBy0FIGeFwOEwbW5i3g5AOiekYtO1e91xdXVFZTx8+0mGxHipTN+pcyIgGyu/K6N3otT4K7myXsnQaizpv7btcOMy/54BdZaZ9RQfa7/dL8XWGDjnfQxmtrKyksuhsdUMO8yaj99GD94FOzvoqKz6bi0FrX+kfZeQAr7rJsj1M6ey1Sr5KaPQzZeS7aLX+XhZQPopb66kOXm1YGbjOj+kzfN0h9xpw7T1HYgriXhb7T8vVUJnbw4Pi7ucG3AnqZB9ra2tpkwxj4pz0GY/HaYWLvhYLKHt2MhQHRDLV6XSahsT0tP1+H9vb22n5lK9r19g6lZmCz00sAmUwVYbODmL+ZKJVyu5MlO3VurhBEaypWG7wCkS+ptvZgYO6K3YOyPicx01zxuexcmd6vIeJ7NU3y7hhOFAoODD5hKozeR8W50YlLhe/zjwmkwkODg7SnJHKjHXx+LPKhUshNYSgej6dTtP6bJWVAoWWx5CCy4TtdhaZIxd0uirbHLCrbJ05qz7pc5qqlmO6owGOX06vpIB56+ccQ3ci40CuiTKnjPQkT7L4druNdrudnLbqh+s4cHK1Wy5slXPcms4NuDOeTrDUeJUCI5k6J0Xr9XoC/tFolNa/sjN10owTZPfu3UtrtDksJQju7u4m9pNbdsaZcVVQ4Hhrdm5ozqTK48qknedHkipjYlJGz+cVZN2wHWAdOHlfDqhzYJ0DXndQfo+2U+uo9dK6ErCc1VMn/Fx7b4+fvaJlKBgxH9UVrbuH3XJtUbl4n2vizl6Gu6hT4/G4NBfEcJyPKBgK4LPcr6Gb7ygzLdsBSvvMnbn3vbJiB/hc+11+bifeZ74GXcHdHYQSOf2uv7OetGkFc96TI19qOzmdVJtT/dV2sh8I9np6rG6S0nCrJtd710GXx2npXIB7jBFra2sIIWBvby+d96LHA3C54+rqalIIBXAOfRi6YaiFAM2JWOB4spZnYB8cHKS157du3Spt63YFUwag4QVlAmyThgxc4TUU4wbGSTU6M2fpanCejyqlx7HVuDUvAFkwcEPyPlPHlwtlsM450GUZPuLQdmr+2h6fsOPzCs7aHmWUWs9ciEuNNgdSalhVsU+VrxIDhv549v18Pk8ruhz0VMf0cLvZbJZGlVwMoKE9ykInXL0vtD1sq8bhvc+rQhc5x6B6oTLwEZfm52CrcjgN2LVd3kbtO7cTdWJaH21LlQxy5fsoCzh52B2AFJ9XORPffBl0Lr1SgD8X4B5CMbHDMx7G4zHu37+fzoEgeIYQ0gQoQzdMXB2jRlGv19OE1aVLl9JKGPXU9Kac+OBBWDEev2RYvakDEdmUDutzjBU4ZghqeCGEE/FwZQbKWtUIaNSqzAqWLFeN0YGGeWpy9qb11HY4a8mxQWe/6hh5r7eXZVLZqfwKXF63KmCoqpcCusY8FexJElw2mo/HZPmbhy4Ivvv7+xgOhyeMU0cY1E/2pW5i4nf+V9BknVUHYoyVm4u0X7hqjH2h7fG+zwEuk8pS+yEXxtC8TutDtsfLchtj3XMEwcM82jeaL/vAJ6bdCToRYjhRySbvZT8xFj+dTksY5E6SdXYbdn3XOlWlhwb3ULxm73kAN2OM7wwhPI3i/amXAHwGwA/HGCchhDaAXwHwLShejP2eGOOXTst7ZWUF165dS8cEbG9v486dOyk0w5lizjqPRqPEXLkR6eDgAOvr61hbW0vMfzAYAAAuXryIlZUVxHh8DAA7Yz6fp/XtL7/8Mvb3908AkguZBswOUJbt691zAE2DYihHlVfv4+TaQv6lVTrOjpTt8X43HA1R+IofN2oFQWeortw5w9Shtsqb11Q26jRZrk4s+UiAz9Kh5hwB79WRhYJy1X054PGyNeWMz0dw8/kcg8GgdDQyn2PogEbP33QDE+XB+x3g9c+dhia2m/99Ga6OcHIO+jSAcTtRW/FRFWWUe071QvvrQeUrAeB3tUX9TfuIoxx3epSXh4xo52rX7uh8fsnrR+xidMH12wmE55FzrLn0Spj7jwF4EcD64vsHAPxsjPGjIYQPAvhRAL+0+L8TY/z6EMJzi/vec1rGMUa84Q1vSKEXZ4jcpHTz5k3cunUL8/kcTzzxBHq9HgDg9u3b+PKXv4xWq5VOf2y322lzBte5r6ysJFYeY0y73AgKL730UopnurKpsgMojRB0d1oubpkzQCqdL9Gi8qgMvPMVTJwZUQmdxThwaT08zEFD5CSb5qX3qtPid62ztlkZj9eX17iqiXMuCso+4UaQ4zI+NUI1Cjd27cMc+POzDuX5vBpozrk5oFEmXCyg+kI2r5P2dFYeaiEYUN68rg6TwKxg5mEtB14lGpTHaSM3TVUgo3VwkHNn6sTHmajmo/XSUSudkoMhl5O6o9W83GZdx7UclUduzo35VYWYtG+1TQR4xwa1IQ/7uI5XpYcC9xDCUwD+NoB/CeDHQ9GitwP4wcUtHwHw0yjA/fsWnwHgYwB+IYQQYq7Fi9Rut7G+vl5SSF2eNRqNcPPmTTSbTTz77LPp/OVOp5PA/01velNiPNqJPCWOW4VDCCncwuVKtVoN9+7dw9bWVhIwdwu64ioIqJKw85wV+ncmGizzBY4BSp2Js1E1ztzIgp+B4+G+M3zmz98ZwsqBtSqrMzIfhjvTVrAEyq8XUxm6Q3dHyLYoKOvIxdlZThYqZwd3dTpsjzNhlaHLlLLSZ5k4guQ6dj/nZT4/Pp+k2+2iVjueb1FdJgg7qFNmlK8COkMy3h9sO7+zPIYHPRzl+qsjLf6uAJ27rnnpPTn91Tr7qJLP6Hp1JxO8rqxY9VQdgYZQ1LarRqc5hu84wfvUWTlm0EFQr3UntOKIjjhz+ntaeljm/nMAfgIAg9yXAOzGGGmtNwA8ufj8JICvLho0DSHsLe6/Z41/H4D3AcDrXvc69Pt9AEhr2jlE5Rbqy5cvo9frlTz10dERGo0GNjY2koIydkjl5Tkrk8kkrfmmYEIIGA6HqNVquH37Nvb390ssk6DHzgFwgs2p9/XYHu9xhXGHAZTjbLxPy9IOz8VWvTyvqwIiY4Eat1UFckdQpUTKklxOyn70fgVi/a+Jjt3LpkNSQ6FTZVJw82cVyLUfFOiYf86x5WKh2ocsX4ftDMcw9KLgrnKnbBzYVdeZv7aRclf2rTqXWx/t93C0yp2q3pc5B8g83JlpGSobB3Hvf3celKOGligvP5QrF9YEyi+FYd21jp6qiJhfc8fvslJbcvtXeWr7fESs5fqolXn8lde5hxDeCeBujPEzIYTvfND9D5tijB8C8CEAePbZZyOPGWi1Wjg8PMTKykpiEQo6unyNxkDgU+bB86yBYpkl42o80oB59Pt91Ot13Lhxo6SIjOf7SECB04fV1r4s2GuHa9zdh3m60gfIv5lGWZwamyun102Zj9ZX76fyqVG6kWgs3ZXdWTHvzwGvyisHyK7cPjz1/JSNOftm+7Q9ShiqHLOPJNi23MiG9eMuYV2XrnsOGFKKsdi74TKL8fgtYOoA3Jm4k1FZaRt4n16nHeiR1c7MmaePqlxv/Blns1Wg6TJl33n/6YiWz2uZzvJp8w7CVc5fr/mo1p2T66/KRPtAdScnBw2B6nuXtX+9X7Uep6WHYe7fAeBdIYR3AFhBEXP/eQAXQgiNWLD3pwDcXNx/E8DrAdwIITQAbKCYWK1MMRZD0m63i16vh9FolBqm26MZSlFmy79arZZ2nrLTW60WxuMxVlZW0nEEXNPOZ65cuYKjo6PE2ilYXU6pLJn1VSZTJXA+r9/VOHi/T5TyLwc0BHxnZ8pQ2H4tV9lADqiUCRD8nckoY2JemnQo6aCjBqhGmqsH28J8crFFHTXwu7JUD5Oog5nNZok0qFNROTmI6XXvFyY3Ph4V7WfFsP2UJ1fD5JwYZaDzMGrs6mRUTxzgHSB8tEaHo232PnOQUhlo/V0u+rzbS67ftf6qVzn70ud15KsnuWo+Dp76vMpPnTSvq+y0Xmo/2ifeR3xG+1InaFmO77TVe08jSJ4eCO4xxvcDeP+isO8E8E9jjD8UQvgtAO9GsWLmvQB+d/HIxxff/2Tx+x/GB7gYrv/l21CA4/dk8mAjhm3YaDJzNpJLJAlMVFYVsnthTuBNJhNsbW2lF3NQUViGDoOZXEFYXyo5DdNkWQqN8BoBUY3OWX8unKEsK/e7K6yCmBuWG4DKuqr7XL5u2M7KHLg1H3UswPEyMs3DQZ6hM38rkIKN1iMnL69zjnkqQ1S5eplaV+5C1ZGfEhLqhwLUbDZLL+hQgGdbc8CijlQB1oFJY7e8xv6gvTAcmmO3Xq72AfNw0qKA6w7JwUnLcYep7dc8PE/tL32Xsfav5qv9pTanzigXS3f58bMug8yRILcXveb66uE37QN32FXpr7LO/Z8B+GgI4WcAfBbAhxfXPwzgV0MILwHYBvDcw2T2/PPP481vfjM6nQ663S4mkwl6vV7qKDZ4MBikw/M1LquTR2T+nEQlgx6NRhgOh6UjTmezWTpThon3s8N0N5krkToY3ZDAe3PsFjg5KeVg74rnbMpZov5XxdXljwwBOaARbFXBmXKMn0bjTM7BXOuiMvN5C9ZB60Vl9yWULF/lzt/VmJkfnTX7RsNRbuAOklpPbZMPlX0kMp/P0yF3BBnG3BWsOYLQa3pUdW4CWvUmF6ZQ4HPA0qWPros8qIyTuvp7FRnIhW/0XnWIGgLzujMfXTGmMtY+UGehwOu6qEuInSgpyOrz7vQdwF0HVa+VVWufaZ65kb/2gZ53pfX1eubIZi69InCPMf4RgD9afP4LAN+WuWcM4PtfSb7j8Rg3btzAlStXSpuODg8P04RnCKEE6io4XQ7HeDt/Y3hBVwMQlNkhL7/8Mg4PD0tDJnZ2vV5P8TCgHJrQTsopgwOD1scVTCfElF07sCsDZ6qKVefYtLI1BXTNK8cQFLhUSbXdDrZaHzVkbYvKWuXljkoTnTJw/AYhdbQO4C4bdUw5VqRMivdrv5FM8GhXrqxiXmTgHAnqskYCPeuga/Vp1AR372+W4SMfD+c46Kme5ggB8+R+D81Ty/XYM+Xlcnbd02u+0kgJh+qPhwSdBOX6jnXm+wdU5jqidt1kO1Qf9TmXozoWtb1cCMidr+u8ytBl6bpK3aI+Piidix2qBGi+r5C7VBuNBnq9Ho6OjjAajVCv10vr1MfjMTY3N9OSSB7OE8LxCw30M3D8KjNlc1/5yldKw0t9K5Ku9VZDBU6yZ2Un7l39vz7vsWXtuBzDZf45ZeW97hR0YtqNhjLyZzROmnM4VWXnGBeArNyqRjJ6XVewEMQZjmF+fIb6QYevQKcGp3mrcaps+Rvfzcm37vCQOT0rZDAYpLpNJpN0CB4ZuuqHAysdSm5Tko+SOKfkpEI/q+65Y3C5KwCPx2MMh8NkQy67HICzTSpH/z0HnNRxjT/n6qv3UIYuR3dEtFN1CFoH1ctc6I7f9Tf978SAKUegcsRJbYpyoA6QROScCOWXm/zPpXMD7rPZDFtbW+nlEsAxkPHl1dzCu7GxgSeeeAI7OzvY2dnBW9/61pJCKusOIZQmYXVlCg1xa2srsXM+x6Eq66B/uXgYUGayOvxVxVQgck/u4JpTRFU0Ld/ZdU7xWC+95vc4gLvR6HdljHze2b4DKtuhjk3z1M/sE+7eJNDp7tVc6EQdj+bpQ2GtS+50Rhob3xMAFHsyLly4kMJ6IYT0UgweRjcYDEovXGc9df6GddPTBKl7vE8ni72/VUdc/5xtOvv1a6oXw+EwvSvBdc5l7PqpYK/6oOE1dyoO1Jq/3qt2oWXl6sOd7DnH5+1Q2eXy8hCc6m0ulMj2um3rNeqh7mj3kRX1z/tWdVT1OJfOBbjTuG7evFliMfV6HZ1OBxsbG+h2u2mn340bNxBjxMWLF7G5uYnV1dVk9DQ2nqXMA8G4tJIGxVnqvb29FG9XVgSUja9Kias6Q0EtZyAe+3Rwdq/tMXln2A76OcXweCd/U4foCuRgrc96HYGTh4LxPi1Lr+eYG4fq6th9Pbveq8PaWq38RqGc48yNRLTPdQTW6XTQ6/XSyIarrigLzuOwbK5jJ5grUOumGgKE1gdA6R6vt/dlbjiv+qdycoedY/Sz2Swdpa0vBs+te9fRVA5w3DY0Dx2JuBMmsfLQpY9mc4DPNozH48oRi9qZ7hr10XbOAeo13eiVC79o2Ezb4HLR79pHGsqraqtjiqdzAe4AEoA//fTTuHz5culFtHyxAE/Tu337NtbX19Hv9/HUU0+loQwBHSiDi26KOTw8TCdPNptN3L17Nxky35xSr9fTxBaH+Q7GqnjKElRZmdgRuXCN38vkrN+fczZcBfp8xjdL6G+5HZdMaoD8z0lAXU/OOmgenqcPMdVgnaUyPwURltVqtRI7Y0hPAVn7QmPlOTmpIfIaHT/z4/G8ly9fTroZYzGS2draSuSAL9ngmd26jNYBnOXpPIH2TY40aJ2rRoXep9ovClDu3CkD2ke32y2FWjTE4BuL9L8mJzWqk3pN26GOV52AOpAqPaVsNd7O3/yzT9S7DSrpcKKkbc05Kh2hKVZ4HF7tTu+t1WrpLCydi8mx/9PSuQD3ZrOJp556Cq1WC/v7+7h16xaazSZWV1fR6/Vw9epVrK6uYjAYYGdnJ71X9Q1veEM6toDCYqyTIRSeV0OD6nQ6WFlZSZNgPFlSl3Jp54UQShs8gDIb0hl+/paLk6kCKZjobx7jU6OuUlQfsqvx5FiUMwIFB81X65Eb/jKp4vO7GqAPx9VJVhmYfqbDJVArCJKhcRczn5lMJifakPusAKL10rbSKC9evIiLFy+mOaC9vT3s7Oyk3acESMbb6fyUEPCaykbl5ixY9cDZe66/HFBUNzSpw3TgirF4V/Hm5mZagJBj+g7cTix8lOfyVx1R1k5ZnZav11evTSaTNDGs9pAbfWhd+J+/c3Rfdb+SB3ecDth8RuXNfL1erC9f+zmbHR/tTP2hvj0S4E6j2NvbS+/q5ITYaDRKyyJ7vR6eeeYZbGxslF6dxwkU4KQC6CoZJnrG6XSKy5cvo9FopHgpnQJDQs6GgJNDXQdKxk1zCuUKzuSsRRVfj+fNxcSVybmj4HMKGsog1DnVasebtnRpaY5xMV/e445AQT7nrDRP3a6v8tI26/nYnNwMISSAZ5upFxry4HOsmzoCrT9loLsjW60WNjc3s/shYozpSAzqsJ4bw34hSChj1PXpOmrJxWAdCDVsofVXmebYqD6v8nU2z6Ove73eiTc05ZhzFdBoPbSu+j8XUuLvVQ7O5aH3cL7Dbc6dhNdT66TgnGtj1ShayRxti32t5Sj5chlRHlydp3bFPH2itiqdC3BfW1vDW97yFqyvr6eYpgqq0Wgko1lfXz/BjHm+uzIY4HiZFVAectN4GKNfXV3FjRs3SobBjuHmKC6JdEbA/9qBGsN25dPrvF8VnqMMVQRdnqkATRBjnasmrbwcZRV6rAETAYeK5PMOyt70Gf7PsUHtT1dq9pX+rtc5Ie5Ojn3OM9K1PSorZchVDtbB3kd/jKfrpiQ9A53nFvGAMA+LaHt8T4EzYoK3b+JyAHeW6H9qI04KqsCYNrK7u5uO0NaVR25jWh+d03EA1jJyoJkjLZSDPqd5qq6wTSRp7Hd1nEqEFOy1390ZqVy0fHXQSpb4XQmhkigt10dWiluqh+7Mcw42l84FuDebTVy/fj2BKE9yjDGWdo1yXTFZFAVI42NYhsvkdFkk2b3uZqQBb25ulibClFHqbkEXrhsbHYaCXtXQDsivWlDH4azKjUBBS/P3+xXMnTE7+GqbgPLJkArcDi5VSdvlAKpOKRd64D3cjMZwB3dSEsin02n6zfNRGXkdvE/cqSi5oOz8xdKccO12u/jSl750YgJVl+R637hs1OgVLNT4PWTo+eick8pZ+1N1jmW5LMbjMe7evVtyYHRYJDlOHlielp+bCM+BNJP26Wm64zH++byYT+OS1Fz73Q5zo1HtA9cd/U+99HCa9pHaH9ug+27UptzhUIdyRED74rR0LsCdYRKgLDTgeDIUOBYsQzchhHRcgXYmmTxZJ++P8fiAJjJyALh8+TJijKWDnbSzNG9fd6zhAuahE1E543FQ10kX9d45dqXx5xzTVWXmdXdqvE83y6hS+rBdn1EG5awyl5yNObPLGa4bFe8lg+dzfmxqji1WMXWVDa8roPJas9lMO6WVSVNPOfk+Ho/xwgsvnDjfXw3XAcmByxmbXnPWqKlKv1TG7iQI0O54tG57e3tYXV3F5cuXS+zdw2cqY62jOh7vBwWy0wA/px/+n+1gCNeBvcpJuC4DODHK03yUMPG6j1p0niLn0IlPmp/qoE/kkvwQwzRk+KB0LsA9hJBepEED9jfPUFgMj+jryI6OjtLZ7N1uN3k9Tq5OJpMTL6cFjs+W0dMoWR9d6aChD4+HurKpJ+ezzNNZTW74puyazwHls1aA8kSl10mdBJkC79W8/FgFTWRrbJtPwrqRa/v0uwM565YD1yqHyH7XuqksqRNkOh4T1bkKtl9HDNo/2s8ahuDvHA06u7x37x76/X5Jpg5+7gBzE57K0hga09+0X3hNAYdM2UHInZo68BwLZFhoa2sLKysrKRzqjiLntJiUqGnbc/Fz1Q+VXRVpUBthnv6mK7ZD26mjIv6esze1R6+v5u/2m3OU1LWcjWj9daJbbUDn+ujIgPJyzFw6F+BOg9fJG17T943qm3oIVjwpUg2FgMZnyTqYNzdDceJlf3+/tGSOIKoAye86nKpiAdquHCvhf3UCMZZPd9Trzu5UmZwVsy4KCqqwzIP3KJPWcp2d+DDVQynabo8ParvdmN3IHXjUgXGOQQFMQYu/+Y5EEgbm53LX/nK5qIPWORySjsFgkPZeMCToIz0nBjpCcuaoMqR+a585COX+O/DmnIvKPwfOlM1oNMLdu3cTcco5Ke0n1Um9Lzcq1KR6nrtH9Uvbynsnkwn29/ez71XI1cnt1ImF9lUOsL1uHEXyPneWStK0nWr3ubqEEEo7bj2cc1o6F+BO4ehMMI2SgK6goExMha/HfDLxICR2Go1Sh6Z812pOcZ1t87MbpXYWcHxKpK+7zxmzAquW6Wzfn1WZVBl7Lq7ItugEMRXSmSzvd8YI5FfGaBv0un7WNqqcndXkHKIzcJ2w0iG5ytYdhzsfDbf4nAzL5VzKwcEBhsNhCu8xJk3WHmNMO1ZzE9Hev6ozPvpS2Xsfeh/lZKmMMVcPz8eZJ3V2d3cXzWYT165dQ6fTOXGvl6tt0vzUfnV0pcDreuh11PxYxtHREfr9fiJrCtAud5WFhzO13szfNxKp7rlt5vpX81BHqzJTB671V51VeVT1o6dzAe61Wg3dbhej0Qj9fh8xFuvP1cC5QYRneozHYwDHcVGgfDYI7+URArp0jmvc5/M59vf309ufqoSlglWAU+VxpSIw+AoWJld6z0eVyNkj/yswAyhN9nldWQ86SmenOuR2BefzCjI6KtF26PAzB9L6DOvhyussVL+zDm50mhfvJdvXPLUeanAEYgfS+byYqNvf309ltlqtdJ7RaDTC/v5+2mDHZ+hodGidK9d1wkFCAUKBkbqRAxi/5kkdr8s8B8rT6RTb29uo1+u4du1amudSx+3M3NvhbWa+OSLjMvG6qz5w3Xe/309hVXcSrieeB/vJ66l5+CSyOuCqZdhun9qP2j8coc3nx4cgahtUtjp6fiTCMgBK57gwPj6bHZ9vzZANmTyH2lzhAhy/YKNWq5VOihwMBphOp2ndOk+N6/f72N3dxcHBQSle696cqYpNuFLqemkmVzBe439VcldKBc4cY8rVjYoE5A9HygGmM51cW3OMi3Xjff5M7iUfOkJTdsm2+GhL89S4qe6WrdVqCVT1bBSXh288UXasThMA9vb20tlDnU6nNG8RQkg7qX0+RJ2syiJXRg7Q9Tm2w51vjpWrE3BAc7BxfdHko67JZIL79++XztbJHSngzkFlq3rtZToI5uxEdVD7lA5WV5boPS6HquThULUJzY+66ftedFezy4X152+6S5lle30pEx6j4ks7fbWcp3MB7jSSyWSSlr2pV2Ssk4DJFS06iUZA+vw0AAAGo0lEQVTA0JUyynAoRM6qc6VIv9/H3t5eyfCcaVYNMx3cVJEJMB7/5bPMSztSwU5/V1Di86qAudi5T8IwP37XJZruRNSAfAJKgYcKqYarfeqjCzV4Jh+SKutUQyWIa92V0bD/3fHNZrNEBHwURXmx30ggGLrjNW7maTabqXwNDXIkyVAMy/KdhN7PuaQ6p8/4kF77R+VGOSnbdBbohEWv+QhTicVoNMLt27cRQsDa2lraRan3ab3dqfA+1SNl/dp+t7Nc2JKjqsFgcGKkpKRB+9tHPe5gVJ4+f6OrXJSBaxkO7CoLb4+O9DQUqHbFurqMiH+npXCaJ3utUgjhAMAXzroer3K6DHsp+COelu053+lxaw/w+LXpa9Ger4sxXsn9cC6YO4AvxBi/9awr8WqmEMLzj1Oblu053+lxaw/w+LXptW7P6QcCL9MyLdMyLdMjmZbgvkzLtEzL9Bim8wLuHzrrCnwN0uPWpmV7znd63NoDPH5tek3bcy4mVJdpmZZpmZbp1U3nhbkv0zIt0zIt06uYluC+TMu0TMv0GKYzB/cQwveGEL4QQngphPCTZ12fh0khhNeHED4ZQvjTEML/DSH82OL6xRDCfw0h/Nni/+bieggh/LtFGz8XQvjms21BPoUQ6iGEz4YQPrH4/nQI4VOLev9GCKG1uN5efH9p8fsbz7LeuRRCuBBC+FgI4fMhhBdDCG97DPrnnyz07YUQwq+HEFYepT4KIfyHEMLdEMILcu0V90kI4b2L+/8shPDes2iL1CXXpn+90LvPhRB+J4RwQX57/6JNXwghfI9cf/Vx0Lclv5Z/AOoA/hzAMwBaAP43gGfPsk4PWe/rAL558XkNwP8D8CyAfwXgJxfXfxLABxaf3wHg9wAEAN8O4FNn3YaKdv04gP8E4BOL778J4LnF5w8C+IeLz/8IwAcXn58D8BtnXfdMWz4C4B8sPrcAXHiU+wfAkwC+CKAjffMjj1IfAfjrAL4ZwAty7RX1CYCLAP5i8X9z8XnznLXpuwE0Fp8/IG16doFxbQBPL7Cv/rXCwbPu7LcB+H35/n4A7z9rJfxLtON3AXwXil221xfXrqPYnAUAvwzgB+T+dN95+QPwFIA/APB2AJ9YGNU9UdLUVwB+H8DbFp8bi/vCWbdB2rKxAMJg1x/l/nkSwFcXoNZY9NH3PGp9BOCNBoSvqE8A/ACAX5brpfvOQ5vst78D4NcWn0v4xj76WuHgWYdlqLBMNxbXHpm0GO5+E4BPAXhdjPHW4qfbAF63+PwotPPnAPwEAB48cgnAboxxuviudU7tWfy+t7j/vKSnAWwB+I+LMNO/DyH08Aj3T4zxJoB/A+ArAG6hkPln8Oj2EdMr7ZNz31eW/j6KEQjwGrfprMH9kU4hhFUA/xnAP44x7utvsXDBj8Q60xDCOwHcjTF+5qzr8iqlBoqh8i/FGL8JwADFkD+lR6l/AGARi/4+FI7rCQA9AN97ppV6ldOj1icPSiGEnwIwBfBrZ1H+WYP7TQCvl+9PLa6d+xRCaKIA9l+LMf724vKdEML1xe/XAdxdXD/v7fwOAO8KIXwJwEdRhGZ+HsCFEALPH9I6p/Ysft8AcP+1rPAD0g0AN2KMn1p8/xgKsH9U+wcA/iaAL8YYt2KMRwB+G0W/Pap9xPRK++RR6CuEEH4EwDsB/NDCaQGvcZvOGtw/DeDNixn/FoqJn4+fcZ0emEIIAcCHAbwYY/y38tPHAXD2/r0oYvG8/ncXKwC+HcCeDEXPPMUY3x9jfCrG+EYUffCHMcYfAvBJAO9e3ObtYTvfvbj/3DCuGONtAF8NIbxlcelvAPhTPKL9s0hfAfDtIYTuQv/YpkeyjyS90j75fQDfHULYXIxmvntx7dykEML3oghxvivGOJSfPg7gucVKpqcBvBnA/8DXCgfPciJioWvvQLHa5M8B/NRZ1+ch6/zXUAwfPwfgfy3+3oEipvkHAP4MwH8DcHFxfwDwi4s2/h8A33rWbTilbd+J49UyzyyU7yUAvwWgvbi+svj+0uL3Z8663pl2fCOA5xd99F9QrKx4pPsHwL8A8HkALwD4VRSrLh6ZPgLw6yjmC45QjK5+9C/TJyji2C8t/v7eOWzTSyhi6MSGD8r9P7Vo0xcA/C25/qrj4PL4gWVapmVapscwnXVYZpmWaZmWaZm+BmkJ7su0TMu0TI9hWoL7Mi3TMi3TY5iW4L5My7RMy/QYpiW4L9MyLdMyPYZpCe7LtEzLtEyPYVqC+zIt0zIt02OY/j+fA5fhfU91mQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 296
-        },
-        "id": "RL0hbFSwXn5-",
-        "outputId": "1156f96c-4a81-4c03-b39d-2ae6f3a7e025"
-      },
-      "source": [
-        "from sklearn.metrics import confusion_matrix\n",
-        "import seaborn as sns\n",
-        "\n",
-        "cf_matrix_1 = confusion_matrix(y_test_1, y_cap_1, labels=classes_1) \n",
-        "ax1= plt.subplot()\n",
-        "sns.heatmap(cf_matrix_1, annot=True, ax = ax1);\n",
-        "# sns.heatmap(cf_matrix, annot=True)\n",
-        "ax1.set_xlabel('Predicted labels');\n",
-        "ax1.set_ylabel('True labels');\n",
-        "ax1.set_title('Confusion Matrix');\n",
-        "ax1.xaxis.set_ticklabels(classes_1);\n",
-        "ax1.yaxis.set_ticklabels(classes_1);\n",
-        "plt.show()"
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_image = cv.imread(X_test[16],cv.IMREAD_GRAYSCALE)\n",
+    "match, num_matches =  sift_match(test_image, X_train[18], plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nWZ2rjMjPBCk"
+   },
+   "source": [
+    "# Testing other team's data (BuggerDebugger Group)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8AOQWsTrPvLx"
+   },
+   "outputs": [],
+   "source": [
+    "other_team_dir = '/gdrive/MyDrive/BitCoders/Assignment2_other_team_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 198
+    },
+    "id": "hTcEmO2WQMrY",
+    "outputId": "8c669c0a-6018-4f4e-8a35-6eb259d647ee"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Image_Sources</th>\n",
+       "      <th>Labels</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
+       "      <td>Rani_Mukherjee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
+       "      <td>Rani_Mukherjee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
+       "      <td>Rani_Mukherjee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
+       "      <td>Rani_Mukherjee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>/gdrive/MyDrive/BitCoders/Assignment2_other_te...</td>\n",
+       "      <td>Rani_Mukherjee</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
+      "text/plain": [
+       "                                       Image_Sources          Labels\n",
+       "0  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee\n",
+       "1  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee\n",
+       "2  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee\n",
+       "3  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee\n",
+       "4  /gdrive/MyDrive/BitCoders/Assignment2_other_te...  Rani_Mukherjee"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JMHWaaqP9enk"
-      },
-      "source": [
-        "# Testing other team's data (YK Group)"
+     },
+     "execution_count": 15,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_database_1 = {}\n",
+    "pathlist_1 = Path(str(other_team_dir)).rglob('*.jpg')\n",
+    "for path in pathlist_1:\n",
+    "  path_in_str = str(path)\n",
+    "  image_database_1[path_in_str] = path_in_str.split('/')[-2]\n",
+    "\n",
+    "image_sources_1 = list(image_database_1.keys())\n",
+    "labels_1 = list(image_database_1.values())\n",
+    "data_1 = pd.DataFrame()\n",
+    "data_1['Image_Sources'] = image_sources_1\n",
+    "data_1['Labels'] = labels_1\n",
+    "data_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9RNieXwWP6NY"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_1 = data_1['Image_Sources']\n",
+    "y_1 = data_1['Labels']\n",
+    "X_train_1, X_test_1, y_train_1, y_test_1 = train_test_split(X_1, y_1, test_size = 0.5, random_state = 0, stratify=y_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 236
+    },
+    "id": "dihZDZgXSHsb",
+    "outputId": "96e47306-3a82-4e3e-eea3-7b55511f431a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Ltne6exBCFxq"
-      },
-      "source": [
-        "other_team_train = '/gdrive/MyDrive/BitCoders/CVAss22/Train'\n",
-        "other_team_test = '/gdrive/MyDrive/BitCoders/CVAss22/Test'\n",
-        "\n",
-        "image_database_1 = {}\n",
-        "pathlist_1 = Path(str(other_team_train)).rglob('*.pgm')\n",
-        "for path in pathlist_1:\n",
-        "  path_in_str = str(path)\n",
-        "  image_database_1[path_in_str] = path_in_str.split('/')[-1].split('_')[0][-2:]\n",
-        "\n",
-        "test_image_database_1 = {}\n",
-        "pathlist_2 = Path(str(other_team_test)).rglob('*.pgm')\n",
-        "for path in pathlist_2:\n",
-        "  path_in_str = str(path)\n",
-        "  test_image_database_1[path_in_str] = path_in_str.split('/')[-1].split('_')[0][-2:]\n",
-        "\n",
-        "X_train_1 = list(image_database_1.keys())\n",
-        "y_train_1 = list(image_database_1.values())\n",
-        "X_test_1 = list(test_image_database_1.keys())\n",
-        "y_test_1 = list(test_image_database_1.values())"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "5Q1SMfpDD-bD"
-      },
-      "source": [
-        "y_cap_1 = []\n",
-        "for test_image_path in X_test_1:\n",
-        "  test_image = cv.imread(test_image_path,cv.IMREAD_GRAYSCALE)\n",
-        "  best_match = ''\n",
-        "  highest_matches = 0\n",
-        "  for train_image, label in zip(X_train_1, y_train_1):\n",
-        "    try:\n",
-        "      match, num_matches =  sift_match(test_image, train_image, MIN_MATCH_COUNT=1)\n",
-        "      if match and num_matches > highest_matches:\n",
-        "        highest_matches = num_matches\n",
-        "        best_match = label\n",
-        "    except:\n",
-        "      pass\n",
-        "      # print('Error')\n",
-        "  if len(best_match)<1:\n",
-        "    y_cap_1.append('Unknown')\n",
-        "  else:\n",
-        "    y_cap_1.append(best_match)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "TuDGkgxTD-X-"
-      },
-      "source": [
-        "classes_1 = list(set(y_test_1))\n",
-        "classes_1.append('Unknown')"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "CDaxf9KED-Tg",
-        "outputId": "3ac800d4-b09e-44b4-c987-7b5731227abd"
-      },
-      "source": [
-        "from sklearn.metrics import accuracy_score\n",
-        "print(\"Accuracy is:\", np.round(accuracy_score(y_test_1, y_cap_1)*100,3),\"%\")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Accuracy is: 98.75 %\n"
-          ],
-          "name": "stdout"
-        }
+     "data": {
+      "text/plain": [
+       "(False, 0)"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cAvQKOt1D-Qb",
-        "outputId": "9e88313f-c0c7-4f81-aca6-2919a38e46ee"
-      },
-      "source": [
-        "from sklearn.metrics import f1_score\n",
-        "f1_1 = f1_score(y_test_1, y_cap_1, average='weighted')\n",
-        "print(\"F1 Score is:\",np.round(f1_1*100, 3), \"%\")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "F1 Score is: 98.667 %\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "execution_count": 44,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# a1 = cv.imread(X_train_1[0], cv.IMREAD_GRAYSCALE)\n",
+    "# cv2_imshow(a1)\n",
+    "a2 = cv.imread(X_test_1[45], cv.IMREAD_GRAYSCALE)\n",
+    "# cv2_imshow(a2)\n",
+    "sift_match(a2, X_train_1[50], plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SPfrtT7rTTSX"
+   },
+   "outputs": [],
+   "source": [
+    "test_image = cv.imread(X_test_1[131],cv.IMREAD_GRAYSCALE)\n",
+    "match, num_matches =  sift_match(test_image, X_train[120], plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "QDeBl9ILQj50"
+   },
+   "outputs": [],
+   "source": [
+    "y_cap_1 = []\n",
+    "for test_image_path in X_test_1:\n",
+    "  test_image = cv.imread(test_image_path,cv.IMREAD_GRAYSCALE)\n",
+    "  best_match = ''\n",
+    "  highest_matches = 0\n",
+    "  for train_image, label in zip(X_train_1, y_train_1):\n",
+    "    try:\n",
+    "      match, num_matches =  sift_match(test_image, train_image, MIN_MATCH_COUNT=1)\n",
+    "      if match and num_matches > highest_matches:\n",
+    "        highest_matches = num_matches\n",
+    "        best_match = label\n",
+    "    except:\n",
+    "      pass\n",
+    "      # print('Error')\n",
+    "  if len(best_match)<1:\n",
+    "    y_cap_1.append('Unknown')\n",
+    "  else:\n",
+    "    y_cap_1.append(best_match)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "w23SfK-fRgDZ"
+   },
+   "outputs": [],
+   "source": [
+    "classes_1 = list(set(y_test_1))\n",
+    "classes_1.append('Unknown')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pRasht6hRfvb",
+    "outputId": "81615c9c-0247-4e25-db08-7cf3a06d379d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy is: 32.5 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "print(\"Accuracy is:\", np.round(accuracy_score(y_test_1, y_cap_1)*100,3),\"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ZDjaAH6jR0ib",
+    "outputId": "a39636a1-924a-4b0c-d921-d4fb8eb2dc8e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Score is: 44.286 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score\n",
+    "f1_1 = f1_score(y_test_1, y_cap_1, average='weighted')\n",
+    "print(\"F1 Score is:\",np.round(f1_1*100, 3), \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 296
+    },
+    "id": "RL0hbFSwXn5-",
+    "outputId": "1156f96c-4a81-4c03-b39d-2ae6f3a7e025"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        },
-        "id": "8v77Yfi8ESyf",
-        "outputId": "ec97595c-e229-4fc2-e7e3-5e609f04efb2"
-      },
-      "source": [
-        "from sklearn.metrics import confusion_matrix\n",
-        "import seaborn as sns\n",
-        "\n",
-        "cf_matrix_1 = confusion_matrix(y_test_1, y_cap_1, labels=classes_1) \n",
-        "ax1= plt.subplot()\n",
-        "sns.heatmap(cf_matrix_1, annot=True, ax = ax1);\n",
-        "# sns.heatmap(cf_matrix, annot=True)\n",
-        "ax1.set_xlabel('Predicted labels');\n",
-        "ax1.set_ylabel('True labels');\n",
-        "ax1.set_title('Confusion Matrix');\n",
-        "ax1.xaxis.set_ticklabels(classes_1);\n",
-        "ax1.yaxis.set_ticklabels(classes_1);\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "import seaborn as sns\n",
+    "\n",
+    "cf_matrix_1 = confusion_matrix(y_test_1, y_cap_1, labels=classes_1) \n",
+    "ax1= plt.subplot()\n",
+    "sns.heatmap(cf_matrix_1, annot=True, ax = ax1);\n",
+    "# sns.heatmap(cf_matrix, annot=True)\n",
+    "ax1.set_xlabel('Predicted labels');\n",
+    "ax1.set_ylabel('True labels');\n",
+    "ax1.set_title('Confusion Matrix');\n",
+    "ax1.xaxis.set_ticklabels(classes_1);\n",
+    "ax1.yaxis.set_ticklabels(classes_1);\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JMHWaaqP9enk"
+   },
+   "source": [
+    "# Testing other team's data (YK Group)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Ltne6exBCFxq"
+   },
+   "outputs": [],
+   "source": [
+    "other_team_train = '/gdrive/MyDrive/BitCoders/CVAss22/Train'\n",
+    "other_team_test = '/gdrive/MyDrive/BitCoders/CVAss22/Test'\n",
+    "\n",
+    "image_database_1 = {}\n",
+    "pathlist_1 = Path(str(other_team_train)).rglob('*.pgm')\n",
+    "for path in pathlist_1:\n",
+    "  path_in_str = str(path)\n",
+    "  image_database_1[path_in_str] = path_in_str.split('/')[-1].split('_')[0][-2:]\n",
+    "\n",
+    "test_image_database_1 = {}\n",
+    "pathlist_2 = Path(str(other_team_test)).rglob('*.pgm')\n",
+    "for path in pathlist_2:\n",
+    "  path_in_str = str(path)\n",
+    "  test_image_database_1[path_in_str] = path_in_str.split('/')[-1].split('_')[0][-2:]\n",
+    "\n",
+    "X_train_1 = list(image_database_1.keys())\n",
+    "y_train_1 = list(image_database_1.values())\n",
+    "X_test_1 = list(test_image_database_1.keys())\n",
+    "y_test_1 = list(test_image_database_1.values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "5Q1SMfpDD-bD"
+   },
+   "outputs": [],
+   "source": [
+    "y_cap_1 = []\n",
+    "for test_image_path in X_test_1:\n",
+    "  test_image = cv.imread(test_image_path,cv.IMREAD_GRAYSCALE)\n",
+    "  best_match = ''\n",
+    "  highest_matches = 0\n",
+    "  for train_image, label in zip(X_train_1, y_train_1):\n",
+    "    try:\n",
+    "      match, num_matches =  sift_match(test_image, train_image, MIN_MATCH_COUNT=1)\n",
+    "      if match and num_matches > highest_matches:\n",
+    "        highest_matches = num_matches\n",
+    "        best_match = label\n",
+    "    except:\n",
+    "      pass\n",
+    "      # print('Error')\n",
+    "  if len(best_match)<1:\n",
+    "    y_cap_1.append('Unknown')\n",
+    "  else:\n",
+    "    y_cap_1.append(best_match)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "TuDGkgxTD-X-"
+   },
+   "outputs": [],
+   "source": [
+    "classes_1 = list(set(y_test_1))\n",
+    "classes_1.append('Unknown')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "CDaxf9KED-Tg",
+    "outputId": "3ac800d4-b09e-44b4-c987-7b5731227abd"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy is: 98.75 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "print(\"Accuracy is:\", np.round(accuracy_score(y_test_1, y_cap_1)*100,3),\"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cAvQKOt1D-Qb",
+    "outputId": "9e88313f-c0c7-4f81-aca6-2919a38e46ee"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Score is: 98.667 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score\n",
+    "f1_1 = f1_score(y_test_1, y_cap_1, average='weighted')\n",
+    "print(\"F1 Score is:\",np.round(f1_1*100, 3), \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "id": "8v77Yfi8ESyf",
+    "outputId": "ec97595c-e229-4fc2-e7e3-5e609f04efb2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EXHpYB-2EWVQ"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 273
-        },
-        "id": "XnGjlk7U92b9",
-        "outputId": "d916d1fe-d26e-45e7-dbc2-b4668418d9f5"
-      },
-      "source": [
-        "test_image = cv2.imread(image_database_2[26],cv.IMREAD_GRAYSCALE)\n",
-        "# cv2_imshow(test_image)\n",
-        "sift_match(test_image,image_database_2[9], plot=True, MIN_MATCH_COUNT=3)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(False, 1)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 39
-        }
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "import seaborn as sns\n",
+    "\n",
+    "cf_matrix_1 = confusion_matrix(y_test_1, y_cap_1, labels=classes_1) \n",
+    "ax1= plt.subplot()\n",
+    "sns.heatmap(cf_matrix_1, annot=True, ax = ax1);\n",
+    "# sns.heatmap(cf_matrix, annot=True)\n",
+    "ax1.set_xlabel('Predicted labels');\n",
+    "ax1.set_ylabel('True labels');\n",
+    "ax1.set_title('Confusion Matrix');\n",
+    "ax1.xaxis.set_ticklabels(classes_1);\n",
+    "ax1.yaxis.set_ticklabels(classes_1);\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EXHpYB-2EWVQ"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 273
+    },
+    "id": "XnGjlk7U92b9",
+    "outputId": "d916d1fe-d26e-45e7-dbc2-b4668418d9f5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wpBxrIHQ_JxA"
-      },
-      "source": [
-        "# And we're DONE !"
+     "data": {
+      "text/plain": [
+       "(False, 1)"
       ]
+     },
+     "execution_count": 39,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "test_image = cv2.imread(image_database_2[26],cv.IMREAD_GRAYSCALE)\n",
+    "# cv2_imshow(test_image)\n",
+    "sift_match(test_image,image_database_2[9], plot=True, MIN_MATCH_COUNT=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wpBxrIHQ_JxA"
+   },
+   "source": [
+    "# And we're DONE !"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Face_Recognition_Challenge.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}