├── Drone Image Classification in GEE PDF.pdf ├── Drone Image Classification in GEE.ipynb ├── Drone Image JPEGs.zip ├── Drone Image Preprocessing.ipynb ├── Image Preprocessing PDF.pdf ├── LICENSE └── README.md /Drone Image Classification in GEE PDF.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bennkat/Drone_Image_Processing_GEE/c773c01d9ffda82ef2cb00de746b5812ce1bd20e/Drone Image Classification in GEE PDF.pdf -------------------------------------------------------------------------------- /Drone Image JPEGs.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bennkat/Drone_Image_Processing_GEE/c773c01d9ffda82ef2cb00de746b5812ce1bd20e/Drone Image JPEGs.zip -------------------------------------------------------------------------------- /Drone Image Preprocessing.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Background\n" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "\n", 15 | "\n", 16 | "+ First we import the python packages and create a series of functions.\n", 17 | "\n", 18 | "+ Then we open each image, roate it, extract its coordinates, and save it as a `*.tiff` file.\n", 19 | "\n", 20 | "+ Later we transform the coordinates of each image from Decimal Degrees to Eastings and Northings. This will allow us to create a `*.tfw` file for each `*.tiff` image.\n", 21 | " \n", 22 | "+ Finally we create the `*.tfw` file. \n", 23 | "\n", 24 | "Once we create the `*.tiff` and `*.tfw` files, we can project them and upload the images to Google Earth Engine\n", 25 | "\n", 26 | "*Note: We assume that the reader has the required knowledge to use Jupyter notebooks or run this code in another development environment. \n", 27 | "\n" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "# Getting Started\n", 35 | "\n", 36 | "## Load python packages\n", 37 | "\n", 38 | "In the following cell we import tha python packages we'll use in this example." 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 1, 44 | "metadata": { 45 | "ExecuteTime": { 46 | "end_time": "2020-08-10T04:34:41.602538Z", 47 | "start_time": "2020-08-10T04:34:37.837131Z" 48 | } 49 | }, 50 | "outputs": [], 51 | "source": [ 52 | "import gdal\n", 53 | "import os\n", 54 | "import sys\n", 55 | "import glob\n", 56 | "import time \n", 57 | "import subprocess\n", 58 | "import pandas as pd\n", 59 | "from PIL import Image" 60 | ] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "## Find the `*.jpeg` files\n", 67 | "Now, we look for the directory or folder where the drone images are located.\n", 68 | "Here, we're interested only in `*.jpg` files, so we ensure that we only find those." 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 2, 74 | "metadata": { 75 | "ExecuteTime": { 76 | "end_time": "2020-08-10T04:40:44.666503Z", 77 | "start_time": "2020-08-10T04:40:43.963142Z" 78 | } 79 | }, 80 | "outputs": [ 81 | { 82 | "name": "stdout", 83 | "output_type": "stream", 84 | "text": [ 85 | "there are 5 *.jpeg files in C:\\Users\\mkben\\Desktop\\Demo Drone JPEGS\n" 86 | ] 87 | } 88 | ], 89 | "source": [ 90 | "# Locate the directory of folder\n", 91 | "path_1 ='C:/Users/mkben/Desktop/Demo Drone JPEGS'\n", 92 | "# path_1 = 'C:/droneImages/'\n", 93 | "\n", 94 | "# Find all `*.jpeg` files in the directory or folder\n", 95 | "os.chdir(path_1)\n", 96 | "files = glob.glob(\"*.JPG\")\n", 97 | "\n", 98 | "# Print the number of `*.jpeg` files in the folder\n", 99 | "print('there are ', len(files), ' *.jpeg files in ', os.getcwd())" 100 | ] 101 | }, 102 | { 103 | "cell_type": "markdown", 104 | "metadata": {}, 105 | "source": [ 106 | "Now that we know how many images we have, and their location, we can begin the image processing" 107 | ] 108 | }, 109 | { 110 | "cell_type": "markdown", 111 | "metadata": {}, 112 | "source": [ 113 | "# Create the functions\n", 114 | "\n", 115 | "In this step, we create some functions that will help us keep our code clean.\n", 116 | "The first fucntion roates the images 180 degrees, and saves it as `*.tiff`.\n", 117 | "The second function uses `exiftool` to extract the metadata from the images\n", 118 | "the third function creates a `*.tfw` file for each image. See more about `*.tfw` files here (https://en.wikipedia.org/wiki/World_file)" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 3, 124 | "metadata": { 125 | "ExecuteTime": { 126 | "end_time": "2020-08-10T04:51:38.699161Z", 127 | "start_time": "2020-08-10T04:51:38.683536Z" 128 | }, 129 | "code_folding": [] 130 | }, 131 | "outputs": [], 132 | "source": [ 133 | "# Function to rotate the JPEG and save image as TIFF\n", 134 | "def rotate_image(file):\n", 135 | " ''' This function uses the PIL packages to open, rotate, and save an image.\n", 136 | " :: file: a string with the image file name\n", 137 | " :: returns a `*.tiff` image\n", 138 | " '''\n", 139 | " with Image.open(file) as im:\n", 140 | "# Give the file a name\n", 141 | " basename = os.path.splitext(file)[0]\n", 142 | "# Rotate the image\n", 143 | " im = im.rotate(180)\n", 144 | "# Save the image as *.tiff\n", 145 | " im.save(str(basename)+'.tiff', format='TIFF')\n", 146 | " print(basename, ' rotated 180deg and saved as TIFF')" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 4, 152 | "metadata": { 153 | "ExecuteTime": { 154 | "end_time": "2019-08-26T01:47:00.743775Z", 155 | "start_time": "2019-08-26T01:47:00.734275Z" 156 | }, 157 | "code_folding": [] 158 | }, 159 | "outputs": [], 160 | "source": [ 161 | "# Function to get the Lat and Lon values from the metadata of the images\n", 162 | "def get_lat_lon(file):\n", 163 | " '''this function gets the latitude and longitude values from an image using the \"exiftool\"\n", 164 | " and exports it to a pandas DataFrame. To use the 'exiftool', ensure that the executable file\n", 165 | " 'exiftool(-k).exe' is in the folder where the images are located.\n", 166 | " ::file (string): the file to extract the metadata from\n", 167 | " Returns pandas Dataframe.\n", 168 | " '''\n", 169 | " exe = 'exiftool(-k).exe' #ensure EXIFtool is on your working directory\n", 170 | " metadata = {}\n", 171 | " cols = ['Image_ID', 'Lat_dd', 'Lon_dd']\n", 172 | " lat_lon = pd.DataFrame(columns=cols)\n", 173 | " #execute exiftool to get the metadata\n", 174 | " process = subprocess.Popen([exe, file], stdout = subprocess.PIPE, \n", 175 | " stderr = subprocess.STDOUT,\n", 176 | " universal_newlines = True)\n", 177 | " #parse each line and store it in a dictionary\n", 178 | " for output in process.stdout:\n", 179 | " #print(output.strip().split(':'))\n", 180 | " info = {}\n", 181 | " line = output.strip().split(':')\n", 182 | " try: \n", 183 | " key = line[0].strip()\n", 184 | " value = line[1].strip()\n", 185 | " metadata[key] = value\n", 186 | " except IndexError as e:\n", 187 | " print(e)\n", 188 | " #transform the dictionary to pandas DataFrame\n", 189 | " metadata = pd.DataFrame.from_dict(metadata, orient='index')\n", 190 | " # Get the Latitude and logitude and transform D-M-S to Decimal Degrees\n", 191 | " lat_dms = str(metadata.loc['GPS Latitude'])\n", 192 | " lat_dms = lat_dms.replace(\"'\",'').replace('\"','').split(' ')\n", 193 | " lat_dd = int(lat_dms[4]) + int(lat_dms[6])/60 + float(lat_dms[7])/3600\n", 194 | " lon_dms = str(metadata.loc['GPS Longitude'])\n", 195 | " lon_dms = lon_dms.replace(\"'\",'').replace('\"','').split(' ')\n", 196 | " lon_dd = int(lon_dms[4]) + int(lon_dms[6])/60 + float(lon_dms[7])/3600\n", 197 | " #create a dataframe of lats and lons\n", 198 | " basename = os.path.splitext(file)[0]\n", 199 | " #here, the latitude has to be negative to be in the correct location\n", 200 | " df2 = pd.DataFrame([[basename, '-'+str(lat_dd), lon_dd]],\n", 201 | " columns = cols)\n", 202 | " lat_lon = lat_lon.append(df2)\n", 203 | " return lat_lon" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": 5, 209 | "metadata": { 210 | "ExecuteTime": { 211 | "end_time": "2019-08-26T01:47:02.377503Z", 212 | "start_time": "2019-08-26T01:47:02.372491Z" 213 | } 214 | }, 215 | "outputs": [], 216 | "source": [ 217 | "# Create a `*.tfw` file for each image\n", 218 | "def create_tfw(file, csv_file):\n", 219 | " '''This function creates a tfw file with the eastings and northings \n", 220 | " for each image.\n", 221 | " :: file (string): *.jpeg file to be transformed\n", 222 | " :: csv_file: CSV file with the eastings and northings of each `file`\n", 223 | " Returns a *.tfw file\n", 224 | " ''' \n", 225 | " #give a name to the file\n", 226 | " basename = os.path.splitext(file)[0]\n", 227 | " \n", 228 | " #Get the Eastings and Northings from the csv file \n", 229 | " easting = float(eastings[eastings['Image_ID'] == basename]['X'].values.round(3))\n", 230 | " northing = float(eastings[eastings['Image_ID'] == basename]['Y'].values.round(3))\n", 231 | " \n", 232 | " #create the *.twf file to store the eastings and northings for each file\n", 233 | " tfw = ('0.0045884815\\n0.0000000000\\n0.0000000000\\n-0.0045884815\\n'+\n", 234 | " str(easting)+'\\n'+str(northing))\n", 235 | " \n", 236 | " with open(basename+'.tfw', \"w\") as file:\n", 237 | " file.write(str(tfw)) \n", 238 | " \n", 239 | " print(basename, 'has a new *.tfw file')" 240 | ] 241 | }, 242 | { 243 | "cell_type": "markdown", 244 | "metadata": {}, 245 | "source": [ 246 | "# Lets put it into practice:\n" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 6, 252 | "metadata": { 253 | "ExecuteTime": { 254 | "end_time": "2019-08-26T01:47:16.096183Z", 255 | "start_time": "2019-08-26T01:47:16.093175Z" 256 | } 257 | }, 258 | "outputs": [ 259 | { 260 | "name": "stdout", 261 | "output_type": "stream", 262 | "text": [ 263 | "C:\\Users\\mkben\\Desktop\\Demo Drone JPEGS\n" 264 | ] 265 | } 266 | ], 267 | "source": [ 268 | "# Print the working directory to ensure that this is where the images are stored\n", 269 | "print(os.getcwd())" 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": 7, 275 | "metadata": { 276 | "ExecuteTime": { 277 | "end_time": "2019-08-26T01:48:36.751646Z", 278 | "start_time": "2019-08-26T01:47:19.900778Z" 279 | } 280 | }, 281 | "outputs": [ 282 | { 283 | "name": "stdout", 284 | "output_type": "stream", 285 | "text": [ 286 | "DJI_0002 rotated 180deg and saved as TIFF\n", 287 | "DJI_0003 rotated 180deg and saved as TIFF\n", 288 | "DJI_0004 rotated 180deg and saved as TIFF\n", 289 | "DJI_0005 rotated 180deg and saved as TIFF\n", 290 | "DJI_0006 rotated 180deg and saved as TIFF\n" 291 | ] 292 | } 293 | ], 294 | "source": [ 295 | "# Use the 'rotate_image' function on each image. \n", 296 | "for file in files:\n", 297 | " rotate_image(file)" 298 | ] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "execution_count": 8, 303 | "metadata": { 304 | "ExecuteTime": { 305 | "end_time": "2019-08-26T01:51:52.366185Z", 306 | "start_time": "2019-08-26T01:51:20.846985Z" 307 | }, 308 | "scrolled": true 309 | }, 310 | "outputs": [ 311 | { 312 | "name": "stdout", 313 | "output_type": "stream", 314 | "text": [ 315 | "working on DJI_0002.JPG\n", 316 | "list index out of range\n", 317 | "working on DJI_0003.JPG\n", 318 | "list index out of range\n", 319 | "working on DJI_0004.JPG\n", 320 | "list index out of range\n", 321 | "working on DJI_0005.JPG\n", 322 | "list index out of range\n", 323 | "working on DJI_0006.JPG\n", 324 | "list index out of range\n" 325 | ] 326 | } 327 | ], 328 | "source": [ 329 | "# Create an empty pandas DataFrame to store the coordinates of all images\n", 330 | "coordinates = pd.DataFrame()\n", 331 | "\n", 332 | "#iterate though the folder where the files are\n", 333 | "for file in files: \n", 334 | " print('working on ', file)\n", 335 | "\n", 336 | " # Extract the latitude and logitude of each image using the 'get_lat_lon' function.\n", 337 | " coords = get_lat_lon(file)\n", 338 | " \n", 339 | " # Add the coordindates of each file to the 'coordinates' DataFrame\n", 340 | " coordinates = coordinates.append(coords)\n", 341 | " \n", 342 | "# Export the DataFrame with the coordinates for all images to a `*.csv` file\n", 343 | "coordinates.to_csv('coordinates.csv')" 344 | ] 345 | }, 346 | { 347 | "attachments": { 348 | "Capture.PNG": { 349 | "image/png": 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vFC7mFUKif63uuHa+qT5mvm4f93itbAzdXnRcUVvRfGPm2n1FwSle9L3r0uLfiRMGQq8173FeXek1efJCSjxf4TpSAp8boffEIjgB5fbu3auOHTuWcfjwYbG/i+DksGFodsLURf82vWNsmMkLQAsNUxf9qx9L47X6RBrOz/HjheQYNzwVzbmYdM96Tz+pNm3aJFijNjn/Kzt+XMAuYnqhjeuEY+0CujRt6qJ/7X33j2v16eK4aefn+LFepKI+005dihlDL2Riu+FfazyuXixNu52ve+124bSPy+YmjRH3CzjOP6ZsvvZ+uvfO1rXur0TqY+5hld+DP2fpGvy60msSxH0iqXozX/98GaZf2XMj9J5Y4pw6Ir2GgdEwNTWljh49mgpN+vHk5KTY3zV2wUm/AWVEYcW2+yHFhqmyICPVh9almMBlw1Zp/0LSPeu9uoJT0YJdtKiH1HV6XIa0qHnsnGM5C1rmPN7CKM6j7Nxee+m1WAHHlc3XD3Wxsvka/tj+71xUMueQayi7JklXwSkS8tzQqtwTghMQZt++fangpB9L/XzsOFnCblAsMMiEhiSpzv94MOR8YaR71nu1BKe8RatkgQyt6/Q4LbXYGWVhwPKPjRe+nGvV5y4MTsJxRXMTxzAqHVc235z20ODk7/TkBYEqc5auPVVXdk1Onavb4OQSnxtG6D0pa6tGeg0Do0PvLtldp9DdJo3gZHkBqcULVHljhIYkv04/Tp13RHacNm7cKFijNk6aRWUu+lk/zmMW2bmof6reHL8YtevHi8JYIXXdHJeaV948y5jj4rHNzxJ7bmlu0r3Qx+TNTRzD1Fc6rmy+3rz840rvlXB+f6yqc85cg19njpf4x7mmF6M+kVS9uf6i4wqZuaSOD7gnljinjkivYWC0HDhwIA5O+/fvF9slBCfLC0gtvdxxksLaqAenyJz+GCcymXqT9pjFp2zxLV0Qc+o6Os47d25dID12vMDlXatDmps9Lj53wNzEMTo5LqovnG9eu3SuHPqc8fHmmGm3XRrHq/PnLF17qq7smnLkhZSg53gBPTd/3MJ74iA4AeF27typms1m/K/ULiE4OaT2nn7HaUyD02S0WOlrLFukpMXOHmsXjdIFMaeuo+O8xTm3LoRZqO3Y0rldUnvqXgTMTTxHh8d1Mt8q98ou/mII6GDOIddQdk2SvJAS+hwXRdfgPjeswnviKGsPJ72GARCcHKH/VZ00RmlIkupyxnfnEP9Xdh3/ZXPpnvWeflJt2LBBsKZlt35zj65zcXe7LhY91v9LvRH93DCLz1wUJuO26F+7oNj+drFrHR9Y19FxOedPzdGnrydq3+3VxzsSTn3mWk2dfRyfJ7pnti0zl4C5SdcXclz8uzK/E9unbL729+ted+m9cpn7pmWeIwFz9q/VvwZ7/90+Zdckicd1fy9+WzRe7nM88LnRUnRPHEVzqkZ6DQMgOHn8L2qHhKG8+qC66E3ZPd9s1Bb/a8ObOca2D8vfcZKebJk3ZmchcPkLl9vmL/xSGAip6/Q4f85zUVv8b8HiahfCFGcRt/xrdfvE8/DG8edaNjfp+kKO0/Sxtt0Pey3eNdngYO025ym6Vy57zkyA0Dq4VvcadD+pT9k1+UpDijdPy84z9LlhFd4Tg+AE9NZYBafxI92z3gsOTggmLfLov/pCSn0ITkBvEZxGmnTPek8/qdavXy9Ygw7Fwektchv65zUmpEht/VLfnKTXMACC00iT7lnvEZzqR3AaTHFIMR+faYuvkfv1XHRedx4EJ6B3CE4jTbpnvUdwAkaB9BoGQHAaadI96z39pFq3bp1gDYC+k16bAEIRnEaadM96Tz+pJiYmBGsArBrpNQigW0MenPpBCiiDSpp/7+kn1dq1awVrutDt8cCo819vAHqB4NQVKawMEmnOvaefVKtrDQDxtQGgF6S1r1dGLDjlkUJMP0hzGyfSPQFGlfQaADDsxiQ4YbBJiw4w7KTnOoBhR3DCAJMWI2BYSM9pAMOO4IQRJS1kwGqSnpcAhh3BCYhJCx9Gn/RcAIB8BCcgmLTwYjhIv08AqI7gBNRGWrCxOqTfBwDUj+AE9I0UAJBPuocAsLoITsDQkMLEqJKuHwD6j+A0Bn7v934PAIBKpPXEd8IJJwwEaW69QnAaA/oF8N73vhcAgCBVgtP27dv7iuCE2hGcAABVEJzyEZzGAMEJAFAFwSkfwWkMEJwAAFUQnPIRnMYAwQkAUAXBKR/BaQwQnAAAVRCc8hGcxgDBCQBQBcEpH8FpDBCcBtGp6vnR7+XEbW8Q2gCgvwhO+YYoODXUwsqKas5OCG0o0p/gJAWDpO73nn+qUzcO8u9FcXAa1/sFoBPve9/71Ac+8AG1f//+Qh/84AfVaaedJo5hEZzy9TE4JUFoRdKcVRO2faGR6k9wqo7g1G8EJwC9d/rpp6sDBw6oZrOpzj333Phn6+yzz1YHDx6Mfz7zzDMJTl3oe3DKD0IEp7oMTnAaV50GJwAI8/73vz8ORDoY6Q2IO+64Q11wwQXxYx2kPv3pT6sbbrhBHTp0iODUpQEOTj6CU6fKgtMbtp0Y90k8X53aajM7Hilue+TU56fbT9ym3pB7nB8W7ONT1bYT233FMOGd58QT9ZxPVNveILcn83COLxQ+j/S98vu0r6/d72R1stM/4d6LonMO1v3yr721EyaO9YZkjsK4pz5frgfQGR2CzjjjjNbu0llnnaW+8pWvqHvuuUdddtll6nOf+5y699571bFjx+IQRXDqzsDvOLXb5f4Ts03nY74F1XDasuwYDTXbtMf4YyZ92mNq7rjyGMnHixMF4yaqzbce+gUgvTC0ZDFsL6jpRe1Udeqp6f5xuw1Pb9imTozGfr7T59Tn22DlL/pSXfJYz681RrwIOwt8q849j1mUbb/CeYQIm4d/r+x5pespvu503/xz+seFzbMX9ytz7Xou+nlSNJY0N/FeAOiU/l7Tvn37WqHJOuecc9RXv/pV9ZOf/ET99Kc/VZdeemmrjeDUnaEOTkkIaarZieRxY8EGGDuGrx2KFhqmrrGQGkP3adg2Ix63FXLyxtB1zjgTs6rp9olUn2899AtAemGIi5iwEKa4i6G4MFrSAunXhfQxi77/PZ/geYTodK42UBSFxbz6Ts4Zckwv7pd0XqPqcyDuXyXUAihSFJz0R3P//u//Hoenj33sY602glN3BvPL4fH3msqCk/84IoSVNOEYsc6TClehY/h1Qp/S+dYjNziJISlZ7Ny6zEc0dqE0x8sfu4Qs8gF98oKcu2AXziNETfMQx8mrD6kreyzU9eJ+5Y3ptOWN5X8sFz/my+5AraSP6nRo+s53vqMuueSS+PtNP/zhD9UVV1zBR3U1GN4dJzF0JH3qCE7pj9S0LoNTR/OtR25wihfTaCETJAtxsiindhRSQUEIVa1FMWCRrysIRI/z5xEiYB7e+VpS85PGyasPqSt7LNT14n6Z54kYnCKFY6XOm8w1bxwAnfO/HH7bbbepw4cPx491WLrqqqvU9ddfz5fDazC8wan18VhW+JhSXfI49bFbHTtOHc23HvoFIL0wCncSNCks5AUI/3s0IYt8jUGgzZ9HiLrmIY2TVx9SV/ZYqOvF/Sp7nrRIYznzi+fAx3RAr7h/juBDH/pQawdK07tQy8vL8c8Ep+6M2I5TmdCA44SmTF3AGFLdKn0sJ8kNTuJC7JAW29wFOJJaYAMW+Y77RILnEaLzeSS7LYP0HSepT6Sr+5UzpkQYK75HJ25T254fOAaAjvAHMFfHEH85vOx4iXSMV9er4CT2WR35wcku/OkF9Q3bnp88Nougvyjb/vrYzALp7Chk/7PzkEU+W5f6L/k0M6/QeZQLm0fmXuXcn/Q4ifJ7IdWVPZbrur1fyeP0mJlr1+eNrucVJWPFvPO36gEMLIJTviEOTtn/Si2pW0iHnhTpnF6d2Rnyz9t9cOpkvvUoCk6aXShb3AU+3qmwbdHCF4Wq1gLYWhCd9tTCmCzqSZteTDsLAloSBpyx3B2U0nmUCZ+Hf69CjnHbkuOke9Hu064re5xX1939koKTW9+iv8sUdO/NR3ip4AhgkOnXs7Se+KoEp9m9ei3dq2Zt3eze6PGK2jtr+8yqvXq93TvbOiYEwSm3Xe6f+RJ3wJ8jKAs46e8jRUEnCjd1BCet2nzroV8A0gtjqMVBwNvZQD7uF4AKCE75+hicsFqGPjhFi37ILktGZjdEUnV3agh0er8AwNDvj9J64qsSnHqF4ITa6ReA9MIYGvFuSTrwEAIKcL8AdEm/b0jriY/ghJGkXwDSCwMAAAnBKR/BaQwQnAAAVRCc8hGcxgDBCQBQBcEpH8FpDBCcAABVEJzyEZzGgH4BAABQhbSe+AhOAAAAgXRoGQTS3HqF4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4DQGpD+lDwDAqJDWvl4hOI2B1X5SAQCwWghOqB3BCQAwqghOqB3BCQAwqghOqB3BCQAwqghOqB3BCQAwqghOqB3BCQAwqghOqB3BCQAwqghOqB3BCQAwqsYoODXUwsqKas5OCG2oE8EJADCqCE6oHcEJADCqCE6oHcEJADCqCE5e+0rKgmrEbRNqthk9Xmh4x5yiGgtRfXNWTZjHE7NN4Xitff52H7fdZfs2kvOa8dJzL5pv/hjJXM31mDrpnuRfRzmCEwBgVBGcnPZGI10XhyIbGhoLQoBIxlwwxyVho6lmJ5LH6VDVDjr5c7Dafe3YyfnbY5fON3cMXeeMMzGrmm6fSPF1lCM4AQBGFcGpSCqsCMenwpTQngolVc4v9Q04vmy+QXVCHyFcFSE4AQBGFcHJk/6IKr074++8xI/tx3diuEjO2cvglD/f0DG8utLrKEdwAgCMKoKT1+4GpczHY3GoMI/dn1t93QDTlpyz7Pwuqa9fVzbfkDGEutLrsMflIzgBAEYVwcnyQ5JYl3ypWo8R7/S43/sp/Tir5PwpoQGnaL4BY0h1pddRjuAEABhVBCcrKDiZj8aaC2ohClDSx1m545e2uwICTq+Ck9inGoITAGBUEZwss9PiB4hMODH9pP9E3/+v0ZK6hYIgk0fq69WVzjdgjJy64usoR3ACAIyqsQtO0nd30n9ywNZFwSEKC5ngFEl9KdyT+bK29+cI0qElT2DoKZxv4Bg588q/jnIEJwDAqBqj4FSTeKcnG6bQRnACAIwqglNFyXecwndfxhHBCQAwqghOlcgfayGN4AQAGFUEJ9SO4AQAGFUEJ9SO4AQAGFUEJ9SO4AQAGFUEJ9SO4AQAGFUEJ9ROP6kAABhV0trXKwQnAACAQAQnAACAQAQnAACAQAQnlFgzIKS5AQCwughOI0EKGuNGui8AANSL4DQSpCCBNumeAQBQHcFpJEhhAeWkewkAQD6C00iQQgHqId1vAMC4IjiNBGnBR29JvwcAwKgjOI0EaWFve8ITMDieAABDRV53TlEnnHDCwJDm1ysEp5EgByZLL9g7dmD07ACAnioLTtu3b+87ghM6IAcmSwen7dsxHuQ3FgDoBMEpa5WCk7ygDzbpOgaVNP82HZy2bcN42QYAXSM4ZRGccknXMaik+bcRnAAMDnmBxmAiOGURnHJJ1zGopPm36eC0dSsADIKtGCIEpyyCUy7pOgaVNP82ghOAwSEv0BhMvQhOeicrpC4UwWlgSNcxqKT5txGcAAwOeYHGYKo7OO3atUvNzMyoRqOhXvCCF8T/5d7OnTvVa17zGjU5OSkeU4bgNDCk6xhU0vzbCE4ABoe8QGMw1RmcXvziF6vzDl+ovvjlG9U737Oo9h/8iDrznPPU2971PnXVZ7+kDpx1UL3oRS8Sjy1CcBoY0nUMKmn+bTo4bdkCAINgC4ZIHcFJfwz3wr/9O3XwIyvqum/coS6/8jPqgkuOqvMv/pg698LLI5eow5f8q7rqC19Vp33g7Kj/DnGcPASngSFdx6CS5t9GcAIwOOQFGoOpjuC086WvUG9/z151xtkH1d4zDqh/fM20Wjqjqebf8W71lrctqPedfoZ6+Stfpd512pJaPPNs9eZ3vF+9aOdLo2PDAhTBaWBI1zGopPm3EZwwCuYPrlEH5+U2DBN5gcZg6jY4/d2uV6tzL7pCfet796k7v/cjddXVx9UFRz6mPnn8a+rQRy5Sy+ddoK747JfVcvTzRUc/oa6+/mb1yWu+pk4783z1wsYrxTF94xmcGmvUykrW7ETSviC0aQ1/nMjEbHjfYtJ1DCpp/m06OG3enG9JuF/WcrRQ5fZZyo41FfXP9IvMeP02T0VjC/3s+YrMROctO07qszTj9Mk5vzUftet+edej2T4t0fhue8i1hIivZTmai1c/H9Xl1vu/m8D7Hfz782SOE54bLvv7ydzDAvo5mPodenR73j1324qe75bfV5pnfJ+dvsOmzud2tefNZgyRboKT/ohu31kfVrfe/UN1w633qK9FTj/zLHX1l29S1379TrUSBaULL7tCXf2Vb0bh6Tr1rtNOVx+/+qvq45//qvrEF29Qb3/fGUH/td3YBafGQvICW4jCU6pNh6lmFISin3VwakaByG2fjdr0cW4gsmPZwOX29evKSdcxqKT5t4UEp7I3f6mPXVTsm2PeYqgXGLfO9pMWQT1m2WKq+6TekM2bujs/fU63j31jL1p4NdvPzsE+LgsO9prK+nXCn5Nl779U796L0Psd+vvzSdcezy2qd/tZ7iJbNG6K+R0X3V//uutqi+fqX0s0bxtE844ddHU9t6s/b+QFGoOp0+Ckv+R92t4z1OGLL1XNcw6plcuPqQ995EJ1xtkfUld+7jr12WtvVOdecKk6+JEj6sqrr1OXfvxq9b7TD6jTmwfVmYfOj34+U73r/fvU6940p174wheK57DGKziZnaZMaPJIwemUKAg13Xr/cdek6xhU0vzbehWc7OIR17s/u318ZgEsCzBVxf/rP+LvvrjiRTBnMbficZw+QYtLwKLeFXNvU/dMnzOa65Lm10d9WwuWeVx6v0N/fwLpuZF73+z8THtocIoX55LfXdHzuJu2JRMM3GuJry+qLzp20NXy3O7oeSMv0Oid+fl5deTIEXXo0KEW/VjXS/1dnQQnHZrOOuss9fXbv6e+9+OfqLvv/Tf1+Wu/qt72jgXVPPQRddah89SZUZh681vfrv55/m3q9P1nR5Zjr/+XOXXh0SvVFZ+7Vn3iSzeoS6JA9fa3vz3+0wXSubSxCk7xrpHZVZLaraDgFBjCwknXMaik+bf1LDiZ+nhBC1ygQwKOK+4fjVsWSkLGLe1jF3VnMQ9ZXPS4vV48/ftvF24dKDL1znyD73fg70/Seg44deJ9M4tsfA7hXhfR11E2t6LncTdt+rz2X7dez73oWM3ehwznfvl9/PHsOeLfZU4fzR+n7DUj/o48pc9t83us9ryRF2j0zt69e9WxY8cyDh8+LPZ3VQ1O+u8y6fN946ab1c3f+kEUnH6q7vnRf8TB6cJLLlfX3XSXuulb31XfuPMetRIFpAsv/1f15ZvvUMdvvFVd9cUb1P6D56mLjl2lPnXtjeqqa76mLrvqi+qSSy9Tb3rTmzLnssYnOFXYIRKDkx+UzHgrC06frkjXMaik+bfp4LRpUz77xiy1WXl9dL1eBDaZRTH+2evTYvqUnctlF4tpoc2yC4BeyKT2WMD84nN57dLC589f34OlqC4e34rGmnL6dGs6mpc7pn68NG3m59W3rqHK/Q75/eWIz+kdKz1fUnXR3PUxhb8zy8ytrG/ec7Tbtsx91nM3PxcdK/KuO/PcNdfqjqnPofvoeUhjaP44/vNFUstz28y32vNmE1bZ1NSUOnr0aCo06cf6D05K/V1Vg9Pf/u3fqpe97GXqX+beoT72iavVV77+TXXrXd9V55x7vrr+m7era268U90cPf76Hd9JgtNlUXC6KQlOV37+enXF1deo9+07M/r3WnVhFKDOOPRRtXvmdfEfyczbdSI4CaTgpOv8kJT6YnjXAUq6jkElzb8tJDi17pvHvhGLC4R5A7dv6Kk3YumN1LzJthaALrX+F3jAm7Zd3HPPLSxGItOvdU67cHiLVHxPSxauKuy9tQFSjx/P1ZzfrW/9nire79LfXwF/EfafK5lQGnq/I/HYAfdSfI7W0BbfP3Mv9Xz1c8n2LzpW4veXjpd+136f0nEq/u5jHT63qz9v5AUavbVv375UcNKPpX6+Tr/jtGnyTeolbz6s/vGty+rl//Qu9Q+vfr067cD56rQzz1P7D12kDh2+VL137371nsUPqua5F6mlA+ept7//LPXW95+tXvKyGdV4zbza+cYDasvrz1ebdr1RPIdFcBLEIclT9mVv/7+uG/f/qk5+A0tIb8y+vDfmvAXNX0jjRSDnzTx+I3aELKYp5g2/LBQVvanHC3vA4qzZEBZfU965Q+fkKLxuc+/ywpJb3zqn/9jpX3Ze8feXI3U/zDn1Y/t8SbVb5vpDftf6d1P2/NSKnsfdtNn7F88juhb/3ofMTcvch5zfj39vpHOk6nLGceceqtvndtjzRl6g0Vt6d8nuOoXuNmmdBqctO3erhYu/qY7/4L/U1d99UH3yW/erIzd8X02986g6+Omb1ZlXXq8Wzr9CvePco+qcS/apq89/rPrSh9fk+D/Up87+b+qf/vF54rkIToLUjlPVj+TMR3oh50mTrmNQSfNv08Fp48Z8i9H9WZ6T2yzdx31D1MqOiZk32bjvZPIGn3uc6TsX9RPbC0wvRsdGC9uk32bOKbZZ5ryL0b9iu2cymn9rnmb8zLFl19qB1u9Jz9e5njm9oNv6qM+0PaZsDqZ/4f02fXLHyLn++PdhxrY/5ym7R/q6Q343Rc/jbtpa5zb3YiW6Hre9bP4xc2zqOkxd5v5791Q6R6rOzksQNDdHbc9tMye5z0b0yYEDB+LgtH//frFd0mlw2vbChnrNBz+vvnTvf6nPf/9h9dnvPqQ+dc9D6g0f/ro68s2fq8vu/KVaue0BdcEtv1BfuOAP1Jc/+hj1jSufq2759Fp1y2fWq1s/syH+9/bP71D3fesc9eUL/0ccnqRzjU9winT85fCKYUj6WK+cdB2DSpp/W13BqeqbsKWPtYuNXuRzQ0zeQhLAvuG3QoMRn1uodxXOSSAtLpl7E7K4VBTPM7qPOoi448bzMfX+ddRxv93fX0beGKY+N/CE/q5Nv6Lfn1U0z6Lnb1mbew3S45Dfse6XmZt5jmTukXdvpHOk6vLG6UCdz23xmmPyAo3e098Tajab8b9Su6TT4KTtfNvl8W7TF6Lg9LnvPaw+HQWndxy5TR24+l51uQ5Otz+gPnrrL+JdpZuufF4cllx3XfMy9cB/3qJ+q5T60Z1nxf2k84xVcLIfp3Xy5why//6Tr8LOVpp0HcNJP/HbL4TsG1nIm39IH5H3JmvfmMU3eW/BqEIKDfEbd15osMw5qyw6fhgRw0kX15InvnfReRY1d776XKbe/x11fb/LFkkzRmb8srFDzh2Jf685YcgnPQdieXM0ip7buq3ouRHyurC/Ayn8Scf7/aU+fl3IPELU9twufN6kF2YMtm6C0wtf+Xb1gSu/pb74gyQ4nX/dv6m5j96iXnHGl9XcRbeq1334G+qfPnxTHIj0TpMbmu654Y3qoV/dG4emB+6/Vd1+9XaCk9XpH8DUUn8E0+xC+d9lkv5QZhjpOobTqgQn82bqLw7xG69XHy9wUV3IYps5Xvfx3silcKDnKy6iHnFhcOhx3LmLQcTMO3P+wAU/mDmPllq0zCKVmZcRdL/NzyG/P5/Up/T63XNL7YYeW7omkbkP/vO07Lmg2/Oe27qt6PxFx8bMnPLGsM+n1n0QrkE6h1+XGcfUFd1fPUbXz23TXu15k16YMdi6CU7a37/zUvXOi29Vrz7zOjX/0W+q/Z/9vvr7peNq6RPfUWdfc586cPy+OBC5oen7tyyq//r1L+LQ9NBDP1Z3XfuKuJ7g5DKhx+f+X66IO0b2+06mrw1JKQEfBcqk6xhO+om/YcMGY01G/EaYQ7852z725zz2zTIlqmsIfTfsFvpGdnv97JhuvTTfueg51Dou+tkGCclidO64n5lD67FAuqbUuSzvesruVafiaxfuqZ2nODct4H5X+v15dptwZpVev5lP7nw183ss7CPIPD+iuUn9rKLntm4ren4UHav590WaUyM63m3zx5POIdX545T97up6bld/3tj3IgyDboPTthefqhoLV6mP3/kL9ZnvPqQ++Z0H1Udv/Knac/Br6tI7fqkuui35qK4Vmm5aUL955NdxaPr1b36lvnvjW+LvOmkEp4EnXcdwKgtOwCCKg0BgcMMwSS/MGGzdBiftBX83pXa+4wp18Iv3qk995yF15d0Pqvf967cjd6uFY9+OA5H2/W++V93yyf9P/eDWJfWbR3+j7rru1a02Sxqf4DQwpOsYTgQnDCO9k1G0m4NhlV6YMdjqCE7WS97QVG86dI1a/Pi31Tlf/KF62Qe/rF72gS/HgegrFz5OPXD/beqhB38ch6dbzQ6TTxqX4DQwpOsYTvqJv379emMNMBTij8leI7dhmNn3IgyDOoOTtvXFr1AvfOU71M43n6u2v/myyOVxILr1c1vNF8FvV/d8/a3q2sP/PROaNGlMgtPAkK5jOBGcAAyO9MKMwVZ3cJJ8+pz/ru669mXq1s9uVNdd+D/EwKTxd5wGnnQdw2k8gtOoXx8wKuxrFcNgNYKT/ovgOhRJYckaw78cjn4ar+BUJ+k8ALojvdYwqFYjOHWL4ITa6Sf+unXrjDUjyl5f3aRz9Zs0TwCoH8Epi+A0BghO3ZDO1QvSuQGgvwhOWQSnMaCf+BMTEwAAVEJwyiI4jQH9xF+7di0AAJUQnLIITmNAP/EBAOiEtK5oOrAMCml+vUJwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACERwAgAACFRLcHrKU54CAAAwNKQ8E6K24CTVAwAA9Mr27ds7QnACAABjRwpFIQhOAABg7EihKATBCQAAjB0pFIUgOAEAgLEjhaIQBCcAADB2pFAUguAEAADGjhSKQhCcAADA2JFCUQiCEwAAGDtSKApBcAIAAGNHCkUhCE4AAGDsSKEoBMEJAACMHSkUhSA4AQCAsSOFohAEJwAAMHakUBSC4AQAAMaOFIpCEJwAAMDYkUJRCIITAAAYO1IoCkFwytiulo7fre6+W7tYzYp9hsmsuji+Fs/xJbVd7F9u9mI7znG1tN1tG7V7BwAYVVIoCkFwyhihxX/7kjpug9LFs6a+++sjOAEAhp0UikKMRHB6+tOfrk444YRCuo90bNaoLP4F1yEGqnD5wQkAgOEghaIQIxGcnvrUp6rf+Z3ficOR72lPe5p6zGMeE/eRjs0KC07t8JB28aztkzeOU+9+XOaGGSsVapyP3MxxyRxy5lgYjty52fCTHn8pdX3tc+Rdd9In59q02Yu9/u690sLOn+nrOL603ekDAEA+KRSFGIngpD3xiU9Uf/Inf6LWrl2b8qQnPSluk46RhQen1MLvBAO7gG9fOt6qa/V1Ak22nz2fFEDygoU8R/fcUqBwA1Ayt3QYSerke9E+tuCjOic4tfvbMZxztUKddP70Ncfj5QRCfb0EJwBAKCkUhRiZ4KQ9/vGPj3eZbGg66aST4jqpb76w4JRVEnbMIt8ONNmdntTC7wSx/GCTryw4ZduFkKJl5lExOAlBUSu6D0Why5132T0AACCPFIpCjFRwOvnkk9Xv//7vq7/4i79Qz3nOc9TjHve4uE7qm69CcBI+forlLvxCsBCCSSwTOHKCTY5eBCc7TpXglBt0MuNWD04Jfw4AAJSTQlGIkQpOmt5leuxjHxvTP0t9ioUEJ2eRby3cQijS3GC01A5DNkRkg0BWL4JTO5DYuQxHcErf+7TU+AAAFJBCUYiRC06a/VK41FYuIDi5Yai1WOcEJ3Ghd8YVx5JUC07dfjl8cIOTJdzXkPsCAEBECkUhRjI4dac8OMlhIC84pXd3Ym6QyfkOUFbF4FR0HWKoksdvX2s7JEl1CeEe1Pwdp/a5rKr3BQAAglONyoOTGAacHZTMAu4GlYi/s5S7K2PmktR1EBDEgJR3fdL4Tp0b9oRdqMzYheFHGle+Pv9Yfa9S5wzesQMAoE0KRSEIThlusBDYRd0NStrFS2JoyI4ZsHPi6Tg4xXLGzYyRf/5UaDLcsJfQ1yUHp5h/vyLpoBMenNwxEv7OFwAAxaRQFILgtBrc0CCEkMHQaTADAGD4SKEoBMFpFbjfcRrcj5MITgCA8SGFohAEp54blkBCcAIAjA8pFIUgOAEAgLEjhaIQBCcAADB2pFAUguAEAADGjhSKQhCcAADA2JFCUQiCEwAAGDtSKApBcAIAAGNHCkUhCE4AAGDsSKEoBMEJAACMHSkUhSA4AQCAsSOFohAEJwAAMHakUBSC4AQAAMaOFIpCEJwAAMDYkUJRCIITAAAYO1IoCkFwAgAAY0cKRSEITgAAYOxIoSgEwQkAAIwdKRSFIDgBAICxI4WiEAQnAAAwdqRQFILgBAAAxo4UikIQnAAAwNiRQlEIghMAABg7UigKQXACAABjRwpFIQhOAABg7EihKATBCQAAjB0pFIUgOAFdeMITngAAqIn0PtsrUigKQXACuqBf6Dt27AAAdIngFIjghGGmX+jSCwsAUA3BKRDBCcNMv9C3bdsGAOgSwSkQwQnDTL/Qt27dCgDoEsEpEMEJw4zgBAD1IDgFIjhhmBGcAKAeBKdABCcMM/1C37JlCwCgSwSnQAQnDDOCEwDUg+AUqK/BqbGgVlZWPE01O6HbG2oh06YtqEZqnLx+iebshNM30Vgw7QsNr614LCsZs/p5SxXej8TEbFPok9yT5Lr8+2NNqNlm1L7wpgrXKI0zWIqD0x61ZK5naY/cdnB+l1cf2bOUuR9iPwAYIQSnQP0KTja8LDS8Nh0emrNqwgST9AJuFv9UOJD6FXEDT17IsIrGrnreYuX3w/ZJB6nknpi6OHj57cbErGpK49d8HatNv9A3b96cY6YVnFaW59WU0LY8P+XUbVYzS0n/pZls3+wYADA6CE6B+hKczM5KdhF35SzoJgC06ysu/DZczHYxh9K2ikLuR+a6JflzSnaqpKBY43X0QUhwWl5aUsvRv1IYSgWnmWSnKd3PMuFpaUZoA4DBMD8/r44cOaIOHTrUoh/reqm/i+AUaPWDk9k1Mrsoch8tZ0HvMjjFuzbOjlb24zpX0dh1BY7A+xEUNt3rc+vtx3TStdZ1Hf0RFJyicBTvJKV2jPzgNKXml/0+aVPzy9HvYEnNCG0AMAj27t2rjh07lnH48GGxv4vgFGjVg1PQzomWs6BnAkSVhT/pmwQI6WM/X9HYNQWO0Pth+hUHvYjdUXM/rjPHyqGrpuvoE/1C37RpU47pVjjaNDWf7DpNC23iY8G02ZFqjQEAg2VqakodPXo0FZr048nJSbG/i+AUaLiCkxt8vDqRFyC80GW/aJ2/i1MUKiqct0jw/fC+GJ4boLJzzv+YTiu6xsEXHJyix3bHaFpos8GqMDhlwhcADJ59+/algpN+LPXzEZwCDXpwKg8k4Qt/JkCUzqVo7JoCR4Xg5EqFKC8U+R/XxY8rBK1hUiU4pR8TnACMJr27ZHedQnebNIJToKHZcTLHZQNA6MIvfZeo7PtFgxucWswuWur41Md1yTw721UbfPqFvnHjxhzTajG6tuW5yVbd5JzedVpU05m2bN+M6cX4Xi9OC20AMEAOHDgQB6f9+/eL7RKCU6BVD06hX4aWFnQpJIQu/DZ4ifI+Wisau67AEXo/8iTzkD6+jMNSfM86/R7X4KsanNp1c17bpJqLvxw+pyZbfdPaoUtuB4BBsXPnTtVsNuN/pXYJwSnQ6genkO8WafKCnvwtI/fYsIU/93s+hTs+RWPXFzjC7keOnPnbj+eKP6bTxi042QCUPI9SbYU7SslYK4vTQhsADD+CU6B+BCctG4AMvUMS777kLej+fw0XsvCbY8QAUbTjszrBSSu9H+LOkX8vHGZ3ThwzZfiD04YNG3LsNsGpIdbre+O37V5M6hd3C/2X51TD6QsAo4TgFKhfwSnmLO5t6e/miAt662M33Tfplx0nER9v+ucFiGTHp+oXzwPOmzmmROH9sCHJk/sRn51f0cd0WtE1Dr7OgtMG1TC7TlLbht3JzpNL7AcAI4TgFKivwQnoUnFwAgCEIjgFIjj1SOGX0a28L6UjlH6hr1+/HgDQJYJTIIIThhnBCQDqQXAKRHDCMCM4AUA9CE6BCE4YZvqFvm7dOgBAlwhOgQhOGGb6hT4xMQEA6BLBKRDBCcNMv9DXrl0LAOgSwSkQwQnDTL/QAQD1kN5ne0UKRSEITgAAYOxIoSgEwWlgrKlIGgMAAISQQlEIglOKFFBQTrqXAAAMLikUhRjR4CQt7ugd6XcAAMDgkkJRiAEKTtKCjOGS/f0CADCIpFAUguCEAZJ9fgAA0AtSKApBcMIAyz5fAACogxSKQhCcMOSyzykAAMpIoSgEwQkjLvucGy7SNQ0j6doAoH+kUBSC4IQxln0+VpX9y7drIPLvEwBkSe+zvSKFohAEJyBX+fNSh4IdO1BuBwAUIjgFIjhhmOngtH07yslvQABgEZwCEZwwzHRw2rYN5bYBQCGCUyCCE4aZDk5bt6LcVgAoRHAKRHDCMCM4hZLfKAHAIjgFIjhhmBGcQslvlABgEZwCEZwwzHRw2rIF5bYAQCGCUyCCE4YZwSmU/EYJABbBKVBfg9PEGtVcWaNWPM3Zdp8Fod1y+8UaAX0idszZ6Px+22wze1zZHIraLTteXt+GOZc7B7fOmojG0W0L0bX6beOoSnDas2Tud/Sv1K7tmjd9PHuEvpL5g/n947bIuwv62PMv7cm2dUd+owQAi+AUqF/BqbGQLBBSANDhwoYa/bMUfnx548VBJQoiE36dFh3j9nWDnB+cQuZgFfWX2qSgFM/Rn19B/bjSwWnz5jBL+t4ZM0L7jAlW81Pp+vnlbF0RfZ7lKAC5dVMmENnzxnOJzuf2Karv3mYAKERwCtSX4GR2hkJ2TYJCS8l4ftjQjxdM0HLDSrybE9X75wyag6Oov9hmAptbb3eW3F0xGw6lnahxFRycZpJ7N293dKLHqfYoGC1H9X7g6YQNSW7Y8sOU1McGNynUdU9+owQwWubn59WRI0fUoUOHWvRjXS/1dxGcAvUjOMU7LN4uUJ6Q0FI2ng0hNnDoMXXIsv/afvqxDir+OUPm4CrqL7YJwUnTfVvXldNn3IUGpziULEeBJfpZ3NUxwSoTqDqkd6nsOeKQZM7t9onnYetrDG4y+Y0SwGjZu3evOnbsWMbhw4fF/i6CU6BVD04VA0BIaCnt4+1I6f765zhQ2WCi+5if/fFC5uAq6i+2efOT6quEzXGig9OmTeVsWNI/x6Emejzt9jHBxfbpmhlvftr8Gz3O9Ina4rAW/RvPSYcov09tNgEYA1NTU+ro0aOp0KQfT05Oiv1dBKdA/QpOmZCQQwcNvbhI4o+xQoKYd049Zvyzqdfj6I/B7Bh+uCmdg+nn9q8SnOLxnY8SXfb7T1roPRsnQcHJCSj6sf2YzD62bH2shgA1HY1RNpYNcdJ86iW/UQIYPfv27UsFJ/1Y6ucjOAUalODkhxMbSIpCSMyMF9InE5yin3UwaerQFNW5H+X5walwfE9Rf93mXqd7rSIzd74QLgsJTjYQtXaYnI/F/L5WKkS5x1bQGqMohNW905VLfqMEMHr07pLddQrdbdIIToH6FZxyg0gUaPRiExycIqV9zJhScLJt/pfH3fFC5uAq6p9qCwxFVc8/TnRw2rix2Jz5GGyypE5kdquW54S2IpNJIFpcTI6fix6L/SKLnYxf2UYAY+TAgQNxcNq/f7/YLiE4BVr14BQp/L5OB8Gp0y+H23bpsXvOqsGlqH+mzVxv0fhVzz9OSoOTCTD6HkuKAo2lg81KFICktjxxMDPHlIU0ghOAuu3cuVM1m834X6ldQnAK1I/gZIOMG1ZaOghO/o6ST4/h7yjl9dX8c1YNLkX9pbaiv2mlVT3/OCkLTpNRINH3dtpvM4GqNLCE9nOZXapWKCsZg+AEYBAQnAL1IzhpuWHBhKBKwSlS9Q9grn5w0ve4EbWtRG0T5rE1oWabK9H8F1SjVdc+tur5x4kOThs25LM7P7ltkYZ+vDt5/uyW+gj1RWwQcusa0eO8caT+9dsAAIUIToH6FZxiJiT53D/wqEOD1EfLhAlhPClw6DGrBid/XCtv/Gy9vsd5wSkyMauaUdvKSjMKjW6bPcYfD1phcIp+x/H3jKJQJLXbMDMX9dOPbUhKscEqUCqMeeKP/IQ2ghOAQUBwClQ+AXnBGi913BN/jE5I4463sh0nWPIbJQBYBKdABKc80r0YJdI1Dx8dnNavXwU7k92rzI6U5y1RP/H4vlsPAIUIToHGJzhJ14Ys6d4NrlULTkNPfqMEAIvgFGh0gpM0d9RPuvf9Q3AKJb9RAoBFcAo0XMFJmh8Gk/T7q58OTuvWdWMdACBCcArU/+AknROjSfr9d0cHp4kJ1wQAoAMEp0DdTADoN/1CX7t2LQCgSwSnQAQnDDP9QgcA1EN6n+0VKRSFIDgBAICxI4WiEAQnAAAwdqRQFILgBAAAxo4UikIQnAAAwNiRQlEIghMAABg7UigKQXACAABjRwpFIQhOAABg7EihKATBCQAAjB0pFIUgOAEAgLEjhaIQBCegC9JfvgUAdEZ6n+0VKRSFIDgBXdAv9B07dgAAukRwCkRwwjDTL3TphQUAqIbgFIjghGGmX+jbtm0DAHSJ4BSI4IRhpl/oW7duBQB0ieAUiOCEYUZwAoB6EJwCEZwwzAhOAFAPglMgghOGmX6hb9myBQDQJYJTIIIThtmgB6c9SytqZSVxcH6X2KeaPWqptrEAoI3gFGj1g1NDLURv/M3ZCaHtFDUx22wtNIkF1YjbkuN03ULDP84fs91XkvQr6NOcVRPu+I2FTJ/s/PPGs/P3+zXV7IRbr02o2WZyXN79aSyYcRcaXlvxNVt54w6reoNTEkqy9+2gmt8l9S+2a/5gdOyS2iO0dc4PTgQpAPUgOAUatOCUkPo4wcAPNpn+nZ4jywaVdFgzc0nNQxrPBiE3PJljNT/8TMyqpmmT5+UcmwlkvrDrG3b6hb558+aazMQhZHl+KlU/Nb+c3PPleTXl1BebUvPL0TFLM0JbN/w5ynMGgKoIToGGLTg1FxbicCEFmXb/Ts/hMTtN2R0uLTm+HX5yxjNhyJ/bQnQdfviJd9ui+tx5xfNpqtnZonlZIfdg+HUXnJJwszRjHxeEkKl5tRy1hQehXgWawOA0s1Qx6AEYBfPz8+rIkSPq0KFDLfqxrpf6uwhOgYYuOEV18S5Q4W5Pp+dwmd2izO5WW/KxYvqjxMx4ecGpYf+1ffVj/fFd/rza1530yX5c58ofZ5R0FpySsJHs3C2pGa8+L+xMxTtPy2p+yq/zxzK7Ta36SBxi3PNaZef369zHReO5be45AIyyvXv3qmPHjmUcPnxY7O8iOAUaxuBkw0g6dLj9Oz1HlfZIakcqp39m1yrppx/HwcsGM93PCUXZ8yb1SViSPgL0Bcx/BOgX+qZNm8LYXSNNB5lMn+lWKEnXG9NL8bFL08njJDTpIJU8ntZfBG+NK401rabNsVZ8TBRspk27dEy6ruyxzw1x7bkCGE1TU1Pq6NGjqdCkH09OTor9XQSnQEMZnKLHxbs9yeNksfDZL2UX9NHhJLNTJEgFuPw5p3eGkrr4mPj4ZD56Nyk5Vhon4gUw+yX6diDz5YwzYoKCkwk8saVpuU+sJISY4JW0C31NexKsygKNEc/NBhrpGL+u7HG+JKRpBChglO3bty8VnPRjqZ+P4BRoWINT+nFRmz3eV9Knw+DUWqBj0n85l/RLjkl2jpLvbRV/5JcOipHS+YXcg+FXGpxMaAoJFqUhxA1OqZBkJceXBaep1Md72uoEJ8uePz13AKNC7y7ZXafQ3SaN4BRoeIOTGyb8tk7PUaU9UvRRnQk22e8hJf1aO0VmjHY/6bzS963KvoMVMP8RoF/oGzduLDY51/6IbnFa7hObVotRn+W5SaEtMr0Yj7E43f5ZkhwvjZXU6aA0N+mOaR/nH9OuK3ucb3rRztE5P4CRdODAgTg47d+/X2yXEJwCDXNwatfNem2dnsNVw5fDTSiS5tz+iE1+nDrGhjCRtKullV3faAgKTpYboJbn1GSmT3EImZzTOzWLalo/NmPFIcrrlxDGSoUkqU46f1lQKp7zxo2Taq71HSczdwAjb+fOnarZbMb/Su0SglOg4Q5O7e/6aO22Ts/h8b5XlJYcX7xTJP0dqKSfPKaWHSfzMZ1V+HFdwPWNgErBqSUJG9kwURBCTFAKDyxC+6oGp6Q+e40AICM4BRr24GTrexKcIt39AUzN/y/gkn7hwckcn/nIz2kTd8XCrm/Y6Rf6hg0bOtSId2MWd9vHu00IaTh9Irt1uInu8+LuVH0j3oGKQk/DrVs0j4WxGjZ82bqkjzvGbv1x2vKcasTtyfz0uf1j3HHTxxh6zn4dABQgOAXqV3BK/pewb0G92tlBsnVFX5rW7K5Tu63oHLZfhWBhv4eUGcPtVzBe66O29t9pCg5O5ti8/sm1538JPej6hlh3wclng4wvHY5cSXhy+rbCSkkIi0XjRkErPb47B13vjyON6x6zqHa36gEgHMEp0OoHJ6A+9QYnABhfBKdABCcMM/1CX79+PQCgSwSnQAQnDDOCEwDUg+AUiOCEYUZwAoB6EJwCEZwwzPQLfd26dQCALhGcAhGcMMz0C31iYgIA0CWCUyCCE4aZfqGvXbsWANAlglMgghOGmX6hAwDqIb3P9ooUikIQnAAAwNiRQlEIghMAABg7UigKQXACAABjRwpFIQhOAABg7EihKATBCQAAjB0pFIUgOAEAgLEjhaIQBCcAADB2dAiS6osQnAAAwFgiOAEAAAQiOAF9IP3lWwBAZ6T32V4hOAF9oF/oO3bsAAB0ieAUiOCEYaZf6PqFBADoDsEpEMEJw0y/0Ldt2wYA6BLBKRDBCcNMv9C3bt0KAOgSwSkQwQnDjOAEAPUgOAUiOGGYEZwAoB4Ep0AEJwwz/ULfsmULAKBLBKdABCcMM4ITANSD4BSoX8FpYrapVlZWPAuqEbc31EKmra05O5HfZ6GROo+rsVDUJ++cdk6y/DGLr8FKrsU5rrFQ3qfDuY6i4OC0Z0m4XwfV/K7yfgfnd6X7bNmjlvKO37JLzR/0j6vaP23PUtK+srRHbAeAOhCcAvUjOCVho6lmJ9z6CTXbtHVJMMgGBpfUxwYKKUC4YSO/PT2enlNef61sTJc0fpoNYQsNt96cozmrJry6anMdTfqFvnnz5kIzJngszXhtM1FIWp5XU4X9ZpLQ4/Rr1WlLM07fyNS8WjZty/NTHfZ3OceuLKmZTDsA1IPgFGjVg9PErGpGi0D1UOTL6ZM3fryTEwWz2WRHJx1OtIrjaaVjukquyew0yWMkx7Z3tTqY64gqDU46HEX3JBOafIX9THhphZ7k8dKSPiYdZqbml6N+S3G7H5zC+zvieS2r+fnA6wAw1ubn59WRI0fUoUOHWvRjXS/1dxGcAq16cCoMCFZJyIjl9fFDRiLezYl3beT2TsJI+ZiuvPlqZrcotauUlny0aXeTqs91VBUHpyk1vxzd19RukaS8XxxwWqHHBKEZ+6/tpx9HIWcqqc8Ep+D+bfEuWDyvpE9mxwoAHHv37lXHjh3LOHz4sNjfRXAK1K8dp85DhpXXRwoxbl3eR1o54+UGvZAxXXnzLWszUvOoOtfRpV/omzZtkpmPwXQgEdtbplvBRW6PTJsdn2n9OOmvf44DlQ42tk/8sz9e1f5WUr+yNB39bMJdFN6mU30AoG1qakodPXo0FZr048nJSbG/i+AUaNWDUyT1xXAxQJlQIir5HpQUHrw6e/50wJDGM/OQ5hg0pitnvlrITpHpk4xfca4jrJbgFNLP9PGDU1Kvd402qeklO0Z+cArrb6TCmgldzmMAkOzbty8VnPRjqZ+P4BSoH8HJlf6v60o+ikoR+tjdLO8jr/THXO1+YvBI8b/A3hY2pqvgmjoMTqFzHWV9D05mJ2h5aSlqt7tBBcEpqH8iCUrODlPo9QAYa3p3ye46he42aQSnQP0OTi1mBycJD+HBKR0epGOk7w9Jdd45bQgTd3BCx3QVXVPA9aZ2uKrMdbTpF/rGjRtlk3NJ0JiblNtbptViWb/pxfj+L07rx0n/5Od228ritOnvj1e1vzap5uLvXc2pycI6AMg6cOBAHJz2798vtksIToEGJjjZIBQv/gFBIqhPxIYKkbtLI4yXCnO2XyR4TFfRfMtCl7/DVWGuI64wOAUHjfJ+k3N692dRTcePvSCU8zg3OJX2j5jQl31+actqbtIeCwBZO3fuVM1mM/5XapcQnAINTHAyYSRZ+OsLTpmP1KzU+XSdPJ70t5XCx3SVzNf7zlRacmx7Ryl8rqOuODjZwOOGlBypHSVfEmz8HaL8Mf0gVLW/H9QcwbtoAFANwSnQqgenOCD4ocPsuBTtqGSE9DHjBn3cljeeP7cqY7rK59vdH8DU/LmOPv1C37BhQ6Hdi8l9Xdztte2OwtLynGoU9tudhCann63LjOe1L881Uo/D+zeSHbDF3U4fy7Sl5gMA3SM4BVr9HSe7uHuEYJDpYySBoTyI2B2gvB2YZOeo5L/S01ofzUV9X1JlTFfAfDWz85S9Xrdf2FwzbSMoJDjFdEhy7mliWc01yvu1A43V4+DUSHaV8vo34t0oYe4A0AWCU6DVD05AfYKDEwCgEMEpEMEJw0y/0NevXw8A6BLBKRDBCcOM4AQA9SA4BSI4YZgRnACgHgSnQAQnDDP9Ql+3bh0AoEsEp0AEJwwz/UKfmJgAAHSJ4BSI4IRhpl/oa9euBQB0ieAUiOCEYaZf6ACAekjvs71CcAIAAAhEcAIAAAhEcAIAAAhEcAIAAAhEcAIAAAhEcAIAAAhEcAIAAAhEcAIAAAhEcAIAAAhEcAL6QPrLtwCAzkjvs71CcAL6QL/Qd+zYAQDoEsEpEMEJw0y/0PULCQDQHYJTIIIThpl+oW/btg0A0CWCUyCCE4aZfqFv3boVANAlglMgghOGGcEJAOpBcApEcMIwIzgBQD0IToEIThhm+oW+ZcsWAECXCE6BCE4YZgQnAKgHwSlQv4LTxGxTrayseBZU45SGWsjUZzVnJ6Jx8vrqcYrOZdvbxy802v0TSVtyHq9tYlY1W2O1iX1DNBYCxiqYT1Fb6dh59zBNHzOI97EoOO2aP+iNvaT2xG171FKq3nFwXu3yxgmxZ8kcv7RHaLfnO6jmd/ltu9T8weTYg/O7TF3+/Jb2JMflX5tj17w6mOqT8M/TfuwqaNuzVDCmVX3s7DVpwnUB6AmCU6B+BKfGgn5DbKrZCbd+Qs02/TqtamDQ4+jx0+FJ7tte8Feas2qitL+de15AkOZfTB7PzCs1J3k+RW3hY2fb5fPktZsxxXGrzE3T/cPvo36hb968ucBMvEgvz0+V1HUjGS++/mihnylqX5pJt03Nq2XT1p6PPL+p+eWo37Kan7J1+dcxY4Lc0ky6PjnGjpF/fF6bPG7Sd2V5Xk15daFjJ+O616ZNqfllvw5ArxCcAq16cDK7DPkLs69oIc9pE88h9TV1CwtxfylgpPqb3ZvsYt+hwvGS868sNFKPg+9DpbGzbfJ58tpNXR/u40AEpxm9AxMt8PPJTowcVqL6Jd2eDlZTOgxF9en55MzPhKzSfvF8pHn4iu6D0FY4btK/HQwrjJ25LgCdmp+fV0eOHFGHDh1q0Y91vdTfRXAKtOrBqfKiWbSQ57RVDU5RXbwDUrjDY3aycndqqiofL/loLP1xWOZaxbaqY7uKzpPX3q5b7fs4CMEp3i2Jd1v88GAl9Usz9l+3Xu+o+PPxHxsmYKSP9/vpXRo7H1uXJ+c8Ylv5uFPxjpgNhhXGDg56AMrs3btXHTt2LOPw4cNifxfBKdCqByf7vRZxt0NStJDntInhrHjBt/NqH+P1r7xTVqbouozUdRT199uqju0qO1Zqd+pW+T7qF/qmTZsKTLcW6eK6TiVjrSxNRz+bcBGFh2mhz9L0piRc6PCh66ejwBD/7M9Hnt90/HGWO7bQz9m9adXlks8jtxX1NfT1RH30dVYa235cGd9Dvy+AKqamptTRo0dToUk/npycFPu7CE6BVj04RVJfMi4NUEULef4inh03v6+tK9zhyQSCLoUEiNQ5pflb8lzDx3YVnSevPV23mvexm+DUeg66qi7eqbBggpHzOJGcL66LQ4LeZUqCUDIvf45580uO88dNXZvdlUqdP49wfF5bSCBLnbvC2JEpc99iBCigK/v27UsFJ/1Y6ucjOAXqR3BypUJU5Y+Okrb28VreF4vLF/z0Y69NXPD981f4cnjlcCPN35LnGj62q+g8ee1+nftYnltd97HfO07Jgu/sAokBIzlfEiiSXanlpaWonz3On488v2THyQ1FQj8xOCX92vfXBjD5PO4xrbYeByfXlBuiMrt3AMro3SW76xS626QRnAL1Ozi1mI+Osgu2tFDntJlFOfxLz9m69m6JPLY8j0g8/yr/VV3RdRnD+FGdqVut+6hf6Bs3biwwrRaj8y3PTZbUdWJSzcXf+5lTk4V1yfkWp83j6cX43q8sTqfa2/PJm19Sn39cZHIuCTh51xafe1nNTerHeeeR2or6Gua6kuusMnYOM15pPwAZBw4ciIPT/v37xXYJwSnQwAQns8CGfcRW0FYpgBXVzXptJV9qrhycOvtyeFgo7N+Xw7N1vb2PfQ1OJqTEISjDhhMtOV8rOOU8bs8nb35JfWFwEoObQwhO7fFc/tgl40Ym5/RO0aKajh9XGTtP0RgAiuzcuVM1m834X6ldQnAKNDDBKXcnomghl9uK/n6RvLinj3c/PnTbbH12lyZSOTiVjGfm5gal7H+xZpiwmBpHqmvJju23yfc7r71/97GfwSkdFByZXZ/kfO2g5PPnkzO/nHH9fsm8cs6XCk4b1fRi9PuRwlBq96igriWZixtyKo0tKds9A1ArglOgVQ9O8cLo73SYXYjKOyB5bdJ44Qu+rddv7n5b7h9u7CA4aUUhLxOSxHCZ0zdSaWyvPXtPitrzjjHnEtrquo/6hb5hw4YCu024aJTUVdVIdmAWd+e3RaGhET9Ozre42+9n+fOR5mfGjILa7tzj2nbr0CKdc7cJTg3zuGHDSfb+tOffJo+b0z907HhO7nVp0vUC6CWCU6DV33GyocbT0UJe0Ga/7xQtwm9+c3vnI5H++Es63u6KiGOb3Rx5zA4I48nXq7XDSIu4c2RUGlsrvydt/b+PRcGpYXZe2uwinB84gplQkBeGknPbgJKcr2pwSs894oSN/GtzxIHE7ZPTTzqfGAgNYdz8exkytg1JHiG4AegdglOg1Q9OKJIEjeo7V+OqfMcJABCC4BSI4FSz1k5XkYJgVPZfnSFFv9DXr19fn51viXeS5N+btazeslM4FgCGGMEpEMFpAHnhixCVr/bgBABjiuAUiOCEYUZwAoB6EJwCEZwwzPQLfd26dQCALhGcAhGcMMz0C31iYgIA0CWCUyCCE4aZfqGvXbsWANAlglMgghOGmX6hAwDqIb3P9grBCQAAIBDBCQAAIBDBCQAAIBDBCQAAIBDBCQAAIBDBCQAAIBDBCQAAIBDBCQAAIBDBCQAAIBDBCeiDC/ecrN778ucDALqk30+l99leITgBffCel/0vtWPHDgBAl/T7qfQ+2ysEJ6AP3n3q8+IXEgCgO/r9VHqf7RV9Tqm+iD6G4AR0Qb/Qt23bBgDoEsEpEMEJw2zhH/5Kbd26FQDQJf1+Kr3P9grBCegDghMA1IPgFIjghGFGcAKAehCcAhGcMMz0C33Lli0AgC4RnAIRnDDMCE4AUA+CU6DVDk6NhRW10pxVE6n6CTXbLKhfaEQ/N9TCSvRzxoJqOMdMzDaF9rxj05qzEwXnSSR93DnmyR9noSH0byxk+mXPZcdsqtkJt14z90o8bnQVB6c9asncy6U9ctvB+V1efWTPUut3YIn9AGCEEJwCrXZwSoKNv/DnBYKk3g006VBgw0I6PMl9Q9vLjg0ljyNdfxwmo77pQJUcnw6Tpk6Lw6TTf2JWNU1b93MfHu966XPV5s2bc8y0gtPK8ryaEtqW56ecus1qZinpvzST7ZsdAwBGh34/ld5ne4XgFMos8KmQoHdbmgtqIQpBmfpWyMgJNGa8dH1Z+ClqLzs2VOB8zU6TuAtlxmiHpOTxwoI+Rthpi+rrmfvwCAlOy0tLajn6VwpDqeA0k+w0pftZJjwtzQhtADAY5ufn1ZEjR9ShQ4da9GNdL/V3EZwCrXpwEgJFsug34p2XTH0rIAQGkaK+Qe1lx4Yqnm8SlPI+omyT7sFCw/5r++nHOmDWNffhERSconAU7ySldoz84DSl5peLd5Wm5pej38WSmhHaAGAQ7N27Vx07dizj8OHDYn8XwSnQ6gen7Pec9GMdAuKQ4NX7uy2ZUCDu2JQFiKL2usKHPE7ysVxJGHSlri/pn7lX8Y6d/rmuuQ+Pd/79X6pNmzblmG6Fo01T88mu07TQJj4WTJsdqdYYADBYpqam1NGjR1OhST+enJwU+7v0+6n0PtsrBKcKsrso5uO4eDcmL1RIoSCpy3zfR+wb2m7GFPnfwSqSN44zhrhb5kntUCVjxj/H9clY7Z26susePcHBKXpsd4ymhTYbrAqDUyZ8AcDg2bdvXyo46cdSPx/BKVA/gpO76GfDklvv7iRJQSQvyJQFiKL2usKHPE6y4+SGn5Jzpe5DMmbyc/IxX3Nhwbt/dcx9eFQJTunHBCcAo0nvLtldp9DdJo3gFKgvwcld4FsfM+l6EwZsfSsQeMfoxyZQZHebhL6V2usKH3njJPXJvAPOlfNRndtW+nHmCNMv9I0bN+aYVovR/Viem2zVTc7pXadFNZ1py/bNmF6M7/fitNAGAAPkwIEDcXDav3+/2C4hOAXqT3AyX4qOFvz2x0xJW/wxnql3v+8khgITHPLCSX6AKGqvK3zkjZPUJ2Gn0y+H23b5cfdzHx5Vg1O7bs5rm1Rz8ZfD59Rkq29aO3TJ7QAwKHbu3KmazWb8r9QuITgF6k9wsl9uzvkTBKY+HQDkUJD66Kukb1h7XeEjZxz/47nUjpIvGcPfUZL7anXNfXhUD042ACXPm1Rb4Y5SMtbK4rTQBgDDj+AUqF/BqfUxk/89JfsRXCQsDJldm6KP9TKK2usKH9I40lyLw5+060ZwanvHrueoDRs25NhtglNDrE+CU7pt92JSv7hb6L88pxpOXwAYJfr9VHqf7RWCU2VSMNBsuPC/+F0QClphq6ne/Gb90Vay+CXSISVRFDDMvHKEh5KccfI+lmsFybbsuZIxCU5tnQWnDaphdp2ktg27k50nl9gPAEYIwSlQ/4IT0L3i4AQACEVwCkRwqsj5KDFf3p9JQN30C339+vUAgC4RnAIRnDDMCE4AUA+CUyCCE4YZwQkA6kFwCkRwwjB7+9Sz1bp16wAAXdLvp9L7bK8QnIA+eNvkX6iJiQkAQJf0+6n0PtsrBCegD/QLfe3atQCALhGcAhGcMMzOe/XT1Fsb/xMA0CX9fiq9z/YKwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwQkAACAQwSly4oknqmc84xliW3Xb1dLxu9Xdd8uOL1W/4UVmL7ZjH1dL2+U+/TGrLvauPXZ8SW0X+5fLv1b3nl+sZp1jAACoE8Ep8kd/9Efqd3/3d9WTn/xksT2cExb8gLB9SR2P6sciOJlrjed18ayp7z7cEJwAAP1GcIro4KT//eM//mP1hCc8IdMeqjTERIHi4pEPTgUhRgxU4QYyJAIAxgrBKaKD07Of/Wz1N3/zN/Gu0x/8wR+ok08+Weybr2C3KcPp6wSIdjBwA4f8kZfeuWr39znHz16cab941o7tjR/Neyk1ZjLO9qXjTl1JaCkMR26osuOUn18fW3ytzrj+va/h+sW+jrp3EQEAg4vgFLHBae3atbFnPetZ6rGPfax65jOfKfYXuQt06W6KswAXBaecEKKDjF2si3ZhskFMOm86DCShIl2XnKsgnDjckCUFCjcASedK6uRdq/xrledWy/Xb8QJ+FwCA0UdwivjBSQemxz3uceqkk04S+0vKAkOatIBnF3p3zPQuSVtumHAWenc+7THlHZ8kdJQFl3adr+w+ZNul80ecIGqvvVJw6ur6O/tdAABGH8EpYoPTX//1X6snPelJ6g//8A8rf1RXFhjSqgenhB8Y8sNE7kLvBJL84FJ1x6et7D5k28uDkx2nSnDq7vo7+10AAEYfwSmig5MOSo9//OPVE5/4RLFPqV58VOf285TtwgxqcGqPYec1HMEp5HcBABh9BKeIDk6PecxjuvtzBO53YNwAIHIW4cLgJPS3zDnywkS/glPed4ES7rh2DDm4DF5wsvJ/F+0+AIBRRXCK1PUHMEuDRRQqkj9H4Cy+QcHJyi7y7YDgnbOn33HKub6YfGxMDFVycJGuK/daheDU3fV39rvI9gEAjBqCU60KFlOzkCeLuBAu3FBh6vQi7y767m5JaxdF2Jmxsou/M7/C4NJNcIqIASkvUEnnl+YZyb1WIThFOr/+7LFBvwsAwMgjONXODQhZ7cXXWbC1aNFu/w2h9mLdam/JhpZsPyeYOAu8lV7opeDQZXCKeddnOeGksJ/mhiZDvlY5OMU6un45OLljJELuAwBglBCc0GdycAEAYBARnNBnBCcAwPAgOKHPCE4AgOGhQ1AnCE4AAGDsSKEoBMEJAACMHSkUhSA4AQCAsSOFohAEJwAAMHakUBSC4AQAAMaOFIpCEJwAAMDYkUJRiIEITgAAAMNCyjMhaglOAAAA44DgBAAAEIjgBAAAEIjgBAAAEIjgBAAAEIjgBAAAEIjgBAAAEIjgBAAAEIjgBAAAEIjgBADomxNOOGFgSPPzSceNMuke+KTjhoF0LSEITgCAvulmAatT6DwGZb6rYZTvSTdzJjgBAPpmUBbdUQ4JnRrle9LNnAlOAIC+GZRFd5RDQqdG+Z50M2eCEwCgbwZl0R3lkNCpUb4n3cyZ4AQA6JtBWXRHOSR0apTvSTdzJjgBAPpmUBbdUQ4JnRrle9LNnAlOAIC+GZRFd5RDQqdG+Z50M2eCEwCgbwZl0R3lkNCpUb4n3cyZ4AQA6JtBWXRHOSR0apTvSTdzJjgBAPpmUBbdUQ4JWQ21sLKimrMTQlvb4N2TsHmH6GbOBCcAQN8EL2CNBbUSLZqu7AKas7BOzKqmPqY5qybcekftIUGY78pKU81OCH1X3WoGp6JzVQ1CBCcAwJgLWcAaC0nwWGi49ckimg5D0sJq+i00nLqsekJCQp5vRIepgvC2esICSD33pOhcYfNoq9o/X+i1SQhOAIC+KV3AzM5NJoTE/FDkL6wTarbphytZPSEhUjjfQREWQOq5J0XnqhqEqvbPF3ptEoITAKBvihew8uAzMduMgsqCasSP0wtrsvNj24rVExJCg1oyz+TjO8udp72ORjKe6SMFhuT6nXGcnbV0mzR+cQCp554UnUtuC593+3HqmBqDsoTgBADom84XXSO1w+MvpOHfKaolJJjvUhXON9ZQDW9HKh3ykutoX1ckvs709WSvMTrOhAa/LR6/FSgC7muklntSeK5sW7V5J4/1fWrXmfBa00ezEoITAKBvug4ipo8bnOzOQ+FxnlpCQnBwEqSCkRQ25NAgn0toE+5T2TxruSfmXPZ3Iim8psJ551yHEDJ9odcmITgBAPqm6yAiBqemasYfcRUvnq5aQkLF4JT6eMnMOzg4pa7bI7Ylx/crOMnnCrmmonnnjF10b4zQa5MQnAAAfdP5omvkfFRnf17V7zgFByc7NyfYVd1xSl23x7RJ3HtTNs9a7knhueRrksjzzhmb4AQAGFXFC1h3Xw63C2jZ9120ekJC4JfDUyFJqpMCgXxtYjgoDQ45gcNTzz0pOleFa4r5Y+WMHY/j3V9P6LVJCE4AgL4pXcCKdlbMwpn/5wjaH4fJC3dbPSGhfb6i3Y5aglNeaChtC2lP1HNPqsyz6rzl/ukwLQu9NgnBCQDQNyELmPwHJZNFM727Iy2kZheoph2Izucb0eFIz9fsrPgBoFpwsgHBva6oj7kf2TZdt1AwflY996ToXCHXVDTv5HFqRzFzb2Wh1yYhOAEA+iZ4ATM7T67s4pi3SJsFtiA81RMSHMJ8U+dPtUf1UThot0vXIV+b3eFqyf07TpFWyMy7T2n13JOicwVeU+68zeNmun/ZdWnBv0cBwQkA0DfdLGB1qickjJbBvydhAVDSzZwJTgCAvhmUIDL4IWH1Df49ITgBAMbMoASRwQ8Jq2/w7wnBCQAwZgYliAx+SFh9o3xPupkzwQkA0DeDsuiOckjo1Cjfk27mTHACAPTNoCy6oxwSOjXK96SbOROcAAB9MyiL7iiHhE6N8j3pZs4EJwBA3wzKojvKIaFTo3xPupkzwQkA0DeDsuiOckjo1Cjfk27mTHACAPSNXsAGhTQ/n3TcKJPugU86bhhI1xKC4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABCI4AQAABAoDk4YHK9+9atjUhsAAOivNd/5znfUnXfeqW677Tb1zW9+U33jG99QN954o7rhhhvU9ddfr77yla+o6667Tl177bXqmmuuUV/60pfU8ePHUbNXvepV6mlPe7o64UlPij39Gc+IA5TUFwAA9JbOOzr36Pyjc5DOQzoXrfnBD36g7rnnHvXtb39bfetb31K33367uvXWW9Utt9yibr755jhIff3rX1df+9rXYjZQoR5fjTzzpJPU+q3b1EtOfaU6dea1sRef+gq1fss29Wd/9mdxH+lYAADQGzrv2Oyjc5DOQzoXrbnvvvvUvffeq77//e/HAeruu+9Wd911Vxyi7rjjjngnSgcpvRul6YNuuukm1OTkk09WL2jsUq947etF217SUKec8ufisQAAoDd03rHZR+cgnYfuuOMO9f8DlCmdYUxFpEEAAAAASUVORK5CYII=" 353 | } 354 | }, 355 | "cell_type": "markdown", 356 | "metadata": {}, 357 | "source": [ 358 | "## Now `coordinates.csv` has the coordinates for all images in Decimal Degrees.\n", 359 | "\n", 360 | "As part of the quality control of this project, we deliberately decided to run the following steps manually in QGIS. this way, we ensured that the extracted coordinates place the drone images in the correct location. We need to change these coordinates to Eastings and Northings. For simplicity, we use the QGIS User Interface in the following way:\n", 361 | "\n", 362 | "1. Open Qgis\n", 363 | "2. Click on *Open Data Source Mangager* and select *delimited text*\n", 364 | "3. File Name --> `coordinates.csv`. \n", 365 | "4. Ensure that on the **geometry CRS** you select ESPG:4236 - WGS 84 (projection for Heron Reef) as shown below. This process will result in a new point layer showing the latitude and longitude coordinates of each image (from the 'coordinates.csv' file).\n", 366 | "\n", 367 | "![Capture.PNG](attachment:Capture.PNG)\n", 368 | "\n", 369 | "5. Right click on the new layer --> Export --> Save feature as. This opens a dialogue table\n", 370 | "6. Select: a)`CSV` as output format, b)the de desired CRS (in this case UTM 56S) and c) `XY` as the geometry (i.e. longitude will be the first column and latitude the second column)\n", 371 | "\n", 372 | "![Capture1.PNG](attachment:Capture1.PNG)" 373 | ] 374 | }, 375 | { 376 | "cell_type": "markdown", 377 | "metadata": { 378 | "ExecuteTime": { 379 | "end_time": "2019-08-15T00:55:52.059348Z", 380 | "start_time": "2019-08-15T00:55:52.043717Z" 381 | } 382 | }, 383 | "source": [ 384 | "# Read the eastings and northings\n", 385 | "\n", 386 | "Now that we have the coordinates of each image in Eastings and Northings, we can proceed to create the `*.tfw` file for each image." 387 | ] 388 | }, 389 | { 390 | "cell_type": "code", 391 | "execution_count": 9, 392 | "metadata": { 393 | "ExecuteTime": { 394 | "end_time": "2019-08-26T02:02:44.210651Z", 395 | "start_time": "2019-08-26T02:02:44.188592Z" 396 | } 397 | }, 398 | "outputs": [ 399 | { 400 | "data": { 401 | "text/html": [ 402 | "
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XYfield_1Image_IDLat_ddLon_dd
0-1.563725e+062.999593e+070DJI_0002-23.440203151.912889
1-1.563737e+062.999593e+070DJI_0003-23.439958151.913181
2-1.563750e+062.999593e+070DJI_0004-23.439711151.913472
3-1.563762e+062.999593e+070DJI_0005-23.439467151.913761
4-1.563776e+062.999593e+070DJI_0006-23.439178151.913997
\n", 476 | "
" 477 | ], 478 | "text/plain": [ 479 | " X Y field_1 Image_ID Lat_dd Lon_dd\n", 480 | "0 -1.563725e+06 2.999593e+07 0 DJI_0002 -23.440203 151.912889\n", 481 | "1 -1.563737e+06 2.999593e+07 0 DJI_0003 -23.439958 151.913181\n", 482 | "2 -1.563750e+06 2.999593e+07 0 DJI_0004 -23.439711 151.913472\n", 483 | "3 -1.563762e+06 2.999593e+07 0 DJI_0005 -23.439467 151.913761\n", 484 | "4 -1.563776e+06 2.999593e+07 0 DJI_0006 -23.439178 151.913997" 485 | ] 486 | }, 487 | "execution_count": 9, 488 | "metadata": {}, 489 | "output_type": "execute_result" 490 | } 491 | ], 492 | "source": [ 493 | "# Read the *.csv file where the eastings and northings are located.\n", 494 | "eastings = pd.read_csv('eastings_northings.csv')\n", 495 | "\n", 496 | "# Print the first 5 records of the file\n", 497 | "eastings.head(5)" 498 | ] 499 | }, 500 | { 501 | "cell_type": "markdown", 502 | "metadata": {}, 503 | "source": [ 504 | "# What goes into a `*.tfw` file?\n", 505 | "\n", 506 | "To create the `*.tfw` files, we need to understand what information they must have.\n", 507 | "Each file has six rows, and each row has only one number. Below we explain the information in the file:\n", 508 | "\n", 509 | "+ 0.0045884815 -> This is the x resolution. In this case ~0.45cm\n", 510 | "\n", 511 | "+ 0.0000000000 -> Rotation in the X axix. In this case there is no rotation, hence the number is zero\n", 512 | "\n", 513 | "+ 0.0000000000 -> Rotation in the Y axis. In this case there is no rotation, hence the number is zero\n", 514 | "\n", 515 | "+ -0.0045884815 -> Y scale (i.e. x resolution). Normally negative because the 'origin' of the image is usually in the top left corner. In this case ~0.45cm\n", 516 | "\n", 517 | "+ 389010.333 -> Easting coordinate of each iamge\n", 518 | "\n", 519 | "+ 12592615.666 -> Northing coordinate of each iamge\n", 520 | "\n", 521 | "\n", 522 | "In our case, because the only parameters that change are the Eastings and Northings while the other paramenters remain constant. Our `create_tfw` function reflects this.\n", 523 | "\n", 524 | "We can now create the `*.tfw` files for each `*.tiff` image." 525 | ] 526 | }, 527 | { 528 | "cell_type": "code", 529 | "execution_count": 10, 530 | "metadata": { 531 | "ExecuteTime": { 532 | "end_time": "2019-08-26T02:03:14.933107Z", 533 | "start_time": "2019-08-26T02:03:13.482901Z" 534 | }, 535 | "scrolled": true 536 | }, 537 | "outputs": [ 538 | { 539 | "name": "stdout", 540 | "output_type": "stream", 541 | "text": [ 542 | "DJI_0002 has a new *.tfw file\n", 543 | "DJI_0003 has a new *.tfw file\n", 544 | "DJI_0004 has a new *.tfw file\n", 545 | "DJI_0005 has a new *.tfw file\n", 546 | "DJI_0006 has a new *.tfw file\n" 547 | ] 548 | } 549 | ], 550 | "source": [ 551 | "\n", 552 | "for file in files:\n", 553 | " create_tfw(file, eastings) " 554 | ] 555 | }, 556 | { 557 | "cell_type": "markdown", 558 | "metadata": {}, 559 | "source": [ 560 | "Now the images are ready to upload into Google Earth Engine or other software." 561 | ] 562 | }, 563 | { 564 | "cell_type": "markdown", 565 | "metadata": {}, 566 | "source": [ 567 | "# License\n", 568 | "\n", 569 | "**License:** The code in this notebook is licensed under the Creative Commons by Attribution 4.0 license.\n", 570 | "\n", 571 | "**Last modified:** August 2020" 572 | ] 573 | } 574 | ], 575 | "metadata": { 576 | "kernelspec": { 577 | "display_name": "Python 3", 578 | "language": "python", 579 | "name": "python3" 580 | }, 581 | "language_info": { 582 | "codemirror_mode": { 583 | "name": "ipython", 584 | "version": 3 585 | }, 586 | "file_extension": ".py", 587 | "mimetype": "text/x-python", 588 | "name": "python", 589 | "nbconvert_exporter": "python", 590 | "pygments_lexer": "ipython3", 591 | "version": "3.7.3" 592 | }, 593 | "toc": { 594 | "base_numbering": 1, 595 | "nav_menu": {}, 596 | "number_sections": true, 597 | "sideBar": true, 598 | "skip_h1_title": false, 599 | "title_cell": "Table of Contents", 600 | "title_sidebar": "Contents", 601 | "toc_cell": false, 602 | "toc_position": {}, 603 | "toc_section_display": true, 604 | "toc_window_display": false 605 | }, 606 | "varInspector": { 607 | "cols": { 608 | "lenName": 16, 609 | "lenType": 16, 610 | "lenVar": 40 611 | }, 612 | "kernels_config": { 613 | "python": { 614 | "delete_cmd_postfix": "", 615 | "delete_cmd_prefix": "del ", 616 | "library": "var_list.py", 617 | "varRefreshCmd": "print(var_dic_list())" 618 | }, 619 | "r": { 620 | "delete_cmd_postfix": ") ", 621 | "delete_cmd_prefix": "rm(", 622 | "library": "var_list.r", 623 | "varRefreshCmd": "cat(var_dic_list()) " 624 | } 625 | }, 626 | "types_to_exclude": [ 627 | "module", 628 | "function", 629 | "builtin_function_or_method", 630 | "instance", 631 | "_Feature" 632 | ], 633 | "window_display": false 634 | } 635 | }, 636 | "nbformat": 4, 637 | "nbformat_minor": 2 638 | } 639 | -------------------------------------------------------------------------------- /Image Preprocessing PDF.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bennkat/Drone_Image_Processing_GEE/c773c01d9ffda82ef2cb00de746b5812ce1bd20e/Image Preprocessing PDF.pdf -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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Code and instructions for image classification and accuracy assessment in Google Earth Engine are included. These files are provided as both Jupyter notebooks and PDFs. 3 | 4 | We have also included three drone images of Heron Reef to use as an example for drone image preprocessing and classification. 5 | 6 | --------------------------------------------------------------------------------