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Elizabethcase/Counting class instance and matching gps data.ipynb

Created November 9, 2017 15:39
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Turning pixels into gps lat lons
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Counting class instance and matching gps data.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 621,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import random\n",
"import exifread\n",
"import piexif\n",
"from PIL import Image\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"\n",
"import pandas as pd\n",
"\n",
"import xml.etree.ElementTree as ET\n",
"\n",
"#from datasets.dataset_utils import int64_feature, float_feature, bytes_feature\n",
"#from datasets.pascalvoc_common import VOC_LABELS\n",
"\n",
"#from datasets.CUSTOM_common import CLASS_LABELS"
]
},
{
"cell_type": "code",
"execution_count": 605,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"os.chdir('/Users/elizabeth/Box Sync/SSD-Tensorflow')"
]
},
{
"cell_type": "code",
"execution_count": 606,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#from datasets.dataset_utils import int64_feature, float_feature, bytes_feature\n",
"#from datasets.pascalvoc_common import VOC_LABELS\n",
"\n",
"CLASS_LABELS = {\n",
" 'none': (0, 'Background'),\n",
" 'flower pot': (1, 'Container'),\n",
" 'container': (2, 'Container'),\n",
" 'porch': (3, 'Cover'),\n",
" 'toy': (4, 'Toy'),\n",
" 'bucket': (5, 'Container'),\n",
" 'basketball': (6, 'Toy'),\n",
" 'car': (7, 'Vehicle'),\n",
" 'tarp': (8, 'Cover'),\n",
" 'gutter': (9, 'Container'),\n",
" 'albo property': (10, 'Presence'),\n",
" 'no albo property': (11, 'Presence'),\n",
" 'trash bin': (12, 'Container'),\n",
" 'pot': (13, 'Container'),\n",
" 'tarp': (14, 'Container'),\n",
" 'table': (15, 'Other'),\n",
" 'umbrella': (16, 'Cover'),\n",
" 'car': (17, 'Other'),\n",
" 'air conditioner': (18, 'Container'),\n",
" 'flower bed': (19, 'Other'),\n",
" 'drain': (20, 'Other')\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 607,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/elizabeth/Box Sync/deeplearningcourse/elizabeth/data/harrison_fieldwork1/annotations_wks1_2/correct_xml\r\n"
]
}
],
"source": [
"directory = '/Users/elizabeth/Box Sync/deeplearningcourse/elizabeth/data/harrison_fieldwork1/annotations_wks1_2/correct_xml/'\n",
"\n",
"os.chdir(directory)\n",
"!pwd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gets a count of each of the instances of the classes, total and # of photos"
]
},
{
"cell_type": "code",
"execution_count": 608,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"COUNT = {key:[0,0] for key in CLASS_LABELS.keys()}\n",
"NEW = {}\n",
"\n",
"def get_counts_of_classes(directory, shape=[512,512]):\n",
"\n",
" ''' This gets the counts of all the classes in a batch of xml files.\n",
" Directory: directory where annotations (.xml) files are stored.\n",
" Shape: desired shape of photos; WEIRDOS will tell you if it's not that shape.\n",
"\n",
" Returns three dicts:\n",
" 1. count (shows labels that you have specified & # of appearances)\n",
" 2. new (shows labels that you did not specify & # of appearances)\n",
" 3. weirdos (returns dimensions of photographs if they don't meet your size)\n",
"\n",
" '''\n",
"\n",
" for r, dirs, files in os.walk(directory):\n",
" \n",
" WEIRDOS = {(file[0:-4]+'.JPG'):[0,0] for file in files}\n",
"\n",
" for f in files:\n",
" #print(f[0:-4])\n",
" # Read the XML annotation file.\n",
" path = os.path.join(directory,f)\n",
" tree = ET.parse(path)\n",
" root = tree.getroot()\n",
" \n",
" f = f[0:-4]+'.JPG'\n",
"\n",
" shape_tree = root.find('size')\n",
" if int(shape_tree.find('width').text) != shape[0]:\n",
" WEIRDOS[f][0] = shape_tree.find('width').text\n",
" elif int(shape_tree.find('height').text) != shape[1]:\n",
" WEIRDOS[f][1] = shape_tree.find('height').text\n",
" else:\n",
" WEIRDOS.pop(f)\n",
" \n",
" for obj in root.findall('object'):\n",
" label = obj.find('name').text\n",
" \n",
" if label in CLASS_LABELS.keys():\n",
" COUNT[label][0] += 1\n",
" elif label == 'garbage bin':\n",
" COUNT['trash bin'][0] +=1\n",
" else:\n",
" if label not in NEW:\n",
" NEW[label] = 1\n",
" else:\n",
" NEW[label] += 1\n",
" \n",
" return COUNT, NEW, WEIRDOS\n"
]
},
{
"cell_type": "code",
"execution_count": 609,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"count, new, weirdos = get_counts_of_classes(directory)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creates panda dataframe of lat/lon info from GPS file"
]
},
{
"cell_type": "code",
"execution_count": 690,
"metadata": {},
"outputs": [],
"source": [
"#os.chdir('../../images_wks1_2/')\n",
"\n",
"photos = pd.read_csv('../../../../../../Downloads/compare.csv',index_col=[0],header=None,names=['photos'])\n",
"files = photos.index.values"
]
},
{
"cell_type": "code",
"execution_count": 719,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"for j in files :\\n\\n f = open(j, 'rb')\\n\\n # Return Exif tags\\n tags = exifread.process_file(f)\\n \\n #extracts lat/lon/date/alt (if present) data \\n lat = tags['GPS GPSLatitude'].values\\n lon = tags['GPS GPSLongitude'].values\\n date = tags['GPS GPSDate'].values\\n \\n try:\\n alt = tags['GPS GPSAltitude'].values\\n alt = float(alt[0].num)/float(alt[0].den)\\n except KeyError:\\n alt = 'NaN'\\n \\n #turns lat/lon deg, minute, second into decimal values\\n lat = dms2dec(lat)\\n lon = dms2dec(lon)\\n \\n #adds to dataframe\\n photos.loc[(j,'lat')]=lat\\n photos.loc[(j,'lon')] = -lon\\n photos.loc[(j,'date_taken')] = date\\n photos.loc[(j,'alt')] = alt\\n #holder for heading info\\n photos['heading'] = ''\\n \\nphotos['notes'] = 'alt values for G00 are not accurate'\\n\""
]
},
"execution_count": 719,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''for j in files :\n",
"\n",
" f = open(j, 'rb')\n",
"\n",
" # Return Exif tags\n",
" tags = exifread.process_file(f)\n",
" \n",
" #extracts lat/lon/date/alt (if present) data \n",
" lat = tags['GPS GPSLatitude'].values\n",
" lon = tags['GPS GPSLongitude'].values\n",
" date = tags['GPS GPSDate'].values\n",
" \n",
" try:\n",
" alt = tags['GPS GPSAltitude'].values\n",
" alt = float(alt[0].num)/float(alt[0].den)\n",
" except KeyError:\n",
" alt = 'NaN'\n",
" \n",
" #turns lat/lon deg, minute, second into decimal values\n",
" lat = dms2dec(lat)\n",
" lon = dms2dec(lon)\n",
" \n",
" #adds to dataframe\n",
" photos.loc[(j,'lat')]=lat\n",
" photos.loc[(j,'lon')] = -lon\n",
" photos.loc[(j,'date_taken')] = date\n",
" photos.loc[(j,'alt')] = alt\n",
" #holder for heading info\n",
" photos['heading'] = ''\n",
" \n",
"photos['notes'] = 'alt values for G00 are not accurate'\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 612,
"metadata": {},
"outputs": [],
"source": [
"#photos.to_csv('../../../../../../Downloads/photo_GPS.csv')"
]
},
{
"cell_type": "code",
"execution_count": 693,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"629"
]
},
"execution_count": 693,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(photos)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read in addresses from csv"
]
},
{
"cell_type": "code",
"execution_count": 720,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"photos = pd.read_csv('../../../../../../Downloads/photo_GPS_with_headings.csv',index_col=[0])\n",
"addresses = pd.read_csv('/Users/elizabeth/Box Sync/deeplearningcourse/elizabeth/data/addresses_lat_lon_wks23.csv')"
]
},
{
"cell_type": "code",
"execution_count": 730,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"178"
]
},
"execution_count": 730,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(photos)"
]
},
{
"cell_type": "code",
"execution_count": 721,
"metadata": {},
"outputs": [],
"source": [
"#use L2 norm to return address shortest distance from photo\n",
"\n",
"compared = addresses.loc[((addresses['Latitude']-photos['lat'][0])**2+\n",
" (addresses['Longitude']-photos['lon'][0])**2).argsort()[:1]]\n"
]
},
{
"cell_type": "code",
"execution_count": 722,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def find_nearest_gps(pd1,pd2,\n",
" pd1_lat='lat',pd1_lon='lon',\n",
" pd2_lat='Latitude',pd2_lon='Longitude',pd2_add='Formatted address'):\n",
"\n",
" '''Functionality is not broad. Works for specific instance where you have two pandas data frames:\n",
" \n",
" takes:\n",
" \n",
" pd1 = DataFrame indexed by photo name with pd1_lat, pd1_lon columns\n",
" pd2 = DataFrame that includes address, lat, lon as specified in assignment\n",
" '''\n",
" \n",
" for f in pd1.index.values:\n",
" top = pd2.loc[((pd2[pd2_lat]-pd1.loc[f,pd1_lat])**2+\n",
" (pd2[pd2_lon]-pd1.loc[f,pd1_lon])**2).argsort()[:1]]\n",
"\n",
" pd1.loc[(f,'nearest_address')] = top[pd2_add].values[0]\n",
" pd1.loc[(f,'add_lat')] = top[pd2_lat].values[0]\n",
" pd1.loc[(f,'add_lon')] = top[pd2_lon].values[0]\n",
" \n",
" \n",
" return pd1"
]
},
{
"cell_type": "code",
"execution_count": 723,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nearest_address = find_nearest_gps(photos, addresses)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## successfully overwrites GPS data"
]
},
{
"cell_type": "code",
"execution_count": 698,
"metadata": {},
"outputs": [],
"source": [
"def deg_rad(inp,key='deg2rad'):\n",
" '''\n",
" Converts degrees to radian or vice versa\n",
" Inp = # to change\n",
" Indicate with key = {deg2rad, rad2deg}\n",
" \n",
" Returns # in radian or degrees, depending on what you specified\n",
" \n",
" '''\n",
" if key=='deg2rad':\n",
" rad = inp*np.pi/180\n",
" return rad\n",
" \n",
" elif key=='rad2deg':\n",
" deg = inp*180/np.pi\n",
" return deg\n",
"\n",
"def len_at_lat(lat):\n",
" \n",
" '''\n",
" Lat = latitude where info is desired\n",
" \n",
" Returns the length in meters of one degree, latitude or longitude, at a specified latitude\n",
" Also returns conversion rate from degrees to meters\n",
" \n",
" '''\n",
" \n",
" #length of 1 degree in meters at a specified latitude\n",
" m_per_lat = 111132.92-559.82*np.cos(2*phi) +1.175*np.cos(4*phi) -0.0023*np.cos(6*phi)\n",
" m_per_lon = 111412.84*np.cos(phi) -93.5*np.cos(3*phi) -0.118*np.cos(5*phi)\n",
" \n",
" #number of seconds per meter\n",
" lat_per_m = 1/m_per_lat\n",
" lon_per_m = 1/m_per_lon\n",
" \n",
" return m_per_lat, m_per_lon, lat_per_m, lon_per_m\n",
"\n",
"def dms2dec(inp):\n",
" \n",
" '''\n",
" Converts inp from degrees, minutes, seconds to decimal\n",
" Expects input to be type Ratio\n",
" Returns converted input\n",
" '''\n",
" \n",
" inp = [float(i.num)/float(i.den) for i in inp]\n",
" inp = inp[0] + (inp[1])/60 + inp[2]/3600\n",
" \n",
" return inp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CHANGE LAT/LON IN PD TABLE AND APPEND"
]
},
{
"cell_type": "code",
"execution_count": 699,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/elizabeth/Box Sync/deeplearningcourse/elizabeth/data/harrison_fieldwork1/images_wks1_2\r\n"
]
}
],
"source": [
"!pwd"
]
},
{
"cell_type": "code",
"execution_count": 704,
"metadata": {},
"outputs": [],
"source": [
"#heading_test = pd.read_csv('../../../../../../Downloads/photo_GPS_with_headings.csv',index_col=[0])"
]
},
{
"cell_type": "code",
"execution_count": 707,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.40740740740740744, 0.025055514433752774)"
]
},
"execution_count": 707,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#px500 = 5.6 #m/500 pixels\n",
"#m_per_px = 5.6/500\n",
"m_per_px = 6.77/270.2\n",
"m_per_500px = m_per_px*500. #meters per 500 pixels\n",
"arcsec_per_px = 0.22/270\n",
"arcsec_per_500px = arcsec_per_px*500\n",
"\n",
"#photo & splicing info\n",
"w = 3000\n",
"h = 2000\n",
"crop = 500\n",
"\n",
"#how many times thigns should be done.. working on making this a generalizable function\n",
"stepw = len(range(0,w,crop))\n",
"steph = len(range(0,h,crop))\n",
"\n",
"arcsec_per_500px, m_per_px"
]
},
{
"cell_type": "code",
"execution_count": 729,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"171"
]
},
"execution_count": 729,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pd1)"
]
},
{
"cell_type": "code",
"execution_count": 725,
"metadata": {},
"outputs": [],
"source": [
"pd1 = nearest_address[np.isfinite(heading_test.heading)].copy()\n",
"\n",
"#splices crop slice number from file name\n",
"\n",
"for i in pd1.index.values:\n",
" \n",
" lat = pd1.loc[i,'lat']\n",
" lon = pd1.loc[i,'lon']\n",
" theta = pd1.loc[i,'heading']*np.pi/180\n",
" \n",
" #conversions between meters per degree and degrees per meter\n",
" m_per_lat, m_per_lon, lat_per_m, lon_per_m = len_at_lat(lat)\n",
" \n",
" #lat change equivalent depending on ground sampling distance of photo \n",
" #note -- explain how you got px500\n",
" lat_step = m_per_500px*lat_per_m #lat step per 500 px\n",
" \n",
" lon_step = m_per_500px*lon_per_m #lon step per 500 px\n",
" \n",
" #accuracy (or precision?) is the difference between theses two\n",
" #print(lat_step, arcsec_per_500px/3600)\n",
"\n",
" #rotaion matrix [x';y'] = [cos -sin; sin cos][x;y]\n",
" del_lon= lat_step*np.cos(theta) - lon_step*np.sin(theta) \n",
" del_lat = lat_step*np.sin(theta) +lon_step*np.cos(theta)\n",
" \n",
" #returns number appended to photo, which tells where it was in the mogrify crop/splice functio\n",
" c = int(re.sub(\"[^0-9]\",\"\",i[-6:]))\n",
" \n",
" #changes lat lon to upper right hand corner of slice for convenience when working with object pixel locations\n",
" if c in [stepw*0, stepw*1, stepw*2, stepw*3]:\n",
" new_lon = lon - (stepw/2)*del_lon\n",
"\n",
" elif c in [stepw*0+1, stepw*1+1, stepw*2+1, stepw*3+1]:\n",
" new_lon = lon - (stepw/2-1)*del_lon\n",
"\n",
" elif c in [stepw*0+2, stepw*1+2, stepw*2+2, stepw*3+2]:\n",
" new_lon = lon - (stepw/2-2)*del_lon\n",
" \n",
" elif c in [stepw*0+3,stepw*1+3,stepw*2+3, stepw*3+3]:\n",
" new_lon = lon - (stepw/2-3)*del_lon\n",
" \n",
" elif c in [stepw*0+4,stepw*1+4,stepw*2+4, stepw*3+4]:\n",
" new_lon = lon - (stepw/2-4)*del_lon\n",
"\n",
" elif c in [stepw*0+5,stepw*1+5,stepw*2+5, stepw*3+5]:\n",
" new_lon = lon - (stepw/2-5)*del_lon\n",
"\n",
" else:\n",
" print('lon num not defined', c)\n",
" new_lon=lon\n",
"\n",
" #latitude\n",
" if c in range(0,1*stepw):\n",
" new_lat = lat + (steph/2)*lat_step\n",
" \n",
" elif c in range(1*stepw,2*stepw):\n",
" new_lat = lat + (steph/2-1)*lat_step\n",
"\n",
" elif c in range(2*stepw,3*stepw):\n",
" new_lat = lat + (steph/2-2)*lat_step\n",
"\n",
" elif c in range(3*stepw,4*stepw):\n",
" new_lat = lat + (steph/2-3)*lat_step\n",
"\n",
" else:\n",
" print('lat num not defined', c)\n",
" new_lat=lat\n",
"\n",
" pd1.loc[(i,'new_lat')] = new_lat\n",
" pd1.loc[(i,'new_lon')] = new_lon\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## import px locations of objects"
]
},
{
"cell_type": "code",
"execution_count": 809,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nn_obj = pd.read_csv('../../../../../../Downloads/itms_val_harrisonwks12_conf35.csv',index_col=[0,1,2])"
]
},
{
"cell_type": "code",
"execution_count": 818,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'G0013879_5.JPG'"
]
},
"execution_count": 818,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"headings_w_nn = nn_obj.join(pd1,how='outer')\n",
"headings_w_nn = headings_w_nn.dropna()\n",
"headings_w_nn.index.values[0][0]"
]
},
{
"cell_type": "code",
"execution_count": 821,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>midx</th>\n",
" <th>midy</th>\n",
" <th>confidence</th>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" <th>date_taken</th>\n",
" <th>alt</th>\n",
" <th>heading</th>\n",
" <th>notes</th>\n",
" <th>nearest_address</th>\n",
" <th>add_lat</th>\n",
" <th>add_lon</th>\n",
" <th>new_lat</th>\n",
" <th>new_lon</th>\n",
" <th>obj_lat</th>\n",
" <th>obj_lon</th>\n",
" </tr>\n",
" <tr>\n",
" <th>photo</th>\n",
" <th>classes</th>\n",
" <th>class instances</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">G0013879_5.JPG</th>\n",
" <th>car</th>\n",
" <th>1</th>\n",
" <td>238.0</td>\n",
" <td>36.5</td>\n",
" <td>0.858202</td>\n",
" <td>40.966592</td>\n",
" <td>-73.733777</td>\n",
" <td>2017:07:20</td>\n",
" <td>5.568</td>\n",
" <td>20.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966819</td>\n",
" <td>-73.733641</td>\n",
" <td>40.966808</td>\n",
" <td>-73.733673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>container</th>\n",
" <th>1</th>\n",
" <td>496.0</td>\n",
" <td>71.0</td>\n",
" <td>0.439825</td>\n",
" <td>40.966592</td>\n",
" <td>-73.733777</td>\n",
" <td>2017:07:20</td>\n",
" <td>5.568</td>\n",
" <td>20.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966819</td>\n",
" <td>-73.733641</td>\n",
" <td>40.966798</td>\n",
" <td>-73.733708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>trash bin</th>\n",
" <th>1</th>\n",
" <td>498.0</td>\n",
" <td>72.5</td>\n",
" <td>0.596141</td>\n",
" <td>40.966592</td>\n",
" <td>-73.733777</td>\n",
" <td>2017:07:20</td>\n",
" <td>5.568</td>\n",
" <td>20.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966819</td>\n",
" <td>-73.733641</td>\n",
" <td>40.966798</td>\n",
" <td>-73.733708</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">G0024032_18.JPG</th>\n",
" <th>table</th>\n",
" <th>1</th>\n",
" <td>82.5</td>\n",
" <td>105.0</td>\n",
" <td>0.748394</td>\n",
" <td>40.966066</td>\n",
" <td>-73.732624</td>\n",
" <td>2017:07:20</td>\n",
" <td>21.282</td>\n",
" <td>243.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1413 Arlington St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966527</td>\n",
" <td>-73.732589</td>\n",
" <td>40.965953</td>\n",
" <td>-73.732771</td>\n",
" <td>40.965985</td>\n",
" <td>-73.732779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hose</th>\n",
" <th>1</th>\n",
" <td>150.0</td>\n",
" <td>18.0</td>\n",
" <td>0.380114</td>\n",
" <td>40.966066</td>\n",
" <td>-73.732624</td>\n",
" <td>2017:07:20</td>\n",
" <td>21.282</td>\n",
" <td>243.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1413 Arlington St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966527</td>\n",
" <td>-73.732589</td>\n",
" <td>40.965953</td>\n",
" <td>-73.732771</td>\n",
" <td>40.965959</td>\n",
" <td>-73.732786</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">G0036497_20.JPG</th>\n",
" <th>car</th>\n",
" <th>1</th>\n",
" <td>116.5</td>\n",
" <td>60.5</td>\n",
" <td>0.496821</td>\n",
" <td>40.967221</td>\n",
" <td>-73.735713</td>\n",
" <td>2017:08:03</td>\n",
" <td>12.586</td>\n",
" <td>47.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1520 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967244</td>\n",
" <td>-73.735619</td>\n",
" <td>40.967107</td>\n",
" <td>-73.735708</td>\n",
" <td>40.967088</td>\n",
" <td>-73.735706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hose</th>\n",
" <th>1</th>\n",
" <td>193.0</td>\n",
" <td>59.0</td>\n",
" <td>0.750789</td>\n",
" <td>40.967221</td>\n",
" <td>-73.735713</td>\n",
" <td>2017:08:03</td>\n",
" <td>12.586</td>\n",
" <td>47.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1520 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967244</td>\n",
" <td>-73.735619</td>\n",
" <td>40.967107</td>\n",
" <td>-73.735708</td>\n",
" <td>40.967088</td>\n",
" <td>-73.735706</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">G0013835_5.JPG</th>\n",
" <th rowspan=\"2\" valign=\"top\">chair</th>\n",
" <th>1</th>\n",
" <td>57.5</td>\n",
" <td>71.0</td>\n",
" <td>0.517677</td>\n",
" <td>40.967491</td>\n",
" <td>-73.732416</td>\n",
" <td>2017:07:20</td>\n",
" <td>0.222</td>\n",
" <td>247.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>507 Chestnut Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967745</td>\n",
" <td>-73.732004</td>\n",
" <td>40.967717</td>\n",
" <td>-73.732297</td>\n",
" <td>40.967739</td>\n",
" <td>-73.732303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>72.0</td>\n",
" <td>56.0</td>\n",
" <td>0.447866</td>\n",
" <td>40.967491</td>\n",
" <td>-73.732416</td>\n",
" <td>2017:07:20</td>\n",
" <td>0.222</td>\n",
" <td>247.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>507 Chestnut Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967745</td>\n",
" <td>-73.732004</td>\n",
" <td>40.967717</td>\n",
" <td>-73.732297</td>\n",
" <td>40.967734</td>\n",
" <td>-73.732305</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">G0013868_18.JPG</th>\n",
" <th>car</th>\n",
" <th>1</th>\n",
" <td>41.0</td>\n",
" <td>90.0</td>\n",
" <td>0.518681</td>\n",
" <td>40.966741</td>\n",
" <td>-73.735203</td>\n",
" <td>2017:07:20</td>\n",
" <td>1.560</td>\n",
" <td>43.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1515 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967128</td>\n",
" <td>-73.735109</td>\n",
" <td>40.966628</td>\n",
" <td>-73.735221</td>\n",
" <td>40.966599</td>\n",
" <td>-73.735222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>table</th>\n",
" <th>1</th>\n",
" <td>465.5</td>\n",
" <td>169.0</td>\n",
" <td>0.812746</td>\n",
" <td>40.966741</td>\n",
" <td>-73.735203</td>\n",
" <td>2017:07:20</td>\n",
" <td>1.560</td>\n",
" <td>43.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1515 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967128</td>\n",
" <td>-73.735109</td>\n",
" <td>40.966628</td>\n",
" <td>-73.735221</td>\n",
" <td>40.966574</td>\n",
" <td>-73.735227</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">air conditioner</th>\n",
" <th>1</th>\n",
" <td>299.5</td>\n",
" <td>241.5</td>\n",
" <td>0.720269</td>\n",
" <td>40.966741</td>\n",
" <td>-73.735203</td>\n",
" <td>2017:07:20</td>\n",
" <td>1.560</td>\n",
" <td>43.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1515 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967128</td>\n",
" <td>-73.735109</td>\n",
" <td>40.966628</td>\n",
" <td>-73.735221</td>\n",
" <td>40.966551</td>\n",
" <td>-73.735225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>224.0</td>\n",
" <td>228.5</td>\n",
" <td>0.625788</td>\n",
" <td>40.966741</td>\n",
" <td>-73.735203</td>\n",
" <td>2017:07:20</td>\n",
" <td>1.560</td>\n",
" <td>43.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1515 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967128</td>\n",
" <td>-73.735109</td>\n",
" <td>40.966628</td>\n",
" <td>-73.735221</td>\n",
" <td>40.966555</td>\n",
" <td>-73.735224</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">G0024040_20.JPG</th>\n",
" <th rowspan=\"4\" valign=\"top\">table</th>\n",
" <th>1</th>\n",
" <td>365.5</td>\n",
" <td>467.0</td>\n",
" <td>0.944633</td>\n",
" <td>40.966898</td>\n",
" <td>-73.733502</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.342</td>\n",
" <td>337.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966784</td>\n",
" <td>-73.733650</td>\n",
" <td>40.966729</td>\n",
" <td>-73.733759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>368.5</td>\n",
" <td>428.5</td>\n",
" <td>0.531788</td>\n",
" <td>40.966898</td>\n",
" <td>-73.733502</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.342</td>\n",
" <td>337.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966784</td>\n",
" <td>-73.733650</td>\n",
" <td>40.966733</td>\n",
" <td>-73.733760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>367.0</td>\n",
" <td>405.0</td>\n",
" <td>0.399011</td>\n",
" <td>40.966898</td>\n",
" <td>-73.733502</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.342</td>\n",
" <td>337.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966784</td>\n",
" <td>-73.733650</td>\n",
" <td>40.966736</td>\n",
" <td>-73.733759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>402.5</td>\n",
" <td>449.0</td>\n",
" <td>0.351321</td>\n",
" <td>40.966898</td>\n",
" <td>-73.733502</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.342</td>\n",
" <td>337.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966784</td>\n",
" <td>-73.733650</td>\n",
" <td>40.966731</td>\n",
" <td>-73.733770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>flower pot</th>\n",
" <th>1</th>\n",
" <td>306.5</td>\n",
" <td>406.0</td>\n",
" <td>0.561530</td>\n",
" <td>40.966898</td>\n",
" <td>-73.733502</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.342</td>\n",
" <td>337.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966784</td>\n",
" <td>-73.733650</td>\n",
" <td>40.966736</td>\n",
" <td>-73.733741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>container</th>\n",
" <th>1</th>\n",
" <td>305.5</td>\n",
" <td>400.5</td>\n",
" <td>0.503890</td>\n",
" <td>40.966898</td>\n",
" <td>-73.733502</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.342</td>\n",
" <td>337.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966784</td>\n",
" <td>-73.733650</td>\n",
" <td>40.966737</td>\n",
" <td>-73.733741</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">G0024061_22.JPG</th>\n",
" <th>table</th>\n",
" <th>1</th>\n",
" <td>281.5</td>\n",
" <td>444.5</td>\n",
" <td>0.748490</td>\n",
" <td>40.965187</td>\n",
" <td>-73.734823</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.272</td>\n",
" <td>177.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>220 Soundview Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965210</td>\n",
" <td>-73.733781</td>\n",
" <td>40.965073</td>\n",
" <td>-73.734942</td>\n",
" <td>40.965168</td>\n",
" <td>-73.734875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>porch</th>\n",
" <th>1</th>\n",
" <td>420.5</td>\n",
" <td>222.5</td>\n",
" <td>0.774700</td>\n",
" <td>40.965187</td>\n",
" <td>-73.734823</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.272</td>\n",
" <td>177.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>220 Soundview Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965210</td>\n",
" <td>-73.733781</td>\n",
" <td>40.965073</td>\n",
" <td>-73.734942</td>\n",
" <td>40.965121</td>\n",
" <td>-73.734842</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">chair</th>\n",
" <th>1</th>\n",
" <td>450.0</td>\n",
" <td>177.0</td>\n",
" <td>0.549229</td>\n",
" <td>40.965187</td>\n",
" <td>-73.734823</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.272</td>\n",
" <td>177.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>220 Soundview Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965210</td>\n",
" <td>-73.733781</td>\n",
" <td>40.965073</td>\n",
" <td>-73.734942</td>\n",
" <td>40.965111</td>\n",
" <td>-73.734835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>451.5</td>\n",
" <td>269.0</td>\n",
" <td>0.424127</td>\n",
" <td>40.965187</td>\n",
" <td>-73.734823</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.272</td>\n",
" <td>177.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>220 Soundview Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965210</td>\n",
" <td>-73.733781</td>\n",
" <td>40.965073</td>\n",
" <td>-73.734942</td>\n",
" <td>40.965131</td>\n",
" <td>-73.734835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>454.0</td>\n",
" <td>297.5</td>\n",
" <td>0.365337</td>\n",
" <td>40.965187</td>\n",
" <td>-73.734823</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.272</td>\n",
" <td>177.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>220 Soundview Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965210</td>\n",
" <td>-73.733781</td>\n",
" <td>40.965073</td>\n",
" <td>-73.734942</td>\n",
" <td>40.965137</td>\n",
" <td>-73.734834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>371.0</td>\n",
" <td>143.5</td>\n",
" <td>0.360516</td>\n",
" <td>40.965187</td>\n",
" <td>-73.734823</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.272</td>\n",
" <td>177.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>220 Soundview Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965210</td>\n",
" <td>-73.733781</td>\n",
" <td>40.965073</td>\n",
" <td>-73.734942</td>\n",
" <td>40.965104</td>\n",
" <td>-73.734854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0024029_22.JPG</th>\n",
" <th>container</th>\n",
" <th>1</th>\n",
" <td>170.0</td>\n",
" <td>331.5</td>\n",
" <td>0.558631</td>\n",
" <td>40.966334</td>\n",
" <td>-73.732228</td>\n",
" <td>2017:07:20</td>\n",
" <td>13.002</td>\n",
" <td>162.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1413 Arlington St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966527</td>\n",
" <td>-73.732589</td>\n",
" <td>40.966221</td>\n",
" <td>-73.732370</td>\n",
" <td>40.966269</td>\n",
" <td>-73.732322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0024044_21.JPG</th>\n",
" <th>car</th>\n",
" <th>1</th>\n",
" <td>197.0</td>\n",
" <td>343.5</td>\n",
" <td>0.978664</td>\n",
" <td>40.966892</td>\n",
" <td>-73.733592</td>\n",
" <td>2017:07:20</td>\n",
" <td>0.700</td>\n",
" <td>250.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.966779</td>\n",
" <td>-73.733592</td>\n",
" <td>40.966879</td>\n",
" <td>-73.733619</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">G0024033_11.JPG</th>\n",
" <th>clutter</th>\n",
" <th>1</th>\n",
" <td>215.5</td>\n",
" <td>425.0</td>\n",
" <td>0.627354</td>\n",
" <td>40.966021</td>\n",
" <td>-73.732860</td>\n",
" <td>2017:07:20</td>\n",
" <td>20.133</td>\n",
" <td>246.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1413 Arlington St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966527</td>\n",
" <td>-73.732589</td>\n",
" <td>40.966135</td>\n",
" <td>-73.732746</td>\n",
" <td>40.966262</td>\n",
" <td>-73.732770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>table</th>\n",
" <th>1</th>\n",
" <td>205.5</td>\n",
" <td>417.0</td>\n",
" <td>0.355831</td>\n",
" <td>40.966021</td>\n",
" <td>-73.732860</td>\n",
" <td>2017:07:20</td>\n",
" <td>20.133</td>\n",
" <td>246.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1413 Arlington St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966527</td>\n",
" <td>-73.732589</td>\n",
" <td>40.966135</td>\n",
" <td>-73.732746</td>\n",
" <td>40.966259</td>\n",
" <td>-73.732769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>porch</th>\n",
" <th>1</th>\n",
" <td>359.0</td>\n",
" <td>112.0</td>\n",
" <td>0.384332</td>\n",
" <td>40.966021</td>\n",
" <td>-73.732860</td>\n",
" <td>2017:07:20</td>\n",
" <td>20.133</td>\n",
" <td>246.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1413 Arlington St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966527</td>\n",
" <td>-73.732589</td>\n",
" <td>40.966135</td>\n",
" <td>-73.732746</td>\n",
" <td>40.966168</td>\n",
" <td>-73.732787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0013877_5.JPG</th>\n",
" <th>container</th>\n",
" <th>1</th>\n",
" <td>444.0</td>\n",
" <td>273.0</td>\n",
" <td>0.362154</td>\n",
" <td>40.966482</td>\n",
" <td>-73.734306</td>\n",
" <td>2017:07:20</td>\n",
" <td>5.404</td>\n",
" <td>20.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966708</td>\n",
" <td>-73.734170</td>\n",
" <td>40.966629</td>\n",
" <td>-73.734230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0036430_21.JPG</th>\n",
" <th>container</th>\n",
" <th>1</th>\n",
" <td>148.5</td>\n",
" <td>173.0</td>\n",
" <td>0.373297</td>\n",
" <td>40.967611</td>\n",
" <td>-73.732276</td>\n",
" <td>2017:08:03</td>\n",
" <td>4.323</td>\n",
" <td>275.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>507 Chestnut Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967745</td>\n",
" <td>-73.732004</td>\n",
" <td>40.967498</td>\n",
" <td>-73.732276</td>\n",
" <td>40.967533</td>\n",
" <td>-73.732313</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">G0013876_23.JPG</th>\n",
" <th rowspan=\"2\" valign=\"top\">toy</th>\n",
" <th>1</th>\n",
" <td>377.5</td>\n",
" <td>38.0</td>\n",
" <td>0.437902</td>\n",
" <td>40.966432</td>\n",
" <td>-73.734562</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.065</td>\n",
" <td>20.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966318</td>\n",
" <td>-73.734427</td>\n",
" <td>40.966307</td>\n",
" <td>-73.734478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>438.5</td>\n",
" <td>214.5</td>\n",
" <td>0.400005</td>\n",
" <td>40.966432</td>\n",
" <td>-73.734562</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.065</td>\n",
" <td>20.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966318</td>\n",
" <td>-73.734427</td>\n",
" <td>40.966256</td>\n",
" <td>-73.734486</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pool</th>\n",
" <th>1</th>\n",
" <td>349.5</td>\n",
" <td>204.0</td>\n",
" <td>0.894474</td>\n",
" <td>40.966432</td>\n",
" <td>-73.734562</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.065</td>\n",
" <td>20.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966318</td>\n",
" <td>-73.734427</td>\n",
" <td>40.966259</td>\n",
" <td>-73.734474</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">G0036508_23.JPG</th>\n",
" <th>flower pot</th>\n",
" <th>1</th>\n",
" <td>129.5</td>\n",
" <td>315.5</td>\n",
" <td>0.488355</td>\n",
" <td>40.966528</td>\n",
" <td>-73.734910</td>\n",
" <td>2017:08:03</td>\n",
" <td>31.699</td>\n",
" <td>47.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966415</td>\n",
" <td>-73.734921</td>\n",
" <td>40.966314</td>\n",
" <td>-73.734919</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">umbrella</th>\n",
" <th>1</th>\n",
" <td>368.5</td>\n",
" <td>151.0</td>\n",
" <td>0.972527</td>\n",
" <td>40.966528</td>\n",
" <td>-73.734910</td>\n",
" <td>2017:08:03</td>\n",
" <td>31.699</td>\n",
" <td>47.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966415</td>\n",
" <td>-73.734921</td>\n",
" <td>40.966367</td>\n",
" <td>-73.734917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>371.0</td>\n",
" <td>201.0</td>\n",
" <td>0.378233</td>\n",
" <td>40.966528</td>\n",
" <td>-73.734910</td>\n",
" <td>2017:08:03</td>\n",
" <td>31.699</td>\n",
" <td>47.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966415</td>\n",
" <td>-73.734921</td>\n",
" <td>40.966351</td>\n",
" <td>-73.734917</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">G0036451_22.JPG</th>\n",
" <th>chair</th>\n",
" <th>1</th>\n",
" <td>78.0</td>\n",
" <td>203.0</td>\n",
" <td>0.469600</td>\n",
" <td>40.967373</td>\n",
" <td>-73.733539</td>\n",
" <td>2017:08:03</td>\n",
" <td>10.148</td>\n",
" <td>275.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>400 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967283</td>\n",
" <td>-73.733141</td>\n",
" <td>40.967260</td>\n",
" <td>-73.733417</td>\n",
" <td>40.967302</td>\n",
" <td>-73.733436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>container</th>\n",
" <th>1</th>\n",
" <td>19.5</td>\n",
" <td>82.5</td>\n",
" <td>0.450302</td>\n",
" <td>40.967373</td>\n",
" <td>-73.733539</td>\n",
" <td>2017:08:03</td>\n",
" <td>10.148</td>\n",
" <td>275.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>400 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967283</td>\n",
" <td>-73.733141</td>\n",
" <td>40.967260</td>\n",
" <td>-73.733417</td>\n",
" <td>40.967277</td>\n",
" <td>-73.733421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0036461_21.JPG</th>\n",
" <th>car</th>\n",
" <th>1</th>\n",
" <td>377.5</td>\n",
" <td>344.0</td>\n",
" <td>0.993506</td>\n",
" <td>40.967047</td>\n",
" <td>-73.734589</td>\n",
" <td>2017:08:03</td>\n",
" <td>16.507</td>\n",
" <td>275.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966934</td>\n",
" <td>-73.734589</td>\n",
" <td>40.967005</td>\n",
" <td>-73.734681</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">G0024043_11.JPG</th>\n",
" <th rowspan=\"2\" valign=\"top\">car</th>\n",
" <th>1</th>\n",
" <td>124.0</td>\n",
" <td>458.5</td>\n",
" <td>0.992442</td>\n",
" <td>40.966901</td>\n",
" <td>-73.733543</td>\n",
" <td>2017:07:20</td>\n",
" <td>0.718</td>\n",
" <td>337.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.967014</td>\n",
" <td>-73.733246</td>\n",
" <td>40.966959</td>\n",
" <td>-73.733283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>123.5</td>\n",
" <td>411.5</td>\n",
" <td>0.717569</td>\n",
" <td>40.966901</td>\n",
" <td>-73.733543</td>\n",
" <td>2017:07:20</td>\n",
" <td>0.718</td>\n",
" <td>337.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1421 N James St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966502</td>\n",
" <td>-73.733680</td>\n",
" <td>40.967014</td>\n",
" <td>-73.733246</td>\n",
" <td>40.966965</td>\n",
" <td>-73.733283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0024017_11.JPG</th>\n",
" <th>car</th>\n",
" <th>1</th>\n",
" <td>163.0</td>\n",
" <td>404.5</td>\n",
" <td>0.903140</td>\n",
" <td>40.967393</td>\n",
" <td>-73.731853</td>\n",
" <td>2017:07:20</td>\n",
" <td>4.796</td>\n",
" <td>260.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>514 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967473</td>\n",
" <td>-73.731890</td>\n",
" <td>40.967506</td>\n",
" <td>-73.731670</td>\n",
" <td>40.967612</td>\n",
" <td>-73.731700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0024067_19.JPG</th>\n",
" <th>table</th>\n",
" <th>1</th>\n",
" <td>312.0</td>\n",
" <td>127.5</td>\n",
" <td>0.620657</td>\n",
" <td>40.965194</td>\n",
" <td>-73.734578</td>\n",
" <td>2017:07:20</td>\n",
" <td>2.036</td>\n",
" <td>86.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>220 Soundview Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965210</td>\n",
" <td>-73.733781</td>\n",
" <td>40.965080</td>\n",
" <td>-73.734369</td>\n",
" <td>40.965050</td>\n",
" <td>-73.734303</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">G0024051_11.JPG</th>\n",
" <th rowspan=\"2\" valign=\"top\">gutter</th>\n",
" <th>1</th>\n",
" <td>247.5</td>\n",
" <td>49.5</td>\n",
" <td>0.889627</td>\n",
" <td>40.966057</td>\n",
" <td>-73.734220</td>\n",
" <td>2017:07:20</td>\n",
" <td>4.024</td>\n",
" <td>156.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>216 Travers Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965890</td>\n",
" <td>-73.733997</td>\n",
" <td>40.966171</td>\n",
" <td>-73.734519</td>\n",
" <td>40.966176</td>\n",
" <td>-73.734445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>239.0</td>\n",
" <td>46.5</td>\n",
" <td>0.373442</td>\n",
" <td>40.966057</td>\n",
" <td>-73.734220</td>\n",
" <td>2017:07:20</td>\n",
" <td>4.024</td>\n",
" <td>156.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>216 Travers Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965890</td>\n",
" <td>-73.733997</td>\n",
" <td>40.966171</td>\n",
" <td>-73.734519</td>\n",
" <td>40.966176</td>\n",
" <td>-73.734447</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">G0013877_23.JPG</th>\n",
" <th rowspan=\"2\" valign=\"top\">pool</th>\n",
" <th>1</th>\n",
" <td>165.0</td>\n",
" <td>412.0</td>\n",
" <td>0.985320</td>\n",
" <td>40.966482</td>\n",
" <td>-73.734306</td>\n",
" <td>2017:07:20</td>\n",
" <td>5.404</td>\n",
" <td>20.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966369</td>\n",
" <td>-73.734170</td>\n",
" <td>40.966249</td>\n",
" <td>-73.734192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>201.5</td>\n",
" <td>330.0</td>\n",
" <td>0.499113</td>\n",
" <td>40.966482</td>\n",
" <td>-73.734306</td>\n",
" <td>2017:07:20</td>\n",
" <td>5.404</td>\n",
" <td>20.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.966369</td>\n",
" <td>-73.734170</td>\n",
" <td>40.966273</td>\n",
" <td>-73.734197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0024052_22.JPG</th>\n",
" <th>pool</th>\n",
" <th>1</th>\n",
" <td>322.5</td>\n",
" <td>448.5</td>\n",
" <td>0.390404</td>\n",
" <td>40.965861</td>\n",
" <td>-73.734138</td>\n",
" <td>2017:07:20</td>\n",
" <td>3.677</td>\n",
" <td>156.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>216 Travers Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965890</td>\n",
" <td>-73.733997</td>\n",
" <td>40.965748</td>\n",
" <td>-73.734287</td>\n",
" <td>40.965799</td>\n",
" <td>-73.734191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0036428_19.JPG</th>\n",
" <th>pool</th>\n",
" <th>1</th>\n",
" <td>236.0</td>\n",
" <td>326.0</td>\n",
" <td>0.992278</td>\n",
" <td>40.967665</td>\n",
" <td>-73.732055</td>\n",
" <td>2017:08:03</td>\n",
" <td>1.169</td>\n",
" <td>275.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>507 Chestnut Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967745</td>\n",
" <td>-73.732004</td>\n",
" <td>40.967551</td>\n",
" <td>-73.732299</td>\n",
" <td>40.967618</td>\n",
" <td>-73.732357</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">G0013868_21.JPG</th>\n",
" <th>chair</th>\n",
" <th>1</th>\n",
" <td>56.5</td>\n",
" <td>224.0</td>\n",
" <td>0.468874</td>\n",
" <td>40.966741</td>\n",
" <td>-73.735203</td>\n",
" <td>2017:07:20</td>\n",
" <td>1.560</td>\n",
" <td>43.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1515 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967128</td>\n",
" <td>-73.735109</td>\n",
" <td>40.966628</td>\n",
" <td>-73.735203</td>\n",
" <td>40.966557</td>\n",
" <td>-73.735204</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">container</th>\n",
" <th>1</th>\n",
" <td>53.0</td>\n",
" <td>248.0</td>\n",
" <td>0.705487</td>\n",
" <td>40.966741</td>\n",
" <td>-73.735203</td>\n",
" <td>2017:07:20</td>\n",
" <td>1.560</td>\n",
" <td>43.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1515 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967128</td>\n",
" <td>-73.735109</td>\n",
" <td>40.966628</td>\n",
" <td>-73.735203</td>\n",
" <td>40.966549</td>\n",
" <td>-73.735204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>56.5</td>\n",
" <td>224.0</td>\n",
" <td>0.361516</td>\n",
" <td>40.966741</td>\n",
" <td>-73.735203</td>\n",
" <td>2017:07:20</td>\n",
" <td>1.560</td>\n",
" <td>43.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1515 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967128</td>\n",
" <td>-73.735109</td>\n",
" <td>40.966628</td>\n",
" <td>-73.735203</td>\n",
" <td>40.966557</td>\n",
" <td>-73.735204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>54.0</td>\n",
" <td>215.5</td>\n",
" <td>0.357651</td>\n",
" <td>40.966741</td>\n",
" <td>-73.735203</td>\n",
" <td>2017:07:20</td>\n",
" <td>1.560</td>\n",
" <td>43.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>1515 Urban St, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.967128</td>\n",
" <td>-73.735109</td>\n",
" <td>40.966628</td>\n",
" <td>-73.735203</td>\n",
" <td>40.966559</td>\n",
" <td>-73.735204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G0024060_19.JPG</th>\n",
" <th>container</th>\n",
" <th>1</th>\n",
" <td>342.5</td>\n",
" <td>204.0</td>\n",
" <td>0.579098</td>\n",
" <td>40.965376</td>\n",
" <td>-73.734865</td>\n",
" <td>2017:07:20</td>\n",
" <td>4.655</td>\n",
" <td>177.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>216 Travers Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.965890</td>\n",
" <td>-73.733997</td>\n",
" <td>40.965263</td>\n",
" <td>-73.734627</td>\n",
" <td>40.965307</td>\n",
" <td>-73.734545</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">G0036460_17.JPG</th>\n",
" <th>porch</th>\n",
" <th>1</th>\n",
" <td>406.5</td>\n",
" <td>325.0</td>\n",
" <td>0.799127</td>\n",
" <td>40.967079</td>\n",
" <td>-73.734482</td>\n",
" <td>2017:08:03</td>\n",
" <td>15.504</td>\n",
" <td>275.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.967079</td>\n",
" <td>-73.734237</td>\n",
" <td>40.967146</td>\n",
" <td>-73.734336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pool</th>\n",
" <th>1</th>\n",
" <td>75.0</td>\n",
" <td>61.0</td>\n",
" <td>0.806519</td>\n",
" <td>40.967079</td>\n",
" <td>-73.734482</td>\n",
" <td>2017:08:03</td>\n",
" <td>15.504</td>\n",
" <td>275.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.967079</td>\n",
" <td>-73.734237</td>\n",
" <td>40.967092</td>\n",
" <td>-73.734255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>container</th>\n",
" <th>1</th>\n",
" <td>471.0</td>\n",
" <td>489.5</td>\n",
" <td>0.532011</td>\n",
" <td>40.967079</td>\n",
" <td>-73.734482</td>\n",
" <td>2017:08:03</td>\n",
" <td>15.504</td>\n",
" <td>275.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.967079</td>\n",
" <td>-73.734237</td>\n",
" <td>40.967180</td>\n",
" <td>-73.734352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>air conditioner</th>\n",
" <th>1</th>\n",
" <td>467.0</td>\n",
" <td>489.0</td>\n",
" <td>0.456745</td>\n",
" <td>40.967079</td>\n",
" <td>-73.734482</td>\n",
" <td>2017:08:03</td>\n",
" <td>15.504</td>\n",
" <td>275.0</td>\n",
" <td>alt values for G00 are not accurate</td>\n",
" <td>212 Warren Ave, Mamaroneck, NY 10543, USA</td>\n",
" <td>40.966977</td>\n",
" <td>-73.734502</td>\n",
" <td>40.967079</td>\n",
" <td>-73.734237</td>\n",
" <td>40.967180</td>\n",
" <td>-73.734351</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>68 rows × 16 columns</p>\n",
"</div>"
],
"text/plain": [
" midx midy confidence \\\n",
"photo classes class instances \n",
"G0013879_5.JPG car 1 238.0 36.5 0.858202 \n",
" container 1 496.0 71.0 0.439825 \n",
" trash bin 1 498.0 72.5 0.596141 \n",
"G0024032_18.JPG table 1 82.5 105.0 0.748394 \n",
" hose 1 150.0 18.0 0.380114 \n",
"G0036497_20.JPG car 1 116.5 60.5 0.496821 \n",
" hose 1 193.0 59.0 0.750789 \n",
"G0013835_5.JPG chair 1 57.5 71.0 0.517677 \n",
" 2 72.0 56.0 0.447866 \n",
"G0013868_18.JPG car 1 41.0 90.0 0.518681 \n",
" table 1 465.5 169.0 0.812746 \n",
" air conditioner 1 299.5 241.5 0.720269 \n",
" 2 224.0 228.5 0.625788 \n",
"G0024040_20.JPG table 1 365.5 467.0 0.944633 \n",
" 2 368.5 428.5 0.531788 \n",
" 3 367.0 405.0 0.399011 \n",
" 4 402.5 449.0 0.351321 \n",
" flower pot 1 306.5 406.0 0.561530 \n",
" container 1 305.5 400.5 0.503890 \n",
"G0024061_22.JPG table 1 281.5 444.5 0.748490 \n",
" porch 1 420.5 222.5 0.774700 \n",
" chair 1 450.0 177.0 0.549229 \n",
" 2 451.5 269.0 0.424127 \n",
" 3 454.0 297.5 0.365337 \n",
" 4 371.0 143.5 0.360516 \n",
"G0024029_22.JPG container 1 170.0 331.5 0.558631 \n",
"G0024044_21.JPG car 1 197.0 343.5 0.978664 \n",
"G0024033_11.JPG clutter 1 215.5 425.0 0.627354 \n",
" table 1 205.5 417.0 0.355831 \n",
" porch 1 359.0 112.0 0.384332 \n",
"... ... ... ... \n",
"G0013877_5.JPG container 1 444.0 273.0 0.362154 \n",
"G0036430_21.JPG container 1 148.5 173.0 0.373297 \n",
"G0013876_23.JPG toy 1 377.5 38.0 0.437902 \n",
" 2 438.5 214.5 0.400005 \n",
" pool 1 349.5 204.0 0.894474 \n",
"G0036508_23.JPG flower pot 1 129.5 315.5 0.488355 \n",
" umbrella 1 368.5 151.0 0.972527 \n",
" 2 371.0 201.0 0.378233 \n",
"G0036451_22.JPG chair 1 78.0 203.0 0.469600 \n",
" container 1 19.5 82.5 0.450302 \n",
"G0036461_21.JPG car 1 377.5 344.0 0.993506 \n",
"G0024043_11.JPG car 1 124.0 458.5 0.992442 \n",
" 2 123.5 411.5 0.717569 \n",
"G0024017_11.JPG car 1 163.0 404.5 0.903140 \n",
"G0024067_19.JPG table 1 312.0 127.5 0.620657 \n",
"G0024051_11.JPG gutter 1 247.5 49.5 0.889627 \n",
" 2 239.0 46.5 0.373442 \n",
"G0013877_23.JPG pool 1 165.0 412.0 0.985320 \n",
" 2 201.5 330.0 0.499113 \n",
"G0024052_22.JPG pool 1 322.5 448.5 0.390404 \n",
"G0036428_19.JPG pool 1 236.0 326.0 0.992278 \n",
"G0013868_21.JPG chair 1 56.5 224.0 0.468874 \n",
" container 1 53.0 248.0 0.705487 \n",
" 2 56.5 224.0 0.361516 \n",
" 3 54.0 215.5 0.357651 \n",
"G0024060_19.JPG container 1 342.5 204.0 0.579098 \n",
"G0036460_17.JPG porch 1 406.5 325.0 0.799127 \n",
" pool 1 75.0 61.0 0.806519 \n",
" container 1 471.0 489.5 0.532011 \n",
" air conditioner 1 467.0 489.0 0.456745 \n",
"\n",
" lat lon \\\n",
"photo classes class instances \n",
"G0013879_5.JPG car 1 40.966592 -73.733777 \n",
" container 1 40.966592 -73.733777 \n",
" trash bin 1 40.966592 -73.733777 \n",
"G0024032_18.JPG table 1 40.966066 -73.732624 \n",
" hose 1 40.966066 -73.732624 \n",
"G0036497_20.JPG car 1 40.967221 -73.735713 \n",
" hose 1 40.967221 -73.735713 \n",
"G0013835_5.JPG chair 1 40.967491 -73.732416 \n",
" 2 40.967491 -73.732416 \n",
"G0013868_18.JPG car 1 40.966741 -73.735203 \n",
" table 1 40.966741 -73.735203 \n",
" air conditioner 1 40.966741 -73.735203 \n",
" 2 40.966741 -73.735203 \n",
"G0024040_20.JPG table 1 40.966898 -73.733502 \n",
" 2 40.966898 -73.733502 \n",
" 3 40.966898 -73.733502 \n",
" 4 40.966898 -73.733502 \n",
" flower pot 1 40.966898 -73.733502 \n",
" container 1 40.966898 -73.733502 \n",
"G0024061_22.JPG table 1 40.965187 -73.734823 \n",
" porch 1 40.965187 -73.734823 \n",
" chair 1 40.965187 -73.734823 \n",
" 2 40.965187 -73.734823 \n",
" 3 40.965187 -73.734823 \n",
" 4 40.965187 -73.734823 \n",
"G0024029_22.JPG container 1 40.966334 -73.732228 \n",
"G0024044_21.JPG car 1 40.966892 -73.733592 \n",
"G0024033_11.JPG clutter 1 40.966021 -73.732860 \n",
" table 1 40.966021 -73.732860 \n",
" porch 1 40.966021 -73.732860 \n",
"... ... ... \n",
"G0013877_5.JPG container 1 40.966482 -73.734306 \n",
"G0036430_21.JPG container 1 40.967611 -73.732276 \n",
"G0013876_23.JPG toy 1 40.966432 -73.734562 \n",
" 2 40.966432 -73.734562 \n",
" pool 1 40.966432 -73.734562 \n",
"G0036508_23.JPG flower pot 1 40.966528 -73.734910 \n",
" umbrella 1 40.966528 -73.734910 \n",
" 2 40.966528 -73.734910 \n",
"G0036451_22.JPG chair 1 40.967373 -73.733539 \n",
" container 1 40.967373 -73.733539 \n",
"G0036461_21.JPG car 1 40.967047 -73.734589 \n",
"G0024043_11.JPG car 1 40.966901 -73.733543 \n",
" 2 40.966901 -73.733543 \n",
"G0024017_11.JPG car 1 40.967393 -73.731853 \n",
"G0024067_19.JPG table 1 40.965194 -73.734578 \n",
"G0024051_11.JPG gutter 1 40.966057 -73.734220 \n",
" 2 40.966057 -73.734220 \n",
"G0013877_23.JPG pool 1 40.966482 -73.734306 \n",
" 2 40.966482 -73.734306 \n",
"G0024052_22.JPG pool 1 40.965861 -73.734138 \n",
"G0036428_19.JPG pool 1 40.967665 -73.732055 \n",
"G0013868_21.JPG chair 1 40.966741 -73.735203 \n",
" container 1 40.966741 -73.735203 \n",
" 2 40.966741 -73.735203 \n",
" 3 40.966741 -73.735203 \n",
"G0024060_19.JPG container 1 40.965376 -73.734865 \n",
"G0036460_17.JPG porch 1 40.967079 -73.734482 \n",
" pool 1 40.967079 -73.734482 \n",
" container 1 40.967079 -73.734482 \n",
" air conditioner 1 40.967079 -73.734482 \n",
"\n",
" date_taken alt heading \\\n",
"photo classes class instances \n",
"G0013879_5.JPG car 1 2017:07:20 5.568 20.0 \n",
" container 1 2017:07:20 5.568 20.0 \n",
" trash bin 1 2017:07:20 5.568 20.0 \n",
"G0024032_18.JPG table 1 2017:07:20 21.282 243.0 \n",
" hose 1 2017:07:20 21.282 243.0 \n",
"G0036497_20.JPG car 1 2017:08:03 12.586 47.0 \n",
" hose 1 2017:08:03 12.586 47.0 \n",
"G0013835_5.JPG chair 1 2017:07:20 0.222 247.0 \n",
" 2 2017:07:20 0.222 247.0 \n",
"G0013868_18.JPG car 1 2017:07:20 1.560 43.0 \n",
" table 1 2017:07:20 1.560 43.0 \n",
" air conditioner 1 2017:07:20 1.560 43.0 \n",
" 2 2017:07:20 1.560 43.0 \n",
"G0024040_20.JPG table 1 2017:07:20 3.342 337.0 \n",
" 2 2017:07:20 3.342 337.0 \n",
" 3 2017:07:20 3.342 337.0 \n",
" 4 2017:07:20 3.342 337.0 \n",
" flower pot 1 2017:07:20 3.342 337.0 \n",
" container 1 2017:07:20 3.342 337.0 \n",
"G0024061_22.JPG table 1 2017:07:20 3.272 177.0 \n",
" porch 1 2017:07:20 3.272 177.0 \n",
" chair 1 2017:07:20 3.272 177.0 \n",
" 2 2017:07:20 3.272 177.0 \n",
" 3 2017:07:20 3.272 177.0 \n",
" 4 2017:07:20 3.272 177.0 \n",
"G0024029_22.JPG container 1 2017:07:20 13.002 162.0 \n",
"G0024044_21.JPG car 1 2017:07:20 0.700 250.0 \n",
"G0024033_11.JPG clutter 1 2017:07:20 20.133 246.0 \n",
" table 1 2017:07:20 20.133 246.0 \n",
" porch 1 2017:07:20 20.133 246.0 \n",
"... ... ... ... \n",
"G0013877_5.JPG container 1 2017:07:20 5.404 20.0 \n",
"G0036430_21.JPG container 1 2017:08:03 4.323 275.0 \n",
"G0013876_23.JPG toy 1 2017:07:20 3.065 20.0 \n",
" 2 2017:07:20 3.065 20.0 \n",
" pool 1 2017:07:20 3.065 20.0 \n",
"G0036508_23.JPG flower pot 1 2017:08:03 31.699 47.0 \n",
" umbrella 1 2017:08:03 31.699 47.0 \n",
" 2 2017:08:03 31.699 47.0 \n",
"G0036451_22.JPG chair 1 2017:08:03 10.148 275.0 \n",
" container 1 2017:08:03 10.148 275.0 \n",
"G0036461_21.JPG car 1 2017:08:03 16.507 275.0 \n",
"G0024043_11.JPG car 1 2017:07:20 0.718 337.0 \n",
" 2 2017:07:20 0.718 337.0 \n",
"G0024017_11.JPG car 1 2017:07:20 4.796 260.0 \n",
"G0024067_19.JPG table 1 2017:07:20 2.036 86.0 \n",
"G0024051_11.JPG gutter 1 2017:07:20 4.024 156.0 \n",
" 2 2017:07:20 4.024 156.0 \n",
"G0013877_23.JPG pool 1 2017:07:20 5.404 20.0 \n",
" 2 2017:07:20 5.404 20.0 \n",
"G0024052_22.JPG pool 1 2017:07:20 3.677 156.0 \n",
"G0036428_19.JPG pool 1 2017:08:03 1.169 275.0 \n",
"G0013868_21.JPG chair 1 2017:07:20 1.560 43.0 \n",
" container 1 2017:07:20 1.560 43.0 \n",
" 2 2017:07:20 1.560 43.0 \n",
" 3 2017:07:20 1.560 43.0 \n",
"G0024060_19.JPG container 1 2017:07:20 4.655 177.0 \n",
"G0036460_17.JPG porch 1 2017:08:03 15.504 275.0 \n",
" pool 1 2017:08:03 15.504 275.0 \n",
" container 1 2017:08:03 15.504 275.0 \n",
" air conditioner 1 2017:08:03 15.504 275.0 \n",
"\n",
" notes \\\n",
"photo classes class instances \n",
"G0013879_5.JPG car 1 alt values for G00 are not accurate \n",
" container 1 alt values for G00 are not accurate \n",
" trash bin 1 alt values for G00 are not accurate \n",
"G0024032_18.JPG table 1 alt values for G00 are not accurate \n",
" hose 1 alt values for G00 are not accurate \n",
"G0036497_20.JPG car 1 alt values for G00 are not accurate \n",
" hose 1 alt values for G00 are not accurate \n",
"G0013835_5.JPG chair 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
"G0013868_18.JPG car 1 alt values for G00 are not accurate \n",
" table 1 alt values for G00 are not accurate \n",
" air conditioner 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
"G0024040_20.JPG table 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
" 3 alt values for G00 are not accurate \n",
" 4 alt values for G00 are not accurate \n",
" flower pot 1 alt values for G00 are not accurate \n",
" container 1 alt values for G00 are not accurate \n",
"G0024061_22.JPG table 1 alt values for G00 are not accurate \n",
" porch 1 alt values for G00 are not accurate \n",
" chair 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
" 3 alt values for G00 are not accurate \n",
" 4 alt values for G00 are not accurate \n",
"G0024029_22.JPG container 1 alt values for G00 are not accurate \n",
"G0024044_21.JPG car 1 alt values for G00 are not accurate \n",
"G0024033_11.JPG clutter 1 alt values for G00 are not accurate \n",
" table 1 alt values for G00 are not accurate \n",
" porch 1 alt values for G00 are not accurate \n",
"... ... \n",
"G0013877_5.JPG container 1 alt values for G00 are not accurate \n",
"G0036430_21.JPG container 1 alt values for G00 are not accurate \n",
"G0013876_23.JPG toy 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
" pool 1 alt values for G00 are not accurate \n",
"G0036508_23.JPG flower pot 1 alt values for G00 are not accurate \n",
" umbrella 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
"G0036451_22.JPG chair 1 alt values for G00 are not accurate \n",
" container 1 alt values for G00 are not accurate \n",
"G0036461_21.JPG car 1 alt values for G00 are not accurate \n",
"G0024043_11.JPG car 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
"G0024017_11.JPG car 1 alt values for G00 are not accurate \n",
"G0024067_19.JPG table 1 alt values for G00 are not accurate \n",
"G0024051_11.JPG gutter 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
"G0013877_23.JPG pool 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
"G0024052_22.JPG pool 1 alt values for G00 are not accurate \n",
"G0036428_19.JPG pool 1 alt values for G00 are not accurate \n",
"G0013868_21.JPG chair 1 alt values for G00 are not accurate \n",
" container 1 alt values for G00 are not accurate \n",
" 2 alt values for G00 are not accurate \n",
" 3 alt values for G00 are not accurate \n",
"G0024060_19.JPG container 1 alt values for G00 are not accurate \n",
"G0036460_17.JPG porch 1 alt values for G00 are not accurate \n",
" pool 1 alt values for G00 are not accurate \n",
" container 1 alt values for G00 are not accurate \n",
" air conditioner 1 alt values for G00 are not accurate \n",
"\n",
" nearest_address \\\n",
"photo classes class instances \n",
"G0013879_5.JPG car 1 1421 N James St, Mamaroneck, NY 10543, USA \n",
" container 1 1421 N James St, Mamaroneck, NY 10543, USA \n",
" trash bin 1 1421 N James St, Mamaroneck, NY 10543, USA \n",
"G0024032_18.JPG table 1 1413 Arlington St, Mamaroneck, NY 10543, USA \n",
" hose 1 1413 Arlington St, Mamaroneck, NY 10543, USA \n",
"G0036497_20.JPG car 1 1520 Urban St, Mamaroneck, NY 10543, USA \n",
" hose 1 1520 Urban St, Mamaroneck, NY 10543, USA \n",
"G0013835_5.JPG chair 1 507 Chestnut Ave, Mamaroneck, NY 10543, USA \n",
" 2 507 Chestnut Ave, Mamaroneck, NY 10543, USA \n",
"G0013868_18.JPG car 1 1515 Urban St, Mamaroneck, NY 10543, USA \n",
" table 1 1515 Urban St, Mamaroneck, NY 10543, USA \n",
" air conditioner 1 1515 Urban St, Mamaroneck, NY 10543, USA \n",
" 2 1515 Urban St, Mamaroneck, NY 10543, USA \n",
"G0024040_20.JPG table 1 1421 N James St, Mamaroneck, NY 10543, USA \n",
" 2 1421 N James St, Mamaroneck, NY 10543, USA \n",
" 3 1421 N James St, Mamaroneck, NY 10543, USA \n",
" 4 1421 N James St, Mamaroneck, NY 10543, USA \n",
" flower pot 1 1421 N James St, Mamaroneck, NY 10543, USA \n",
" container 1 1421 N James St, Mamaroneck, NY 10543, USA \n",
"G0024061_22.JPG table 1 220 Soundview Ave, Mamaroneck, NY 10543, USA \n",
" porch 1 220 Soundview Ave, Mamaroneck, NY 10543, USA \n",
" chair 1 220 Soundview Ave, Mamaroneck, NY 10543, USA \n",
" 2 220 Soundview Ave, Mamaroneck, NY 10543, USA \n",
" 3 220 Soundview Ave, Mamaroneck, NY 10543, USA \n",
" 4 220 Soundview Ave, Mamaroneck, NY 10543, USA \n",
"G0024029_22.JPG container 1 1413 Arlington St, Mamaroneck, NY 10543, USA \n",
"G0024044_21.JPG car 1 1421 N James St, Mamaroneck, NY 10543, USA \n",
"G0024033_11.JPG clutter 1 1413 Arlington St, Mamaroneck, NY 10543, USA \n",
" table 1 1413 Arlington St, Mamaroneck, NY 10543, USA \n",
" porch 1 1413 Arlington St, Mamaroneck, NY 10543, USA \n",
"... ... \n",
"G0013877_5.JPG container 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
"G0036430_21.JPG container 1 507 Chestnut Ave, Mamaroneck, NY 10543, USA \n",
"G0013876_23.JPG toy 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
" 2 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
" pool 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
"G0036508_23.JPG flower pot 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
" umbrella 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
" 2 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
"G0036451_22.JPG chair 1 400 Warren Ave, Mamaroneck, NY 10543, USA \n",
" container 1 400 Warren Ave, Mamaroneck, NY 10543, USA \n",
"G0036461_21.JPG car 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
"G0024043_11.JPG car 1 1421 N James St, Mamaroneck, NY 10543, USA \n",
" 2 1421 N James St, Mamaroneck, NY 10543, USA \n",
"G0024017_11.JPG car 1 514 Warren Ave, Mamaroneck, NY 10543, USA \n",
"G0024067_19.JPG table 1 220 Soundview Ave, Mamaroneck, NY 10543, USA \n",
"G0024051_11.JPG gutter 1 216 Travers Ave, Mamaroneck, NY 10543, USA \n",
" 2 216 Travers Ave, Mamaroneck, NY 10543, USA \n",
"G0013877_23.JPG pool 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
" 2 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
"G0024052_22.JPG pool 1 216 Travers Ave, Mamaroneck, NY 10543, USA \n",
"G0036428_19.JPG pool 1 507 Chestnut Ave, Mamaroneck, NY 10543, USA \n",
"G0013868_21.JPG chair 1 1515 Urban St, Mamaroneck, NY 10543, USA \n",
" container 1 1515 Urban St, Mamaroneck, NY 10543, USA \n",
" 2 1515 Urban St, Mamaroneck, NY 10543, USA \n",
" 3 1515 Urban St, Mamaroneck, NY 10543, USA \n",
"G0024060_19.JPG container 1 216 Travers Ave, Mamaroneck, NY 10543, USA \n",
"G0036460_17.JPG porch 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
" pool 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
" container 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
" air conditioner 1 212 Warren Ave, Mamaroneck, NY 10543, USA \n",
"\n",
" add_lat add_lon \\\n",
"photo classes class instances \n",
"G0013879_5.JPG car 1 40.966502 -73.733680 \n",
" container 1 40.966502 -73.733680 \n",
" trash bin 1 40.966502 -73.733680 \n",
"G0024032_18.JPG table 1 40.966527 -73.732589 \n",
" hose 1 40.966527 -73.732589 \n",
"G0036497_20.JPG car 1 40.967244 -73.735619 \n",
" hose 1 40.967244 -73.735619 \n",
"G0013835_5.JPG chair 1 40.967745 -73.732004 \n",
" 2 40.967745 -73.732004 \n",
"G0013868_18.JPG car 1 40.967128 -73.735109 \n",
" table 1 40.967128 -73.735109 \n",
" air conditioner 1 40.967128 -73.735109 \n",
" 2 40.967128 -73.735109 \n",
"G0024040_20.JPG table 1 40.966502 -73.733680 \n",
" 2 40.966502 -73.733680 \n",
" 3 40.966502 -73.733680 \n",
" 4 40.966502 -73.733680 \n",
" flower pot 1 40.966502 -73.733680 \n",
" container 1 40.966502 -73.733680 \n",
"G0024061_22.JPG table 1 40.965210 -73.733781 \n",
" porch 1 40.965210 -73.733781 \n",
" chair 1 40.965210 -73.733781 \n",
" 2 40.965210 -73.733781 \n",
" 3 40.965210 -73.733781 \n",
" 4 40.965210 -73.733781 \n",
"G0024029_22.JPG container 1 40.966527 -73.732589 \n",
"G0024044_21.JPG car 1 40.966502 -73.733680 \n",
"G0024033_11.JPG clutter 1 40.966527 -73.732589 \n",
" table 1 40.966527 -73.732589 \n",
" porch 1 40.966527 -73.732589 \n",
"... ... ... \n",
"G0013877_5.JPG container 1 40.966977 -73.734502 \n",
"G0036430_21.JPG container 1 40.967745 -73.732004 \n",
"G0013876_23.JPG toy 1 40.966977 -73.734502 \n",
" 2 40.966977 -73.734502 \n",
" pool 1 40.966977 -73.734502 \n",
"G0036508_23.JPG flower pot 1 40.966977 -73.734502 \n",
" umbrella 1 40.966977 -73.734502 \n",
" 2 40.966977 -73.734502 \n",
"G0036451_22.JPG chair 1 40.967283 -73.733141 \n",
" container 1 40.967283 -73.733141 \n",
"G0036461_21.JPG car 1 40.966977 -73.734502 \n",
"G0024043_11.JPG car 1 40.966502 -73.733680 \n",
" 2 40.966502 -73.733680 \n",
"G0024017_11.JPG car 1 40.967473 -73.731890 \n",
"G0024067_19.JPG table 1 40.965210 -73.733781 \n",
"G0024051_11.JPG gutter 1 40.965890 -73.733997 \n",
" 2 40.965890 -73.733997 \n",
"G0013877_23.JPG pool 1 40.966977 -73.734502 \n",
" 2 40.966977 -73.734502 \n",
"G0024052_22.JPG pool 1 40.965890 -73.733997 \n",
"G0036428_19.JPG pool 1 40.967745 -73.732004 \n",
"G0013868_21.JPG chair 1 40.967128 -73.735109 \n",
" container 1 40.967128 -73.735109 \n",
" 2 40.967128 -73.735109 \n",
" 3 40.967128 -73.735109 \n",
"G0024060_19.JPG container 1 40.965890 -73.733997 \n",
"G0036460_17.JPG porch 1 40.966977 -73.734502 \n",
" pool 1 40.966977 -73.734502 \n",
" container 1 40.966977 -73.734502 \n",
" air conditioner 1 40.966977 -73.734502 \n",
"\n",
" new_lat new_lon \\\n",
"photo classes class instances \n",
"G0013879_5.JPG car 1 40.966819 -73.733641 \n",
" container 1 40.966819 -73.733641 \n",
" trash bin 1 40.966819 -73.733641 \n",
"G0024032_18.JPG table 1 40.965953 -73.732771 \n",
" hose 1 40.965953 -73.732771 \n",
"G0036497_20.JPG car 1 40.967107 -73.735708 \n",
" hose 1 40.967107 -73.735708 \n",
"G0013835_5.JPG chair 1 40.967717 -73.732297 \n",
" 2 40.967717 -73.732297 \n",
"G0013868_18.JPG car 1 40.966628 -73.735221 \n",
" table 1 40.966628 -73.735221 \n",
" air conditioner 1 40.966628 -73.735221 \n",
" 2 40.966628 -73.735221 \n",
"G0024040_20.JPG table 1 40.966784 -73.733650 \n",
" 2 40.966784 -73.733650 \n",
" 3 40.966784 -73.733650 \n",
" 4 40.966784 -73.733650 \n",
" flower pot 1 40.966784 -73.733650 \n",
" container 1 40.966784 -73.733650 \n",
"G0024061_22.JPG table 1 40.965073 -73.734942 \n",
" porch 1 40.965073 -73.734942 \n",
" chair 1 40.965073 -73.734942 \n",
" 2 40.965073 -73.734942 \n",
" 3 40.965073 -73.734942 \n",
" 4 40.965073 -73.734942 \n",
"G0024029_22.JPG container 1 40.966221 -73.732370 \n",
"G0024044_21.JPG car 1 40.966779 -73.733592 \n",
"G0024033_11.JPG clutter 1 40.966135 -73.732746 \n",
" table 1 40.966135 -73.732746 \n",
" porch 1 40.966135 -73.732746 \n",
"... ... ... \n",
"G0013877_5.JPG container 1 40.966708 -73.734170 \n",
"G0036430_21.JPG container 1 40.967498 -73.732276 \n",
"G0013876_23.JPG toy 1 40.966318 -73.734427 \n",
" 2 40.966318 -73.734427 \n",
" pool 1 40.966318 -73.734427 \n",
"G0036508_23.JPG flower pot 1 40.966415 -73.734921 \n",
" umbrella 1 40.966415 -73.734921 \n",
" 2 40.966415 -73.734921 \n",
"G0036451_22.JPG chair 1 40.967260 -73.733417 \n",
" container 1 40.967260 -73.733417 \n",
"G0036461_21.JPG car 1 40.966934 -73.734589 \n",
"G0024043_11.JPG car 1 40.967014 -73.733246 \n",
" 2 40.967014 -73.733246 \n",
"G0024017_11.JPG car 1 40.967506 -73.731670 \n",
"G0024067_19.JPG table 1 40.965080 -73.734369 \n",
"G0024051_11.JPG gutter 1 40.966171 -73.734519 \n",
" 2 40.966171 -73.734519 \n",
"G0013877_23.JPG pool 1 40.966369 -73.734170 \n",
" 2 40.966369 -73.734170 \n",
"G0024052_22.JPG pool 1 40.965748 -73.734287 \n",
"G0036428_19.JPG pool 1 40.967551 -73.732299 \n",
"G0013868_21.JPG chair 1 40.966628 -73.735203 \n",
" container 1 40.966628 -73.735203 \n",
" 2 40.966628 -73.735203 \n",
" 3 40.966628 -73.735203 \n",
"G0024060_19.JPG container 1 40.965263 -73.734627 \n",
"G0036460_17.JPG porch 1 40.967079 -73.734237 \n",
" pool 1 40.967079 -73.734237 \n",
" container 1 40.967079 -73.734237 \n",
" air conditioner 1 40.967079 -73.734237 \n",
"\n",
" obj_lat obj_lon \n",
"photo classes class instances \n",
"G0013879_5.JPG car 1 40.966808 -73.733673 \n",
" container 1 40.966798 -73.733708 \n",
" trash bin 1 40.966798 -73.733708 \n",
"G0024032_18.JPG table 1 40.965985 -73.732779 \n",
" hose 1 40.965959 -73.732786 \n",
"G0036497_20.JPG car 1 40.967088 -73.735706 \n",
" hose 1 40.967088 -73.735706 \n",
"G0013835_5.JPG chair 1 40.967739 -73.732303 \n",
" 2 40.967734 -73.732305 \n",
"G0013868_18.JPG car 1 40.966599 -73.735222 \n",
" table 1 40.966574 -73.735227 \n",
" air conditioner 1 40.966551 -73.735225 \n",
" 2 40.966555 -73.735224 \n",
"G0024040_20.JPG table 1 40.966729 -73.733759 \n",
" 2 40.966733 -73.733760 \n",
" 3 40.966736 -73.733759 \n",
" 4 40.966731 -73.733770 \n",
" flower pot 1 40.966736 -73.733741 \n",
" container 1 40.966737 -73.733741 \n",
"G0024061_22.JPG table 1 40.965168 -73.734875 \n",
" porch 1 40.965121 -73.734842 \n",
" chair 1 40.965111 -73.734835 \n",
" 2 40.965131 -73.734835 \n",
" 3 40.965137 -73.734834 \n",
" 4 40.965104 -73.734854 \n",
"G0024029_22.JPG container 1 40.966269 -73.732322 \n",
"G0024044_21.JPG car 1 40.966879 -73.733619 \n",
"G0024033_11.JPG clutter 1 40.966262 -73.732770 \n",
" table 1 40.966259 -73.732769 \n",
" porch 1 40.966168 -73.732787 \n",
"... ... ... \n",
"G0013877_5.JPG container 1 40.966629 -73.734230 \n",
"G0036430_21.JPG container 1 40.967533 -73.732313 \n",
"G0013876_23.JPG toy 1 40.966307 -73.734478 \n",
" 2 40.966256 -73.734486 \n",
" pool 1 40.966259 -73.734474 \n",
"G0036508_23.JPG flower pot 1 40.966314 -73.734919 \n",
" umbrella 1 40.966367 -73.734917 \n",
" 2 40.966351 -73.734917 \n",
"G0036451_22.JPG chair 1 40.967302 -73.733436 \n",
" container 1 40.967277 -73.733421 \n",
"G0036461_21.JPG car 1 40.967005 -73.734681 \n",
"G0024043_11.JPG car 1 40.966959 -73.733283 \n",
" 2 40.966965 -73.733283 \n",
"G0024017_11.JPG car 1 40.967612 -73.731700 \n",
"G0024067_19.JPG table 1 40.965050 -73.734303 \n",
"G0024051_11.JPG gutter 1 40.966176 -73.734445 \n",
" 2 40.966176 -73.734447 \n",
"G0013877_23.JPG pool 1 40.966249 -73.734192 \n",
" 2 40.966273 -73.734197 \n",
"G0024052_22.JPG pool 1 40.965799 -73.734191 \n",
"G0036428_19.JPG pool 1 40.967618 -73.732357 \n",
"G0013868_21.JPG chair 1 40.966557 -73.735204 \n",
" container 1 40.966549 -73.735204 \n",
" 2 40.966557 -73.735204 \n",
" 3 40.966559 -73.735204 \n",
"G0024060_19.JPG container 1 40.965307 -73.734545 \n",
"G0036460_17.JPG porch 1 40.967146 -73.734336 \n",
" pool 1 40.967092 -73.734255 \n",
" container 1 40.967180 -73.734352 \n",
" air conditioner 1 40.967180 -73.734351 \n",
"\n",
"[68 rows x 16 columns]"
]
},
"execution_count": 821,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"headings_w_nn = px2gps(headings_w_nn)\n",
"headings_w_nn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add lat/lon to object location based on pixel value"
]
},
{
"cell_type": "code",
"execution_count": 819,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def px2gps(frame):\n",
" \n",
" files = [f[0] for f in frame.index.values]\n",
" for name in files:\n",
"\n",
" i = name\n",
" #i = 'G0013876_23.JPG'\n",
"\n",
" lat = frame.loc[(i,),('lat')][0]\n",
" lon = frame.loc[(i,),('lon')][0]\n",
" theta = frame.loc[(i,),('heading')][0]*np.pi/180\n",
"\n",
" rhc_lat = frame.loc[(i,),('new_lat')].values\n",
" rhc_lon = frame.loc[(i,),('new_lon')].values\n",
"\n",
" xval = frame.loc[(i,),('midx')].values\n",
" yval = frame.loc[(i,),('midy')].values\n",
"\n",
" #conversions between meters per degree and degrees per meter\n",
" m_per_lat, m_per_lon, lat_per_m, lon_per_m = len_at_lat(lat)\n",
"\n",
" #lat change equivalent depending on ground sampling distance of photo \n",
" #note -- explain how you got px500\n",
" lat_step = m_per_px*lat_per_m #lat step per px\n",
" lon_step = m_per_px*lon_per_m #lon step per px\n",
"\n",
" #accuracy (or precision?) is the difference between theses two\n",
" #print(np.sqrt(lat_step**2 + lon_step**2), arcsec_per_500px/3600)\n",
"\n",
" #rotaion matrix [x';y'] = [cos -sin; sin cos][x;y]\n",
" del_lon= lat_step*np.cos(theta) - lon_step*np.sin(theta) \n",
" del_lat = lat_step*np.sin(theta) +lon_step*np.cos(theta)\n",
"\n",
" lx = []\n",
" for j in range(len(xval)):\n",
" obj_lon = rhc_lon[j] - del_lon*xval[j]\n",
" lx.append(obj_lon)\n",
"\n",
" ly = []\n",
" for j in range(len(yval)):\n",
" obj_lat = rhc_lat[j] - del_lat*yval[j]\n",
" ly.append(obj_lat)\n",
"\n",
" frame.loc[(i,),'obj_lat'] = ly\n",
" frame.loc[(i,),'obj_lon'] = lx\n",
" \n",
" return frame"
]
},
{
"cell_type": "code",
"execution_count": 625,
"metadata": {},
"outputs": [],
"source": [
"def plotgps(pd, title='Altered GPS of photos in go pro wks 1/2 set in Mamaroneck\\n',col1='lat',col2='lon'):\n",
" '''\n",
" Making my life easier by putting my pandas plot preferences in a function\n",
" '''\n",
" #pd.plot.scatter(col2,col1)\n",
" plt.scatter(pd[col2],pd[col1])\n",
" plt.xlim(pd[col2].min()-.001, pd[col2].max()+.001)\n",
" plt.ylim(pd[col1].min()-.001, pd[col1].max()+.001)\n",
" plt.gca().invert_xaxis\n",
" plt.title(title)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 663,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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6rMBIw9W4Fc6iZiROBB5R1U2quhl4BNexfwz4RyA66KO4dRsAvomL2LkZQFXfThxMRGaI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\nPzzCE9uEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e03a850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(1)\n",
"plotgps(pd1,col1='new_lat',col2='new_lon')\n",
"plotgps(test,col1='obj_lat',col2='obj_lon')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## COMPARE VALIDATE ANNOTATED VS. FOUND"
]
},
{
"cell_type": "code",
"execution_count": 775,
"metadata": {},
"outputs": [],
"source": [
"all_annos = pd.read_csv('/Users/elizabeth/Downloads/allbboxes.csv',index_col=[0,1,6])\n",
"#all_annos"
]
},
{
"cell_type": "code",
"execution_count": 776,
"metadata": {},
"outputs": [],
"source": [
"#chooses only the items in the val_keys set\n",
"valid_annos = all_annos[all_annos['valid']]\n"
]
},
{
"cell_type": "code",
"execution_count": 789,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"288"
]
},
"execution_count": 789,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#removing .jpgs from the photo names\n",
"#pd1.to_csv('~/Downloads/hackthesystem.csv')\n",
"new_pd1 = pd.read_csv('~/Downloads/hackthesystem.csv',index_col=[0])\n",
"all_objs = all_annos.join(new_pd1,how='inner')\n",
"len(all_objs)"
]
},
{
"cell_type": "code",
"execution_count": 790,
"metadata": {},
"outputs": [],
"source": [
"#converts cooords in valid_annos into GPS locations\n",
"valid_annos = all_objs[all_objs['valid']]\n"
]
},
{
"cell_type": "code",
"execution_count": 798,
"metadata": {},
"outputs": [],
"source": [
"all_objs_gs = px2gps(all_objs)\n",
"valid_objs_gps = px2gps(valid_annos)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plots"
]
},
{
"cell_type": "code",
"execution_count": 871,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[u'seaborn-darkgrid', u'Solarize_Light2', u'seaborn-notebook', u'classic', u'seaborn-ticks', u'grayscale', u'bmh', u'seaborn-talk', u'dark_background', u'ggplot', u'fivethirtyeight', u'_classic_test', u'seaborn-colorblind', u'seaborn-deep', u'seaborn-whitegrid', u'seaborn-bright', u'seaborn-poster', u'seaborn-muted', u'seaborn-paper', u'seaborn-white', u'fast', u'seaborn-pastel', u'seaborn-dark', u'seaborn', u'seaborn-dark-palette']\n"
]
}
],
"source": [
"print(plt.style.available)\n",
"plt.style.use('seaborn')"
]
},
{
"cell_type": "code",
"execution_count": 881,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(-73.736369179999997, -73.731005530000004)\n",
"(40.964088910000001, 40.968400080000002)\n"
]
}
],
"source": [
"col1='new_lat'\n",
"col2='new_lon'\n",
"print(new_pd1[col2].min()-.0005,new_pd1[col2].max()+.0005)\n",
"print(new_pd1[col1].min()-.0005, new_pd1[col1].max()+.0005)\n"
]
},
{
"cell_type": "code",
"execution_count": 887,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11ada6750>"
]
},
"execution_count": 887,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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sS01NxcKFC/H555+jY8eO6NixI4BqYfv2229jxYoV0Ol04DgOK1assDlOADh+/DgyMzPB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MBH+AQT5tHZtHX04A8EGDliBFdeYcHU83FMBK/jUhddPT7GZEn5It3dbkb0foR9/iAen6iQqfHj\nx+NyuQYsgR9uREa3kz1biTqDNBrYd6QVe0sXQTGcNCo7XNLjociRzyCDywHxyKtWq8VoNMaQ23jw\n+XwYDAbMZjMGg4GGhgasVuugyGs6kCM3kZJoGfFKTXR3d5OTk6PKT208dlIhM7sbGhFFkXKrlcVV\ns7A7HNjsDnJzDGjDOi72dGE2GiguGklluZWGpiZ8bg8GozqXTyB+/mgyzHj+j9StvENV6iUSdfaz\nTH3+ZUVOWX+hmXmlfY3a+pZmJUfS6/Uq+a9NP36QlgP7yc02MWrZlxh9973Kueju7h5QQ7ikpIQ/\nL7peirZGQM4RdrrdnG5pY1xpbORocfUc0Gl5/hM797/6pqqkTHQ+aSqIdNpuNeqZfe3VqunbVy4n\nVycdo8ytDq5cTm3VrH5JH8D8imkxpG12WRn7HA4glrTJmPzs76lbuZyFCUoRnWhp58r7/yFmvHKN\nc6Vz0djljCG5MmY8/0f+tHI5tUX9OyBXTZmCu8fHhUAgrqKgppcEb9+zl0XWPvm7NyJ6WrXhCfjN\n0xB1z6jl0X3kWyQYQ+Jn/Or/KZ1fqd57E9b/Cx9/9x6mxpk26b+eT2kdw4HhlhHLiCSxw0FeIUNg\nLzvEIylynuBw3ADpGkj98o+HaHOpjZrEkJQjeVtNdFWwDIYS0XV+p1w1lYPP7uP0yfOMHFmAxyfS\n4w0QDAU54xMZkWfg5DkXPp+fUrM/pc6QeEqAHW+9zv+851YOn3Jx8nwnHp9IKCjSeqaJ5TVVhILj\nY+S17RddTJ7U9+rOyTHT1nae4uJiDHqdUooG+gjUFVeMvazMgdKVA0d2BgUCAbq73eTkmGnv9NPh\n8lJgMSnPWrREf7ilx4PJkc8gg8sNsktt9DcxlcZSc3MzVquV2tpaNm/erKoFmy76y62MhiiK/Zqc\nROdSCoKA1+ul0mrtIy9aKcd1556GGEJmLSujyX6aOVerSRGA3eHA6XTzF+cRnred4ie1iym19HkL\n1FRVEnZ3K7+jcyAjcwLrmo4zFYm8nLh7FRML1ZHYE+1OpdyILKfMHmmR3Fl7kT1ypPK/y+Wi8fZl\n1FTPoTLX2CfpPe/Avvrr2OwOpj7/csrtoNeuu4bSwhKsZZKQtPHwkV5yEp8AWoxGLKVGtu/dz0vH\nT/Lck79UyUQFjdShWGaxxEhOZalqOmZOx1Z9NaEcWB6/fc97iny40JKv4lUtzi4e3b4T6M1pvbXv\nO2k0Gtl38DDXTuiLJCarNxuJqc//EfGB+6P5HqIIF7MExlfPjbuc3etmnEnywihdeEPceUAqpbO4\ncqbq3EbWc11RPla5bl6vF0IiTfbTSR2Va6vn8MknNibk51JnP8v87buUaeZZ11L38EMsjIqqxrsP\n6mynWGS1Eslgd9ntzOg9rnTqFGssI/oLZv9V8GnIiGXIJHY4uAtkCOxlh2iSEu3oOtRIZCDl8/nJ\nMwTw+7wIvfvS7fFz+GT8umkdLh8+3+X4uH4+EK/Ob1ePH2eXn6wsHYFAgIudHnzKhzDEnz86T5ZG\nZHSBwN03j0+rMyRSCVBQkIfZnEP19ByqppYoJktvbjuL1+MhOzs7rrz2w2NtEXLaLHp6fPh8fq4a\nX6REF6MJ1OVkDpRObjhInUE6AY4fP0Z2tgGLxUJr6znOXBAZUVCAoOt7vqIl+uluayDIuAdn8HlA\nZNQ1HuLlyEbDbrdTXV1NdXV10vmGGgNtyLlcLiLjfJXl5exu2C9FBqOdZvc0SARIjO2WtpaOwVo6\nhu173gPg0e07VdFEIO5y8aYJgkYhIQ6vh3GimsA6vB4l+mRAx+lAAOvTz7J31Qrm5I6g/lKHqp7m\nsVVf7T2eJPu9crkq/zIRzn/3mzFSajmC13D4sFQbNQEK83Mijrfv3FrLxkDTSR6ZPzdGcipTq1TJ\nq5TLOod+5cBVs2jrVS5NKStT7Y9MXgElRzQS/hApRWtFd2zUva5FckGOHpfs3BdXzYYTpzjg+f/s\nvXt4E+eZ/v+RNDrYlm35bIyxhTGEgzEGHHACBVJI41CapqTQlLZsm36bZrP9Zn+bsrvZNG2zaTZN\nN7TfXmmbTdrNtk1bSqAJaSFAE9jgFIg5hBpCiAEZBAh8xJZtSdZhJP3+GM1Yo5MP4dTW93XpsjyH\nd955ZyTN/T7Pc9/9lH31/ybdzvE//02Zdbyqb49ue5P5JcXMLSmgNjpLyGxW0rp37d0XyTpIjAs9\nTsqMaWQWj4tbN+Wl37LvvjUsiBabSjA0k/JyVf1qcLSqznmktdp/7anCw8HVIq8w5gN7QyERSQG1\n0MrVQM3kAqzjMtEAPp/IadtJwt5WaiZnqjw9z7b2Rk+eQlTcNhSGjb/fmVre3i/S3uORVIzHMCKk\n8vlNM5no7IkmrxLChBER6PUZ6AlYWLFiBatWfYo33tg+6uPKIkuCThvngRrrIRx9XwUCISZVVtJq\nP0HH2UMcP36crVu3snnzljgCNZQX8bXAULXhifyOBZ2WyxdPMm3aTUyZMoXCwkKsFZXk5OYy0H+Z\noCjidPYoXpNyin6qY51x9GBraUn42fd4PJw9e2bY3wtyHW919Tw8niDV1fNYvvyuq/oDM4YxXGkI\ngpCSKIxETOd6YKg03YyMjLhlip1L5LW36ahE9mKws/FACh/XwZeKCMR4Z15ub0+yH8r7BttJKv5n\nA4WFhRQWFkYiXoPbHnV2sXDnbuU6WB96GEfE91JWmI0WGjq2ZuWw+y2KIunp6eoI3o+e4dTaz3Jq\n7WdpWftZiaAm8ZutnTqVXY2HaXM6+UXjn+N8RmsqK/ncTfHZZONN5khEWt0nkSAdHR0J65eTYWHV\njKT9i34JkbEBMMZ6rMbguKMtzgN1n90ed31jXw1tjri2bnron+l1u1TnmSh1OBrFDz8Coogzwf0d\nPb4eZ1fc+vKMNO6YPjnldVtWN59dh5u4b+MW7tu4hTaXS3UqS2qqaOxsY9Yvfp2wf5nVc2IGTz0Q\n+xx2ivVG1WGzUxDm4eB6pgr/LWDsyeUGQiqScjWFVqLTRre9vo3H/v6TZEVqGapnzlQiwJay2qi9\nwhBjrDK3dh5btmxhyZJ61XIxFOK1hhZaLvXhC4Qw6rVMKsni7sWTEJLMoo9BjUQ1jJnpBvIsaZxs\n6SEQSpRmqiEcljx6L7T1IwZDI1ad/TC1k4mFySpvmAhrKoy0NhwkQlllTadwYgFnLvXi8wfRC1qK\n88w4Q91cunSe/Px8Ojou4fH4mFRZicmgS3isUChES4sNg9HA3Ep1KQHwocoMxtSDx/DXgFAolDQK\nO5xUYtlW58NipGnEfX19Cb1OvV6vIgSVqN9S+u5gSU8sSf/vvYf4PwtvHjZ5X11Zht0bIpYN2QIB\n8pK1EVk+5aVNcanQ+9o6WFCQGzkXaZIuIyNDqjmuuwUxYhealZcHflH6G4FMTFPh8Aencbr7ENZ+\nVupKhADX182nRBAgSuBKrousT0DyYVDoSBZmio1Ch4N+1fkCXPS6GB/jAdpgO6uK0B1bszIubdkr\niqptzv1/X+PmsnGqUY+2K4pN686PeKQebXNQE6XYG13TCrJQ02Cr2YUl5N98My27dzMpkYgTkk9r\noqiqse4Wmp59hsWR+3RfZyfTk6QOyxBFkXOBAAVVNRiNxoQTMR0dHZKtTMy1/vi0ypQpwjKW1dUi\nEmST7TyPbnsz7rrpjGo7v1AopEx2jVv3iFTfK0dho7rQ0tWFeeoM6Bq0x2loa6X6B4k9bjMyMigo\nUDuEdHZ24na7E24/hquDMQJ7A+F6C634fV6KC7MU8irDZEqjN2jmwpnOqKXxhKl8XA6Bnow4cvRa\nQwunHL1otRqlBvCUo5fNu09SV2kclVDVXwKupCBPohpGQadl3rR8TrecIxyOV5eVr5BGIykKy8JJ\nI5kMuRK1k7Eqyn8JBGqkteEgTUBVVFRQVTVOlWp9+EQ7+//swaDXk5ubS2FhIT6fn1b7CQRdJSYD\nccdqabExY8ZU9Ho9c2qmIMydpUwkAde0zGAMY7gR0dXVlbKe9GqKh1wpiKKoRO4EQUjZ14bm0yye\nOhmIpLNGkYBfN33AxBhRJhiaGC2N8bo82tZKx/3/wO5n/zOxR6kIux32hMI08nqAeb/ZBAwSWABT\nhHxbf/Qzzt23FusLLwFw8cnHKUxX9z223zb7mYQiPnaHI2IlFLtOR33drVKqtFki1cumqoMDVZVT\n2GST6vyOO9qoKi1W7Q/E+YCOt0ZHZkXGrfw0AOfvvw9BCCdPW37wS5Q893MAfN2XoURNfqJtbWLT\nukuLiwkRUe+NSo+Va1ovegcYb0pT9RWg/Ic/BuDE69uYlGRuYPzkeDVqeSIhe/x48PgjZ5o81Tm6\nrnuP18WS9T9Mum1WVhbCvz+N49F/YrxpkGzWVFbGTWDsaj5LTWlBnEhW9HWLjUR7PP0qj1b58yT/\nnfTIt7j8k2fJiNnZ4R1g+qPfhn98UFlm/crg+2hYrVa+ue3OhOu+s2LHiCazxvDhMBb+uoEQTRai\nca2EVpJFgA++34YoWNCF/Qn2kmDQQVmJhfLyclVaqdcv0nKpT5UeGQ6Hcfb0cNzWimDQqNKU/xoQ\nCoc5cqqD1/fb2fHOOV7fb+fIqQ5C4RS1RcOAXMO4detWJQX33Pv7+cSimWSY9ERzZA2RGLkG0k0C\n2WajIpx09uxZioqGnu1MddxEqb9/TZBrw0Mh9TULhcJMKDInnJAoKipWfryiU63nzSjGEOonMzMT\nn196EJg2sYAqazoejyfuWLLlkE4nUFGSrfJszcgwYrFkXvMygzGM4UZDtJLraHGlfnNGEsmV+93R\n0aGoJg+HZAuCRqoPFcNxXpZv22wYY9KMEcU4YhS9rrS4WGlPfnU5e/nYxz7GXfuP0NLVRWzOaktX\nF9UbXkUQhLiU2ZrHvq20rY2KrspjPCWq3tUeSScGcJ86GZMefZy7Skt4asXt/PTeTyGKPryiyOGj\nx+L6ay0ez7K6+ZGa3sQpx0b/AJuamnj4te2q4xSb0/hChNRWFeerxy4BYTPq9arx2G23k/PpzwBQ\nnJ89ZNryiTWrACm1N3pYYz1apUEbfJkj90bp/AVx548YZrxgilvW0HxaaWr6hs1c7nXGXEuRBscF\nsv/9u6rrZDQalbTw2S/+RlmXlZMb38dRQFGZtp9Tlh11XIgbr/s3bmFTUxOPbnsz4XVLOFCIZE+e\nonyeEn2mjNU1NPU7I+cbVF5yLfaBni4QgyAGybotXpU8FXkF+Oa2O7FarRQVXXvP+pFgftaZIbc5\n780ccpurCTnlPBVu3KnJv1FcT6GVRBFgMRjizKVeep1OiorGU14Y5HyHhzAh5PkPo15LfV0ZJoPA\nuXPnmDFjsG6g1+3HF1FVldHT3UNBYR5iEMaVVjCnpvovJoI0nKjqaAR5vH6RXref7AwDJkPij2Uq\nL9ILXW7eOd7KgC+oEOVgKIxe0JJvSWdKWQ6CTjuqyZDR+Av/NUC2FIpWBraOy1SWxyJZtNrrHaAo\nY4BP3TlDicoKOi3HNRVKJDz6WM6+fnJycqgstTBvhnqiISMjgwkTEltWjPm5juFvDTKRkn1UZfT1\n9eH1eockhg6HY1RpxI2HT1NXO3nE+8kYTVR4ykub2L1mJUutlThcXhWJfbB2Ji6XVxWVbYuk8a6e\nXIEQDlNptYIg4Ojqkrwui4vIMw9+TzXYbNy1/4jyf5dWQ3lMVK9Lq1F8LWMfLs0LF7H3yX8HoDoy\n/qnO1fbg/fidPWRnZbGr6SgCOkSC6NGxbGEdO/fuo7ZyEtbSUqyR1NldextVXrgy6uvm0+XswWKO\nf+iW7dqiybwMnxiMWMqoz9Mgi+9EznG3zSZFpCOnLBN5iBZkSo1ldbXsXLOS7MISoqN/sR6t80pL\nVeubHQ5KgNx//LqUAluZ2u3hcnsXlf/2TdWy4/39LMgYJH697gHKln9ciohGrk+ibIYD/T2IYlgl\nthULQRAU5WyTPi3pdqIo0tfXhyinVH/rXwDocqnTbmMJfaLr9kL97SBo4rIHyiNR7lRQUqYjgi4N\nnW2K5Y9cn72vpzsuy6C0tDSOvH67fisA/75zMGL+zW138p0VO8jKyhqVN/S1wHC+fy54cigzXR9T\nzFcBq9QAACAASURBVOiofqpJyjECe4PhepKFRA/gAz4Rl8fLwIAfnU6HKeSkMMuAy6fF7wezIciS\nugrWLp+Bx+PB7Xar+pudYcAYlR4ZCoYQ9Dp0Wh06XZicLJNy7JHUZl4JjCTFd7g2J0OJ/1RPysfv\n89Ie8fgVxQx+99bpEdUHJ0rBvWfJJAAOnminfyBAOBTGKGhJ0/opTXeSThdbtx7+UJMhfwmpv1cS\niWt4U98nsRNQLS1naG7+gK9//etKVFZGdFlA9LF6evM5ffIIdTPj1RTdbjdnzpwd83MdwxiSQE4b\nHi5JjLWtGQlsNhuVEUJRWlqKwxEviHMlUb3hVd69bw0ur5fxUUq6s4rHcfDMOdW2xSY9qyvLqF+g\nJlcyIXS6XOxuOspSqxXR68OQrZ4EKHv+Rfj6PxBdd1vx379U3seOWSgUUlJNhzOmMyYU8Z7Lyc3V\n8QnJdoeDm2fO4EJrF+e6mpk7VRrjZXW17DncxMKa+O+/w82n2GQ7H2cpU1NZycrLTl49eyGu9jKR\nLcrRtlZuGR+pNY1sflsMqYxWWK6qnJIw/RUSpy1rn3ia8BOPqpa/sOJjSiowqFOXu5y9lETeF89f\ngHjuHIIp+dg29TupnjnowiqKIlNe+i3n7r9P5dM6/d7PD3mNvENEwGR4PB6ysrKo27pDOaZMVmXE\npvPuttlYaq3EbEhTXZdYQh9pMMEydXZU9DgND9IY59UO/mZav/IgbPxtwpTpRGMlL7OabsXu3T/k\n9mMYHqLrp1OViYyN8A2K60UWEj2A2057mTRpMi0tNmbOlOryRDHEgF/kU4vKeGXTb9mxw053dx+f\n+9xn6OkZUNozGQQmlWQpNbD+gJ80k4lQKMS0iXmqaOO1iiCNxnNzuFHVVOI/Xp/Itte3Ma5oUHzn\nvzZ78Wpy0em0qvrg1xpa+PRtw5/hF7RaPvPRyXxy4UQ6ezwEwyGy0oxkmY0KYb4ekVN5IkZOWZbf\n/yVFcP0+L52RfgtD9Dt2AqqmZj5OZz9+v1/1g+bxeOjouBw3DoJOS0FuFod6+hLWHbvdPtxu35if\n6xjGEIWOjo5R17yONgoL6tTha/XA6k1CDudVlKuIz9snT0uRwSQkxGIyUV83n92NhwkQpO61naxb\nt05ZX19fz6nObkWYKWFfvF78fr8S7U43xwv3xOLE5z9Dfd187G1tLKmdB2KYnY37ESJEWSRIaYH0\ne1FgTqPV2aPa32w2cf/GLUnFjhJZypiNpkikNXppkNLMjLhtnU4XWCTrmt12m6pfi4vHcaCrU4lI\nnlq7elBROIL7f7dVeb+9+QN+EOWlW2xOY+/a1ZwQw3GRVCUVOAal8wfrauUo7NIk92uD/YISYRRF\nkYyMDCU7YY/XRXmmRJCNw5zgMRGv9ZAIXq9Xpa4dS1YTYeZzL9Ly8D8yLdcSc13UAlWJrluiFO9o\nMaquHz3DpXfeUV07c0YWFT/7xeAOIpwbGGD8w48oi7JuW8a5X73EhJWfISsrS5XZ8eKu76iO950V\nO5T3X172TVV09sVd32Fp5ReSnvsYkkP2+B4OxgjsdUL0g/2N9NCZ6AHclO/i9Plu0tIM6PWS2IJW\nq2FWZQGFeRaKioqZPLmKvLz8hF9Ydy+epKgQazQ6vF4Pc6aX8vn6aartrlUEaaQpvsOJqsqRuVTi\nP+cvtKgUniunTOXts/vp7egiNy9X1W7LpT68fjFpOnEymAwCE4qyVMuE6zAZIoqiSin3wIEGzp49\nyx133DFi1dzrhdhzOHLkHbq7nSxf/smE/ZYj+oJOQ0ijZ0KZFUGnVSaFcnKymDjRysmTJ+ns7KSk\npITt2/+QcByGKiUY83MdwxgGIacSJ/xcRshFfn4+AwMDiqhQ7DYj+S6S04ftdrsSgR0JZHKRkZEx\nYtI95aVNnFizit3NzSyNOfZum42llZIwj06nYWfjAUyCQN20mZgy4oX+AJbW1XLpoX8lGAzy2GOP\nYbFIfq6NjY30CZo4oZxoyDX3cv8rv5dYtVXGuW8/wrJ5cyLCS7XsajzAsoV1ceJHIPm1pqdlMH3C\nBMkeKGJpUlNZyWpnb5zY0dTSUjj8nvRP0sjhIPHZ3dzMUmul6vx2223clFfAUWcPt0LCfoltrcr7\noSKUidJfvaLI1H97nMs//iF5RYnVgWU02GxMf+K7qmXVG16VUsmjFIllTHnpt0kf/s16A4hwdKCf\nm17alPK4MornDq0ODZHP3yubcLyykaz8fIq+8CXSbk6tWqyx5CB+fAXp+/byflcXM/JzlHWyQNUg\nYq+bVdXWbrtdSQM+sWYVyxbWUZ3onnrwS4QysrFufg1EEbvXRTVgNBopKChAEAT+4HVx17p/jds3\nNsIqpwpDfDq9tO2NTWAX5J5nX3fZ9e6GCtGpw8PBjfv0+FeK2IfiG/VhPjoCXDM5jfb2dsScHHz+\nIEaDTlWfN2XKZPr6+sjLS/xlLGi1fPq2yUqd54F9u1j90YkIwmCU8lpFkEZCRmWMxFJFFuSRCbIM\nn89Pab5RpfDc0+dFDIYR9DpCwRDaqOP6AiF63f4RE9gbAWIwxB9e384nPrGcwnxpVry8YgrhoJ8/\n7tzBqlWrboiaZzEYwj0QsXtI08dd9zfe2J5Q7fd733uGBx54SPm8yhH98239XOhw4fGJGPVaLKYQ\nN00sYn7VeJYvv4stWzZTUTGR5cuXK20mG4ehSgn+FmuSxzCGkUAmpdEPRNGquNEYbRS2yxkmmkIO\nZakjiiL5+fkq+5/c3NwR18qZMjKYlZlFi6ONSVH2I7eUlrPbdpKp1nKVaq/T5WJP4wHaMPD52plx\n7Z3+8lomv/gSaWlprF+/nra2Nh544AF+N28RU9+T6mKzh9OxnJyUq92nWzjpKmJJbY1CYhFFDh07\ngdvvRxCgsCCPKeMnUDt1Kva2NhqPn8AkC1RFoPwqRi1zRmp+EyE9Zn/R5eWmLAux7HxObgGezHRq\niyvi2pexrG4+u9asYvqGzVIK8lAEL0HasmFmNU39TpbmWZLv5vVRPD+ehAFM+cKXEN98U7VsX5uD\nKSm6Ubv1Dex3r8Dp9SLTlr6+PrKyspLuY/2P/1RZ0STCsTUrmWotp6q0lCqZNB7az87/9wwznv5/\n6MrKk+6be89qGl7ZKEVKI9H0gxdb6R9wq6KnE4sKqMjMRnR5mV+sLq1psF+gfMlSIEJeI/dUwjGI\n3FMA5wIBsgtLhv25j00Tjo7Axj67W023DqvNMQxipOQVxgjsNYf8UGwwmhjwiUydNh2/z8uvf72R\nmpq5N1xEFqT6vMVzJ9F09CAfXVqniNDIGG7k1GQQMBkEltdfP6Gq0fh7jtRSJZH4T5YxQE2VOgqa\nk2XCaNAimkz4A35MukFvQKNeS3aSGfMbFaFwmD+f6uSd45dwtJk5/dv3CBGiwJJGUW4GJqNAtzsN\nl8uN2ZyByaQf8gf06vWzg0MnOrjc70MD5GQbmTe1iNlTpDRyj8dDbm5WQrXfm2+ey49//AO+9rWH\nEQRBiei3d3voH/Dj7HEi6HWEczJ49/gpjh8/ymc+/hHGjSuIq10dqvY7VSnB31pN8hjGMFykehj6\nMDWvVwKx3rXRIjjDRcXPfsGBNasQ0DApioQdcjgSRg0tZjP1Cxewa28j92/cwrN33ymRwggGBgZV\ngR988EHlu2j9+vVsv/VmAMoAi8WSkswMhUprKc32c5Tk57KsrpaGd98lHCbOImdP4wGqyiZgLS7G\nZndQUTo+piVdROxoEG1d7UnFfWYXj1Nx1YY2h0qUCaDBYQd0LJsy9HfqsrpaztvPkJWZGefPO1T6\na1amRNSqN7yK+PBDSetZJR/SnyRcZ7rzExzd/FtmWQYnDPQmE4IgpPRHtntdGKP66vV6k/7+DuVL\nDBEBqwT3G0jR66b1T6NftERRa06Emc+9SO4Tj7HPbscrionbE0V2NR5GJChFniMEVRSh+J5VZN+z\nepC8DgHZb9bhHeD2Ta/Erc/MS6wgHJ0mvGLSP8etXzHpn9nW8oyy7V+Dnc61/K4cKXmFMQJ7TeHx\neMjJyeRYSy82xzncAwHSjTo6HB9QnpdNerruho7IOnv60CEi6AaJ1V+aqu1o/D1jo6qBQAC320Va\nWgaTy3LjIneJxH/8Pi/vvXeI6pmDs98mg8BNZbnsPdxNZuZgLU8oFGZKafZfXPS16XQnjSfa6Oxx\ngVZPa7cLvz9I+2U3PX0+Zk0pwK/N5M13TvKp2+cwY8Z0Xn31Zb74xa9ch3620+f2o9NJUXJnv5/G\nE21oNFIaeTJLKZBqtVtbW9m6dQufuOseLrRLD3+9Hom8FhTmodPqCAOTK8YTDIpse/33LP3ooqTt\njakHj2EM1wbJfld9Ph9GY7yoTypUlGXHKRKnEnMSBAGv16vYicgYTRRW0OuYb8mmwX6BxaXj2O1w\npKx5BYl4WdJNNH7wAWIwRIbBwPybblKiXU6nE4vFQnp6Ot3d3Tz11FMsikrf/DDkFaDUbMaGjgPv\nfRDpT4S4xtR+LokI6+xs3M+y2rlSfWV0BJUgU80GXJFraXe00eXshfziuLbcLjdYcthttyvnCXCw\nq5M5EQJ41NnF9A2vUviD/1DSQQ1ID/DJ/EhPPfqvTN+wWUrnjRLUGir9NbpWs7GtlYWl8WJ9ux0O\n1XbKeUf6lpWVhdcbgGBENbezkymRtOBU/sgmdNTt3J0wbb27u1tpP1kN64nPfwYxFBhUjNYIKe+3\nmkor4nkbqcyuNJYcjnV1Yi0dL5HLJO1JKeeHVav3dbQz/Z7VAEytnBSXytvlctHk6IwT0zIg3UP6\n/HhHgdu2DkZWu7u7lc+lKIpKFHZbyzMKWU0Em812Qz2/3+gY7ViNjfAVRipl2/b2NjzaAvYcuYDT\n5cMfCNHb083E8vGMn1JOVVWJkqZ4vdMrE+FKW/yMNII0EtXgZBgpGZVRM7mAoBhiz4E/YzAYyc/L\nIextpfX0KcSKxJMNgk47mFocUXjudvbhE7XkZJkwGQRWLirn+Hvvgy5bUSGeUprN3Ysnjer8rhfE\nYAh7Wz8uT4BAUIt7wEtYq0Or1SIGoaPHw/nWPjx9veRYchCDIc6fP8/EiWXXXHn6XGs//Z4AmijB\nLo0GXJ4A9rZ+qiflU1RUzJEj7yRU+z137hyzZ8/myJEj9PRKUfYwYfwBUVHYlo8VCIYgrKW4ZDzN\nzc1j6sFjGMM1QDJ7HZDIYqIH+NbW1hGnEcuembHHToW+vr44AjsqW51fbuTcfWsRBC32gC9OTCgR\ndh1+l9qaWViiyJjT5VKprjqdTp588klqa2tZv349e5YtvmJ+uQ6XNNmXlpbGglkzSCTGE41ltXM5\ndOIUIFIbSUU92tYaF6WrmVoJVHL4+HEIaZlVKPnQNthsgI6LhBNG9vY0HmB+fgEuf5Bz336Ewgy1\nDcz9G7cAsKkJSd04SpCp0ipFgIvzCodOI0ZKWy7OU98rN23YTMOaVSyISYut/NzfqcikHAk7sXa1\nKrX23X4PIJJtzBrWPaTPMCptyn87OjqUKGyyNgYOvYNuyyaW3Rr/O7Wn8QAzKyvIS2BhBBLJOLZm\nZUJCLqNfDDO/eGhvepnELiiWxnH53gPKOo9ZrWb88LY3lBrkTU1NqnppQJWBIGNgYID29vbE5yEI\ncWJNifCdFTv+KqKv1xKpMgFSYYzAXiEMR9k2P7+Q3+w+SECbgdcfIhgK4Q9oudTppqHJwc0zihF0\n2utiKTMcXK/IaSgc5t3mDs629REOSVHUoVSDU2E0ZFSr0dBpP8hjf/9JNDqDkkY93MkGMRSi3ziV\nr//gDTQaPeYME1kGLzPH+fn6F29HDDGkD+xocCVI/3Dg9Qfx+EREMYw/GCYYCgE6tBoIE8bnD9J8\nvhutGMSncbLrwBlcLjfTp0+7ptFHrz9In9uLxzOAyWhU1R0Hg2E8PlFKI09Pp7vbmUQJ2E1HRwez\nZ8+mt6cTg15LMBQmFAyQFvVgKui06HVatFoNU8sreOOPb46pB49hDCNEbFRopPtdD4wm1XY06XoO\nr4uqTAtN7Z1MnFCmquj8772HOOhwMK+0lP+z8Gb2HG5iWar04khdpyzidO+99wIwf/2z/PSnP2XZ\niHqWGHbHRUSCDAwMDIv0CYDb41EIdkPz6eHVOMoppjDo0ZpA4XdJ7Tx2NR5g+obNHFuzEmpmJe1L\nrCBTvtlMH1D4o+fZt3Y1C0qtKc+loS1xVFVv0hNN5I86e6j5wpcAeHfNSga6e6m0WinMtzDVWo7N\n7kAQBJbU1CievmkWo0ISk0X5Aea+sl2JtA5HKRigd8dW0v70v1iLxycdQ3vbRTp7evnPfYcA+Om9\nn1Jtk4gsqtuoGfbn1WIxQzDMvs5OUoVOYq+Xw+mk1DJYcywLcEVHWYeCzWbjOyt2JCWx31mxg9//\n/vfMmpX8PhpDPOSJFFnYbtj7XcU+/dUiESkYjrJtSKOnvTeIKV1Eq9ESDgURBB1efxD7pT76PX5y\nMqUvnRs5rfBa1t6FwmFe3WPDdrGXUEhSVszKMCCGpC/SRKrBQ2E0ZFSuiYwWYYLkNYyx94iswpyb\nm08oGMIf8NMfyGAgo1LyLIQrSlxHYxX0YWAy6Eg3Cmg0EA6FSUsz0e/ySf9rNPiDYfy+AEV5Zpy9\nPTTs7+Ljy27h1KlDzJ17bQQPRFFkz//uoL/PjF4w4Xb3EwgEycnNQaPRoNNpSDcKShr58uWf5Hvf\ne4abb57LxIkTOXfuHG63mzvuuIM//vGPBINBqqvn0Rd0YW/tJy/bTGtHNxkZGYTDYXIjHscVJdmc\ntx9m5crPjKkHj2EMQ0B+kI19mBmpWm+qbQsLC+nu7o5bbrfbR22pE41UBFZ+WJMjt319fYoNzUih\nQ0dTv1OKysUQALkO86DDQc3x9JTED6ToVuN9a7C8up0lS5YoYk4A659LXIuZDPI1NJlMZGVlKdfO\nOLGCtPMO5s6cpiLbD7+2XSEcsfY4S2pr2H/0fXY3Nw+S0QRou3iR4xcc1NbMQh+JMNeXjqep2UZb\nV3vSfZfV1bJrzSrSLfkjEmTqcrmQi6lK71pJy/btTErkYQq0tHUljUBO/OlL7Fv7WcWuyBsRo9pb\nv5RlC+tU28oevgC79jYiEkRAR6ezDwEd/kf/CcelNsr++1cKgU1Uyzpsj2T7GXIO7ae4uJhU0XJr\ncTFtUff7/Ru3qBWiKycRitknNzeXrKws3lwyn8Lpai/gaCui2Mh3jdVK4NwlNLqYcq8hrl2p2aza\nxiQYRxwpFQQBu91ObfABZsyYoSiQt7a2cvDgQex2+18UeZ2Q3sMFT3LhtUtiDmVC/zXpiyAI+Hw+\n3G73sOthxwjsCJCMFFRV5A1L2VYMhtAb0/C4B9DqtOh0WoJiEL3egM8fRAwOfsTH0golvHuyg9MX\n+9BoNGh1EAacLj8AglaTUDV4KIyUjAJD1kTKkw2J7pGivHRsl/oS3B9aTp7vZunskjjy+mFtlmIn\nVPyBAE3N5/H5/NwyM15+/8NC0GmxFmdyqctNd78PNGAy6fH7Rem+14E5w4yg01JaXIS1xEKLowd/\nd+81iz6+8cZ2PrP6Uxxr6ZXS+Pt9hMIhOjsuk5ObS2aGHmtxpnI/CYLAAw88xI9//ANaW1uZPXs2\nHR0d/PGPf+SOO+5gy5atpKenUzNZSjvTaqCto51gKER2hpHxhWYmjbdQNTGLV45IYlVj6sFjGEM8\nomv7EkWOYHRptsnSiOVjXs86tWjiOtp+WB96mJPPRmrxop7fj0eIJ8DqyjLqhpFeDFBXNYMzO7ax\n8M4VLFy4UInGyuMoe+0mg3wdYx9AZfXn8n9/mlNrPxvXl+hoWaw9DsCMvHwGzOlxUbpdzWcBWDZ1\nIl6dLmGEWU4v3rl3n2LFE4sltTUcOHVmRH6kNrsDmXalf/peTrz6CpOSDLHDO8B01FkBcWRShF4x\nQO3Pfsne+iVSfXCCa3bEdoo+Z38cuQUpndecJU36yNfpw9xfJx5dx7La4Vnp5JvNfKS0hD85Linn\nI8MsCOQmmRga8KvVox0xEz+JrIgQRZb8/FeqRbFiWi+s+Bhf3fYGIF3H2GubP3dowadkkElqNAH+\nSyKuMspM/SkJ7PVA7ARfym2vQX/+apAsytrbP0DH5W4sWZmKTypAIBDA2ddPT28+BblZCDotRr0O\nrcZEOBxGDAYJhkKEwiFMhkFl37G0QgliMIT9Uh+hUFgR2wGpXrHP7Sc305RQNXgoDJeMRqOoqJj3\n3js0ZA1jonvk9HknPf1+LGY9Pd096PU6TCYpAtjjDNLd66GkQMr/vxI2S9FWQaFQiJYWG+npRiwW\nC0feP8Xl80eov+PKi4RVVeTh8Yk4+71c7vMhaDVojQKiZwCDoMc/4MYXDqIPC5w61YHjUjuPfOWT\nV7QPyRCtKjxvRhrhcJi3my5y2TmAVqcjM01H3fRiRUFahiAIfO1rD7N16xaOHDnC7NmzCQaDbNmy\nVYmcRot23Ta7hIaGN8nKzmRKSTrn7Yd55Yg6yjqmHjyGMQxiuPYJubm5CaOmQ7Udi9gUylhcqSjs\nUGJOV4JAG+tuIfuV8fRevEg0Y6iKigTmW7LjrFzl2k6IT/c89aMfYLpFIq/r1q3DbDbz0EMPAdJ3\nl9FoVFkRRXvaJkNGRgZutzuKrI3Meua9rk4WFk9VLY+O0llED3U1s1K2W183n52NB9hkOy+ddwxJ\n7u/r4X2DYdh+pBmT1ToVsqDT4mL1BHFDm4PpL21SJlLe+6cHKRhwUxKlolyysI6de/eRptEyfuMv\nJaKd4Fx2HW4aVNpNsH5JhKDvWvtZpm/YnGwoho2p1nLVcbyiyEOvDYocxapZj4soKksK0YP7NTsc\nJMuzirUiKo0RywLiznVffx/TBT1EEchjzc0qMS2QSOwg1BHk3H/8uur/oqLE6sPRSFYf+2EQfdyr\n0X6q4wEwxFdqUVERFy9eTJq1cjUgaxiA9P2SrD52yG/PUCjE448/zsmTJzEYDDz55JOUlw/6Om3a\ntImNGzciCAJ///d/z2233UZ3dzfr1q3D6/VSWFjId7/7Xex2O0899ZSyX1NTEz/5yU+oqqqK2zYt\nLS1RV64rEvmHyuTAlGbAYrHQ0XEJj8fHxIkVnD17RiENp08e4VBPHx9dWs+EwnRsjj68gSDhMGi0\nelwuD5nZOpqbm+m4dB6ns38srRCpXlFOGwYIh8OEQuEIMQONloSqwUNhuGQ0GukREaZUNYzJPGbT\njALBUJDubheFEYVakH7Ug6EQhw/s4a4VUtpyMu/RkYh6RVsFtbTYmDFjqjKxkm3J4+O3lLLj9W1X\nTCRMrlE+09pL++UBdDodWel6XP29ZBo05BRmEBZ9BNw9pOmMdHd309V1mdo588hIH5nq52gRPWmh\n1Wq4pbqEm2cU0+/x09zcDEGonJT4IVoQBD71qVVDRk4FnZbszDTuWjEWZR3DGGQMRdRS1ex9WAiC\ngNvtJiMj44pEpEZ67GuB8md+xJkvr1VZx4D08H7ROyCp58aI20Tj4de2q1I0o5V6H3jgASorK1m3\nbh3r169n165dVFdXq/YXBGFEdWvG3LyU62PtcQC8YoIoXAQjiTAvrJqBU6PhjdPn4moiBXR0dXUp\nfqRA0jZFl5fyHzwbt7xy5WrY/yfl/173AFVr1pIfmaDZU78kIiYVH/WqX7iApmYbjj/9CWv1DNU6\nuZ75qRW3D3mOAFVTK+NSdkeDWDIZTV7l/6MnAgR0CmmMvh+7nL1Jj1H2kUWEWy+plg1lRSQmSGcu\nX7IUsfkkgnn03yN3P13Ba4+ciXv/6MZ7eOreVxSV8ugJHHlCRxAEdDqdSs080XZDHTcRkrUXDAZT\nRsdjj5mRkcEx+zt8a+PnlGUvfv0SDbZEvhwS2tvbuRw4T66YOJPlaiPlb8dQO+/atQu/38/LL79M\nU1MTTz/9NP/1X/8FQGdnJ7/61a945ZVX8Pl8rFmzhgULFvDcc8+xYsUKVq5cyU9/+lNefvllvvjF\nL/KrX0kh/x07dlBYWMiiRYt48sknE257oyGRf6hMDkJhLdZxWVzsdBEMiuzb9w4LFtyCTidQWWqh\nbuY4hYQUZE/F0elBo4FgCLSaMGG9Ca3BwFtNPZjTC7Fk5uP1+TD/jctwmww6TEYdWWl6HJfd+ANB\nwhHiatTrsBZljkqYaDhkNBGGUmFO5jErCFoy0wQ87kGFWpAmQKZPzCMvLPUFSOo9OhJRL9kqyB8I\nkJZmUGUFGA06cnMyr5hIWHSNcp87gF8MYjII+Ab6uWnSeIrzM9FqQKfV4vNbabGdRqvVceutt9Jq\nP4Ggmzz0Qa4AEk1aCDotmekGHBfOM7fm5iHbGEnkdCzKOoa/dYiiiMlkIj09Xfl+S4ZEyrzJ2hwp\nMXS73ero3yiRKh05GUbrmzpS4aqKF1+iYc0qFkdq8mSMF0zEhl/llMyb8i3MsmQhoGNP03FEgozP\nVWegPP/886xfv16p9Vu2TJJyik6dHK74Tn5+Pk6nk/If/pimB7/ErCjl3RdWfEwSCBIik9UxPq6x\nUbpomAQhaYT5qRW3q+xvTCYTlrD0sC4kaHP6hs1qQaYkp9bQ5qA6Zpkoihju/jTntm+nPFKe1NTv\nZMna+wA4vGb1kDZHNZVWDjc3q7b5ddMHHHQ4uK9qGvlm87DTeRvv+wIurxuTIFBTJRFiu6ONtq72\nlIrA6enpSkbEeVEkcVWv6sRj/o8nQwu++33lfdM//xPn9zWo1JQnVFZC1L2eKvLd0Hya7MlT4o6R\nff8/0LBmJUutlXHrorHPYedjKbdQR0JdLhdP3Sv5xVqiJjxkHLO/w9zJiwgEAqr3LpdLtf0x+ztU\nW29hYGBAqUnOzc3l7qcreOLe3wCJo8B6vZ5AIEB7e7tqvdyevD7RPvI2MsxmMxu3rVeR5csBWFyp\n4eLFtxg//jZVOxcvHuXF48/w6FLpfpGDi9ciUjwcDPn0/+677/KRj3wEgJqaGo4fP66sO3bsU6gC\nYgAAIABJREFUGLNnz8ZgMJCZmUlZWRnNzc2qfRYtWsT+/fuVfTweDz/60Y/4xje+Edd+7LY3EmRS\nICMQRQ6MBh0LZpVQWSrdrLl5+ej1eipLLcybIUmDp6enY7FkEgqHyEjTQyRIFwiG0WhAp9PS091F\nd3cHXa4A2946yPbtf7guSooej4ezZ88M+dBxtSFb3oQJowmDMmhhDWkGHf5gCDEYGlV/ZTK6detW\njh8/ztatW9m8eUvKyLeswlxdPQ+PRxLwWb78LuUhI/YeicaEXB1Ty7NBE8YXCIImzLSJeXy+fpqS\ntjyc1ObhQB63flc/OTmDM72hUJiKkmwEnXZE7aWCXKMcBvzBIGg0DPgCBMM6XANBDIKWjp4BLna5\nOH2hjy6Pgb4BDdZxmVQUakfsfThaRE9agDQWje+18psd79N4ysv/NnVw5FQHoXDymcgxjGEMQ0Oa\ncJPqJeWa1iv1OzYaEjqaiGsicZdTZy6P+NiJHniTQRRFRFFUxq6wsHBE4zZxzeeRHvTVr6NtDk60\nO9h1/Ch7jh9nTeVEVleWcedNFdQvXMCyhXUsW1hH/cIFzJw+BYvFTM/vXsbpdPIv//Iv2Gw2Hnjg\nAQA2btzI448/rhpPOR16KGi1WmU7t8cjEZ3oF8QvE8OSj6smjMPlIiwGlZcc6audepNEoiKvh1/b\nrhzz0W1vqtYhigjomFdaSpGgJyyivOSo3uRnX6BFqSEW414NdhvVG15VrpfRaFRdM7vXBWIQxKBK\nZ6OmpCCuL4letZWV7Go6qhzybZsNAK/XrdrO6/Vy/8Yt3L9xC785cJBdh99lz+Em9hxuYtfhd5lQ\nWkj9wgUsqZuPxWzGYjZTM7WS+oULKHn2e5LqchSsVitWq1WVzt9sa1GN0Qsr1Oq7L6y4M8EYql8N\ntpP4p0zlz+u/h+ehrzBdI8bdd/riIhpk4p7iJTpdjF+yhPJHvp3wHqve8CoNdlvC6wYiLW1tzF//\nI4C4tNRoEvjVnw+Svs//uBq9Xs/dT0sT0u3t7WzYtx69Xs9Tu1cyd/Ii7HY7LpeLuZMX8c+/kjLb\nzGYzdz9dwYZ963lq90qqrbdw99MVqgxTvV7P6o88SLX1Fh7deA+AcpxHN96DXq/nl3u+C0jk9u6n\nK7h48SKPbrxHaS96H5CIbV9fn+qY0Thh/7Py/u6nKyhOD/Ll75cwfvxtvPvWH7h48S2+/8rHefet\nP1BtvYWDh5pUx7hRyCsMIwLrcrkwR81g6XQ6ZRbU5XKRmTmYbpGRkYHL5VItz8jIoL9/UMXqd7/7\nHfX19cpMZqptbyTE+oe63S5ycnIIhcJUllow6HXUzRxHrtHNtHGTmFMzJS46OK60nD81tKPV6MnP\nTicYCtHT70MDtHY6+ei8yWSky7PQRay4tZQtr/7+mvnBXon6yw/dhxj13qqKPA580E5WhoFAIESI\nMBpAo9Gw510H+w8dpazQxOLaiUp/P7q0Hl9A+kHMSNMnjNJ+GEugZNG12HtERigUZlrFOLSeFpZ9\nrJ6ePq/iAwvqtOWRpjYnQ83kAnw+P0feP0W2JQ+jQaeaULkSImHRNcoQVqLjwVAIrVZHIBjifHs/\nzn4f0yvymFCYSWtbiP7+XpzOHqqqpvPqqy/zxS9+5UP1Y7iIjqB7tAW8b2vH6/UzpXJyQtXwMYxh\nDCNHokjqUL8fqWpCE6moXi+MJgo7VARariEdabuxSFvxSRo2/JrF1gmq5V3OXurr5lN9k7o+EFFk\n5959ceq8tVOnYj/SiG/KTYy7406ys7N5/vnneeCBB6itreXw4cOKl66M7u7uYSuHAkz+xctc/up9\n5BWlju812GwsKCim8/vPcmLtasYnqnFMEmGWcdzRRlWMx6gU3YtKQ/WKlC9ZCoDGkoPD75MirDFt\nt3R1cdPT/w8A+0MP4nf2UGm1kp1vUSxtzMbBspg5m7cB0Ld/L7kmk6q5LpdLItgks52Rtn6wdibP\nHX6PaIEiGEznXVM5UaqLjf1sJLm+Murr5iMUFKBNkf4tWc3Ep6YPQr1ufvE4VURW9PqoePgR+g4d\nIONII0JxcdII9LK6+exqPMxia+JJfIhEvhOkbkej4gtfgrfeSrjO4R2gep40Hrm5uaoJdK/XC0gE\n7Sdf+BM/+ZNU9z3dOhuAF770jkLiXvyKZBf06NJXCQQC/ORPD/HUva8QCAQ4Yf+zijRu+tNzCdOD\ni4qKeGr3SiW6ecL+Z4XQyv/L+2/603PKfr9s/r88dW/yCPrcyYv4+Hek74C7DyXOArv76QqmW2fz\n668dA+CJe3/Dl79fIp3bbZc4Yf8cX7/nLi4HBrd/7ZEzNxR5hWEQWLPZjNvtVv4PhULKj0jsOrfb\nTWZmprJcEqpxq2Y6tm7dyrPPPhvXfqJtk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2tXs6RuPiVAlXxOh/bDqs8iaAQQ4ehAPzVP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u7pZJRiejTjMaURSloDV2/rkwh1q2bBlNTU1YrdaEEuJU5IjJ+muOl2h5sM/nY+PGjdxwww08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\nng2DXJJkbRHo7/Jogy6h0OecTwi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iy+XimKjQ01MVsPHSAtkPy4kezrONUqlkHpg0Gg1k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ezs7FAKpxS1Hc3oqCQYLRZL2Psmckl2uVwjImCNRmNUOmtXV9egzofdbo8SujpB4FB7O1fn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\nLBERERERETmF/wddI4s1lnLHxgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a83d890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#all objects vs. unique photos\n",
"\n",
"plt.figure(1,figsize = (16,6))\n",
"plt.subplot(1,2,1)\n",
"\n",
"col1='new_lat'\n",
"col2='new_lon'\n",
"plt.scatter(new_pd1[col2],new_pd1[col1],facecolor='white',edgecolors='black')\n",
"\n",
"plt.xlim(new_pd1[col2].min()-.0005, new_pd1[col2].max()+.0005)\n",
"plt.ylim(new_pd1[col1].min()-.0005, new_pd1[col1].max()+.0005)\n",
"\n",
"col1='obj_lat'\n",
"col2='obj_lon'\n",
"plt.scatter(all_objs_gs[col2],all_objs_gs[col1],alpha=.5)\n",
"\n",
"plt.legend(labels=['unique photos','training objects'])\n",
"plt.gca().invert_xaxis\n",
"\n",
"plt.text(-73.736, 40.965,r'N$\\uparrow$',fontsize=16)\n",
"plt.text(-73.736, 40.9645,r'Mamaroneck',fontsize=16)\n",
"plt.title('All objects vs. unique photographed locations\\n')\n",
"\n",
"plt.subplot(1,2,2)\n",
"mm = plt.imread('/Users/elizabeth/Desktop/Mamaroneck.png')\n",
"plt.imshow(mm)"
]
},
{
"cell_type": "code",
"execution_count": 885,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,u'Validation objects vs. NN ID\\n')"
]
},
"execution_count": 885,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7hpYtW17QOeV1FUII0TA1yi2GL5Zl2kTMi17y/6zs/9X/s336nFpdc8KEcdxxxzC6d09m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\nJiuK6O7rdAIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118ecc490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#all objects vs. unique photos\n",
"\n",
"plt.figure(1)\n",
"\n",
"col1='obj_lat'\n",
"col2='obj_lon'\n",
"\n",
"plt.scatter(valid_objs_gps[col2],valid_objs_gps[col1],facecolor='red')\n",
"\n",
"plt.xlim(-73.736369179999997, -73.731005530000004)\n",
"plt.ylim(40.964088910000001, 40.968400080000002)\n",
"\n",
"plt.scatter(headings_w_nn[col2],headings_w_nn[col1],alpha=.5,facecolor='blue')\n",
"\n",
"plt.legend(labels=['validation objects','NN id\\'d objects'])\n",
"plt.gca().invert_xaxis\n",
"\n",
"plt.text(-73.736, 40.965,r'N$\\uparrow$',fontsize=16)\n",
"plt.text(-73.736, 40.9645,r'Mamaroneck',fontsize=16)\n",
"plt.title('Validation objects vs. NN ID\\n')\n"
]
},
{
"cell_type": "code",
"execution_count": 888,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(52, 68)\n"
]
}
],
"source": [
"print(len(valid_objs_gps), len(headings_w_nn))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CHANGE LAT/LON IN EXIF"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('new lon=', 73.734519917327106)\n",
"('new lat=', 40.9670199173271)\n",
"('new_lat = ', 40.9670199173271, 'old lat =', 40.966944444444444)\n",
"('new_lon = ', 73.734519917327106, 'old lon =', 73.73444444444445)\n"
]
}
],
"source": [
"path_to_open = '/Users/elizabeth/Box Sync/deeplearningcourse/elizabeth/data/harrison_fieldwork1/images_wks1_2/testingexif/'\n",
"path_to_save = '/Users/elizabeth/Box Sync/deeplearningcourse/elizabeth/data/harrison_fieldwork1/images_wks1_2/testingexif/'\n",
"img = 'G0036562_4.JPG'\n",
"\n",
"f = open(path_to_open+img, 'rb')\n",
"\n",
"# Return Exif tags\n",
"exif_dict = piexif.load\n",
"\n",
"#convert to decimal\n",
"lat = tags['GPS GPSLatitude'].values\n",
"lon = tags['GPS GPSLongitude'].values\n",
"\n",
"lat = [float(i.num/i.den) for i in lat]\n",
"lon = [float(i.num/i.den) for i in lon]\n",
"\n",
"lat = lat[0] + (lat[1])/60 + lat[2]/3600\n",
"lon = lon[0] + (lon[1])/60 + lon[2]/3600\n",
"\n",
"#splices crop slice number from file name\n",
"c = int(re.sub(\"[^0-9]\",\"\",img[-6:]))\n",
"\n",
"#longitude\n",
"#for i in range(stepw)\n",
"\n",
"if c==stepw*0 | c==stepw*1 | c==stepw*2 | c==stepw*3:# | stepw*4 | stepw*5:\n",
" new_lon = lon - (stepw/2-0.5)*del_lon\n",
" print('new lon=',new_lon)\n",
"\n",
"elif c==(stepw*0+1) | c==stepw*1+1 | c==stepw*2+1 | c==stepw*3+1:#| stepw*4+1 | stepw*5+1:\n",
" new_lon = lon - (stepw/2-1.5)*del_lon\n",
" print('new lon=',new_lon)\n",
" \n",
"elif c==stepw*0+2 | c==stepw*1+2 | c==stepw*2+2 | c==stepw*3+2:\n",
" new_lon = lon - (stepw/2-2.5)*del_lon\n",
" print('new lon=',new_lon)\n",
" \n",
"elif c in [stepw*0+3,stepw*1+3,stepw*2+3,c==stepw*3+3]:\n",
" new_lon = lon + (stepw/2-2.5)*del_lon\n",
" print('new lon=',new_lon)\n",
" \n",
"elif c in [stepw*0+4,stepw*1+4,stepw*2+4,c==stepw*3+4]:\n",
" new_lon = lon + (stepw/2-1.5)*del_lon\n",
" print('new lon=',new_lon)\n",
" \n",
"elif c in [stepw*0+5,stepw*1+5,stepw*2+5,c==stepw*3+5]:\n",
" new_lon = lon + (stepw/2-.5)*del_lon\n",
" print('new lon=',new_lon)\n",
" \n",
"else:\n",
" print('lon num not defined', c)\n",
" new_lon=lon\n",
"\n",
"#latitude\n",
"if c in range(0,1*stepw):\n",
" new_lat = lat + (steph/2-0.5)*lat_step\n",
" print('new lat=',new_lat)\n",
" \n",
"elif c in range(1*stepw,2*stepw):\n",
" new_lat = lat + (steph/2-1.5)*lat_step\n",
" print('new lat=',new_lat)\n",
" \n",
"elif c in range(2*stepw,3*stepw):\n",
" new_lat = lat + (steph/2-1.5)*lat_step\n",
" print('new lat=',new_lat)\n",
" \n",
"elif c in range(3*stepw,4*stepw):\n",
" new_lat = lat + (steph/2-0.5)*lat_step\n",
" print('new lat=',new_lat)\n",
" \n",
"else:\n",
" print('lat num not defined', c)\n",
" new_lat=lat\n",
"\n",
"print('new_lat = ', new_lat, \"old lat =\", lat)\n",
"print('new_lon = ',new_lon, \"old lon =\", lon)"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-128-1a373f807d39>, line 23)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-128-1a373f807d39>\"\u001b[0;36m, line \u001b[0;32m23\u001b[0m\n\u001b[0;31m def change_GPS_exif(table,path_to_directory,)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"px500 = 5.6 #m\n",
"\n",
"lat_step = 5.6*lat_per_m # whatever step sizes you're taking (250px to centerline since photos are 500x500)\n",
"lon_step = 5.6*lon_per_m\n",
"\n",
"#heading -- 0 for north, 90 if photo is turned east, etc; heading from north\n",
"theta = 0\n",
"\n",
"#rotaion matrix [x';y'] = [cos -sin; sin cos][x;y]\n",
"del_lon= lat_step*np.cos(theta) - lon_step*np.sin(theta) \n",
"del_lat = lat_step*np.sin(theta) +lon_step*np.cos(theta)\n",
"\n",
"w = 3000\n",
"h = 2000\n",
"crop = 500\n",
"\n",
"stepw = len(range(0,w,crop))\n",
"steph = len(range(0,h,crop))\n",
"\n",
"def change_GPS_exif(dict_of_files,path_to_open,path_to_save)\n",
" \n",
" photo_info = {key:[0,0,0] for key in pd.keys()} #lat, lon\n",
"\n",
" files = [key for key in dict_of_files.keys()]\n",
"\n",
" for j in files :\n",
"\n",
" f = open(path_to_open+j, 'rb')\n",
"\n",
" # Return Exif tags\n",
" exif_dict = piexif.load\n",
"\n",
" lat = dms2dec(tags['GPS GPSLatitude'].values)\n",
" lon = dms2dec(tags['GPS GPSLongitude'].values)\n",
" \n",
" #longitude\n",
" if c==stepw*0 | c==stepw*1 | c==stepw*2 | c==stepw*3:# | stepw*4 | stepw*5:\n",
" new_lon = lon - (stepw/2-0.5)*del_lon\n",
"\n",
" elif c==stepw*0+1 | c==stepw*1+1 | c==stepw*2+1 | c==stepw*3+1:#| stepw*4+1 | stepw*5+1:\n",
" new_lon = lon - (stepw/2-1.5)*del_lon\n",
"\n",
" elif c==stepw*0+2 | c==stepw*1+2 | c==stepw*2+2 | c==stepw*3+2:\n",
" new_lon = lon - (stepw/2-2.5)*del_lon\n",
"\n",
" elif c==stepw*0+3 | c==stepw*1+3 | c==stepw*2+3 | c==stepw*3+3:\n",
" new_lon = lon + (stepw/2-2.5)*del_lon\n",
"\n",
" elif c==stepw*0+4 | c==stepw*1+4 | c==stepw*2+4 | c==stepw*3+4:\n",
" new_lon = lon + (stepw/2-1.5)*del_lon\n",
"\n",
" elif c==stepw*0+5 | c==stepw*1+5 | c==stepw*2+5 | c==stepw*3+5:\n",
" new_lon = lon + (stepw/2-.5)*del_lon\n",
" else:\n",
" print('lon num not defined')\n",
" new_lon=lon\n",
" \n",
" #latitude\n",
" if c in range(0,1*stepw):\n",
" new_lat = lat + (steph/2-0.5)*lat_step\n",
" elif c in range(1*stepw,2*stepw):\n",
" new_lat = lat + (steph/2-1.5)*lat_step\n",
" elif c in range(2*stepw,3*stepw):\n",
" new_lat = lat + (steph/2-1.5)*lat_step\n",
" elif c in range(3*stepw,4*stepw):\n",
" new_lat = lat + (steph/2-0.5)*lat_step\n",
" else:\n",
" print('lat num not defined' )\n",
" new_lat=lat\n",
" \n",
" exif_dict['GPS'][2] = new_lat\n",
" exif_dict['GPS'][4] = new_lon\n",
" \n",
" exif_bytes = piexif.dump(exifdict)\n",
" im = Image.open(f)\n",
" im.save(path_to_save+f, exif=exif_bytes)\n",
" \n",
" return lat,lon,new_lat,new_lon \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 733,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
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"execution_count": 733,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bounding_boxes = {}\n",
"image_name = 'test'\n",
"class_name = 'box'\n",
"n = 5\n",
"ymin = 2\n",
"xmin = 3\n",
"bounding_box = [class_name,n,xmin,ymin]\n",
"bounding_boxes[image_name]=bounding_box\n",
"bounding_boxes['test'][1]"
]
},
{
"cell_type": "code",
"execution_count": 735,
"metadata": {},
"outputs": [],
"source": [
"filenames = [1,2,3,4]\n",
"bounding_boxes = pd.DataFrame(index=filenames)"
]
},
{
"cell_type": "code",
"execution_count": 736,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
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" </tr>\n",
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"Columns: []\n",
"Index: [1, 2, 3, 4]"
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"execution_count": 736,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"bounding_boxes.loc"
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},
{
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"outputs": [],
"source": []
}
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@Elizabethcase
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Elizabethcase commented Nov 9, 2017 •
edited
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Note -- there are many issues to be addressed right now:

  1. commenting
  2. making general, or better specifying input
  3. adding the code from the CNN output
  4. make consistent removing & adding extensions in index names (photo names)

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