def main(args: list):
if len(argv) != 3:
error(f"Invalid Arguments #: {len(argv)}!")
ast: dict = loadJson(args[1])
patterns: list = loadJson(args[2])
parser: Parser = Parser(patterns)
parser.parseFunc(ast["type"], ast)
vulnerabilities: Set[Vulnerability] = parser.getEvaluator().getVulnerabilities()
dumpJson(args[1].replace(".json", ".output.json"), vulnerabilities)
def getContextList(self, channel, position):
utils.log('getContextList, channel = ' + str(channel) + ', position = ' + str(position))
position = self.fixPlaylistIndex(channel, position)
item = self.channelLST.channels[channel - 1].guidedata[position]
contextLST = (utils.loadJson(item.get('contextmenu','[]') or '[]'))
utils.log('getContextList, contextLST = ' + str(contextLST))
return contextLST
def _loadFuncs(self):
self.log('Start files loading...')
filesMap = self.filesMap
for key in filesMap:
filePath = filesMap[key]
self.log(f'Try to load {key} file: {filePath}')
func = None
if filePath.endswith('.pickle'):
grid = utils.loadPickle(filePath, 'rb')
if grid is None:
self.error(
f'Unable to load {key} function as pickle:\n{filePath}.'
)
func = num_methods.interpolation.SplineInterpolation(grid)
elif filePath.endswith('.json'):
jsn = utils.loadJson(filePath, 'r')
if jsn is None:
self.error(
f'Unable to load {key} function interpolation as json:\n{filePath}.'
)
func = num_methods.interpolation.SplineInterpolation(
None).load_from_dict(jsn)
else:
grid = utils.loadCSV(filePath)
if grid is None:
self.error(
f'Unable to load {key} function as csv:\n{filePath}.')
func = num_methods.interpolation.SplineInterpolation(grid)
self.funcDict[key] = func
self.log('Successful')
self.log('All files loaded')
def predict(string):
dic = vars(params)
dic['embedding'] = None
dic['is_train'] = False
saved = loadJson()
# merging config, using dic to overwrite saved when same key
dic = {**saved, **dic}
return inference_online(dic, string)
def main(verbose=False):
try:
#Initialize
stats_authors = dict()
# Get current location
path = utils.getPath()
params = utils.loadJson(path + "/docs/parameters.json")
# Get selenium driver
driver = sf.getDriver(path)
# Iterate over tags
for tagName in params["tags"]:
print("--------------- Starting tag " + tagName +
" ---------------")
# Get tag URL
driver.get('https://www.tiktok.com/tag/' + tagName)
# Scroll in tags
login_form, driver = sf.scrollPage(driver,
scope="tag",
maxNScrolls=50)
#Get authors names:
authors_list = sf.get_authors(login_form, driver)
print("--------------- Found ", len(authors_list),
"users: ---------------")
#Extract statistics from each author:
stats_authors = sf.get_stats_author(driver,
authors_list,
params,
stats_authors,
useTikster=True)
#Compute metrics for each author:
metrics_author = sf.compute_metrics(stats_authors)
#print(metrics_author)
except Exception as e:
traceback.print_exc()
finally:
# Check whether driver has been initialized yet
try:
driver
except NameError:
driver = None
# Always close drivers
if not driver is None:
driver.close()
driver.quit()
print("Driver closed")
def main():
src = sys.argv[1]
dest = sys.argv[2]
stats = utils.loadJson(sys.argv[3])
threshold = float(sys.argv[4])
# keep executing only if stats were loaded
if stats != None:
stats = stats['classes']
cache = utils.loadJson(getCacheName(stats))
if cache == None:
cache = {}
applySegmentation(src, dest, threshold, stats, cache)
utils.saveJson(cache, getCacheName(stats))
else:
print('No stats available, cant process images')
print('Finished')
def __init__(self, parent=None):
super(ControlPanel, self).__init__(parent)
self._config = loadJson()
self.ctrlpanelWidth = self._config['controlpanel']['width']
self.ctrlpanelHeight = self._config['controlpanel']['height']
self.initUI()
self.timer = QtCore.QTimer()
self.timer.setInterval(50)
self.timer.timeout.connect(self.on_update)
self.timer.start()
def run():
# convert to dict
dic = vars(params)
dic['embedding'] = None
if dic['is_train']:
train(dic)
else:
# load config
saved = loadJson()
# merging config, using dic to overwrite saved when same key
dic = {**saved, **dic}
inference(dic)
def main():
src = sys.argv[1]
dest = sys.argv[2]
pixels = {}
for f in os.listdir(src):
print('Processing ' + f)
data = utils.loadJson(src + f)
for key in data:
if key != 'height' and key != 'width' and len(data[key]) > 0:
if not key in pixels:
pixels[key] = []
extracted = utils.extractRGB(data[key])
pixels[key] = pixels[key] + extracted
for key in pixels:
print('Extracted ' + str(len(pixels[key])) + ' pixels for ' + key)
print('Saving')
utils.saveJson(pixels, dest + 'pixels.json')
print('Finished')
def __init__(self, parent=None):
super(TouchPanel, self).__init__(parent)
self._config = loadJson()
self.touchpanelWidth = self._config['touchpanel']['width']
self.touchpanelHeight = self._config['touchpanel']['height']
if sys.platform == "win32":
self.setWindowFlags(QtCore.Qt.WindowDoesNotAcceptFocus
| QtCore.Qt.Tool
| QtCore.Qt.FramelessWindowHint
| QtCore.Qt.WindowStaysOnTopHint)
self.setGeometry(self._config['touchpanel']['left_win32'],
self._config['touchpanel']['top_win32'],
self.touchpanelWidth, self.touchpanelHeight)
else:
self.setWindowFlags(QtCore.Qt.WindowDoesNotAcceptFocus
| QtCore.Qt.Tool
| QtCore.Qt.FramelessWindowHint
| QtCore.Qt.WindowStaysOnTopHint
| QtCore.Qt.X11BypassWindowManagerHint)
self.setGeometry(self._config['touchpanel']['left'],
self._config['touchpanel']['top'],
self.touchpanelWidth, self.touchpanelHeight)
self.initUI()
if __name__ == '__main__':
if utils.getProperty('PseudoTVRunning') != "True":
try: params = dict(arg.split('=') for arg in sys.argv[1].split('&'))
except: params = {}
dataType = None
utils.log('params = ' + str(params))
for type in ['json','property','listitem']:
try:
data = params[type]
dataType = type
break
except: pass
hasChannels= False
channelLST = ChannelList()
channelLST.incHDHR = (utils.loadJson(utils.unquote(params.get('include_hdhr',''))) or channelLST.incHDHR)
channelLST.skinPath = ((utils.loadJson(utils.unquote(params.get('skin_path','')))) or channelLST.chkSkinPath())
channelLST.mediaFolder = os.path.join(channelLST.skinPath,'resources','skins','default','media')
channelLST.refreshPath = utils.loadJson(utils.unquote(params.get('refresh_path','')) or utils.ADDON_ID)
channelLST.refreshIntvl = int(utils.loadJson(utils.unquote(params.get('refresh_interval',''))) or '0')
channelLST.skinFolder = os.path.join(channelLST.skinPath,'resources','skins','default','1080i',) if xbmcvfs.exists(os.path.join(channelLST.skinPath,'resources','skins','default','1080i','%s.guide.xml'%utils.ADDON_ID)) else os.path.join(channelLST.skinPath,'resources','skins','default','720p')
utils.setProperty('uEPG.rowCount',utils.loadJson(utils.unquote(params.get('row_count','')) or '9'))
channelLST.pluginName, channelLST.pluginAuthor, channelLST.pluginIcon, channelLST.pluginFanart, channelLST.pluginPath = utils.getPluginMeta(channelLST.refreshPath)
utils.log('dataType = ' + str(dataType))
utils.log('skinPath = ' + str(channelLST.skinPath))
utils.log('skinFolder = ' + str(channelLST.skinFolder))
utils.log('rowCount = ' + utils.getProperty('uEPG.rowCount'))
utils.log('refreshPath = ' + str(channelLST.refreshPath))
utils.log('refreshIntvl = ' + str(channelLST.refreshIntvl))
utils.setProperty('PluginName' ,channelLST.pluginName)
def initChannels(params, data, dataType):
utils.log('initChannels, params = ' + str(params))
with busy_dialog():
hasChannels = False
channelLST = ChannelList()
channelLST.incHDHR = (
params.get('include_hdhr', '')
or utils.REAL_SETTINGS.getSetting('Enable_HDHR')) == 'true'
channelLST.skinPath = (utils.unquote(params.get('skin_path', ''))
or channelLST.chkSkinPath())
channelLST.mediaFolder = os.path.join(channelLST.skinPath, 'resources',
'skins', 'default', 'media')
channelLST.refreshPath = (utils.unquote(params.get('refresh_path', ''))
or utils.ADDON_ID)
channelLST.refreshIntvl = int(
params.get('refresh_interval', '') or '0')
channelLST.skinFolder = os.path.join(
channelLST.skinPath,
'resources',
'skins',
'default',
'1080i',
) if xbmcvfs.exists(
os.path.join(channelLST.skinPath, 'resources', 'skins', 'default',
'1080i', '%s.guide.xml' %
utils.ADDON_ID)) else os.path.join(
channelLST.skinPath, 'resources', 'skins',
'default', '720p')
utils.setProperty('uEPG.rowCount',
(params.get('row_count', '') or '9'))
channelLST.pluginName, channelLST.pluginAuthor, channelLST.pluginIcon, channelLST.pluginFanart, channelLST.pluginPath = utils.getPluginMeta(
channelLST.refreshPath)
utils.log('dataType = ' + str(dataType))
utils.log('skinPath = ' + str(channelLST.skinPath))
utils.log('skinFolder = ' + str(channelLST.skinFolder))
utils.log('rowCount = ' + utils.getProperty('uEPG.rowCount'))
utils.log('refreshPath = ' + str(channelLST.refreshPath))
utils.log('refreshIntvl = ' + str(channelLST.refreshIntvl))
utils.setProperty('PluginName', channelLST.pluginName)
utils.setProperty('PluginIcon', channelLST.pluginIcon)
utils.setProperty('PluginFanart', channelLST.pluginFanart)
utils.setProperty('PluginAuthor', channelLST.pluginAuthor)
utils.setProperty('pluginPath', channelLST.pluginPath)
#show optional load screen
# if channelLST.uEPGRunning == False and utils.getProperty('uEPGSplash') != 'True' and xbmcvfs.exists(os.path.join(channelLST.skinFolder,'%s.splash.xml'%utils.ADDON_ID)) == True:
# mySplash = epg.Splash('%s.splash.xml'%utils.ADDON_ID,channelLST.skinPath,'default')
# mySplash.show()
# xbmc.sleep(100)
if utils.HDHR().hasHDHR():
if utils.REAL_SETTINGS.getSetting(
'FirstTime_HDHR') == "true" and not channelLST.incHDHR:
utils.REAL_SETTINGS.setSetting('FirstTime_HDHR', 'false')
if utils.yesnoDialog(
(utils.LANGUAGE(30012) % (channelLST.pluginName)),
custom='Later'):
utils.REAL_SETTINGS.setSetting('Enable_HDHR', 'true')
channelLST.incHDHR = True
utils.log('incHDHR = ' + str(channelLST.incHDHR))
if dataType == 'wrap': hasChannels = channelLST.prepareJson(data)
elif dataType == 'json':
hasChannels = channelLST.prepareJson(
utils.loadJson(utils.unquote(data)))
elif dataType == 'property':
hasChannels = channelLST.prepareJson(
utils.loadJson(utils.unquote(utils.getProperty(data))))
elif dataType == 'listitem':
hasChannels = channelLST.prepareVFS(utils.unquote(data))
if hasChannels == True:
if utils.REAL_SETTINGS.getSetting('FirstTime_Run') == "true":
utils.REAL_SETTINGS.setSetting('FirstTime_Run', 'false')
utils.textViewer(
utils.LANGUAGE(30008),
'%s / %s' % (utils.ADDON_NAME, channelLST.pluginName))
# if utils.getProperty('uEPGSplash') == 'True':
# mySplash.close()
# del mySplash
# xbmc.sleep(100)
if channelLST.refreshIntvl > 0 and channelLST.refreshPath is not None:
channelLST.startRefreshTimer()
if channelLST.uEPGRunning == False and utils.getProperty(
'uEPGGuide') != 'True':
channelLST.myEPG = epg.uEPG('%s.guide.xml' % utils.ADDON_ID,
channelLST.skinPath, 'default')
channelLST.myEPG.channelLST = channelLST
channelLST.myEPG.doModal()
del channelLST.myEPG
xbmc.sleep(100)
else:
utils.log("invalid uEPG information", xbmc.LOGERROR)
# utils.notificationDialog(utils.LANGUAGE(30002)%(channelLST.pluginName,channelLST.pluginAuthor),icon=channelLST.pluginIcon)
# utils.REAL_SETTINGS.openSettings()
del utils.KODI_MONITOR
text_dir = [
os.path.join(dest_dir, 'train'),
os.path.join(dest_dir, 'test')
]
generate_text([train_data, test_data],
data_ext,
text_dir,
args.data_type,
topn_sent=args.topn_sent,
is_stat=args.is_stat)
elif args.data_type == 'tree':
test_file = os.path.join(dest_dir, '{}_test_seg'.format(data_name))
test_data = loadJson(test_file)
train_file = os.path.join(dest_dir, '{}_train_seg'.format(data_name))
train_data = loadJson(train_file)
data_ext = []
summary, Y = text_train.ref[:train_ids], text_train.Y[:train_ids]
name, pos = text_train.name[:train_ids], text_train.pos[:train_ids]
# text = text_train.text[:train_ids]
data_ext.append([summary, Y, name, pos])
summary, Y = text_test.ref[:test_ids], text_test.Y[:test_ids]
name, pos = text_test.name[:test_ids], text_test.pos[:test_ids]
# text = text_test.text[:train_ids]
data_ext.append([summary, Y, name, pos])
text_dir = [
os.path.join(dest_dir, 'train'),
os.path.join(dest_dir, 'test')
def main():
src = sys.argv[1]
dest = sys.argv[2]
attempts = 10
ratio = 0.5
thres = 0.95
pixels = utils.loadJson(src)
bestSamples = {}
bestStats = {'classes': []}
minVal = [1e10000]
minTrain = []
minTest = []
for n in range(attempts):
print('** Attempt ' + str(n))
samples = {}
stats = {'classes': []}
# get sets and stats from data
for key in pixels:
print('\tProcessing color ' + key)
sets = getBestSets(key, pixels[key], ratio, attempts)
stats['classes'].append(sets['stats'])
samples[key] = sets
# calculate the classification error
totalTrain = 0.0
totalVal = 0.0
totalTest = 0.0
errTrain = 0
errVal = 0
errTest = 0
cache = {}
for key in samples:
clsTrain = classify(samples[key]['train'], thres, stats['classes'], cache)
errTrain += classificationError(clsTrain, key)
totalTrain += len(samples[key]['train'][utils.R])
clsVal = classify(samples[key]['val'], thres, stats['classes'], cache)
errVal += classificationError(clsVal, key)
totalVal += len(samples[key]['val'][utils.R])
clsTest = classify(samples[key]['test'], thres, stats['classes'], cache)
errTest += classificationError(clsTest, key)
totalTest += len(samples[key]['test'][utils.R])
print('\tError train: \t' + str(errTrain) + '\t' + str(errTrain / totalTrain))
print('\tError val: \t' + str(errVal) + '\t' + str(errVal / totalVal))
print('\tError test: \t' + str(errTest) + '\t' + str(errTest / totalTest))
if errVal < minVal[0]:
minVal = [errVal, totalVal]
minTrain = [errTrain, totalTrain]
minTest = [errTest, totalTest]
bestSamples = samples
bestStats = stats
if minVal[0] == 0:
break
print('Min error')
print('\ttrain: \t' + str(minTrain[0]) + '\t' + str(minTrain[0] / minTrain[1]))
print('\tval: \t' + str(minVal[0]) + '\t' + str(minVal[0] / minVal[1]))
print('\ttest: \t' + str(minTest[0]) + '\t' + str(minTest[0] / minTest[1]))
bestSamples = {'samples': bestSamples, 'errors': {}}
bestSamples['errors']['train'] = minTrain
bestSamples['errors']['val'] = minVal
bestSamples['errors']['test'] = minTest
print('Saving data')
utils.saveJson(bestStats, dest + 'learnedStats.json')
utils.saveJson(bestSamples, dest + 'learnedSamples.json')
print('Finished')
def main():
src = sys.argv[1]
dest = sys.argv[2]
attempts = 10
ratio = 0.5
thres = 0.95
pixels = utils.loadJson(src)
bestSamples = {}
bestStats = {'classes': []}
minVal = [1e10000]
minTrain = []
minTest = []
for n in range(attempts):
print('** Attempt ' + str(n))
samples = {}
stats = {'classes': []}
# get sets and stats from data
for key in pixels:
print('\tProcessing color ' + key)
sets = getBestSets(key, pixels[key], ratio, attempts)
stats['classes'].append(sets['stats'])
samples[key] = sets
# calculate the classification error
totalTrain = 0.0
totalVal = 0.0
totalTest = 0.0
errTrain = 0
errVal = 0
errTest = 0
cache = {}
for key in samples:
clsTrain = classify(samples[key]['train'], thres, stats['classes'],
cache)
errTrain += classificationError(clsTrain, key)
totalTrain += len(samples[key]['train'][utils.R])
clsVal = classify(samples[key]['val'], thres, stats['classes'],
cache)
errVal += classificationError(clsVal, key)
totalVal += len(samples[key]['val'][utils.R])
clsTest = classify(samples[key]['test'], thres, stats['classes'],
cache)
errTest += classificationError(clsTest, key)
totalTest += len(samples[key]['test'][utils.R])
print('\tError train: \t' + str(errTrain) + '\t' +
str(errTrain / totalTrain))
print('\tError val: \t' + str(errVal) + '\t' + str(errVal / totalVal))
print('\tError test: \t' + str(errTest) + '\t' +
str(errTest / totalTest))
if errVal < minVal[0]:
minVal = [errVal, totalVal]
minTrain = [errTrain, totalTrain]
minTest = [errTest, totalTest]
bestSamples = samples
bestStats = stats
if minVal[0] == 0:
break
print('Min error')
print('\ttrain: \t' + str(minTrain[0]) + '\t' +
str(minTrain[0] / minTrain[1]))
print('\tval: \t' + str(minVal[0]) + '\t' + str(minVal[0] / minVal[1]))
print('\ttest: \t' + str(minTest[0]) + '\t' + str(minTest[0] / minTest[1]))
bestSamples = {'samples': bestSamples, 'errors': {}}
bestSamples['errors']['train'] = minTrain
bestSamples['errors']['val'] = minVal
bestSamples['errors']['test'] = minTest
print('Saving data')
utils.saveJson(bestStats, dest + 'learnedStats.json')
utils.saveJson(bestSamples, dest + 'learnedSamples.json')
print('Finished')
else:
word = re.findall('\d+', word)
if not word:
return og
if len(word) > 1:
return False
elif og[-2:] in ["st" "nd", "rd", "th"]:
word = word[0]
return num2words(int(word), ordinal=True)
else:
return False
#data to make
for z in ["val", "train"]:
data = loadJson(
"data_vqa/v2_mscoco_{}2014_annotations".format(z))["annotations"]
total_data += len(data)
if z == "train":
train_data = data
else:
val_data = data
for a in tqdm(data):
label2score = {}
qid = a["question_id"]
img_id = a["image_id"]
a_type = a["answer_type"]
gt = stripPunc(a["multiple_choice_answer"].lower(), a_type)
if gt:
ans_count[gt] += 1
if gt and gt not in ans2num:
ans2num[gt] = num_ans
params = dict(arg.split('=') for arg in sys.argv[1].split('&'))
except:
params = {}
dataType = None
utils.log('params = ' + str(params))
for type in ['json', 'property', 'listitem']:
try:
data = params[type]
dataType = type
break
except:
pass
hasChannels = False
channelLST = ChannelList()
channelLST.incHDHR = (utils.loadJson(
utils.unquote(params.get('include_hdhr', '')))
or channelLST.incHDHR)
channelLST.skinPath = ((utils.loadJson(
utils.unquote(params.get('skin_path', ''))))
or channelLST.chkSkinPath())
channelLST.mediaFolder = os.path.join(channelLST.skinPath, 'resources',
'skins', 'default', 'media')
channelLST.refreshPath = utils.loadJson(
utils.unquote(params.get('refresh_path', '')) or utils.ADDON_ID)
channelLST.refreshIntvl = int(
utils.loadJson(utils.unquote(params.get('refresh_interval', '')))
or '0')
channelLST.skinFolder = os.path.join(
channelLST.skinPath,
'resources',
'skins',
split = word.split(" ")
if (split[0] in attributes \
or split[0].isdigit()\
or len(split[0]) ==1)\
or split[0] in relations\
and split[1] in set2check:
return [split[0]]
if word in INVALID_NOUNS or (objs and word in relations):
return []
cands = word2alternatives(word)
if og not in cands:
cands.append(og)
return cands
print("LOADING")
vg = loadJson("data_visual_genome/scene_graphs")
for scene in tqdm(vg):
img_id = scene["image_id"]
objs = scene["objects"]
relationships = scene["relationships"]
img_info = []
#GET OBJECTS
for o in objs:
#get attributes
try:
attrs = o["attributes"]
attrs = None
fil_attrs = []
if attrs is not None:
def __init__(self, parent=None):
super(Keyboard, self).__init__(parent)
self._config = loadJson()
self.keyboardWidth = self._config['keyboard']['width']
self.keyboardHeight = self._config['keyboard']['height']
self.initUI()
from nltk import word_tokenize, pos_tag, download
from nltk.corpus import wordnet as wn #download('wordnet')`
import inflect
from copy import deepcopy
from substring import substring, getLCS
PRINT = False
ISGQA = True
relations = loadTxt("phase_1/relations")
attributes = loadTxt("phase_1/attributes")
objects = loadTxt("phase_1/objects")
vg_relations = loadTxt("phase_2/vg_relations")
vg_objects = loadTxt("phase_2/vg_relations")
vg_attributes = loadTxt("phase_2/vg_relations")
vg_id2obj = loadJson("phase_2/id2obj")
graphs = loadJson("phase_2/v
splits = ["val"]
subsets = ["all"]
train_annos = []
val_annos = []
all_answers = set()
qid2ambgOverlap = defaultdict(list)
qid2normOverlap = defaultdict(list)
ambigous_objects = defaultdict(list)
find = FindInParens(id2obj, ("OBJECTS", objects))
uniq_rels = deepcopy(relations)
uniq_attrs = deepcopy(attributes)
uniq_objs = deepcopy(objects)
#!/usr/bin/env python3
import os
import json
import math
import utils
import datetime
from consts import *
# check cache file
data = utils.loadJson(CACHE_USAGE)
if not data:
try:
# get config
gSheetId = utils.getConfigValue(None, XFINITY_GSHEET)
gSheetUrl = utils.getGoogleSheetUrl(gSheetId)
# update current usage
book = utils.openGoogleSheet(gSheetId)
dataSheet = book.get_worksheet(0)
date = dataSheet.acell(DATE_CELL).value
cap = int(dataSheet.acell(CAP_CELL).value.split()[0])
target = int(dataSheet.acell(TARGET_CELL).value.split()[0])
usage = int(dataSheet.acell(USAGE_CELL).value.split()[0])
today = int(dataSheet.acell(TODAY_CELL).value.split()[0])
# build data
data = {
'date': date,
'cap': cap,
import os
import requests
from utils import loadJson
from tablulate import tablulate
from lib.query import QueryCurrentFoodTrucks
userInput = loadJson()
def query_api(offset):
query = QueryCurrentFoodTrucks(
offset=offset
).build_query()
url = "{0}{1}".format(base_url,query)
if os.environ.get('FT_APP_TOKEN') is not None:
header={'X-App-token': os.environ.get('FT_APP_TOKEN')}
request = requests.get(url, headers=header)
else:
request =requests.get(url)
return request
def go_to_page(page, num):
request = query_api(page)
page += num
keep_paging = True
return (page,request, keep_paging)
def print_results_to_terminal(results):
trucks = []
for foodtruck in data:
trucks.append([foodtruck['applicant'],foodtruck['location']])
columns = ["NAME", "LOCATION"]
from collections import defaultdict, Counter
from tqdm import tqdm
import re
from shapely.geometry import Polygon
import statistics
from nltk import word_tokenize, pos_tag, download
from nltk.corpus import wordnet as wn
import inflect
from copy import deepcopy
PRINT = False
ISGQA = True
relations = loadTxt("phase_1/relations")
attributes = loadTxt("phase_1/attributes")
objects = loadTxt("phase_1/objects")
id2obj = loadJson("phase_1/gqa_id2obj")
if ISGQA:
graphs = loadJson("phase_1/gqa_img2info")
splits = ["val", "train"]
subsets = ["balanced", "all"]
train_annos = []
val_annos = []
all_answers = set()
qid2ambgOverlap = defaultdict(list)
qid2normOverlap = defaultdict(list)
ambigous_objects = defaultdict(list)
find = FindInParens(id2obj, ("OBJECTS", objects))
uniq_rels = deepcopy(relations)
uniq_attrs = deepcopy(attributes)
uniq_objs = deepcopy(objects)
import re
from shapely.geometry import Polygon
import statistics
from nltk import word_tokenize, pos_tag, download
from nltk.corpus import wordnet as wn #download('wordnet')`
import inflect
from copy import deepcopy
from substring import substring, getLCS
PRINT = False
ISGQA = True
relations = loadTxt("phase_1/relations")
attributes = loadTxt("phase_1/attributes")
objects = loadTxt("phase_1/objects")
id2obj = loadJson("phase_1/id2obj")
obj_synonyms = loadJson("phase_2/visual_genome_obj_synonyms.json")
obj_synonyms = loadJson("phase_2/visual_genome_rel_synonyms.json")
vg_atts = loadJson("phase_2/visual_genome_attributes")
vg_objs = loadJson("phase_2/visual_genome_attributes")
vg_rels = loadJson("phase_2/visual_genome_attributes")
graphs = loadJson("phase_2/visual_genome_graphs")
vqa_ans = loadJson("phase_2/vqa_val_qid2ans")
vg_objs = loadJson("phase_2/visual_genome_attributes")
if ISGQA:
img2sg_bb = loadJson("phase_1/img2sg_bb")
imgId2objs = loadJson("phase_1/img2objs")
splits = ["val"]
subsets = ["all"]
train_annos = []
params = dict(arg.split('=') for arg in sys.argv[1].split('&'))
except:
params = {}
dataType = None
utils.log('params = ' + str(params))
for type in ['json', 'property', 'listitem']:
try:
data = params[type]
dataType = type
break
except:
pass
channelLST = ChannelList()
channelLST.skinPath = ((utils.loadJson(
utils.unquote(params.get('skin_path', ''))))
or channelLST.chkSkinPath())
channelLST.mediaFolder = os.path.join(channelLST.skinPath, 'resources',
'skins', 'default', 'media')
channelLST.refreshPath = utils.loadJson(
utils.unquote(params.get('refresh_path', None) or None))
channelLST.refreshIntvl = int(
utils.loadJson(utils.unquote(params.get('refresh_interval', '0')))
or '0')
channelLST.skinFolder = os.path.join(
channelLST.skinPath,
'resources',
'skins',
'default',
'1080i',
) if xbmcvfs.exists(