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1820 1821 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from presidio_analyzer import AnalyzerEngine, PatternRecognizer\n",
"from presidio_anonymizer import AnonymizerEngine\n",
"from presidio_anonymizer.entities import OperatorConfig\n",
"import json\n",
"from presidio_analyzer import RecognizerRegistry\n",
"from presidio_analyzer import Pattern\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# text = \"Karan is working in Infosys. He is from Mumbai. His appointment for renewing passport is booked on March 12 and his old Passport Number is P2096457. Also, he want to link his Aadhaar Number is 567845678987 with his Pan Number is BNZAA2318A. and has 35$\"\n",
"text = \"Karan is working in Infosys.He has email id asv@gmail.com\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"registry = RecognizerRegistry()\n",
"registry.load_predefined_recognizers()\n",
"analyzer = AnalyzerEngine(registry=registry)\n",
"anonymize=AnonymizerEngine()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result = analyzer.analyze(text=text, language=\"en\", entities=[\"PERSON\",\"EMAIL_ADDRESS\"])\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l=['Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'ClientListRecognizer', 'PhoneRecognizer', 'UrlRecognizer', 'Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'SpacyRecognizer', 'PhoneRecognizer', 'UrlRecognizer']\n",
"l1=list(set(l))\n",
"l1==l\n",
"d={}\n",
"for i in l:\n",
" if i in d:\n",
" d[i]+=1\n",
" else:\n",
" d[i]=1\n",
"print(d)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x1=['Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'ClientListRecognizer', 'PhoneRecognizer', 'UrlRecognizer', 'Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'SpacyRecognizer', 'PhoneRecognizer', 'UrlRecognizer']\n",
"x2=['Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'ClientListRecognizer', 'PhoneRecognizer', 'UrlRecognizer', 'Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'SpacyRecognizer', 'PhoneRecognizer', 'UrlRecognizer']\n",
"x3=['Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'ClientListRecognizer', 'PhoneRecognizer', 'UrlRecognizer', 'Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'SpacyRecognizer', 'PhoneRecognizer', 'UrlRecognizer']\n",
"x4=['Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'ClientListRecognizer', 'PhoneRecognizer', 'UrlRecognizer', 'Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'SpacyRecognizer', 'PhoneRecognizer', 'UrlRecognizer']\n",
"\n",
"\n",
"s=['Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'SpacyRecognizer', 'PhoneRecognizer', 'UrlRecognizer']\n",
"\n",
"ss=['Aadhaar_Number', 'PAN_Number', 'UsBankRecognizer', 'UsLicenseRecognizer', 'UsItinRecognizer', 'UsPassportRecognizer', 'UsSsnRecognizer', 'NhsRecognizer', 'SgFinRecognizer', 'AuAbnRecognizer', 'AuAcnRecognizer', 'AuTfnRecognizer', 'AuMedicareRecognizer', 'InPanRecognizer', 'CreditCardRecognizer', 'CryptoRecognizer', 'DateRecognizer', 'EmailRecognizer', 'IbanRecognizer', 'IpRecognizer', 'MedicalLicenseRecognizer', 'ClientListRecognizer', 'PhoneRecognizer', 'UrlRecognizer']\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"d={}\n",
"for i in ss:\n",
" if i in d:\n",
" \n",
" d[i]+=1\n",
" else:\n",
" d[i]=1\n",
"print(d)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"patternObj = Pattern(name=\"Currency\",\n",
" regex='[1-9]*\\$',\n",
" score=0.8)\n",
"patternRecog = PatternRecognizer(supported_entity=\"CURRENCY\",\n",
" patterns=[patternObj])\n",
"registry.add_recognizer(patternRecog)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result = analyzer.analyze(text=text, language=\"en\",allow_list=[\"Karan\"])\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from faker import Faker\n",
"fake=Faker()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fake.name()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"anonymized_results = anonymize.anonymize(\n",
" text=text,\n",
" analyzer_results=result, \n",
" operators= {\"DEFAULT\": OperatorConfig(\"replace\", {\"new_value\": fake.name()})}\n",
")\n",
"\n",
"print(f\"text: {anonymized_results.text}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"from privacy.dao.privacy.DatabaseConnection import DB\n",
"DB.connect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import string\n",
"\n",
"def generate_value_with_ranges(pattern):\n",
" value = \"\"\n",
" for char in pattern:\n",
" if char in \"[a-z]\":\n",
" value += random.choice(string.ascii_lowercase)\n",
" elif char in \"[A-Z]\":\n",
" value += random.choice(string.ascii_uppercase)\n",
" else:\n",
" value += char\n",
" return value\n",
"\n",
"pattern = r\"[A-Z][a-z]{2}\" # Example: AaX\n",
"generated_value = generate_value_with_ranges(pattern)\n",
"print(generated_value) # Output: something like Pbq\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import re"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def valueGen(pattern):\n",
" if(\"|\" in pattern):\n",
" pattern=pattern.split(\"|\")\n",
" pattern=pattern[random.randrange(0,len(pattern))]\n",
" res=\"\" \n",
" while True:\n",
" \n",
" r=re.search(r\"(\\[[A-Za-z0-9\\-\\,]*\\](\\{[0-9\\,]*\\})?)|(\\\\s)|((\\\\d)(\\{[0-9\\,]*\\})?)|((\\\\w)(\\{[0-9\\,]*\\})?)|(\\w*)\",pattern)\n",
" # print(r.group(2)) \n",
" # print(pattern)\n",
" # print(r.span())\n",
" print(\"gp\",r.group(),r.group(4))\n",
" if(r.group(1)):\n",
" ptr=r.group()\n",
"\n",
" # print(ptr)\n",
" t=re.match(r\"\\[[A-Za-z0-9\\-\\,]*\\]\",ptr).group()[1:-1].split(\",\")\n",
" s=string.ascii_lowercase+string.ascii_uppercase+string.digits+string.punctuation\n",
" # print(t)\n",
" s1=\"\"\n",
" for x in t:\n",
" # print(x)\n",
" l=x.split('-')\n",
" # print(l)\n",
" s1+=s[s.index(l[0]):s.index(l[1])+1]\n",
" count=re.search(r\"\\{[0-9\\,]*\\}\",ptr)\n",
" k=1\n",
" if count: \n",
" k=int(random.choice(count.group()[1:-1].split(',')))\n",
" print(\"==\",k)\n",
" # if k==0:\n",
" # k=1\n",
" v=\"\".join(random.choices(s1,k=k))\n",
" # print(v)\n",
" pattern=pattern[r.span()[1]:]\n",
" res+=v\n",
" # print(pattern)\n",
" print(v)\n",
" \n",
" if(r.group()=='\\s'):\n",
" print(\" a\")\n",
" pattern=pattern[r.span()[1]:]\n",
" res+=\" \"\n",
" \n",
" if(r.group()=='\\d'):\n",
" print(\"b\")\n",
" pattern=pattern[r.span()[1]:]\n",
" res+=random.choice(string.digits)\n",
" if(r.group()==r.group(4)):\n",
" pattern=pattern[r.span()[1]:]\n",
" res+=random.choice(string.ascii_lowercase)\n",
" if(r.group(5)):\n",
" print(\"tt======\",r.group())\n",
" pattern=pattern[r.span()[1]:]\n",
" res+=r.group()\n",
" print(pattern)\n",
" \n",
" if(re.search(r\"\\[[A-Za-z0-9\\-\\,]*\\]\\{[0-9\\,]*\\}\",pattern)==None):\n",
" break\n",
" print(pattern)\n",
" print(\"======\",res)\n",
" print(\"===\",res) \n",
" \n",
" \n",
" \n",
"p=\"[A-Z]{2}\\s[0-9]{2}\\s[A-Z]{1,2}\\s[0-9]{4}\"\n",
"# p=\"[A-Za-z]{6}\\d{2}\"\n",
"valueGen(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# p=\"\\b([A-Z][0-9]{3,6}|[A-Z][0-9]{5,9}|[A-Z][0-9]{6,8}|[A-Z][0-9]{4,8}|[A-Z][0-9]{9,11}|[A-Z]{1,2}[0-9]{5,6}|H[0-9]{8}|V[0-9]{6}|X[0-9]{8}|A-Z]{2}[0-9]{2,5}|[A-Z]{2}[0-9]{3,7}|[0-9]{2}[A-Z]{3}[0-9]{5,6}|[A-Z][0-9]{13,14}|[A-Z][0-9]{18}|[A-Z][0-9]{6}R|[A-Z][0-9]{9}|[A-Z][0-9]{1,12}|[0-9]{9}[A-Z]|[A-Z]{2}[0-9]{6}[A-Z]|[0-9]{8}[A-Z]{2}|[0-9]{3}[A-Z]{2}[0-9]{4}|[A-Z][0-9][A-Z][0-9][A-Z]|[0-9]{7,8}[A-Z])\\b\"\n",
"p=\"[A-Z,a-z]{2}\\s[0-9]{2}\\s[A-Z,a-z]{1,2}\\s[0-9]{4}\"\n",
"valueGen(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from xeger import Xeger\n",
"x = Xeger()\n",
"t=x.xeger(p)\n",
"print(t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"payload={\"portfolio\":\"RAI\",\"account\":\"TEST\"}\n",
" \n",
"# payload={\"accName\":\"Infosys\",\"subAccName\":\"Impact\"}\n",
"# api_url = os.getenv(\"PRIVADMIN_API\")\n",
"\n",
"# print(api_url)\n",
"aurl=\"http://10.66.155.13:30016/api/v1/rai/admin/PrivacyDataList\"\n",
"# log.debug(aurl)\n",
"# log.debug(str(type(aurl)))\n",
"# log.debug(\"Calling Admin Api ======\")\n",
"# log.debug(\"api payload:\"+str(payload))\n",
"# print(payload)\n",
"response1 = requests.post(\n",
" url=aurl\n",
" , headers={'Content-Type': \"application/json\",\n",
" 'accept': \"application/json\"}\n",
" , json=payload\n",
" )\n",
"print(response1.json()[\"datalist\"])\n",
"# response1=httpx.post(aurl, json=payload)\n",
"# response1=httpx.post('http://10.66.155.13:30016/api/v1/rai/admin/PrivacyDataList', json=payload)\n",
"# log.debug(\"response=\"+str(response1))\n",
"# log.debug(\"response11=\"+str(response1.text))\n",
"# response1=PrivacyData.getDataList(payload)\n",
"entityType,datalist,preEntity,records,encryptionList,scoreTreshold=response1.json()[\"datalist\"]\n",
"entityType,datalist,preEntity,records,encryptionList,scoreTreshold"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"from typing import Optional, List, Tuple, Set\n",
"import spacy\n",
"from spacy.matcher import PhraseMatcher\n",
"from presidio_analyzer.predefined_recognizers.spacy_recognizer import SpacyRecognizer\n",
"# from presidio_analyzer.predefined_recognizers import SpacyRecognizer\n",
"from presidio_analyzer import RecognizerResult\n",
"import copy\n",
"\n",
"\n",
"\n",
"\n",
"from presidio_analyzer import (\n",
" RecognizerResult,\n",
" LocalRecognizer,\n",
" AnalysisExplanation,\n",
")\n",
"\n",
"logger = logging.getLogger(\"presidio_analyzer\")\n",
"# terms = [\"1&1 Telecommunication SE\",\"1010 data services LLC\",\"AMA\",\n",
"# \"A O Smith Corporations\",\"ABBMST\",\"Addidas India\",\"CITI\",\"Cisco Systems\",\"ERICSSON\",\"Gati Ltd\",\"IBM\",\n",
"# \"Infosys Ltd\",\"Intel Corporation\",\"Johnson\",\"JTC Corporation\",\"NSC Global\",\"SUZUKI MOTOR CORPORATION\",\n",
"# \"Synopsys Ltd\",\"TIBCOO\", \"T-Mobile UK\",\"Toyota Systems Corporation\",\"TSB Bank\",\"UBS Bank\"\n",
"# ,\"United Health Corporation\",\"Vodafone quickcom\",\"Voltas\",\"VOLVO CARS\",\"WIPRO LIMITED\",\n",
"# \"Walmart\", \"CVS Health\", \"Walgreens Boots Alliance\"]\n",
"# terms=[]\n",
"class DataList:\n",
" # def __init__(self,val) -> None:\n",
" # self.Entiity=val\n",
" entity=[]\n",
" def setData(values):\n",
" terms.extend(values)\n",
" # print(terms)\n",
" def resetData():\n",
" terms.clear()\n",
" # def setEntity(val):\n",
" # DataList.Entity=val\n",
" # ClientListRecognizer(supported_entities=val)\n",
" # def getE():\n",
" # return self.Entiity\n",
" \n",
"\n",
"nlp = spacy.load(\"en_core_web_lg\")\n",
" \n",
"\n",
"\n",
"\n",
"\n",
"class TESTR(SpacyRecognizer): \n",
" \"\"\"\n",
" Recognize PII entities using a spaCy NLP model.\n",
"\n",
" Since the spaCy pipeline is ran by the AnalyzerEngine,\n",
" this recognizer only extracts the entities from the NlpArtifacts\n",
" and replaces their types to align with Presidio's.\n",
"\n",
" :param supported_language: Language this recognizer supports\n",
" :param supported_entities: The entities this recognizer can detect\n",
" :param ner_strength: Default confidence for NER prediction\n",
" :param check_label_groups: Tuple containing Presidio entity names\n",
" and spaCy entity names, for verifying that the right entity\n",
" is translated into a Presidio entity.\n",
" \"\"\"\n",
"\n",
" # ENTITIES = DataList.entity\n",
" # ENTITIES =[]\n",
" # terms=[]\n",
"\n",
" DEFAULT_EXPLANATION = \"Identified as {} by Spacy's Named Entity Recognition\"\n",
"\n",
" CHECK_LABEL_GROUPS = [\n",
" # ({\"LOCATION\"}, {\"GPE\", \"LOC\"}),\n",
" # ({\"PERSON\", \"PER\"}, {\"PERSON\", \"PER\"}),\n",
" # ({\"DATE_TIME\"}, {\"DATE\", \"TIME\"}),\n",
" # ({\"NRP\"}, {\"NORP\"}),\n",
" # ({\"ORGANIZATION\"}, {\"ORG\"}),\n",
" # ()\n",
" ]\n",
" \n",
" \n",
"\n",
" \n",
"\n",
" def __init__(\n",
" self,\n",
" terms,entitie,\n",
" supported_language: str = \"en\",\n",
" supported_entities: Optional[List[str]] = None,\n",
" ner_strength: float = 0.85,\n",
" check_label_groups: Optional[Tuple[Set, Set]] = None,\n",
" context: Optional[List[str]] = None,\n",
" \n",
" \n",
" ):\n",
" self.terms=terms\n",
" self.ENTITIES=entitie\n",
" self.ner_strength = ner_strength\n",
" self.check_label_groups = (\n",
" check_label_groups if check_label_groups else self.CHECK_LABEL_GROUPS\n",
" )\n",
" supported_entities = supported_entities if supported_entities else self.ENTITIES\n",
" # print(\"=========\",supported_entities)\n",
" super().__init__(\n",
" supported_entities=supported_entities,\n",
" supported_language=supported_language,\n",
" context=context,\n",
" )\n",
"\n",
" def load(self) -> None: # noqa D102\n",
" # no need to load anything as the analyze method already receives\n",
" # preprocessed nlp artifacts\n",
" pass\n",
" \n",
" \n",
" def build_spacy_explanation(\n",
" self, original_score: float, explanation: str\n",
" ) -> AnalysisExplanation:\n",
" \"\"\"\n",
" Create explanation for why this result was detected.\n",
"\n",
" :param original_score: Score given by this recognizer\n",
" :param explanation: Explanation string\n",
" :return:\n",
" \"\"\"\n",
" explanation = AnalysisExplanation(\n",
" recognizer=self.__class__.__name__,\n",
" original_score=original_score,\n",
" textual_explanation=explanation,\n",
" )\n",
" return explanation\n",
" \n",
" def analyze(self, text, entities, nlp_artifacts=None): # noqa D102\n",
" \n",
" # print(\"=========\",self.supported_entities)\n",
" \n",
" # matcher = PhraseMatcher(nlp.vocab)\n",
" \n",
" # # Only run nlp.make_doc to speed things up\n",
" # patterns = [nlp.make_doc(text) for text in terms]\n",
" \n",
" # matcher.add(\"TerminologyList\", patterns)\n",
" # result = []\n",
" \n",
" matcher = PhraseMatcher(nlp.vocab)\n",
" \n",
" # Only run nlp.make_doc to speed things up\n",
" patterns = [nlp.make_doc(text) for text in self.terms]\n",
" \n",
" matcher.add(\"TerminologyList\", patterns)\n",
" \n",
" results = []\n",
" # result =[]\n",
" \n",
" doc = nlp(text)\n",
" doc1 = str(doc)\n",
" \n",
" matches = matcher(doc)\n",
" for match_id, start, end in matches:\n",
" span = doc[start:end]\n",
" \n",
" if doc1.find(str(span)):\n",
" doc1=doc1.replace(str(span.text),\"<COMPANY_NAME>\")\n",
" # etype=copy.deepcopy(DataList.entity) \n",
" etype=self.ENTITIES \n",
" spacy_result = RecognizerResult(\n",
" \n",
" entity_type=etype[0],\n",
" start=span.start_char,\n",
" end=span.end_char,\n",
" score=self.ner_strength,\n",
" # analysis_explanation=explanation,\n",
" recognition_metadata={\n",
" RecognizerResult.RECOGNIZER_NAME_KEY: self.name,\n",
" RecognizerResult.RECOGNIZER_IDENTIFIER_KEY: self.id,\n",
" },\n",
" )\n",
" \n",
"\n",
" results.append(spacy_result)\n",
"\n",
" \n",
" \n",
"\n",
" return results\n",
"\n",
" @staticmethod\n",
" def __check_label(\n",
" entity: str, label: str, check_label_groups: Tuple[Set, Set]\n",
" ) -> bool:\n",
" return any(\n",
" [entity in egrp and label in lgrp for egrp, lgrp in check_label_groups]\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from presidio_analyzer import AnalyzerEngine, RecognizerRegistry\n",
"from presidio_anonymizer import AnonymizerEngine\n",
"\n",
"yaml_file = \"recognizers.yaml\"\n",
"registry = RecognizerRegistry()\n",
"registry.load_predefined_recognizers()\n",
"analyzer = AnalyzerEngine(registry=registry)\n",
"anonymize=AnonymizerEngine()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# DataList.entity.clear()\n",
"# DataList.resetData()\n",
"# DataList.entity.append(\"XX\")\n",
"# DataList.setData([\"alex\",\"amit\"])\n",
"r=(TESTR(terms=[\"alex\",\"amit\"],entitie=[\"XX\"]))\n",
"registry.add_recognizer(r)\n",
"# DataList.entity.clear()\n",
"# DataList.resetData()\n",
"# DataList.entity.append(\"YY\")\n",
"# DataList.setData([\"game\",\"race\"])\n",
"r1=(TESTR(terms=[\"game\",\"race\"],entitie=[\"YY\"]))\n",
"registry.add_recognizer(r1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"txt=\"My name is alex and amit live in Pune play race game.\"\n",
"results = analyzer.analyze(\n",
" txt,\n",
" language=\"en\",\n",
" return_decision_process=True,\n",
" )\n",
"print(results)\n",
"\n",
"anonymize_text = anonymize.anonymize(text=txt,\n",
" operators={},\n",
" analyzer_results=results)\n",
"\n",
"anonymize_text\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from detectron2.utils.visualizer import Visualizer\n",
"from detectron2.data import MetadataCatalog\n",
"from detectron2.config import get_cfg\n",
"\n",
"# Replace \"TextDetectionModel\" with your chosen pre-trained model name from Detectron2 model zoo \n",
"cfg = get_cfg()\n",
"cfg.MODEL.ROI_HEADS.NAME = \"TextDetectionModel\"\n",
"\n",
"# Load model and weights (adjust paths as needed)\n",
"cfg.MODEL.WEIGHTS = \"path/to/model_weights.pth\"\n",
"predictor = Detectron2Demo(cfg)\n",
"\n",
"# Define function to extract text from video frames\n",
"def extract_text_from_video(video_path):\n",
" cap = cv2.VideoCapture(video_path)\n",
" text_list = []\n",
" while True:\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
" \n",
" # Use Detectron2 predictor to get text detections\n",
" outputs = predictor(frame)\n",
" \n",
" # Extract text from detected regions (replace with your logic based on model outputs)\n",
" for text_obj in outputs[\"instances\"].pred_boxes:\n",
" x1, y1, x2, y2 = text_obj.intBounds()\n",
" text_region = frame[y1:y2, x1:x2]\n",
" # You might need to use OCR library like pytesseract to extract text from the region\n",
" extracted_text = \"your_ocr_function(text_region)\" # Replace with actual OCR logic\n",
" text_list.append(extracted_text)\n",
" \n",
" cap.release()\n",
" return text_list\n",
"\n",
"# Example usage\n",
"video_path = \"path/to/your/video.mp4\"\n",
"extracted_text = extract_text_from_video(video_path)\n",
"print(f\"Extracted text: {extracted_text}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install detectron2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import pytesseract\n",
"\n",
"# Function to extract text from a single frame\n",
"def extract_text_from_frame(frame):\n",
" # Optional image processing (adjust as needed)\n",
" gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
" thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n",
" output_type = pytesseract.Output.DICT\n",
" s=pytesseract.image_to_data(frame, output_type=output_type)\n",
" s=\" \".join(s[\"text\"])\n",
" print(\"======\",s)\n",
" # Use OCR library (replace with your preferred OCR implementation)\n",
" text = pytesseract.image_to_string(thresh, config='--psm 6') # Adjust config for better results\n",
" return text\n",
"\n",
"# Open video and iterate through frames\n",
"cap = cv2.VideoCapture(r\"C:\\Users\\amitumamaheshwar.h\\Downloads\\piivdo 1.mp4\")\n",
"extracted_text = []\n",
"while True:\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
"\n",
" text = extract_text_from_frame(frame)\n",
" \n",
" print(\"==\",text)\n",
" extracted_text.append(text)\n",
"\n",
"cap.release()\n",
"\n",
"# Print or process the extracted text\n",
"print(f\"Extracted text: {extracted_text}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import pytesseract\n",
"\n",
"def extract_text_with_bounding_boxes(image_path):\n",
" \"\"\"\n",
" Extracts text with bounding boxes from an image using TesseractOCR.\n",
"\n",
" Args:\n",
" image_path (str): Path to the image file.\n",
"\n",
" Returns:\n",
" list: List of dictionaries containing extracted text and bounding box coordinates.\n",
" \"\"\"\n",
" img = cv2.imread(image_path)\n",
"\n",
" # Convert to grayscale (optional, might improve OCR accuracy)\n",
" gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
"\n",
" # Apply image processing techniques (optional, adjust based on your image)\n",
" # thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] # Example thresholding\n",
"\n",
" # Use Tesseract to detect text regions (config option for bounding boxes)\n",
" boxes = pytesseract.image_to_data(gray, config='--oem 1 --psm 6')\n",
"# print(boxes)\n",
" # Extract text and bounding box coordinates\n",
" extracted_data = []\n",
" for i, line in enumerate(boxes.splitlines()[1:]):\n",
" line=line.split('\\t')[6::]\n",
" # print(line)\n",
" if line[-2] != '-1':\n",
" # print(line)\n",
" x, y, w, h, conf, text = line\n",
" extracted_data.append({\n",
" 'text': text,\n",
" 'x': int(x),\n",
" 'y': int(y),\n",
" 'width': int(w),\n",
" 'height': int(h),\n",
" 'confidence': float(conf)\n",
" })\n",
"\n",
" return extracted_data\n",
"\n",
"# Example usage\n",
"image_path = r\"C:\\WORK\\GIT\\responsible-ai-admin\\responsible-ai-admin\\src\\rai_admin\\temp\\Karan (2).png\"\n",
"extracted_text = extract_text_with_bounding_boxes(image_path)\n",
"print(extracted_text)\n",
"# Print extracted text and bounding box data\n",
"for data in extracted_text:\n",
" print(f\"Text: {data['text']}, Confidence: {data['confidence']}\")\n",
" print(f\"Bounding Box: ({data['x']},{data['y']}), Width: {data['width']}, Height: {data['height']}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install moviepy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import pytesseract\n",
"from moviepy.editor import ImageClip\n",
"# Function to extract text from a single frame\n",
"def extract_text_from_frame(frame):\n",
" # Optional image processing (adjust as needed)\n",
" gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
" thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n",
"# output_type = pytesseract.Output.DICT\n",
"# s=pytesseract.image_to_data(frame, output_type=output_type)\n",
"# s=\" \".join(s[\"text\"])\n",
"# print(\"======\",s)\n",
" # Use OCR library (replace with your preferred OCR implementation)\n",
" # text=extract_text_with_bounding_boxes(frame)\n",
"# text = pytesseract.image_to_string(thresh, config='--psm 6') # Adjust config for better results\n",
"\n",
" boxes = pytesseract.image_to_data(thresh, config='--oem 1 --psm 6')\n",
"# print(boxes)\n",
" # Extract text and bounding box coordinates\n",
" extracted_data = []\n",
" for i, line in enumerate(boxes.splitlines()[1:]):\n",
" line=line.split('\\t')[6::]\n",
" # print(line)\n",
" if line[-2] != '-1':\n",
" # print(line)\n",
" x, y, w, h, conf, text = line\n",
" x = int(x)\n",
" y = int(y)\n",
" w = int(w)\n",
" h = int(h)\n",
" cv2.rectangle(frame, (x + 1, y + 1), (x + w - 1, y + h - 1), (0, 255, 0), -1) # Adjust padding for fill\n",
"\n",
" # extracted_data.append({\n",
" # 'text': text,\n",
" # 'x': int(x),\n",
" # 'y': int(y),\n",
" # 'width': int(w),\n",
" # 'height': int(h),\n",
" # 'confidence': float(conf)\n",
" # })\n",
" \n",
" \n",
" \n",
" return frame\n",
" # return extracted_data\n",
" # return text\n",
"\n",
"# Open video and iterate through frames\n",
"cap = cv2.VideoCapture(r\"C:\\Users\\amitumamaheshwar.h\\Downloads\\piivdo 1.mp4\")\n",
"extracted_text = []\n",
"processed_frames = []\n",
"while True:\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
"\n",
" proc_frame = extract_text_from_frame(frame.copy())\n",
" processed_frames.append(proc_frame)\n",
"\n",
" \n",
" \n",
" # print(\"==\",text)\n",
" # extracted_text.append(text)\n",
"\n",
"\n",
"cap.release()\n",
"\n",
"\n",
"# Print or process the extracted text\n",
"# print(f\"Extracted text: {extracted_text}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(processed_frames)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from moviepy.editor import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clip = ImageClip(processed_frames).set_duration(5)\n",
"clip.write_videofile(\"test.mp4\", fps=24)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clip = ImageClip.from_array(processed_frames, fps=25)\n",
"clip.write_videofile(\"output.mp4\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"height, width = processed_frames[0].shape[:2] # Get frame dimensions from the first frame\n",
"fourcc = cv2.VideoWriter_fourcc(*'XVID') # Adjust codec if needed\n",
"video = cv2.VideoWriter(\"test.mp4\", fourcc, 25, (width, height))\n",
"for frame in processed_frames:\n",
" video.write(frame)\n",
"video.release()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import base64\n",
"import io\n",
"from typing import Tuple\n",
"import cv2\n",
"from PIL import Image\n",
"from privacy.config.logger import request_id_var\n",
"request_id_var.set(\"aa\")\n",
"from privacy.service.service import PrivacyService,AttributeDict\n",
"import numpy as np\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"\n",
"\n",
"async def videoPrivacy(payload) -> Tuple[str, str]:\n",
" # upload_file = payload['video']\n",
" # video_data = await upload_file.read()\n",
" s=time.time()\n",
" temp_file_path = r\"C:\\Users\\amitumamaheshwar.h\\Downloads\\piivdo 1.mp4\"\n",
" output_file_path = \"output.mp4\"\n",
"\n",
" # with open(temp_file_path, \"wb\") as temp_file:\n",
" # temp_file.write(video_data)\n",
"\n",
" video = cv2.VideoCapture(temp_file_path)\n",
"\n",
" # Get video properties\n",
" width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
" height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
" fps = video.get(cv2.CAP_PROP_FPS)\n",
"\n",
" # Define the codec and create a VideoWriter object\n",
" fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
" out = cv2.VideoWriter(output_file_path, fourcc, fps, (width, height))\n",
" \n",
" while(video.isOpened()):\n",
" ret, frame = video.read()\n",
" print(ret)\n",
" if ret==True:\n",
" # Convert the frame to PIL Image\n",
" # base64.b64encode(frame).decode()\n",
" # Image.open(base64.b64encode(frame).decode())\n",
" # print(type(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)))\n",
" imagef = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
" imagef.save(\"test.jpg\")\n",
" # image=open(\"test.jpg\",\"rb\")\n",
" print(type(imagef))\n",
" image={\"file\":\"test.jpg\"}\n",
" image=AttributeDict(image)\n",
" # ocr=None\n",
" # global imageAnalyzerEngine\n",
"\n",
" # imageAnalyzerEngine = ImageAnalyzerEngine(analyzer_engine=analyzer,ocr=ocr) \n",
" # imageRedactorEngine = ImageRedactorEngine(image_analyzer_engine=imageAnalyzerEngine)\n",
" # redacted_image = imageRedactorEngine.redact(image, (255, 192, 203))\n",
" payload={\"easyocr\":\"Tesseract\",\"mag_ratio\":False,\"rotationFlag\":False,\"image\":image,\"portfolio\":None,\"account\":None,\"exclusion\":None}\n",
" \n",
" redacted_image=PrivacyService.image_anonymize(payload)\n",
" decoded_bytes = base64.b64decode(redacted_image)\n",
"\n",
" # Create a BytesIO object to simulate a file-like object\n",
" bio = io.BytesIO(decoded_bytes)\n",
"\n",
" # Use OpenCV (assuming it's an image) or other libraries to load the image from the BytesIO object\n",
" img = cv2.imdecode(np.fromstring(bio.getvalue(), np.uint8), cv2.IMREAD_COLOR)\n",
"\n",
" # Convert the PIL Image back to OpenCV frame\n",
" frame = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
"\n",
" # Write the frame into the file 'output.avi'\n",
" out.write(frame)\n",
"\n",
" else:\n",
" break\n",
"\n",
" # Release everything when job is finished\n",
" video.release()\n",
" out.release()\n",
"\n",
" # Remove temporary file\n",
" # os.remove(temp_file_path)\n",
"\n",
" # Read the processed video file\n",
" # with open(output_file_path, \"rb\") as video_file:\n",
" # video_data = video_file.read()\n",
"\n",
" # # Convert the video to base64\n",
" # video_str = base64.b64encode(video_data).decode()\n",
"\n",
" # Remove the output file\n",
" # os.remove(output_file_path)\n",
" print(\"====\",time.time()-s)\n",
" return \"video_str\"\n",
"\n",
"s=await videoPrivacy({})\n",
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import asyncio\n",
"import cv2\n",
"from PIL import Image\n",
"import base64\n",
"import io\n",
"from concurrent.futures import ThreadPoolExecutor\n",
"\n",
"async def video_privacy_parallel(payload) -> Tuple[str, str]:\n",
" \"\"\"\n",
" Processes a video, anonymizes frames in parallel using PrivacyService,\n",
" and returns a tuple containing the output video and processing time.\n",
"\n",
" Args:\n",
" payload (dict): The input payload for the video processing function.\n",
"\n",
" Returns:\n",
" Tuple[str, str]: A tuple containing the anonymized video as a base64\n",
" encoded string and the processing time in seconds.\n",
" \"\"\"\n",
"\n",
" start_time = time.time()\n",
"\n",
" temp_file_path = \"piivdo 1.mp4\" # Replace with your actual video path\n",
" output_file_path = \"output.mp4\"\n",
"\n",
" cap = cv2.VideoCapture(temp_file_path)\n",
"\n",
" if not cap.isOpened():\n",
" print(\"Error: Could not open video file.\")\n",
" return \"\", time.time() - start_time\n",
"\n",
" width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
" height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
" fps = cap.get(cv2.CAP_PROP_FPS)\n",
" fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
" out = cv2.VideoWriter(output_file_path, fourcc, fps, (width, height))\n",
"\n",
" async def process_frame(frame):\n",
" \"\"\"\n",
" Processes a single video frame.\n",
"\n",
" Args:\n",
" frame (numpy.ndarray): The frame to be processed.\n",
"\n",
" Returns:\n",
" bytes: The anonymized frame data as bytes.\n",
" \"\"\"\n",
"\n",
" imagef = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
" imagef.save(\"test.jpg\")\n",
"\n",
" image = {\"file\": \"test.jpg\"}\n",
" image = AttributeDict(image)\n",
"\n",
" try:\n",
" redacted_image_bytes = await PrivacyService.image_anonymize(payload, image)\n",
" except Exception as e:\n",
" print(f\"Error anonymizing frame: {e}\")\n",
" return None\n",
"\n",
" decoded_bytes = base64.b64decode(redacted_image_bytes)\n",
" bio = io.BytesIO(decoded_bytes)\n",
" img = cv2.imdecode(np.fromstring(bio.getvalue(), np.uint8), cv2.IMREAD_COLOR)\n",
" frame = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
"\n",
" return frame\n",
"\n",
" async def main_loop():\n",
" \"\"\"\n",
" Processes frames in a loop, using a thread pool for parallelization.\n",
" \"\"\"\n",
"\n",
" tasks = []\n",
" with ThreadPoolExecutor(max_workers=4) as executor: # Adjust max_workers as needed\n",
" while True:\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
"\n",
" task = executor.submit(process_frame, frame.copy())\n",
" tasks.append(task)\n",
"\n",
" processed_frames = []\n",
" for task in tasks:\n",
" try:\n",
" processed_frame = await task\n",
" if processed_frame is not None:\n",
" processed_frames.append(processed_frame)\n",
" except Exception as e:\n",
" print(f\"Error processing frame: {e}\")\n",
"\n",
" for frame in processed_frames:\n",
" out.write(frame)\n",
"\n",
" await main_loop()\n",
"\n",
" cap.release()\n",
" out.release()\n",
" # Remove temporary files (if needed)\n",
"\n",
" processing_time = time.time() - start_time\n",
" print(f\"Processing time: {processing_time:.2f} seconds\")\n",
"\n",
" # Read the processed video file and convert to base64 if needed\n",
" # ...\n",
"\n",
" return \"video_str\", processing_time\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Neither CUDA nor MPS are available - defaulting to CPU. Note: This module is much faster with a GPU.\n"
]
}
],
"source": [
"import base64\n",
"import io\n",
"from typing import Tuple\n",
"import cv2\n",
"from PIL import Image\n",
"from privacy.config.logger import request_id_var\n",
"request_id_var.set(\"aa\")\n",
"from privacy.service.service import PrivacyService,AttributeDict\n",
"import numpy as np\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"samp 9\n",
"totalFrame: 117\n",
"after sampling 13\n",
"Entering in image_anonymize function\n",
"Entering in image_anonymize function\n",
"remaining: 6 / 13\n",
"Time taken to duplicate image: Entering in image_anonymize function\n",
" 0.01303410530090332\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Entering in image_anonymize function\n",
"Time taken to duplicate image: 0.022548913955688477\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Entering in image_anonymize function\n",
"Entering in image_anonymize function\n",
"Time taken to duplicate image: Time taken to duplicate image: 0.01099252700805664\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Time taken to duplicate image: 0.008993864059448242\n",
"Time taken to parse ocr kwargs: 0.0\n",
" 0.019997358322143555\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Time taken to duplicate image: 0.0069980621337890625\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Time taken to perform ocr: 1.9020135402679443\n",
"Time taken to threshold ocr result: 0.0010013580322265625\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Time taken to perform ocr: 1.8870036602020264\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.c02@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Time taken to perform ocr: 1.9049980640411377\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Time taken to perform ocr: 1.9230256080627441\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.000989675521850586\n",
"Time taken to perform ocr: 1.9470298290252686\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Time taken to perform ocr: 1.971557378768921\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Time taken to analyze text: 0.23116564750671387\n",
"Time taken to map analyzer results to bounding boxes: 0.005988597869873047\n",
"Time taken to analyze image: 2.1241579055786133\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Time taken to analyze text: 0.2351548671722412\n",
"Time taken to map analyzer results to bounding boxes: 0.0\n",
"Time taken to analyze image: 2.140152931213379\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Returning from image_anonymize function\n",
"Returning from image_anonymize function\n",
"Time taken to analyze text: 0.39892077445983887\n",
"Time taken to map analyzer results to bounding boxes: 0.0010018348693847656\n",
"Time taken to analyze image: 2.310929298400879\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Time taken to analyze text: 0.3519287109375\n",
"Time taken to map analyzer results to bounding boxes: 0.0\n",
"Time taken to analyze image: 2.2759439945220947\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Time taken to analyze text: 0.34737491607666016\n",
"Time taken to map analyzer results to bounding boxes: 0.0\n",
"Time taken to analyze image: 2.3199360370635986\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Returning from image_anonymize function\n",
"Returning from image_anonymize function\n",
"Time taken to analyze text: 0.38391971588134766\n",
"Time taken to map analyzer results to bounding boxes: 0.000997304916381836\n",
"Time taken to analyze image: 2.331946849822998\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Returning from image_anonymize function\n",
"Returning from image_anonymize function\n",
"Entering in image_anonymize function\n",
"Entering in image_anonymize function\n",
"Entering in image_anonymize function\n",
"remaining: 12 / 13\n",
"Time taken to duplicate image: 0.013000965118408203\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Entering in image_anonymize function\n",
"Entering in image_anonymize function\n",
"Time taken to duplicate image: 0.013574600219726562\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Time taken to duplicate image: 0.012556791305541992\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Entering in image_anonymize function\n",
"Time taken to duplicate image: 0.003995180130004883\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Time taken to duplicate image: 0.003995180130004883\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Time taken to duplicate image: 0.00799870491027832\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Time taken to perform ocr: 1.925011157989502\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Time taken to analyze text: 0.12255573272705078\n",
"Time taken to map analyzer results to bounding boxes: 0.0\n",
"Time taken to analyze image: 2.0475668907165527\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Time taken to perform ocr: 2.081622362136841\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Returning from image_anonymize function\n",
"Time taken to analyze text: 0.15117764472961426\n",
"Time taken to map analyzer results to bounding boxes: 0.0020067691802978516\n",
"Time taken to analyze image: 2.234806776046753\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Time taken to perform ocr: 2.280395030975342\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Returning from image_anonymize function\n",
"Time taken to perform ocr: 2.340979814529419\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Time taken to perform ocr: 2.366431713104248\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Time taken to analyze text: 0.16561484336853027\n",
"Time taken to map analyzer results to bounding boxes: 0.0010027885437011719\n",
"Time taken to analyze image: 2.4480113983154297\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Time taken to perform ocr: 2.478013277053833\n",
"Time taken to threshold ocr result: 0.0\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Returning from image_anonymize function\n",
"Time taken to analyze text: 0.22170615196228027\n",
"Time taken to map analyzer results to bounding boxes: 0.0009951591491699219\n",
"Time taken to analyze image: 2.5901355743408203\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Time taken to analyze text: 0.31073760986328125\n",
"Time taken to map analyzer results to bounding boxes: 0.0\n",
"Time taken to analyze image: 2.6517174243927\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Returning from image_anonymize function\n",
"Time taken to analyze text: 0.22667837142944336\n",
"Time taken to map analyzer results to bounding boxes: 0.0009975433349609375\n",
"Time taken to analyze image: 2.7056891918182373\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Returning from image_anonymize function\n",
"Returning from image_anonymize function\n",
"remaining: 13 / 13\n",
"Entering in image_anonymize function\n",
"Time taken to duplicate image: 0.004000663757324219\n",
"Time taken to parse ocr kwargs: 0.0\n",
"Time taken to perform ocr: 1.4694664478302002\n",
"Time taken to threshold ocr result: 0.001005411148071289\n",
" Phone : 9159236847 E-mail : krishna@gmail.com Teams-Id: Krishnakumar.cO2@infosys.com Aadhar : 7629 5476 3472 5008 Welcome to the team O-\n",
"Time taken to get text from ocr dict: 0.0\n",
"Time taken to analyze text: 0.0729987621307373\n",
"Time taken to map analyzer results to bounding boxes: 0.0\n",
"Time taken to analyze image: 1.54447340965271\n",
"Time taken to draw rectangle: 0.0\n",
"Time taken to redact image: 0.0\n",
"Returning from image_anonymize function\n",
"==== 7.518835544586182\n",
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HYMNkqCQHApoOlYPhf+ro9TL8A8JXSxPJZ8IfZU+lvvriOHY6BhskQWA4FPB2rB8L/1+ltgxaXFqkHAgCEoEEcjgcgHlhYHwgAaB4P/bEEQByDh+DIw/A2DAVBTA+FAHvB6XcnQgCQHSadvwh9uqvjn7awjgXHoMiSolQfA4FNg6Tg+F/6/S26PUiADwlQPE+dnwh9mq/lrX1xJDseAw2SIA+BwKeDtWD4X/qwYtLi1SDgQBCUCCORwOQDywsD4QANA8H/tiCIA5Bw/BkYGwNrB+CmB8KAP+ltweplugeErpYmlt+EPtqbS3/1hHDsdgw2SoJAcCmg6Vg+F/6uj1MvwDwldLE8lnwh9lT6W++uI4djoGGyRBYDgU8HasHwv/X6W2DFpcWqQcCAISgQRyOByAeWFgfCABoHg/9sQRAHIOH4MjD8DYMBUFMD4UAe8HpdydCAJAdJp2/CH26q+OftrCOBcegyJKiVB8DgU2DpOD4X/r9Lbo9SIAPCVA8T52fCH2ar+WtfXEkOx4DDZIgD4HAp4O1YPhf+rBi0uLVIOBAEJQII5HA5APLCwPhAA0Dwf+2IIgDkHD8GRgbA2sH4KYHwoA/70lt5F+rThCIFQMJQl4EBOITY9A4mCCOx+IQ7TBAViO1g73Eg+AP8WVgGDlWDdaA1AYpBhsQoCEEMfFw7EhX8Qi6eSJxC/9v2iXrIPBQC4/abTq898S6wOR21BwnlYA0DwsAmqSU7IhgwkAdCGAalA+Ok5cyB4dj/478AbB6mxUkVN/LQ9BWiC0DcBkQMBI59Qa06ZkuVtpk/tYVsf3zbXxv7fAlKiEGZAPEgfgpRJTqh0PBJVUvziVXolzUvkwkMDocA4uHqZgEQctA3Y2wwqT9BgKgaoMAoa4P0kk6EASA6STt+ENTdVNjn7awkh2PYCsSolQfA4FNg6Tg+F/6/S26PUiADwlQPE+dnwh9mq/lrX1xJDseAw2SIA+BwKeDtWD4X/qwYtLi1SDgQBCUCCORwOQDywsD4QANA8H/tiCIA5Bw/BkYGwNrB+CmB8KAP+ltweplugeErpYmlt+EPtqbS3/1hHDsdgw2SoJAcCmg6Vg+F/6uj1MvwDwldLE8lnwh9lT6W++uI4djoGGyRBYDgU8HasHwv/X6W2DFpcWqQcCAISgQRyOByAeWFgfCABoHg/9sQRAHIOH4MjD8DYMBUFMD4UAe8HpdydCAJAdJp2/CH26q+OftrCOBcegyJKiVB8DgU2DpOD4X/r9Lbo9SIAPCVA8T52fCH2ar+WtfXEkOx4DDZIgD4HAp4O1YPhf+rBi0uLVIOBAEJQII5HA5APLCwPhAA0Dwf+2IIgDkHD8GRgbA2sH4KYHwoA/6W3B6mW6B4SuliaW34Q+2ptLf/WEcOx2DDZKgkBwKaDpWD4X/q6PUy/APCV0sTyWfCH2VPpb764jh2OgYbJEFgOBTwdqwfC/9fpbYMWlxapBwIAhKBBHI4HIB5YWB8IAGgeD/2xBEAcg4fgyMPwNgwFQUwPhQB7wel3J0IAkB0mnb8Ifbqr45+2sI4Fx6DIkqJUHwOBTYOk4Phf+v0tuj1IgA8JUDxPnZ8IfZqv5a19cSQ7HgMNkiAPgcCng7Vg+F/6sGLS4tUg4EAQlAgjkcDkA8sLA+EADQPB/7YgiAOQcPwZGBsDawfgpgfCgD/pbcHqZboHhK6WJpbfhD7am0t/9YRw7HYMNkqCQHApoOlYPhf+ro9TL8A8JXSxPJZ8IfZU+lvvriOHY6BhskQWA4FPB2rB8L/1+ltgxaXFqkHAgCEoEEcjgcgHlhYHwgAaB4P/bEEQByDh+DIw/A2DAVBTA+FAHvB6XcnQgCQHSadvwh9uqvjn7awjgXHoMiSolQfA4FNg6Tg+F/6/S26PUiADwlQPE+dnwh9mq/lrX1xJDseAw2SIA+BwKeDtWD4X/qwYtLi1SDgQBCUCCORwOQDywsD4QANA8H/tiCIA5Bw/BkYGwNrB+CmB8KAP+ltweplugeErpYmlt+EPtqbS3/1hHDsdgw2SoJAcCmg6Vg+F/6uj1MvwDwldLE8lnwh9lT6W++uI4djoGGyRBYDgU8HasHwv/X6W2DFpcWqQcCAISgQRyOByAeWFgfCABoHg/9sQRAHIOH4MjD8DYMBUFMD4UAe8HpdydCAJAdJp2/CH26q+OftrCOBcegyJKiVB8DgU2DpOD4X/r9Lbo9SIAPCVA8T52fCH2ar+WtfXEkOx4DDZIgD4HAp4O1YPhf+rBi0uLVIOBAEJQII5HA5APLCwPhAA0Dwf+2IIgDkHD8GRgbA2sH4KYHwoA/6W3B6mW6B4SuliaW34Q+2ptLf/WEcOx2DDZKgkBwKaDpWD4X/q6PUy/APCV0sTyWfCH2VPpb764jh2OgYbJEFgOBTwdqwfC/9fpbYMWlxapBwIAhKBBHI4HIB5YWB8IAGgeD/2xBEAcg4fgyMPwNgwFQUwPhQB7wel3J0IAkB0mnb8Ifbqr45+2sI4Fx6DIkqJUHwOBTYOk4Phf+v0tuj1IgA8JUDxPnZ8IfZqv5a19cSQ7HgMNkiAPgcCng7Vg+F/6sGLS4tUg4EAQlAgjkcDkA8sLA+EADQPB/7YgiAOQcPwZGBsDawfgpgfCgD/pbcHqZboHhK6WJpbfhD7am0t/9YRw7HYMNkqCQHApoOlYPhf+ro9TL8A8JXSxPJZ8IfZU+lvvriOHY6BhskQWA4FPB2rB8L/1+ltgxaXFqkHAgCEoEEcjgcgHlhYHwgAaB4P/bEEQByDh+DIw/A2DAVBTA+FAHvB6XcnQgCQHSadvwh9uqvjn7awjgXHoMiSolQfA4FNg6Tg+F/6/W9JbeRfq04Quj1IgA8JUDxPnZ8IfZqv5a19cSQ7HgMNkiAPgcCng7Vg+F/6lhGA+OkwQx4X/A4OvMKhHSD/Rx8Q7rTDQ49fNCCJKsCsBVgrQ/oG1gcCmByR/2FvAYEAIYeMfEsEJMJLeKfAcTiH7wh0tAt4PhwOBy2gB8L/1DArTKy5W2kTta0rVf1ptrRv7fB3jyIhgwlAdEMA1KB8dJ0jYHh2X6O/AGweqoqSMN/LQ9BlxBB4KAXBkQMBI4KwYSR2mEcG0IY/LtLVW/Y95sGNgaTMJvtg8R/6ttfZE/xqscv422/jbb+Njlv5QytzgQh5qj3aJYQ8v4Xfxu8SAeKvjvo4T+hXwfQFaOi1lWrWEsBh5t3oQh5in3YJYQ9voX/1udSAeK/jrg4TepXwfwFaOyxlUrWEoBj+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv52kk9G06r1G31bHqGn/ucjwHAqx8Dw//iwDxf/yLPsEqJIMXA8BBC+BgULAPAQOsBhGH6Zsdlw5aBQj4GoQ22WtBuMjsHg4BkvbVqkSagZtVg+PAHjQSgYEJoHgYF8HgP4EIAlBAYBDBShCBh6mAOCEPcHnmxDHCQSQhNfA1/g5SAxV9XU8YZECQQQN0DUP2DwH7eDwH8WEEGLgYRy8fAGCEDYqHoMOB9wFL8D3mQUuYlVLtA3W2xwIEQMMpqcNA3hLSg8DBZg8B/Cl4KdmD9WChEqsp4z+J2wbo/AM8lHP0If++nisGDmNtx/2DBBqDCQDfBi5lMmBShAZEj5fNqYPUydpksLC0cKy5lWw3+FvutRvsHLRtwtoPAQSoPAfyMHQMIOgyYdgiTC3haPqP1APDwD4IoMIIPFQB58FqDDkeDoGEgHgIMURwg/mj4fApWfq221xAYSD1vxaDFA8LGgfJgCRQPAUQIIQgeA/dx2DwH8mI4Qx14GCFsEtODgQaB8fUfD8Sk4kNtDoSvexms/A0yOAYRA+oIgMVdoWfZ0AwGA4DJQbR5RHBAAOTiOPIJYMH2ssfSFQ/HDTZZ9rWYz70aZ8DL1oDTBbOgFrAowYdA2gyovVDoEEICseJy9dJ7qpPE65YDLNlzKthn/f4BhhvbLslhEGJoR58HgIKsHgP5MfiNv0jA7BgUrNa81RxQVQ9BwKAGKueBkAgUcDhFwjF1EMShKA4DwEGqI4B/8o+SKxJrLKu9xUWAiNNJ//AgOmQNMg+RAF/ZASQeAggQYA0IAhaAeJYN4fqhKzw5bbLR99tU0Iv04+YbbH6TIjtVqmhAIQyNNfVt+Yal1tn+NeyCL/+lnjAhEPfgoBLBi8GbSAwBwhA3AgiUIfk4MO0oPAfxpaJQB48/4c+LAgNtD0sbZHHmEg+YVAywOBVJE4OBZAYFI19tOqaYpY22yx5r9qhR//w80tBiD0yQIYKID4MwJPwgAGlwMoEtrGv6qrKpkqZxT7S1I2ywiNBeWEoSi5WXNNtsd3S3pX+AyH8i2bIiNRxAGA6BwEMEAGqcSAb4jgwIoH/MJvsjuD1OJJbn1TX2sA2P40ro4aBjbpCADwcA2qB3weL/9xaMiXu4xLluB7EV4Imk2iFgYDQ6BgIA4PWQfN/+xgDAiCUDAQB4r/5ZB83/7KsfD9kGDlsGGOhLUTAwGh0DAQB4r/5ZB83/7FYMCIJQMBAHB6yD5v/23h+yDBy2DDHQl0TAwGh0DAQB4r/5ZB83/74MBoSgYCAOD1sHzf/tvD9kGDlsGGOhLomBgNDoGAgDg9bBwEQciNO4MCIJQMBAHiv/lkHzf/tvD9kGDlsGGOhLojBgRB0DAQB4r/5ZB83/7wMBodAwEAcHrYOAihQmyFvD9kGDlsGGOhLonBgNDoGAgDxX/yyDgIg7p1gwGhKBgIA4PWQcBEHDF7eH7IMHLYMMdCXSMDAaHQMBAHiv/lkHzf/sciS1jA/b96KPRRyLoWvVDVyuogmeA+Dwf/OrBgJA8XAFiwWEr/9Zs3+6HlR7aIuE/GabYhI22/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/lDKqLQcAcJIFywGEEIHeB8BotBEANHA5B4T/vEEC4GhABirgfgilYOBZHm1OhCHuqPdglhCyfpd/W51IB8q8O+jhN6lXR9QVo7LGFapYSgGv422/jbb+Ntv422/lastBwBwkAXHAMIIQSzofAaLQRADByWA8J/3iCHQGxBBirgfAiFYOBZttzgQh5qj3aJYQ8v4Xfxu8SAeKvjvo4T+hXwfQFaOi1lWrWEsBj+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv42u38YOxt/G238bbfxtt+gwwJ4nT+ijytnMiLMb/IMI9qC/JUZ3RJBVl4PC/+KcHif/dsGCVgqy8Hhf/FODxP/u2DBK2FKjA4wYOiIwVZeDwv/inB4n/3bBglYKsvB4X/xTg8T/7tgwSt6jA4wYO0Rgqy8Hhf/FODxP/u2DBKwVZeDwv/inB4n/3bBglbyzA4wYO0Rgqy8Hhf/FODxP/u2DBKwVZeDwv/inB4n/3bBglb1GBxgwdojBVl4PC/+KcHif/dsGCVgqy8Hhf/FODxP/u2DBK3qMDjBg7RGCrLweF/8U4PE/+7YMErBVl4PC/+KcHif/dsGCVvUYHGDB2iMFWXg8L/4pweJ/92wYJVd3f1T/bu1Hu3tGL25yRFJBNxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238vaWCEPfd8poliFZ6pf5+yJgglfh4BZX6d8pH9BWpC1hUqWEsBrzCkHAHCWDIywGEEIHeB8BotBEANLC0HhP+8QQLgaD4GKuB+CKVg4Fk/jbb+Ntv422/jbb+V/5QhD7O4pg7EO3Il/f8A0EAbjoOk3ireD+LpSxtjVhKAY2rLQcAcJAFywGEEIPOh8BotBEAMHI4B4T/vEECwGxBBirgfAiFYOBZv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+dlAhMeTiGJCZmdaVj0uYbnedxtX72hS5AMB4P/XA8Dw//CJQPF/+IXfUC3BgUIPAwPoMIwKMSwbGgeAge4PdTJk18JYPA/2IIaYrSj5pP7QRAYrBkRaDARB8L/1BphQB4D99TA8B+uj8RwDS4GzByDCMlEgPvtdBk2KVXxILBD8pEvgQ17U6JcHhYA8gC3gIYMJAOBRAw40GAMUghgqtYB4H/JB4H+/CHgOSgUHoOA8OAVQGAYCHRAp77CkXA8BBIgwKUexNqQG8nZaSiUqENUyCnEjFXx4lhaqBl2y+MCDYuDcWSmwClQeBghwYFCDcLxJB4GBDEnEg61mK62IDTIMVAX1oeNtwfpWoDBymVMqqvx4SQYII7TtAfB4CC980lZxjBCBkxfnsZ0QI3qsSmg/ngZEWjgDAgoxwcEg6H6eg8B+LgHsAbbqqAcxJGhz30YBwIQQi8PlAEAU6lWOIDwn/md+zANAYEAGH4MP2m2QUQBzI7TMaIGpxymHyYQRuOANjtlUq+zz/usNM9D1h4sBh4DDptgGBBHwN4GSgyoQxJ0sS/ZBCTMqi5Or8DcZbabxO2IYHmBxAZGOYJKqB4DCKDDZ4YAYIAMPgOCODCMrHrAQk4MIY/YBqP8ZBmAeB/s9BsEYEBIDwv++2BlgDNYSJusgxWH8EDgMHSs8FQdttNg8B+cgwQG/b7cbBgDtvvtKwN+bzQeB/xf+HCcHhP/GFi8Z7RBI/sUD4A4A6g8BBGg8B/BiPgHk3i8dAysfJhIZL2FScRxGSAwGwDvApvD0Fa37jSdthPWFYMv14fQgBAEsEES0gHhCVApxLHuliRNPtNDnVF+Bdn7DHgMeBkN4Jw5pLAeBgnQeA/ZzYPA/1qQHi/9UHwIC0qDFwkBDCCDUIAlAGD+CQENOlUDwe1KrHgGmZ7fqQRUnWWkIMi6ygjvTsSR0XhCHZeJA7aA0Px4rZSJVVuaOGWtUN+UtAYWoMa64SpEybUyq1UqtrDVtHHl++nREIVgUaQdjoA0IY8EdXB+JZcJZemHpfFf0oIqVpO35OrA23I2yOWVNHLIK2kKZcDgUyQGBXMA4OnDYk2z3l7F6iFFREwUxcDwsAimB4mAL8EjBTFwPCwCKYHiYAvwSJMeLfhzoxfojBTFwPCwCKoHiYAvwSMFMXA8LAIqgeJgC/BI3lvw50Yv0RgpkgPCwB6oHiYAvwSMFMXA8LAIpgeJgC/BI3qdDnRi/RGCmLgeFgEVQPEwBfgkYKZIDwsAeqB4mAL8Ejep0OdGL9EYKouB4WARTA8TAF+CTgqi4HhYBFMDxMAX4GCRvLfgR0Ed+iYFUXA8LAIpgeJgC/BIwUyQHhYBFUDxMAX4JG8t0OdGL9EYKYuB4WARTA8TAF+CRgqi4HhYBFMDxMAX4JG8t+HOjF+iMFMXA8LAIqgeJgC/BIwUxcDwsAiqB4mAL8EjeW/DnRi/RGCqLgeFgEUwPEwBfgkYKYuB4WARTA8TAF+CRvLfhzoxfojBTFwPCwCKYHiYAvwSMFMXA8LAIpgeJgC/BI3luhzoxfpDBTJAeFgD1QPEwBfgkHoQ5mD1nMyZkLFsnIBb1ROeCCDwf/OrBgJA8XAFhcLCRbWb22gYqElMSM22wv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+V5yhAHu88pgliFJ8ET+/kA0EMq8OwLJvUqUj6grUpY0rxYSgGt5yBAHud8poliFZ4EX+fsA0EMr8OgLJ/QqUj+grUha0qxYSwGv422/jbb+Ntv422/lbcgMCIPN55TRLCHC2F38bsiQDwdjsCydpf/C6ArR0Wsq1awlgMbbsBgRB5nfKYJYQ6WQv/rclSAeDsdAWTNXn+F8BWjssZVK1hKAY/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv42db+MDBNv4wcbb+Ntv422/QbO4CGPuQGKvsLQFYxOtNAqGqrT/ZvvDb1Z/9u5IW7u9ex2Q2xjMk+gJtEkHAhlwcKwRvA+b/8sHAhlwcKwRvA+b/85heDFiYFSNYiMHAhlwcKwRvA+b/8sHAhlwcKwRvA+b/8t4MWJgVI10Rg4EMuDhWCN4Hzf/lg4EMuDhWCN4Hzf/lvBixMCpGuiMHAhlwEFYI3gfN/+WDgQS4CCcEbwPm//LeDFiYFSNakMHAglwEE4I3gfN/+WDgQS4CCsEbwPm//LwmwZQqNaD5P/3GRg4A1IBBOMGikHxf/dg4EFIBBOCN4Hzf/mngxYqJRrg4JERg4EEuDhWCN4Hzf/lg4EEuDhWCN4Hzf/nPBixMCpGuiMHAglwcKwRvA+b/8sHAglwcKwRvA+b/8t4MWJgVI10Rg4EEuDhWCN4Hzf/lg4EEuDhWCN4Hzf/lvBixMCpGuiMHAglwcKwRvA+b/8sHAhlwcKwRvA+b/8t4MWJgVI10Tg4EMuAgnBG8D5v/zY9bidL9nOzNLOKVqHUhuwlG8W4K+y2kzTFU+73nF71ZahNxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfytUWg4A4SQLlgMIIQO8D4DRaCIAaOByDwn/eIIFwNCADFXA/BFKwcCybzgMCIPd55TBLEKT4Gv7+QDQQ+54dgWVeoeKR9QVqUsaV4sJQDX8bbfxtt/G238bbfytWWg4A4SALlgMIIQedD4DRaCIAYORwDwn/eIIFgNiCDFXA+BEKwcCzb3gMCIPN55TRLEOX4Gv5+wDQQuZ8dgWV+geqB9AVqQtbV6sJYDH8bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxttlyPtsW422/jbb+Ntvkbbfxtt/H238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfyv3IEAf53ykGUiFeUv/n7ANBB7uDoCyfwddH9XLi1hVFhLC9tUWg4A4SQLjgGEEIBbwPgNFoIgBo4LQeE/7xBDsDQgAxVwPwRSsHAsn8bbfxtt/G238bbfyv/KEAfZ3ykGUiHeQv/v5ANBA5ujoCybwd8H8XLyxlVVhKC5tWWg4A4SALjgGEEIJZ0PgNFoIgBg5LAeE/7xBDoDYggxVwPgRCsHAs38bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfyv3KEIf7zykGUiFFNSfv5ANBBKsHYFk3g66PquXljCuLCUF7fuQIQ/zvlIMpEKqKl/P2AaCCV4OgLJ/B10f1cuLWFUWEsL38bbfxtt/G238bbfyv/IEIfbzykGUiHFMSfn7ANBAKtHYFk/g74PouXFrKurCWFzf+UIQ+zvlIMpEOqIl/fyAaCAV6OgLJvB3wfxcvLGVVWEoLn8bbfxtt/G238bbfxtt/G238bbfw2VHTaZUOx8rHpcDFo8YSqmx+OhB+XA3Unv59OPgNsAZSB+1NYD9iKVRapczX2CqVD4HgIINkHgIG8dgwIIPAwPagFUEPBCBgRAeB/sQUujkPUng/BlKxYDgU4FgRAZARhv8DAogZODF4IoKMGSAwkJR8CiV/348SUS/MjhjAKFycQ2Uw/CGHvvgZYYbXWFR8SqDwUG+CCSgw9akXBUg+BATgq/UfAowOAwIAB4KQGEpMDD2AoAgCRnvtAwH2wafBlQ9HmMFvk4jAyL4gNpl2QZdlPGgZEykD6mfsbgeBhKBmQQRHTgeEIA4epxJH13w/TXANAqt98svh/9KIDBcHqwMuy0hJkgDwYDgMXgpBLHgjAdA4yPC8Q2/MCVL9Iy0lZ//zRY0lTNslxaWt+7A/BWFp0dhCHQjAwHwYeg8B/Ej0fqoPxLmCEHwMIwIAMnA0JAKH6QuHCUtA/5gdQdtDlJAVogNRIrL2+gwiAFlQYD7SpIEIHgIJ1I0JSoA6DwGHrSb4QPAfBwIOCF9gephyPviGDCK2qBgKgwEKDFSJp32L6DwEEyDwH8b7B+3QZIPVA6vFahsff3QeHgFWvF45EsHiIBmAEmxHB4CBvBhDAOA9AUIhgoi8fBCqjar/qYtVFtLGgRUnmaPy4Pf+gMVqmASUmB6lB4H8PSpgNsiBglAiY0OfjjGAeBgR0g7qrPAxoFODFdaBgInR+DFydsegzQPAfmoKdX9PCzyeDlvd6rLQUYGSkGWTAwIzTvqyQHgIHcGCGPQDqClVAoi/4Hoq1KBstTZv0meHIfsJPswfstDhE2qVMuDIIwDgPMg1BmAOA8D/TjoSi4DwBgM16AgA8B/HggAzbSTR0wCqYLB8lHggxn5eqYBWJRBVB8H7DPJRBBgFg0cBDBhGBwKIGHGgwBikEMFVrAPA/5IPA/34Q8ByWh0PQcB4cAqgMAwEOiAc+whgwKEGSBCCEB4HgIH1kAwSv+EsG+DeSD8S5Gx+lo7ElOmCF8PPNssjj7badsFYmjED2Q8FsHgII8HgIIcEEdAyvzIMI4MPvfA8lA4P6xBHYEgSU0S3S4cB+mLU4IiWNFvANsMKwLisKAQRLEoRhLB4CDfHoj77B4mBoJGs/b0sxUWiQEJppP/4FPKmSxkGAk8QgxcCkBRhAB4D9xBvggiGJKSAeBtwS/gcgMCKq34B7X2/JR0Hw8aZEEcBAEj32BBVscYgMHXYrP+mDLaBvAgpwUA9BmWBK34IQkF+DwfiWOUyhhL8EQs8OByH4PCf96SDlpisAZaleZEbM0esZuTZS3s2KYs19fiJHw3AqF48BABtVgwBgHQUKoIOMA3h8EFIkEsDgMOU7VSj34KocJwYQIDh8lHAG0wGWaH4eN8B8OAPHIHgeD/51QMBMHi4AsWuyDImwYo+DlD9EEYKYuB4WARVA8TAF+CRgqi4HhYBFMDxMAX4JEmGxboc6MX1EYKYuB4WARVA8TAF+CRgpi4HhYBFMDxMAX4JG8t0OdGL9EYKYuB4WARTA8TAF+CRgpi4HhYBFMDxMAX4JG8t0OdGL9EYKouB4WARTA8TAF+CRgpi4HhYBFMDxMAX4JG8t0OdGL9EYKYuB4WARVA8TAF+CRgpi4HhYBFMDxMAX4JG8t0OdGL9EYKYuB4WARTA8TAF+CRgqi4HhYBFMDxMAX4JG8t0OdGL9EYKYuB4WARTA8TAF+CTgqi4HhYBFMDxMAX4GCRvLdDnRi/RMCmLgeFgEUwPEwBfgkYKYuB4WARTA8TAF+CRvLdDnRi/RGCmLgeFgEUwPEwBfgkYKouB4WARTA8TAF+CRvLdDnRi/RGCqLgeFgEUwPEwBfgkYKouB4WARTA8TAF+CRvLfhzoxfojBTJAeFgD1QPEwBfgkYKYuB4WARTA8TAF+CRvLdDnRi/RGCmLgeFgEUwPEwBfgkYKouB4WARTA8TAF+CRvLdDnRi/RGCmLgeFgEUwPEwBfgkYKYuB4WARVA8TAF+CRvLdDnRi/RGCqLgeFgEUwPEwBfgkYKouB4WARTA8TAF+CRvLfhzoxfojBVFwPCwCKYHiYAvwSMFUXA8LAIpgeJgC/BI3luhzoxfojBTFwPCwCKYHiYAvwSMFMXA8LAIpgeJgC/BI3luhzoxfonBVFwPCwCKYHiYAvwMEmBVFwPCwCKYHiYAvwSN5boc6MX6QwVRcDwsAimB4mAL8EgYy+SJU8k5IrZkkWkk/VnPD8HAp0oPD/+bIPF//JkUEi27/ttKojorkarbEeNtv422/jbb+Ntv422/jbb+Ntv5WqLQcAcJIFywGEEIHeB8BotBEANHA5B4T/vEEC4GhABirgfgilYOBZN5wGBEHu88pgliFJ8DX9/IBoIfc8OwLKvUPFI+oK1KWNK8WEoBr+Ntv422/jbb+K2xMDCQJSQQwb4Qh8P4pTN/Z//wMbA0mZT40DxH/q21GBP9TVloOAOEgC5YDCCEHnQ+A0WgiAGDkcA8J/3iCBYDYggxVwPgRCsHAs2JRCBhLA6IYB6YDw9SFysDw/L8HXwQgZQqipMx/44DwFYH8bHgMjgK1H5cVBnA+k8PAgiGPmLawXCWPUzVqHyZW214Hy4Af42zj+Ntv422/jbb+Ntv422/jbb+Ntv4waDb+Ntv422/jbb+MHW2/QbQHSvEiVpn/bmKeqOdX9uIbDZO11sUy5UVllE+iDEHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8jxhcDFiY1oMNYiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/nA4EMuAgnGDQPm/+7eDFiYNwYa6IwcCCXAQVjBoHjf/kKWDgQy4CCcEZoHzf/dvBixMG4MNdE4OBQlwEE4IzQPm//LBwKEuAgnBGaB83/5bwYsTGtBhrojBwKEuAgnBGaB83/5YOBQlwEE4IzQPm//LeDFiY1oMNdEYOBQlwEE4IzQPm//OBwIJcBBWCN4Hzf/lvBixMa0GGuiMHAhlwEE4I3gfN/+eDgUJcBBOCM0D5v/y3gxYmDcGGuiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/mlf42rZbiL7f/zqPm7adbRpN5jt91cV8bbfxtt/G238bbfxtt/G238bbfxW2TA8k8PQgiEPmbYwXiWPEzdiDytW018GCn6n7kCEP53FNHYhVRUv5+wDQQRsOg6T+Dro/RpC1hiLCWA1haEMGEsDoQgDUoHh0mSMBAHhfo68AZB4mqpKw18sD0GWEEHgv+sGRgwE1j46BhJHSYRgbQhj4uwsTZ9V/zQMaA2mYT/aB4iANbb+wJvjbHL+Ntv422/hsaKk7JcraSpv60rY/n2m/DfN8Irmr8xhkGCCJYKrw9YHwKRMB9K2PUysIaQfZgB6hpMOgZf4MaD8P2wZdcDd82DFSoPgfC/9Tdtt7V7V70hHqstBwBwkAXHAMIIQSzofAaLQRADByWA8J/3iCHQGxBBirgfAiFYOBZ/422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbbnG22L8bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238Nm5bL2LWLzhA/8y7QMIYj0ffEtsuAOTiGXMj5IwENWAcIIlgZTDv4GwYcjkQGmVQIng+YZVAxWWs3nEys+NFadkvVtpU/9bVq/6223o33fh3rx80sEIe+75TRLELSyl/8/ZEgQSvw8Asr8Hvuj+grUhawqTLCWAWr8bbfxtt/G238bbfytsHg4CMef55TRLEPL/C7+fsiQIHM+PQLK/Tv1BdAVqQtZVq1hLAY22DwcBGPPd8pgliHt9hf/fyVIEDm/HgFlXrz6gvgK1KWMqlawlAMfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G236EbCuXAwFkwMCuYBwdOSHaI/LYICK8DsHzoAfQ226DB+DBsDg9B86AHqG23gwfgwbA4PQfOgB9DbbwYPwYNgcHoPnQA+ktjUFCDwkAiDxH/qDxkASSPTB4GBPEcHgYE0Hgf60PxKBgNAwGwYEQQA/+DFoGxwOS0PhAEAQAeI/8xBBgVB4UYbEFrP853qyNFbCQmlLZoAwA1sEBWyraHQHhLv2BAHAB4Qh8EFNCv3y1pUXtMteZDwC5o+E8FGJKrQeAgdx8XiOCgBkw+A6DKwg6DgPfCACiCEEJOkAMA8DAXH5aXNCD0eiTwuneRWmaC9OlsUMgw+BQgpMHQHgUgMBwGgB/8EYeF4jiOCjYCAOx2AeJacfAykv8BxMEMfsMgw4HhaDcB4KAlTAbD9psGKwRSIPwhAgsAGMiP/4N4Dw/EdtvMYgHUjacfBAHH2QRWPAebBWNtsgYVA8F/1iCBqNNrWs2hd9LYUADwaAGAdAPB4CCL2iQkBQJwYQ2B5oKERhG8CqTRhsSfJmh+PEiIuBgRWC0DQ4BWIudOpCR8DwMrBvgowYA5kGHwKAICSKgQ2AQE46EoDQB4hD8DjSVOm+lEmg4fiWOQNUFOwOxkIB76WwZI+k1KkzZ3NVsSw3L+HwV4PAfxY8BmQgD8G+0PgYFECgLgZVlTqkwQUgMIQQlKoeN+LAN4I7BcOUnw9WLwVgfbFl476Ww4AwlCUPmsEkGLwD1TSUDgNPYOB4rilgDoKFOWg4DgQP+BkKoc++PxHgPCwDqRuWArXC8GBBZVAyoHgP4MGoMHwQADAREivPKhGShAHycITUZEhIWlgkfaBVAVHIMiWq4p+lsX/A+0rB4CB1BBA6B8GgMOmE4QxLVAoAZrRGBmgDhIH+M+ByUDol42kYjI4TMDhMqZYB4T/zbOkx4DD4A8DwBgN4HAoAYdAoADRLBBAOSAHj0IIN+AcTsUGD9v/xKCAB5IAaP1Q+HKofAabSh8wnSq1aUGApw99LYMQA+J8CCO4PxK8EBLB+zFYjNJtSMDpstD1N5J/o48wiHCAUBTBgOD9WlVggAcEoGBQAGAdTMpcVAHjsSwgNhDTj/G1YMVtgwgsgYAMaHg+bWBuRVFRYCKX0An6WxolBvFwQwYEMdg0BvK2gYPqAePWkw/3Y2kHwhJvttpx/9X0DJcPEjTbIg9BWqmu9rxKDwH8eDwMBzoN4GA4JINgHi4SB99KDD8dNK8SewfiGkA4nCEkHw/A/8sjaUvTwtVgy4MNqn+76WyoMAcDUHAhgdboQQbwMAd4FAAcJQ/ANBDbaBhCBCzYJQ8aBkTXwUwFx+z9pUxO9D04GodAzKQSGRHL2gZgFInLx2PdTsUIY+BhyPBCSf3/wVQ8EZsHgoBkCoMjVqldir3E0VU79LYuB4D9/EgSB4OwUReOhKTAcTgggyQDwBoQQgFweJB0JI9EEtxMmB4SATaZ97zYNwGAoJxcDwED6JCUfg3gYA1OEAFIEIGoII+bBkkEZUI4Hh8B0cYlThDg7D5od+54GWanQYoIfpbGPwPAdBSgwQQUQKIFCCEkBlSsfl48HzaYSgQkwIUBVfVgZHoGmmlXk/xwOeMogMcIBCBxKCkBkoMPS8GBCB4GBFBSCMB4f6B8QgODtIOlAIIkJR0mL/joeqwgg4FWO22W0wOSsK6HwGRV9LY0gKYGCEDCUDMCPAQADghgggyVWB9KyyCjEjwQ2BIEoIQ/aHTA8TDxKDLRlW2Wsp1YgB/AYbEQsgIAMOmwYSQYSBHTlw+EoEAGSg2A4DoMCI2z9UBxoFKOC9MDDgDbbfWhIViOkaoMNwZCBoHw4A/4ja4M2PNaBh4AfoMJYMqBCBlQKFoRx4wDD6AeEhMBxKO+K6DgOjlMkB4P/pA8qHxYHwjsg8J/5ttQEVPXgx/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv5WqLQcAcJIFxwDCCEAt4HwGi0EQA0cFoPCf94gh2BoQAYq4H4IpWDgWTbU6EIe6o92CWELJ+l39bnUgHyrw76OE3qVdH1BWjssYVqlhKAa/jbb+Ntv422/jbb+Vqy0HAHCQBcsBhBCDzofAaLQRADByOAeE/7xBAsBsQQYq4HwIhWDgWbe8BgRB5vPKaJYhy/A1/P2AaCFzPjsCyv0D1QPoCtSFravVhLAY/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv0E0gMBZMDArmgcHUNLDRhofRG7bRBR0CAPnf/ehtt4MHwMG4ODwHzv/vQ228GD4GDcHB4D53/3obbeDB8DBuDg8B87/70TgpQeEgEweI/9QfMgC2DgUAhA8DAlg8D/kg8D/kjoGD8GD8GLAeBgQSoGHIGiwHg/+MHApxyOAeIgEQ+B4b/x6D4UAW3sNCA1nu951dEjtpKT8iwQh94SRHHQ6+lSDrw/L2E3x+P2cTJEnm/ln1adv4GGmlQF70gHgPAfx4BpcB8GuqghbKCgBQJkwKIFAo2bAYQBCwcRWWgyEFWyOR+IKGNrk7DN8gVRLwDgNQYvBCBgUPgYeghBCHU8CkH4H1SUSRwAaIQ/AOVJPJUw6A9xkfpaDh9ARG2A/YTqwvCwAckBvAwlgyUGHoMnA+PQPD4SvCOnEMSAUgQQOj4AxkEFWO0qVtIqbSpWWBwkYWZENtUILJWHyadC8Gg/kHQMlVAGpQUZcB0Qwb4MqbHojiWw0wXgHAox8P0gIHmvteSbfBCbVsiQJQlNFggWMW89+grU4JIVmgYeghgpGBJCGDUGA6CkAPV0Qh6PAhCQCkbCEPRLCGPU4+BTJE4Hx6EIfKmQYcDxr4N0Hgv91OH4G2muJgcAS38mSMYnTqmNVqkkZbaYqdPf4mVFulgfpyxplkQGgLqxAYV3neTpwYgw+TMAwBg/HYKEIIMXg2D8dAwGhDLqAcAYCC0B9KChZH48bTJEzY8LgLJBy35XWR2BoQAY8wYvyF0gMOwbwPAwHoHgbAhgw+H49wQmR6P9Vj4GZBgNYOx+O/g4FOlSA4Dg9BWqkjTQ+VAy9BlvAwb8dgeA/bx2AYAaDAGiXoBwfArVbHlYH8APVDsRhL36ou80l9vmhAHTCoCysGK08DwGApyub+Qbg8B/FiOOqXgeBmgDC9ovBCBh8ITAkJEqZuMpB2JbIOSgfEf32gMfA2X/HA5aRwAgWCGPhIBghjwGEYSVQMnBSAc+BwGwIQNgB6QIIQwZQXjrQOa35oejxI3BBHflaS+ZB4KAZD8GXWRkTfyCoHgP48EMfAzYMI4IYB4lKaPkqtV5N/ZzWcHg8SDwP9wGBUgaEEHyP/tMGSKgDR4XCQnBQA8B/AgcA4Ov3C8SxIHoKH4QgZQnRJQRQUiUFYEEPGpyqmFYMWsRqcC5v5BIJYMEEGHg6BmwYEH4hA2UegGAzfkg6TMiUJOgpGVQ89ngcB2JAeCgHR6WJAUJf4cp2BAVAxUDLc4yFlAw9ANBh0PwYFEDDweCEELUw9aTg21UkB4H/FaEtOCgHIjiF9v4jqgYcMNl4MCK17QUw5glAaLLYDgWbfyDnR2OgQAeAgd8EgeD0uVgGgwjiSBsA3yZX5tkSgDAOfB4KAZEpL5pdIyPR/8G6OEKry7hSDwEDiAcAYnglgoQPiSDTU4Qv5AOApAOgHjoDjAhiXQ9xWPfl7bAPBQEo+WbrDAMBdmAa6xOn2/kGgMI6oD9CHoHwDQDQQgDR2AeyyyyyBwA8SgPjgcDjw4ZBlDLIKZqNf80XMAwcNcVLKqeDSAYDTQDWm60AaDQSq02oA0AeB/wHt/CwGDtrS8QB99oGQUGKw/BgVB5v5AoDsGH46BAHoPAQQ6QGA34GpeDAeVpWwDwgjz/0qRtgdiO02DCAPkrbUZLwYsHzX2FSsGGwewHAsuIyQFADD4GHo/BgDQeB/vwUghgeH3wPiOCAPh4JYGwOiSlHbRe0Iw+TiE0Dh+JQgNpQclZTiAqBWCsGQ/kygfAwQwYQgZMJHgUIBgjAGgyZkA0uSpAUYjtBCg/EoQx60O0g/SJUoMs2HwGVStgQQNh53iIGAXCQFCDJvAycEIQgYPxK+mBlQMB5WCEBwFKPVX2wDsBh3/zafwHxw2wDwcA6qH6esJlatMDCJ5P0GGwBLfwsOx+yPgYfAysSgYSwYegcBi9OPQNfBvJB8AbgNpekEMFXG0ok+HrQggwfMVgch+wyyqA2vA+6Rtv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+V+5AhD/O4po7EKzKl/P8A0EEbDoOk/ivOj+rpC1pjFhLAa2qLQcAcJIFywGEEIHeB8BotBEANHA5B4T/vEEC4GhABirgfgilYOBZP422/jbb+Ntv422/le2AwIg8zvlMHYh7ciX+/mAiBC4WjoOk3huoH8BWl5Y2q1YSgGNqy0HAHCQBcsBhBCDzofAaLQRADByOAeE/7xBAsBsQQYq4HwIhWDgWb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbbEuNtvkbbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238rakBgRB7vPKYJYQoW0u/rclSAfDodgWTNLe6XUFaOyxhWqWEoBrbVgMCIPc75TRLCFSyl/8bsiQD4dDoCydqd90voK0dFrCpUsJYDX8bbfxtt/G238bbfyv6wQh9vMUgykQ4pBE/NsA0EAq0dh0n8HfB8jSFrLOrCWFzf1whD7O4pBlIh1QCL+7IBoIBXo6DpN4O+D9GlLGWNWEoLn8bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxW2FIGwfDwD4MBwGCCXApklA6AYDB8DfHiWJvA3dEppWDKAOqqnBkYKphpOmH4gAyIP17CL6WxIJAM0BxkHgII8vylzGgxerSCSDBBLkogNAp2gQgOCSXAcTqhzwPwUzbAMpH8Y6oDwDfKcHAOA5mAwIQNiRUJDReDaDCODK0rYkNxV7R3AhCF5oITQ+aboK1rw9A+lbBgVCsERcHw4A36W1x+DwEDeI5cXCOnTAdBvBBVgHNanA+IQHhH+XiOJCpr44Tp22gUzDDbA8+slbHitPFRCRBBHSURwYRgeAgnRKHjGqx+PfAxekTMYN22WwZK37ws+lsaAHgwQmQDkiVnQUYMqEtkdN4wOxGCEEMDYlj5v+jjn/fLlTCUQGQVicGUMpQ+qM4R8DwEESCDW2B02EMGaSApU6rytUDKi8IIgAg/HPp9pUWAiA3GC9nyIfKxLVKzbvpbGABgMEAD4IKoA1P74lCU0DRWPlQHwcB9KDKAVQKfR20Of79ocDhV8dNtK50DdV/YVJEydls8Q+DwEEGENViQfpwQwZIqBAHrWfHgN9sDu4Bwf7/7bQIiQHFwMIPy9U218EVgGLAckB4T/xBgFfS2MaDBABR4DNqwYfVUxAhpFOq7qdrw+aSYmBugxb8e/BWNFwKZQrEFYFYK5A+DCEXiEI1A6kCCCgBQiE0rYSD9KCgHfy8GLE6ctA1g9aSlw6VtB+OGw+ZECqqiD8/9LZIHgIIUG8EIGZBmWB0B9kdQfYmElv48L049SMjvfBCHf2gRPlzQPCQC7LTQ9TqVaoGKmmwYbgw2ecTAwQh6JIH2whBAEMEAIeJh0DCCPQD0herYBAEgfeWbbHo9EsuHn2A+DzoMtQ7nByQfS3wDwYQh6EAQsANYEkAwEMDlSgqmRJBCL207SQf3w58kStJS4eD9M235edTCASonEgeAghQbwQgZkGZYHQH2R1B9iYSW/jwvTj1IyO98EId/aBE+XNA8JALstND1OpVqgYqabBhuDDZ/0tikGAMBRXBLBgDQODoRwOAH/Sg8BA8gfzyURkv8ErPtiSBwq+lCGwJUaEDg5Tst8VKgYCJGGMA0GEIdgGCEkAPBhyAcB0ICsvokUdghD/R+wkEvPDkG4nHKpMWMjhSzOdVM4vGX/S2TSA8BA+gfTgpmPiGDQdAfEtUwnEoFIOwhiAAeP8T+LS7PeD4G4wyPegxUqHo7TK6scDLEmwvBTM8LvgbA0yBtT7E2lo4bA2sW+EC9EFbrvpbCcDAhgwQAhAgKgQlYQggghb9WDNAomVYlJh+ENOOwgiWP/AxWEEeYlHw4BiwftgZVAy6YFYDwkAmwRGweBggQDQ9LgYcgpKChYTan+DJMCGpBRzzTQG/+A0IAKr6UQAVoNyDsEUHxoA36WxiAcDF1A6XA3WwUIM0CAqqvUzQOANCGBptlnw4LSxIXMD0flkHOAy6tUwmB8b/1LqgYFIClVgzYlgw+mJPAHiUPko/rGqwViVpMPoPfAiJferGg3WP+BE1WICoPYBtWAR9LZUHgYIEIOlwMIKYA8GTD0ITTNSCOIaQA/AZQAYzmFoPBf8Lf2FRviqjqCXE7I4YTB+IKoLRsDUGVD0Ga+PARVYlCWXCGXA3RGEkIScDYlD1Mpvv61/7TDMrDfq0rYZVKmZUB742xy/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Vqi0HAHCSBcsBhBCB3gfAaLQRADRwOQeE/7xBAuBoQAYq4H4IpWDgWTecBgRB7vPKYJYhSfA1/fyAaCH3PDsCyr1DxSPqCtSljSvFhKAa/jbb+Ntv422/jbb+Vqy0HAHCQBcsBhBCDzofAaLQRADByOAeE/7xBAsBsQQYq4HwIhWDgWbe8BgRB5vPKaJYhy/A1/P2AaCFzPjsCyv0D1QPoCtSFravVhLAY/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+G1QeAgeRDHwjAydI0IavyoDwB4+EsFEB1tv/vMYOgOj5lJft/bBWeYYZBEVsjlA1RMw0fJMBqB4SgeA/gQDB9B2XJQZUIwHBIBgDlY98n8DKFYhJwhAygRy8DfRABTAw5HqUe1X1RSwGAWJ04M2AYCiB4D+PBhIHCVMJIKQGCAOhCA9R6OlCdMl0DTbYhhDSDjo5B4L/lHadsFYjBEON/IgAaB0A8dJAQ21QMXg8BA90SB6PB2XNaAcJAKAHAdCGPB3laBVAqhKEYGD8uZZa0GKwZSHyZMDwv/irPLgcBmQPAHMA8BBWj1OlVN+BA+DKwcB1hV/zKURsHoIaUcNp0jQK1oDDYN1UxwQVarwMVRdzfyFxLBmlQKRXQPg8D/fgzQHVSQS0zI8TCUEHw7EcIbAKf3y1tI1LR0BgFarBlIfGsBgtEIljoEAdgwBsBgQ2gYRwUngUjYHx8DXcbEksBtVCA20XtiUDgQI0DdBVjlqqk1Y6qUCArBwBLfyDT4KUShDBCB4D9lLxyqSMg2g1BSCSIwlNstYXq2AcAeB0vZLFQ5HLYfMJWgRax2YW9IhgCjHpcyDAoAeAgbx54fqmR+Ok4MI9Ly7ytr6cfYlEkRvpg/YAukiss+2qHIMjAzTDfyBGVAyUA4eA8B/IiWr0RxGBEAPBgUA8BoqCC2OAhiMOhDZBQBCLPjvzQIrTKpgvSA3BJYRQctgiyJgYBY6Bk4QQUKoFAJIkF4KUGBQMlwjtjzPiMDFyQA5tKOwRE4IrSX6sA4RmAUzTH/A8J/2q1SUDIfB8CtALBsP5BsDwEEGJI6CADAfAOH4hpfD4eKAhpGUrYFlQMpAOEsdNfbBUDlP4+JQbFQlBC8DCGDCEIYNQUCZkG+B4SsHQIaQS8H4IEH4lCT4etDxOI6RplphoetAw3VolUWTgxWD4UAW38iisGUAw+BgOApQUDYB4HgOgdBko7AM2JgDB7gQ0wQh4BxWlSh+nHqQHD6F4/bbD4QGFaegaXoqGAPAQQYkjoIAMB8A4fiGl8Ph4oCGkZStgWVAykA4Sx019sFQOU/j7fyHAbFSYGYVgwII8HYBoKBn6cHgIHED6ZIrEoIKYvHyVKnH4hDgeJErQGE5e217Ww+Y6rKOANLFw/LsB4CB/APEoFOmg9ZBh+AcnYH1Z8CL9U0Iwl+bV+TAyMcNf/5ocojrfyBIBBBk6UDoQWAZkGgNQeAgdRGEplgQk8wEEeAhYCjAMSpFRc0377ReJCsA8v+qZYHKZWOgNgxUOIkBkQDAbIhl7Y7EcSx6yzFbI/HqUvbV40OIz9On//W/KlhwIN+ORwQA5H8iANqcdN4DAhAw/SBDA4P1QIAKQGHgjCWBwGHLAOAPwfgGfLEqoGD4GRiBSwQQ/Bg65SgGC0JgHQZkSwcAaXg2gogYfg8BA7pAgMtsiWXD9WIwHAeB/xU2f8WgaVjwGD5kFH8S+FrA5XA2jqeHwYD+QeAw+gN6AzCQvTMAwKBKDfokj0HAgg8D/Pj9WxiYeB4DKPhAEZr6povaLByBsQA/Sh+sHyuA+HAHi4SQQwDPg8B/GgoxLaCGB4GLE4PAQN6UuTJ0zONDrU/wYuaaweJBy2BhsQQZZUPAYOweEgDwfD/82/kDYEIel3geAgcwYfjpsvYwvTghg3mUg+1X7E6vGPCGBv7SQGRt+YLfB+zFgZFDgtBmx+Pk4MB4HgIHlIwlSNpR0kBleD9M0nabVlwGwUv203k7QF0ggsAZVCCjYXixG38bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/K/cgQB/nfKQZSIV5S/+fsA0EHu4OgLJ/B10f1cuLWFUWEsL21RaDgDhJAuOAYQQgFvA+A0WgiAGjgtB4T/vEEOwNCADFXA/BFKwcCyfxtt/G238bbfxtt/K9sBgRB5nfKYOxD25Ev9/MBECFwtHQdJvDdQP4CtLyxtVqwlAMbVloOAOEgC5YDCCEHnQ+A0WgiAGDkcA8J/3iCBYDYggxVwPgRCsHAs38bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfyv3KEIf7zykGUiFFNSfv5ANBBKsHYFk3g66PquXljCuLCUF7fuQIQ/zvlIMpEKqKl/P2AaCCV4OgLJ/B10f1cuLWFUWEsL38bbfxtt/G238Vti0GgQAcCCB0eCEWjiJ/6W/DhhprU7agFSOGvnvqe8BgRB5vPKaJYhy/A1/P2AaCFzPjsCyv0D1QPoCtSFravVhLAYxGmSgwQhHSsAosA8JZckZV6wPAPgwIqttmJUpY15P8RmwcPvB+WJgN8Bg4aJh4lBCEMuVpWgbhe35Jol5qdpIr1NrAhKwND1P744BkSZlZO2iVKYpPfS2oDgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEjwDwQbJsEIS7N8k/LB8nuXB38cDz4MiDoetgYLkCtlZNaRfS26B4IcltEIS5MaS/to+TzJg6+OB78GRAWHjYGC+olTKys9gcCAAepLAcB4RxyOQNDgGHBeWgih8CnA0oEAGRgqgcPxwjA0DLgkfS34B4IdmbBCEu5vkjcsLk6jcEv44HXwLB0PWw8SA8L/50GGx90DwQ5N2iEJczPJW7aXJ1GYJXxwO/gWDoeNh4lB4WAPoMNz30t4HAgAHqSwHAeEccjkDQ4BhwXloIofApwNKBABkYKoHD8cIwNAy4JHgHgg2TYIQl2b5J+WD5PcuDv44HnwZEHQ9bAwXIFbKya0i+lt0DwQ5LaIQlyY0l/bR8nmTB18cD34MiAsPGwMF9RKmVlZ7A4EAA9SWA4DwjjkcgaHAMOC8tBFD4FOBpQIAMjBVA4fjhGBoGXBI+lvwDwQ7M2CEJdzfJG5YXJ1G4JfxwOvgWDoeth4kB4X/zoMNj7oHghybtEIS5meSt20uTqMwSvjgd/AsHQ8bDxKDwsAfQYbnvpbwOBAAPUlgOA8I45HIGhwDDgvLQRQ+BTgaUCADIwVQOH44RgaBlwSPAPBBsmwQhLs3yT8sHye5cHfxwPPgyIOh62BguQK2Vk1pF9LboHghyW0QhLkxpL+2j5PMmDr44HvwZEBYeNgYL6iVMrKz2BwIAB6ksBwHhHHI5A0OAYcF5aCKHwKcDSgQAZGCqBw/HCMDQMuCR9LfgHgh2ZsEIS7m+SNywuTqNwS/jgdfAsHQ9bDxIDwv/nQYbH3QPBDk3aIQlzM8lbtpcnUZglfHA7+BYOh42HiUHhYA+gw3PfS3gcCAAepLAcB4RxyOQNDgGHBeWgih8CnA0oEAGRgqgcPxwjA0DLgkeAeCDZNghCXZvkn5YPk9y4O/jgefBkQdD1sDBcgVsrJrSL6W3QPBDktohCXJjSX9tHyeZMHXxwPfgyICw8bAwX1EqZWVnsDgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEj6W/APBDszYIQl3N8kblhcnUbgl/HA6+BYOh62HiQHhf/Ogw2PugeCHJu0QhLmZ5K3bS5OozBK+OB38CwdDxsPEoPCwB9Bhue+lvA4EAA9SWA4DwjjkcgaHAMOC8tBFD4FOBpQIAMjBVA4fjhGBoGXBI8A8EGybBCEuzfJPywfJ7lwd/HA8+DIg6HrYGC5ArZWTWkX0tugeCHJbRCEuTGkv7aPk8yYOvjge/BkQFh42BgvqJUysrPYHAgAHqSwHAeEccjkDQ4BhwXloIofApwNKBABkYKoHD8cIwNAy4JH0t+AeCHZmwQhLub5I3LC5Oo3BL+OB18CwdD1sPEgPC/+dBhsfdA8EOTdohCXMzyVu2lydRmCV8cDv4Fg6HjYeJQeFgD6DDc99LeBwIAB6ksBwHhHHI5A0OAYcF5aCKHwKcDSgQAZGCqBw/HCMDQMuCR4B4INk2CEJdm+Sflg+T3Lg7+OB58GRB0PWwMFyBWysmtIvpbdA8EOS2iEJcmNJf20fJ5kwdfHA9+DIgLDxsDBfUSplZWewOBAAPUlgOA8I45HIGhwDDgvLQRQ+BTgaUCADIwVQOH44RgaBlwSPpb8A8EOzNghCXc3yRuWFydRuCX8cDr4Fg6HrYeJAeF/86DDY+6B4Icm7RCEuZnkrdtLk6jMEr44HfwLB0PGw8Sg8LAH0GG576W8DgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEjwDwQbJsEIS7N8k/LB8nuXB38cDz4MiDoetgYLkCtlZNaRfS2iPgaAdHpemLsEYetanZ1ib8uSz3hwnaLkv/h4BYeMt55LQeEgD6DDc8JS/QYDwhDv4BzIIA/L8SM6Ph+CCDCCraS4z7SxP/w88Dh98QPoohgm+lsZAHgzAkiEIY/HiouH49YY1MOE7SRXs+wO9HA6D3zRYCtZBw+WWVpWm1QBB6geCDJdohCXJnkv7aPk8yYOvjge/BkQdDxsDBegVMrJ7CL6W8DgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEjwDwQbJsEIS7N8k/LB8nuXB38cDz4MiDoetgYLkCtlZNaRfS26B4IcltEIS5MaS/to+TzJg6+OB78GRAWHjYGC+olTKys9gcCAAepLAcB4RxyOQNDgGHBeWgih8CnA0oEAGRgqgcPxwjA0DLgkfS34B4IdmbBCEu5vkjcsLk6jcEv44HXwLB0PWw8SA8L/50GGx90DwQ5N2iEJczPJW7aXJ1GYJXxwO/gWDoeNh4lB4WAPoMNz30t4HAgAHqSwHAeEccjkDQ4BhwXloIofApwNKBABkYKoHD8cIwNAy4JHgHgg2TYIQl2b5J+WD5PcuDv44HnwZEHQ9bAwXIFbKya0i+lt0DwQ5LaIQlyY0l/bR8nmTB18cD34MiAsPGwMF9RKmVlZ7A4EAA9SWA4DwjjkcgaHAMOC8tBFD4FOBpQIAMjBVA4fjhGBoGXBI+lvwDwQ7M2CEJdzfJG5YXJ1G4JfxwOvgWDoeth4kB4X/zoMNj7oHghybtEIS5meSt20uTqMwSvjgd/AsHQ8bDxKDwsAfQYbnvpbwOBAAPUlgOA8I45HIGhwDDgvLQRQ+BTgaUCADIwVQOH44RgaBlwSPAPBBsmwQhLs3yT8sHye5cHfxwPPgyIOh62BguQK2Vk1pF9LboHghyW0QhLkxpL+2j5PMmDr44HvwZEBYeNgYL6iVMrKz2BwIAB6ksBwHhHHI5A0OAYcF5aCKHwKcDSgQAZGCqBw/HCMDQMuCR9LfgHgh2ZsEIS7m+SNywuTqNwS/jgdfAsHQ9bDxIDwv/nQYbH3QPBDk3aIQlzM8lbtpcnUZglfHA7+BYOh42HiUHhYA+gw3PfS3gcCAAepLAcB4RxyOQNDgGHBeWgih8CnA0oEAGRgqgcPxwjA0DLgkeAeCDZNghCXZvkn5YPk9y4O/jgefBkQdD1sDBcgVsrJrSL6W3QPBDktohCXJjSX9tHyeZMHXxwPfgyICw8bAwX1EqZWVnsDgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEj6W/APBDszYIQl3N8kblhcnUbgl/HA6+BYOh62HiQHhf/Ogw2PugeCHJu0QhLmZ5K3bS5OozBK+OB38CwdDxsPEoPCwB9Bhue+lvA4EAA9SWA4DwjjkcgaHAMOC8tBFD4FOBpQIAMjBVA4fjhGBoGXBI8A8EGybBCEuzfJPywfJ7lwd/HA8+DIg6HrYGC5ArZWTWkX0tugeCHJbRCEuTGkv7aPk8yYOvjge/BkQFh42BgvqJUysrPYHAgAHqSwHAeEccjkDQ4BhwXloIofApwNKBABkYKoHD8cIwNAy4JH0t+AeCHZmwQhLub5I3LC5Oo3BL+OB18CwdD1sPEgPC/+dBhsfdA8EOTdohCXMzyVu2lydRmCV8cDv4Fg6HjYeJQeFgD6DDc99LeBwIAB6ksBwHhHHI5A0OAYcF5aCKHwKcDSgQAZGCqBw/HCMDQMuCR4B4INk2CEJdm+Sflg+T3Lg7+OB58GRB0PWwMFyBWysmtIvpbdA8EOS2iEJcmNJf20fJ5kwdfHA9+DIgLDxsDBfUSplZWewOBAAPUlgOA8I45HIGhwDDgvLQRQ+BTgaUCADIwVQOH44RgaBlwSPpb8A8EOzNghCXc3yRuWFydRuCX8cDr4Fg6HrYeJAeF/86DDY+6B4Icm7RCEuZnkrdtLk6jMEr44HfwLB0PGw8Sg8LAH0GG576W8DgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEhNICEIZerSNA3C9ryXRKzE7aRXibGBDVgaHif/xwDIk7KyZtErUVQf+pqi0HAHCSBcsBhBCB3gfAaLQRADRwOQeE/7xBAuBoQAYq4H4IpWDgWTESdWDBCCEXqwUTIICcvaVJwND0EOj3cEnEqYcsN/TJhAEAcKm/tgywMNk5YxU4GxAVPHoKMIQOAMA6PRCjYgxN/BwyBFUy15Om+CvEBsWfG2Mn8bbfxtt/DaoHBDZAOEcfCGm5rZd9Soo2/7/q021QYNWP/C5nfkUaA6DBCHRfAOjwIABoMOWqBtIJCdkfA4DyRKHfta+0DwX/WOFTYGQCjiVO2P06svV1SylZZTt022z8t+L3hqfxrNv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv5EUitsfJ1RexVDKRlpW1ocMtfHPheYpkDoMEIej+gdHoQgDcEn/wNpRKTMj4HAeTpUfy37YPBf9Y4Vtg+H/5rBr+EeAPENkA8Qx+IafkbL/qVEG3ve9GmmoDBox7zQmOt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/DaKRO2Pk6ovYqhlIywnavETbXxy0FDW+RVsDoMEIel9A6OwhAGwSf/A2lEpMyPgcB5OlR/LW2weC/6xwrbXALVAPENkA0Qx+IafmNl/1JZBt73vRppqAwaMe8Fwan8bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G234AAAG2WfAz///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+AAABtlpgM////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////gAAAbZa8DP///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////4AAAG2W2Az///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+AAABtlvwM////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////gAAAbZcYDP//////////////////////////////////////WwYE/UwfygCQfygCdTB/KAJB/KAJ1MH8oAkH8oAnUwfygCQfygCdTB/KAJB/KAJ1MH8oAkH8oAn6mD+UASD+UATqYP5QBIP5QBOpg/lAEg/lAE6mD+UASD+UATqYP5QBIP5QBOpg/lAEg/lAE////qYIwPj//Y3Sg85AGkH//////////////t5UD83/zt5aDAt/////XwuC//////////////////////////////////////////////////7GKv//////7eD0X/6EX//////////r4XBf////sYSf/////////////////////////rYPPf/oZf//////////////+8AAAG2XPAz///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+AAABtl1gM////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////gAAAbZd8DP///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////4AAAG2XmAz///////////////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AJAdJp2/CH26q+OftrCOBcegyJKiVB8DgU2DpOD4X/r9Lbo9SIAPCVA8T52fCH2ar+WtfXEkOx4DDZIgD4HAp4O1YPhf+rBi0uLVIOBAEJQII5HA5APLCwPhAA0Dwf+2IIgDkHD8GRgbA2sH4KYHwoA/6W3B6mW6B4SuliaW34Q+2ptLf/WEcOx2DDZKgkBwKaDpWD4X/q6PUy/APCV0sTyWfCH2VPpb764jh2OgYbJEFgOBTwdqwfC/9fpbYMWlxapBwIAhKBBHI4HIB5YWB8IAGgeD/2xBEAcg4fgyMPwNgwFQUwPhQB7wel3J0IAkB0mnb8Ifbqr45+2sI4Fx6DIkqJUHwOBTYOk4Phf+v0tuj1IgA8JUDxPnZ8IfZqv5a19cSQ7HgMNkiAPgcCng7Vg+F/6sGLS4tUg4EAQlAgjkcDkA8sLA+EADQPB/7YgiAOQcPwZGBsDawfgpgfCgD/pbcHqZboHhK6WJpbfhD7am0t/9YRw7HYMNkqCQHApoOlYPhf+ro9TL8A8JXSxPJZ8IfZU+lvvriOHY6BhskQWA4FPB2rB8L/1+ltgxaXFqkHAgCEoEEcjgcgHlhYHwgAaB4P/bEEQByDh+DIw/A2DAVBTA+FAHvB6XcnQgCQHSadvwh9uqvjn7awjgXHoMiSolQfA4FNg6Tg+F/6/S26PUiADwlQPE+dnwh9mq/lrX1xJDseAw2SIA+BwKeDtWD4X/qwYtLi1SDgQBCUCCORwOQDywsD4QANA8H/tiCIA5Bw/BkYGwNrB+CmB8KAP+ltweplugeErpYmlt+EPtqbS3/1hHDsdgw2SoJAcCmg6Vg+F/6uj1MvwDwldLE8lnwh9lT6W++uI4djoGGyRBYDgU8HasHwv/X6W2DFpcWqQcCAISgQRyOByAeWFgfCABoHg/9sQRAHIOH4MjD8DYMBUFMD4UAe8HpdydCAJAdJp2/CH26q+OftrCOBcegyJKiVB8DgU2DpOD4X/r9Lbo9SIAPCVA8T52fCH2ar+WtfXEkOx4DDZIgD4HAp4O1YPhf+rBi0uLVIOBAEJQII5HA5APLCwPhAA0Dwf+2IIgDkHD8GRgbA2sH4KYHwoA/70lt5F+rThCIFQMJQl4EBOITY9A4mCCOx+IQ7TBAViO1g73Eg+AP8WVgGDlWDdaA1AYpBhsQoCEEMfFw7EhX8Qi6eSJxC/9v2iXrIPBQC4/abTq898S6wOR21BwnlYA0DwsAmqSU7IhgwkAdCGAalA+Ok5cyB4dj/478AbB6mxUkVN/LQ9BWiC0DcBkQMBI59Qa06ZkuVtpk/tYVsf3zbXxv7fAlKiEGZAPEgfgpRJTqh0PBJVUvziVXolzUvkwkMDocA4uHqZgEQctA3Y2wwqT9BgKgaoMAoa4P0kk6EASA6STt+ENTdVNjn7awkh2PYCsSolQfA4FNg6Tg+F/6/S26PUiADwlQPE+dnwh9mq/lrX1xJDseAw2SIA+BwKeDtWD4X/qwYtLi1SDgQBCUCCORwOQDywsD4QANA8H/tiCIA5Bw/BkYGwNrB+CmB8KAP+ltweplugeErpYmlt+EPtqbS3/1hHDsdgw2SoJAcCmg6Vg+F/6uj1MvwDwldLE8lnwh9lT6W++uI4djoGGyRBYDgU8HasHwv/X6W2DFpcWqQcCAISgQRyOByAeWFgfCABoHg/9sQRAHIOH4MjD8DYMBUFMD4UAe8HpdydCAJAdJp2/CH26q+OftrCOBcegyJKiVB8DgU2DpOD4X/r9Lbo9SIAPCVA8T52fCH2ar+WtfXEkOx4DDZIgD4HAp4O1YPhf+rBi0uLVIOBAEJQII5HA5APLCwPhAA0Dwf+2IIgDkHD8GRgbA2sH4KYHwoA/6W3B6mW6B4SuliaW34Q+2ptLf/WEcOx2DDZKgkBwKaDpWD4X/q6PUy/APCV0sTyWfCH2VPpb764jh2OgYbJEFgOBTwdqwfC/9fpbYMWlxapBwIAhKBBHI4HIB5YWB8IAGgeD/2xBEAcg4fgyMPwNgwFQUwPhQB7wel3J0IAkB0mnb8Ifbqr45+2sI4Fx6DIkqJUHwOBTYOk4Phf+v0tuj1IgA8JUDxPnZ8IfZqv5a19cSQ7HgMNkiAPgcCng7Vg+F/6sGLS4tUg4EAQlAgjkcDkA8sLA+EADQPB/7YgiAOQcPwZGBsDawfgpgfCgD/pbcHqZboHhK6WJpbfhD7am0t/9YRw7HYMNkqCQHApoOlYPhf+ro9TL8A8JXSxPJZ8IfZU+lvvriOHY6BhskQWA4FPB2rB8L/1+ltgxaXFqkHAgCEoEEcjgcgHlhYHwgAaB4P/bEEQByDh+DIw/A2DAVBTA+FAHvB6XcnQgCQHSadvwh9uqvjn7awjgXHoMiSolQfA4FNg6Tg+F/6/S26PUiADwlQPE+dnwh9mq/lrX1xJDseAw2SIA+BwKeDtWD4X/qwYtLi1SDgQBCUCCORwOQDywsD4QANA8H/tiCIA5Bw/BkYGwNrB+CmB8KAP+ltweplugeErpYmlt+EPtqbS3/1hHDsdgw2SoJAcCmg6Vg+F/6uj1MvwDwldLE8lnwh9lT6W++uI4djoGGyRBYDgU8HasHwv/X6W2DFpcWqQcCAISgQRyOByAeWFgfCABoHg/9sQRAHIOH4MjD8DYMBUFMD4UAe8HpdydCAJAdJp2/CH26q+OftrCOBcegyJKiVB8DgU2DpOD4X/r9Lbo9SIAPCVA8T52fCH2ar+WtfXEkOx4DDZIgD4HAp4O1YPhf+rBi0uLVIOBAEJQII5HA5APLCwPhAA0Dwf+2IIgDkHD8GRgbA2sH4KYHwoA/6W3B6mW6B4SuliaW34Q+2ptLf/WEcOx2DDZKgkBwKaDpWD4X/q6PUy/APCV0sTyWfCH2VPpb764jh2OgYbJEFgOBTwdqwfC/9fpbYMWlxapBwIAhKBBHI4HIB5YWB8IAGgeD/2xBEAcg4fgyMPwNgwFQUwPhQB7wel3J0IAkB0mnb8Ifbqr45+2sI4Fx6DIkqJUHwOBTYOk4Phf+v0tuj1IgA8JUDxPnZ8IfZqv5a19cSQ7HgMNkiAPgcCng7Vg+F/6sGLS4tUg4EAQlAgjkcDkA8sLA+EADQPB/7YgiAOQcPwZGBsDawfgpgfCgD/pbcHqZboHhK6WJpbfhD7am0t/9YRw7HYMNkqCQHApoOlYPhf+ro9TL8A8JXSxPJZ8IfZU+lvvriOHY6BhskQWA4FPB2rB8L/1+ltgxaXFqkHAgCEoEEcjgcgHlhYHwgAaB4P/bEEQByDh+DIw/A2DAVBTA+FAHvB6XcnQgCQHSadvwh9uqvjn7awjgXHoMiSolQfA4FNg6Tg+F/6/W9JbeRfq04Quj1IgA8JUDxPnZ8IfZqv5a19cSQ7HgMNkiAPgcCng7Vg+F/6lhGA+OkwQx4X/A4OvMKhHSD/Rx8Q7rTDQ49fNCCJKsCsBVgrQ/oG1gcCmByR/2FvAYEAIYeMfEsEJMJLeKfAcTiH7wh0tAt4PhwOBy2gB8L/1DArTKy5W2kTta0rVf1ptrRv7fB3jyIhgwlAdEMA1KB8dJ0jYHh2X6O/AGweqoqSMN/LQ9BlxBB4KAXBkQMBI4KwYSR2mEcG0IY/LtLVW/Y95sGNgaTMJvtg8R/6ttfZE/xqscv422/jbb+Njlv5QytzgQh5qj3aJYQ8v4Xfxu8SAeKvjvo4T+hXwfQFaOi1lWrWEsBh5t3oQh5in3YJYQ9voX/1udSAeK/jrg4TepXwfwFaOyxlUrWEoBj+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv52kk9G06r1G31bHqGn/ucjwHAqx8Dw//iwDxf/yLPsEqJIMXA8BBC+BgULAPAQOsBhGH6Zsdlw5aBQj4GoQ22WtBuMjsHg4BkvbVqkSagZtVg+PAHjQSgYEJoHgYF8HgP4EIAlBAYBDBShCBh6mAOCEPcHnmxDHCQSQhNfA1/g5SAxV9XU8YZECQQQN0DUP2DwH7eDwH8WEEGLgYRy8fAGCEDYqHoMOB9wFL8D3mQUuYlVLtA3W2xwIEQMMpqcNA3hLSg8DBZg8B/Cl4KdmD9WChEqsp4z+J2wbo/AM8lHP0If++nisGDmNtx/2DBBqDCQDfBi5lMmBShAZEj5fNqYPUydpksLC0cKy5lWw3+FvutRvsHLRtwtoPAQSoPAfyMHQMIOgyYdgiTC3haPqP1APDwD4IoMIIPFQB58FqDDkeDoGEgHgIMURwg/mj4fApWfq221xAYSD1vxaDFA8LGgfJgCRQPAUQIIQgeA/dx2DwH8mI4Qx14GCFsEtODgQaB8fUfD8Sk4kNtDoSvexms/A0yOAYRA+oIgMVdoWfZ0AwGA4DJQbR5RHBAAOTiOPIJYMH2ssfSFQ/HDTZZ9rWYz70aZ8DL1oDTBbOgFrAowYdA2gyovVDoEEICseJy9dJ7qpPE65YDLNlzKthn/f4BhhvbLslhEGJoR58HgIKsHgP5MfiNv0jA7BgUrNa81RxQVQ9BwKAGKueBkAgUcDhFwjF1EMShKA4DwEGqI4B/8o+SKxJrLKu9xUWAiNNJ//AgOmQNMg+RAF/ZASQeAggQYA0IAhaAeJYN4fqhKzw5bbLR99tU0Iv04+YbbH6TIjtVqmhAIQyNNfVt+Yal1tn+NeyCL/+lnjAhEPfgoBLBi8GbSAwBwhA3AgiUIfk4MO0oPAfxpaJQB48/4c+LAgNtD0sbZHHmEg+YVAywOBVJE4OBZAYFI19tOqaYpY22yx5r9qhR//w80tBiD0yQIYKID4MwJPwgAGlwMoEtrGv6qrKpkqZxT7S1I2ywiNBeWEoSi5WXNNtsd3S3pX+AyH8i2bIiNRxAGA6BwEMEAGqcSAb4jgwIoH/MJvsjuD1OJJbn1TX2sA2P40ro4aBjbpCADwcA2qB3weL/9xaMiXu4xLluB7EV4Imk2iFgYDQ6BgIA4PWQfN/+xgDAiCUDAQB4r/5ZB83/7KsfD9kGDlsGGOhLUTAwGh0DAQB4r/5ZB83/7FYMCIJQMBAHB6yD5v/23h+yDBy2DDHQl0TAwGh0DAQB4r/5ZB83/74MBoSgYCAOD1sHzf/tvD9kGDlsGGOhLomBgNDoGAgDg9bBwEQciNO4MCIJQMBAHiv/lkHzf/tvD9kGDlsGGOhLojBgRB0DAQB4r/5ZB83/7wMBodAwEAcHrYOAihQmyFvD9kGDlsGGOhLonBgNDoGAgDxX/yyDgIg7p1gwGhKBgIA4PWQcBEHDF7eH7IMHLYMMdCXSMDAaHQMBAHiv/lkHzf/sciS1jA/b96KPRRyLoWvVDVyuogmeA+Dwf/OrBgJA8XAFiwWEr/9Zs3+6HlR7aIuE/GabYhI22/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/lDKqLQcAcJIFywGEEIHeB8BotBEANHA5B4T/vEEC4GhABirgfgilYOBZHm1OhCHuqPdglhCyfpd/W51IB8q8O+jhN6lXR9QVo7LGFapYSgGv422/jbb+Ntv422/lastBwBwkAXHAMIIQSzofAaLQRADByWA8J/3iCHQGxBBirgfAiFYOBZttzgQh5qj3aJYQ8v4Xfxu8SAeKvjvo4T+hXwfQFaOi1lWrWEsBj+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv42u38YOxt/G238bbfxtt+gwwJ4nT+ijytnMiLMb/IMI9qC/JUZ3RJBVl4PC/+KcHif/dsGCVgqy8Hhf/FODxP/u2DBK2FKjA4wYOiIwVZeDwv/inB4n/3bBglYKsvB4X/xTg8T/7tgwSt6jA4wYO0Rgqy8Hhf/FODxP/u2DBKwVZeDwv/inB4n/3bBglbyzA4wYO0Rgqy8Hhf/FODxP/u2DBKwVZeDwv/inB4n/3bBglb1GBxgwdojBVl4PC/+KcHif/dsGCVgqy8Hhf/FODxP/u2DBK3qMDjBg7RGCrLweF/8U4PE/+7YMErBVl4PC/+KcHif/dsGCVvUYHGDB2iMFWXg8L/4pweJ/92wYJVd3f1T/bu1Hu3tGL25yRFJBNxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238vaWCEPfd8poliFZ6pf5+yJgglfh4BZX6d8pH9BWpC1hUqWEsBrzCkHAHCWDIywGEEIHeB8BotBEANLC0HhP+8QQLgaD4GKuB+CKVg4Fk/jbb+Ntv422/jbb+V/5QhD7O4pg7EO3Il/f8A0EAbjoOk3ireD+LpSxtjVhKAY2rLQcAcJAFywGEEIPOh8BotBEAMHI4B4T/vEECwGxBBirgfAiFYOBZv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+dlAhMeTiGJCZmdaVj0uYbnedxtX72hS5AMB4P/XA8Dw//CJQPF/+IXfUC3BgUIPAwPoMIwKMSwbGgeAge4PdTJk18JYPA/2IIaYrSj5pP7QRAYrBkRaDARB8L/1BphQB4D99TA8B+uj8RwDS4GzByDCMlEgPvtdBk2KVXxILBD8pEvgQ17U6JcHhYA8gC3gIYMJAOBRAw40GAMUghgqtYB4H/JB4H+/CHgOSgUHoOA8OAVQGAYCHRAp77CkXA8BBIgwKUexNqQG8nZaSiUqENUyCnEjFXx4lhaqBl2y+MCDYuDcWSmwClQeBghwYFCDcLxJB4GBDEnEg61mK62IDTIMVAX1oeNtwfpWoDBymVMqqvx4SQYII7TtAfB4CC980lZxjBCBkxfnsZ0QI3qsSmg/ngZEWjgDAgoxwcEg6H6eg8B+LgHsAbbqqAcxJGhz30YBwIQQi8PlAEAU6lWOIDwn/md+zANAYEAGH4MP2m2QUQBzI7TMaIGpxymHyYQRuOANjtlUq+zz/usNM9D1h4sBh4DDptgGBBHwN4GSgyoQxJ0sS/ZBCTMqi5Or8DcZbabxO2IYHmBxAZGOYJKqB4DCKDDZ4YAYIAMPgOCODCMrHrAQk4MIY/YBqP8ZBmAeB/s9BsEYEBIDwv++2BlgDNYSJusgxWH8EDgMHSs8FQdttNg8B+cgwQG/b7cbBgDtvvtKwN+bzQeB/xf+HCcHhP/GFi8Z7RBI/sUD4A4A6g8BBGg8B/BiPgHk3i8dAysfJhIZL2FScRxGSAwGwDvApvD0Fa37jSdthPWFYMv14fQgBAEsEES0gHhCVApxLHuliRNPtNDnVF+Bdn7DHgMeBkN4Jw5pLAeBgnQeA/ZzYPA/1qQHi/9UHwIC0qDFwkBDCCDUIAlAGD+CQENOlUDwe1KrHgGmZ7fqQRUnWWkIMi6ygjvTsSR0XhCHZeJA7aA0Px4rZSJVVuaOGWtUN+UtAYWoMa64SpEybUyq1UqtrDVtHHl++nREIVgUaQdjoA0IY8EdXB+JZcJZemHpfFf0oIqVpO35OrA23I2yOWVNHLIK2kKZcDgUyQGBXMA4OnDYk2z3l7F6iFFREwUxcDwsAimB4mAL8EjBTFwPCwCKYHiYAvwSJMeLfhzoxfojBTFwPCwCKoHiYAvwSMFMXA8LAIqgeJgC/BI3lvw50Yv0RgpkgPCwB6oHiYAvwSMFMXA8LAIpgeJgC/BI3qdDnRi/RGCmLgeFgEVQPEwBfgkYKZIDwsAeqB4mAL8Ejep0OdGL9EYKouB4WARTA8TAF+CTgqi4HhYBFMDxMAX4GCRvLfgR0Ed+iYFUXA8LAIpgeJgC/BIwUyQHhYBFUDxMAX4JG8t0OdGL9EYKYuB4WARTA8TAF+CRgqi4HhYBFMDxMAX4JG8t+HOjF+iMFMXA8LAIqgeJgC/BIwUxcDwsAiqB4mAL8EjeW/DnRi/RGCqLgeFgEUwPEwBfgkYKYuB4WARTA8TAF+CRvLfhzoxfojBTFwPCwCKYHiYAvwSMFMXA8LAIpgeJgC/BI3luhzoxfpDBTJAeFgD1QPEwBfgkHoQ5mD1nMyZkLFsnIBb1ROeCCDwf/OrBgJA8XAFhcLCRbWb22gYqElMSM22wv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+V5yhAHu88pgliFJ8ET+/kA0EMq8OwLJvUqUj6grUpY0rxYSgGt5yBAHud8poliFZ4EX+fsA0EMr8OgLJ/QqUj+grUha0qxYSwGv422/jbb+Ntv422/lbcgMCIPN55TRLCHC2F38bsiQDwdjsCydpf/C6ArR0Wsq1awlgMbbsBgRB5nfKYJYQ6WQv/rclSAeDsdAWTNXn+F8BWjssZVK1hKAY/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv42db+MDBNv4wcbb+Ntv422/QbO4CGPuQGKvsLQFYxOtNAqGqrT/ZvvDb1Z/9u5IW7u9ex2Q2xjMk+gJtEkHAhlwcKwRvA+b/8sHAhlwcKwRvA+b/85heDFiYFSNYiMHAhlwcKwRvA+b/8sHAhlwcKwRvA+b/8t4MWJgVI10Rg4EMuDhWCN4Hzf/lg4EMuDhWCN4Hzf/lvBixMCpGuiMHAhlwEFYI3gfN/+WDgQS4CCcEbwPm//LeDFiYFSNakMHAglwEE4I3gfN/+WDgQS4CCsEbwPm//LwmwZQqNaD5P/3GRg4A1IBBOMGikHxf/dg4EFIBBOCN4Hzf/mngxYqJRrg4JERg4EEuDhWCN4Hzf/lg4EEuDhWCN4Hzf/nPBixMCpGuiMHAglwcKwRvA+b/8sHAglwcKwRvA+b/8t4MWJgVI10Rg4EEuDhWCN4Hzf/lg4EEuDhWCN4Hzf/lvBixMCpGuiMHAglwcKwRvA+b/8sHAhlwcKwRvA+b/8t4MWJgVI10Tg4EMuAgnBG8D5v/zY9bidL9nOzNLOKVqHUhuwlG8W4K+y2kzTFU+73nF71ZahNxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfytUWg4A4SQLlgMIIQO8D4DRaCIAaOByDwn/eIIFwNCADFXA/BFKwcCybzgMCIPd55TBLEKT4Gv7+QDQQ+54dgWVeoeKR9QVqUsaV4sJQDX8bbfxtt/G238bbfytWWg4A4SALlgMIIQedD4DRaCIAYORwDwn/eIIFgNiCDFXA+BEKwcCzb3gMCIPN55TRLEOX4Gv5+wDQQuZ8dgWV+geqB9AVqQtbV6sJYDH8bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxttlyPtsW422/jbb+Ntvkbbfxtt/H238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfyv3IEAf53ykGUiFeUv/n7ANBB7uDoCyfwddH9XLi1hVFhLC9tUWg4A4SQLjgGEEIBbwPgNFoIgBo4LQeE/7xBDsDQgAxVwPwRSsHAsn8bbfxtt/G238bbfyv/KEAfZ3ykGUiHeQv/v5ANBA5ujoCybwd8H8XLyxlVVhKC5tWWg4A4SALjgGEEIJZ0PgNFoIgBg5LAeE/7xBDoDYggxVwPgRCsHAs38bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfyv3KEIf7zykGUiFFNSfv5ANBBKsHYFk3g66PquXljCuLCUF7fuQIQ/zvlIMpEKqKl/P2AaCCV4OgLJ/B10f1cuLWFUWEsL38bbfxtt/G238bbfyv/IEIfbzykGUiHFMSfn7ANBAKtHYFk/g74PouXFrKurCWFzf+UIQ+zvlIMpEOqIl/fyAaCAV6OgLJvB3wfxcvLGVVWEoLn8bbfxtt/G238bbfxtt/G238bbfw2VHTaZUOx8rHpcDFo8YSqmx+OhB+XA3Unv59OPgNsAZSB+1NYD9iKVRapczX2CqVD4HgIINkHgIG8dgwIIPAwPagFUEPBCBgRAeB/sQUujkPUng/BlKxYDgU4FgRAZARhv8DAogZODF4IoKMGSAwkJR8CiV/348SUS/MjhjAKFycQ2Uw/CGHvvgZYYbXWFR8SqDwUG+CCSgw9akXBUg+BATgq/UfAowOAwIAB4KQGEpMDD2AoAgCRnvtAwH2wafBlQ9HmMFvk4jAyL4gNpl2QZdlPGgZEykD6mfsbgeBhKBmQQRHTgeEIA4epxJH13w/TXANAqt98svh/9KIDBcHqwMuy0hJkgDwYDgMXgpBLHgjAdA4yPC8Q2/MCVL9Iy0lZ//zRY0lTNslxaWt+7A/BWFp0dhCHQjAwHwYeg8B/Ej0fqoPxLmCEHwMIwIAMnA0JAKH6QuHCUtA/5gdQdtDlJAVogNRIrL2+gwiAFlQYD7SpIEIHgIJ1I0JSoA6DwGHrSb4QPAfBwIOCF9gephyPviGDCK2qBgKgwEKDFSJp32L6DwEEyDwH8b7B+3QZIPVA6vFahsff3QeHgFWvF45EsHiIBmAEmxHB4CBvBhDAOA9AUIhgoi8fBCqjar/qYtVFtLGgRUnmaPy4Pf+gMVqmASUmB6lB4H8PSpgNsiBglAiY0OfjjGAeBgR0g7qrPAxoFODFdaBgInR+DFydsegzQPAfmoKdX9PCzyeDlvd6rLQUYGSkGWTAwIzTvqyQHgIHcGCGPQDqClVAoi/4Hoq1KBstTZv0meHIfsJPswfstDhE2qVMuDIIwDgPMg1BmAOA8D/TjoSi4DwBgM16AgA8B/HggAzbSTR0wCqYLB8lHggxn5eqYBWJRBVB8H7DPJRBBgFg0cBDBhGBwKIGHGgwBikEMFVrAPA/5IPA/34Q8ByWh0PQcB4cAqgMAwEOiAc+whgwKEGSBCCEB4HgIH1kAwSv+EsG+DeSD8S5Gx+lo7ElOmCF8PPNssjj7badsFYmjED2Q8FsHgII8HgIIcEEdAyvzIMI4MPvfA8lA4P6xBHYEgSU0S3S4cB+mLU4IiWNFvANsMKwLisKAQRLEoRhLB4CDfHoj77B4mBoJGs/b0sxUWiQEJppP/4FPKmSxkGAk8QgxcCkBRhAB4D9xBvggiGJKSAeBtwS/gcgMCKq34B7X2/JR0Hw8aZEEcBAEj32BBVscYgMHXYrP+mDLaBvAgpwUA9BmWBK34IQkF+DwfiWOUyhhL8EQs8OByH4PCf96SDlpisAZaleZEbM0esZuTZS3s2KYs19fiJHw3AqF48BABtVgwBgHQUKoIOMA3h8EFIkEsDgMOU7VSj34KocJwYQIDh8lHAG0wGWaH4eN8B8OAPHIHgeD/51QMBMHi4AsWuyDImwYo+DlD9EEYKYuB4WARVA8TAF+CRgqi4HhYBFMDxMAX4JEmGxboc6MX1EYKYuB4WARVA8TAF+CRgpi4HhYBFMDxMAX4JG8t0OdGL9EYKYuB4WARTA8TAF+CRgpi4HhYBFMDxMAX4JG8t0OdGL9EYKouB4WARTA8TAF+CRgpi4HhYBFMDxMAX4JG8t0OdGL9EYKYuB4WARVA8TAF+CRgpi4HhYBFMDxMAX4JG8t0OdGL9EYKYuB4WARTA8TAF+CRgqi4HhYBFMDxMAX4JG8t0OdGL9EYKYuB4WARTA8TAF+CTgqi4HhYBFMDxMAX4GCRvLdDnRi/RMCmLgeFgEUwPEwBfgkYKYuB4WARTA8TAF+CRvLdDnRi/RGCmLgeFgEUwPEwBfgkYKouB4WARTA8TAF+CRvLdDnRi/RGCqLgeFgEUwPEwBfgkYKouB4WARTA8TAF+CRvLfhzoxfojBTJAeFgD1QPEwBfgkYKYuB4WARTA8TAF+CRvLdDnRi/RGCmLgeFgEUwPEwBfgkYKouB4WARTA8TAF+CRvLdDnRi/RGCmLgeFgEUwPEwBfgkYKYuB4WARVA8TAF+CRvLdDnRi/RGCqLgeFgEUwPEwBfgkYKouB4WARTA8TAF+CRvLfhzoxfojBVFwPCwCKYHiYAvwSMFUXA8LAIpgeJgC/BI3luhzoxfojBTFwPCwCKYHiYAvwSMFMXA8LAIpgeJgC/BI3luhzoxfonBVFwPCwCKYHiYAvwMEmBVFwPCwCKYHiYAvwSN5boc6MX6QwVRcDwsAimB4mAL8EgYy+SJU8k5IrZkkWkk/VnPD8HAp0oPD/+bIPF//JkUEi27/ttKojorkarbEeNtv422/jbb+Ntv422/jbb+Ntv5WqLQcAcJIFywGEEIHeB8BotBEANHA5B4T/vEEC4GhABirgfgilYOBZN5wGBEHu88pgliFJ8DX9/IBoIfc8OwLKvUPFI+oK1KWNK8WEoBr+Ntv422/jbb+K2xMDCQJSQQwb4Qh8P4pTN/Z//wMbA0mZT40DxH/q21GBP9TVloOAOEgC5YDCCEHnQ+A0WgiAGDkcA8J/3iCBYDYggxVwPgRCsHAs2JRCBhLA6IYB6YDw9SFysDw/L8HXwQgZQqipMx/44DwFYH8bHgMjgK1H5cVBnA+k8PAgiGPmLawXCWPUzVqHyZW214Hy4Af42zj+Ntv422/jbb+Ntv422/jbb+Ntv4waDb+Ntv422/jbb+MHW2/QbQHSvEiVpn/bmKeqOdX9uIbDZO11sUy5UVllE+iDEHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8jxhcDFiY1oMNYiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/nA4EMuAgnGDQPm/+7eDFiYNwYa6IwcCCXAQVjBoHjf/kKWDgQy4CCcEZoHzf/dvBixMG4MNdE4OBQlwEE4IzQPm//LBwKEuAgnBGaB83/5bwYsTGtBhrojBwKEuAgnBGaB83/5YOBQlwEE4IzQPm//LeDFiY1oMNdEYOBQlwEE4IzQPm//OBwIJcBBWCN4Hzf/lvBixMa0GGuiMHAhlwEE4I3gfN/+eDgUJcBBOCM0D5v/y3gxYmDcGGuiMHAoS4CCcEZoHzf/lg4FCXAQTgjNA+b/8t4MWJjWgw10Rg4FCXAQTgjNA+b/8sHAoS4CCcEZoHzf/lvBixMa0GGuiMHAoS4CCcEZoHzf/mlf42rZbiL7f/zqPm7adbRpN5jt91cV8bbfxtt/G238bbfxtt/G238bbfxW2TA8k8PQgiEPmbYwXiWPEzdiDytW018GCn6n7kCEP53FNHYhVRUv5+wDQQRsOg6T+Dro/RpC1hiLCWA1haEMGEsDoQgDUoHh0mSMBAHhfo68AZB4mqpKw18sD0GWEEHgv+sGRgwE1j46BhJHSYRgbQhj4uwsTZ9V/zQMaA2mYT/aB4iANbb+wJvjbHL+Ntv422/hsaKk7JcraSpv60rY/n2m/DfN8Irmr8xhkGCCJYKrw9YHwKRMB9K2PUysIaQfZgB6hpMOgZf4MaD8P2wZdcDd82DFSoPgfC/9Tdtt7V7V70hHqstBwBwkAXHAMIIQSzofAaLQRADByWA8J/3iCHQGxBBirgfAiFYOBZ/422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbbnG22L8bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238Nm5bL2LWLzhA/8y7QMIYj0ffEtsuAOTiGXMj5IwENWAcIIlgZTDv4GwYcjkQGmVQIng+YZVAxWWs3nEys+NFadkvVtpU/9bVq/6223o33fh3rx80sEIe+75TRLELSyl/8/ZEgQSvw8Asr8Hvuj+grUhawqTLCWAWr8bbfxtt/G238bbfytsHg4CMef55TRLEPL/C7+fsiQIHM+PQLK/Tv1BdAVqQtZVq1hLAY22DwcBGPPd8pgliHt9hf/fyVIEDm/HgFlXrz6gvgK1KWMqlawlAMfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G236EbCuXAwFkwMCuYBwdOSHaI/LYICK8DsHzoAfQ226DB+DBsDg9B86AHqG23gwfgwbA4PQfOgB9DbbwYPwYNgcHoPnQA+ktjUFCDwkAiDxH/qDxkASSPTB4GBPEcHgYE0Hgf60PxKBgNAwGwYEQQA/+DFoGxwOS0PhAEAQAeI/8xBBgVB4UYbEFrP853qyNFbCQmlLZoAwA1sEBWyraHQHhLv2BAHAB4Qh8EFNCv3y1pUXtMteZDwC5o+E8FGJKrQeAgdx8XiOCgBkw+A6DKwg6DgPfCACiCEEJOkAMA8DAXH5aXNCD0eiTwuneRWmaC9OlsUMgw+BQgpMHQHgUgMBwGgB/8EYeF4jiOCjYCAOx2AeJacfAykv8BxMEMfsMgw4HhaDcB4KAlTAbD9psGKwRSIPwhAgsAGMiP/4N4Dw/EdtvMYgHUjacfBAHH2QRWPAebBWNtsgYVA8F/1iCBqNNrWs2hd9LYUADwaAGAdAPB4CCL2iQkBQJwYQ2B5oKERhG8CqTRhsSfJmh+PEiIuBgRWC0DQ4BWIudOpCR8DwMrBvgowYA5kGHwKAICSKgQ2AQE46EoDQB4hD8DjSVOm+lEmg4fiWOQNUFOwOxkIB76WwZI+k1KkzZ3NVsSw3L+HwV4PAfxY8BmQgD8G+0PgYFECgLgZVlTqkwQUgMIQQlKoeN+LAN4I7BcOUnw9WLwVgfbFl476Ww4AwlCUPmsEkGLwD1TSUDgNPYOB4rilgDoKFOWg4DgQP+BkKoc++PxHgPCwDqRuWArXC8GBBZVAyoHgP4MGoMHwQADAREivPKhGShAHycITUZEhIWlgkfaBVAVHIMiWq4p+lsX/A+0rB4CB1BBA6B8GgMOmE4QxLVAoAZrRGBmgDhIH+M+ByUDol42kYjI4TMDhMqZYB4T/zbOkx4DD4A8DwBgN4HAoAYdAoADRLBBAOSAHj0IIN+AcTsUGD9v/xKCAB5IAaP1Q+HKofAabSh8wnSq1aUGApw99LYMQA+J8CCO4PxK8EBLB+zFYjNJtSMDpstD1N5J/o48wiHCAUBTBgOD9WlVggAcEoGBQAGAdTMpcVAHjsSwgNhDTj/G1YMVtgwgsgYAMaHg+bWBuRVFRYCKX0An6WxolBvFwQwYEMdg0BvK2gYPqAePWkw/3Y2kHwhJvttpx/9X0DJcPEjTbIg9BWqmu9rxKDwH8eDwMBzoN4GA4JINgHi4SB99KDD8dNK8SewfiGkA4nCEkHw/A/8sjaUvTwtVgy4MNqn+76WyoMAcDUHAhgdboQQbwMAd4FAAcJQ/ANBDbaBhCBCzYJQ8aBkTXwUwFx+z9pUxO9D04GodAzKQSGRHL2gZgFInLx2PdTsUIY+BhyPBCSf3/wVQ8EZsHgoBkCoMjVqldir3E0VU79LYuB4D9/EgSB4OwUReOhKTAcTgggyQDwBoQQgFweJB0JI9EEtxMmB4SATaZ97zYNwGAoJxcDwED6JCUfg3gYA1OEAFIEIGoII+bBkkEZUI4Hh8B0cYlThDg7D5od+54GWanQYoIfpbGPwPAdBSgwQQUQKIFCCEkBlSsfl48HzaYSgQkwIUBVfVgZHoGmmlXk/xwOeMogMcIBCBxKCkBkoMPS8GBCB4GBFBSCMB4f6B8QgODtIOlAIIkJR0mL/joeqwgg4FWO22W0wOSsK6HwGRV9LY0gKYGCEDCUDMCPAQADghgggyVWB9KyyCjEjwQ2BIEoIQ/aHTA8TDxKDLRlW2Wsp1YgB/AYbEQsgIAMOmwYSQYSBHTlw+EoEAGSg2A4DoMCI2z9UBxoFKOC9MDDgDbbfWhIViOkaoMNwZCBoHw4A/4ja4M2PNaBh4AfoMJYMqBCBlQKFoRx4wDD6AeEhMBxKO+K6DgOjlMkB4P/pA8qHxYHwjsg8J/5ttQEVPXgx/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv5WqLQcAcJIFxwDCCEAt4HwGi0EQA0cFoPCf94gh2BoQAYq4H4IpWDgWTbU6EIe6o92CWELJ+l39bnUgHyrw76OE3qVdH1BWjssYVqlhKAa/jbb+Ntv422/jbb+Vqy0HAHCQBcsBhBCDzofAaLQRADByOAeE/7xBAsBsQQYq4HwIhWDgWbe8BgRB5vPKaJYhy/A1/P2AaCFzPjsCyv0D1QPoCtSFravVhLAY/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv0E0gMBZMDArmgcHUNLDRhofRG7bRBR0CAPnf/ehtt4MHwMG4ODwHzv/vQ228GD4GDcHB4D53/3obbeDB8DBuDg8B87/70TgpQeEgEweI/9QfMgC2DgUAhA8DAlg8D/kg8D/kjoGD8GD8GLAeBgQSoGHIGiwHg/+MHApxyOAeIgEQ+B4b/x6D4UAW3sNCA1nu951dEjtpKT8iwQh94SRHHQ6+lSDrw/L2E3x+P2cTJEnm/ln1adv4GGmlQF70gHgPAfx4BpcB8GuqghbKCgBQJkwKIFAo2bAYQBCwcRWWgyEFWyOR+IKGNrk7DN8gVRLwDgNQYvBCBgUPgYeghBCHU8CkH4H1SUSRwAaIQ/AOVJPJUw6A9xkfpaDh9ARG2A/YTqwvCwAckBvAwlgyUGHoMnA+PQPD4SvCOnEMSAUgQQOj4AxkEFWO0qVtIqbSpWWBwkYWZENtUILJWHyadC8Gg/kHQMlVAGpQUZcB0Qwb4MqbHojiWw0wXgHAox8P0gIHmvteSbfBCbVsiQJQlNFggWMW89+grU4JIVmgYeghgpGBJCGDUGA6CkAPV0Qh6PAhCQCkbCEPRLCGPU4+BTJE4Hx6EIfKmQYcDxr4N0Hgv91OH4G2muJgcAS38mSMYnTqmNVqkkZbaYqdPf4mVFulgfpyxplkQGgLqxAYV3neTpwYgw+TMAwBg/HYKEIIMXg2D8dAwGhDLqAcAYCC0B9KChZH48bTJEzY8LgLJBy35XWR2BoQAY8wYvyF0gMOwbwPAwHoHgbAhgw+H49wQmR6P9Vj4GZBgNYOx+O/g4FOlSA4Dg9BWqkjTQ+VAy9BlvAwb8dgeA/bx2AYAaDAGiXoBwfArVbHlYH8APVDsRhL36ou80l9vmhAHTCoCysGK08DwGApyub+Qbg8B/FiOOqXgeBmgDC9ovBCBh8ITAkJEqZuMpB2JbIOSgfEf32gMfA2X/HA5aRwAgWCGPhIBghjwGEYSVQMnBSAc+BwGwIQNgB6QIIQwZQXjrQOa35oejxI3BBHflaS+ZB4KAZD8GXWRkTfyCoHgP48EMfAzYMI4IYB4lKaPkqtV5N/ZzWcHg8SDwP9wGBUgaEEHyP/tMGSKgDR4XCQnBQA8B/AgcA4Ov3C8SxIHoKH4QgZQnRJQRQUiUFYEEPGpyqmFYMWsRqcC5v5BIJYMEEGHg6BmwYEH4hA2UegGAzfkg6TMiUJOgpGVQ89ngcB2JAeCgHR6WJAUJf4cp2BAVAxUDLc4yFlAw9ANBh0PwYFEDDweCEELUw9aTg21UkB4H/FaEtOCgHIjiF9v4jqgYcMNl4MCK17QUw5glAaLLYDgWbfyDnR2OgQAeAgd8EgeD0uVgGgwjiSBsA3yZX5tkSgDAOfB4KAZEpL5pdIyPR/8G6OEKry7hSDwEDiAcAYnglgoQPiSDTU4Qv5AOApAOgHjoDjAhiXQ9xWPfl7bAPBQEo+WbrDAMBdmAa6xOn2/kGgMI6oD9CHoHwDQDQQgDR2AeyyyyyBwA8SgPjgcDjw4ZBlDLIKZqNf80XMAwcNcVLKqeDSAYDTQDWm60AaDQSq02oA0AeB/wHt/CwGDtrS8QB99oGQUGKw/BgVB5v5AoDsGH46BAHoPAQQ6QGA34GpeDAeVpWwDwgjz/0qRtgdiO02DCAPkrbUZLwYsHzX2FSsGGwewHAsuIyQFADD4GHo/BgDQeB/vwUghgeH3wPiOCAPh4JYGwOiSlHbRe0Iw+TiE0Dh+JQgNpQclZTiAqBWCsGQ/kygfAwQwYQgZMJHgUIBgjAGgyZkA0uSpAUYjtBCg/EoQx60O0g/SJUoMs2HwGVStgQQNh53iIGAXCQFCDJvAycEIQgYPxK+mBlQMB5WCEBwFKPVX2wDsBh3/zafwHxw2wDwcA6qH6esJlatMDCJ5P0GGwBLfwsOx+yPgYfAysSgYSwYegcBi9OPQNfBvJB8AbgNpekEMFXG0ok+HrQggwfMVgch+wyyqA2vA+6Rtv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+V+5AhD/O4po7EKzKl/P8A0EEbDoOk/ivOj+rpC1pjFhLAa2qLQcAcJIFywGEEIHeB8BotBEANHA5B4T/vEEC4GhABirgfgilYOBZP422/jbb+Ntv422/le2AwIg8zvlMHYh7ciX+/mAiBC4WjoOk3huoH8BWl5Y2q1YSgGNqy0HAHCQBcsBhBCDzofAaLQRADByOAeE/7xBAsBsQQYq4HwIhWDgWb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbbEuNtvkbbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238rakBgRB7vPKYJYQoW0u/rclSAfDodgWTNLe6XUFaOyxhWqWEoBrbVgMCIPc75TRLCFSyl/8bsiQD4dDoCydqd90voK0dFrCpUsJYDX8bbfxtt/G238bbfyv6wQh9vMUgykQ4pBE/NsA0EAq0dh0n8HfB8jSFrLOrCWFzf1whD7O4pBlIh1QCL+7IBoIBXo6DpN4O+D9GlLGWNWEoLn8bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxW2FIGwfDwD4MBwGCCXApklA6AYDB8DfHiWJvA3dEppWDKAOqqnBkYKphpOmH4gAyIP17CL6WxIJAM0BxkHgII8vylzGgxerSCSDBBLkogNAp2gQgOCSXAcTqhzwPwUzbAMpH8Y6oDwDfKcHAOA5mAwIQNiRUJDReDaDCODK0rYkNxV7R3AhCF5oITQ+aboK1rw9A+lbBgVCsERcHw4A36W1x+DwEDeI5cXCOnTAdBvBBVgHNanA+IQHhH+XiOJCpr44Tp22gUzDDbA8+slbHitPFRCRBBHSURwYRgeAgnRKHjGqx+PfAxekTMYN22WwZK37ws+lsaAHgwQmQDkiVnQUYMqEtkdN4wOxGCEEMDYlj5v+jjn/fLlTCUQGQVicGUMpQ+qM4R8DwEESCDW2B02EMGaSApU6rytUDKi8IIgAg/HPp9pUWAiA3GC9nyIfKxLVKzbvpbGABgMEAD4IKoA1P74lCU0DRWPlQHwcB9KDKAVQKfR20Of79ocDhV8dNtK50DdV/YVJEydls8Q+DwEEGENViQfpwQwZIqBAHrWfHgN9sDu4Bwf7/7bQIiQHFwMIPy9U218EVgGLAckB4T/xBgFfS2MaDBABR4DNqwYfVUxAhpFOq7qdrw+aSYmBugxb8e/BWNFwKZQrEFYFYK5A+DCEXiEI1A6kCCCgBQiE0rYSD9KCgHfy8GLE6ctA1g9aSlw6VtB+OGw+ZECqqiD8/9LZIHgIIUG8EIGZBmWB0B9kdQfYmElv48L049SMjvfBCHf2gRPlzQPCQC7LTQ9TqVaoGKmmwYbgw2ecTAwQh6JIH2whBAEMEAIeJh0DCCPQD0herYBAEgfeWbbHo9EsuHn2A+DzoMtQ7nByQfS3wDwYQh6EAQsANYEkAwEMDlSgqmRJBCL207SQf3w58kStJS4eD9M235edTCASonEgeAghQbwQgZkGZYHQH2R1B9iYSW/jwvTj1IyO98EId/aBE+XNA8JALstND1OpVqgYqabBhuDDZ/0tikGAMBRXBLBgDQODoRwOAH/Sg8BA8gfzyURkv8ErPtiSBwq+lCGwJUaEDg5Tst8VKgYCJGGMA0GEIdgGCEkAPBhyAcB0ICsvokUdghD/R+wkEvPDkG4nHKpMWMjhSzOdVM4vGX/S2TSA8BA+gfTgpmPiGDQdAfEtUwnEoFIOwhiAAeP8T+LS7PeD4G4wyPegxUqHo7TK6scDLEmwvBTM8LvgbA0yBtT7E2lo4bA2sW+EC9EFbrvpbCcDAhgwQAhAgKgQlYQggghb9WDNAomVYlJh+ENOOwgiWP/AxWEEeYlHw4BiwftgZVAy6YFYDwkAmwRGweBggQDQ9LgYcgpKChYTan+DJMCGpBRzzTQG/+A0IAKr6UQAVoNyDsEUHxoA36WxiAcDF1A6XA3WwUIM0CAqqvUzQOANCGBptlnw4LSxIXMD0flkHOAy6tUwmB8b/1LqgYFIClVgzYlgw+mJPAHiUPko/rGqwViVpMPoPfAiJferGg3WP+BE1WICoPYBtWAR9LZUHgYIEIOlwMIKYA8GTD0ITTNSCOIaQA/AZQAYzmFoPBf8Lf2FRviqjqCXE7I4YTB+IKoLRsDUGVD0Ga+PARVYlCWXCGXA3RGEkIScDYlD1Mpvv61/7TDMrDfq0rYZVKmZUB742xy/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Vqi0HAHCSBcsBhBCB3gfAaLQRADRwOQeE/7xBAuBoQAYq4H4IpWDgWTecBgRB7vPKYJYhSfA1/fyAaCH3PDsCyr1DxSPqCtSljSvFhKAa/jbb+Ntv422/jbb+Vqy0HAHCQBcsBhBCDzofAaLQRADByOAeE/7xBAsBsQQYq4HwIhWDgWbe8BgRB5vPKaJYhy/A1/P2AaCFzPjsCyv0D1QPoCtSFravVhLAY/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+G1QeAgeRDHwjAydI0IavyoDwB4+EsFEB1tv/vMYOgOj5lJft/bBWeYYZBEVsjlA1RMw0fJMBqB4SgeA/gQDB9B2XJQZUIwHBIBgDlY98n8DKFYhJwhAygRy8DfRABTAw5HqUe1X1RSwGAWJ04M2AYCiB4D+PBhIHCVMJIKQGCAOhCA9R6OlCdMl0DTbYhhDSDjo5B4L/lHadsFYjBEON/IgAaB0A8dJAQ21QMXg8BA90SB6PB2XNaAcJAKAHAdCGPB3laBVAqhKEYGD8uZZa0GKwZSHyZMDwv/irPLgcBmQPAHMA8BBWj1OlVN+BA+DKwcB1hV/zKURsHoIaUcNp0jQK1oDDYN1UxwQVarwMVRdzfyFxLBmlQKRXQPg8D/fgzQHVSQS0zI8TCUEHw7EcIbAKf3y1tI1LR0BgFarBlIfGsBgtEIljoEAdgwBsBgQ2gYRwUngUjYHx8DXcbEksBtVCA20XtiUDgQI0DdBVjlqqk1Y6qUCArBwBLfyDT4KUShDBCB4D9lLxyqSMg2g1BSCSIwlNstYXq2AcAeB0vZLFQ5HLYfMJWgRax2YW9IhgCjHpcyDAoAeAgbx54fqmR+Ok4MI9Ly7ytr6cfYlEkRvpg/YAukiss+2qHIMjAzTDfyBGVAyUA4eA8B/IiWr0RxGBEAPBgUA8BoqCC2OAhiMOhDZBQBCLPjvzQIrTKpgvSA3BJYRQctgiyJgYBY6Bk4QQUKoFAJIkF4KUGBQMlwjtjzPiMDFyQA5tKOwRE4IrSX6sA4RmAUzTH/A8J/2q1SUDIfB8CtALBsP5BsDwEEGJI6CADAfAOH4hpfD4eKAhpGUrYFlQMpAOEsdNfbBUDlP4+JQbFQlBC8DCGDCEIYNQUCZkG+B4SsHQIaQS8H4IEH4lCT4etDxOI6RplphoetAw3VolUWTgxWD4UAW38iisGUAw+BgOApQUDYB4HgOgdBko7AM2JgDB7gQ0wQh4BxWlSh+nHqQHD6F4/bbD4QGFaegaXoqGAPAQQYkjoIAMB8A4fiGl8Ph4oCGkZStgWVAykA4Sx019sFQOU/j7fyHAbFSYGYVgwII8HYBoKBn6cHgIHED6ZIrEoIKYvHyVKnH4hDgeJErQGE5e217Ww+Y6rKOANLFw/LsB4CB/APEoFOmg9ZBh+AcnYH1Z8CL9U0Iwl+bV+TAyMcNf/5ocojrfyBIBBBk6UDoQWAZkGgNQeAgdRGEplgQk8wEEeAhYCjAMSpFRc0377ReJCsA8v+qZYHKZWOgNgxUOIkBkQDAbIhl7Y7EcSx6yzFbI/HqUvbV40OIz9On//W/KlhwIN+ORwQA5H8iANqcdN4DAhAw/SBDA4P1QIAKQGHgjCWBwGHLAOAPwfgGfLEqoGD4GRiBSwQQ/Bg65SgGC0JgHQZkSwcAaXg2gogYfg8BA7pAgMtsiWXD9WIwHAeB/xU2f8WgaVjwGD5kFH8S+FrA5XA2jqeHwYD+QeAw+gN6AzCQvTMAwKBKDfokj0HAgg8D/Pj9WxiYeB4DKPhAEZr6povaLByBsQA/Sh+sHyuA+HAHi4SQQwDPg8B/GgoxLaCGB4GLE4PAQN6UuTJ0zONDrU/wYuaaweJBy2BhsQQZZUPAYOweEgDwfD/82/kDYEIel3geAgcwYfjpsvYwvTghg3mUg+1X7E6vGPCGBv7SQGRt+YLfB+zFgZFDgtBmx+Pk4MB4HgIHlIwlSNpR0kBleD9M0nabVlwGwUv203k7QF0ggsAZVCCjYXixG38bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/K/cgQB/nfKQZSIV5S/+fsA0EHu4OgLJ/B10f1cuLWFUWEsL21RaDgDhJAuOAYQQgFvA+A0WgiAGjgtB4T/vEEOwNCADFXA/BFKwcCyfxtt/G238bbfxtt/K9sBgRB5nfKYOxD25Ev9/MBECFwtHQdJvDdQP4CtLyxtVqwlAMbVloOAOEgC5YDCCEHnQ+A0WgiAGDkcA8J/3iCBYDYggxVwPgRCsHAs38bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfyv3KEIf7zykGUiFFNSfv5ANBBKsHYFk3g66PquXljCuLCUF7fuQIQ/zvlIMpEKqKl/P2AaCCV4OgLJ/B10f1cuLWFUWEsL38bbfxtt/G238Vti0GgQAcCCB0eCEWjiJ/6W/DhhprU7agFSOGvnvqe8BgRB5vPKaJYhy/A1/P2AaCFzPjsCyv0D1QPoCtSFravVhLAYxGmSgwQhHSsAosA8JZckZV6wPAPgwIqttmJUpY15P8RmwcPvB+WJgN8Bg4aJh4lBCEMuVpWgbhe35Jol5qdpIr1NrAhKwND1P744BkSZlZO2iVKYpPfS2oDgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEjwDwQbJsEIS7N8k/LB8nuXB38cDz4MiDoetgYLkCtlZNaRfS26B4IcltEIS5MaS/to+TzJg6+OB78GRAWHjYGC+olTKys9gcCAAepLAcB4RxyOQNDgGHBeWgih8CnA0oEAGRgqgcPxwjA0DLgkfS34B4IdmbBCEu5vkjcsLk6jcEv44HXwLB0PWw8SA8L/50GGx90DwQ5N2iEJczPJW7aXJ1GYJXxwO/gWDoeNh4lB4WAPoMNz30t4HAgAHqSwHAeEccjkDQ4BhwXloIofApwNKBABkYKoHD8cIwNAy4JHgHgg2TYIQl2b5J+WD5PcuDv44HnwZEHQ9bAwXIFbKya0i+lt0DwQ5LaIQlyY0l/bR8nmTB18cD34MiAsPGwMF9RKmVlZ7A4EAA9SWA4DwjjkcgaHAMOC8tBFD4FOBpQIAMjBVA4fjhGBoGXBI+lvwDwQ7M2CEJdzfJG5YXJ1G4JfxwOvgWDoeth4kB4X/zoMNj7oHghybtEIS5meSt20uTqMwSvjgd/AsHQ8bDxKDwsAfQYbnvpbwOBAAPUlgOA8I45HIGhwDDgvLQRQ+BTgaUCADIwVQOH44RgaBlwSPAPBBsmwQhLs3yT8sHye5cHfxwPPgyIOh62BguQK2Vk1pF9LboHghyW0QhLkxpL+2j5PMmDr44HvwZEBYeNgYL6iVMrKz2BwIAB6ksBwHhHHI5A0OAYcF5aCKHwKcDSgQAZGCqBw/HCMDQMuCR9LfgHgh2ZsEIS7m+SNywuTqNwS/jgdfAsHQ9bDxIDwv/nQYbH3QPBDk3aIQlzM8lbtpcnUZglfHA7+BYOh42HiUHhYA+gw3PfS3gcCAAepLAcB4RxyOQNDgGHBeWgih8CnA0oEAGRgqgcPxwjA0DLgkeAeCDZNghCXZvkn5YPk9y4O/jgefBkQdD1sDBcgVsrJrSL6W3QPBDktohCXJjSX9tHyeZMHXxwPfgyICw8bAwX1EqZWVnsDgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEj6W/APBDszYIQl3N8kblhcnUbgl/HA6+BYOh62HiQHhf/Ogw2PugeCHJu0QhLmZ5K3bS5OozBK+OB38CwdDxsPEoPCwB9Bhue+lvA4EAA9SWA4DwjjkcgaHAMOC8tBFD4FOBpQIAMjBVA4fjhGBoGXBI8A8EGybBCEuzfJPywfJ7lwd/HA8+DIg6HrYGC5ArZWTWkX0tugeCHJbRCEuTGkv7aPk8yYOvjge/BkQFh42BgvqJUysrPYHAgAHqSwHAeEccjkDQ4BhwXloIofApwNKBABkYKoHD8cIwNAy4JH0t+AeCHZmwQhLub5I3LC5Oo3BL+OB18CwdD1sPEgPC/+dBhsfdA8EOTdohCXMzyVu2lydRmCV8cDv4Fg6HjYeJQeFgD6DDc99LeBwIAB6ksBwHhHHI5A0OAYcF5aCKHwKcDSgQAZGCqBw/HCMDQMuCR4B4INk2CEJdm+Sflg+T3Lg7+OB58GRB0PWwMFyBWysmtIvpbdA8EOS2iEJcmNJf20fJ5kwdfHA9+DIgLDxsDBfUSplZWewOBAAPUlgOA8I45HIGhwDDgvLQRQ+BTgaUCADIwVQOH44RgaBlwSPpb8A8EOzNghCXc3yRuWFydRuCX8cDr4Fg6HrYeJAeF/86DDY+6B4Icm7RCEuZnkrdtLk6jMEr44HfwLB0PGw8Sg8LAH0GG576W8DgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEjwDwQbJsEIS7N8k/LB8nuXB38cDz4MiDoetgYLkCtlZNaRfS2iPgaAdHpemLsEYetanZ1ib8uSz3hwnaLkv/h4BYeMt55LQeEgD6DDc8JS/QYDwhDv4BzIIA/L8SM6Ph+CCDCCraS4z7SxP/w88Dh98QPoohgm+lsZAHgzAkiEIY/HiouH49YY1MOE7SRXs+wO9HA6D3zRYCtZBw+WWVpWm1QBB6geCDJdohCXJnkv7aPk8yYOvjge/BkQdDxsDBegVMrJ7CL6W8DgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEjwDwQbJsEIS7N8k/LB8nuXB38cDz4MiDoetgYLkCtlZNaRfS26B4IcltEIS5MaS/to+TzJg6+OB78GRAWHjYGC+olTKys9gcCAAepLAcB4RxyOQNDgGHBeWgih8CnA0oEAGRgqgcPxwjA0DLgkfS34B4IdmbBCEu5vkjcsLk6jcEv44HXwLB0PWw8SA8L/50GGx90DwQ5N2iEJczPJW7aXJ1GYJXxwO/gWDoeNh4lB4WAPoMNz30t4HAgAHqSwHAeEccjkDQ4BhwXloIofApwNKBABkYKoHD8cIwNAy4JHgHgg2TYIQl2b5J+WD5PcuDv44HnwZEHQ9bAwXIFbKya0i+lt0DwQ5LaIQlyY0l/bR8nmTB18cD34MiAsPGwMF9RKmVlZ7A4EAA9SWA4DwjjkcgaHAMOC8tBFD4FOBpQIAMjBVA4fjhGBoGXBI+lvwDwQ7M2CEJdzfJG5YXJ1G4JfxwOvgWDoeth4kB4X/zoMNj7oHghybtEIS5meSt20uTqMwSvjgd/AsHQ8bDxKDwsAfQYbnvpbwOBAAPUlgOA8I45HIGhwDDgvLQRQ+BTgaUCADIwVQOH44RgaBlwSPAPBBsmwQhLs3yT8sHye5cHfxwPPgyIOh62BguQK2Vk1pF9LboHghyW0QhLkxpL+2j5PMmDr44HvwZEBYeNgYL6iVMrKz2BwIAB6ksBwHhHHI5A0OAYcF5aCKHwKcDSgQAZGCqBw/HCMDQMuCR9LfgHgh2ZsEIS7m+SNywuTqNwS/jgdfAsHQ9bDxIDwv/nQYbH3QPBDk3aIQlzM8lbtpcnUZglfHA7+BYOh42HiUHhYA+gw3PfS3gcCAAepLAcB4RxyOQNDgGHBeWgih8CnA0oEAGRgqgcPxwjA0DLgkeAeCDZNghCXZvkn5YPk9y4O/jgefBkQdD1sDBcgVsrJrSL6W3QPBDktohCXJjSX9tHyeZMHXxwPfgyICw8bAwX1EqZWVnsDgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEj6W/APBDszYIQl3N8kblhcnUbgl/HA6+BYOh62HiQHhf/Ogw2PugeCHJu0QhLmZ5K3bS5OozBK+OB38CwdDxsPEoPCwB9Bhue+lvA4EAA9SWA4DwjjkcgaHAMOC8tBFD4FOBpQIAMjBVA4fjhGBoGXBI8A8EGybBCEuzfJPywfJ7lwd/HA8+DIg6HrYGC5ArZWTWkX0tugeCHJbRCEuTGkv7aPk8yYOvjge/BkQFh42BgvqJUysrPYHAgAHqSwHAeEccjkDQ4BhwXloIofApwNKBABkYKoHD8cIwNAy4JH0t+AeCHZmwQhLub5I3LC5Oo3BL+OB18CwdD1sPEgPC/+dBhsfdA8EOTdohCXMzyVu2lydRmCV8cDv4Fg6HjYeJQeFgD6DDc99LeBwIAB6ksBwHhHHI5A0OAYcF5aCKHwKcDSgQAZGCqBw/HCMDQMuCR4B4INk2CEJdm+Sflg+T3Lg7+OB58GRB0PWwMFyBWysmtIvpbdA8EOS2iEJcmNJf20fJ5kwdfHA9+DIgLDxsDBfUSplZWewOBAAPUlgOA8I45HIGhwDDgvLQRQ+BTgaUCADIwVQOH44RgaBlwSPpb8A8EOzNghCXc3yRuWFydRuCX8cDr4Fg6HrYeJAeF/86DDY+6B4Icm7RCEuZnkrdtLk6jMEr44HfwLB0PGw8Sg8LAH0GG576W8DgQAD1JYDgPCOORyBocAw4Ly0EUPgU4GlAgAyMFUDh+OEYGgZcEhNICEIZerSNA3C9ryXRKzE7aRXibGBDVgaHif/xwDIk7KyZtErUVQf+pqi0HAHCSBcsBhBCB3gfAaLQRADRwOQeE/7xBAuBoQAYq4H4IpWDgWTESdWDBCCEXqwUTIICcvaVJwND0EOj3cEnEqYcsN/TJhAEAcKm/tgywMNk5YxU4GxAVPHoKMIQOAMA6PRCjYgxN/BwyBFUy15Om+CvEBsWfG2Mn8bbfxtt/DaoHBDZAOEcfCGm5rZd9Soo2/7/q021QYNWP/C5nfkUaA6DBCHRfAOjwIABoMOWqBtIJCdkfA4DyRKHfta+0DwX/WOFTYGQCjiVO2P06svV1SylZZTt022z8t+L3hqfxrNv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv5EUitsfJ1RexVDKRlpW1ocMtfHPheYpkDoMEIej+gdHoQgDcEn/wNpRKTMj4HAeTpUfy37YPBf9Y4Vtg+H/5rBr+EeAPENkA8Qx+IafkbL/qVEG3ve9GmmoDBox7zQmOt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/DaKRO2Pk6ovYqhlIywnavETbXxy0FDW+RVsDoMEIel9A6OwhAGwSf/A2lEpMyPgcB5OlR/LW2weC/6xwrbXALVAPENkA0Qx+IafmNl/1JZBt73vRppqAwaMe8Fwan8bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G234AAAG2XPAz///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+AAABtl1gM////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////gAAAbZd8DP///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////4AAAG2XmAz///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+AAABtl7wM////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////gAAAbZoMBn//////////////////////////////////////rYMCfqYP5QBIP5QBOpg/lAEg/lAE6mD+UASD+UATqYP5QBIP5QBOpg/lAEg/lAE6mD+UASD+UAT9TB/KAJB/KAJ1MH8oAkH8oAnUwfygCQfygCdTB/KAJB/KAJ1MH8oAkH8oAnUwfygCQfygCf///1MEYHx//sbpQecgDSD//////////////28qB+b/528tBgW/////r4XBf/////////////////////////////////////////////////9jFX//////9vB6L/9CL//////////18Lgv////2MJP/////////////////////////1sHnv/0Mv///////////////cAAAG2UPAz///////////////////////////////////////////////////////////////////////////////////////////////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]
}
],
"source": [
"\n",
"import os\n",
"import shutil\n",
"import threading\n",
"import time\n",
"import uuid\n",
"\n",
"path=\"../video/\"\n",
"\n",
"\n",
"def frameAnonymization(frame,indx,procFrame,request_id):\n",
" # request_id_var.set(request_id)\n",
" id = uuid.uuid4().hex\n",
" request_id_var.set(id)\n",
" ipath=path+str(request_id)+\"/\"+str(indx)+\".jpg\"\n",
" # print(ipath)\n",
" # Convert the frame to PIL Image\n",
" # base64.b64encode(frame).decode()\n",
" # Image.open(base64.b64encode(frame).decode())\n",
" # print(type(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)))\n",
" imagef = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
" imagef.save(ipath)\n",
" # image=open(\"test.jpg\",\"rb\")\n",
" # print(type(imagef))\n",
" image={\"file\":ipath}\n",
" image=AttributeDict(image)\n",
" # ocr=None\n",
" # global imageAnalyzerEngine\n",
"\n",
" # imageAnalyzerEngine = ImageAnalyzerEngine(analyzer_engine=analyzer,ocr=ocr) \n",
" # imageRedactorEngine = ImageRedactorEngine(image_analyzer_engine=imageAnalyzerEngine)\n",
" # redacted_image = imageRedactorEngine.redact(image, (255, 192, 203))\n",
" payload={\"easyocr\":\"Tesseract\",\"mag_ratio\":False,\"rotationFlag\":False,\"image\":image,\"portfolio\":None,\"account\":None,\"exclusion\":None}\n",
" \n",
" redacted_image=PrivacyService.image_anonymize(payload)\n",
" decoded_bytes = base64.b64decode(redacted_image)\n",
"\n",
" # Create a BytesIO object to simulate a file-like object\n",
" bio = io.BytesIO(decoded_bytes)\n",
"\n",
" # Use OpenCV (assuming it's an image) or other libraries to load the image from the BytesIO object\n",
" img = cv2.imdecode(np.fromstring(bio.getvalue(), np.uint8), cv2.IMREAD_COLOR)\n",
"\n",
" # Convert the PIL Image back to OpenCV frame\n",
" frame = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
" procFrame[indx]=frame\n",
" return (frame,indx)\n",
" # Write the frame into the file 'output.avi'\n",
" # out.write(frame)\n",
"\n",
" # else:\n",
" # break\n",
"\n",
"\n",
"async def videoPrivacy(payload) -> Tuple[str, str]:\n",
" # upload_file = payload['video']\n",
" id = uuid.uuid4().hex\n",
" request_id_var.set(id)\n",
" _path=path+str(id)\n",
" if(not os.path.exists(_path)):\n",
" os.makedirs(_path)\n",
" # video_data = await upload_file.read()\n",
" s=time.time()\n",
" temp_file_path = r\"C:\\WORK\\GIT\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\src\\temp.avi\"\n",
" output_file_path = \"output3.mp4\"\n",
" # with open(temp_file_path, \"wb\") as temp_file:\n",
" # temp_file.write(video_data)\n",
" video = cv2.VideoCapture(temp_file_path)\n",
" # Get video properties\n",
" width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
" height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
" fps = video.get(cv2.CAP_PROP_FPS)\n",
" sampling_rate=int(fps*0.3)\n",
" # Define the codec and create a VideoWriter object\n",
" fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
" out = cv2.VideoWriter(output_file_path, fourcc, fps, (width, height))\n",
" frameList=[]\n",
" indxList=[]\n",
" first=True\n",
" count=0\n",
" last_frame=None\n",
" print(\"samp \",sampling_rate)\n",
"\n",
"\n",
" # audio_fps = video.get(cv2.CAP_PROP_FPS)\n",
" # fourcc = int(video.get(cv2.CAP_PROP_FOURCC)) \n",
" # print(\"aud\",audio_fps,fourcc)\n",
" # sampling_rate=1\n",
" while(video.isOpened()):\n",
" ret, frame = video.read()\n",
" # print(ret)\n",
" if ret==True:\n",
" if first:\n",
" frameList.append(frame)\n",
" indxList.append(count)\n",
" first=False \n",
" else:\n",
" if count % sampling_rate == 0:\n",
" frameList.append(frame)\n",
" indxList.append(count)\n",
" # else:\n",
" # frameList.append(None)\n",
" last_frame=frame\n",
" count+=1 \n",
" else:\n",
" break\n",
" if(count%sampling_rate!=0):\n",
" frameList.append(last_frame)\n",
" indxList.append(count)\n",
" print(\"totalFrame:\",count)\n",
" # print(indxList,len(indxList)) \n",
" print(\"after sampling\",len(frameList))\n",
" rcount=len(frameList)\n",
" framecopy=frameList.copy()\n",
" procFrame=[None]*(count+1)\n",
" # print(len(procFrame))\n",
" # indx=0\n",
" while framecopy:\n",
" threads = []\n",
" for _ in range(min(6, len(framecopy))): # Limit calls to remaining arguments\n",
" arg = framecopy.pop(0) # Get the first argument and remove it\n",
" indx=indxList.pop(0)\n",
" thread = threading.Thread(target=frameAnonymization, args=(arg,indx,procFrame,request_id_var.get()))\n",
" thread.start()\n",
" threads.append(thread)\n",
" # print(thread)\n",
" indx+=1\n",
" # Wait for all threads in the current set to finish\n",
"\n",
" print(\"remaining:\",rcount-len(framecopy),\"/\",rcount)\n",
" for thread in threads:\n",
" thread.join() \n",
" # print(\"===\",procFrame) \n",
" # Release everything when job is finished\n",
" # print(procFrame)\n",
" lstFrame=None\n",
" for frm in procFrame:\n",
" # print(frm,frm.any())\n",
" # print(frm,frm.all())\n",
" if(lstFrame is None):\n",
" lstFrame=frm\n",
" if(frm is not None):\n",
" lstFrame=frm \n",
" else:\n",
" frm=lstFrame\n",
" out.write(frm)\n",
" video.release()\n",
" out.release()\n",
" # Remove temporary file\n",
" # os.remove(temp_file_path)\n",
" # Read the processed video file\n",
" with open(output_file_path, \"rb\") as video_file:\n",
" video_data = video_file.read()\n",
" # Convert the video to base64\n",
" video_str = base64.b64encode(video_data).decode()\n",
" # Remove the output file\n",
" # os.remove(output_file_path)\n",
" shutil.rmtree(_path)\n",
" print(\"====\",time.time()-s)\n",
" del procFrame\n",
" del indxList\n",
" del frameList\n",
" return video_str\n",
"\n",
"s=await videoPrivacy({})\n",
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lstFrame=None\n",
"for frm in procFrame:\n",
" # print(frm,frm.any())\n",
" # print(frm,frm.all())\n",
" if(lstFrame is None):\n",
" lstFrame=frm\n",
" if(frm is not None):\n",
" lstFrame=frm \n",
" else:\n",
" frm=lstFrame\n",
" out.write(frm)\n",
"video.release()\n",
"out.release()\n",
"# Remove temporary file\n",
"# os.remove(temp_file_path)\n",
"# Read the processed video file\n",
"# with open(output_file_path, \"rb\") as video_file:\n",
"# video_data = video_file.read()\n",
"# # Convert the video to base64\n",
"# video_str = base64.b64encode(video_data).decode()\n",
"# Remove the output file\n",
"# os.remove(output_file_path)\n",
"shutil.rmtree(_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import os\n",
"def sample_frames(video_path, output_dir=\"sampled_frames\"):\n",
" \"\"\"\n",
" Samples frames from a video at a specified interval and saves them to a directory.\n",
"\n",
" Args:\n",
" video_path (str): Path to the input video.\n",
" sampling_rate (int, optional): Interval between frames to sample. Defaults to 10.\n",
" output_dir (str, optional): Directory to save the sampled frames. Defaults to \"sampled_frames\".\n",
" \"\"\"\n",
"\n",
" cap = cv2.VideoCapture(video_path)\n",
" fps = cap.get(cv2.CAP_PROP_FPS) # Get the video's frame rate (informational)\n",
" sampling_rate=int(fps*0.3)\n",
" print(sampling_rate)\n",
" # print(fps)\n",
" count = 0\n",
" while True:\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
"\n",
" if count % sampling_rate == 0:\n",
" # Create output directory if it doesn't exist\n",
" if not os.path.exists(output_dir):\n",
" os.makedirs(output_dir)\n",
"\n",
" # Generate frame filename with frame number\n",
" filename = f\"{output_dir}/frame_{count}.jpg\"\n",
" cv2.imwrite(filename, frame)\n",
"\n",
" count += 1\n",
"\n",
" cap.release()\n",
"\n",
" print(f\"Sampled frames at a rate of 1 frame every {sampling_rate / fps:.2f} seconds (based on video FPS).\")\n",
"\n",
"if __name__ == \"__main__\":\n",
" temp_file_path = r\"C:\\WORK\\GIT\\responsible-ai-admin\\responsible-ai-admin\\src\\rai_admin\\temp\\Recording 2024-05-28 181908.mp4\"\n",
" # Replace with your video path\n",
" sample_frames(temp_file_path, sampling_rate=10)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://infyartifactory.ad.infosys.com/artifactory/api/pypi/pypi-remote/simple, https://infyartifactory.ad.infosys.com/artifactory/api/pypi/pypi-remote/simple\n",
"Requirement already satisfied: moviepy in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (1.0.3)\n",
"Requirement already satisfied: decorator<5.0,>=4.0.2 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from moviepy) (4.4.2)\n",
"Requirement already satisfied: imageio<3.0,>=2.5 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from moviepy) (2.33.0)\n",
"Requirement already satisfied: imageio-ffmpeg>=0.2.0 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from moviepy) (0.4.9)\n",
"Requirement already satisfied: numpy>=1.17.3 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from moviepy) (1.26.2)\n",
"Requirement already satisfied: proglog<=1.0.0 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from moviepy) (0.1.10)\n",
"Requirement already satisfied: tqdm<5.0,>=4.11.2 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from moviepy) (4.66.1)\n",
"Requirement already satisfied: requests<3.0,>=2.8.1 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from moviepy) (2.31.0)\n",
"Requirement already satisfied: pillow>=8.3.2 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from imageio<3.0,>=2.5->moviepy) (10.1.0)\n",
"Requirement already satisfied: setuptools in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from imageio-ffmpeg>=0.2.0->moviepy) (65.5.0)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from requests<3.0,>=2.8.1->moviepy) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from requests<3.0,>=2.8.1->moviepy) (3.6)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from requests<3.0,>=2.8.1->moviepy) (2.1.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from requests<3.0,>=2.8.1->moviepy) (2023.11.17)\n",
"Requirement already satisfied: colorama in c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages (from tqdm<5.0,>=4.11.2->moviepy) (0.4.6)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Ignoring invalid distribution -ensorflow-intel (c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages)\n",
"WARNING: Ignoring invalid distribution -ensorflow-intel (c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages)\n",
"WARNING: Ignoring invalid distribution -ensorflow-intel (c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages)\n",
"WARNING: Ignoring invalid distribution -ensorflow-intel (c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages)\n",
"WARNING: Ignoring invalid distribution -ensorflow-intel (c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages)\n",
"WARNING: Ignoring invalid distribution -ensorflow-intel (c:\\work\\git\\cpy1\\responsible-ai-privacy\\responsible-ai-privacy\\myenv\\lib\\site-packages)\n",
"\n",
"[notice] A new release of pip is available: 23.0.1 -> 24.0\n",
"[notice] To update, run: python.exe -m pip install --upgrade pip\n"
]
}
],
"source": [
"!pip install moviepy"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "error",
"evalue": "OpenCV(4.8.1) :-1: error: (-5:Bad argument) in function 'VideoCapture'\n> Overload resolution failed:\n> - Can't convert object to 'str' for 'filename'\n> - VideoCapture() missing required argument 'apiPreference' (pos 2)\n> - Argument 'index' is required to be an integer\n> - VideoCapture() missing required argument 'apiPreference' (pos 2)\n",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31merror\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[5], line 12\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# print(len(clip))\u001b[39;00m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# clipping of the video \u001b[39;00m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m# getting video for only starting 10 seconds \u001b[39;00m\n\u001b[0;32m 11\u001b[0m clip \u001b[38;5;241m=\u001b[39m clip\u001b[38;5;241m.\u001b[39msubclip(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m10\u001b[39m) \n\u001b[1;32m---> 12\u001b[0m cap \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mVideoCapture\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclip\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;66;03m# rotating video by 180 degree \u001b[39;00m\n",
"\u001b[1;31merror\u001b[0m: OpenCV(4.8.1) :-1: error: (-5:Bad argument) in function 'VideoCapture'\n> Overload resolution failed:\n> - Can't convert object to 'str' for 'filename'\n> - VideoCapture() missing required argument 'apiPreference' (pos 2)\n> - Argument 'index' is required to be an integer\n> - VideoCapture() missing required argument 'apiPreference' (pos 2)\n"
]
}
],
"source": [
"# Import everything needed to edit video clips \n",
"from moviepy.editor import *\n",
"import cv2\n",
"temp_file_path = r\"C:\\Users\\amitumamaheshwar.h\\Downloads\\OCS - Bulk Upload 2.mp4\"\n",
"# loading video dsa gfg intro video \n",
"clip = VideoFileClip(temp_file_path) \n",
"\n",
"# print(len(clip))\n",
"# clipping of the video \n",
"# getting video for only starting 10 seconds \n",
"clip = clip.subclip(0, 10) \n",
"# cap = cv2.VideoCapture(clip)\n",
"# rotating video by 180 degree \n",
"print(clip)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import cv2"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"ename": "error",
"evalue": "OpenCV(4.8.1) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\core\\include\\opencv2/core/private.cuda.hpp:106: error: (-216:No CUDA support) The library is compiled without CUDA support in function 'throw_no_cuda'\n",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31merror\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcuda\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetDevice\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[1;31merror\u001b[0m: OpenCV(4.8.1) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\core\\include\\opencv2/core/private.cuda.hpp:106: error: (-216:No CUDA support) The library is compiled without CUDA support in function 'throw_no_cuda'\n"
]
}
],
"source": [
"cv2.cuda.getDevice()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"async def videoPrivacy(payload) -> Tuple[str, str]:\n",
" payload=AttributeDict(payload)\n",
" upload_file = payload.video\n",
" video_data = await upload_file.read()\n",
"\n",
" temp_file_path = \"temp.avi\"\n",
" output_file_path = \"output.avi\"\n",
"\n",
" with open(temp_file_path, \"wb\") as temp_file:\n",
" temp_file.write(video_data)\n",
"\n",
" video = cv2.VideoCapture(temp_file_path)\n",
"\n",
" # Get video properties\n",
" width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
" height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
" fps = video.get(cv2.CAP_PROP_FPS)\n",
"\n",
" # Define the codec and create a VideoWriter object\n",
" fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
" out = cv2.VideoWriter(output_file_path, fourcc, fps, (width, height))\n",
"\n",
" while(video.isOpened()):\n",
" ret, frame = video.read()\n",
" if ret==True:\n",
" # Convert the frame to PIL Image\n",
" imagef = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
" imagef.save(\"videoframe.jpg\")\n",
" # image=open(\"test.jpg\",\"rb\")\n",
" # print(type(imagef))\n",
" image={\"file\":\"videoframe.jpg\"}\n",
" image=AttributeDict(image)\n",
" # ocr=None\n",
" # global imageAnalyzerEngine\n",
"\n",
" # imageAnalyzerEngine = ImageAnalyzerEngine(analyzer_engine=analyzer,ocr=ocr) \n",
" # imageRedactorEngine = ImageRedactorEngine(image_analyzer_engine=imageAnalyzerEngine)\n",
" # redacted_image = imageRedactorEngine.redact(image, (255, 192, 203))\n",
" payload[\"image\"]=image\n",
" redacted_image=PrivacyService.image_anonymize(payload)\n",
" decoded_bytes = base64.b64decode(redacted_image)\n",
"\n",
" # Create a BytesIO object to simulate a file-like object\n",
" bio = io.BytesIO(decoded_bytes)\n",
"\n",
" # Use OpenCV (assuming it's an image) or other libraries to load the image from the BytesIO object\n",
" img = cv2.imdecode(np.fromstring(bio.getvalue(), np.uint8), cv2.IMREAD_COLOR)\n",
"\n",
" # Convert the PIL Image back to OpenCV frame\n",
" frame = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
"\n",
" # Write the frame into the file 'output.avi'\n",
" out.write(frame)\n",
"\n",
" else:\n",
" break\n",
" \n",
" # Release everything when job is finished\n",
" video.release()\n",
" out.release()\n",
"\n",
" # Remove temporary file\n",
" # os.remove(temp_file_path)\n",
"\n",
" # Read the processed video file\n",
" with open(output_file_path, \"rb\") as video_file:\n",
" video_data = video_file.read()\n",
"\n",
" # Convert the video to base64\n",
" video_str = base64.b64encode(video_data).decode()\n",
"\n",
" # Remove the output file\n",
" # os.remove(output_file_path)\n",
"\n",
" return video_str"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "myenv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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|