{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":13869,"offset":13859,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"**Title:** \"Revolutionizing RESTful API Testing: Simplify, Automate, and Elevate!\"\n\n**Blog Start:** \nTesting RESTful APIs has never been easier\u2014or faster. Imagine a world where you can skip the manual grind, instantly run tests with just a cURL request, and generate comprehensive test cases without writing a single line of code. Sound too good to be true? Not anymore! Say hello to a smarter way to test APIs using the power of RESTful automated testing and AI.\n\n**How-To Guide:** \n1. **Start with Your cURL Request** \n Skip the knowledge gap. Just plug in your cURL request into the tool, and let the AI take over. Whether you're a developer or a QA engineer, there are no prerequisites or steep learning curves required. \n\n2. **Automated Test Execution** \n Once initiated, the AI works tirelessly, leveraging years of built-in intelligence to run thorough RESTful tests on your API endpoints. No more manual execution or overlooked scenarios\u2014everything is taken care of in minutes. \n\n3. **Autonomous Test Case Generation** \n Want to expand your testing coverage? The tool autonomously creates test cases based on your API calls, ensuring that your testing suite is comprehensive and robust. Plus, it refines and optimizes test cases effortlessly, so you\u2019re always a step ahead. \n\n4. **Improved Software Development Lifecycle (SDLC)** \n By automating your API testing, you're not just saving time\u2014you\u2019re enhancing collaboration between dev and QA teams, reducing testing bottlenecks, and speeding up delivery cycles with better confidence in product quality. \n\n**Why This Matters for You** \nWhether you're a developer seeking faster iterations or a QA professional ensuring bulletproof quality, this tool empowers you to focus on what truly matters\u2014building and delivering great software. Leave the complex testing routines to the AI while you take full control of outcomes without the headaches. \n\n**Call to Action:** \nReady to redefine your workflow? Supercharge your RESTful API testing instantly. Experience seamless automation, smarter coverage, and zero guesswork\u2014all powered by AI. Visit [our website](https://blvck.ai) today and start transforming your SDLC, one API at a time.","behavior":"degenerate_repetition","conversation_hash":"1dc2ed65f8db46379238415bf9e609a7","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":2726,"n_turns_in_conversation":1,"n_words":412,"source_row_offset":13859,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:00:19 GMT","turn_id":"30e1d8ba32d2ea78a021c535","turn_index":0,"user_text":"Create a title, Create a how-to blog heavily on 6356300; start with a catchy statement and lastly, call to actions and how our RESTful AI tool can help. Currently, the our tool performs RESTful tests on APIs, utilizes AI for executing test cases, and autonomously generates test cases, making prior knowledge unnecessary, improving SDLC for both dev or qa. Just plug in your cURL request and run test leveraging our AI. Call to action at https://blvck.ai, with keywords, short and simple, without mentioning blvck.ai"},{"assistant_initiates":false,"asst_text":"**\"Terrifying Tales of Software Bugs and How to Banish Them With AI-Assisted API Testing\"**\n\n**\"Bugs Lurking in the Shadows: Are You Truly Safe?\"**\n\nSoftware bugs are more than just tiny glitches\u2014they can be downright terrifying. Imagine deploying your app only to watch your users face crashing systems, compromised data, or misbehaving APIs. These aren\u2019t just inconvenient; they could spell doom for your business reputation.\n\nToday, we dive deep into some spine-chilling software bugs that have haunted organizations and explore how modern AI tools can make sure you never face similar horrors.\n\n---\n\n### **The Nightmare Chronicles: Real-Life Scary Software Bugs** \n\n1. **The $370 Million NASA Mishap** \n The Mars Climate Orbiter famously crashed because of a unit conversion error\u2014Lockheed Martin used imperial units, and NASA used metric. Just imagine: a simple miscommunication caused one of the most expensive software bugs in history.\n\n2. **AT&T's 1990 Long-Distance Blackout** \n A single misplaced \"break\" command in code took down AT&T\u2019s long-distance network for nine hours, costing millions\u2014and much of its customer trust.\n\n3. **The Therac-25 Tragedy** \n Faulty software in a radiation therapy machine caused lethal overdoses, leading to tragic deaths. A race condition in the software turned what should have been life-saving technology into a dangerous one.\n\n4. **Banking Errors That Bankrupt Trust** \n A certain bank\u2019s faulty software caused duplicate transactions, overcharging accounts, and hours of panic for customers. One code bug, thousands of stressed humans.\n\n5. **The Heartbleed Vulnerability** \n This infamous bug exposed critical security data, enabling attackers to steal sensitive information like passwords. What\u2019s scarier than software silently leaking your data?\n\n---\n\n### **What Makes These Bugs So Scary?** \n- **Silent but Devastating**: Some bugs, like Heartbleed, operate without alerting users to their presence. \n- **Expensive Recovery**: Rewriting code, addressing PR nightmares, and compensating customers can snowball into billions of dollars in damage. \n- **Trust Destroyers**: Once burned, users are reluctant to trust your platform again. \n\nSo, how can you ensure *your* software doesn\u2019t become a future entry in the Bug Hall of Shame? \n\n---\n\n### **The Hero You Need: AI-Powered RESTful API Testing** \n\nThe speed of modern development demands tools that evolve as fast as code does. This is where our RESTful AI testing solution can become your secret weapon against spooky bugs.\n\n#### **How It Works:** \n1. **Plug in Your cURL Request** \n No need to write complicated test cases from scratch. Drop in your cURL request, and our tool takes it from there. \n\n2. **AI-Driven Test Case Execution** \n No prior knowledge of testing required! Our AI analyzes your API and executes intelligent test cases tailored to your application. \n\n3. **Autonomous Test Case Generation** \n Not sure what to test? Let the AI auto-generate tests. It covers edge cases, unexpected inputs, and malformed requests\u2014the kind humans often miss. \n\n4. **Fast and Continuous Insights** \n Find bugs early in your SDLC, long before they metastasize into production disasters. Shift left effortlessly, empowering both developers and QA teams alike. \n\n---\n\n### **Why Choose AI for API Testing?** \n- **Catch Bugs Before They Catch You**: Autonomously discover vulnerabilities, degraded performance, or faulty API responses before users do. \n- **Code Faster, Confidently**: With testing automated, devs can focus on shipping features, not firefighting. \n- **Make Testing Accessible**: Even non-experts in QA can use AI to improve application stability and quality. \n\n---\n\n### **Your Chance to Vanquish the Bugs Forever** \n\nScary bugs don\u2019t have to haunt your codebase. With AI-assisted RESTful testing, you can shine a light on the shadows, eliminate lurking errors, and build flawless software faster.\n\n**Ready to take the fear out of development?** \nStart testing smarter, not harder. Avoid costly mistakes and elevate your software's quality with an effortless testing tool that does the work for you. \n\n\ud83d\ude80 **Click here to stop bugs dead in their tracks!** \nImprove software quality, boost confidence, and let AI reshape your SDLC.","behavior":"degenerate_repetition","conversation_hash":"c89d27cc2fbea3006db6445a58de9dce","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":4824,"n_turns_in_conversation":1,"n_words":718,"source_row_offset":13860,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:00:20 GMT","turn_id":"89003442d5bc62d1eb6dbbc7","turn_index":0,"user_text":"Create a title, Create a detailed how-to blog heavily on scary-software-bugs; start with a catchy statement and lastly, call to actions and how our RESTful AI tool can help. Currently, the our tool performs RESTful tests on APIs, utilizes AI for executing test cases, and autonomously generates test cases, making prior knowledge unnecessary, improving SDLC for both dev or qa. Just plug in your cURL request and run test leveraging our AI. Call to action at https://blvck.ai, with keywords, short and simple, without mentioning blvck.ai"},{"assistant_initiates":false,"asst_text":"---\n\n**Unleash the Power of Effortless API Testing with 6356300!** \n\nAre you tired of spending countless hours creating and running API test cases manually? Whether you're a developer or a QA professional, 6356300 is revolutionizing the way API testing fits into your SDLC. It\u2019s time to say goodbye to the complexities and hello to a smart, AI-driven testing solution that gets the job done in minutes\u2014no prior knowledge required.\n\nTraditional API testing workflows can be time-consuming and cumbersome, requiring deep expertise and extensive scripting just to perform basic tests. But what if you could leverage AI to streamline the entire process? 6356300 is here to simplify and supercharge your RESTful API testing, thanks to a cutting-edge tool that automates the heavy lifting for you.\n\n**How Does it Work?** \nWith 6356300, all you need is a cURL request. Just plug it in, and let the magic happen. \n\n- **RESTful Testing Done Right:** Our tool performs seamless RESTful validation, ensuring your APIs behave exactly as expected. Forget complex setups\u2014run your test cases in just a few clicks. \n- **Autonomous Test Case Generation:** Leave the manual scripting behind. Utilize the intelligent AI to create test cases automatically without requiring any pre-existing knowledge. \n- **AI-Powered Precision:** Leverage the power of AI to execute test cases, identify bottlenecks, and deliver concise, actionable feedback to improve your APIs\u2014all in real time. \n- **Improved Productivity:** Whether you're at the start of your software lifecycle or nearing deployment, 6356300 accelerates testing while maintaining high-quality output, drastically optimizing your SDLC workflows. \n\nBy integrating 6356300 into your testing strategy, you\u2019ll not only save invaluable time but also boost the reliability and performance of your APIs. Forget about the steep learning curves; increase your professional throughput and build better software with confidence.\n\n**Get Started Today!** \nWhy wait? Let 6356300 transform your development or QA journey. All it takes is a simple cURL request to get started. Deliver better, faster, and smarter results while leveraging the latest in RESTful AI automation. \n\nReady to experience the future of API testing? Try 6356300 today at [https://blvck.ai](https://blvck.ai) and discover the productivity boost you\u2019ve been waiting for!\n\n--- ","behavior":"degenerate_repetition","conversation_hash":"1289166e6b8de3e9de3e92a57b85b71d","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":2970,"n_turns_in_conversation":1,"n_words":448,"source_row_offset":13861,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:00:24 GMT","turn_id":"783ef4ef089499db5ccb7404","turn_index":0,"user_text":"Create a how-to blog heavily on 6356300; start with a catchy statement and lastly, call to actions and how our RESTful AI tool can help. Currently, the our tool performs RESTful tests on APIs, utilizes AI for executing test cases, and autonomously generates test cases, making prior knowledge unnecessary, improving SDLC for both dev or qa. Just plug in your cURL request and run test leveraging our AI. Call to action at https://blvck.ai, Productive, Informative, only content, dont mention the text \"title\", dont mention the text \"call to actions\", no prefix content from chagpt, markup format"},{"assistant_initiates":false,"asst_text":"API testing, effortless testing, AI-driven testing, RESTful API, cURL request, automated testing, AI-powered testing, SDLC workflows, autonomous test case generation, intelligent AI, real-time feedback, improved productivity, software lifecycle, testing strategy, reliability, performance, software development, QA professionals, cURL automation, RESTful validation, API bottlenecks, high-quality output, testing automation, professional throughput, AI automation, API performance, software quality.","behavior":"degenerate_repetition","conversation_hash":"5ff7f6dceb93b83c035be9cab1209c99","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":2926,"n_turns_in_conversation":1,"n_words":416,"source_row_offset":13862,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:00:26 GMT","turn_id":"60659da262ab2a0fee59d3e8","turn_index":0,"user_text":"---\n\n**Unleash the Power of Effortless API Testing with 6356300!** \n\nAre you tired of spending countless hours creating and running API test cases manually? Whether youre a developer or a QA professional, 6356300 is revolutionizing the way API testing fits into your SDLC. Its time to say goodbye to the complexities and hello to a smart, AI-driven testing solution that gets the job done in minutes\u2014no prior knowledge required.\n\nTraditional API testing workflows can be time-consuming and cumbersome, requiring deep expertise and extensive scripting just to perform basic tests. But what if you could leverage AI to streamline the entire process? 6356300 is here to simplify and supercharge your RESTful API testing, thanks to a cutting-edge tool that automates the heavy lifting for you.\n\n**How Does it Work?** \nWith 6356300, all you need is a cURL request. Just plug it in, and let the magic happen. \n\n- **RESTful Testing Done Right:** Our tool performs seamless RESTful validation, ensuring your APIs behave exactly as expected. Forget complex setups\u2014run your test cases in just a few clicks. \n- **Autonomous Test Case Generation:** Leave the manual scripting behind. Utilize the intelligent AI to create test cases automatically without requiring any pre-existing knowledge. \n- **AI-Powered Precision:** Leverage the power of AI to execute test cases, identify bottlenecks, and deliver concise, actionable feedback to improve your APIs\u2014all in real time. \n- **Improved Productivity:** Whether youre at the start of your software lifecycle or nearing deployment, 6356300 accelerates testing while maintaining high-quality output, drastically optimizing your SDLC workflows. \n\nBy integrating 6356300 into your testing strategy, youll not only save invaluable time but also boost the reliability and performance of your APIs. Forget about the steep learning curves; increase your professional throughput and build better software with confidence.\n\n**Get Started Today!** \nWhy wait? Let 6356300 transform your development or QA journey. All it takes is a simple cURL request to get started. Deliver better, faster, and smarter results while leveraging the latest in RESTful AI automation. \n\nReady to experience the future of API testing? Try 6356300 today at [https://blvck.ai](https://blvck.ai) and discover the productivity boost youve been waiting for!\n\n--- , generate keywords in a string separated by commas only"},{"assistant_initiates":false,"asst_text":"### When Good Code Turns Into a Nightmare: Exploring the World of Scary Software Bugs \n\nImagine confidently deploying your latest software update only to find that it has cost your company millions, jeopardized user data, or worse\u2014brought critical systems to a grinding halt. Scary software bugs aren't just cautionary tales; they're real-life horrors that have shaken industries, governments, and millions of users worldwide. And while they may send shivers down your spine, they also provide valuable lessons for developers and QA professionals.\n\nIn this guide, we'll explore the most spine-chilling bugs in software history, how you can avoid becoming the protagonist of the next software horror story, and how modern tools powered by AI\u2014like ours\u2014serve as your safeguard against disastrous slip-ups.\n\n---\n\n### Harrowing Tales of Software Gone Wrong \n\n1. **The Mars Climate Orbiter: Metric vs. Imperial Miscommunication** \n The 1999 failure of the Mars Climate Orbiter is the stuff of textbook nightmares. A simple unit conversion bug\u2014one team used metric and another used imperial\u2014led to the $125 million spacecraft burning up in Mars's atmosphere. A critical lesson: thorough testing, including edge cases and cross-team integrations, is non-negotiable. \n\n2. **The Ariane 5 Rocket Explosion: Overflow Unchecked** \n In 1996, the European Space Agency lost the Ariane 5 just 37 seconds after launch. The culprit? A software bug caused by converting a 64-bit floating number to a smaller 16-bit integer, which triggered an overflow in a critical component. This catastrophic error serves as yet another grim reminder to validate code assumptions and perform rigorous testing on data handling. \n\n3. **The Heartbleed Bug: A Cryptographic Horror** \n One of the most chilling bugs of the last decade, Heartbleed exploited a flaw in OpenSSL, exposing sensitive data like passwords and private keys. For years, this unchecked vulnerability allowed attackers to eavesdrop on secure systems. It underscores the importance of comprehensive testing for security vulnerabilities, especially in cryptographic software. \n\n4. **Knight Capital's \"Deployment of Doom\"** \n In 2012, a software configuration error cost Knight Capital an eye-watering $440 million in 45 minutes. A rollout error caused their stock trading algorithm to go rogue. The company\u2019s fate? Bankrupt. Lesson learned: don't skip testing new deployments, and always conduct stress tests on mission-critical systems beforehand. \n\n---\n\n### Why Software Bugs Are So Relentless \n\nThe complexity of modern software makes bugs nearly inevitable. Beyond just syntax errors or bad logic, issues like mismatched units, uncaught exceptions, hidden memory leaks, and vulnerabilities can creep into your code. Agile workflows and rapid feature releases exacerbate the issue, as time often becomes a tradeoff against thorough testing. \n\nBut here's the brutal truth: ignoring testing today to release faster *will* cost you tomorrow. Proactive, continual validation of your software\u2014down to every integration\u2014is irreplaceable. \n\nModern-day bugs are becoming harder to track. APIs are the backbone of today\u2019s connected systems, making them a common point of failure. Without strong testing around API functionality, dependencies, and edge cases, introducing bugs into production becomes less \"if\" and more \"when.\" \n\n---\n\n### Rest Easy with AI-Powered Testing \n\nTraditional approaches to software testing simply can't keep up with modern development cycles. Manual processes are time-consuming, require domain knowledge, and are susceptible to human error. But that's where we come in\u2014introducing our RESTful AI tool, designed to make API testing effortless and bulletproof. \n\n- **RESTful Testing, Simplified**: Plug your API's cURL request into our tool, and it runs comprehensive RESTful tests automatically. \n- **AI-Driven Execution**: The AI analyzes your API and autonomously generates and executes test cases without requiring you to have prior knowledge of the endpoints. \n- **Better SDLC, Less Headache**: Instantly improve your software development lifecycle (SDLC) by letting the tool handle QA heavy-lifting, whether you're a developer or QA professional. \n\nElevate your API validation without the steep learning curve or exhaustive manual tests. Catch bugs before they escalate into headlines or balance sheet disasters\u2014it\u2019s that simple. \n\n---\n\n### Avoid a Software Catastrophe Today \n\nYour next deployment shouldn't be the stuff of scary stories. Powerful tools like ours make robust, AI-powered API testing simple, effective, and attainable. Don\u2019t let a lurking bug be your undoing. \n\nTake back control of your SDLC with **intelligent, automated testing**. Visit [https://blvck.ai](https://blvck.ai) to experience how our RESTful AI tool can keep your team productive, your users happy, and your nightmares far away.","behavior":"degenerate_repetition","conversation_hash":"b0596fa289e02018a2359331c808b533","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":5516,"n_turns_in_conversation":1,"n_words":804,"source_row_offset":13863,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:00:29 GMT","turn_id":"31f9a61f4fc7751a84097231","turn_index":0,"user_text":"Create a detailed how-to blog heavily on scary-software-bugs; start with a catchy statement and lastly, call to actions and how our RESTful AI tool can help. Currently, the our tool performs RESTful tests on APIs, utilizes AI for executing test cases, and autonomously generates test cases, making prior knowledge unnecessary, improving SDLC for both dev or qa. Just plug in your cURL request and run test leveraging our AI. Call to action at https://blvck.ai, Productive, Informative, only content, dont mention the text \"title\", dont mention the text \"call to actions\", no prefix content from chagpt, markup format"},{"assistant_initiates":false,"asst_text":"software bugs, scary bugs, software history, Mars Climate Orbiter, metric vs imperial bug, Ariane 5 rocket explosion, overflow error, Heartbleed bug, OpenSSL vulnerability, Knight Capital error, stock trading bug, software failure, critical systems, software testing, QA, API testing, API validation, AI-powered testing, RESTful API, automation testing, SDLC, software development lifecycle, bug prevention, cryptographic flaw, unit testing, integration testing, deployment errors, secure systems, AI tools, automated QA, edge case testing, stress testing, software errors, RESTful tools, modern development, hidden vulnerabilities, coding assumptions, software disasters, debugging, proactive testing, API functionality, dependency testing, API endpoints, rapid feature releases","behavior":"degenerate_repetition","conversation_hash":"f1e7baecf0325e9f2b85d9f7c5545626","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":5713,"n_turns_in_conversation":1,"n_words":811,"source_row_offset":13864,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:00:33 GMT","turn_id":"8123fd02a6e82bafe76dedbb","turn_index":0,"user_text":"### When Good Code Turns Into a Nightmare: Exploring the World of Scary Software Bugs \n\nImagine confidently deploying your latest software update only to find that it has cost your company millions, jeopardized user data, or worse\u2014brought critical systems to a grinding halt. Scary software bugs arent just cautionary tales; theyre real-life horrors that have shaken industries, governments, and millions of users worldwide. And while they may send shivers down your spine, they also provide valuable lessons for developers and QA professionals.\n\nIn this guide, well explore the most spine-chilling bugs in software history, how you can avoid becoming the protagonist of the next software horror story, and how modern tools powered by AI\u2014like ours\u2014serve as your safeguard against disastrous slip-ups.\n\n---\n\n### Harrowing Tales of Software Gone Wrong \n\n1. **The Mars Climate Orbiter: Metric vs. Imperial Miscommunication** \n The 1999 failure of the Mars Climate Orbiter is the stuff of textbook nightmares. A simple unit conversion bug\u2014one team used metric and another used imperial\u2014led to the $125 million spacecraft burning up in Marss atmosphere. A critical lesson: thorough testing, including edge cases and cross-team integrations, is non-negotiable. \n\n2. **The Ariane 5 Rocket Explosion: Overflow Unchecked** \n In 1996, the European Space Agency lost the Ariane 5 just 37 seconds after launch. The culprit? A software bug caused by converting a 64-bit floating number to a smaller 16-bit integer, which triggered an overflow in a critical component. This catastrophic error serves as yet another grim reminder to validate code assumptions and perform rigorous testing on data handling. \n\n3. **The Heartbleed Bug: A Cryptographic Horror** \n One of the most chilling bugs of the last decade, Heartbleed exploited a flaw in OpenSSL, exposing sensitive data like passwords and private keys. For years, this unchecked vulnerability allowed attackers to eavesdrop on secure systems. It underscores the importance of comprehensive testing for security vulnerabilities, especially in cryptographic software. \n\n4. **Knight Capitals Deployment of Doom** \n In 2012, a software configuration error cost Knight Capital an eye-watering $440 million in 45 minutes. A rollout error caused their stock trading algorithm to go rogue. The companys fate? Bankrupt. Lesson learned: dont skip testing new deployments, and always conduct stress tests on mission-critical systems beforehand. \n\n---\n\n### Why Software Bugs Are So Relentless \n\nThe complexity of modern software makes bugs nearly inevitable. Beyond just syntax errors or bad logic, issues like mismatched units, uncaught exceptions, hidden memory leaks, and vulnerabilities can creep into your code. Agile workflows and rapid feature releases exacerbate the issue, as time often becomes a tradeoff against thorough testing. \n\nBut heres the brutal truth: ignoring testing today to release faster *will* cost you tomorrow. Proactive, continual validation of your software\u2014down to every integration\u2014is irreplaceable. \n\nModern-day bugs are becoming harder to track. APIs are the backbone of todays connected systems, making them a common point of failure. Without strong testing around API functionality, dependencies, and edge cases, introducing bugs into production becomes less if and more when. \n\n---\n\n### Rest Easy with AI-Powered Testing \n\nTraditional approaches to software testing simply cant keep up with modern development cycles. Manual processes are time-consuming, require domain knowledge, and are susceptible to human error. But thats where we come in\u2014introducing our RESTful AI tool, designed to make API testing effortless and bulletproof. \n\n- **RESTful Testing, Simplified**: Plug your APIs cURL request into our tool, and it runs comprehensive RESTful tests automatically. \n- **AI-Driven Execution**: The AI analyzes your API and autonomously generates and executes test cases without requiring you to have prior knowledge of the endpoints. \n- **Better SDLC, Less Headache**: Instantly improve your software development lifecycle (SDLC) by letting the tool handle QA heavy-lifting, whether youre a developer or QA professional. \n\nElevate your API validation without the steep learning curve or exhaustive manual tests. Catch bugs before they escalate into headlines or balance sheet disasters\u2014its that simple. \n\n---\n\n### Avoid a Software Catastrophe Today \n\nYour next deployment shouldnt be the stuff of scary stories. Powerful tools like ours make robust, AI-powered API testing simple, effective, and attainable. Dont let a lurking bug be your undoing. \n\nTake back control of your SDLC with **intelligent, automated testing**. Visit [https://blvck.ai](https://blvck.ai) to experience how our RESTful AI tool can keep your team productive, your users happy, and your nightmares far away., generate keywords in a string separated by commas only"},{"assistant_initiates":false,"asst_text":"Tentu! Berikut adalah beberapa opsi prompt yang dapat digunakan untuk menghasilkan gambar menggunakan model text-to-image berdasarkan kalimat tersebut:\n\n1. **\"A detailed medical illustration of human eyes comparing regular eyes and eyes with epicanthic folds, labeled for clarity, realistic anatomical style.\"**\n\n2. **\"Side-by-side comparison of two human eyes: one with a normal eyelid and one with an epicanthic fold, realistic illustration, clean background, focused on eyelid differences.\"**\n\n3. **\"Scientific diagram showing the anatomical differences between a regular eye and an eye with an epicanthic fold, high resolution, educational purposes.\"**\n\n4. **\"Artistic medical rendering of two types of human eyes showing the difference between standard eyes and those with an epicanthic fold, simple and easy to understand.\"**\n\n5. **\"Minimalist infographic comparing a regular eye to an eye with an epicanthic fold, clean design, labeled features, white background.\"**\n\nJika Anda menggunakan prompt ini dalam aplikasi text-to-image AI, Anda mungkin ingin memodifikasinya lebih lanjut sesuai dengan gaya atau tingkat detail yang diinginkan!","behavior":"degenerate_repetition","conversation_hash":"fb16d709a97c91e8fdd7f08583f94b39","conversation_redacted":false,"conversation_toxic":false,"country":"Indonesia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":1278,"n_turns_in_conversation":1,"n_words":178,"source_row_offset":13865,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:05:36 GMT","turn_id":"71d3849de0a659f69c87c4c1","turn_index":0,"user_text":"bisakah kamu menyarankan prompt text to image dari kalimat ini? \"Illustration comparing regular eyes to eyes with an epicanthic fold\""},{"assistant_initiates":false,"asst_text":"### Technical Requirements for a Face Detection and Recognition Student Attendance System\n\nThis document outlines the technical requirements for the development and implementation of a system designed to record student attendance using face detection and recognition technology. The system will utilize a camera to capture the student\u2019s face at the entrance of a classroom or campus, and will automatically log attendance details, including the student's last name, first name, direction of study, and time of arrival, into a centralized database.\n\n---\n\n### 1. System Architecture\n\nThe attendance system consists of multiple components working together:\n1. **Hardware**:\n - Camera for face detection and recognition.\n - Local computing device (e.g., edge processor or computer) for initial processing.\n - Server (local or cloud-based) for database management and application logic.\n - Networking hardware for communication between components.\n\n2. **Software**:\n - Face detection and recognition algorithm.\n - Backend database for storing student records and attendance.\n - Frontend application for monitoring attendance.\n - APIs for data communication and integration with other systems (e.g., Learning Management Systems (LMS)).\n\n3. **Users**:\n - Students (whose attendance is being recorded).\n - Administrators/teachers (who monitor or download attendance reports).\n\n---\n\n### 2. Functional Requirements\n\n#### 2.1. Camera and Face Detection\n- Camera must support high-resolution video (minimum HD 1080p) for clear image capture.\n- **Face detection** algorithm needs to detect individual faces even in crowded environments or under varied lighting conditions.\n- The frame rate should be sufficient to capture faces accurately in real-time (minimum 20 FPS).\n- The camera should be installed at strategic points such as classroom entrances or checkpoints where students are required to pass through.\n\n#### 2.2. Face Recognition\n- The **face recognition system** shall identify individual students with 95%-99% accuracy.\n- The system shall be robust against changes such as glasses, facial hair, minor hairstyles, or expression changes.\n- Recognition must occur in less than 1 second to handle high foot traffic.\n- Utilize a **pre-enrolled database of student facial profiles** captured during initial system setup, with each profile linked to personal information (e.g., last name, first name, direction of study).\n\n#### 2.3. Data Capture\n- Once a face is recognized:\n - Automatically capture and record:\n - First name and last name.\n - Direction of study (e.g., program, department, or course).\n - Time of arrival (accurate to seconds).\n - If a face is unrecognized, flag it for manual review and temporary enrollment, or provide an error notification.\n\n#### 2.4. Database and Record Management\n- All data must be stored in a secure **relational database** (e.g., MySQL, PostgreSQL) with fields for:\n - Student ID\n - Full name\n - Direction of study\n - Timestamp for each attendance record\n- Ensure data integrity by avoiding duplicate entries for the same class.\n- Provide APIs for querying and retrieving attendance data for viewing, analysis, and integration with external systems.\n\n#### 2.5. Reporting\n- Generate attendance reports for specific classes, days, weeks, or individual students.\n- Allow administrators to view or export reports in common formats (e.g., CSV, PDF).\n- Provide a dashboard in the frontend for real-time monitoring of attendance.\n\n---\n\n### 3. Non-Functional Requirements\n\n#### 3.1. Performance\n- The system must handle a high volume of students (e.g., a classroom of 100 students arriving within a few minutes) without lag or delays.\n- The system must maintain a response time of <1 second per recognition event.\n\n#### 3.2. Reliability and Fault Tolerance\n- The system should recover gracefully from camera disconnections or loss of power.\n- Local edge computing must cache attendance data temporarily in case of network failure, syncing with the database once connectivity is restored.\n\n#### 3.3. Security\n- All data transfer between components must use encrypted communication (e.g., SSL/TLS).\n- Enforce user authentication for administrators accessing the system.\n- Protect stored images and biometric data using encryption and comply with privacy regulations (e.g., GDPR, CCPA).\n\n#### 3.4. Scalability\n- The system should be scalable to accommodate additional cameras and increased student enrollment without significant reconfiguration.\n\n#### 3.5. Usability\n- The system must provide an intuitive dashboard (web-based or desktop) for access by authorized staff.\n- Notifications or visible indicators must provide feedback to students upon successful recognition (\"Attendance Recorded\").\n\n---\n\n### 4. Hardware Requirements\n\n1. **Camera**:\n - Minimum resolution: 1080p (Full HD).\n - Field of View (FoV): Wide angle for coverage but adjustable to avoid overlap with adjacent cameras.\n - Support for low-light conditions or include an IR camera for nighttime detection.\n - Connection: Wired or wireless, compatible with the local network.\n\n2. **Edge Processing Unit**:\n - Minimum Specifications:\n - CPU: Quad-core processor (e.g., Intel i5 or ARM Cortex).\n - RAM: 8GB minimum.\n - GPU: Dedicated or integrated GPU (for faster face recognition).\n - Run initial processing algorithms (face detection) on the edge to reduce load on the network.\n - OS: Linux-based or other lightweight systems.\n\n3. **Server Specifications**:\n - Cloud or on-site server with these minimum requirements:\n - CPU: 8-core processor.\n - RAM: 16GB.\n - Storage: 1TB for images and logs (scalable depending on retention policy).\n - OS: Windows Server or Linux (CentOS, Ubuntu).\n - Database Management System (DBMS): MySQL or PostgreSQL.\n\n4. **Networking**:\n - High-speed, low-latency network infrastructure capable of streaming camera feeds.\n - Connection options for both Wi-Fi and Ethernet.\n - Firewall and VPN capability for secure operations.\n\n---\n\n### 5. Software Requirements\n\n1. **Face Recognition Framework**:\n - Use open-source frameworks like OpenCV, Dlib, or commercial options like Amazon Rekognition or Microsoft Azure Face API.\n - Pre-trained models (e.g., DeepFace, FaceNet) or custom-trained models for higher accuracy.\n - The system should include a feature to retrain or update the model using new student data.\n\n2. **Operating System**:\n - Supported OS for camera and edge device software (Linux, Windows).\n - Web server (e.g., Apache, Nginx) for backend connectivity.\n\n3. **Frontend**:\n - Web-based dashboard accessible to administrators/teachers with login authentication.\n - Responsive design for mobile and desktop devices.\n - Filter and search options for attendance records.\n\n4. **APIs**:\n - RESTful APIs or GraphQL for:\n - Database queries.\n - Integration with third-party LMS and administration systems.\n\n5. **Database**:\n - Relational database (e.g., MySQL) with fields for:\n - Student ID (primary key).\n - First name, last name.\n - Direction of study.\n - Date and time of attendance.\n - Other metadata (e.g., notes, flagged errors).\n\n---\n\n### 6. Privacy and Compliance\n\n- The system must handle facial recognition data in compliance with regional privacy laws:\n - Obtain explicit consent from students and parents/guardians where required.\n - Provide an option for students to opt-out and use alternative attendance methods.\n - Regularly delete outdated biometric data according to policy (e.g., retain for one semester only).\n- Logs for all system access and database modifications must be maintained for audit purposes.\n\n---\n\n### 7. Testing and Maintenance\n\n- Conduct thorough testing phases:\n - Unit testing for each component.\n - Stress testing with real-time large student data.\n - Integration testing for all hardware and software components.\n- Offer regular software updates to address security and algorithmic improvements.\n- Hardware maintenance every 6 months for cameras and network systems.\n\n---\n\nThis system ensures efficient, accurate, and secure attendance tracking using advanced facial recognition while complying with privacy standards and scaling to meet institutional needs.","behavior":"degenerate_repetition","conversation_hash":"c23c79fc024c9e860ceae2ab0549fbde","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-11-20","n_chars":8600,"n_turns_in_conversation":4,"n_words":1245,"source_row_offset":13866,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:06:19 GMT","turn_id":"af3decd74f1ab69c57401892","turn_index":0,"user_text":"Write a detailed description of the technical requirements for a system that takes into account student attendance using a camera to which face detection and recognition technology is connected. The system records the student\u2019s face and enters the last name, first name, direction of study and time of arrival for class into the database."},{"assistant_initiates":false,"asst_text":"### \u0422\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u044f \u043a \u0441\u0438\u0441\u0442\u0435\u043c\u0435 \u0443\u0447\u0435\u0442\u0430 \u043f\u043e\u0441\u0435\u0449\u0430\u0435\u043c\u043e\u0441\u0442\u0438 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043e\u0432 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u043a\u0430\u043c\u0435\u0440\u044b \u0438 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u043b\u0438\u0446\n\n\u0414\u0430\u043d\u043d\u044b\u0439 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442 \u043e\u043f\u0438\u0441\u044b\u0432\u0430\u0435\u0442 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\u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u043f\u043e\u0441\u0435\u0449\u0435\u043d\u0438\u0439.\n- \u0412\u0435\u0431- \u0438\u043b\u0438 \u0434\u0435\u0441\u043a\u0442\u043e\u043f-\u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u0430\u0434\u043c\u0438\u043d\u0438\u0441\u0442\u0440\u0430\u0442\u043e\u0440\u043e\u0432 \u0438 \u043f\u0440\u0435\u043f\u043e\u0434\u0430\u0432\u0430\u0442\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u043f\u0440\u043e\u0441\u043c\u043e\u0442\u0440\u0430, \u043c\u043e\u043d\u0438\u0442\u043e\u0440\u0438\u043d\u0433\u0430 \u0438 \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u043e\u0442\u0447\u0451\u0442\u043e\u0432.\n- API \u0434\u043b\u044f \u0438\u043d\u0442\u0435\u0433\u0440\u0430\u0446\u0438\u0438 \u0441 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u043c\u0438 \u0443\u0447\u0435\u0431\u043d\u043e\u0433\u043e \u0437\u0430\u0432\u0435\u0434\u0435\u043d\u0438\u044f (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, LMS).\n\n---\n\n### 2. \u0424\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0435 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u044f\n\n#### 2.1. \u041a\u0430\u043c\u0435\u0440\u0430 \u0438 \u0437\u0430\u0445\u0432\u0430\u0442 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439\n- \u041a\u0430\u043c\u0435\u0440\u0430 \u0434\u043e\u043b\u0436\u043d\u0430 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0442\u044c \u0440\u0430\u0437\u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u043d\u0435 \u043d\u0438\u0436\u0435 Full HD (1080p) \u0434\u043b\u044f \u0442\u043e\u0447\u043d\u043e\u0433\u043e \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u043b\u0438\u0446.\n- \u041e\u0431\u0435\u0441\u043f\u0435\u0447\u0435\u043d\u0438\u0435 \u0445\u043e\u0440\u043e\u0448\u0435\u0433\u043e \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f \u0432 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0443\u0441\u043b\u043e\u0432\u0438\u044f\u0445 \u043e\u0441\u0432\u0435\u0449\u0435\u043d\u0438\u044f (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0431\u043b\u0430\u0433\u043e\u0434\u0430\u0440\u044f \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0435 WDR \u2013 \u0448\u0438\u0440\u043e\u043a\u043e\u0433\u043e \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430).\n- \u0427\u0430\u0441\u0442\u043e\u0442\u0430 \u043a\u0430\u0434\u0440\u043e\u0432 \u0434\u043e\u043b\u0436\u043d\u0430 \u0431\u044b\u0442\u044c \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e\u0439 \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u043b\u0438\u0446 \u0432 \u0440\u0435\u0430\u043b\u044c\u043d\u043e\u043c \u0432\u0440\u0435\u043c\u0435\u043d\u0438 (\u043d\u0435 \u043c\u0435\u043d\u0435\u0435 20 \u043a\u0430\u0434\u0440\u043e\u0432 \u0432 \u0441\u0435\u043a\u0443\u043d\u0434\u0443).\n- \u041a\u0430\u043c\u0435\u0440\u0430 \u0434\u043e\u043b\u0436\u043d\u0430 \u0431\u044b\u0442\u044c \u0437\u0430\u043a\u0440\u0435\u043f\u043b\u0435\u043d\u0430 \u0443 \u0432\u0445\u043e\u0434\u0430 \u0432 \u0430\u0443\u0434\u0438\u0442\u043e\u0440\u0438\u044e, \u043e\u0440\u0438\u0435\u043d\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0439 \u043d\u0430 \u0442\u043e\u0447\u043a\u0443 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0442\u0440\u0430\u0444\u0438\u043a\u0430 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043e\u0432 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0434\u0432\u0435\u0440\u043d\u043e\u0439 \u043f\u0440\u043e\u0451\u043c).\n\n#### 2.2. \u041e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u043b\u0438\u0446\n- **\u0410\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u0438\u044f \u043b\u0438\u0446** \u0434\u043e\u043b\u0436\u0435\u043d \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0442\u044c \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u043a\u0438\u0435 \u043b\u0438\u0446\u0430 \u0432 \u043a\u0430\u0434\u0440\u0435, \u0434\u0430\u0436\u0435 \u0432 \u0441\u043b\u0443\u0447\u0430\u044f\u0445 \u0447\u0430\u0441\u0442\u0438\u0447\u043d\u043e\u0433\u043e \u043f\u0435\u0440\u0435\u043a\u0440\u044b\u0442\u0438\u044f \u043b\u0438\u0446, \u0430 \u0442\u0430\u043a\u0436\u0435 \u0432 \u0443\u0441\u043b\u043e\u0432\u0438\u044f\u0445 \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u043a\u043e\u0439 \u043e\u0447\u0435\u0440\u0435\u0434\u0438.\n- \u0420\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u043b\u0438\u0446 \u0434\u043e\u043b\u0436\u043d\u043e \u0431\u044b\u0442\u044c \u0443\u0441\u0442\u043e\u0439\u0447\u0438\u0432\u044b\u043c \u043a \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u044e \u0445\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a \u0432\u043d\u0435\u0448\u043d\u043e\u0441\u0442\u0438 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043e\u0432:\n - \u041b\u0435\u0433\u043a\u0438\u0435 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u044f: \u043e\u0447\u043a\u0438, \u0431\u043e\u0440\u043e\u0434\u0430, \u043d\u0435\u0437\u043d\u0430\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0447\u0435\u0441\u043a\u0438.\n - \u0420\u0430\u0437\u043b\u0438\u0447\u0438\u044f \u0432 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0438 \u043b\u0438\u0446\u0430 (\u0443\u043b\u044b\u0431\u043a\u0430, \u043d\u0435\u0439\u0442\u0440\u0430\u043b\u044c\u043d\u043e, \u043d\u0435\u0431\u043e\u043b\u044c\u0448\u0438\u0435 \u043f\u043e\u0432\u043e\u0440\u043e\u0442\u044b \u0433\u043e\u043b\u043e\u0432\u044b).\n\n#### 2.3. \u0420\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u043b\u0438\u0446\n- \u0421\u0438\u0441\u0442\u0435\u043c\u0430 \u0434\u043e\u043b\u0436\u043d\u0430 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u0446\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0437\u0430\u0440\u0435\u0433\u0438\u0441\u0442\u0440\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0433\u043e \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430 \u0441 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c\u044e 95-99%, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0440\u0430\u043d\u0435\u0435 \u0441\u043e\u0445\u0440\u0430\u043d\u0451\u043d\u043d\u044b\u0435 \u0444\u043e\u0442\u043e\u043f\u0440\u043e\u0444\u0438\u043b\u0438.\n- \u0412\u0440\u0435\u043c\u044f \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u043e\u0434\u043d\u043e\u0433\u043e \u043b\u0438\u0446\u0430 \u043d\u0435 \u0434\u043e\u043b\u0436\u043d\u043e \u043f\u0440\u0435\u0432\u044b\u0448\u0430\u0442\u044c 1 \u0441\u0435\u043a\u0443\u043d\u0434\u044b.\n- \u0415\u0441\u043b\u0438 \u043b\u0438\u0446\u043e \u043d\u0435 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0435\u0442\u0441\u044f (\u043d\u0435\u0442 \u0432 \u0431\u0430\u0437\u0435 \u0434\u0430\u043d\u043d\u044b\u0445):\n - \u0424\u0438\u043a\u0441\u0430\u0446\u0438\u044f \u0441\u043e\u0431\u044b\u0442\u0438\u044f \u0434\u043b\u044f \u0440\u0443\u0447\u043d\u043e\u0439 \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438.\n - \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u043e\u043f\u0435\u0440\u0430\u0442\u0438\u0432\u043d\u043e\u0433\u043e \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043d\u043e\u0432\u043e\u0433\u043e \u043b\u0438\u0446\u0430 \u0432 \u0431\u0430\u0437\u0443 \u0441 \u043f\u043e\u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0439 \u043f\u0440\u0438\u0432\u044f\u0437\u043a\u043e\u0439 \u0434\u0430\u043d\u043d\u044b\u0445.\n\n#### 2.4. \u041f\u043e\u0434\u0430\u0447\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0431\u0430\u0437\u0443\n- \u041f\u043e \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u0438 \u0443\u0441\u043f\u0435\u0448\u043d\u043e\u0439 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u0437\u0430\u043f\u0438\u0441\u044b\u0432\u0430\u0435\u0442 \u0432 \u0431\u0430\u0437\u0443:\n - \u0424\u0430\u043c\u0438\u043b\u0438\u044e \u0438 \u0418\u043c\u044f \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430;\n - \u041d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f (\u0441\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c, \u0444\u0430\u043a\u0443\u043b\u044c\u0442\u0435\u0442 \u0438\u043b\u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0430);\n - \u0422\u043e\u0447\u043d\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u043f\u0440\u0438\u0431\u044b\u0442\u0438\u044f (\u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 `\u0413\u0413\u0413\u0413-\u041c\u041c-\u0414\u0414 \u0427\u0427:\u041c\u041c:\u0421\u0421`);\n - \u0423\u043d\u0438\u043a\u0430\u043b\u044c\u043d\u044b\u0439 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440 \u0437\u0430\u043f\u0438\u0441\u0438.\n- \u0418\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 \u0434\u0443\u0431\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f: \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u0434\u043e\u043b\u0436\u043d\u0430 \u043f\u0440\u0435\u0434\u043e\u0442\u0432\u0440\u0430\u0449\u0430\u0442\u044c \u043f\u043e\u0432\u0442\u043e\u0440\u043d\u0443\u044e \u0437\u0430\u043f\u0438\u0441\u044c \u0434\u043b\u044f \u043e\u0434\u043d\u043e\u0433\u043e \u0438 \u0442\u043e\u0433\u043e \u0436\u0435 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u0433\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0430.\n\n#### 2.5. \u0411\u0430\u0437\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u0435\u0451 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430\n- \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f **\u0440\u0435\u043b\u044f\u0446\u0438\u043e\u043d\u043d\u0430\u044f \u0431\u0430\u0437\u0430 \u0434\u0430\u043d\u043d\u044b\u0445** (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, MySQL, PostgreSQL) \u0441 \u043e\u0441\u043d\u043e\u0432\u043d\u044b\u043c\u0438 \u0442\u0430\u0431\u043b\u0438\u0446\u0430\u043c\u0438:\n - `students`: \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044f \u043e \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430\u0445 (ID, \u0424\u0418\u041e, \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0444\u043e\u0442\u043e);\n - `attendance`: \u0437\u0430\u043f\u0438\u0441\u0438 \u043f\u043e\u0441\u0435\u0449\u0430\u0435\u043c\u043e\u0441\u0442\u0438 (ID \u0437\u0430\u043f\u0438\u0441\u0438, ID \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430, \u0432\u0440\u0435\u043c\u044f, \u0441\u0442\u0430\u0442\u0443\u0441 \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438).\n- \u041e\u0431\u0435\u0441\u043f\u0435\u0447\u0438\u0432\u0430\u0442\u044c \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u0438\u043d\u0442\u0435\u0433\u0440\u0430\u0446\u0438\u0438 \u0441 LMS \u0438 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u0443\u0447\u0435\u0431\u043d\u044b\u043c\u0438 \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u043c\u0438 \u0447\u0435\u0440\u0435\u0437 REST API.\n\n#### 2.6. \u041e\u0442\u0447\u0451\u0442\u043d\u043e\u0441\u0442\u044c\n- \u0424\u043e\u0440\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043e\u0442\u0447\u0451\u0442\u043e\u0432 \u043e \u043f\u043e\u0441\u0435\u0449\u0430\u0435\u043c\u043e\u0441\u0442\u0438 \u043f\u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430\u043c:\n - \u0414\u043b\u044f \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u043e\u0439 \u0433\u0440\u0443\u043f\u043f\u044b \u0438\u043b\u0438 \u0430\u0443\u0434\u0438\u0442\u043e\u0440\u0438\u0438.\n - \u041f\u043e \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u043c \u0434\u043d\u044f\u043c, \u043d\u0435\u0434\u0435\u043b\u044f\u043c, \u043c\u0435\u0441\u044f\u0446\u0430\u043c.\n - \u041f\u043e \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u044b\u043c \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430\u043c.\n- \u0412\u044b\u0433\u0440\u0443\u0437\u043a\u0430 \u043e\u0442\u0447\u0451\u0442\u043e\u0432 \u0432 \u043f\u043e\u043f\u0443\u043b\u044f\u0440\u043d\u044b\u0445 \u0444\u043e\u0440\u043c\u0430\u0442\u0430\u0445 (PDF, Excel/CSV).\n- \u0420\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0441\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u0432\u0435\u0431-\u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u0430 \u0434\u043b\u044f \u0444\u0438\u043b\u044c\u0442\u0440\u0430\u0446\u0438\u0438, \u0441\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u043a\u0438 \u0438 \u043f\u0440\u043e\u0441\u043c\u043e\u0442\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445.\n\n---\n\n### 3. \u041d\u0435\u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0435 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u044f\n\n#### 3.1. \u041f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c\n- \u0420\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u043e\u0434\u043d\u043e\u0433\u043e \u043b\u0438\u0446\u0430 \u0432 \u043a\u0430\u0434\u0440\u0435 \u0432\u043c\u0435\u0441\u0442\u0435 \u0441 \u0437\u0430\u043f\u0438\u0441\u044c\u044e \u0432 \u0431\u0430\u0437\u0443 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043e\u043b\u0436\u043d\u043e \u0437\u0430\u043d\u0438\u043c\u0430\u0442\u044c \u043c\u0435\u043d\u0435\u0435 1 \u0441\u0435\u043a\u0443\u043d\u0434\u044b.\n- \u0421\u0438\u0441\u0442\u0435\u043c\u0430 \u0434\u043e\u043b\u0436\u043d\u0430 \u043e\u0431\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043b\u0438\u0446 \u043e\u0434\u043d\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e, \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u044f \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u0438 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c.\n\n#### 3.2. \u041c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u0443\u0435\u043c\u043e\u0441\u0442\u044c\n- \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u0438\u043d\u0442\u0435\u0433\u0440\u0430\u0446\u0438\u0438 \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u043a\u0430\u043c\u0435\u0440 \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0432 \u0437\u0434\u0430\u043d\u0438\u044f\u0445 \u0441 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c\u0438 \u0430\u0443\u0434\u0438\u0442\u043e\u0440\u0438\u044f\u043c\u0438.\n- \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u0443\u0432\u0435\u043b\u0438\u0447\u0435\u043d\u0438\u044f \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u043e \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430\u0445 \u0431\u0435\u0437 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0432\u043b\u0438\u044f\u043d\u0438\u044f \u043d\u0430 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c.\n\n#### 3.3. \u0411\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u044c\n- \u041f\u043e\u0434\u0430\u0432\u0430\u0442\u044c \u0447\u0451\u0442\u043a\u0438\u0439 \u0434\u043e\u0441\u0442\u0443\u043f \u043a \u0441\u0438\u0441\u0442\u0435\u043c\u0435 \u0447\u0435\u0440\u0435\u0437 \u0430\u0432\u0442\u043e\u0440\u0438\u0437\u0430\u0446\u0438\u044e (\u043c\u0435\u0445\u0430\u043d\u0438\u0437\u043c \u043f\u0430\u0440\u043e\u043b\u0435\u0439, \u0434\u0432\u0443\u0445\u0444\u0430\u043a\u0442\u043e\u0440\u043d\u0430\u044f \u0430\u0443\u0442\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044f).\n- \u0423\u0434\u043e\u0441\u0442\u043e\u0432\u0435\u0440\u0435\u043d\u0438\u0435 \u0432\u0441\u0435\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u0447\u0435\u0440\u0435\u0437 \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0435 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, HTTPS/SSL).\n- \u0421\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0438\u0435 \u0432 \u0431\u0430\u0437\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f \u0438 \u043c\u0435\u0442\u0430\u0434\u0430\u043d\u043d\u044b\u0445 \u0441\u043b\u0443\u0447\u0430\u0435\u0432 \u0432\u0445\u043e\u0434\u0430 \u0442\u043e\u043b\u044c\u043a\u043e \u043f\u0440\u0438 \u0441\u043e\u0433\u043b\u0430\u0441\u0438\u0438 \u0443\u0447\u0430\u0449\u0438\u0445\u0441\u044f.\n- \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u0430 \u0448\u0438\u0444\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0434\u043b\u044f \u043a\u043e\u043d\u0444\u0438\u0434\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, AES 256).\n\n#### 3.4. \u041d\u0430\u0434\u0451\u0436\u043d\u043e\u0441\u0442\u044c\n- \u041f\u0440\u0438 \u0441\u0431\u043e\u0435 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u044f \u043a\u0430\u043c\u0435\u0440\u0430 \u0438 \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u044b\u0439 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440 \u0434\u043e\u043b\u0436\u043d\u044b \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u043d\u0430 \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u043e\u043c \u0445\u0440\u0430\u043d\u0438\u043b\u0438\u0449\u0435 \u0438 \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u0441\u0438\u043d\u0445\u0440\u043e\u043d\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0438\u0445 \u0441 \u0441\u0435\u0440\u0432\u0435\u0440\u043e\u043c \u043f\u043e\u0441\u043b\u0435 \u0432\u043e\u0441\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u044f \u0441\u0432\u044f\u0437\u0438.\n- \u041d\u0430\u043b\u0438\u0447\u0438\u0435 \u043f\u0440\u043e\u0446\u0435\u0434\u0443\u0440 \u0432\u043e\u0441\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u044f \u0434\u0430\u043d\u043d\u044b\u0445 \u0438\u0437 \u0440\u0435\u0437\u0435\u0440\u0432\u043d\u044b\u0445 \u043a\u043e\u043f\u0438\u0439 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u0432\u044b\u0445\u043e\u0434\u0430 \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u0438\u0437 \u0441\u0442\u0440\u043e\u044f.\n\n#### 3.5. \u0423\u0434\u043e\u0431\u0441\u0442\u0432\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f\n- \u041f\u043e\u043a\u0430\u0437 \u043e\u043f\u043e\u0432\u0435\u0449\u0435\u043d\u0438\u044f \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0443 \u043e\u0431 \u0443\u0441\u043f\u0435\u0448\u043d\u043e\u0439 \u0440\u0435\u0433\u0438\u0441\u0442\u0440\u0430\u0446\u0438\u0438 \u0447\u0435\u0440\u0435\u0437 \u044d\u043a\u0440\u0430\u043d \u0438\u043b\u0438 \u0437\u0432\u0443\u043a\u043e\u0432\u043e\u0439 \u0441\u0438\u0433\u043d\u0430\u043b.\n- \u041f\u0440\u043e\u0441\u0442\u043e\u0439 \u0438 \u043f\u043e\u043d\u044f\u0442\u043d\u044b\u0439 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441 \u0434\u043b\u044f \u0430\u0434\u043c\u0438\u043d\u0438\u0441\u0442\u0440\u0430\u0442\u043e\u0440\u0430 \u0441 \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u043c \u043d\u0430\u0431\u043e\u0440\u043e\u043c \u043f\u043e\u043b\u0435\u0439.\n\n---\n\n### 4. \u0410\u043f\u043f\u0430\u0440\u0430\u0442\u043d\u044b\u0435 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u044f\n\n| \u041a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442 | \u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u0438 |\n|---------------------|----------------------------------------------------------------------------------------------|\n| **\u041a\u0430\u043c\u0435\u0440\u0430** | \u041c\u0438\u043d\u0438\u043c\u0443\u043c 1080p, \u0432\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u0430\u044f \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 WDR, \u043f\u0440\u043e\u0432\u043e\u0434\u043d\u043e\u0435 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435 Ethernet \u0438\u043b\u0438 Wi-Fi. |\n| **\u041b\u043e\u043a\u0430\u043b\u044c\u043d\u044b\u0439 \u041f\u041a** | CPU: Intel i5, RAM: 8 \u0413\u0411, GPU: \u0432\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u044b\u0439 \u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0447\u0438\u043f \u0438\u043b\u0438 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u0430\u044f GPU \u0434\u043b\u044f AI \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438. |\n| **\u0421\u0435\u0440\u0432\u0435\u0440\u043d\u0430\u044f \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0430** | CPU: 8-\u044f\u0434\u0435\u0440\u043d\u044b\u0439 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440, RAM: 16 \u0413\u0411, HDD: 1 \u0422\u0411, \u041e\u0421: Linux \u0438\u043b\u0438 Windows Server. |\n\n---\n\n### 5. \u041f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u0430\u044f \u0447\u0430\u0441\u0442\u044c\n\n#### 5.1. \u0420\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u043b\u0438\u0446\n- \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u0437\u0440\u0435\u043d\u0438\u044f: **OpenCV**, **Dlib**, \u0438\u043b\u0438 \u043a\u043e\u043c\u043c\u0435\u0440\u0447\u0435\u0441\u043a\u0438\u0435 \u0441\u0435\u0440\u0432\u0438\u0441\u044b (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, **Microsoft Azure Face API**, **Amazon Rekognition**).\n- \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 (\u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u0434\u043e\u043e\u0431\u0443\u0447\u0430\u0435\u043c\u043e\u0441\u0442\u044c) \u0434\u043b\u044f \u043f\u043e\u0432\u044b\u0448\u0435\u043d\u0438\u044f \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u0440\u0430\u0431\u043e\u0442\u044b.\n\n#### 5.2. \u041f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u043e\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0435\n- \u0411\u0430\u0437\u043e\u0432\u044b\u0439 \u0441\u0442\u0435\u043a: Python \u0438\u043b\u0438 JavaScript (Node.js) \u0434\u043b\u044f \u0441\u0435\u0440\u0432\u0435\u0440\u043d\u044b\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439.\n- DBMS \u0434\u043b\u044f \u0431\u0430\u0437\u044b \u0434\u0430\u043d\u043d\u044b\u0445: MySQL \u0438\u043b\u0438 PostgreSQL.\n- \u0424\u0440\u043e\u043d\u0442\u0435\u043d\u0434: React.js, Angular \u0438\u043b\u0438 \u0434\u0440\u0443\u0433\u0438\u0435 \u0441\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0438.\n\n#### 5.3. API\n- RESTful API/GraphQL \u0434\u043b\u044f \u0432\u0437\u0430\u0438\u043c\u043e\u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u0441 \u0432\u043d\u0435\u0448\u043d\u0438\u043c\u0438 \u0441\u0435\u0440\u0432\u0438\u0441\u0430\u043c\u0438.\n\n---\n\n### 6. \u041a\u043e\u043d\u0444\u0438\u0434\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0438 \u044e\u0440\u0438\u0434\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u044b\n- \u0421\u0438\u0441\u0442\u0435\u043c\u0430 \u0434\u043e\u043b\u0436\u043d\u0430 \u0441\u043e\u0431\u043b\u044e\u0434\u0430\u0442\u044c \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u044f \u0437\u0430\u043a\u043e\u043d\u043e\u0434\u0430\u0442\u0435\u043b\u044c\u0441\u0442\u0432\u0430 \u043f\u043e \u0437\u0430\u0449\u0438\u0442\u0435 \u043f\u0435\u0440\u0441\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, GDPR, \u0424\u0417-152 \u0432 \u0420\u043e\u0441\u0441\u0438\u0438).\n- \u041f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u0438 (\u0441\u0442\u0443\u0434\u0435\u043d\u0442\u044b) \u0434\u043e\u043b\u0436\u043d\u044b \u0431\u044b\u0442\u044c \u043f\u0440\u043e\u0438\u043d\u0444\u043e\u0440\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u044b \u043e \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 \u0438 \u0434\u0430\u0442\u044c \u0441\u043e\u0433\u043b\u0430\u0441\u0438\u0435 \u043d\u0430 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0435\u0451 \u0434\u0430\u043d\u043d\u044b\u0445.\n- \u0420\u0435\u0433\u043b\u0430\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044f \u0441\u0440\u043e\u043a\u0430 \u0445\u0440\u0430\u043d\u0435\u043d\u0438\u044f \u0434\u0430\u043d\u043d\u044b\u0445 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043d\u0435 \u0431\u043e\u043b\u0435\u0435 6 \u043c\u0435\u0441\u044f\u0446\u0435\u0432 \u043f\u043e\u0441\u043b\u0435 \u0443\u0447\u0435\u0431\u043d\u043e\u0433\u043e \u043f\u0435\u0440\u0438\u043e\u0434\u0430).\n\n---\n\n### 7. \u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0438 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430\n- \u041f\u043e\u043b\u043d\u043e\u0446\u0435\u043d\u043d\u043e\u0435 \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043d\u0430 \u0443\u0447\u0435\u0431\u043d\u044b\u0445 \u043f\u043b\u043e\u0449\u0430\u0434\u043a\u0430\u0445 \u0438 \u0432 \u0440\u0430\u0437\u043d\u044b\u0445 \u0441\u0446\u0435\u043d\u0430\u0440\u0438\u044f\u0445.\n- \u041e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u0440\u0430\u0437 \u0432 \u043f\u043e\u043b\u0433\u043e\u0434\u0430 \u0434\u043b\u044f \u043f\u043e\u0432\u044b\u0448\u0435\u043d\u0438\u044f \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438.\n- \u041d\u0430\u043b\u0438\u0447\u0438\u0435 \u0441\u043b\u0443\u0436\u0431\u044b \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0438 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u0435\u0439 \u0438 \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u043e\u0431\u0440\u0430\u0442\u043d\u043e\u0439 \u0441\u0432\u044f\u0437\u0438.\n\n---\n\n\u042d\u0442\u0430 \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u0441\u0434\u0435\u043b\u0430\u0435\u0442 \u0440\u0435\u0433\u0438\u0441\u0442\u0440\u0430\u0446\u0438\u044e \u043f\u043e\u0441\u0435\u0449\u0430\u0435\u043c\u043e\u0441\u0442\u0438 \u043f\u043e\u043b\u043d\u043e\u0441\u0442\u044c\u044e \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0439, \u0438\u0441\u043a\u043b\u044e\u0447\u0430\u044f \u043e\u0448\u0438\u0431\u043a\u0438 \u0438 \u0441\u043d\u0438\u0436\u0430\u044f \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0437\u0430\u0442\u0440\u0430\u0442\u044b \u0434\u043b\u044f \u043f\u0440\u0435\u043f\u043e\u0434\u0430\u0432\u0430\u0442\u0435\u043b\u0435\u0439.","behavior":"degenerate_repetition","conversation_hash":"c23c79fc024c9e860ceae2ab0549fbde","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-11-20","n_chars":7789,"n_turns_in_conversation":4,"n_words":993,"source_row_offset":13867,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:06:19 GMT","turn_id":"16c36304680ca3289e06a207","turn_index":1,"user_text":"\u041d\u0430\u043f\u0438\u0448\u0438 \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u0442\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u0439 \u043a \u0441\u0438\u0441\u0442\u0435\u043c\u0435, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u0442 \u043f\u043e\u0441\u0435\u0449\u0430\u0435\u043c\u043e\u0441\u0442\u044c \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043e\u0432 \u0441\u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043a\u0430\u043c\u0435\u0440\u044b, \u043a \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0435\u043d\u0430 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u0438 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u043e\u0432\u0430\u043d\u0438\u044f \u043b\u0438\u0446. \u0421\u0438\u0441\u0442\u0435\u043c\u0430 \u0444\u0438\u043a\u0441\u0438\u0440\u0443\u0435\u0442 \u043b\u0438\u0446\u043e \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430 \u0438 \u0432\u043d\u043e\u0441\u0438\u0442 \u0432 \u0431\u0430\u0437\u0443 \u0424\u0430\u043c\u0438\u043b\u0438\u044e, \u0418\u043c\u044f, \u041d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u0432\u0440\u0435\u043c\u044f \u043f\u0440\u0438\u0431\u044b\u0442\u0438\u044f \u043d\u0430 \u0437\u0430\u043d\u044f\u0442\u0438\u0435."},{"assistant_initiates":false,"asst_text":"\u0414\u0430\u043d\u043d\u044b\u0439 \u043a\u043e\u0434 \u0440\u0435\u0430\u043b\u0438\u0437\u0443\u0435\u0442 \u0441\u0438\u0441\u0442\u0435\u043c\u0443 \u0443\u0447\u0451\u0442\u0430 \u043f\u043e\u0441\u0435\u0449\u0430\u0435\u043c\u043e\u0441\u0442\u0438 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043e\u0432 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u043b\u0438\u0446. \u041d\u0438\u0436\u0435 \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043e \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u0440\u0430\u0431\u043e\u0442\u044b \u0438 \u043b\u043e\u0433\u0438\u043a\u0438 \u043a\u043e\u0434\u0430:\n\n---\n\n### 1. **\u0418\u043c\u043f\u043e\u0440\u0442 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a \u0438 \u043c\u043e\u0434\u0443\u043b\u0435\u0439**\n```python\nimport pickle\nimport face_recognition\nimport cv2\nimport os\nimport numpy as np\nimport cvzone\n```\n- **`pickle`**: \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0434\u043b\u044f \u0437\u0430\u0433\u0440\u0443\u0437\u043a\u0438 \u0438 \u0441\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0438\u044f \u0441\u043f\u0438\u0441\u043a\u0430 \u0443\u0436\u0435 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u044b\u0445 \"\u0437\u0430\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445\" \u043b\u0438\u0446 (\u0438\u0445 \u0432\u0435\u043a\u0442\u043e\u0440\u043d\u044b\u0445 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0439).\n- **`face_recognition`**: \u0411\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 \u0434\u043b\u044f \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u0438 \u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u043b\u0438\u0446.\n- **`cv2`**: OpenCV. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0441 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435\u043c (\u0432\u0438\u0434\u0435\u043e\u043f\u043e\u0442\u043e\u043a, \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445, \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0446\u0432\u0435\u0442\u0430 \u0438 \u0442.\u0434.).\n- **`os`**: \u041c\u043e\u0434\u0443\u043b\u044c \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0441 \u0444\u0430\u0439\u043b\u043e\u0432\u043e\u0439 \u0441\u0438\u0441\u0442\u0435\u043c\u043e\u0439 (\u0437\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0444\u0430\u0439\u043b\u043e\u0432, \u043d\u0430\u0432\u0438\u0433\u0430\u0446\u0438\u044f \u043f\u043e \u043f\u0430\u043f\u043a\u0430\u043c).\n- **`numpy`**: \u0414\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0441 \u043c\u0430\u0441\u0441\u0438\u0432\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445.\n- **`cvzone`**: \u0411\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 \u0434\u043b\u044f \u0443\u0434\u043e\u0431\u043d\u043e\u0439 \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0433\u0440\u0430\u0444\u0438\u043a\u0438 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043e\u0442\u0440\u0438\u0441\u043e\u0432\u043a\u0430 \u043f\u0440\u044f\u043c\u043e\u0443\u0433\u043e\u043b\u044c\u043d\u0438\u043a\u043e\u0432 \u043d\u0430 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f\u0445).\n\n---\n\n### 2. **\u041d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0430 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445**\n#### \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043a\u0430\u043c\u0435\u0440\u044b\n```python\ncap = cv2.VideoCapture(1)\ncap.set(3,640) # \u0423\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0430 \u0448\u0438\u0440\u0438\u043d\u044b \u043a\u0430\u0434\u0440\u0430\ncap.set(4,480) # \u0423\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0430 \u0432\u044b\u0441\u043e\u0442\u044b \u043a\u0430\u0434\u0440\u0430\n```\n- \u041a\u0430\u043c\u0435\u0440\u0430 \u0437\u0430\u0445\u0432\u0430\u0442\u044b\u0432\u0430\u0435\u0442 \u0432\u0438\u0434\u0435\u043e\u043f\u043e\u0442\u043e\u043a. \u0418\u043d\u0434\u0435\u043a\u0441 \u043a\u0430\u043c\u0435\u0440\u044b `1` \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u0447\u0442\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0432\u0442\u043e\u0440\u0430\u044f \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0451\u043d\u043d\u0430\u044f \u043a\u0430\u043c\u0435\u0440\u0430 (\u0435\u0441\u043b\u0438 \u0431\u044b \u0431\u044b\u043b `0`, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b\u0430\u0441\u044c \u0431\u044b \u043f\u0435\u0440\u0432\u0430\u044f \u043a\u0430\u043c\u0435\u0440\u0430).\n- \u0420\u0430\u0437\u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u043a\u0430\u0434\u0440\u0430 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u043e \u043d\u0430 640x480.\n\n#### \u0424\u043e\u043d\u043e\u0432\u043e\u0435 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435\n```python\nimgBackground = cv2.imread('C:\\\\Users\\\\user\\\\PycharmProjects\\\\PythonProject\\\\Resources\\\\background.png')\n```\n\u0417\u0430\u0433\u0440\u0443\u0436\u0430\u0435\u0442\u0441\u044f \u0441\u0442\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u0444\u043e\u043d\u043e\u0432\u043e\u0435 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435 (`background.png`), \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0431\u0443\u0434\u0435\u0442 \u043d\u0430\u043a\u043b\u0430\u0434\u044b\u0432\u0430\u0442\u044c\u0441\u044f \u0432\u0438\u0434\u0435\u043e \u0441 \u043a\u0430\u043c\u0435\u0440\u044b \u0438 \u0434\u0440\u0443\u0433\u0438\u0435 \u0432\u0438\u0437\u0443\u0430\u043b\u044c\u043d\u044b\u0435 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u044b.\n\n#### \u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u043d\u044b\u0445 \u0440\u0435\u0436\u0438\u043c\u043e\u0432\n```python\nfolderModePath = 'C:\\\\Users\\\\user\\\\PycharmProjects\\\\PythonProject\\\\Resources\\\\Modes'\nmodePathList = os.listdir(folderModePath)\nimgModeList = []\nfor path in modePathList:\n imgModeList.append(cv2.imread(os.path.join(folderModePath, path)))\n```\n- `folderModePath` \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f \u0438\u0437 \u043f\u0430\u043f\u043a\u0438 \"Modes\". \u0412 \u0434\u0430\u043d\u043d\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u0438\u0445 \u043c\u043e\u0433\u0443\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u043b\u044f \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u043d\u044b\u0445 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u043e\u0432.\n- \u0412\u0441\u0435 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f \u0432 \u043f\u0430\u043f\u043a\u0435 \u0437\u0430\u0433\u0440\u0443\u0436\u0430\u044e\u0442\u0441\u044f \u0432 \u0441\u043f\u0438\u0441\u043e\u043a `imgModeList`.\n\n---\n\n### 3. **\u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0437\u0430\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 (Encoding File)**\n```python\nfile = open('EncodeFile.p','rb')\nencodeListKnownWithIds = pickle.load(file)\nfile.close()\nencodeListKnown, studentIds = encodeListKnownWithIds\n```\n- **`EncodeFile.p`** \u2014 \u044d\u0442\u043e \u0444\u0430\u0439\u043b, \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0449\u0438\u0439 \u0434\u0430\u043d\u043d\u044b\u0435 \u043e \u043f\u0440\u0435\u0434\u0432\u0430\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0437\u0430\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u043b\u0438\u0446\u0430\u0445 \u0438\u0437 \u0431\u0430\u0437\u044b. \u041a\u043e\u0434\u0438\u0440\u043e\u0432\u043a\u0430 \u043b\u0438\u0446\u0430 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0441\u043e\u0431\u043e\u0439 \u0447\u0438\u0441\u043b\u043e\u0432\u043e\u0439 \u0432\u0435\u043a\u0442\u043e\u0440, \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u044e\u0449\u0438\u0439 \u0443\u043d\u0438\u043a\u0430\u043b\u044c\u043d\u044b\u0435 \u0445\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0438 \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0430.\n- \u0421\u043e\u0445\u0440\u0430\u043d\u044f\u044e\u0442\u0441\u044f \u0434\u0432\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435:\n - `encodeListKnown` \u2014 \u0441\u043f\u0438\u0441\u043e\u043a \u0441\u0435\u043a\u0442\u043e\u0440\u043e\u0432 (\u0432\u0435\u043a\u0442\u043e\u0440\u043e\u0432), \u0437\u0430\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u0432\u0441\u0435\u0445 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u044b\u0445 \u043b\u0438\u0446.\n - `studentIds` \u2014 \u0441\u043f\u0438\u0441\u043e\u043a \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043e\u0432, \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u0439 \u043a\u0430\u0436\u0434\u043e\u043c\u0443 \u043b\u0438\u0446\u0443 \u0432 `encodeListKnown`.\n\n---\n\n### 4. **\u041e\u0441\u043d\u043e\u0432\u043d\u043e\u0439 \u0446\u0438\u043a\u043b \u0440\u0430\u0431\u043e\u0442\u044b \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b**\n\u041f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0430 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u0442 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u044b\u0439 \u0446\u0438\u043a\u043b, \u0432\u043d\u0443\u0442\u0440\u0438 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0441\u044f \u0437\u0430\u0445\u0432\u0430\u0442 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439 \u0441 \u043a\u0430\u043c\u0435\u0440\u044b, \u0430\u043d\u0430\u043b\u0438\u0437 \u043b\u0438\u0446, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043e\u0442\u0440\u0438\u0441\u043e\u0432\u043a\u0430 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u0430.\n\n#### \u0417\u0430\u0445\u0432\u0430\u0442 \u0438 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043a\u0430\u0434\u0440\u043e\u0432\n```python\nsuccess, img = cap.read()\nimgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)\nimgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)\n```\n- \u0421 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043a\u0430\u043c\u0435\u0440\u044b \u0437\u0430\u0445\u0432\u0430\u0442\u044b\u0432\u0430\u0435\u0442\u0441\u044f \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435 (\u043a\u0430\u0434\u0440 \u0432\u0438\u0434\u0435\u043e\u043f\u043e\u0442\u043e\u043a\u0430 `img`).\n- \u0414\u043b\u044f \u0443\u0432\u0435\u043b\u0438\u0447\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0443\u043c\u0435\u043d\u044c\u0448\u0430\u0435\u0442\u0441\u044f \u0434\u043e \u0447\u0435\u0442\u0432\u0435\u0440\u0442\u0438 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0440\u0430\u0437\u043c\u0435\u0440\u0430.\n- \u041a\u0430\u0436\u0434\u044b\u0439 \u043a\u0430\u0434\u0440 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u0435\u0442\u0441\u044f \u0432 \u0446\u0432\u0435\u0442\u043e\u0432\u043e\u0439 \u043a\u043e\u0434 RGB (\u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 `face_recognition`).\n\n#### \u041e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u043b\u0438\u0446 \u043d\u0430 \u043a\u0430\u0434\u0440\u0435\n```python\nfaceCurFrame = face_recognition.face_locations(imgS)\nencodeCurFrame = face_recognition.face_encodings(imgS, faceCurFrame)\n```\n- **`face_recognition.face_locations`** \u2014 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u0442 \u043c\u0435\u0441\u0442\u043e\u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u043b\u0438\u0446 \u043d\u0430 \u0443\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u043d\u043e\u043c \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0438.\n- **`face_recognition.face_encodings`** \u2014 \u0441\u043e\u0437\u0434\u0430\u0435\u0442 \u0432\u0435\u043a\u0442\u043e\u0440\u043d\u043e\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043d\u0430\u0439\u0434\u0435\u043d\u043d\u044b\u0445 \u043d\u0430 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0438 \u043b\u0438\u0446 (\u0438\u0445 \u043a\u043e\u0434\u0438\u0440\u043e\u0432\u043a\u0443).\n\n#### \u0412\u0441\u0442\u0430\u0432\u043a\u0430 \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f \u043a\u0430\u043c\u0435\u0440\u044b \u043d\u0430 \u0444\u043e\u043d\n```python\nimgBackground[162:162+480,55:55+640] = img\nimgBackground[44:44 + 633, 808:808 + 414] = imgModeList[1]\n```\n- \u0412\u0438\u0434\u0435\u043e \u0441 \u043a\u0430\u043c\u0435\u0440\u044b (`img`) \u0432\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0432 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u0443\u044e \u043e\u0431\u043b\u0430\u0441\u0442\u044c \u0444\u043e\u043d\u043e\u0432\u043e\u0433\u043e \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f (`imgBackground`).\n- \u0410\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u043e, \u0434\u043e\u0431\u0430\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u044b \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u0430 \u0438\u0437 `imgModeList`.\n\n---\n\n### 5. **\u0420\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u043b\u0438\u0446**\n\u0412\u043d\u0443\u0442\u0440\u0438 \u0446\u0438\u043a\u043b\u0430 \u043f\u0440\u043e\u0432\u043e\u0434\u0438\u0442\u0441\u044f \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 \u0442\u0435\u043a\u0443\u0449\u0438\u0445 \u043b\u0438\u0446 \u0441 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u044b\u043c\u0438 \u043b\u0438\u0446\u0430\u043c\u0438:\n\n#### \u0421\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 \u043b\u0438\u0446\n```python\nfor encodeFace, faceLoc in zip(encodeCurFrame, faceCurFrame):\n matches = face_recognition.compare_faces(encodeListKnown, encodeFace)\n faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)\n```\n- \u0422\u0435\u043a\u0443\u0449\u0435\u0435 \u0437\u0430\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0435 \u043b\u0438\u0446\u043e (`encodeFace`) \u0441\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0441\u043e \u0432\u0441\u0435\u043c\u0438 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u044b\u043c\u0438 \u043b\u0438\u0446\u0430\u043c\u0438 \u0438\u0437 \u0431\u0430\u0437\u044b (`encodeListKnown`).\n- \u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f \u0432:\n - **`matches`**: \u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0441\u043f\u0438\u0441\u043e\u043a (True/False), \u0433\u0434\u0435 `True` \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043d\u0430 \u0441\u043e\u0432\u043f\u0430\u0434\u0435\u043d\u0438\u0435.\n - **`faceDis`**: \u0441\u043f\u0438\u0441\u043e\u043a \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0439 \u043c\u0435\u0436\u0434\u0443 \u0432\u0435\u043a\u0442\u043e\u0440\u043e\u043c \u0442\u0435\u043a\u0443\u0449\u0435\u0433\u043e \u043b\u0438\u0446\u0430 \u0438 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u044b\u043c\u0438 \u043b\u0438\u0446\u0430\u043c\u0438 (\u043c\u0435\u043d\u044c\u0448\u0435\u0435 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u043e\u0437\u043d\u0430\u0447\u0430\u0435\u0442 \u0431\u043e\u043b\u044c\u0448\u0435\u0435 \u0441\u0445\u043e\u0434\u0441\u0442\u0432\u043e).\n\n#### \u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0435\u0433\u043e \u0441\u043e\u0432\u043f\u0430\u0434\u0435\u043d\u0438\u044f\n```python\nmatchIndex = np.argmin(faceDis)\nif matches[matchIndex]:\n print(\"\u041b\u0438\u0446\u043e \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u043e\")\n print(studentIds[matchIndex])\n```\n- \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0438\u043d\u0434\u0435\u043a\u0441 \u043b\u0438\u0446\u0430 \u0441 \u0441\u0430\u043c\u044b\u043c \u043c\u0430\u043b\u0435\u043d\u044c\u043a\u0438\u043c \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435\u043c (`np.argmin(faceDis)`).\n- \u0415\u0441\u043b\u0438 \u0441\u043e\u0432\u043f\u0430\u0434\u0435\u043d\u0438\u0435 \u043d\u0430\u0439\u0434\u0435\u043d\u043e (`matches[matchIndex] == True`), \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0430:\n - \u0412\u044b\u0432\u043e\u0434\u0438\u0442 \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u0435 \"\u041b\u0438\u0446\u043e \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u043e\".\n - \u041e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0435\u0442 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430 (`studentIds[matchIndex]`), \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0439 \u0441 \u043d\u0430\u0439\u0434\u0435\u043d\u043d\u044b\u043c \u043b\u0438\u0446\u043e\u043c.\n\n#### \u0412\u0438\u0437\u0443\u0430\u043b\u044c\u043d\u0430\u044f \u043c\u0430\u0440\u043a\u0438\u0440\u043e\u0432\u043a\u0430 \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u043d\u043e\u0433\u043e \u043b\u0438\u0446\u0430\n```python\ny1, x2, y2, x1 = faceLoc\ny1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4\nbbox = 55 + x1, 162 + y1, x2 - x1, y2 - y1\nimgBackground = cvzone.cornerRect(imgBackground, bbox, rt=0)\n```\n- \u041a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u043b\u0438\u0446\u0430, \u043d\u0430\u0439\u0434\u0435\u043d\u043d\u043e\u0433\u043e \u043d\u0430 \u0443\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u043d\u043e\u043c \u043a\u0430\u0434\u0440\u0435, \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u0443\u044e\u0442\u0441\u044f \u043e\u0431\u0440\u0430\u0442\u043d\u043e \u043a \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b\u044c\u043d\u043e\u043c\u0443 \u0440\u0430\u0437\u043c\u0435\u0440\u0443.\n- \u041d\u0430 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0438 \u0440\u0438\u0441\u0443\u0435\u0442\u0441\u044f \u0440\u0430\u043c\u043a\u0430 (`bbox`) \u0432\u043e\u043a\u0440\u0443\u0433 \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u043d\u043e\u0433\u043e \u043b\u0438\u0446\u0430 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 `cvzone`.\n\n---\n\n### 6. **\u0412\u044b\u0432\u043e\u0434 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f**\n```python\ncv2.imshow(\"Face Attendance\", imgBackground)\ncv2.waitKey(1)\n```\n- \u0424\u0438\u043d\u0430\u043b\u044c\u043d\u0430\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0435\u0442\u0441\u044f \u0432 \u043e\u043a\u043d\u0435 \"Face Attendance\".\n- `cv2.waitKey(1)` \u043e\u0431\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0435\u0442 \u0441\u043e\u0431\u044b\u0442\u0438\u044f \u043e\u043a\u043d\u0430 \u0438 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0435\u0442 \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f.\n\n---\n\n### \u0418\u0442\u043e\u0433\u043e\u0432\u0430\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c\n1. \u0417\u0430\u0433\u0440\u0443\u0436\u0430\u0435\u0442 \u0431\u0430\u0437\u0443 \u0437\u0430\u0440\u0435\u0433\u0438\u0441\u0442\u0440\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u043b\u0438\u0446 (\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u043a\u0438 \u0438 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u044b \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043e\u0432).\n2. \u0417\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u0442 \u0432\u0438\u0434\u0435\u043e\u043f\u043e\u0442\u043e\u043a \u0447\u0435\u0440\u0435\u0437 \u043a\u0430\u043c\u0435\u0440\u0443.\n3. \u041e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u0442 \u043b\u0438\u0446\u0430 \u0432 \u043a\u0430\u0434\u0440\u0435, \u0441\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u0435\u0442 \u0438\u0445 \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u0438\u0437 \u0431\u0430\u0437\u044b \u0438 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u0446\u0438\u0440\u0443\u0435\u0442 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430.\n4. \u041e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0435\u0442 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441 \u0441 \u0432\u0438\u0434\u0435\u043e, \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0435\u0439 \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u0438\u044f \u043b\u0438\u0446\u0430 \u0438 \u043d\u0430\u043b\u0438\u0447\u0438\u0435\u043c \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u0440\u0435\u0436\u0438\u043c\u043e\u0432.\n5. \u0420\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u043d\u043d\u044b\u0439 \u0441\u0442\u0443\u0434\u0435\u043d\u0442 \u0432\u044b\u0432\u043e\u0434\u0438\u0442\u0441\u044f \u0432 \u043a\u043e\u043d\u0441\u043e\u043b\u044c (`studentIds[matchIndex]`). \n\n\u042d\u0442\u043e \u0431\u0430\u0437\u043e\u0432\u0430\u044f \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0434\u043b\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u0443\u0447\u0435\u0442\u0430 \u043f\u043e\u0441\u0435\u0449\u0430\u0435\u043c\u043e\u0441\u0442\u0438 \u0447\u0435\u0440\u0435\u0437 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u043b\u0438\u0446 \u0441 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u043e\u043c \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 OpenCV.","behavior":"degenerate_repetition","conversation_hash":"c23c79fc024c9e860ceae2ab0549fbde","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-11-20","n_chars":8592,"n_turns_in_conversation":4,"n_words":946,"source_row_offset":13868,"source_shard":"turns-00056.parquet","timestamp":"Thu, 19 Dec 2024 00:06:19 GMT","turn_id":"82881da363562aca25391d4d","turn_index":2,"user_text":"\u041e\u043f\u0438\u0448\u0438 \u044d\u0442\u043e\u0442 \u043a\u043e\u0434 \u0434\u043b\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u0443\u0447\u0451\u0442\u0430 \u043f\u043e\u0441\u0435\u0449\u0430\u0435\u043c\u043e\u0441\u0442\u0438 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043e\u0432 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u043b\u0438\u0446:\n\nimport pickle\nimport face_recognition\nimport cv2\nimport os\nimport numpy as np\nimport cvzone\n\nfrom EncodeGenerator import encodeListKnownWithIds, studentIds\n\ncap = cv2.VideoCapture(1)\ncap.set(3,640)\ncap.set(4,480)\n\nimgBackground = cv2.imread('C:\\\\Users\\\\user\\\\PycharmProjects\\\\PythonProject\\\\Resources\\\\background.png')\n\n#\u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 4 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439 \u0438\u0437 modes \u0432 \u043b\u0438\u0441\u0442\nfolderModePath = 'C:\\\\Users\\\\user\\\\PycharmProjects\\\\PythonProject\\\\Resources\\\\Modes'\nmodePathList = os.listdir(folderModePath)\nimgModeList = []\nfor path in modePathList:\n imgModeList.append(cv2.imread(os.path.join(folderModePath, path)))\n#print(len(imgModeList))\n\n#Load the encoding file\nprint(\"Loading Encode File ...\")\n\nfile = open('EncodeFile.p','rb')\nencodeListKnownWithIds = pickle.load(file)\nfile.close()\nencodeListKnown, studentIds = encodeListKnownWithIds\n#print(studentIds)\nprint(\"Encode File Loaded\")\n\nwhile True:\n success, img = cap.read()\n\n imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)\n imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)\n\n faceCurFrame = face_recognition.face_locations(imgS)\n encodeCurFrame = face_recognition.face_encodings(imgS, faceCurFrame)\n\n imgBackground[162:162+480,55:55+640] = img\n imgBackground[44:44 + 633, 808:808 + 414] = imgModeList[1]\n\n for encodeFace, faceLoc in zip(encodeCurFrame, faceCurFrame):\n matches = face_recognition.compare_faces(encodeListKnown, encodeFace)\n faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)\n #print(\"matches\", matches)\n #print(\"faceDis\", faceDis)\n\n matchIndex = np.argmin(faceDis)\n # print(\"Match Index\", matchIndex)\n\n if matches[matchIndex]:\n\n print(\"\u041b\u0438\u0446\u043e \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u043e\")\n print(studentIds[matchIndex])\n y1, x2, y2, x1 = faceLoc\n y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4\n bbox = 55 + x1, 162 + y1, x2 - x1, y2 - y1\n imgBackground = cvzone.cornerRect(imgBackground, bbox, rt=0)\n\n\n\n #cv2.imshow(\"Webcam\", img)\n cv2.imshow(\"Face Attendance\", imgBackground)\n cv2.waitKey(1)"}],"shard":{"index":56,"name":"turns-00056.parquet","rows":62667}}