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FaceRecognition

This project is about the comparision of two images and giving confidence of how much similar are the two images.

Contributors

  • Tanmay Bhatt - Worked on backend frontend integration , routing and hosted on AWS
  • Rahil Modi - Worked on UI development , Web cam integration and Frontend Integration .
  • Sanket Dhami - Worked on Image comparison algorithm , storing information in database .
  • Dhrumil Shah - Worked on UI Development, routing and testing of the app.
  • Akhilesh Deowanshi - Worked on m-lab DB connection , image comaprison alogorithm & Dockerize the app

Features!

  • Create account and Upload Images .
  • Click live images and compare with existing image .
  • Web App is hosted on AWS EC2 instance and can be accessed from anywhere .

Tech

Face Recogniton App uses a number of open source projects to work properly:

  • [Python] - Python is a widely used high-level programming language for general-purpose programming.
  • [AngularJS] - HTML enhanced for web apps!
  • [Opencv] - Open Source Computer Vision Library
  • [Twitter Bootstrap] - great UI boilerplate for modern web apps
  • [Flask] - evented I/O for the backend
  • [Docker] - Docker is a container technology for Linux
  • [AWS] - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services
  • [mLab's - Mongo DB] - mLab's MongoDB hosting platform is the fastest growing cloud Database-as-a-Service

Docker

Face Recogniton is very easy to install and deploy in a Docker container.

By default, the Docker will expose port 80, so change this within the Dockerfile if necessary. When ready, simply use the Dockerfile to build the image.

cd Project
docker build -t akhilesh1312/facerecog

This will create the Face Recognition image and pull in the necessary dependencies.

Once done, run the Docker image and map the port to whatever you wish on your host. In this example, we simply map port 5000 of the host to port 5000 of the Docker (or whatever port was exposed in the Dockerfile as we have exposed 5000):

docker run -d -p 5000:5000 --name FaceRecognition akhilesh1312/facerecog

Verify the deployment by navigating to your server address in your preferred browser.

localhost:5000

About

Face recognition web application

Topics

angularjsaws-ec2dockerflask-applicationmongolabopencv3python

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
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FaceRecognition
This project is about the comparision of two images and giving confidence of how much similar are the two images.
Contributors
  • Tanmay Bhatt - Worked on backend frontend integration , routing and hosted on AWS
  • Rahil Modi - Worked on UI development , Web cam integration and Frontend Integration .
  • Sanket Dhami - Worked on Image comparison algorithm , storing information in database .
  • Dhrumil Shah - Worked on UI Development, routing and testing of the app.
  • Akhilesh Deowanshi - Worked on m-lab DB connection , image comaprison alogorithm & Dockerize the app
Features!
  • Create account and Upload Images .
  • Click live images and compare with existing image .
  • Web App is hosted on AWS EC2 instance and can be accessed from anywhere .
Tech
Face Recogniton App uses a number of open source projects to work properly:
  • [Python] - Python is a widely used high-level programming language for general-purpose programming.
  • [AngularJS] - HTML enhanced for web apps!
  • [Opencv] - Open Source Computer Vision Library
  • [Twitter Bootstrap] - great UI boilerplate for modern web apps
  • [Flask] - evented I/O for the backend
  • [Docker] - Docker is a container technology for Linux
  • [AWS] - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services
  • [mLab's - Mongo DB] - mLab's MongoDB hosting platform is the fastest growing cloud Database-as-a-Service
Docker
Face Recogniton is very easy to install and deploy in a Docker container.
By default, the Docker will expose port 80, so change this within the Dockerfile if necessary. When ready, simply use the Dockerfile to build the image.
cd Project
docker build -t akhilesh1312/facerecog
This will create the Face Recognition image and pull in the necessary dependencies.
Once done, run the Docker image and map the port to whatever you wish on your host. In this example, we simply map port 5000 of the host to port 5000 of the Docker (or whatever port was exposed in the Dockerfile as we have exposed 5000):
docker run -d -p 5000:5000 --name FaceRecognition akhilesh1312/facerecog
Verify the deployment by navigating to your server address in your preferred browser.
localhost:5000
About
Face recognition web application
Topics
angularjsaws-ec2dockerflask-applicationmongolabopencv3python
Resources
Readme
Activity
Stars
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Watchers
0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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FaceRecognition
This project is about the comparision of two images and giving confidence of how much similar are the two images.
Contributors
  • Tanmay Bhatt - Worked on backend frontend integration , routing and hosted on AWS
  • Rahil Modi - Worked on UI development , Web cam integration and Frontend Integration .
  • Sanket Dhami - Worked on Image comparison algorithm , storing information in database .
  • Dhrumil Shah - Worked on UI Development, routing and testing of the app.
  • Akhilesh Deowanshi - Worked on m-lab DB connection , image comaprison alogorithm & Dockerize the app
Features!
  • Create account and Upload Images .
  • Click live images and compare with existing image .
  • Web App is hosted on AWS EC2 instance and can be accessed from anywhere .
Tech
Face Recogniton App uses a number of open source projects to work properly:
  • [Python] - Python is a widely used high-level programming language for general-purpose programming.
  • [AngularJS] - HTML enhanced for web apps!
  • [Opencv] - Open Source Computer Vision Library
  • [Twitter Bootstrap] - great UI boilerplate for modern web apps
  • [Flask] - evented I/O for the backend
  • [Docker] - Docker is a container technology for Linux
  • [AWS] - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services
  • [mLab's - Mongo DB] - mLab's MongoDB hosting platform is the fastest growing cloud Database-as-a-Service
Docker
Face Recogniton is very easy to install and deploy in a Docker container.
By default, the Docker will expose port 80, so change this within the Dockerfile if necessary. When ready, simply use the Dockerfile to build the image.
cd Project
docker build -t akhilesh1312/facerecog
This will create the Face Recognition image and pull in the necessary dependencies.
Once done, run the Docker image and map the port to whatever you wish on your host. In this example, we simply map port 5000 of the host to port 5000 of the Docker (or whatever port was exposed in the Dockerfile as we have exposed 5000):
docker run -d -p 5000:5000 --name FaceRecognition akhilesh1312/facerecog
Verify the deployment by navigating to your server address in your preferred browser.
localhost:5000
About
Face recognition web application
Topics
angularjsaws-ec2dockerflask-applicationmongolabopencv3python
Resources
Readme
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Stars
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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FaceRecognition
This project is about the comparision of two images and giving confidence of how much similar are the two images.
Contributors
  • Tanmay Bhatt - Worked on backend frontend integration , routing and hosted on AWS
  • Rahil Modi - Worked on UI development , Web cam integration and Frontend Integration .
  • Sanket Dhami - Worked on Image comparison algorithm , storing information in database .
  • Dhrumil Shah - Worked on UI Development, routing and testing of the app.
  • Akhilesh Deowanshi - Worked on m-lab DB connection , image comaprison alogorithm & Dockerize the app
Features!
  • Create account and Upload Images .
  • Click live images and compare with existing image .
  • Web App is hosted on AWS EC2 instance and can be accessed from anywhere .
Tech
Face Recogniton App uses a number of open source projects to work properly:
  • [Python] - Python is a widely used high-level programming language for general-purpose programming.
  • [AngularJS] - HTML enhanced for web apps!
  • [Opencv] - Open Source Computer Vision Library
  • [Twitter Bootstrap] - great UI boilerplate for modern web apps
  • [Flask] - evented I/O for the backend
  • [Docker] - Docker is a container technology for Linux
  • [AWS] - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services
  • [mLab's - Mongo DB] - mLab's MongoDB hosting platform is the fastest growing cloud Database-as-a-Service
Docker
Face Recogniton is very easy to install and deploy in a Docker container.
By default, the Docker will expose port 80, so change this within the Dockerfile if necessary. When ready, simply use the Dockerfile to build the image.
cd Project
docker build -t akhilesh1312/facerecog
This will create the Face Recognition image and pull in the necessary dependencies.
Once done, run the Docker image and map the port to whatever you wish on your host. In this example, we simply map port 5000 of the host to port 5000 of the Docker (or whatever port was exposed in the Dockerfile as we have exposed 5000):
docker run -d -p 5000:5000 --name FaceRecognition akhilesh1312/facerecog
Verify the deployment by navigating to your server address in your preferred browser.
localhost:5000
About
Face recognition web application
Topics
angularjsaws-ec2dockerflask-applicationmongolabopencv3python
Resources
Readme
Activity
Stars
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0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
FaceRecognition
This project is about the comparision of two images and giving confidence of how much similar are the two images.
Contributors
  • Tanmay Bhatt - Worked on backend frontend integration , routing and hosted on AWS
  • Rahil Modi - Worked on UI development , Web cam integration and Frontend Integration .
  • Sanket Dhami - Worked on Image comparison algorithm , storing information in database .
  • Dhrumil Shah - Worked on UI Development, routing and testing of the app.
  • Akhilesh Deowanshi - Worked on m-lab DB connection , image comaprison alogorithm & Dockerize the app
Features!
  • Create account and Upload Images .
  • Click live images and compare with existing image .
  • Web App is hosted on AWS EC2 instance and can be accessed from anywhere .
Tech
Face Recogniton App uses a number of open source projects to work properly:
  • [Python] - Python is a widely used high-level programming language for general-purpose programming.
  • [AngularJS] - HTML enhanced for web apps!
  • [Opencv] - Open Source Computer Vision Library
  • [Twitter Bootstrap] - great UI boilerplate for modern web apps
  • [Flask] - evented I/O for the backend
  • [Docker] - Docker is a container technology for Linux
  • [AWS] - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services
  • [mLab's - Mongo DB] - mLab's MongoDB hosting platform is the fastest growing cloud Database-as-a-Service
Docker
Face Recogniton is very easy to install and deploy in a Docker container.
By default, the Docker will expose port 80, so change this within the Dockerfile if necessary. When ready, simply use the Dockerfile to build the image.
cd Project
docker build -t akhilesh1312/facerecog
This will create the Face Recognition image and pull in the necessary dependencies.
Once done, run the Docker image and map the port to whatever you wish on your host. In this example, we simply map port 5000 of the host to port 5000 of the Docker (or whatever port was exposed in the Dockerfile as we have exposed 5000):
docker run -d -p 5000:5000 --name FaceRecognition akhilesh1312/facerecog
Verify the deployment by navigating to your server address in your preferred browser.
localhost:5000
About
Face recognition web application
Topics
angularjsaws-ec2dockerflask-applicationmongolabopencv3python
Resources
Readme
Activity
Stars
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Watchers
0 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
FaceRecognition
This project is about the comparision of two images and giving confidence of how much similar are the two images.
Contributors
  • Tanmay Bhatt - Worked on backend frontend integration , routing and hosted on AWS
  • Rahil Modi - Worked on UI development , Web cam integration and Frontend Integration .
  • Sanket Dhami - Worked on Image comparison algorithm , storing information in database .
  • Dhrumil Shah - Worked on UI Development, routing and testing of the app.
  • Akhilesh Deowanshi - Worked on m-lab DB connection , image comaprison alogorithm & Dockerize the app
Features!
  • Create account and Upload Images .
  • Click live images and compare with existing image .
  • Web App is hosted on AWS EC2 instance and can be accessed from anywhere .
Tech
Face Recogniton App uses a number of open source projects to work properly:
  • [Python] - Python is a widely used high-level programming language for general-purpose programming.
  • [AngularJS] - HTML enhanced for web apps!
  • [Opencv] - Open Source Computer Vision Library
  • [Twitter Bootstrap] - great UI boilerplate for modern web apps
  • [Flask] - evented I/O for the backend
  • [Docker] - Docker is a container technology for Linux
  • [AWS] - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services
  • [mLab's - Mongo DB] - mLab's MongoDB hosting platform is the fastest growing cloud Database-as-a-Service
Docker
Face Recogniton is very easy to install and deploy in a Docker container.
By default, the Docker will expose port 80, so change this within the Dockerfile if necessary. When ready, simply use the Dockerfile to build the image.
cd Project
docker build -t akhilesh1312/facerecog
This will create the Face Recognition image and pull in the necessary dependencies.
Once done, run the Docker image and map the port to whatever you wish on your host. In this example, we simply map port 5000 of the host to port 5000 of the Docker (or whatever port was exposed in the Dockerfile as we have exposed 5000):
docker run -d -p 5000:5000 --name FaceRecognition akhilesh1312/facerecog
Verify the deployment by navigating to your server address in your preferred browser.
localhost:5000
About
Face recognition web application
Topics
angularjsaws-ec2dockerflask-applicationmongolabopencv3python
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
FaceRecognition
This project is about the comparision of two images and giving confidence of how much similar are the two images.
Contributors
  • Tanmay Bhatt - Worked on backend frontend integration , routing and hosted on AWS
  • Rahil Modi - Worked on UI development , Web cam integration and Frontend Integration .
  • Sanket Dhami - Worked on Image comparison algorithm , storing information in database .
  • Dhrumil Shah - Worked on UI Development, routing and testing of the app.
  • Akhilesh Deowanshi - Worked on m-lab DB connection , image comaprison alogorithm & Dockerize the app
Features!
  • Create account and Upload Images .
  • Click live images and compare with existing image .
  • Web App is hosted on AWS EC2 instance and can be accessed from anywhere .
Tech
Face Recogniton App uses a number of open source projects to work properly:
  • [Python] - Python is a widely used high-level programming language for general-purpose programming.
  • [AngularJS] - HTML enhanced for web apps!
  • [Opencv] - Open Source Computer Vision Library
  • [Twitter Bootstrap] - great UI boilerplate for modern web apps
  • [Flask] - evented I/O for the backend
  • [Docker] - Docker is a container technology for Linux
  • [AWS] - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services
  • [mLab's - Mongo DB] - mLab's MongoDB hosting platform is the fastest growing cloud Database-as-a-Service
Docker
Face Recogniton is very easy to install and deploy in a Docker container.
By default, the Docker will expose port 80, so change this within the Dockerfile if necessary. When ready, simply use the Dockerfile to build the image.
cd Project
docker build -t akhilesh1312/facerecog
This will create the Face Recognition image and pull in the necessary dependencies.
Once done, run the Docker image and map the port to whatever you wish on your host. In this example, we simply map port 5000 of the host to port 5000 of the Docker (or whatever port was exposed in the Dockerfile as we have exposed 5000):
docker run -d -p 5000:5000 --name FaceRecognition akhilesh1312/facerecog
Verify the deployment by navigating to your server address in your preferred browser.
localhost:5000
About
Face recognition web application
Topics
angularjsaws-ec2dockerflask-applicationmongolabopencv3python
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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FaceRecognition
This project is about the comparision of two images and giving confidence of how much similar are the two images.
Contributors
  • Tanmay Bhatt - Worked on backend frontend integration , routing and hosted on AWS
  • Rahil Modi - Worked on UI development , Web cam integration and Frontend Integration .
  • Sanket Dhami - Worked on Image comparison algorithm , storing information in database .
  • Dhrumil Shah - Worked on UI Development, routing and testing of the app.
  • Akhilesh Deowanshi - Worked on m-lab DB connection , image comaprison alogorithm & Dockerize the app
Features!
  • Create account and Upload Images .
  • Click live images and compare with existing image .
  • Web App is hosted on AWS EC2 instance and can be accessed from anywhere .
Tech
Face Recogniton App uses a number of open source projects to work properly:
  • [Python] - Python is a widely used high-level programming language for general-purpose programming.
  • [AngularJS] - HTML enhanced for web apps!
  • [Opencv] - Open Source Computer Vision Library
  • [Twitter Bootstrap] - great UI boilerplate for modern web apps
  • [Flask] - evented I/O for the backend
  • [Docker] - Docker is a container technology for Linux
  • [AWS] - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services
  • [mLab's - Mongo DB] - mLab's MongoDB hosting platform is the fastest growing cloud Database-as-a-Service
Docker
Face Recogniton is very easy to install and deploy in a Docker container.
By default, the Docker will expose port 80, so change this within the Dockerfile if necessary. When ready, simply use the Dockerfile to build the image.
cd Project
docker build -t akhilesh1312/facerecog
This will create the Face Recognition image and pull in the necessary dependencies.
Once done, run the Docker image and map the port to whatever you wish on your host. In this example, we simply map port 5000 of the host to port 5000 of the Docker (or whatever port was exposed in the Dockerfile as we have exposed 5000):
docker run -d -p 5000:5000 --name FaceRecognition akhilesh1312/facerecog
Verify the deployment by navigating to your server address in your preferred browser.
localhost:5000
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Face recognition web application
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angularjsaws-ec2dockerflask-applicationmongolabopencv3python
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