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Real-Time Multi-Face Tracking and Masking

Or something like a Snapchat filter....

Click on the image to watch a demo!

WATCH A DEMO

or download directly

Description

The traditional OpenCV examples just provide face detections on a frame by frame basis, and are not correlated through time. Additionally they can have a high false-positive rate under different lighting conditions / video quailty, causing a lot of noise. The following program builds upon OpenCV's face classification tools to build a real-time face masking program that independently tracks multiple faces frame-to-frame using a primitive tracking algorithm.

Details

Face detections on each frame are passed to the FaceTracker, which will keep an internal record of "potential" and "definite" faces. Incoming face detections are compared for both size and proximity similiarity to known faces to detect matches. If a potential face has enough detections, it will be upgraded to a "definite" face. Detected faces as given a "mask", or an overlay with some transparent alpha channel, that follows the face and scales with it as the face moves on scrren. Ultimately this works similiar to something like a Snapchat filter.

If a face has not been seen for some time, it will be dropped from the FaceTracker, however the Tracker can tolerate several frames worth of missed detections, and pick back up the face.

Requirements

Install

make

Run

./facedetect

File Walkthrough

Future Updates

  • Incoroporate real logging
  • Add more face cascade models
  • Multithread image processing with work queues
  • Make matching algorithm do nearest-neighbor search instead of return first match
  • Add event toggles for classifiers / debug mode

About

OpenCV Face Tracking

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

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Packages

Contributors

Languages

, '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
Repository files navigation
Real-Time Multi-Face Tracking and Masking
Or something like a Snapchat filter....
Click on the image to watch a demo!
WATCH A DEMO
or download directly
Description
The traditional OpenCV examples just provide face detections on a frame by frame basis, and are not correlated through time. Additionally they can have a high false-positive rate under different lighting conditions / video quailty, causing a lot of noise. The following program builds upon OpenCV's face classification tools to build a real-time face masking program that independently tracks multiple faces frame-to-frame using a primitive tracking algorithm.
Details
Face detections on each frame are passed to the FaceTracker, which will keep an internal record of "potential" and "definite" faces. Incoming face detections are compared for both size and proximity similiarity to known faces to detect matches. If a potential face has enough detections, it will be upgraded to a "definite" face. Detected faces as given a "mask", or an overlay with some transparent alpha channel, that follows the face and scales with it as the face moves on scrren. Ultimately this works similiar to something like a Snapchat filter.
If a face has not been seen for some time, it will be dropped from the FaceTracker, however the Tracker can tolerate several frames worth of missed detections, and pick back up the face.
Requirements
Install
make

Run
./facedetect

File Walkthrough
Future Updates
  • Incoroporate real logging
  • Add more face cascade models
  • Multithread image processing with work queues
  • Make matching algorithm do nearest-neighbor search instead of return first match
  • Add event toggles for classifiers / debug mode
About
OpenCV Face Tracking
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
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
Repository files navigation
Real-Time Multi-Face Tracking and Masking
Or something like a Snapchat filter....
Click on the image to watch a demo!
WATCH A DEMO
or download directly
Description
The traditional OpenCV examples just provide face detections on a frame by frame basis, and are not correlated through time. Additionally they can have a high false-positive rate under different lighting conditions / video quailty, causing a lot of noise. The following program builds upon OpenCV's face classification tools to build a real-time face masking program that independently tracks multiple faces frame-to-frame using a primitive tracking algorithm.
Details
Face detections on each frame are passed to the FaceTracker, which will keep an internal record of "potential" and "definite" faces. Incoming face detections are compared for both size and proximity similiarity to known faces to detect matches. If a potential face has enough detections, it will be upgraded to a "definite" face. Detected faces as given a "mask", or an overlay with some transparent alpha channel, that follows the face and scales with it as the face moves on scrren. Ultimately this works similiar to something like a Snapchat filter.
If a face has not been seen for some time, it will be dropped from the FaceTracker, however the Tracker can tolerate several frames worth of missed detections, and pick back up the face.
Requirements
Install
make

Run
./facedetect

File Walkthrough
Future Updates
  • Incoroporate real logging
  • Add more face cascade models
  • Multithread image processing with work queues
  • Make matching algorithm do nearest-neighbor search instead of return first match
  • Add event toggles for classifiers / debug mode
About
OpenCV Face Tracking
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
Repository files navigation
Real-Time Multi-Face Tracking and Masking
Or something like a Snapchat filter....
Click on the image to watch a demo!
WATCH A DEMO
or download directly
Description
The traditional OpenCV examples just provide face detections on a frame by frame basis, and are not correlated through time. Additionally they can have a high false-positive rate under different lighting conditions / video quailty, causing a lot of noise. The following program builds upon OpenCV's face classification tools to build a real-time face masking program that independently tracks multiple faces frame-to-frame using a primitive tracking algorithm.
Details
Face detections on each frame are passed to the FaceTracker, which will keep an internal record of "potential" and "definite" faces. Incoming face detections are compared for both size and proximity similiarity to known faces to detect matches. If a potential face has enough detections, it will be upgraded to a "definite" face. Detected faces as given a "mask", or an overlay with some transparent alpha channel, that follows the face and scales with it as the face moves on scrren. Ultimately this works similiar to something like a Snapchat filter.
If a face has not been seen for some time, it will be dropped from the FaceTracker, however the Tracker can tolerate several frames worth of missed detections, and pick back up the face.
Requirements
Install
make

Run
./facedetect

File Walkthrough
Future Updates
  • Incoroporate real logging
  • Add more face cascade models
  • Multithread image processing with work queues
  • Make matching algorithm do nearest-neighbor search instead of return first match
  • Add event toggles for classifiers / debug mode
About
OpenCV Face Tracking
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
Real-Time Multi-Face Tracking and Masking
Or something like a Snapchat filter....
Click on the image to watch a demo!
WATCH A DEMO
or download directly
Description
The traditional OpenCV examples just provide face detections on a frame by frame basis, and are not correlated through time. Additionally they can have a high false-positive rate under different lighting conditions / video quailty, causing a lot of noise. The following program builds upon OpenCV's face classification tools to build a real-time face masking program that independently tracks multiple faces frame-to-frame using a primitive tracking algorithm.
Details
Face detections on each frame are passed to the FaceTracker, which will keep an internal record of "potential" and "definite" faces. Incoming face detections are compared for both size and proximity similiarity to known faces to detect matches. If a potential face has enough detections, it will be upgraded to a "definite" face. Detected faces as given a "mask", or an overlay with some transparent alpha channel, that follows the face and scales with it as the face moves on scrren. Ultimately this works similiar to something like a Snapchat filter.
If a face has not been seen for some time, it will be dropped from the FaceTracker, however the Tracker can tolerate several frames worth of missed detections, and pick back up the face.
Requirements
Install
make

Run
./facedetect

File Walkthrough
Future Updates
  • Incoroporate real logging
  • Add more face cascade models
  • Multithread image processing with work queues
  • Make matching algorithm do nearest-neighbor search instead of return first match
  • Add event toggles for classifiers / debug mode
About
OpenCV Face Tracking
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
Real-Time Multi-Face Tracking and Masking
Or something like a Snapchat filter....
Click on the image to watch a demo!
WATCH A DEMO
or download directly
Description
The traditional OpenCV examples just provide face detections on a frame by frame basis, and are not correlated through time. Additionally they can have a high false-positive rate under different lighting conditions / video quailty, causing a lot of noise. The following program builds upon OpenCV's face classification tools to build a real-time face masking program that independently tracks multiple faces frame-to-frame using a primitive tracking algorithm.
Details
Face detections on each frame are passed to the FaceTracker, which will keep an internal record of "potential" and "definite" faces. Incoming face detections are compared for both size and proximity similiarity to known faces to detect matches. If a potential face has enough detections, it will be upgraded to a "definite" face. Detected faces as given a "mask", or an overlay with some transparent alpha channel, that follows the face and scales with it as the face moves on scrren. Ultimately this works similiar to something like a Snapchat filter.
If a face has not been seen for some time, it will be dropped from the FaceTracker, however the Tracker can tolerate several frames worth of missed detections, and pick back up the face.
Requirements
Install
make

Run
./facedetect

File Walkthrough
Future Updates
  • Incoroporate real logging
  • Add more face cascade models
  • Multithread image processing with work queues
  • Make matching algorithm do nearest-neighbor search instead of return first match
  • Add event toggles for classifiers / debug mode
About
OpenCV Face Tracking
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
Real-Time Multi-Face Tracking and Masking
Or something like a Snapchat filter....
Click on the image to watch a demo!
WATCH A DEMO
or download directly
Description
The traditional OpenCV examples just provide face detections on a frame by frame basis, and are not correlated through time. Additionally they can have a high false-positive rate under different lighting conditions / video quailty, causing a lot of noise. The following program builds upon OpenCV's face classification tools to build a real-time face masking program that independently tracks multiple faces frame-to-frame using a primitive tracking algorithm.
Details
Face detections on each frame are passed to the FaceTracker, which will keep an internal record of "potential" and "definite" faces. Incoming face detections are compared for both size and proximity similiarity to known faces to detect matches. If a potential face has enough detections, it will be upgraded to a "definite" face. Detected faces as given a "mask", or an overlay with some transparent alpha channel, that follows the face and scales with it as the face moves on scrren. Ultimately this works similiar to something like a Snapchat filter.
If a face has not been seen for some time, it will be dropped from the FaceTracker, however the Tracker can tolerate several frames worth of missed detections, and pick back up the face.
Requirements
Install
make

Run
./facedetect

File Walkthrough
Future Updates
  • Incoroporate real logging
  • Add more face cascade models
  • Multithread image processing with work queues
  • Make matching algorithm do nearest-neighbor search instead of return first match
  • Add event toggles for classifiers / debug mode
About
OpenCV Face Tracking
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Real-Time Multi-Face Tracking and Masking
Or something like a Snapchat filter....
Click on the image to watch a demo!
WATCH A DEMO
or download directly
Description
The traditional OpenCV examples just provide face detections on a frame by frame basis, and are not correlated through time. Additionally they can have a high false-positive rate under different lighting conditions / video quailty, causing a lot of noise. The following program builds upon OpenCV's face classification tools to build a real-time face masking program that independently tracks multiple faces frame-to-frame using a primitive tracking algorithm.
Details
Face detections on each frame are passed to the FaceTracker, which will keep an internal record of "potential" and "definite" faces. Incoming face detections are compared for both size and proximity similiarity to known faces to detect matches. If a potential face has enough detections, it will be upgraded to a "definite" face. Detected faces as given a "mask", or an overlay with some transparent alpha channel, that follows the face and scales with it as the face moves on scrren. Ultimately this works similiar to something like a Snapchat filter.
If a face has not been seen for some time, it will be dropped from the FaceTracker, however the Tracker can tolerate several frames worth of missed detections, and pick back up the face.
Requirements
Install
make

Run
./facedetect

File Walkthrough
Future Updates
  • Incoroporate real logging
  • Add more face cascade models
  • Multithread image processing with work queues
  • Make matching algorithm do nearest-neighbor search instead of return first match
  • Add event toggles for classifiers / debug mode
About
OpenCV Face Tracking
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages