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Face recognition concierge to announce new visitors

This tutorial shows you how to train a facial recognition model to identify visitors and send an email announcing their arrival.

Tutorial Requirements

  • Python version 3
  • A webcam (your laptop’s built-in webcam or an external one)
  • A free Twilio SendGrid account to send up to 100 free emails per day

Step 0: Clone repo and install dependencies

Clone this example project, and change into the directory from the command line.

$ git clone git@github.com:loopDelicious/facial-recognition.git
$ cd facial-recognition

Create a virtual environment called venv. Activate the virtual environment, and then install the required Python packages inside the virtual environment. If you’re on Unix or Mac operating systems, enter these commands in a terminal.

$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt

If you’re on a Windows machine, enter these commands in a command prompt window.

$ python -m venv venv
$ venv\Scripts\activate
(venv) $ pip install -r requirements.txt

Step 1: Create a custom face recognition dataset

Create a new subfolder inside the dataset directory using your first name, like Joyce, to contain your photos.

(venv) $ python headshots.py Joyce

Then run this command to open a new webcam window, passing in the name of your new subfolder. Use headshots_picam.py if using a Pi camera. Press the spacebar to take at least 10 pictures of your face from different angles. When you're done, ESC to close the window. Repeat this step to add more friends, creating a separate folder for each person.

Step 2: Train the model

(venv) $ python encode_faces.py

Run this command to analyze the photos and output a new file encodings.pickle that contains criteria for identifying these faces.

Step 3: Test the model

(venv) $ python facial_req.py

Run this command to open a new webcam window. If your face is highlighted with a yellow box alongside your name, the model has been properly trained. Hit q to quit the program.

Step 4: Set up SendGrid email notifications

Create a new file called .env (notice the dot in front of the filename), formatted like .env.example. Save your API key from the SendGrid settings and other configuration details in this file.

(venv) $ python send_test_email.py

Run this command to send a test email.

Step 5: Add email notifications to facial recognition

(venv) $ python facial_req_email.py

Run this command to open a new webcam window and try it out. If someone from your dataset is recognized, the webcam will snap a photo and send an email notification to announce the new arrival.

Attributions

Forked from this Raspberry Pi 4 Facial Recognition tutorial

https://www.tomshardware.com/how-to/raspberry-pi-facial-recognition

Included code samples from these Face Recognition tutorials

https://www.pyimagesearch.com/2018/06/11/how-to-build-a-custom-face-recognition-dataset/https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/

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how to train a facial recognition model

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face-recognitionopencv-python

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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BranchesTags
Repository files navigation
Face recognition concierge to announce new visitors
This tutorial shows you how to train a facial recognition model to identify visitors and send an email announcing their arrival.
Tutorial Requirements
  • Python version 3
  • A webcam (your laptop’s built-in webcam or an external one)
  • A free Twilio SendGrid account to send up to 100 free emails per day
Step 0: Clone repo and install dependencies
Clone this example project, and change into the directory from the command line.
$ git clone git@github.com:loopDelicious/facial-recognition.git
$ cd facial-recognition

Create a virtual environment called venv. Activate the virtual environment, and then install the required Python packages inside the virtual environment. If you’re on Unix or Mac operating systems, enter these commands in a terminal.
$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt

If you’re on a Windows machine, enter these commands in a command prompt window.
$ python -m venv venv
$ venv\Scripts\activate
(venv) $ pip install -r requirements.txt

Step 1: Create a custom face recognition dataset
Create a new subfolder inside the dataset directory using your first name, like Joyce, to contain your photos.
(venv) $ python headshots.py Joyce

Then run this command to open a new webcam window, passing in the name of your new subfolder. Use headshots_picam.py if using a Pi camera. Press the spacebar to take at least 10 pictures of your face from different angles. When you're done, ESC to close the window. Repeat this step to add more friends, creating a separate folder for each person.
Step 2: Train the model
(venv) $ python encode_faces.py

Run this command to analyze the photos and output a new file encodings.pickle that contains criteria for identifying these faces.
Step 3: Test the model
(venv) $ python facial_req.py

Run this command to open a new webcam window. If your face is highlighted with a yellow box alongside your name, the model has been properly trained. Hit q to quit the program.
Step 4: Set up SendGrid email notifications
Create a new file called .env (notice the dot in front of the filename), formatted like .env.example. Save your API key from the SendGrid settings and other configuration details in this file.
(venv) $ python send_test_email.py

Run this command to send a test email.
Step 5: Add email notifications to facial recognition
(venv) $ python facial_req_email.py

Run this command to open a new webcam window and try it out. If someone from your dataset is recognized, the webcam will snap a photo and send an email notification to announce the new arrival.
Attributions
Forked from this Raspberry Pi 4 Facial Recognition tutorial
https://www.tomshardware.com/how-to/raspberry-pi-facial-recognition
Included code samples from these Face Recognition tutorials
https://www.pyimagesearch.com/2018/06/11/how-to-build-a-custom-face-recognition-dataset/https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
About
how to train a facial recognition model
Topics
face-recognitionopencv-python
Resources
Readme
Activity
Stars
20 stars
Watchers
3 watching
Forks
7 forks
Report repository
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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");
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} 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
Face recognition concierge to announce new visitors
This tutorial shows you how to train a facial recognition model to identify visitors and send an email announcing their arrival.
Tutorial Requirements
  • Python version 3
  • A webcam (your laptop’s built-in webcam or an external one)
  • A free Twilio SendGrid account to send up to 100 free emails per day
Step 0: Clone repo and install dependencies
Clone this example project, and change into the directory from the command line.
$ git clone git@github.com:loopDelicious/facial-recognition.git
$ cd facial-recognition

Create a virtual environment called venv. Activate the virtual environment, and then install the required Python packages inside the virtual environment. If you’re on Unix or Mac operating systems, enter these commands in a terminal.
$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt

If you’re on a Windows machine, enter these commands in a command prompt window.
$ python -m venv venv
$ venv\Scripts\activate
(venv) $ pip install -r requirements.txt

Step 1: Create a custom face recognition dataset
Create a new subfolder inside the dataset directory using your first name, like Joyce, to contain your photos.
(venv) $ python headshots.py Joyce

Then run this command to open a new webcam window, passing in the name of your new subfolder. Use headshots_picam.py if using a Pi camera. Press the spacebar to take at least 10 pictures of your face from different angles. When you're done, ESC to close the window. Repeat this step to add more friends, creating a separate folder for each person.
Step 2: Train the model
(venv) $ python encode_faces.py

Run this command to analyze the photos and output a new file encodings.pickle that contains criteria for identifying these faces.
Step 3: Test the model
(venv) $ python facial_req.py

Run this command to open a new webcam window. If your face is highlighted with a yellow box alongside your name, the model has been properly trained. Hit q to quit the program.
Step 4: Set up SendGrid email notifications
Create a new file called .env (notice the dot in front of the filename), formatted like .env.example. Save your API key from the SendGrid settings and other configuration details in this file.
(venv) $ python send_test_email.py

Run this command to send a test email.
Step 5: Add email notifications to facial recognition
(venv) $ python facial_req_email.py

Run this command to open a new webcam window and try it out. If someone from your dataset is recognized, the webcam will snap a photo and send an email notification to announce the new arrival.
Attributions
Forked from this Raspberry Pi 4 Facial Recognition tutorial
https://www.tomshardware.com/how-to/raspberry-pi-facial-recognition
Included code samples from these Face Recognition tutorials
https://www.pyimagesearch.com/2018/06/11/how-to-build-a-custom-face-recognition-dataset/https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
About
how to train a facial recognition model
Topics
face-recognitionopencv-python
Resources
Readme
Activity
Stars
20 stars
Watchers
3 watching
Forks
7 forks
Report repository
Used by
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('^' + ".*" + '

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BranchesTags
Repository files navigation
Face recognition concierge to announce new visitors
This tutorial shows you how to train a facial recognition model to identify visitors and send an email announcing their arrival.
Tutorial Requirements
  • Python version 3
  • A webcam (your laptop’s built-in webcam or an external one)
  • A free Twilio SendGrid account to send up to 100 free emails per day
Step 0: Clone repo and install dependencies
Clone this example project, and change into the directory from the command line.
$ git clone git@github.com:loopDelicious/facial-recognition.git
$ cd facial-recognition

Create a virtual environment called venv. Activate the virtual environment, and then install the required Python packages inside the virtual environment. If you’re on Unix or Mac operating systems, enter these commands in a terminal.
$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt

If you’re on a Windows machine, enter these commands in a command prompt window.
$ python -m venv venv
$ venv\Scripts\activate
(venv) $ pip install -r requirements.txt

Step 1: Create a custom face recognition dataset
Create a new subfolder inside the dataset directory using your first name, like Joyce, to contain your photos.
(venv) $ python headshots.py Joyce

Then run this command to open a new webcam window, passing in the name of your new subfolder. Use headshots_picam.py if using a Pi camera. Press the spacebar to take at least 10 pictures of your face from different angles. When you're done, ESC to close the window. Repeat this step to add more friends, creating a separate folder for each person.
Step 2: Train the model
(venv) $ python encode_faces.py

Run this command to analyze the photos and output a new file encodings.pickle that contains criteria for identifying these faces.
Step 3: Test the model
(venv) $ python facial_req.py

Run this command to open a new webcam window. If your face is highlighted with a yellow box alongside your name, the model has been properly trained. Hit q to quit the program.
Step 4: Set up SendGrid email notifications
Create a new file called .env (notice the dot in front of the filename), formatted like .env.example. Save your API key from the SendGrid settings and other configuration details in this file.
(venv) $ python send_test_email.py

Run this command to send a test email.
Step 5: Add email notifications to facial recognition
(venv) $ python facial_req_email.py

Run this command to open a new webcam window and try it out. If someone from your dataset is recognized, the webcam will snap a photo and send an email notification to announce the new arrival.
Attributions
Forked from this Raspberry Pi 4 Facial Recognition tutorial
https://www.tomshardware.com/how-to/raspberry-pi-facial-recognition
Included code samples from these Face Recognition tutorials
https://www.pyimagesearch.com/2018/06/11/how-to-build-a-custom-face-recognition-dataset/https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
About
how to train a facial recognition model
Topics
face-recognitionopencv-python
Resources
Readme
Activity
Stars
20 stars
Watchers
3 watching
Forks
7 forks
Report repository
Used by
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" + '

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BranchesTags
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Face recognition concierge to announce new visitors
This tutorial shows you how to train a facial recognition model to identify visitors and send an email announcing their arrival.
Tutorial Requirements
  • Python version 3
  • A webcam (your laptop’s built-in webcam or an external one)
  • A free Twilio SendGrid account to send up to 100 free emails per day
Step 0: Clone repo and install dependencies
Clone this example project, and change into the directory from the command line.
$ git clone git@github.com:loopDelicious/facial-recognition.git
$ cd facial-recognition

Create a virtual environment called venv. Activate the virtual environment, and then install the required Python packages inside the virtual environment. If you’re on Unix or Mac operating systems, enter these commands in a terminal.
$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt

If you’re on a Windows machine, enter these commands in a command prompt window.
$ python -m venv venv
$ venv\Scripts\activate
(venv) $ pip install -r requirements.txt

Step 1: Create a custom face recognition dataset
Create a new subfolder inside the dataset directory using your first name, like Joyce, to contain your photos.
(venv) $ python headshots.py Joyce

Then run this command to open a new webcam window, passing in the name of your new subfolder. Use headshots_picam.py if using a Pi camera. Press the spacebar to take at least 10 pictures of your face from different angles. When you're done, ESC to close the window. Repeat this step to add more friends, creating a separate folder for each person.
Step 2: Train the model
(venv) $ python encode_faces.py

Run this command to analyze the photos and output a new file encodings.pickle that contains criteria for identifying these faces.
Step 3: Test the model
(venv) $ python facial_req.py

Run this command to open a new webcam window. If your face is highlighted with a yellow box alongside your name, the model has been properly trained. Hit q to quit the program.
Step 4: Set up SendGrid email notifications
Create a new file called .env (notice the dot in front of the filename), formatted like .env.example. Save your API key from the SendGrid settings and other configuration details in this file.
(venv) $ python send_test_email.py

Run this command to send a test email.
Step 5: Add email notifications to facial recognition
(venv) $ python facial_req_email.py

Run this command to open a new webcam window and try it out. If someone from your dataset is recognized, the webcam will snap a photo and send an email notification to announce the new arrival.
Attributions
Forked from this Raspberry Pi 4 Facial Recognition tutorial
https://www.tomshardware.com/how-to/raspberry-pi-facial-recognition
Included code samples from these Face Recognition tutorials
https://www.pyimagesearch.com/2018/06/11/how-to-build-a-custom-face-recognition-dataset/https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
About
how to train a facial recognition model
Topics
face-recognitionopencv-python
Resources
Readme
Activity
Stars
20 stars
Watchers
3 watching
Forks
7 forks
Report repository
Used by
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
Face recognition concierge to announce new visitors
This tutorial shows you how to train a facial recognition model to identify visitors and send an email announcing their arrival.
Tutorial Requirements
  • Python version 3
  • A webcam (your laptop’s built-in webcam or an external one)
  • A free Twilio SendGrid account to send up to 100 free emails per day
Step 0: Clone repo and install dependencies
Clone this example project, and change into the directory from the command line.
$ git clone git@github.com:loopDelicious/facial-recognition.git
$ cd facial-recognition

Create a virtual environment called venv. Activate the virtual environment, and then install the required Python packages inside the virtual environment. If you’re on Unix or Mac operating systems, enter these commands in a terminal.
$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt

If you’re on a Windows machine, enter these commands in a command prompt window.
$ python -m venv venv
$ venv\Scripts\activate
(venv) $ pip install -r requirements.txt

Step 1: Create a custom face recognition dataset
Create a new subfolder inside the dataset directory using your first name, like Joyce, to contain your photos.
(venv) $ python headshots.py Joyce

Then run this command to open a new webcam window, passing in the name of your new subfolder. Use headshots_picam.py if using a Pi camera. Press the spacebar to take at least 10 pictures of your face from different angles. When you're done, ESC to close the window. Repeat this step to add more friends, creating a separate folder for each person.
Step 2: Train the model
(venv) $ python encode_faces.py

Run this command to analyze the photos and output a new file encodings.pickle that contains criteria for identifying these faces.
Step 3: Test the model
(venv) $ python facial_req.py

Run this command to open a new webcam window. If your face is highlighted with a yellow box alongside your name, the model has been properly trained. Hit q to quit the program.
Step 4: Set up SendGrid email notifications
Create a new file called .env (notice the dot in front of the filename), formatted like .env.example. Save your API key from the SendGrid settings and other configuration details in this file.
(venv) $ python send_test_email.py

Run this command to send a test email.
Step 5: Add email notifications to facial recognition
(venv) $ python facial_req_email.py

Run this command to open a new webcam window and try it out. If someone from your dataset is recognized, the webcam will snap a photo and send an email notification to announce the new arrival.
Attributions
Forked from this Raspberry Pi 4 Facial Recognition tutorial
https://www.tomshardware.com/how-to/raspberry-pi-facial-recognition
Included code samples from these Face Recognition tutorials
https://www.pyimagesearch.com/2018/06/11/how-to-build-a-custom-face-recognition-dataset/https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
About
how to train a facial recognition model
Topics
face-recognitionopencv-python
Resources
Readme
Activity
Stars
20 stars
Watchers
3 watching
Forks
7 forks
Report repository
Used by
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('^' + ".*" + '

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Face recognition concierge to announce new visitors
This tutorial shows you how to train a facial recognition model to identify visitors and send an email announcing their arrival.
Tutorial Requirements
  • Python version 3
  • A webcam (your laptop’s built-in webcam or an external one)
  • A free Twilio SendGrid account to send up to 100 free emails per day
Step 0: Clone repo and install dependencies
Clone this example project, and change into the directory from the command line.
$ git clone git@github.com:loopDelicious/facial-recognition.git
$ cd facial-recognition

Create a virtual environment called venv. Activate the virtual environment, and then install the required Python packages inside the virtual environment. If you’re on Unix or Mac operating systems, enter these commands in a terminal.
$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt

If you’re on a Windows machine, enter these commands in a command prompt window.
$ python -m venv venv
$ venv\Scripts\activate
(venv) $ pip install -r requirements.txt

Step 1: Create a custom face recognition dataset
Create a new subfolder inside the dataset directory using your first name, like Joyce, to contain your photos.
(venv) $ python headshots.py Joyce

Then run this command to open a new webcam window, passing in the name of your new subfolder. Use headshots_picam.py if using a Pi camera. Press the spacebar to take at least 10 pictures of your face from different angles. When you're done, ESC to close the window. Repeat this step to add more friends, creating a separate folder for each person.
Step 2: Train the model
(venv) $ python encode_faces.py

Run this command to analyze the photos and output a new file encodings.pickle that contains criteria for identifying these faces.
Step 3: Test the model
(venv) $ python facial_req.py

Run this command to open a new webcam window. If your face is highlighted with a yellow box alongside your name, the model has been properly trained. Hit q to quit the program.
Step 4: Set up SendGrid email notifications
Create a new file called .env (notice the dot in front of the filename), formatted like .env.example. Save your API key from the SendGrid settings and other configuration details in this file.
(venv) $ python send_test_email.py

Run this command to send a test email.
Step 5: Add email notifications to facial recognition
(venv) $ python facial_req_email.py

Run this command to open a new webcam window and try it out. If someone from your dataset is recognized, the webcam will snap a photo and send an email notification to announce the new arrival.
Attributions
Forked from this Raspberry Pi 4 Facial Recognition tutorial
https://www.tomshardware.com/how-to/raspberry-pi-facial-recognition
Included code samples from these Face Recognition tutorials
https://www.pyimagesearch.com/2018/06/11/how-to-build-a-custom-face-recognition-dataset/https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
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how to train a facial recognition model
Topics
face-recognitionopencv-python
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Readme
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3 watching
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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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Face recognition concierge to announce new visitors
This tutorial shows you how to train a facial recognition model to identify visitors and send an email announcing their arrival.
Tutorial Requirements
  • Python version 3
  • A webcam (your laptop’s built-in webcam or an external one)
  • A free Twilio SendGrid account to send up to 100 free emails per day
Step 0: Clone repo and install dependencies
Clone this example project, and change into the directory from the command line.
$ git clone git@github.com:loopDelicious/facial-recognition.git
$ cd facial-recognition

Create a virtual environment called venv. Activate the virtual environment, and then install the required Python packages inside the virtual environment. If you’re on Unix or Mac operating systems, enter these commands in a terminal.
$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt

If you’re on a Windows machine, enter these commands in a command prompt window.
$ python -m venv venv
$ venv\Scripts\activate
(venv) $ pip install -r requirements.txt

Step 1: Create a custom face recognition dataset
Create a new subfolder inside the dataset directory using your first name, like Joyce, to contain your photos.
(venv) $ python headshots.py Joyce

Then run this command to open a new webcam window, passing in the name of your new subfolder. Use headshots_picam.py if using a Pi camera. Press the spacebar to take at least 10 pictures of your face from different angles. When you're done, ESC to close the window. Repeat this step to add more friends, creating a separate folder for each person.
Step 2: Train the model
(venv) $ python encode_faces.py

Run this command to analyze the photos and output a new file encodings.pickle that contains criteria for identifying these faces.
Step 3: Test the model
(venv) $ python facial_req.py

Run this command to open a new webcam window. If your face is highlighted with a yellow box alongside your name, the model has been properly trained. Hit q to quit the program.
Step 4: Set up SendGrid email notifications
Create a new file called .env (notice the dot in front of the filename), formatted like .env.example. Save your API key from the SendGrid settings and other configuration details in this file.
(venv) $ python send_test_email.py

Run this command to send a test email.
Step 5: Add email notifications to facial recognition
(venv) $ python facial_req_email.py

Run this command to open a new webcam window and try it out. If someone from your dataset is recognized, the webcam will snap a photo and send an email notification to announce the new arrival.
Attributions
Forked from this Raspberry Pi 4 Facial Recognition tutorial
https://www.tomshardware.com/how-to/raspberry-pi-facial-recognition
Included code samples from these Face Recognition tutorials
https://www.pyimagesearch.com/2018/06/11/how-to-build-a-custom-face-recognition-dataset/https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
About
how to train a facial recognition model
Topics
face-recognitionopencv-python
Resources
Readme
Activity
Stars
20 stars
Watchers
3 watching
Forks
7 forks
Report repository
Used by
Contributors
Languages