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Deploy Django + AI ML Face Recognition Web App in AWS

Name: Deploy Django + AI ML Face Recognition Web App in AWS
Rating: 4.5 (189 reviews)

Develop & Deploy Face Recognition, Facial Emotion using OpenCV, Machine Learning, Django, Database in Python in AWS

Created byG Sudheer, datascience Anywhere, VedicSkill Academy

Last updated 1/2024

English

What you'll learn

Deploy Face Recognition Django Web App in AWS and Heroku Cloud
Train your own Machine Learning based Face Recognition Model in Python
Train own Facial Emotion Recognition using Machine Learning in Python
Develop Django Web App using MVT Framework
Design SQLlite Database in Django
Train Support Vector Machines, Random Forest Model for Face Recognition in Python
Debuging error while Deploying in Heroku
Interphase Machine Learning Models with MVT Framework
Build Ensemble (stacking) Machine Learning Model combining SVM and Random Forest Models in Python
Face Detection with Deep Neural Networks
OpenCV Essentials for Face Recognition
Managing Heroku Cloud
Styling Django Web App with Bootstrap

Course content

14 sections • 104 lectures • 8h 14m total length

Introduction5:25
What you will Develop ?1:23
Facing any Issue with the Course? Don't Panic. Here is the solution6:03

Install Python1:04
In this lecture we will explain how to install Python 3.8 and install install python packages using pip
Download requirements.txt1:36
Download requirements.txt and install the following packages:
numpy
pandas
matplotlib
jupyter
pillow
opencv-python
scikit-learn
Install CMake & Dlib on Windows2:49
How to install Dlib in windows without anaconda. Here we will explains to install Dlib with pip and CMAKE.

1. Install CMAKE
2. install dlib using pip (pip install dlib)

Download the Syntax commands commonly used0:01
Download Resources
What will you learn ?0:55
What is Pixel in Image.4:51
What Does Pixel Mean?
A pixel is the smallest unit of a digital image or graphic that can be displayed and represented on a digital display device.
A pixel is the basic logical unit in digital graphics. Pixels are combined to form a complete image, video, text, or any visible thing on a computer display.
A pixel is also known as a picture element (pix = picture, el = element).
Load Image9:12
Reading, displaying, and writing images are basic to image processing and computer vision. Even when cropping, resizing, rotating, or applying different filters to process images, you’ll need to first read in the images. So it’s important that you master these basic operations.
OpenCV, the largest computer vision library in the world has these three built-in functions, let’s find out what exactly each one does:
imread() helps us read an image
imshow() displays an image in a window
imwrite() writes an image into the file directory
Display Image7:42
Save Image1:49
Acessing Pixels5:47
Accessing and manipulating pixels in images with OpenCV
To access pixel data in Image, use numpy and opencv-python library. Import numpy and cv2(opencv-python) module inside your Python program file.
Once you read the image with you will get image in numpy array. Using numpy we can access and slice image using indexing.
Manipulate Pixels2:27
Color Space - Split BGR6:24
Color Space - Convert Colors10:00
Drawings - Line5:58
Drawings - Line part22:26
Drawings - Rectangle5:23
Drawings - Polygon5:43
Drawings - Circles5:30
Put Text2:41
What you will Learn ?0:46
Download the Resources
Viola-Jones Object Detection Intuition2:39
The Viola–Jones object detection framework is an object detection framework which was proposed in 2001 by Paul Viola and Michael Jones. Although it can be trained to detect a variety of object classes, it was motivated primarily by the problem of face detection.

In this section we will explain how to use Viola-Jones cascade classifier to detect faces, eyes, smile etc.
Download Cascade Classifier3:02
Face Detection with Cascade Classifier12:15
Multiple Faces Detection6:57
Eyes Detection8:21
Smile Detection5:22
What you will Learn ?0:59
Download the Resourses
Face Detection with Deep Neural Network Framework2:12
Face Detection with DNN part-16:17
Face Detection with DNN part -21:33
Face Detection with DNN part-39:39
Feature Extraction Framework1:55
Facial Feature Extraction: part-17:20
Facial Feature Extraction: part-2 (Landmark Detection)3:40
Facial Feature Extraction: part-3 (Face Descriptors)1:44

Project phase -1, Face Recognition1:14
Face Recognition Framework3:51
How to Download the Resource4:28
Download the Resource
Data Preprocessing4:15
Data Preprocessing - face detection8:47
Data Preprocessing - feature extraction2:44
Data Preprocessing - Helper Function1:58
Data Preprocessing - Feature Embedding and Labeling4:53
Save Data in Pickle file2:15
Machine Learning - Data6:20
Machine Learning and Evaluation - Logistic Regression, Accuracy and F1score10:05
Machine Learning & Evaluation - Support Vector Classifier, Accuracy and F1score2:37
Machine Learning & Evaluation - Random Forest, Accuracy and F1score2:56
Machine Learning & Evaluation - Voting Classifier4:23
Grid Search Parameter Tuning9:50
Save Face Recognition Model in Pickle3:52

Model Views Templates + Machine Learning Framework2:17
Download the Django Project0:01
Upload Images into Models - part-113:57
Connect Models, Views, Template part-28:51
Connect Models, Views, Template part-33:04
Import Machine Learning Models in Django App12:44
Get Face Recognition Predictions from Machine Learning in Django6:46
Display Face Recognition Output in Django Templates7:32
Display Face Recognition Output in Django Templates part-28:39

Requirements

Should be at least beginner to Python
Basic knowledge in Machine Learning
Understanding HTML, Bootstrap
Should know how to install Python Libraries using pip

Description

Welcome to the AI and ML Enthusiast Course: Building a Face Recognition Web App with Django, Machine Learning, and Cloud Deployment on AWS!

Embark on an exciting journey into Artificial Intelligence as we delve into the realms of Computer Vision and Face Recognition within the expansive field of AI and ML. This course is designed to guide you through the entire development process of an end-to-end project, catering to both machine learning and web development enthusiasts.

Course Phases:

Phase 1: Machine Learning - Face Identity Recognition

Image processing techniques with OpenCV
Prerequisites of the course: Python installation and library setup
Face Detection using OpenCV and Deep Neural Networks
Feature extraction using deep neural networks
Training machine learning models: logistic regression, support vector machines, random forest
Combining models with a Voting Classifier (stacking method)
Model selection and hyperparameter tuning for face recognition

Phase 2: Machine Learning - Facial Emotion Recognition

Application of machine learning techniques from face identity recognition
Integration of detection and recognition models into a pipeline

Phase 3: Django Web App Development

Web application development in Django
Rendering HTML, CSS, and Bootstrap for the frontend
Backend development in Python using the MVT (Models, Views, and Templates) framework
Designing a SQLite database for the Django app
Interfacing machine learning pipeline models with the MVT framework
Styling the app using Bootstrap

Phase 4: Deployment / Production on AWS Cloud

Deployment of the Django Web App on AWS Elastic Beanstalk
Utilizing the AWS Free Tier for 12 months
Accessing the app globally through a provided URL/domain
Troubleshooting and error resolution during deployment

Course Highlights:

In-depth learning of OpenCV for image processing
Training models for Face Recognition and Facial Emotion Recognition
Django web app development with MVT framework
Integration of machine learning models into the web app
Deployment on AWS Elastic Beanstalk with a focus on the AWS Free Tier

If you aspire to be an AI developer, this course is your gateway to mastering AI and ML concepts while gaining hands-on experience. Don't miss out – start your journey now!

See you inside the course!

Who this course is for:

Anyone who want to learn OpenCV project
Python Developers curious about Artificial Intelligence Projects

Deploy Django + AI ML Face Recognition Web App in AWS

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 13min

Setting Up Course3 lectures • 5min

OpenCV Crash Course35 lectures • 2hr 32min

Practice Test0

Phase-1: Face Recognition Project (Person Identity)17 lectures • 1hr 14min

Facial Emotion Recognition4 lectures • 15min

Pipeline All Models4 lectures • 29min

Phase-2: Setting Up Web App Project5 lectures • 14min

Django Basics5 lectures • 27min

Face Recognition Webapp with Django9 lectures • 1hr 4min

Requirements

Description

Who this course is for: