https://github.com/prudhvideep/video-face-recognition
An application to detect faces in video streams, seamlessly adjusting to varying workloads. It's designed for both small-scale and large-scale, high-performance deployments.
https://github.com/prudhvideep/video-face-recognition
aws docker python-lambda python3 reactjs tailwindcss terraform
Last synced: 3 months ago
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An application to detect faces in video streams, seamlessly adjusting to varying workloads. It's designed for both small-scale and large-scale, high-performance deployments.
- Host: GitHub
- URL: https://github.com/prudhvideep/video-face-recognition
- Owner: prudhvideep
- Created: 2024-06-25T22:49:04.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-01-14T06:17:22.000Z (5 months ago)
- Last Synced: 2025-01-14T07:26:15.388Z (5 months ago)
- Topics: aws, docker, python-lambda, python3, reactjs, tailwindcss, terraform
- Language: JavaScript
- Homepage: https://main.d2g4ycz7ogc8gz.amplifyapp.com/
- Size: 30.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Video Face Recognition
In this project, I built an elastic application that can scale out on-demand and detect faces present in a video. I built this using the PaaS offerings from AWS - specifically AWS Lambda and other supporting services from AWS.
**Try it here :** https://main.d2g4ycz7ogc8gz.amplifyapp.com/
## Components
In this section, I'll describe the main components of this application.
### Frontend
The front end is built using **React.js** and styled with **TailwindCss**, ensuring a responsive design. The application is secured with **Firebase Authentication**.
### Backend
Our video analysis application uses four Lambda functions to implement a multi-stage pipeline to process videos sent by users.
- The pipeline starts with a user uploading a video to the input bucket (through the web interface).
- ***video-splitting function*** splits the video into frames and chunks them into the group-of-pictures (GoP) using FFmpeg. It stores this group of pictures in an intermediate stage-1 bucket.
- ***face-recognition function*** extracts the faces in the pictures using a Single Shot MultiBox Detector (SSD) algorithm and uses only the frames with faces for face recognition. It uses a pre-trained CNN model (ResNet-34) for face recognition and outputs the name of the extracted face. The final output is stored in the output bucket.
The structure of the application is shown in the figure below. I used AWS Lambda for serverless computation and S3 for storing the data required for the functions.
> [!IMPORTANT]
>
> The demo only accepts video files and the file size is capped to 5mb to stay within the free tier limits.
## Setup
### Clone the repository
```
git clone [email protected]:prudhvideep/Video-Face-Recognition.git && cd Video-Face-Recognition/
```
### Prerequisites
- Terraform CLI (1.2.0+)
- Aws CLI installed.
- npm
- AWS account and associated credentials.
### Initialize Infrastructure
Execute the script **setup_infrastructure.sh** to install all the prerequisites and initialize the infrastructure in AWS (***using terraform*** )
```
chmod +X ./scripts/setup_infrastructure.sh && sh ./scripts/setup_infrastructure.sh
```
Enter your *aws_access_key_id* and *aws_secret_access_key* when prompted
```
pmutyal@Prudhvi:~/video-face-recognition$ ./scripts/setup_infrastructure.sh
Checking and installing prerequisites...
Terraform is already installed.
AWS CLI is already installed.
Docker is already installed.
npm is already installed.
All prerequisites are installed.
Setting AWS credentials...
Enter your AWS Access Key ID:
Enter your AWS Secret Access Key:
```
This script initializes the infrastructure and outputs the necessary lambda function urls.
To teardown the infrastructure, execute
```
chmod +X ./scripts/destroy_infrastructure.sh && sh ./scripts/destroy_infrastructure.sh
```
### Firebase
Create a Firebase project, set the authentication with email, and add the Gmail provider to implement sign-in with Gmail.
- **References**: please refer this article [Firebase Authentication](https://medium.com/@Adekola_Olawale/firebase-authentication-413626c5234d).
### Environment Variables
setup a .env file
```
touch .env
```
Update the following environment variables
```
#firebase keys
REACT_APP_FIREBASE_API_KEY ="<>"
REACT_APP_AUTH_DOMAIN = "<>"
REACT_APP_PROJECT_ID = "<>"
REACT_APP_STORAGE_BUCKET = "<>"
REACT_APP_MESSAGING_SENDER_ID = "<>"
#Aws keys
REACT_APP_S3_BUCKET_INPUT = "<>"
REACT_APP_S3_BUCKET_OUTPUT = "<>"
REACT_APP_PRESIGNED_LAMBDA_URL = "<>"
REACT_APP_OUTPUT_LAMBDA_URL = "<>"
REACT_APP_DELETE_LAMBDA_URL = "<>"
```
### Start React Application
Install the project dependencies by running:
```
npm install
```
Start the development server
```
npm start
```
To build the production application, run the following:
```
npm run build
```
### Upcoming Features
- Standard Logging.
- Support for multiple media types.
- Detecting videos with multiple characters.