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VideoFramesExtractorBulk-lambda

AWS Lambda function responsible for retrieving videos from the S3 bucket oriane-videos, extracting frames (by default one frame per second) using FFmpeg, and storing the images in the S3 bucket oriane-frames.

Overview A high-performance, serverless solution for extracting frames from videos at scale. Built with AWS Lambda using Node.js and TypeScript, this service efficiently processes videos stored in S3, extracts frames using FFmpeg, and manages workloads through SQS queuing.

Architecture

![Architecture Diagram]

Key Features

  • Serverless Architecture: Leverages AWS Lambda for scalable, cost-effective processing
  • Containerized Solution: Docker-based deployment ensures consistent environments
  • Batch Processing: Efficient handling of multiple videos through SQS queuing
  • Intelligent Frame Extraction:
    • Multiple extraction strategies (uniform/keyframe)
    • Configurable frame intervals
    • Quality control parameters
  • Progress Tracking: Real-time logging and progress monitoring
  • Memory Optimization: Efficient handling of large video files
  • State Management: Supabase integration for reliable state tracking

Technical Stack

  • AWS Services: Lambda, S3, SQS, ECR
  • Database: Supabase
  • Core Technologies: Python 3.8+, OpenCV, FFmpeg
  • Container: Docker
  • Dependencies: boto3, opencv-python, ffmpeg-python

Setup and Deployment

Prerequisites

  • AWS CLI configured with appropriate permissions
  • Docker installed
  • Python 3.8 or higher
  • Supabase account and project

Environment Configuration

  1. Create .env file:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_key
  1. Configure AWS resources:
# Create S3 buckets
aws s3 mb s3://oriane-videos
aws s3 mb s3://oriane-frames
# Create ECR repository
aws ecr create-repository --repository-name video-frames-extractor

Installation

  1. Install dependencies:
pip install -r requirements.txt
  1. Build and deploy Docker image:
./scripts/build_docker.sh

Usage

Running Batch Processing

# Queue videos for processingpythonrun_sqs_batches.py

Lambda Function Configuration

  • Memory: 2048 MB (recommended)
  • Timeout: 5 minutes
  • Environment variables:
    • S3_BUCKET_VIDEOS
    • S3_BUCKET_FRAMES
    • SUPABASE_URL
    • SUPABASE_KEY

Customization Options

# Frame extraction parametersextract_frames(
video_path,
output_dir,
frame_interval=1, # Extract every nth framequality=95, # JPEG quality (1-100)max_frames=None, # Limit total framesstrategy='uniform'# 'uniform' or 'keyframe'
)

Project Structure

├── lambda_function.py # Main Lambda handler
├── run_sqs_batches.py # SQS batch processor
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── scripts/
│ └── build_docker.sh # Docker build script
└── .env # Environment variables

Database Schema

Supabase Table: watched_content

ColumnTypeDescription
codestringVideo identifier
is_fetchedbooleanVideo download status
is_extractedbooleanFrame extraction status

Error Handling

  • Comprehensive error logging
  • Automatic retries for transient failures
  • Dead-letter queue support
  • State management through Supabase

Performance Optimization

  • Memory-efficient frame processing
  • Batch operations for SQS messages
  • Configurable processing intervals
  • Docker layer optimization

Monitoring and Logging

  • CloudWatch integration
  • Progress tracking
  • Performance metrics
  • Error reporting

Security

  • IAM role-based access
  • Environment variable encryption
  • S3 bucket policies
  • Network isolation

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Implement changes with tests
  4. Submit a pull request

Support

For issues and feature requests, please create an issue in the repository.

Maintained by Oriane XYZ

About

No description, website, or topics provided.

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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var __m = "github.com";
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GitHub - orianexxx/VideoFramesExtractorBulk-lambda · GitHub
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VideoFramesExtractorBulk-lambda
AWS Lambda function responsible for retrieving videos from the S3 bucket oriane-videos, extracting frames (by default one frame per second) using FFmpeg, and storing the images in the S3 bucket oriane-frames.
Overview
A high-performance, serverless solution for extracting frames from videos at scale. Built with AWS Lambda using Node.js and TypeScript, this service efficiently processes videos stored in S3, extracts frames using FFmpeg, and manages workloads through SQS queuing.
Architecture
![Architecture Diagram]
Key Features
  • Serverless Architecture: Leverages AWS Lambda for scalable, cost-effective processing
  • Containerized Solution: Docker-based deployment ensures consistent environments
  • Batch Processing: Efficient handling of multiple videos through SQS queuing
  • Intelligent Frame Extraction:

    • Multiple extraction strategies (uniform/keyframe)
    • Configurable frame intervals
    • Quality control parameters
  • Progress Tracking: Real-time logging and progress monitoring
  • Memory Optimization: Efficient handling of large video files
  • State Management: Supabase integration for reliable state tracking
Technical Stack
  • AWS Services: Lambda, S3, SQS, ECR
  • Database: Supabase
  • Core Technologies: Python 3.8+, OpenCV, FFmpeg
  • Container: Docker
  • Dependencies: boto3, opencv-python, ffmpeg-python
Setup and Deployment
Prerequisites
  • AWS CLI configured with appropriate permissions
  • Docker installed
  • Python 3.8 or higher
  • Supabase account and project
Environment Configuration
  1. Create .env file:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_key
  1. Configure AWS resources:
# Create S3 buckets
aws s3 mb s3://oriane-videos
aws s3 mb s3://oriane-frames
# Create ECR repository
aws ecr create-repository --repository-name video-frames-extractor
Installation
  1. Install dependencies:
pip install -r requirements.txt
  1. Build and deploy Docker image:
./scripts/build_docker.sh
Usage
Running Batch Processing
# Queue videos for processingpythonrun_sqs_batches.py
Lambda Function Configuration
  • Memory: 2048 MB (recommended)
  • Timeout: 5 minutes
  • Environment variables:

    • S3_BUCKET_VIDEOS
    • S3_BUCKET_FRAMES
    • SUPABASE_URL
    • SUPABASE_KEY
Customization Options
# Frame extraction parametersextract_frames(
video_path,
output_dir,
frame_interval=1, # Extract every nth framequality=95, # JPEG quality (1-100)max_frames=None, # Limit total framesstrategy='uniform'# 'uniform' or 'keyframe'
)
Project Structure
├── lambda_function.py # Main Lambda handler
├── run_sqs_batches.py # SQS batch processor
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── scripts/
│ └── build_docker.sh # Docker build script
└── .env # Environment variables

Database Schema
Supabase Table: watched_content
ColumnTypeDescription
codestringVideo identifier
is_fetchedbooleanVideo download status
is_extractedbooleanFrame extraction status
Error Handling
  • Comprehensive error logging
  • Automatic retries for transient failures
  • Dead-letter queue support
  • State management through Supabase
Performance Optimization
  • Memory-efficient frame processing
  • Batch operations for SQS messages
  • Configurable processing intervals
  • Docker layer optimization
Monitoring and Logging
  • CloudWatch integration
  • Progress tracking
  • Performance metrics
  • Error reporting
Security
  • IAM role-based access
  • Environment variable encryption
  • S3 bucket policies
  • Network isolation
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Implement changes with tests
  4. Submit a pull request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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GitHub - orianexxx/VideoFramesExtractorBulk-lambda · GitHub
Skip to content
BranchesTags
Repository files navigation
VideoFramesExtractorBulk-lambda
AWS Lambda function responsible for retrieving videos from the S3 bucket oriane-videos, extracting frames (by default one frame per second) using FFmpeg, and storing the images in the S3 bucket oriane-frames.
Overview
A high-performance, serverless solution for extracting frames from videos at scale. Built with AWS Lambda using Node.js and TypeScript, this service efficiently processes videos stored in S3, extracts frames using FFmpeg, and manages workloads through SQS queuing.
Architecture
![Architecture Diagram]
Key Features
  • Serverless Architecture: Leverages AWS Lambda for scalable, cost-effective processing
  • Containerized Solution: Docker-based deployment ensures consistent environments
  • Batch Processing: Efficient handling of multiple videos through SQS queuing
  • Intelligent Frame Extraction:

    • Multiple extraction strategies (uniform/keyframe)
    • Configurable frame intervals
    • Quality control parameters
  • Progress Tracking: Real-time logging and progress monitoring
  • Memory Optimization: Efficient handling of large video files
  • State Management: Supabase integration for reliable state tracking
Technical Stack
  • AWS Services: Lambda, S3, SQS, ECR
  • Database: Supabase
  • Core Technologies: Python 3.8+, OpenCV, FFmpeg
  • Container: Docker
  • Dependencies: boto3, opencv-python, ffmpeg-python
Setup and Deployment
Prerequisites
  • AWS CLI configured with appropriate permissions
  • Docker installed
  • Python 3.8 or higher
  • Supabase account and project
Environment Configuration
  1. Create .env file:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_key
  1. Configure AWS resources:
# Create S3 buckets
aws s3 mb s3://oriane-videos
aws s3 mb s3://oriane-frames
# Create ECR repository
aws ecr create-repository --repository-name video-frames-extractor
Installation
  1. Install dependencies:
pip install -r requirements.txt
  1. Build and deploy Docker image:
./scripts/build_docker.sh
Usage
Running Batch Processing
# Queue videos for processingpythonrun_sqs_batches.py
Lambda Function Configuration
  • Memory: 2048 MB (recommended)
  • Timeout: 5 minutes
  • Environment variables:

    • S3_BUCKET_VIDEOS
    • S3_BUCKET_FRAMES
    • SUPABASE_URL
    • SUPABASE_KEY
Customization Options
# Frame extraction parametersextract_frames(
video_path,
output_dir,
frame_interval=1, # Extract every nth framequality=95, # JPEG quality (1-100)max_frames=None, # Limit total framesstrategy='uniform'# 'uniform' or 'keyframe'
)
Project Structure
├── lambda_function.py # Main Lambda handler
├── run_sqs_batches.py # SQS batch processor
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── scripts/
│ └── build_docker.sh # Docker build script
└── .env # Environment variables

Database Schema
Supabase Table: watched_content
ColumnTypeDescription
codestringVideo identifier
is_fetchedbooleanVideo download status
is_extractedbooleanFrame extraction status
Error Handling
  • Comprehensive error logging
  • Automatic retries for transient failures
  • Dead-letter queue support
  • State management through Supabase
Performance Optimization
  • Memory-efficient frame processing
  • Batch operations for SQS messages
  • Configurable processing intervals
  • Docker layer optimization
Monitoring and Logging
  • CloudWatch integration
  • Progress tracking
  • Performance metrics
  • Error reporting
Security
  • IAM role-based access
  • Environment variable encryption
  • S3 bucket policies
  • Network isolation
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Implement changes with tests
  4. Submit a pull request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - orianexxx/VideoFramesExtractorBulk-lambda · GitHub
Skip to content
BranchesTags
Repository files navigation
VideoFramesExtractorBulk-lambda
AWS Lambda function responsible for retrieving videos from the S3 bucket oriane-videos, extracting frames (by default one frame per second) using FFmpeg, and storing the images in the S3 bucket oriane-frames.
Overview
A high-performance, serverless solution for extracting frames from videos at scale. Built with AWS Lambda using Node.js and TypeScript, this service efficiently processes videos stored in S3, extracts frames using FFmpeg, and manages workloads through SQS queuing.
Architecture
![Architecture Diagram]
Key Features
  • Serverless Architecture: Leverages AWS Lambda for scalable, cost-effective processing
  • Containerized Solution: Docker-based deployment ensures consistent environments
  • Batch Processing: Efficient handling of multiple videos through SQS queuing
  • Intelligent Frame Extraction:

    • Multiple extraction strategies (uniform/keyframe)
    • Configurable frame intervals
    • Quality control parameters
  • Progress Tracking: Real-time logging and progress monitoring
  • Memory Optimization: Efficient handling of large video files
  • State Management: Supabase integration for reliable state tracking
Technical Stack
  • AWS Services: Lambda, S3, SQS, ECR
  • Database: Supabase
  • Core Technologies: Python 3.8+, OpenCV, FFmpeg
  • Container: Docker
  • Dependencies: boto3, opencv-python, ffmpeg-python
Setup and Deployment
Prerequisites
  • AWS CLI configured with appropriate permissions
  • Docker installed
  • Python 3.8 or higher
  • Supabase account and project
Environment Configuration
  1. Create .env file:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_key
  1. Configure AWS resources:
# Create S3 buckets
aws s3 mb s3://oriane-videos
aws s3 mb s3://oriane-frames
# Create ECR repository
aws ecr create-repository --repository-name video-frames-extractor
Installation
  1. Install dependencies:
pip install -r requirements.txt
  1. Build and deploy Docker image:
./scripts/build_docker.sh
Usage
Running Batch Processing
# Queue videos for processingpythonrun_sqs_batches.py
Lambda Function Configuration
  • Memory: 2048 MB (recommended)
  • Timeout: 5 minutes
  • Environment variables:

    • S3_BUCKET_VIDEOS
    • S3_BUCKET_FRAMES
    • SUPABASE_URL
    • SUPABASE_KEY
Customization Options
# Frame extraction parametersextract_frames(
video_path,
output_dir,
frame_interval=1, # Extract every nth framequality=95, # JPEG quality (1-100)max_frames=None, # Limit total framesstrategy='uniform'# 'uniform' or 'keyframe'
)
Project Structure
├── lambda_function.py # Main Lambda handler
├── run_sqs_batches.py # SQS batch processor
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── scripts/
│ └── build_docker.sh # Docker build script
└── .env # Environment variables

Database Schema
Supabase Table: watched_content
ColumnTypeDescription
codestringVideo identifier
is_fetchedbooleanVideo download status
is_extractedbooleanFrame extraction status
Error Handling
  • Comprehensive error logging
  • Automatic retries for transient failures
  • Dead-letter queue support
  • State management through Supabase
Performance Optimization
  • Memory-efficient frame processing
  • Batch operations for SQS messages
  • Configurable processing intervals
  • Docker layer optimization
Monitoring and Logging
  • CloudWatch integration
  • Progress tracking
  • Performance metrics
  • Error reporting
Security
  • IAM role-based access
  • Environment variable encryption
  • S3 bucket policies
  • Network isolation
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Implement changes with tests
  4. Submit a pull request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
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observer.observe(document.body, { childList: true, subtree: true });
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - orianexxx/VideoFramesExtractorBulk-lambda · GitHub
Skip to content
BranchesTags
Repository files navigation
VideoFramesExtractorBulk-lambda
AWS Lambda function responsible for retrieving videos from the S3 bucket oriane-videos, extracting frames (by default one frame per second) using FFmpeg, and storing the images in the S3 bucket oriane-frames.
Overview
A high-performance, serverless solution for extracting frames from videos at scale. Built with AWS Lambda using Node.js and TypeScript, this service efficiently processes videos stored in S3, extracts frames using FFmpeg, and manages workloads through SQS queuing.
Architecture
![Architecture Diagram]
Key Features
  • Serverless Architecture: Leverages AWS Lambda for scalable, cost-effective processing
  • Containerized Solution: Docker-based deployment ensures consistent environments
  • Batch Processing: Efficient handling of multiple videos through SQS queuing
  • Intelligent Frame Extraction:

    • Multiple extraction strategies (uniform/keyframe)
    • Configurable frame intervals
    • Quality control parameters
  • Progress Tracking: Real-time logging and progress monitoring
  • Memory Optimization: Efficient handling of large video files
  • State Management: Supabase integration for reliable state tracking
Technical Stack
  • AWS Services: Lambda, S3, SQS, ECR
  • Database: Supabase
  • Core Technologies: Python 3.8+, OpenCV, FFmpeg
  • Container: Docker
  • Dependencies: boto3, opencv-python, ffmpeg-python
Setup and Deployment
Prerequisites
  • AWS CLI configured with appropriate permissions
  • Docker installed
  • Python 3.8 or higher
  • Supabase account and project
Environment Configuration
  1. Create .env file:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_key
  1. Configure AWS resources:
# Create S3 buckets
aws s3 mb s3://oriane-videos
aws s3 mb s3://oriane-frames
# Create ECR repository
aws ecr create-repository --repository-name video-frames-extractor
Installation
  1. Install dependencies:
pip install -r requirements.txt
  1. Build and deploy Docker image:
./scripts/build_docker.sh
Usage
Running Batch Processing
# Queue videos for processingpythonrun_sqs_batches.py
Lambda Function Configuration
  • Memory: 2048 MB (recommended)
  • Timeout: 5 minutes
  • Environment variables:

    • S3_BUCKET_VIDEOS
    • S3_BUCKET_FRAMES
    • SUPABASE_URL
    • SUPABASE_KEY
Customization Options
# Frame extraction parametersextract_frames(
video_path,
output_dir,
frame_interval=1, # Extract every nth framequality=95, # JPEG quality (1-100)max_frames=None, # Limit total framesstrategy='uniform'# 'uniform' or 'keyframe'
)
Project Structure
├── lambda_function.py # Main Lambda handler
├── run_sqs_batches.py # SQS batch processor
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── scripts/
│ └── build_docker.sh # Docker build script
└── .env # Environment variables

Database Schema
Supabase Table: watched_content
ColumnTypeDescription
codestringVideo identifier
is_fetchedbooleanVideo download status
is_extractedbooleanFrame extraction status
Error Handling
  • Comprehensive error logging
  • Automatic retries for transient failures
  • Dead-letter queue support
  • State management through Supabase
Performance Optimization
  • Memory-efficient frame processing
  • Batch operations for SQS messages
  • Configurable processing intervals
  • Docker layer optimization
Monitoring and Logging
  • CloudWatch integration
  • Progress tracking
  • Performance metrics
  • Error reporting
Security
  • IAM role-based access
  • Environment variable encryption
  • S3 bucket policies
  • Network isolation
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Implement changes with tests
  4. Submit a pull request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
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function tryTheater() {
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - orianexxx/VideoFramesExtractorBulk-lambda · GitHub
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VideoFramesExtractorBulk-lambda
AWS Lambda function responsible for retrieving videos from the S3 bucket oriane-videos, extracting frames (by default one frame per second) using FFmpeg, and storing the images in the S3 bucket oriane-frames.
Overview
A high-performance, serverless solution for extracting frames from videos at scale. Built with AWS Lambda using Node.js and TypeScript, this service efficiently processes videos stored in S3, extracts frames using FFmpeg, and manages workloads through SQS queuing.
Architecture
![Architecture Diagram]
Key Features
  • Serverless Architecture: Leverages AWS Lambda for scalable, cost-effective processing
  • Containerized Solution: Docker-based deployment ensures consistent environments
  • Batch Processing: Efficient handling of multiple videos through SQS queuing
  • Intelligent Frame Extraction:

    • Multiple extraction strategies (uniform/keyframe)
    • Configurable frame intervals
    • Quality control parameters
  • Progress Tracking: Real-time logging and progress monitoring
  • Memory Optimization: Efficient handling of large video files
  • State Management: Supabase integration for reliable state tracking
Technical Stack
  • AWS Services: Lambda, S3, SQS, ECR
  • Database: Supabase
  • Core Technologies: Python 3.8+, OpenCV, FFmpeg
  • Container: Docker
  • Dependencies: boto3, opencv-python, ffmpeg-python
Setup and Deployment
Prerequisites
  • AWS CLI configured with appropriate permissions
  • Docker installed
  • Python 3.8 or higher
  • Supabase account and project
Environment Configuration
  1. Create .env file:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_key
  1. Configure AWS resources:
# Create S3 buckets
aws s3 mb s3://oriane-videos
aws s3 mb s3://oriane-frames
# Create ECR repository
aws ecr create-repository --repository-name video-frames-extractor
Installation
  1. Install dependencies:
pip install -r requirements.txt
  1. Build and deploy Docker image:
./scripts/build_docker.sh
Usage
Running Batch Processing
# Queue videos for processingpythonrun_sqs_batches.py
Lambda Function Configuration
  • Memory: 2048 MB (recommended)
  • Timeout: 5 minutes
  • Environment variables:

    • S3_BUCKET_VIDEOS
    • S3_BUCKET_FRAMES
    • SUPABASE_URL
    • SUPABASE_KEY
Customization Options
# Frame extraction parametersextract_frames(
video_path,
output_dir,
frame_interval=1, # Extract every nth framequality=95, # JPEG quality (1-100)max_frames=None, # Limit total framesstrategy='uniform'# 'uniform' or 'keyframe'
)
Project Structure
├── lambda_function.py # Main Lambda handler
├── run_sqs_batches.py # SQS batch processor
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── scripts/
│ └── build_docker.sh # Docker build script
└── .env # Environment variables

Database Schema
Supabase Table: watched_content
ColumnTypeDescription
codestringVideo identifier
is_fetchedbooleanVideo download status
is_extractedbooleanFrame extraction status
Error Handling
  • Comprehensive error logging
  • Automatic retries for transient failures
  • Dead-letter queue support
  • State management through Supabase
Performance Optimization
  • Memory-efficient frame processing
  • Batch operations for SQS messages
  • Configurable processing intervals
  • Docker layer optimization
Monitoring and Logging
  • CloudWatch integration
  • Progress tracking
  • Performance metrics
  • Error reporting
Security
  • IAM role-based access
  • Environment variable encryption
  • S3 bucket policies
  • Network isolation
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Implement changes with tests
  4. Submit a pull request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
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0 stars
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0 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
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});
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var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - orianexxx/VideoFramesExtractorBulk-lambda · GitHub
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VideoFramesExtractorBulk-lambda
AWS Lambda function responsible for retrieving videos from the S3 bucket oriane-videos, extracting frames (by default one frame per second) using FFmpeg, and storing the images in the S3 bucket oriane-frames.
Overview
A high-performance, serverless solution for extracting frames from videos at scale. Built with AWS Lambda using Node.js and TypeScript, this service efficiently processes videos stored in S3, extracts frames using FFmpeg, and manages workloads through SQS queuing.
Architecture
![Architecture Diagram]
Key Features
  • Serverless Architecture: Leverages AWS Lambda for scalable, cost-effective processing
  • Containerized Solution: Docker-based deployment ensures consistent environments
  • Batch Processing: Efficient handling of multiple videos through SQS queuing
  • Intelligent Frame Extraction:

    • Multiple extraction strategies (uniform/keyframe)
    • Configurable frame intervals
    • Quality control parameters
  • Progress Tracking: Real-time logging and progress monitoring
  • Memory Optimization: Efficient handling of large video files
  • State Management: Supabase integration for reliable state tracking
Technical Stack
  • AWS Services: Lambda, S3, SQS, ECR
  • Database: Supabase
  • Core Technologies: Python 3.8+, OpenCV, FFmpeg
  • Container: Docker
  • Dependencies: boto3, opencv-python, ffmpeg-python
Setup and Deployment
Prerequisites
  • AWS CLI configured with appropriate permissions
  • Docker installed
  • Python 3.8 or higher
  • Supabase account and project
Environment Configuration
  1. Create .env file:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_key
  1. Configure AWS resources:
# Create S3 buckets
aws s3 mb s3://oriane-videos
aws s3 mb s3://oriane-frames
# Create ECR repository
aws ecr create-repository --repository-name video-frames-extractor
Installation
  1. Install dependencies:
pip install -r requirements.txt
  1. Build and deploy Docker image:
./scripts/build_docker.sh
Usage
Running Batch Processing
# Queue videos for processingpythonrun_sqs_batches.py
Lambda Function Configuration
  • Memory: 2048 MB (recommended)
  • Timeout: 5 minutes
  • Environment variables:

    • S3_BUCKET_VIDEOS
    • S3_BUCKET_FRAMES
    • SUPABASE_URL
    • SUPABASE_KEY
Customization Options
# Frame extraction parametersextract_frames(
video_path,
output_dir,
frame_interval=1, # Extract every nth framequality=95, # JPEG quality (1-100)max_frames=None, # Limit total framesstrategy='uniform'# 'uniform' or 'keyframe'
)
Project Structure
├── lambda_function.py # Main Lambda handler
├── run_sqs_batches.py # SQS batch processor
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── scripts/
│ └── build_docker.sh # Docker build script
└── .env # Environment variables

Database Schema
Supabase Table: watched_content
ColumnTypeDescription
codestringVideo identifier
is_fetchedbooleanVideo download status
is_extractedbooleanFrame extraction status
Error Handling
  • Comprehensive error logging
  • Automatic retries for transient failures
  • Dead-letter queue support
  • State management through Supabase
Performance Optimization
  • Memory-efficient frame processing
  • Batch operations for SQS messages
  • Configurable processing intervals
  • Docker layer optimization
Monitoring and Logging
  • CloudWatch integration
  • Progress tracking
  • Performance metrics
  • Error reporting
Security
  • IAM role-based access
  • Environment variable encryption
  • S3 bucket policies
  • Network isolation
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Implement changes with tests
  4. Submit a pull request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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var enabled = true;
function applyDarkMode() {
if (!enabled) return;
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var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
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html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - orianexxx/VideoFramesExtractorBulk-lambda · GitHub
Skip to content
BranchesTags
Repository files navigation
VideoFramesExtractorBulk-lambda
AWS Lambda function responsible for retrieving videos from the S3 bucket oriane-videos, extracting frames (by default one frame per second) using FFmpeg, and storing the images in the S3 bucket oriane-frames.
Overview
A high-performance, serverless solution for extracting frames from videos at scale. Built with AWS Lambda using Node.js and TypeScript, this service efficiently processes videos stored in S3, extracts frames using FFmpeg, and manages workloads through SQS queuing.
Architecture
![Architecture Diagram]
Key Features
  • Serverless Architecture: Leverages AWS Lambda for scalable, cost-effective processing
  • Containerized Solution: Docker-based deployment ensures consistent environments
  • Batch Processing: Efficient handling of multiple videos through SQS queuing
  • Intelligent Frame Extraction:

    • Multiple extraction strategies (uniform/keyframe)
    • Configurable frame intervals
    • Quality control parameters
  • Progress Tracking: Real-time logging and progress monitoring
  • Memory Optimization: Efficient handling of large video files
  • State Management: Supabase integration for reliable state tracking
Technical Stack
  • AWS Services: Lambda, S3, SQS, ECR
  • Database: Supabase
  • Core Technologies: Python 3.8+, OpenCV, FFmpeg
  • Container: Docker
  • Dependencies: boto3, opencv-python, ffmpeg-python
Setup and Deployment
Prerequisites
  • AWS CLI configured with appropriate permissions
  • Docker installed
  • Python 3.8 or higher
  • Supabase account and project
Environment Configuration
  1. Create .env file:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_key
  1. Configure AWS resources:
# Create S3 buckets
aws s3 mb s3://oriane-videos
aws s3 mb s3://oriane-frames
# Create ECR repository
aws ecr create-repository --repository-name video-frames-extractor
Installation
  1. Install dependencies:
pip install -r requirements.txt
  1. Build and deploy Docker image:
./scripts/build_docker.sh
Usage
Running Batch Processing
# Queue videos for processingpythonrun_sqs_batches.py
Lambda Function Configuration
  • Memory: 2048 MB (recommended)
  • Timeout: 5 minutes
  • Environment variables:

    • S3_BUCKET_VIDEOS
    • S3_BUCKET_FRAMES
    • SUPABASE_URL
    • SUPABASE_KEY
Customization Options
# Frame extraction parametersextract_frames(
video_path,
output_dir,
frame_interval=1, # Extract every nth framequality=95, # JPEG quality (1-100)max_frames=None, # Limit total framesstrategy='uniform'# 'uniform' or 'keyframe'
)
Project Structure
├── lambda_function.py # Main Lambda handler
├── run_sqs_batches.py # SQS batch processor
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── scripts/
│ └── build_docker.sh # Docker build script
└── .env # Environment variables

Database Schema
Supabase Table: watched_content
ColumnTypeDescription
codestringVideo identifier
is_fetchedbooleanVideo download status
is_extractedbooleanFrame extraction status
Error Handling
  • Comprehensive error logging
  • Automatic retries for transient failures
  • Dead-letter queue support
  • State management through Supabase
Performance Optimization
  • Memory-efficient frame processing
  • Batch operations for SQS messages
  • Configurable processing intervals
  • Docker layer optimization
Monitoring and Logging
  • CloudWatch integration
  • Progress tracking
  • Performance metrics
  • Error reporting
Security
  • IAM role-based access
  • Environment variable encryption
  • S3 bucket policies
  • Network isolation
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Implement changes with tests
  4. Submit a pull request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages