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@muditbhargava66 muditbhargava66 released this 08 Aug 08:58
· 3 commits to main since this release
v1.1.0
3fc2d5a

🚀 Advanced Image Sensor Interface v1.1.0 Release Notes

📋 Overview

Version 1.1.0 represents a major milestone for the Advanced Image Sensor Interface project, transforming it into a production-ready, enterprise-grade Python simulation and modeling framework. This release includes comprehensive security enhancements, advanced image processing capabilities, AI-based calibration, and professional documentation suitable for deployment in development environments.

🎯 What's New in v1.1.0

🔒 Security Framework

  • Comprehensive Input Validation: All data types validated with detailed error messages
  • Buffer Overflow Protection: Size limits and memory safety checks
  • Operation Timeout Management: Prevents hanging operations
  • Security Policy Enforcement: Configurable security limits and constraints
  • 20 Dedicated Security Tests: Comprehensive validation coverage

🧠 AI-Based Calibration System

  • Neural Network Parameter Tuning: Automated optimization using machine learning
  • Multiple Model Support: Linear and MLP calibration models
  • Optimization Algorithms: Advanced parameter tuning for sensor optimization
  • Model Validation: Comprehensive validation and performance tracking

📡 Complete MIPI CSI-2 Protocol Implementation

  • Packet-Level Simulation: Short and long packet handling with proper validation
  • ECC/CRC Validation: Error correction code and cyclic redundancy check
  • Protocol Compliance: Full MIPI CSI-2 specification compliance
  • Frame/Line Tracking: Comprehensive packet statistics and monitoring
  • 20 Protocol Tests: Extensive validation of protocol implementation

🖼️ Advanced Image Processing

  • Multiple Denoising Algorithms: Bilateral, guided filter, non-local means, BM3D
  • Proper Color Space Handling: sRGB ↔ linear ↔ XYZ ↔ LAB conversions
  • Quality Metrics: PSNR, SSIM, and Delta E 2000 calculations
  • Bit-Depth Safety: Comprehensive validation across 8-16 bit depths
  • 18 Image Validation Tests: Thorough image processing validation

⚡ Pluggable Power Management

  • Backend Architecture: Simulation and hardware-ready backends
  • Realistic Power Modeling: Temperature-dependent behavior simulation
  • Power Rail Management: Main, IO, analog, and digital rail support
  • Thermal Protection: Temperature monitoring and safety limits
  • 19 Power Management Tests: Comprehensive power system validation

🏗️ Enhanced Architecture

  • Multi-Protocol Support: MIPI CSI-2, GigE Vision, CoaXPress protocol models
  • Modular Design: Clean separation of concerns with dependency injection
  • Professional Code Quality: Strict type checking with MyPy and Pyright
  • PEP 517/518 Compliance: Modern Python packaging standards

📊 Technical Achievements

🧪 Testing Excellence

  • 122 Comprehensive Tests (100% passing rate)
  • Multi-Python Support: Python 3.10, 3.11, 3.12, 3.13
  • 37% Code Coverage: Focused on core functionality
  • CI/CD Pipeline: Automated testing, linting, and type checking

📚 Documentation Overhaul

  • Professional Presentation: Removed excessive emojis, clean formatting
  • ReadTheDocs Integration: Comprehensive online documentation
  • API Documentation: Complete with examples and usage patterns
  • Design Specifications: Detailed system architecture documentation
  • Performance Analysis: Realistic benchmarking with proper disclaimers

🔧 Development Experience

  • Modern Tooling: Ruff for linting, Black for formatting
  • Type Safety: Dual-layer type checking (MyPy + Pyright)
  • Flexible Installation: Multiple installation methods available
  • Developer-Friendly: Comprehensive contributing guidelines

📈 Performance Metrics

Note: All performance metrics are simulation-based, not hardware measurements.

Component Performance Notes
MIPI Simulation 7,769.9 ops/sec Protocol simulation in Python
Signal Processing 8.6 ops/sec Pure Python processing pipeline
Power Modeling 18.4 ops/sec Power simulation calculations
Test Suite 122 tests in 5.87s Comprehensive validation

🛠️ Installation & Usage

Quick Installation

# Method 1: Using requirements files
pip install -r requirements.txt
pip install -e .

# Method 2: Using pyproject.toml (recommended)
pip install -e ".[dev,docs]"

# Method 3: Development setup
pip install -r requirements.txt -r requirements-dev.txt
pip install -e .

Basic Usage

from advanced_image_sensor_interface import MIPIDriver, MIPIConfig

# Initialize MIPI driver
config = MIPIConfig(lanes=4, data_rate=2.5, channel=0)
driver = MIPIDriver(config)

# Get status
status = driver.get_status()
print(f"Driver status: {status}")

🔄 Migration from v1.0.x

Breaking Changes

  • Python Version: Minimum requirement updated to Python 3.10
  • Dependencies: Updated dependency versions with stricter constraints
  • Import Structure: Enhanced package organization (backward compatible)

Migration Steps

  1. Update Python: Ensure Python 3.10+ is installed
  2. Update Dependencies: Run pip install -r requirements.txt
  3. Reinstall Package: Run pip install -e .
  4. Run Tests: Verify with pytest tests/

📦 What's Included

Core Components

  • MIPI Driver: Complete CSI-2 protocol simulation
  • Signal Processor: Advanced image processing pipeline
  • Power Manager: Comprehensive power modeling system
  • Security Framework: Input validation and protection
  • AI Calibration: Neural network-based parameter tuning

Development Tools

  • Testing Suite: 122 comprehensive tests
  • Benchmarking: Performance measurement tools
  • Documentation: Complete API and design documentation
  • CI/CD Pipeline: Automated quality assurance

Utilities

  • Performance Metrics: SNR, dynamic range, color accuracy calculations
  • Test Patterns: Comprehensive test pattern generation
  • Noise Reduction: Advanced denoising algorithms
  • Protocol Validation: MIPI packet validation tools

🎯 Use Cases

Development & Testing

  • Algorithm Prototyping: Test image processing algorithms
  • Protocol Validation: Validate MIPI CSI-2 implementations
  • Performance Benchmarking: Measure and optimize performance
  • Educational Tool: Learn about sensor interfaces

Research & Analysis

  • Power Modeling: Simulate power consumption patterns
  • Quality Assessment: Measure image quality metrics
  • Parameter Optimization: AI-based calibration and tuning
  • System Validation: End-to-end pipeline testing

🤝 Contributing

We welcome contributions! See our Contributing Guide for details.

How to Contribute

  1. Fork the Repository: Create your own fork
  2. Create Feature Branch: git checkout -b feature/amazing-feature
  3. Make Changes: Implement your improvements
  4. Add Tests: Ensure comprehensive test coverage
  5. Submit PR: Create a pull request with detailed description

📞 Support & Community

Getting Help

Community

  • Star the Repository: Show your support
  • Report Issues: Help us improve
  • Contribute Code: Join our development community
  • Share Feedback: Let us know how you're using the project

🔗 Links

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