meteor_detect/TODO_DISPLAY_OPTIMIZATION.md

Display System Optimization Roadmap

Phase 2: GStreamer Integration

Overview

Implement GStreamer-based display backend for hardware-accelerated rendering, particularly optimized for embedded systems like Raspberry Pi.

Goals

• Performance: 60-80% reduction in CPU usage
• Hardware Acceleration: Utilize GPU/VPU when available
• Zero-Copy: Minimize memory bandwidth usage
• Flexibility: Support multiple output formats (window, file, network stream)

Implementation Plan

1. Core GStreamer Backend (src/display/gstreamer_backend.rs)

pub struct GStreamerDisplay {
    pipeline: gst::Pipeline,
    appsrc: gst_app::AppSrc,
    bus: gst::Bus,
    config: DisplayConfig,
    stats: DisplayStats,
}

impl DisplayBackend for GStreamerDisplay {
    // Implement trait methods using GStreamer pipeline
}

Key Features:

• Dynamic pipeline creation based on output requirements
• Hardware encoder detection and utilization
• Efficient buffer management with memory pools
• Error handling and pipeline state management

2. Pipeline Configurations

Basic Window Display

appsrc ! videoconvert ! videoscale ! xvimagesink

Hardware-Accelerated (Raspberry Pi)

appsrc ! v4l2convert ! video/x-raw,format=NV12 ! v4l2h264enc ! h264parse ! v4l2h264dec ! xvimagesink

Network Streaming

appsrc ! videoconvert ! x264enc ! rtph264pay ! udpsink host=192.168.1.100 port=5000

File Recording

appsrc ! videoconvert ! x264enc ! mp4mux ! filesink location=output.mp4

3. Dependencies and Setup

Cargo.toml additions:

[dependencies]
gstreamer = "0.20"
gstreamer-app = "0.20"
gstreamer-video = "0.20"

[features]
gstreamer-display = ["gstreamer", "gstreamer-app", "gstreamer-video"]

System Dependencies:

# Ubuntu/Debian
sudo apt install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev

# Raspberry Pi additional packages
sudo apt install gstreamer1.0-omx gstreamer1.0-plugins-bad

4. Buffer Management Optimization

struct BufferPool {
    pool: gst::BufferPool,
    allocator: gst::Allocator,
}

impl BufferPool {
    fn get_buffer(&self, mat: &opencv::Mat) -> Result<gst::Buffer> {
        // Zero-copy conversion from OpenCV Mat to GStreamer Buffer
        // Use memory mapping when possible
    }
}

5. Auto-Detection and Fallback

pub fn create_optimal_display(config: DisplayConfig) -> Box<dyn DisplayBackend> {
    if gstreamer_available() && hardware_acceleration_available() {
        Box::new(GStreamerDisplay::new(config))
    } else {
        Box::new(OpenCVDisplay::new(config))
    }
}

Hardware-Specific Optimizations

Raspberry Pi 4/5

• GPU Memory Split: gpu_mem=128 in /boot/config.txt
• V4L2 Codecs: Utilize hardware H.264 encoder/decoder
• DMA Buffers: Use DMA-BUF for zero-copy operations

NVIDIA Jetson

• NVENC/NVDEC: Hardware encoding/decoding
• CUDA Integration: GPU-accelerated image processing

Intel Systems

• VAAPI: Video Acceleration API support
• Quick Sync: Hardware encoding acceleration

Performance Targets

Metric	Current (OpenCV)	Target (GStreamer)	Improvement
CPU Usage	60-80%	15-25%	60-75% reduction
Memory Bandwidth	2-3 GB/s	0.5-1 GB/s	50-75% reduction
Latency	3-5 frames	1-2 frames	60% reduction
Power Consumption	High	Low	30-50% reduction

Implementation Timeline

Week 1-2: Foundation

• Set up GStreamer Rust bindings
• Create basic pipeline structure
• Implement DisplayBackend trait
• Basic window display working

Week 3-4: Optimization

• Buffer pool implementation
• Zero-copy Mat to Buffer conversion
• Hardware acceleration detection
• Pipeline optimization

Week 5-6: Advanced Features

• Multiple output support (file, network)
• Error recovery and robustness
• Performance profiling and tuning
• Documentation and examples

Week 7-8: Integration and Testing

• Update demos to use GStreamer backend
• Raspberry Pi testing and optimization
• Benchmark comparison with OpenCV
• Production readiness assessment

Compatibility Matrix

Platform	GStreamer Support	Hardware Accel	Recommended Pipeline
Raspberry Pi 4/5	✅	V4L2	v4l2convert + omx
NVIDIA Jetson	✅	NVENC/NVDEC	nvvidconv + nvenc
Intel x86	✅	VAAPI	vaapi + intel-media
Generic Linux	✅	Software	videoconvert + x264
macOS	✅	VideoToolbox	vtenc + osxvideosink
Windows	✅	DirectShow	d3d11 + mf

Testing Strategy

Unit Tests

• Pipeline creation and teardown
• Buffer management
• Error handling

Integration Tests

• End-to-end display functionality
• Performance benchmarking
• Memory leak detection

Hardware Tests

• Raspberry Pi 4/5 validation
• NVIDIA Jetson compatibility
• Various resolution and framerate combinations

Risk Mitigation

Potential Issues

1. GStreamer Version Compatibility: Different distributions have different GStreamer versions
2. Hardware Driver Issues: Proprietary drivers may not be available
3. Memory Alignment: OpenCV Mat and GStreamer Buffer memory layout differences

Solutions

1. Runtime Detection: Probe capabilities at startup
2. Graceful Fallback: Fall back to OpenCV backend if GStreamer fails
3. Memory Copy Fallback: Use memory copy if zero-copy fails

Success Metrics

Must-Have

• 50%+ reduction in CPU usage on Raspberry Pi
• Maintain visual quality parity with OpenCV
• No memory leaks during extended operation
• Graceful error handling and recovery

Nice-to-Have

• Network streaming capability
• Recording to file functionality
• Multi-window support
• Real-time performance monitoring

Documentation Requirements

• API documentation for new backend
• Hardware setup guides for different platforms
• Performance tuning recommendations
• Troubleshooting guide for common issues
• Migration guide from OpenCV backend

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Phase 3: Future Enhancements

WebRTC Integration

• Real-time streaming to web browsers
• Low-latency remote monitoring

Custom Shaders

• GPU-accelerated overlay rendering
• Advanced visual effects

Multi-Display Support

• Simultaneous output to multiple displays
• Picture-in-picture functionality

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This roadmap will be updated as Phase 2 development progresses.