# 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`)

```rust
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:**
```toml
[dependencies]
gstreamer = "0.20"
gstreamer-app = "0.20"
gstreamer-video = "0.20"

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

**System Dependencies:**
```bash
# 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

```rust
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

```rust
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

---

## 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

---

*This roadmap will be updated as Phase 2 development progresses.*