# Performance Optimization Guide

This guide provides comprehensive strategies for optimizing NowYouSeeMe performance across all system components. Follow these guidelines to achieve the best possible performance for your specific use case.

## 🎯 Performance Targets

### Real-time Requirements
| Metric | Target | Acceptable Range | Critical |
|--------|--------|------------------|----------|
| **Latency** | <20ms | 15-25ms | >30ms |
| **Accuracy** | <10cm | 8-15cm | >20cm |
| **Frame Rate** | 30-60 FPS | 25-60 FPS | <20 FPS |
| **CSI Rate** | ≥100 pkt/s | 80-120 pkt/s | <50 pkt/s |

### Resource Utilization
| Component | CPU Target | GPU Target | Memory Target |
|-----------|------------|------------|---------------|
| **Camera Capture** | <10% | N/A | <500MB |
| **CSI Processing** | <15% | N/A | <1GB |
| **Vision SLAM** | <40% | <60% | <2GB |
| **RF SLAM** | <20% | N/A | <1GB |
| **Sensor Fusion** | <15% | <20% | <1GB |
| **Rendering** | <10% | <80% | <2GB |

## 🔧 Hardware Optimization

### GPU Configuration

#### NVIDIA GPU Setup
```bash
# Check GPU status
nvidia-smi

# Set GPU power management
sudo nvidia-smi -pm 1

# Set GPU memory allocation
export CUDA_VISIBLE_DEVICES=0
export CUDA_MEMORY_FRACTION=0.8

# Optimize GPU settings
nvidia-settings --assign GPUPowerMizerMode=1
```

#### GPU Memory Optimization
```python
# In your application
import torch
import cupy as cp

# Set memory fraction
torch.cuda.set_per_process_memory_fraction(0.8)

# Clear cache periodically
torch.cuda.empty_cache()
cp.get_default_memory_pool().free_all_blocks()
```

### CPU Optimization

#### Multi-threading Configuration
```python
# Configure thread pools
import multiprocessing as mp

# Set optimal thread count
optimal_threads = min(mp.cpu_count(), 8)
mp.set_start_method('spawn', force=True)

# Configure OpenMP
import os
os.environ['OMP_NUM_THREADS'] = str(optimal_threads)
os.environ['MKL_NUM_THREADS'] = str(optimal_threads)
```

#### CPU Affinity
```bash
# Set CPU affinity for critical processes
sudo taskset -cp 0-3 <process_id>

# Or in Python
import os
os.sched_setaffinity(0, {0, 1, 2, 3})
```

### Memory Optimization

#### Memory Management
```python
# Monitor memory usage
import psutil
import gc

def optimize_memory():
    """Optimize memory usage"""
    # Force garbage collection
    gc.collect()
    
    # Clear caches
    torch.cuda.empty_cache()
    
    # Monitor memory
    process = psutil.Process()
    memory_mb = process.memory_info().rss / 1024 / 1024
    print(f"Memory usage: {memory_mb:.1f} MB")
```

#### Memory Pooling
```python
# Use memory pools for frequent allocations
import numpy as np
from memory_profiler import profile

class MemoryPool:
    def __init__(self, size=1000):
        self.pool = []
        self.size = size
    
    def get_array(self, shape, dtype=np.float32):
        if self.pool:
            return self.pool.pop().reshape(shape)
        return np.zeros(shape, dtype=dtype)
    
    def return_array(self, array):
        if len(self.pool) < self.size:
            self.pool.append(array.flatten())
```

## 📊 Performance Monitoring

### Real-time Monitoring
```python
import time
import threading
from collections import deque

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {
            'latency': deque(maxlen=100),
            'fps': deque(maxlen=100),
            'accuracy': deque(maxlen=100),
            'cpu_usage': deque(maxlen=100),
            'gpu_usage': deque(maxlen=100),
            'memory_usage': deque(maxlen=100)
        }
        self.running = False
        self.monitor_thread = None
    
    def start_monitoring(self):
        """Start performance monitoring"""
        self.running = True
        self.monitor_thread = threading.Thread(target=self._monitor_loop)
        self.monitor_thread.start()
    
    def stop_monitoring(self):
        """Stop performance monitoring"""
        self.running = False
        if self.monitor_thread:
            self.monitor_thread.join()
    
    def _monitor_loop(self):
        """Main monitoring loop"""
        while self.running:
            # Collect metrics
            self._collect_metrics()
            time.sleep(0.1)  # 10Hz monitoring
    
    def _collect_metrics(self):
        """Collect current performance metrics"""
        # CPU usage
        cpu_percent = psutil.cpu_percent()
        self.metrics['cpu_usage'].append(cpu_percent)
        
        # Memory usage
        memory = psutil.virtual_memory()
        self.metrics['memory_usage'].append(memory.percent)
        
        # GPU usage (if available)
        try:
            import pynvml
            pynvml.nvmlInit()
            handle = pynvml.nvmlDeviceGetHandleByIndex(0)
            gpu_util = pynvml.nvmlDeviceGetUtilizationRates(handle)
            self.metrics['gpu_usage'].append(gpu_util.gpu)
        except:
            self.metrics['gpu_usage'].append(0)
    
    def get_average_metrics(self):
        """Get average metrics over the last 100 samples"""
        return {
            metric: sum(values) / len(values) if values else 0
            for metric, values in self.metrics.items()
        }
    
    def get_performance_report(self):
        """Generate performance report"""
        avg_metrics = self.get_average_metrics()
        
        report = {
            'status': 'optimal' if self._check_targets(avg_metrics) else 'needs_optimization',
            'metrics': avg_metrics,
            'recommendations': self._generate_recommendations(avg_metrics)
        }
        
        return report
    
    def _check_targets(self, metrics):
        """Check if metrics meet targets"""
        return (
            metrics.get('latency', 0) < 20 and
            metrics.get('fps', 0) > 30 and
            metrics.get('accuracy', 0) < 10
        )
    
    def _generate_recommendations(self, metrics):
        """Generate optimization recommendations"""
        recommendations = []
        
        if metrics.get('latency', 0) > 20:
            recommendations.append("High latency detected - consider reducing processing load")
        
        if metrics.get('fps', 0) < 30:
            recommendations.append("Low frame rate - check GPU utilization and rendering settings")
        
        if metrics.get('cpu_usage', 0) > 80:
            recommendations.append("High CPU usage - consider reducing thread count or processing quality")
        
        if metrics.get('memory_usage', 0) > 80:
            recommendations.append("High memory usage - consider clearing caches or reducing buffer sizes")
        
        return recommendations
```

### Profiling Tools

#### CPU Profiling
```python
import cProfile
import pstats
import io

def profile_function(func, *args, **kwargs):
    """Profile a function's performance"""
    pr = cProfile.Profile()
    pr.enable()
    
    result = func(*args, **kwargs)
    
    pr.disable()
    s = io.StringIO()
    ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')
    ps.print_stats(20)
    
    print(s.getvalue())
    return result
```

#### Memory Profiling
```python
from memory_profiler import profile

@profile
def memory_intensive_function():
    """Function to profile memory usage"""
    # Your memory-intensive code here
    pass
```

#### GPU Profiling
```python
import torch

def profile_gpu_operations():
    """Profile GPU operations"""
    with torch.profiler.profile(
        activities=[
            torch.profiler.ProfilerActivity.CPU,
            torch.profiler.ProfilerActivity.CUDA,
        ],
        record_shapes=True,
        with_stack=True
    ) as prof:
        # Your GPU operations here
        pass
    
    print(prof.key_averages().table(sort_by="cuda_time_total"))
```

## ⚡ Algorithm Optimization

### Vision SLAM Optimization

#### Feature Detection Optimization
```python
import cv2
import numpy as np

class OptimizedFeatureDetector:
    def __init__(self, max_features=1000, quality_level=0.01):
        self.max_features = max_features
        self.quality_level = quality_level
        self.detector = cv2.FastFeatureDetector_create(
            threshold=10,
            nonmaxSuppression=True
        )
    
    def detect_features(self, image):
        """Optimized feature detection"""
        # Convert to grayscale if needed
        if len(image.shape) == 3:
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        else:
            gray = image
        
        # Detect features
        keypoints = self.detector.detect(gray)
        
        # Limit number of features
        if len(keypoints) > self.max_features:
            keypoints = sorted(keypoints, key=lambda x: x.response, reverse=True)
            keypoints = keypoints[:self.max_features]
        
        return keypoints
```

#### Tracking Optimization
```python
class OptimizedTracker:
    def __init__(self):
        self.prev_frame = None
        self.prev_keypoints = None
        self.prev_descriptors = None
        
    def track_features(self, frame, keypoints, descriptors):
        """Optimized feature tracking"""
        if self.prev_frame is None:
            self.prev_frame = frame
            self.prev_keypoints = keypoints
            self.prev_descriptors = descriptors
            return keypoints, descriptors
        
        # Use optical flow for fast tracking
        if len(self.prev_keypoints) > 0:
            prev_pts = np.float32([kp.pt for kp in self.prev_keypoints]).reshape(-1, 1, 2)
            curr_pts, status, error = cv2.calcOpticalFlowPyrLK(
                self.prev_frame, frame, prev_pts, None
            )
            
            # Filter good matches
            good_old = self.prev_keypoints[status.ravel() == 1]
            good_new = keypoints[status.ravel() == 1]
            
            # Update tracking state
            self.prev_frame = frame
            self.prev_keypoints = good_new
            self.prev_descriptors = descriptors[status.ravel() == 1]
            
            return good_new, self.prev_descriptors
        
        return keypoints, descriptors
```

### RF SLAM Optimization

#### CSI Processing Optimization
```python
import numpy as np
from scipy import signal

class OptimizedCSIProcessor:
    def __init__(self, sample_rate=1000, window_size=64):
        self.sample_rate = sample_rate
        self.window_size = window_size
        self.window = signal.windows.hann(window_size)
    
    def process_csi_packet(self, csi_data):
        """Optimized CSI packet processing"""
        # Apply window function
        windowed_data = csi_data * self.window
        
        # FFT with optimized size
        fft_size = 2**int(np.log2(len(windowed_data)))
        spectrum = np.fft.fft(windowed_data, fft_size)
        
        # Extract relevant frequency bins
        relevant_bins = spectrum[:fft_size//2]
        
        return relevant_bins
    
    def estimate_aoa(self, csi_packets):
        """Optimized AoA estimation"""
        # Process multiple packets
        processed_packets = [self.process_csi_packet(packet) for packet in csi_packets]
        
        # Use MUSIC algorithm for AoA estimation
        # (Simplified implementation)
        correlation_matrix = np.corrcoef(processed_packets)
        eigenvalues, eigenvectors = np.linalg.eigh(correlation_matrix)
        
        # Estimate AoA from eigenstructure
        noise_subspace = eigenvectors[:, :-3]  # Assume 3 sources
        aoa_spectrum = self._music_spectrum(noise_subspace)
        
        return np.argmax(aoa_spectrum)
    
    def _music_spectrum(self, noise_subspace):
        """MUSIC algorithm spectrum"""
        # Simplified MUSIC implementation
        angles = np.linspace(-np.pi/2, np.pi/2, 180)
        spectrum = np.zeros(len(angles))
        
        for i, angle in enumerate(angles):
            steering_vector = np.exp(1j * 2 * np.pi * np.arange(4) * np.sin(angle))
            spectrum[i] = 1 / (steering_vector.conj() @ noise_subspace @ noise_subspace.conj().T @ steering_vector)
        
        return spectrum
```

### Sensor Fusion Optimization

#### EKF Optimization
```python
import numpy as np
from scipy.linalg import solve_discrete_lyapunov

class OptimizedEKF:
    def __init__(self, state_dim=6, measurement_dim=3):
        self.state_dim = state_dim
        self.measurement_dim = measurement_dim
        
        # Initialize state and covariance
        self.x = np.zeros(state_dim)
        self.P = np.eye(state_dim) * 0.1
        
        # Process and measurement noise
        self.Q = np.eye(state_dim) * 0.01
        self.R = np.eye(measurement_dim) * 0.1
    
    def predict(self, dt):
        """Optimized prediction step"""
        # State transition matrix (constant velocity model)
        F = np.eye(self.state_dim)
        F[:3, 3:6] = np.eye(3) * dt
        
        # Predict state
        self.x = F @ self.x
        
        # Predict covariance
        self.P = F @ self.P @ F.T + self.Q
    
    def update(self, measurement):
        """Optimized update step"""
        # Measurement matrix
        H = np.zeros((self.measurement_dim, self.state_dim))
        H[:3, :3] = np.eye(3)
        
        # Kalman gain
        S = H @ self.P @ H.T + self.R
        K = self.P @ H.T @ np.linalg.inv(S)
        
        # Update state and covariance
        y = measurement - H @ self.x
        self.x = self.x + K @ y
        self.P = (np.eye(self.state_dim) - K @ H) @ self.P
    
    def get_pose(self):
        """Get current pose estimate"""
        return {
            'position': self.x[:3],
            'velocity': self.x[3:6],
            'covariance': self.P
        }
```

## 🎨 Rendering Optimization

### OpenGL Optimization
```python
import OpenGL.GL as gl
import numpy as np

class OptimizedRenderer:
    def __init__(self):
        self.shader_program = None
        self.vao = None
        self.vbo = None
        self.ebo = None
        
        self.setup_gl()
    
    def setup_gl(self):
        """Setup OpenGL for optimal performance"""
        # Enable optimizations
        gl.glEnable(gl.GL_DEPTH_TEST)
        gl.glEnable(gl.GL_CULL_FACE)
        gl.glEnable(gl.GL_BLEND)
        gl.glBlendFunc(gl.GL_SRC_ALPHA, gl.GL_ONE_MINUS_SRC_ALPHA)
        
        # Set clear color
        gl.glClearColor(0.1, 0.1, 0.1, 1.0)
    
    def create_shader_program(self, vertex_source, fragment_source):
        """Create optimized shader program"""
        vertex_shader = gl.glCreateShader(gl.GL_VERTEX_SHADER)
        gl.glShaderSource(vertex_shader, vertex_source)
        gl.glCompileShader(vertex_shader)
        
        fragment_shader = gl.glCreateShader(gl.GL_FRAGMENT_SHADER)
        gl.glShaderSource(fragment_shader, fragment_source)
        gl.glCompileShader(fragment_shader)
        
        program = gl.glCreateProgram()
        gl.glAttachShader(program, vertex_shader)
        gl.glAttachShader(program, fragment_shader)
        gl.glLinkProgram(program)
        
        # Clean up shaders
        gl.glDeleteShader(vertex_shader)
        gl.glDeleteShader(fragment_shader)
        
        return program
    
    def setup_buffers(self, vertices, indices):
        """Setup optimized vertex buffers"""
        # Create VAO
        self.vao = gl.glGenVertexArrays(1)
        gl.glBindVertexArray(self.vao)
        
        # Create VBO
        self.vbo = gl.glGenBuffers(1)
        gl.glBindBuffer(gl.GL_ARRAY_BUFFER, self.vbo)
        gl.glBufferData(gl.GL_ARRAY_BUFFER, vertices.nbytes, vertices, gl.GL_STATIC_DRAW)
        
        # Create EBO
        self.ebo = gl.glGenBuffers(1)
        gl.glBindBuffer(gl.GL_ELEMENT_ARRAY_BUFFER, self.ebo)
        gl.glBufferData(gl.GL_ELEMENT_ARRAY_BUFFER, indices.nbytes, indices, gl.GL_STATIC_DRAW)
        
        # Set vertex attributes
        gl.glVertexAttribPointer(0, 3, gl.GL_FLOAT, gl.GL_FALSE, 24, None)
        gl.glEnableVertexAttribArray(0)
        
        gl.glVertexAttribPointer(1, 3, gl.GL_FLOAT, gl.GL_FALSE, 24, ctypes.c_void_p(12))
        gl.glEnableVertexAttribArray(1)
    
    def render_frame(self, pose_data):
        """Optimized frame rendering"""
        # Clear buffers
        gl.glClear(gl.GL_COLOR_BUFFER_BIT | gl.GL_DEPTH_BUFFER_BIT)
        
        # Use shader program
        gl.glUseProgram(self.shader_program)
        
        # Update uniform matrices
        self.update_matrices(pose_data)
        
        # Bind VAO and draw
        gl.glBindVertexArray(self.vao)
        gl.glDrawElements(gl.GL_TRIANGLES, self.index_count, gl.GL_UNSIGNED_INT, None)
    
    def update_matrices(self, pose_data):
        """Update transformation matrices"""
        # Calculate view and projection matrices
        view_matrix = self.calculate_view_matrix(pose_data)
        projection_matrix = self.calculate_projection_matrix()
        
        # Upload to GPU
        gl.glUniformMatrix4fv(self.view_location, 1, gl.GL_FALSE, view_matrix)
        gl.glUniformMatrix4fv(self.projection_location, 1, gl.GL_FALSE, projection_matrix)
```

### NeRF Rendering Optimization
```python
import torch
import torch.nn as nn

class OptimizedNeRFRenderer:
    def __init__(self, model_path, device='cuda'):
        self.device = device
        self.model = self.load_model(model_path)
        self.model.to(device)
        self.model.eval()
        
        # Optimization settings
        self.chunk_size = 4096
        self.num_samples = 64
        
    def load_model(self, model_path):
        """Load optimized NeRF model"""
        # Load pre-trained model
        model = torch.load(model_path, map_location=self.device)
        return model
    
    @torch.no_grad()
    def render_rays(self, rays_o, rays_d, near, far):
        """Optimized ray rendering"""
        # Process rays in chunks
        outputs = []
        
        for i in range(0, rays_o.shape[0], self.chunk_size):
            chunk_o = rays_o[i:i+self.chunk_size]
            chunk_d = rays_d[i:i+self.chunk_size]
            
            # Render chunk
            chunk_output = self._render_chunk(chunk_o, chunk_d, near, far)
            outputs.append(chunk_output)
        
        # Combine outputs
        return torch.cat(outputs, dim=0)
    
    def _render_chunk(self, rays_o, rays_d, near, far):
        """Render a chunk of rays"""
        # Sample points along rays
        t_vals = torch.linspace(0., 1., self.num_samples, device=self.device)
        z_vals = near * (1. - t_vals) + far * t_vals
        
        # Expand dimensions
        z_vals = z_vals.unsqueeze(0).expand(rays_o.shape[0], -1)
        
        # Sample points
        pts = rays_o.unsqueeze(1) + rays_d.unsqueeze(1) * z_vals.unsqueeze(-1)
        
        # Query network
        rgb, sigma = self.model(pts, rays_d)
        
        # Volume rendering
        rgb_final = self._volume_render(rgb, sigma, z_vals)
        
        return rgb_final
    
    def _volume_render(self, rgb, sigma, z_vals):
        """Volume rendering integration"""
        # Calculate distances
        dists = z_vals[..., 1:] - z_vals[..., :-1]
        dists = torch.cat([dists, torch.tensor([1e10], device=self.device).expand(dists[..., :1].shape)], -1)
        
        # Calculate alpha
        alpha = 1. - torch.exp(-sigma * dists)
        
        # Calculate weights
        weights = alpha * torch.cumprod(torch.cat([torch.ones((alpha.shape[0], 1), device=self.device), 1.-alpha + 1e-10], -1), -1)[:, :-1]
        
        # Integrate
        rgb_final = torch.sum(weights.unsqueeze(-1) * rgb, -2)
        
        return rgb_final
```

## 🔧 Configuration Optimization

### Performance Configuration
```json
{
  "performance": {
    "target_latency": 20,
    "target_fps": 30,
    "target_accuracy": 10,
    "max_cpu_usage": 80,
    "max_gpu_usage": 90,
    "max_memory_usage": 80
  },
  "processing": {
    "vision_slam": {
      "max_features": 1000,
      "min_features": 100,
      "update_rate": 30,
      "quality_level": 0.01
    },
    "rf_slam": {
      "packet_rate": 100,
      "aoa_estimation": "music",
      "filter_window": 10
    },
    "sensor_fusion": {
      "fusion_method": "ekf",
      "vision_weight": 0.7,
      "rf_weight": 0.3,
      "process_noise": 0.01
    }
  },
  "rendering": {
    "quality": "high",
    "resolution": [1280, 720],
    "vsync": true,
    "antialiasing": true,
    "shadow_quality": "medium"
  },
  "optimization": {
    "use_gpu": true,
    "use_multithreading": true,
    "memory_pooling": true,
    "chunk_processing": true
  }
}
```

### Dynamic Configuration
```python
class DynamicConfigManager:
    def __init__(self, base_config):
        self.base_config = base_config
        self.current_config = base_config.copy()
        self.performance_monitor = PerformanceMonitor()
    
    def optimize_config(self):
        """Dynamically optimize configuration based on performance"""
        metrics = self.performance_monitor.get_average_metrics()
        
        # Adjust based on latency
        if metrics.get('latency', 0) > 25:
            self._reduce_processing_load()
        
        # Adjust based on frame rate
        if metrics.get('fps', 0) < 25:
            self._reduce_rendering_quality()
        
        # Adjust based on CPU usage
        if metrics.get('cpu_usage', 0) > 85:
            self._reduce_thread_count()
        
        # Adjust based on memory usage
        if metrics.get('memory_usage', 0) > 85:
            self._reduce_buffer_sizes()
    
    def _reduce_processing_load(self):
        """Reduce processing load"""
        self.current_config['processing']['vision_slam']['max_features'] = max(
            500, self.current_config['processing']['vision_slam']['max_features'] - 100
        )
        self.current_config['processing']['vision_slam']['update_rate'] = max(
            20, self.current_config['processing']['vision_slam']['update_rate'] - 5
        )
    
    def _reduce_rendering_quality(self):
        """Reduce rendering quality"""
        quality_levels = ['high', 'medium', 'low']
        current_quality = self.current_config['rendering']['quality']
        current_index = quality_levels.index(current_quality)
        
        if current_index < len(quality_levels) - 1:
            self.current_config['rendering']['quality'] = quality_levels[current_index + 1]
    
    def _reduce_thread_count(self):
        """Reduce thread count"""
        # Implementation for reducing thread count
        pass
    
    def _reduce_buffer_sizes(self):
        """Reduce buffer sizes"""
        # Implementation for reducing buffer sizes
        pass
```

## 📊 Performance Testing

### Benchmark Suite
```python
import time
import statistics

class PerformanceBenchmark:
    def __init__(self):
        self.results = {}
    
    def benchmark_latency(self, func, *args, iterations=100):
        """Benchmark function latency"""
        times = []
        
        for _ in range(iterations):
            start_time = time.perf_counter()
            func(*args)
            end_time = time.perf_counter()
            times.append((end_time - start_time) * 1000)  # Convert to ms
        
        return {
            'mean': statistics.mean(times),
            'median': statistics.median(times),
            'std': statistics.stdev(times),
            'min': min(times),
            'max': max(times),
            'p95': statistics.quantiles(times, n=20)[18],  # 95th percentile
            'p99': statistics.quantiles(times, n=100)[98]  # 99th percentile
        }
    
    def benchmark_throughput(self, func, *args, duration=10):
        """Benchmark function throughput"""
        start_time = time.perf_counter()
        count = 0
        
        while time.perf_counter() - start_time < duration:
            func(*args)
            count += 1
        
        return count / duration  # Operations per second
    
    def benchmark_memory(self, func, *args):
        """Benchmark memory usage"""
        import psutil
        import gc
        
        # Force garbage collection
        gc.collect()
        
        # Get initial memory
        process = psutil.Process()
        initial_memory = process.memory_info().rss
        
        # Run function
        func(*args)
        
        # Get final memory
        final_memory = process.memory_info().rss
        
        return final_memory - initial_memory  # Memory increase in bytes
    
    def run_full_benchmark(self):
        """Run complete performance benchmark"""
        benchmark_results = {}
        
        # Benchmark camera capture
        benchmark_results['camera_capture'] = self.benchmark_latency(
            self.camera_capture_test
        )
        
        # Benchmark CSI processing
        benchmark_results['csi_processing'] = self.benchmark_latency(
            self.csi_processing_test
        )
        
        # Benchmark SLAM processing
        benchmark_results['slam_processing'] = self.benchmark_latency(
            self.slam_processing_test
        )
        
        # Benchmark rendering
        benchmark_results['rendering'] = self.benchmark_latency(
            self.rendering_test
        )
        
        # Benchmark end-to-end
        benchmark_results['end_to_end'] = self.benchmark_latency(
            self.end_to_end_test
        )
        
        return benchmark_results
    
    def generate_report(self, results):
        """Generate performance report"""
        report = {
            'summary': {
                'total_latency': sum(r['mean'] for r in results.values()),
                'bottleneck': max(results.items(), key=lambda x: x[1]['mean'])[0],
                'performance_grade': self._calculate_grade(results)
            },
            'details': results,
            'recommendations': self._generate_recommendations(results)
        }
        
        return report
    
    def _calculate_grade(self, results):
        """Calculate overall performance grade"""
        total_latency = sum(r['mean'] for r in results.values())
        
        if total_latency < 20:
            return 'A'
        elif total_latency < 30:
            return 'B'
        elif total_latency < 40:
            return 'C'
        else:
            return 'D'
    
    def _generate_recommendations(self, results):
        """Generate optimization recommendations"""
        recommendations = []
        
        for component, metrics in results.items():
            if metrics['mean'] > 10:  # High latency threshold
                recommendations.append(f"Optimize {component} - current latency: {metrics['mean']:.2f}ms")
            
            if metrics['p99'] > metrics['mean'] * 2:  # High variance
                recommendations.append(f"Reduce variance in {component} - p99: {metrics['p99']:.2f}ms")
        
        return recommendations
```

## 🚀 Deployment Optimization

### Production Configuration
```yaml
# docker-compose.prod.yml
version: '3.8'

services:
  nowyouseeme:
    build:
      context: .
      dockerfile: Dockerfile
      target: production
    container_name: nowyouseeme-prod
    ports:
      - "8080:8080"
    volumes:
      - ./config:/app/config:ro
      - ./data:/app/data
      - ./logs:/app/logs
    environment:
      - PYTHONPATH=/app/src
      - NOWYOUSEE_DEBUG=0
      - CUDA_VISIBLE_DEVICES=0
      - OMP_NUM_THREADS=4
      - MKL_NUM_THREADS=4
    devices:
      - /dev/video0:/dev/video0
      - /dev/bus/usb:/dev/bus/usb
    network_mode: host
    restart: unless-stopped
    deploy:
      resources:
        limits:
          cpus: '4.0'
          memory: 8G
        reservations:
          cpus: '2.0'
          memory: 4G
    healthcheck:
      test: ["CMD", "python3", "-c", "import sys; sys.exit(0)"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s
```

### Monitoring Setup
```python
# monitoring.py
import prometheus_client
from prometheus_client import Counter, Histogram, Gauge

class PerformanceMetrics:
    def __init__(self):
        # Define metrics
        self.latency_histogram = Histogram(
            'nowyouseeme_latency_seconds',
            'End-to-end latency in seconds',
            buckets=[0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.5]
        )
        
        self.fps_gauge = Gauge(
            'nowyouseeme_fps',
            'Current frame rate'
        )
        
        self.accuracy_gauge = Gauge(
            'nowyouseeme_accuracy_cm',
            'Current tracking accuracy in cm'
        )
        
        self.cpu_usage_gauge = Gauge(
            'nowyouseeme_cpu_usage_percent',
            'CPU usage percentage'
        )
        
        self.gpu_usage_gauge = Gauge(
            'nowyouseeme_gpu_usage_percent',
            'GPU usage percentage'
        )
        
        self.memory_usage_gauge = Gauge(
            'nowyouseeme_memory_usage_percent',
            'Memory usage percentage'
        )
    
    def record_latency(self, latency_ms):
        """Record latency measurement"""
        self.latency_histogram.observe(latency_ms / 1000.0)
    
    def record_fps(self, fps):
        """Record frame rate"""
        self.fps_gauge.set(fps)
    
    def record_accuracy(self, accuracy_cm):
        """Record accuracy measurement"""
        self.accuracy_gauge.set(accuracy_cm)
    
    def record_system_metrics(self, cpu_percent, gpu_percent, memory_percent):
        """Record system metrics"""
        self.cpu_usage_gauge.set(cpu_percent)
        self.gpu_usage_gauge.set(gpu_percent)
        self.memory_usage_gauge.set(memory_percent)

# Start metrics server
if __name__ == '__main__':
    prometheus_client.start_http_server(8000)
```

---

For more detailed optimization strategies, see:
- [Architecture Guide](architecture.md) - System design and optimization
- [Troubleshooting Guide](troubleshooting.md) - Performance issue resolution
- [API Reference](API_REFERENCE.md) - Performance-related API calls