Distributed Image Processing Pipeline

System Architecture

A scalable distributed system for real-time image and video processing, leveraging Ray for distributed computing, OpenCV for image processing, and PyTorch for deep learning inference.

Core Components

1. Distributed Worker System

import ray
from typing import List, Tuple, Dict
import cv2
import numpy as np

@ray.remote(num_gpus=0.5)
class ImageProcessor:
    def __init__(self, config: Dict[str, Any]):
        self.models = self._load_models(config['model_paths'])
        self.preprocessing = PreprocessingPipeline(
            resize_dims=config['input_size'],
            normalization=config['normalization']
        )
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        
    def process_batch(self, image_batch: np.ndarray) -> List[Dict[str, Any]]:
        processed = self.preprocessing(image_batch)
        with torch.no_grad():
            results = {
                'segmentation': self.models['segmentation'](processed),
                'detection': self.models['detection'](processed),
                'classification': self.models['classification'](processed)
            }
        return self._postprocess_results(results)

2. Video Stream Handler

class VideoStreamManager:
    def __init__(self, num_streams: int, buffer_size: int = 30):
        self.streams = [
            StreamBuffer(buffer_size=buffer_size)
            for _ in range(num_streams)
        ]
        self.frame_processors = [
            ImageProcessor.remote()
            for _ in range(ray.available_resources()['GPU'])
        ]
        
    async def process_streams(self) -> None:
        while True:
            frames = await self._gather_frames()
            batches = self._create_batches(frames, batch_size=16)
            
            # Distribute processing across workers
            futures = [
                processor.process_batch.remote(batch)
                for processor, batch in zip(self.frame_processors, batches)
            ]
            results = await ray.get(futures)
            
            self._update_streams(results)
            
    def _create_batches(
        self, 
        frames: List[np.ndarray], 
        batch_size: int
    ) -> List[np.ndarray]:
        return np.array_split(frames, max(1, len(frames) // batch_size))

3. Image Enhancement Pipeline

class EnhancementPipeline:
    def __init__(self):
        self.denoiser = cv2.cuda.createNonLocalMeans()
        self.super_res = SuperResolutionModel()
        self.color_correction = ColorCorrectionModule()
        
    def enhance(self, image: np.ndarray) -> np.ndarray:
        # Convert to GPU memory
        gpu_image = cv2.cuda_GpuMat()
        gpu_image.upload(image)
        
        # Apply enhancements
        denoised = self.denoiser.apply(gpu_image)
        enhanced = self.color_correction(denoised)
        super_res = self.super_res(enhanced)
        
        # Download result
        return super_res.download()
        
    @torch.cuda.amp.autocast()
    def _apply_super_resolution(self, image: torch.Tensor) -> torch.Tensor:
        return self.super_res(image)

4. Real-time Analysis System

class AnalysisSystem:
    def __init__(self, config: Dict[str, Any]):
        self.feature_extractor = FeatureExtractor(
            backbone=config['backbone'],
            pretrained=True
        )
        self.object_detector = YOLOv5(
            weights=config['detector_weights'],
            conf_threshold=0.5
        )
        self.tracker = DeepSORT(
            model_weights=config['tracker_weights'],
            max_age=30
        )
        
    def analyze_frame(
        self, 
        frame: np.ndarray
    ) -> Tuple[List[Detection], List[Track]]:
        # Extract features
        features = self.feature_extractor(frame)
        
        # Detect objects
        detections = self.object_detector(frame)
        
        # Update tracking
        tracks = self.tracker.update(
            detections=detections,
            features=features
        )
        
        return detections, tracks

5. Data Management System

class DataManager:
    def __init__(self, storage_path: str):
        self.storage = StorageHandler(storage_path)
        self.index = faiss.IndexFlatL2(512)  # Feature dimension
        self.metadata = SQLiteDict('./metadata.sqlite')
        
    async def store_frame_data(
        self, 
        frame_id: str, 
        frame: np.ndarray, 
        metadata: Dict[str, Any]
    ) -> None:
        # Extract features for indexing
        features = self.feature_extractor(frame)
        
        # Store frame and metadata
        await asyncio.gather(
            self.storage.store_frame(frame_id, frame),
            self.storage.store_metadata(frame_id, metadata)
        )
        
        # Update search index
        self.index.add(features.numpy())
        self.metadata[frame_id] = metadata

Performance Optimization

class PerformanceOptimizer:
    def __init__(self):
        self.profiler = cProfile.Profile()
        self.memory_tracker = memory_tracker.SummaryTracker()
        
    def optimize_batch_size(
        self, 
        sample_data: np.ndarray
    ) -> Tuple[int, float]:
        batch_sizes = [1, 2, 4, 8, 16, 32, 64]
        timings = []
        
        for batch_size in batch_sizes:
            timing = self._measure_processing_time(
                sample_data, 
                batch_size
            )
            timings.append(timing)
            
        return self._find_optimal_batch_size(batch_sizes, timings)

Deployment Configuration

version: '3.8'
services:
  processor:
    image: image-processor:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    environment:
      - CUDA_VISIBLE_DEVICES=0
      - MODEL_PATH=/models
      - BATCH_SIZE=16
    volumes:
      - ./models:/models
      - ./data:/data

  redis:
    image: redis:latest
    ports:
      - "6379:6379"

System Requirements

1. Hardware

   • NVIDIA GPU (8GB+ VRAM)
   • 32GB+ RAM
   • NVMe SSD for storage
   • 10Gbps network connection

2. Software

   • CUDA 11.x
   • Python 3.8+
   • OpenCV with CUDA support
   • PyTorch 1.9+
   • Ray 2.0+

Getting Started

1. Install dependencies:

pip install -r requirements.txt
python -m spacy download en_core_web_lg

2. Configure the system:

config = {
    'input_size': (640, 640),
    'batch_size': 16,
    'num_workers': 4,
    'model_paths': {
        'detection': 'models/yolov5x.pt',
        'segmentation': 'models/mask_rcnn.pt',
        'classification': 'models/efficientnet.pt'
    }
}

3. Start the processing pipeline:

ray.init(num_gpus=4)
manager = VideoStreamManager(num_streams=8)
await manager.process_streams()

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