Enterprise Computer Vision Analytics

System Architecture

A scalable computer vision system that combines multiple deep learning models for real-time video analysis and object tracking.

Core Components

1. Video Processing Pipeline

class VideoProcessor:
    def __init__(self, config: Dict[str, Any]):
        self.frame_buffer = FrameBuffer(
            max_size=config['buffer_size']
        )
        self.gpu_preprocessor = GPUPreprocessor(
            batch_size=config['batch_size'],
            input_shape=config['input_shape']
        )
        self.models = self._initialize_models(config['model_configs'])
        
    async def process_video_stream(
        self,
        stream: AsyncVideoStream
    ) -> AsyncIterator[Dict[str, Any]]:
        async for frames in self.frame_buffer.get_batch(stream):
            # Preprocess on GPU
            processed_frames = await self.gpu_preprocessor(frames)
            
            # Run inference in parallel
            results = await asyncio.gather(*[
                model.infer(processed_frames)
                for model in self.models.values()
            ])
            
            # Merge results
            yield self._merge_results(results)
            
    @torch.cuda.amp.autocast()
    def _preprocess_batch(
        self,
        frames: torch.Tensor
    ) -> torch.Tensor:
        return self.gpu_preprocessor.preprocess(frames)

2. Object Detection and Tracking

class ObjectTracker:
    def __init__(self, config: Dict[str, Any]):
        self.detector = YOLOV8(
            weights=config['detector_weights'],
            confidence=config['confidence_threshold']
        )
        self.tracker = DeepSORT(
            model_weights=config['tracker_weights'],
            max_age=config['max_age'],
            n_init=config['n_init']
        )
        self.feature_extractor = ResNet50(
            weights='imagenet',
            include_top=False
        )
        
    def track_objects(
        self,
        frame: np.ndarray,
        detections: List[Detection]
    ) -> List[Track]:
        # Extract appearance features
        crops = self._get_detection_crops(frame, detections)
        features = self.feature_extractor(crops)
        
        # Update tracker
        tracks = self.tracker.update(
            detections=detections,
            features=features
        )
        
        return self._post_process_tracks(tracks)
        
    def _get_detection_crops(
        self,
        frame: np.ndarray,
        detections: List[Detection]
    ) -> torch.Tensor:
        crops = []
        for det in detections:
            crop = self._crop_and_resize(
                frame,
                det.bbox,
                size=(224, 224)
            )
            crops.append(crop)
        return torch.stack(crops)

3. Instance Segmentation

class SegmentationModule:
    def __init__(self, config: Dict[str, Any]):
        self.model = MaskRCNN(
            backbone=config['backbone'],
            num_classes=config['num_classes'],
            min_confidence=config['min_confidence']
        )
        self.post_processor = SegmentationPostProcessor(
            score_threshold=config['score_threshold'],
            mask_threshold=config['mask_threshold']
        )
        
    @torch.no_grad()
    def segment_instances(
        self,
        image: torch.Tensor
    ) -> Dict[str, torch.Tensor]:
        # Run inference
        outputs = self.model(image)
        
        # Post-process results
        instances = self.post_processor(outputs)
        
        return {
            'masks': instances.pred_masks,
            'boxes': instances.pred_boxes,
            'scores': instances.scores,
            'labels': instances.pred_classes
        }
        
    def _apply_mask_refinement(
        self,
        masks: torch.Tensor,
        boxes: torch.Tensor
    ) -> torch.Tensor:
        return self.post_processor.refine_masks(masks, boxes)

4. Action Recognition

class ActionRecognizer:
    def __init__(self, config: Dict[str, Any]):
        self.model = SlowFast(
            num_classes=config['num_classes'],
            frame_length=config['frame_length']
        )
        self.temporal_pool = TemporalROIPool(
            output_size=config['roi_size']
        )
        
    def recognize_actions(
        self,
        video_clip: torch.Tensor,
        tracks: List[Track]
    ) -> List[Dict[str, Any]]:
        # Extract track-specific clips
        track_clips = self._extract_track_clips(
            video_clip,
            tracks
        )
        
        # Run action recognition
        features = self.model.extract_features(track_clips)
        actions = self.model.classify_actions(features)
        
        return self._associate_actions_with_tracks(
            actions,
            tracks
        )

5. Scene Understanding

class SceneAnalyzer:
    def __init__(self, config: Dict[str, Any]):
        self.scene_classifier = EfficientNet(
            model_name=config['model_name'],
            num_classes=config['num_scenes']
        )
        self.relationship_detector = SceneGraphGenerator(
            config['relationship_config']
        )
        
    def analyze_scene(
        self,
        frame: torch.Tensor,
        detections: List[Detection]
    ) -> Dict[str, Any]:
        # Classify scene
        scene_features = self.scene_classifier.extract_features(frame)
        scene_type = self.scene_classifier.classify(scene_features)
        
        # Generate scene graph
        scene_graph = self.relationship_detector(
            frame,
            detections,
            scene_type
        )
        
        return {
            'scene_type': scene_type,
            'scene_graph': scene_graph,
            'spatial_relationships': self._extract_spatial_relationships(
                scene_graph
            )
        }

Performance Optimization

class PerformanceOptimizer:
    def __init__(self):
        self.profiler = torch.profiler.profile(
            activities=[
                torch.profiler.ProfilerActivity.CPU,
                torch.profiler.ProfilerActivity.CUDA
            ]
        )
        
    def optimize_pipeline(
        self,
        pipeline: VideoProcessor,
        sample_batch: torch.Tensor
    ) -> Dict[str, float]:
        with self.profiler as prof:
            pipeline.process_batch(sample_batch)
            
        bottlenecks = self._identify_bottlenecks(prof)
        optimizations = self._suggest_optimizations(bottlenecks)
        
        return {
            'bottlenecks': bottlenecks,
            'optimizations': optimizations,
            'metrics': self._compute_performance_metrics(prof)
        }

Usage Example

# Initialize system
config = {
    'buffer_size': 30,
    'batch_size': 16,
    'input_shape': (3, 640, 640),
    'model_configs': {
        'detector': {
            'weights': 'yolov8x.pt',
            'confidence_threshold': 0.5
        },
        'tracker': {
            'weights': 'deepsort.pt',
            'max_age': 30
        },
        'segmentation': {
            'backbone': 'resnet101',
            'num_classes': 80
        }
    }
}

processor = VideoProcessor(config)

# Process video stream
async for results in processor.process_video_stream(video_stream):
    detections = results['detections']
    tracks = results['tracks']
    segments = results['segments']
    actions = results['actions']
    scene = results['scene']
    
    # Handle results
    await handle_analytics_results(results)

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