Advanced Computer Vision

// Real-time Multi-Object Detection en Tracking Pipeline
import cv2
import torch
import numpy as np
from typing import List, Dict, Tuple
from collections import defaultdict

class AdvancedVisionPipeline:
    def __init__(self, config: Dict):
        # Initialize detection models
        self.detector = self.load_detection_model(config['detector'])
        self.tracker = DeepSORTTracker(config['tracker'])
        self.pose_estimator = PoseEstimationModel(config['pose'])
        self.segmentation_model = SegmentationModel(config['segmentation'])

        # Performance optimization
        self.tensorrt_engine = self.build_tensorrt_engine()
        self.cuda_stream = torch.cuda.Stream()

        # Temporal consistency
        self.temporal_buffer = TemporalBuffer(size=5)
        self.kalman_filters = defaultdict(lambda: ExtendedKalmanFilter())

    def process_frame(self, frame: np.ndarray, timestamp: float) -> Dict:
        with torch.cuda.stream(self.cuda_stream):
            # Preprocessing met GPU acceleratie
            preprocessed = self.preprocess_frame(frame)

            # Parallelle inferentie voor multiple tasks
            detection_results = self.detect_objects(preprocessed)
            segmentation_mask = self.segment_frame(preprocessed)

            # Post-processing en tracking
            tracked_objects = self.tracker.update(detection_results, timestamp)

            # 3D pose estimation voor gedetecteerde objecten
            poses_3d = []
            for obj in tracked_objects:
                if obj['confidence'] > self.pose_threshold:
                    pose = self.estimate_3d_pose(frame, obj['bbox'], obj['class'])
                    poses_3d.append({
                        'track_id': obj['track_id'],
                        'pose_6dof': pose,
                        'uncertainty': self.calculate_pose_uncertainty(pose)
                    })

            # Temporal consistency en filtering
            consistent_results = self.apply_temporal_filtering(
                tracked_objects, poses_3d, timestamp
            )

            return {
                'objects': consistent_results,
                'segmentation': segmentation_mask,
                'poses_3d': poses_3d,
                'processing_time': self.get_processing_time(),
                'frame_id': self.frame_counter
            }

class DeepSORTTracker:
    def __init__(self, config: Dict):
        self.max_age = config.get('max_age', 30)
        self.min_hits = config.get('min_hits', 3)
        self.iou_threshold = config.get('iou_threshold', 0.3)

        # Deep appearance descriptor
        self.feature_extractor = self.load_reid_model()
        self.tracks = []
        self.track_id_counter = 0

    def update(self, detections: List[Dict], timestamp: float) -> List[Dict]:
        # Prediction step voor alle actieve tracks
        for track in self.tracks:
            track.predict(timestamp)

        # Data association met Hungarian algorithm
        matched_tracks, unmatched_dets, unmatched_tracks = self.associate_detections(
            detections, self.tracks
        )

        # Update matched tracks
        for track_idx, det_idx in matched_tracks:
            self.tracks[track_idx].update(detections[det_idx], timestamp)

        # Initialize nieuwe tracks voor unmatched detections
        for det_idx in unmatched_dets:
            if detections[det_idx]['confidence'] > self.confidence_threshold:
                new_track = Track(
                    detection=detections[det_idx],
                    track_id=self.track_id_counter,
                    timestamp=timestamp
                )
                self.tracks.append(new_track)
                self.track_id_counter += 1

        # Remove tracks die te lang niet geupdate zijn
        self.tracks = [track for track in self.tracks
                      if track.time_since_update < self.max_age]

        # Return confirmed tracks
        return [track.to_dict() for track in self.tracks
                if track.hits >= self.min_hits]

    def associate_detections(self, detections, tracks):
        # Bereken cost matrix (IoU + appearance features)
        cost_matrix = np.zeros((len(tracks), len(detections)))

        for t, track in enumerate(tracks):
            for d, detection in enumerate(detections):
                # IoU distance
                iou_cost = 1 - self.calculate_iou(track.bbox, detection['bbox'])

                # Appearance feature distance
                feature_cost = self.calculate_feature_distance(
                    track.features, detection['features']
                )

                # Combined cost
                cost_matrix[t, d] = 0.7 * iou_cost + 0.3 * feature_cost

        # Hungarian algorithm voor optimale assignment
        matched_indices = self.solve_assignment(cost_matrix)

        # Filter matches met te hoge cost
        matched_tracks = []
        unmatched_detections = list(range(len(detections)))
        unmatched_tracks = list(range(len(tracks)))

        for track_idx, det_idx in matched_indices:
            if cost_matrix[track_idx, det_idx] <= self.iou_threshold:
                matched_tracks.append((track_idx, det_idx))
                unmatched_detections.remove(det_idx)
                unmatched_tracks.remove(track_idx)

        return matched_tracks, unmatched_detections, unmatched_tracks

# Stereo Vision Depth Estimation
class StereoVisionProcessor:
    def __init__(self, camera_params: Dict):
        self.left_camera = camera_params['left']
        self.right_camera = camera_params['right']
        self.baseline = camera_params['baseline']  # in meters

        # Stereo matching algorithm
        self.stereo_matcher = cv2.StereoSGBM_create(
            minDisparity=0,
            numDisparities=64,
            blockSize=11,
            P1=8 * 3 * 11**2,
            P2=32 * 3 * 11**2,
            disp12MaxDiff=1,
            uniquenessRatio=15,
            speckleWindowSize=100,
            speckleRange=32
        )

    def compute_depth_map(self, left_frame: np.ndarray,
                         right_frame: np.ndarray) -> np.ndarray:
        # Rectificatie van stereo images
        left_rect, right_rect = self.rectify_stereo_pair(left_frame, right_frame)

        # Dispariteit berekening
        disparity = self.stereo_matcher.compute(left_rect, right_rect)
        disparity = disparity.astype(np.float32) / 16.0

        # Conversie naar depth map
        focal_length = self.left_camera['focal_length']
        depth_map = (focal_length * self.baseline) / (disparity + 1e-6)

        # Post-processing filters
        depth_map = cv2.medianBlur(depth_map.astype(np.uint16), 5)
        depth_map = self.apply_temporal_filtering(depth_map)

        return depth_map

    def get_3d_coordinates(self, pixel_coords: Tuple[int, int],
                          depth_map: np.ndarray) -> Tuple[float, float, float]:
        u, v = pixel_coords
        depth = depth_map[v, u]

        if depth > 0:
            # Back-project naar 3D coordinates
            cx, cy = self.left_camera['principal_point']
            fx, fy = self.left_camera['focal_length']

            x = (u - cx) * depth / fx
            y = (v - cy) * depth / fy
            z = depth

            return (x, y, z)
        else:
            return None