import numpy as np
from typing import List, Dict

class PseudoLabelPipeline:
    """
    多视角车内标注伪标签生成管道
    
    论文: Gorelik et al., 2026
    流程: 2D检测 → 3D姿态估计 → 跨视角追踪 → 3D包围盒提取
    """
    
    def __init__(self, camera_intrinsics: List[dict], extrinsics: List[np.ndarray]):
        """
        Args:
            camera_intrinsics: 各相机内参列表
            extrinsics: 各相机外参列表 (4x4 变换矩阵)
        """
        self.num_cameras = len(camera_intrinsics)
        self.intrinsics = camera_intrinsics
        self.extrinsics = extrinsics
        
    def process_frame(
        self, 
        rgb_images: List[np.ndarray],
        depth_images: List[np.ndarray],
        lidar_points: np.ndarray
    ) -> Dict:
        """
        处理一帧多视角数据
        
        Args:
            rgb_images: 4个相机的RGB图, shape=(4, H, W, 3)
            depth_images: 4个相机的深度图, shape=(4, H, W)
            lidar_points: LiDAR点云, shape=(N, 3)
        
        Returns:
            annotations: 标注结果
        """
        results = {}
        
        # Step 1: 2D检测 (每视角独立)
        detections_2d = []
        for i in range(self.num_cameras):
            dets = self._detect_2d(rgb_images[i])
            detections_2d.append(dets)
        
        # Step 2: 3D姿态估计 (每视角)
        poses_3d = []
        for i in range(self.num_cameras):
            pose = self._estimate_3d_pose(
                rgb_images[i], 
                depth_images[i],
                detections_2d[i]
            )
            poses_3d.append(pose)
        
        # Step 3: 跨视角追踪关联
        tracks = self._cross_view_track(detections_2d, poses_3d)
        
        # Step 4: 3D包围盒提取
        boxes_3d = self._extract_3d_bbox(poses_3d, tracks, lidar_points)
        
        results['detections_2d'] = detections_2d
        results['poses_3d'] = poses_3d
        results['tracks'] = tracks
        results['boxes_3d'] = boxes_3d
        
        return results
    
    def _detect_2d(self, rgb: np.ndarray) -> list:
        """2D人员检测 (模拟YOLO)"""
        # 实际使用YOLO检测
        return [{'bbox': [100, 200, 150, 400], 'score': 0.95}]
    
    def _estimate_3d_pose(self, rgb, depth, detections):
        """单视角3D姿态估计"""
        poses = []
        for det in detections:
            # 提取2D关键点 → 映射深度 → 3D坐标
            pose_3d = self._depth_to_3d(depth, det['bbox'])
            poses.append(pose_3d)
        return poses
    
    def _depth_to_3d(self, depth, bbox):
        """深度图 → 3D坐标"""
        h, w = depth.shape
        cx, cy = w // 2, h // 2
        fx = fy = 615.0  # RealSense D435i
        
        x1, y1, x2, y2 = bbox
        center_u = (x1 + x2) // 2
        center_v = (y1 + y2) // 2
        
        z = depth[center_v, center_x]
        x = (center_u - cx) * z / fx
        y = (center_v - cy) * z / fy
        
        return {'center_3d': [x, y, z], 'confidence': 0.9}
    
    def _cross_view_track(self, detections, poses):
        """跨视角追踪关联"""
        # 基于IoU和3D距离的关联
        tracks = []
        for i in range(len(detections)):
            for j in range(i+1, len(detections)):
                # 简化: 检查3D中心距离
                if poses[i] and poses[j]:
                    dist = np.linalg.norm(
                        np.array(poses[i][0]['center_3d']) -
                        np.array(poses[j][0]['center_3d'])
                    )
                    if dist < 500:  # 500mm内视为同一人
                        tracks.append({'view_a': i, 'view_b': j, 'dist': dist})
        return tracks
    
    def _extract_3d_bbox(self, poses, tracks, lidar):
        """从姿态和LiDAR提取3D包围盒"""
        boxes = []
        for pose_list in poses:
            for pose in pose_list:
                center = pose['center_3d']
                # 默认人体尺寸 (mm)
                w, h, l = 600, 1700, 400
                box = {
                    'center': center,
                    'dimensions': [w, h, l],
                    'orientation': [0, 0, 0]
                }
                boxes.append(box)
        return boxes


# 测试
if __name__ == "__main__":
    # 模拟4相机系统
    intrinsics = [{'fx': 615, 'fy': 615, 'cx': 320, 'cy': 240}] * 4
    extrinsics = [np.eye(4)] * 4  # 简化
    
    pipeline = PseudoLabelPipeline(intrinsics, extrinsics)
    
    # 模拟输入
    rgb = np.random.randint(0, 255, (4, 480, 640, 3), dtype=np.uint8)
    depth = np.random.randint(500, 2000, (4, 480, 640), dtype=np.uint16)
    lidar = np.random.randn(10000, 3)
    
    result = pipeline.process_frame(rgb, depth, lidar)
    print(f"检测到的3D包围盒: {len(result['boxes_3d'])}")