"""
四种视线估计模型的统一推理接口

适用场景: DMS 开发选型评估
"""

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

class GazeModelComparison:
    """四种视线模型统一对比接口"""
    
    def __init__(self, device='cuda'):
        self.device = device
        self.models = {}
        
    def load_gazelle(self, model_path):
        """加载 Gaze-LLE"""
        # from gazelle.model import GazeLLE
        # self.models['gazelle'] = GazeLLE.load(model_path).to(self.device)
        pass
        
    def load_l2cs(self, model_path):
        """加载 L2CS-Net"""
        # from l2cs.model import L2CSNet
        # self.models['l2cs'] = L2CSNet(arch='ResNet50').to(self.device)
        pass
        
    def load_gaze3d(self, model_path):
        """加载 Gaze3D"""
        # from gaze3d.model import Gaze3DModel
        # self.models['gaze3d'] = Gaze3DModel.load(model_path).to(self.device)
        pass
        
    def load_gazeanywhere(self, model_path):
        """加载 GazeAnywhere"""
        # from gazeanywhere.model import GazeAnywhereModel
        # self.models['gazeanywhere'] = GazeAnywhereModel.load(model_path).to(self.device)
        pass
    
    def benchmark(self, image, head_boxes, num_runs=100):
        """基准测试"""
        import time
        
        results = {}
        
        for name, model in self.models.items():
            times = []
            for _ in range(num_runs):
                torch.cuda.synchronize()
                start = time.perf_counter()
                
                if name == 'gazelle':
                    output = model(image, head_boxes)
                elif name == 'l2cs':
                    crops = [self._crop_face(image, box) for box in head_boxes]
                    output = model(crops)
                elif name == 'gaze3d':
                    crops = [self._crop_face(image, box) for box in head_boxes]
                    output = model(crops, image)
                elif name == 'gazeanywhere':
                    output = model(image, "driver")
                
                torch.cuda.synchronize()
                times.append((time.perf_counter() - start) * 1000)
            
            results[name] = {
                'mean_ms': np.mean(times),
                'p99_ms': np.percentile(times, 99),
                'fps': 1000 / np.mean(times),
            }
            
        return results
    
    def _crop_face(self, image, box):
        """裁剪人脸区域"""
        x1, y1, x2, y2 = map(int, box)
        return cv2.resize(image[y1:y2, x1:x2], (224, 224))


# ============================================
# DMS 选型决策矩阵
# ============================================

def dms_model_selection_guide():
    """DMS 视线模型选型决策指南"""
    
    scenarios = {
        '高速实时DMS（<20ms）': {
            'recommend': 'L2CS-Net',
            'reason': '25M参数，ResNet-50骨干，推理<10ms',
            'tradeoff': '仅单人，需额外映射视线落点',
        },
        '多乘员全舱监测': {
            'recommend': 'GazeAnywhere / Gaze-LLE',
            'reason': '原生多人物支持，无需循环',
            'tradeoff': '推理延迟较高，需优化',
        },
        '视线落点直接输出': {
            'recommend': 'Gaze-LLE / GazeAnywhere',
            'reason': '直接输出热图，无需角度映射',
            'tradeoff': '热图分辨率有限(64×64)',
        },
        '车规NPU部署': {
            'recommend': 'L2CS-Net / Gaze3D',
            'reason': 'ResNet骨干量化友好，INT8后<5MB',
            'tradeoff': '功能有限，需扩展',
        },
        '研发验证+最高精度': {
            'recommend': 'GazeAnywhere',
            'reason': 'CVPR 2026 SOTA，提示驱动灵活',
            'tradeoff': '模型大，需大幅压缩才能车规部署',
        },
        '与EyeCue认知分心融合': {
            'recommend': 'GazeAnywhere → EyeCue',
            'reason': 'GazeAnywhere提供凝视坐标→EyeCue分类',
            'tradeoff': '两阶段管线，延迟需评估',
        },
    }
    
    for scenario, info in scenarios.items():
        print(f"\n场景: {scenario}")
        print(f"  推荐: {info['recommend']}")
        print(f"  原因: {info['reason']}")
        print(f"  权衡: {info['tradeoff']}")


if __name__ == "__main__":
    dms_model_selection_guide()