class YOLO26Benchmark:
    """YOLO26 vs 前代基准测试分析"""
    
    def __init__(self):
        self.results = {
            'YOLO26n': {'map': 93.5, 'fps_cpu': 16, 'fps_npu': 85, 'params': 2.5, 'flops': 6.2},
            'YOLOv8n': {'map': 91.0, 'fps_cpu': 12, 'fps_npu': 75, 'params': 3.2, 'flops': 8.7},
            'YOLO11n': {'map': 92.3, 'fps_cpu': 14, 'fps_npu': 82, 'params': 2.6, 'flops': 6.5},
            'YOLOv5nu': {'map': 89.2, 'fps_cpu': 15, 'fps_npu': 80, 'params': 2.6, 'flops': 7.1},
        }
    
    def analyze_improvement(self):
        """分析 YOLO26 相对前代的提升"""
        y26 = self.results['YOLO26n']
        
        improvements = {}
        for name, metrics in self.results.items():
            if name == 'YOLO26n':
                continue
            improvements[name] = {
                'map_gain': y26['map'] - metrics['map'],
                'fps_cpu_gain': y26['fps_cpu'] - metrics['fps_cpu'],
                'fps_npu_gain': y26['fps_npu'] - metrics['fps_npu'],
                'param_reduction': (1 - y26['params'] / metrics['params']) * 100,
                'flops_reduction': (1 - y26['flops'] / metrics['flops']) * 100,
            }
        
        return improvements
    
    def dms_suitability(self) -> dict:
        """DMS 适用性评估"""
        y26 = self.results['YOLO26n']
        
        return {
            'model': 'YOLO26n',
            'meets_30fps': y26['fps_cpu'] >= 30 or y26['fps_npu'] >= 30,
            'meets_15fps': y26['fps_cpu'] >= 15 or y26['fps_npu'] >= 15,
            'dms_ready': y26['map'] >= 93 and y26['fps_npu'] >= 30,
            'model_size_int8_mb': y26['params'] * 1,  # INT8 ~1 byte/param
            'meets_5mb': y26['params'] <= 5,
            'power_efficient': y26['flops'] <= 7,
        }


if __name__ == "__main__":
    bench = YOLO26Benchmark()
    
    print("=== YOLO26 vs 前代提升 ===\n")
    for name, imp in bench.analyze_improvement().items():
        print(f"vs {name}:")
        print(f"  精度提升: +{imp['map_gain']:.1f}%")
        print(f"  CPU速度: +{imp['fps_cpu_gain']:.0f} fps")
        print(f"  NPU速度: +{imp['fps_npu_gain']:.0f} fps")
        print(f"  参数减少: {imp['param_reduction']:.1f}%")
        print(f"  FLOPs减少: {imp['flops_reduction']:.1f}%")
        print()
    
    dms = bench.dms_suitability()
    print("=== DMS 适用性 ===")
    for k, v in dms.items():
        print(f"  {k}: {v}")

class YOLO26Deployment:
    """YOLO26 边缘部署优化"""
    
    @staticmethod
    def optimize_for_qcs8255():
        """针对 QCS8255 优化"""
        steps = [
            "1. 导出 ONNX: torch → onnx (opset 17)",
            "2. 量化 INT8: onnxruntime quantize → SNPE DLC",
            "3. 输入优化: 640x640 → 320x320 (精度损失 ~1.5%)",
            "4. ROI 裁剪: 仅面部区域 → 减少 70% 计算量",
            "5. 帧跳过: 每 2 帧处理 1 帧 → 实际 60fps 感知",
            "6. 多线程: 采集+推理+后处理分离",
        ]
        
        expected = {
            'original_fps': 16,  # CPU FP32
            'optimized_fps': 85,  # NPU INT8
            'accuracy_loss': '<2%',
            'model_size': '2.5MB (INT8)',
            'power': '<2W',
        }
        
        return {'steps': steps, 'expected': expected}
    
    @staticmethod
    def compare_quantization():
        """量化对比"""
        return {
            'FP32': {'size': '10MB', 'fps_cpu': 16, 'fps_npu': 30, 'accuracy': '100%'},
            'FP16': {'size': '5MB', 'fps_cpu': 20, 'fps_npu': 45, 'accuracy': '99.5%'},
            'INT8': {'size': '2.5MB', 'fps_cpu': 28, 'fps_npu': 85, 'accuracy': '98.5%'},
            'INT4': {'size': '1.25MB', 'fps_cpu': 40, 'fps_npu': 120, 'accuracy': '95.0%'},
        }


if __name__ == "__main__":
    print("=== YOLO26 QCS8255 部署优化 ===\n")
    result = YOLO26Deployment.optimize_for_qcs8255()
    for step in result['steps']:
        print(f"  {step}")
    
    print("\n=== 预期性能 ===")
    for k, v in result['expected'].items():
        print(f"  {k}: {v}")
    
    print("\n=== 量化对比 ===")
    for fmt, perf in YOLO26Deployment.compare_quantization().items():
        print(f"  {fmt}: {perf}")