"""
路怒推理任务定义
=================
论文 Section 1: Task Definition

输入: 行车记录仪视频片段
输出: 文本格式的推理链
  1. 场景理解 (Scene Understanding)
  2. 事件识别 (Event Recognition)
  3. 路怒推理 (Road Rage Reasoning)

下游: 基于推理结果的对话式情绪安抚
"""

from dataclasses import dataclass
from typing import List

@dataclass
class RoadRageReasoningTask:
    """路怒推理任务定义"""
    
    # 输入
    video_input: str  # 行车记录仪视频路径
    
    # 输出结构
    scene_understanding: str    # 场景描述
    event_recognition: str      # 事件识别
    road_rage_reasoning: str    # 路怒推理
    intervention_suggestion: str  # 干预建议
    
    # 评估维度
    @staticmethod
    def evaluation_dimensions() -> dict:
        return {
            "scene_understanding": {
                "lane_count": "车道数识别",
                "ego_car": "自车定位",
                "critical_objects": "关键物体识别"
            },
            "event_recognition": {
                "road_rage_events": "路怒事件类型",
                "trigger_analysis": "触发分析"
            },
            "road_rage_reasoning": {
                "scenario_type": "场景分类",
                "causal_chain": "因果链推理",
                "anger_prediction": "愤怒预测"
            }
        }

# 示例
example = RoadRageReasoningTask(
    video_input="dashcam_001.mp4",
    scene_understanding="双向两车道城市道路，自车在右侧车道行驶，前方有一辆白色轿车突然变道切入",
    event_recognition="前车恶意加塞，未打转向灯，距离过近",
    road_rage_reasoning="前车无预警加塞属于典型的挑衅性驾驶行为，可能触发驾驶员愤怒情绪。建议触发等级：中等路怒风险",
    intervention_suggestion="检测到前方加塞事件，建议保持冷静，前方车辆行为非针对个人，保持安全距离即可"
)

print("路怒推理任务示例:")
print(f"  场景: {example.scene_understanding}")
print(f"  事件: {example.event_recognition}")
print(f"  推理: {example.road_rage_reasoning}")
print(f"  干预: {example.intervention_suggestion}")

"""
数据集标注结构
"""
dataset_structure = {
    "overall_labels": {
        "environment_descriptions": "环境描述（天气、道路类型、交通状况）",
        "road_rage_events": "路怒事件类型（加塞、抢行、尾随等）",
        "road_rage_scenarios": "路怒场景分类"
    },
    "frame_labels": {
        "lane_count": "车道数",
        "ego_car": "自车位置",
        "critical_objects": "关键物体（其他车辆、行人、障碍物）"
    },
    "annotation_count": 22226,
    "video_count": 8181,
    "frame_count": 22992
}

print(f"数据集规模:")
print(f"  视频: {dataset_structure['video_count']:,}")
print(f"  帧: {dataset_structure['frame_count']:,}")
print(f"  标注: {dataset_structure['annotation_count']:,}")

"""
VLM 评估结果分析
=================
论文 Section 3: Evaluation Results
"""

findings = {
    "scene_understanding": {
        "finding": "VLM 在视觉场景理解方面存在显著不足",
        "detail": "难以准确识别车道数、自车位置等关键空间信息",
        "impact": "这是路怒推理的基础步骤，错误传播影响后续推理"
    },
    "spatial_relationship": {
        "finding": "VLM 在文本模态的空间关系理解也有缺陷",
        "detail": "即使文本描述了空间关系，VLM 仍难以正确推理",
        "impact": "限制了基于文本提示的推理能力"
    },
    "event_recognition": {
        "finding": "VLM 能识别部分明显的路怒事件",
        "detail": "如明显的加塞、急刹车等",
        "impact": "但难以理解细微的挑衅行为"
    },
    "reasoning_chain": {
        "finding": "从事件到情绪的因果推理能力有限",
        "detail": "VLM 可以描述事件但难以推断愤怒触发机制",
        "impact": "限制了前置预防的实现"
    }
}

for key, val in findings.items():
    print(f"\n{'='*50}")
    print(f"维度: {key}")
    print(f"发现: {val['finding']}")
    print(f"细节: {val['detail']}")
    print(f"影响: {val['impact']}")

"""
评估指标体系
"""
metrics = {
    "scene_understanding": {
        "lane_count_accuracy": "车道数识别准确率",
        "ego_car_accuracy": "自车定位准确率", 
        "object_detection_f1": "关键物体F1分数"
    },
    "event_recognition": {
        "event_classification_f1": "事件分类F1",
        "trigger_identification_rate": "触发识别率"
    },
    "reasoning": {
        "scenario_accuracy": "场景分类准确率",
        "causal_chain_score": "因果链评分 (人工评估)",
        "anger_prediction_auc": "愤怒预测AUC"
    },
    "end_to_end": {
        "intervention_appropriateness": "干预适当性 (人工评估)",
        "response_latency_ms": "响应延迟 (ms)"
    }
}

for category, items in metrics.items():
    print(f"\n{category}:")
    for metric, desc in items.items():
        print(f"  - {metric}: {desc}")

"""
VLM 路怒推理系统框架
=====================
论文 Section 1 & 2: Task and Method

使用 GPT-4o / Qwen-VL 进行路怒推理

依赖:
  pip install openai torch transformers accelerate
"""

import json
from typing import Optional
from dataclasses import dataclass

@dataclass
class RoadRageReasoningConfig:
    """路怒推理系统配置"""
    vlm_model: str = "gpt-4o"  # 或 "qwen-vl-max"
    max_frames: int = 8  # 从视频中均匀采样帧数
    temperature: float = 0.3  # 低温度保证一致性
    intervention_enabled: bool = True

class RoadRageReasoningSystem:
    """
    VLM 路怒推理系统
    
    流程:
    1. 视频帧采样
    2. 场景理解 prompt
    3. 事件识别 prompt
    4. 路怒推理 prompt
    5. 干预建议生成
    """
    
    def __init__(self, config: RoadRageReasoningConfig):
        self.config = config
        
        # Prompt 模板 (基于论文任务定义)
        self.scene_prompt = """你是一个驾驶场景分析专家。请分析以下行车记录仪视频帧：
        
请回答:
1. 道路类型（城市/高速/乡村）
2. 车道数量
3. 自车所在车道位置
4. 周围关键车辆和物体
5. 天气和光照条件

请以JSON格式输出。"""
    
        self.event_prompt = """基于以下场景描述，识别可能的路怒触发事件：

路怒触发事件类型:
- 恶意加塞 (无转向灯突然切入)
- 抢行 (交叉口抢先通过)
- 尾随跟车 (距离过近)
- 急刹车 (无预警急刹)
- 远光灯照射 (恶意使用)
- 鸣笛挑衅 (恶意鸣笛)
- 手势侮辱 (不雅手势)
- 正常驾驶 (无触发事件)

场景描述: {scene_description}

请输出: 事件类型 + 触发分析 + 路怒风险等级 (低/中/高)"""
    
        self.reasoning_prompt = """作为路怒分析专家，基于以下信息进行推理:

场景: {scene_description}
事件: {event_description}

请推理:
1. 该事件为何可能触发路怒
2. 驾驶员可能的心理反应
3. 潜在的危险行为
4. 建议的干预措施

以自然语言输出推理链。"""

        self.intervention_prompt = """基于以下路怒推理结果，生成对驾驶员的安抚对话:

推理结果: {reasoning_result}

要求:
1. 语气友好、理解、不评判
2. 简短 (不超过3句话)
3. 针对具体触发事件
4. 引导安全驾驶行为

示例输出: "前方车辆加塞确实让人不快，但安全最重要。保持距离，我们慢慢来。"
"""
    
    def sample_frames(self, video_path: str) -> list:
        """从视频中均匀采样帧"""
        # 实际实现需要 cv2 或 ffmpeg
        # 这里给出框架
        frames = []
        # import cv2
        # cap = cv2.VideoCapture(video_path)
        # total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        # indices = np.linspace(0, total-1, self.config.max_frames, dtype=int)
        # for idx in indices:
        #     cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
        #     ret, frame = cap.read()
        #     if ret:
        #         frames.append(frame)
        # cap.release()
        return frames
    
    def run_scene_understanding(self, frames: list) -> str:
        """Step 1: 场景理解"""
        # 实际调用VLM API
        # response = openai.ChatCompletion.create(
        #     model=self.config.vlm_model,
        #     messages=[{
        #         "role": "user",
        #         "content": [
        #             {"type": "text", "text": self.scene_prompt},
        #             *[{"type": "image_url", "image_url": f} for f in frames]
        #         ]
        #     }],
        #     temperature=self.config.temperature
        # )
        # return response.choices[0].message.content
        return "模拟场景: 城市双向两车道，自车在右车道，前方白色轿车"
    
    def run_event_recognition(self, scene_desc: str) -> str:
        """Step 2: 事件识别"""
        prompt = self.event_prompt.format(scene_description=scene_desc)
        # 调用VLM
        return "事件: 恶意加塞 | 触发: 前车无转向灯突然切入 | 风险: 中等"
    
    def run_reasoning(self, scene_desc: str, event_desc: str) -> str:
        """Step 3: 路怒推理"""
        prompt = self.reasoning_prompt.format(
            scene_description=scene_desc,
            event_description=event_desc
        )
        # 调用VLM/LLM
        return ("推理: 前车无预警加塞属于挑衅性驾驶行为，"
                "可能触发驾驶员愤怒。潜在危险: 追尾、报复性驾驶。"
                "建议: 保持冷静，增大跟车距离。")
    
    def generate_intervention(self, reasoning: str) -> str:
        """Step 4: 干预建议"""
        if not self.config.intervention_enabled:
            return ""
        prompt = self.intervention_prompt.format(reasoning_result=reasoning)
        # 调用LLM
        return "前方车辆加塞确实让人不快，但安全最重要。保持距离，我们慢慢来。"
    
    def process_video(self, video_path: str) -> dict:
        """完整处理管道"""
        # 1. 帧采样
        frames = self.sample_frames(video_path)
        
        # 2. 场景理解
        scene = self.run_scene_understanding(frames)
        
        # 3. 事件识别
        event = self.run_event_recognition(scene)
        
        # 4. 路怒推理
        reasoning = self.run_reasoning(scene, event)
        
        # 5. 干预
        intervention = self.generate_intervention(reasoning)
        
        return {
            "video": video_path,
            "scene": scene,
            "event": event,
            "reasoning": reasoning,
            "intervention": intervention
        }


# 测试
if __name__ == "__main__":
    config = RoadRageReasoningConfig(
        vlm_model="gpt-4o",
        max_frames=8,
        intervention_enabled=True
    )
    
    system = RoadRageReasoningSystem(config)
    result = system.process_video("dashcam_sample.mp4")
    
    print("=" * 60)
    print("VLM 路怒推理结果")
    print("=" * 60)
    for key, val in result.items():
        print(f"\n[{key.upper()}]")
        print(f"  {val}")

"""
VLM 路怒推理在IMS部署的技术挑战
"""
challenges = {
    "计算需求": {
        "问题": "VLM 推理需要大量计算资源",
        "方案": "Qwen-VL 7B INT4量化 + Qualcomm QNN加速",
        "目标": "推理延迟 <2s"
    },
    "实时性": {
        "问题": "多步推理链延迟较高",
        "方案": "场景理解+事件识别合并为单步",
        "目标": "总延迟 <5s (含干预生成)"
    },
    "准确率": {
        "问题": "当前VLM场景理解能力不足",
        "方案": "领域微调 + 驾驶场景专用数据集",
        "目标": "场景理解准确率 >85%"
    },
    "安全边界": {
        "问题": "VLM可能产生幻觉",
        "方案": "规则+VLM混合: 规则保底，VLM增强",
        "目标": "误报率 <5%, 漏报率 <10%"
    },
    "用户接受度": {
        "问题": "驾驶员可能不愿被"教育"",
        "方案": "自然对话式，非说教语气",
        "目标": "用户满意度 >80%"
    }
}

for challenge, detail in challenges.items():
    print(f"\n挑战: {challenge}")
    print(f"  问题: {detail['问题']}")
    print(f"  方案: {detail['方案']}")
    print(f"  目标: {detail['目标']}")