AI智能拍照×AI智能助手：2026年4月十大深度技术演进与面试考点全解析

import numpy as np
from typing import List, Tuple

class MultiFrameFusion:
    """多帧图像融合——AI智能拍照的核心算法"""
    
    def __init__(self, num_frames: int = 8):
        self.num_frames = num_frames   连续拍摄帧数
        
    def capture_burst(self) -> List[np.ndarray]:
        """模拟连续捕获多帧RAW数据"""
        frames = []
        for i in range(self.num_frames):
             实际场景中每帧曝光参数略有差异
            raw_frame = self._simulate_capture(exposure_bias=i  0.1)
            frames.append(raw_frame)
        return frames
    
    def align_frames(self, frames: List[np.ndarray]) -> List[np.ndarray]:
        """帧对齐：消除手抖和运动物体位移"""
         关键步骤1：选取参考帧（通常选中间帧）
        ref_frame = frames[len(frames)//2]
        aligned = []
        for frame in frames:
             实际使用光流或特征匹配算法
            displacement = self._calculate_displacement(ref_frame, frame)
            aligned_frame = self._apply_transform(frame, displacement)
            aligned.append(aligned_frame)
        return aligned
    
    def semantic_segmentation(self, frame: np.ndarray) -> np.ndarray:
        """语义分割：区分天空、人物、建筑等区域"""
         关键步骤2：用深度学习模型分割场景
         2026年主流方案：端侧CNN/Transformer混合模型
        return self._model_inference(frame)   返回像素级标签
    
    def fuse_with_weights(self, aligned_frames: List[np.ndarray]) -> np.ndarray:
        """带权重的帧融合——AI智能拍照的核心"""
        seg_map = self.semantic_segmentation(aligned_frames[0])
        result = np.zeros_like(aligned_frames[0], dtype=np.float32)
        
        for y in range(result.shape[0]):
            for x in range(result.shape[1]):
                region_type = seg_map[y, x]   0:天空 1:人物 2:建筑 3:暗部
                 不同区域采用不同的融合权重策略
                if region_type == 0:   天空：选曝光适中的帧
                    weights = self._sky_weights(aligned_frames, y, x)
                elif region_type == 1:   人物：优先选清晰且肤色自然的帧
                    weights = self._portrait_weights(aligned_frames, y, x)
                elif region_type == 3:   暗部：优先选高曝光的帧
                    weights = self._shadow_weights(aligned_frames, y, x)
                else:
                    weights = np.ones(len(aligned_frames)) / len(aligned_frames)
                 加权融合
                for i, frame in enumerate(aligned_frames):
                    result[y, x] += frame[y, x]  weights[i]
        return np.clip(result, 0, 255).astype(np.uint8)
    
    def process(self) -> np.ndarray:
        """完整AI拍照处理流程"""
         1. 多帧连续拍摄（~0.5秒完成）
        raw_frames = self.capture_burst()
         2. 帧对齐（消除手抖）
        aligned = self.align_frames(raw_frames)
         3. 语义分割（理解场景）
         4. 加权融合（生成最优解）
        result = self.fuse_with_weights(aligned)
         5. 后续还有AI降噪、HDR色调映射、超分等...
        return result

from typing import Dict, List, Optional
import json

class AIAssistantAgent:
    """AI智能助手——基于ReAct框架的Agent实现"""
    
    def __init__(self, llm_client):
        self.llm = llm_client   大语言模型作为“大脑”
        self.memory = []   对话记忆
        self.tools = {
            "search": self._search_web,
            "calculate": self._calculate,
            "book_flight": self._book_flight_api,
            "get_weather": self._get_weather_api
        }
    
    def think_and_act(self, user_input: str) -> str:
        """
        ReAct框架：Reasoning + Acting 交替执行
        核心循环：思考 → 行动 → 观察 → 思考 → ...
        """
        self.memory.append({"role": "user", "content": user_input})
        max_iterations = 5
        iteration = 0
        
        while iteration < max_iterations:
             思考阶段：生成推理链
            reasoning_prompt = self._build_reasoning_prompt()
            thought = self.llm.generate(reasoning_prompt)
            
             检查是否需要采取行动
            action_needed, action_name, action_params = self._parse_action(thought)
            
            if not action_needed:
                 直接生成最终回答
                final_answer = self.llm.generate(
                    self._build_final_prompt()
                )
                return final_answer
            
             行动阶段：调用工具
            action_result = self._execute_action(action_name, action_params)
            
             观察阶段：将结果加入上下文
            self.memory.append({
                "role": "tool", 
                "content": f"{action_name}返回: {action_result}"
            })
            
            iteration += 1
        
        return "抱歉，任务过于复杂，请尝试简化需求。"
    
    def _execute_action(self, action_name: str, params: Dict) -> str:
        """工具调用：Agent的“手脚”"""
        if action_name in self.tools:
            return self.tools[action_name](params)
        return f"未知操作: {action_name}"