AI与电影预告片：机器学习生成的营销内容

# Python: AI预告片素材分析与剪辑引擎
import cv2
import numpy as np
from typing import List, Dict, Tuple
import json
from moviepy.editor import VideoFileClip, concatenate_videoclips, AudioFileClip
from transformers import pipeline

class TrailerAIEngine:
    def __init__(self):
        self.scene_classifier = pipeline("image-classification", model="microsoft/resnet-50")
        self.emotion_analyzer = pipeline("image-classification", model="trpakov/vit-face-expression")
        self.audio_analyzer = None
        self.scenes = []
        self.metadata = {}
    
    def analyze_film_material(self, film_path: str) -> Dict:
        """分析电影素材，提取元数据"""
        clip = VideoFileClip(film_path)
        duration = clip.duration
        fps = clip.fps
        
        # 逐帧分析（简化为每隔1秒采样）
        scenes = []
        for t in np.arange(0, duration, 1.0):
            frame = clip.get_frame(t)
            
            # 场景分类
            scene_type = self._classify_scene(frame)
            
            # 情感分析
            emotion = self._analyze_emotion(frame)
            
            # 动作检测
            motion_level = self._detect_motion(clip, t)
            
            # 音频分析
            audio_level = self._get_audio_level(clip, t)
            
            scenes.append({
                'timestamp': t,
                'scene_type': scene_type,
                'emotion': emotion,
                'motion_level': motion_level,
                'audio_level': audio_level
            })
        
        self.scenes = scenes
        self.metadata = {
            'duration': duration,
            'fps': fps,
            'total_frames': int(duration * fps),
            'scene_count': len(scenes)
        }
        
        clip.close()
        return self.metadata
    
    def _classify_scene(self, frame: np.ndarray) -> str:
        """分类场景类型"""
        # 简化分类
        brightness = np.mean(frame)
        if brightness < 50:
            return 'night'
        elif brightness > 200:
            return 'daylight'
        else:
            return 'interior'
    
    def _analyze_emotion(self, frame: np.ndarray) -> Dict:
        """分析画面情感"""
        # 简化情感分析
        return {
            'valence': np.random.uniform(0, 1),  # 0负面-1正面
            'arousal': np.random.uniform(0, 1)   # 0平静-1激动
        }
    
    def _detect_motion(self, clip: VideoFileClip, timestamp: float) -> float:
        """检测动作强度"""
        if timestamp < 1:
            return 0
        try:
            frame1 = clip.get_frame(timestamp - 0.5)
            frame2 = clip.get_frame(timestamp)
            diff = np.mean(np.abs(frame1.astype(float) - frame2.astype(float)))
            return min(diff / 255, 1.0)
        except:
            return 0
    
    def _get_audio_level(self, clip: VideoFileClip, timestamp: float) -> float:
        """获取音频音量"""
        try:
            audio = clip.audio.subclip(timestamp, min(timestamp + 0.5, clip.duration))
            if audio is not None:
                samples = audio.to_soundarray(fps=22050)
                return float(np.mean(np.abs(samples)))
            return 0
        except:
            return 0
    
    def generate_trailer(self, target_duration: int = 120) -> List[Dict]:
        """生成预告片剪辑方案"""
        # 提取关键场景
        key_scenes = self._extract_key_scenes()
        
        # 构建三幕结构
        first_act = key_scenes[:len(key_scenes)//3]
        second_act = key_scenes[len(key_scenes)//3:2*len(key_scenes)//3]
        third_act = key_scenes[2*len(key_scenes)//3:]
        
        # 安排时间线
        timeline = []
        first_act_end = target_duration * 0.25
        second_act_end = target_duration * 0.7
        third_act_end = target_duration
        
        timeline.extend(self._arrange_scenes(first_act, 0, first_act_end, 'slow'))
        timeline.extend(self._arrange_scenes(second_act, first_act_end, second_act_end, 'medium'))
        timeline.extend(self._arrange_scenes(third_act, second_act_end, third_act_end, 'fast'))
        
        return timeline
    
    def _extract_key_scenes(self) -> List[Dict]:
        """提取关键场景"""
        # 按情感强度排序
        scored_scenes = []
        for scene in self.scenes:
            score = (
                scene['emotion']['arousal'] * 0.4 +
                scene['motion_level'] * 0.3 +
                scene['audio_level'] * 0.3
            )
            scored_scenes.append((score, scene))
        
        scored_scenes.sort(key=lambda x: x[0], reverse=True)
        return [s[1] for s in scored_scenes[:30]]
    
    def _arrange_scenes(self, scenes: List[Dict], start_time: float, 
                        end_time: float, pace: str) -> List[Dict]:
        """安排场景时间线"""
        available_time = end_time - start_time
        scene_duration = available_time / len(scenes) if scenes else 0
        
        # 节奏调整
        pace_multiplier = {'slow': 0.8, 'medium': 1.0, 'fast': 1.3}
        actual_duration = scene_duration * pace_multiplier.get(pace, 1.0)
        
        timeline = []
        current_time = start_time
        
        for scene in scenes:
            timeline.append({
                'timestamp': scene['timestamp'],
                'duration': min(actual_duration, 8),
                'start_time': current_time,
                'type': scene['scene_type'],
                'emotion': scene['emotion']
            })
            current_time += actual_duration
        
        return timeline
    
    def export_edit_decision_list(self, timeline: List[Dict], output_path: str):
        """导出剪辑决策列表（EDL）"""
        edl = {
            'version': '1.0',
            'generated_by': 'TrailerAIEngine',
            'total_duration': sum(t['duration'] for t in timeline),
            'clips': []
        }
        
        for i, clip_info in enumerate(timeline):
            edl['clips'].append({
                'clip_number': i + 1,
                'source_timestamp': clip_info['timestamp'],
                'duration': clip_info['duration'],
                'output_start': clip_info['start_time'],
                'transition': 'cut' if i == 0 else 'crossfade'
            })
        
        with open(output_path, 'w') as f:
            json.dump(edl, f, indent=2)
        
        return output_path

// JavaScript: AI预告片配乐生成器
const axios = require('axios');
const fs = require('fs');

class TrailerMusicGenerator {
  constructor(apiKey) {
    this.apiKey = apiKey;
    this.musicGenEndpoint = 'https://api.musicgen.ai/v1/generate';
  }

  async analyzeEmotionCurve(visualTimeline) {
    const emotionCurve = [];
    let currentTime = 0;
    
    for (const clip of visualTimeline) {
      const emotion = clip.emotion;
      emotionCurve.push({
        time: currentTime,
        valence: emotion.valence,
        arousal: emotion.arousal,
        description: this._emotionToDescription(emotion)
      });
      currentTime += clip.duration;
    }
    
    return emotionCurve;
  }

  _emotionToDescription(emotion) {
    const valence = emotion.valence;
    const arousal = emotion.arousal;
    
    if (valence > 0.7 && arousal > 0.7) return '激动、欢乐、振奋';
    if (valence > 0.7 && arousal <= 0.7) return '平静、温馨、感动';
    if (valence <= 0.7 && arousal > 0.7) return '紧张、恐惧、激烈';
    if (valence <= 0.7 && arousal <= 0.7) return '悲伤、沉重、压抑';
    return '中性、平静';
  }

  async generateMusic(emotionCurve, duration, style = 'cinematic') {
    const prompt = this._buildPrompt(emotionCurve, style);
    
    const response = await axios.post(this.musicGenEndpoint, {
      prompt: prompt,
      duration: duration,
      temperature: 0.8,
      top_k: 250,
      top_p: 0.95,
      format: 'mp3'
    }, {
      headers: {
        'Authorization': `Bearer ${this.apiKey}`,
        'Content-Type': 'application/json'
      }
    });
    
    return {
      audioUrl: response.data.audio_url,
      prompt: prompt,
      duration: duration,
      generatedAt: new Date().toISOString()
    };
  }

  _buildPrompt(emotionCurve, style) {
    // 从情感曲线生成音乐提示
    const segments = emotionCurve.map((point, i) => {
      return `第${i + 1}段: ${point.description}, 时长${i + 1 === emotionCurve.length ? '剩余' : ''}部分`;
    });
    
    return `生成一段${style}风格的预告片配乐，总时长${emotionCurve.reduce((sum, p) => sum + 1, 0)}秒。情感变化: ${segments.join('; ')}。需要配合画面切换的节奏卡点。`;
  }

  async syncToEditPoints(audioUrl, editPoints) {
    // 分析音频节拍
    const beatAnalysis = await this.analyzeBeats(audioUrl);
    
    // 将编辑点对齐到节拍
    const syncedEdits = editPoints.map(edit => {
      const closestBeat = beatAnalysis.beats.reduce((prev, curr) => {
        return Math.abs(curr - edit.time) < Math.abs(prev - edit.time) ? curr : prev;
      });
      
      return {
        ...edit,
        originalTime: edit.time,
        syncedTime: closestBeat,
        timeOffset: closestBeat - edit.time
      };
    });
    
    return syncedEdits;
  }

  async analyzeBeats(audioUrl) {
    // 简化节拍分析
    return {
      bpm: 120,
      beats: Array.from({ length: 60 }, (_, i) => i * 0.5), // 假设120BPM
      totalBeats: 60
    };
  }

  async generateMultipleVersions(emotionCurve, baseDuration, count = 3) {
    const styles = ['cinematic', 'electronic', 'orchestral', 'minimalist'];
    const versions = [];
    
    for (let i = 0; i < count; i++) {
      const style = styles[i % styles.length];
      const version = await this.generateMusic(emotionCurve, baseDuration, style);
      versions.push(version);
    }
    
    return versions;
  }
}

module.exports = { TrailerMusicGenerator };