AI 视频编辑智能体：自动化后期制作

原始素材输入
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┌────────────────────────────────────────┐
│           AI 视频编辑智能体              │
│                                        │
│  1. 素材分析                            │
│     ├── 画面质量评估                     │
│     ├── 内容理解                         │
│     └── 场景检测                         │
│                                        │
│  2. 智能剪辑                            │
│     ├── 粗剪（去废片）                    │
│     ├── 精剪（节奏匹配）                  │
│     └── 转场选择                         │
│                                        │
│  3. 音频处理                            │
│     ├── 降噪                             │
│     ├── 音量平衡                         │
│     ├── BGM 匹配                        │
│     └── 音效添加                         │
│                                        │
│  4. 视觉处理                            │
│     ├── 智能调色                         │
│     ├── 画面稳定                         │
│     └── 特效添加                         │
│                                        │
│  5. 文字处理                            │
│     ├── 语音识别 → 字幕                  │
│     ├── 多语言翻译                       │
│     └── 标题/片尾生成                    │
│                                        │
│  6. 输出                               │
│     ├── 多分辨率导出                     │
│     └── 多平台格式适配                    │
└────────────────────────────────────────┘
    ↓
成品视频

import cv2
import numpy as np

class SceneDetector:
    """自动场景检测"""
    
    def __init__(self, threshold=30.0):
        self.threshold = threshold
    
    def detect(self, video_path):
        """检测场景切换点"""
        cap = cv2.VideoCapture(video_path)
        fps = cap.get(cv2.CAP_PROP_FPS)
        
        scenes = []
        prev_frame = None
        frame_idx = 0
        
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            
            # 计算帧间差异
            if prev_frame is not None:
                diff = self._frame_diff(prev_frame, frame)
                if diff > self.threshold:
                    scenes.append({
                        "frame": frame_idx,
                        "timestamp": frame_idx / fps,
                        "diff_score": diff
                    })
            
            prev_frame = frame
            frame_idx += 1
        
        cap.release()
        return scenes
    
    def _frame_diff(self, f1, f2):
        """计算帧间差异"""
        # 使用直方图比较
        h1 = cv2.calcHist([f1], [0,1,2], None, [8,8,8], [0,256,0,256,0,256])
        h2 = cv2.calcHist([f2], [0,1,2], None, [8,8,8], [0,256,0,256,0,256])
        return cv2.compareHist(h1, h2, cv2.HISTCMP_BHATTACHARYYA) * 100

class QualityAssessor:
    """画面质量评估"""
    
    def assess(self, frame):
        """评估单帧质量"""
        return {
            "sharpness": self._sharpness(frame),    # 清晰度
            "brightness": self._brightness(frame),  # 亮度
            "contrast": self._contrast(frame),      # 对比度
            "stability": self._stability(frame),    # 稳定性
            "face_detected": self._detect_face(frame),  # 人脸检测
            "score": 0  # 综合评分
        }
    
    def _sharpness(self, frame):
        """清晰度（拉普拉斯方差）"""
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        return cv2.Laplacian(gray, cv2.CV_64F).var()
    
    def _brightness(self, frame):
        """亮度"""
        return np.mean(frame)
    
    def _contrast(self, frame):
        """对比度"""
        return np.std(frame)

class ContentUnderstanding:
    """使用 GPT-4o 理解视频内容"""
    
    def __init__(self):
        self.client = OpenAI()
    
    async def analyze_video(self, key_frames):
        """分析关键帧内容"""
        frames_content = []
        
        for timestamp, frame_path in key_frames:
            base64_img = self._encode_image(frame_path)
            
            response = await self.client.chat.completions.acreate(
                model="gpt-4o",
                messages=[{
                    "role": "user",
                    "content": [
                        {"type": "text", "text": 
                            "分析这个视频截图，返回JSON："
                            "1.场景描述 2.人物动作 3.情绪 4.画面质量(1-10)"},
                        {"type": "image_url", "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_img}"
                        }}
                    ]
                }]
            )
            
            frames_content.append({
                "timestamp": timestamp,
                "analysis": response.choices[0].message.content
            })
        
        return frames_content

class AutoRoughCut:
    """自动粗剪：去除废片"""
    
    def __init__(self):
        self.quality_assessor = QualityAssessor()
        self.scene_detector = SceneDetector()
    
    def process(self, video_path):
        """自动粗剪"""
        cap = cv2.VideoCapture(video_path)
        fps = cap.get(cv2.CAP_PROP_FPS)
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        
        keep_segments = []
        current_segment_start = 0
        bad_frame_count = 0
        
        for i in range(0, total_frames, int(fps)):  # 每秒检测一帧
            cap.set(cv2.CAP_PROP_POS_FRAMES, i)
            ret, frame = cap.read()
            if not ret:
                break
            
            quality = self.quality_assessor.assess(frame)
            
            # 判断是否为废片
            if self._is_bad_frame(quality):
                bad_frame_count += 1
                if bad_frame_count > fps * 2:  # 连续2秒废片
                    if current_segment_start < i / fps:
                        keep_segments.append({
                            "start": current_segment_start,
                            "end": i / fps - 2
                        })
                    current_segment_start = i / fps + bad_frame_count / fps
                    bad_frame_count = 0
            else:
                bad_frame_count = 0
        
        # 添加最后一段
        if current_segment_start < total_frames / fps:
            keep_segments.append({
                "start": current_segment_start,
                "end": total_frames / fps
            })
        
        cap.release()
        return keep_segments
    
    def _is_bad_frame(self, quality):
        """判断是否为废片"""
        return (
            quality["sharpness"] < 50 or      # 模糊
            quality["brightness"] < 20 or     # 太暗
            quality["brightness"] > 240 or    # 过曝
            quality["stability"] < 0.3        # 抖动严重
        )

class RhythmEditor:
    """节奏匹配精剪"""
    
    def __init__(self):
        self.audio_analyzer = AudioAnalyzer()
    
    def edit_to_beat(self, video_path, music_path):
        """根据音乐节拍剪辑"""
        # 1. 检测音乐 BPM 和节拍点
        beats = self.audio_analyzer.detect_beats(music_path)
        
        # 2. 检测视频场景切换点
        scenes = self.scene_detector.detect(video_path)
        
        # 3. 将视频片段对齐到节拍点
        edited_timeline = self._align_scenes_to_beats(scenes, beats)
        
        return edited_timeline
    
    def _align_scenes_to_beats(self, scenes, beats):
        """将场景对齐到节拍"""
        timeline = []
        beat_idx = 0
        
        for scene in scenes:
            if beat_idx >= len(beats):
                break
            
            # 每个场景持续到下一个节拍
            start_beat = beats[beat_idx]
            end_beat = beats[beat_idx + 1] if beat_idx + 1 < len(beats) else start_beat + 2
            
            timeline.append({
                "source_start": scene["timestamp"],
                "source_duration": min(scene["duration"], end_beat - start_beat),
                "target_start": start_beat,
                "target_duration": end_beat - start_beat
            })
            
            beat_idx += 1
        
        return timeline

class TransitionSelector:
    """智能转场选择"""
    
    TRANSITION_MAP = {
        ("indoor", "outdoor"): "fade_black",
        ("outdoor", "indoor"): "fade_white",
        ("close_up", "wide_shot"): "zoom_in",
        ("wide_shot", "close_up"): "zoom_out",
        ("day", "night"): "crossfade",
        ("action", "calm"): "slow_dissolve",
        ("calm", "action"): "quick_cut",
    }
    
    def select_transition(self, scene_a, scene_b):
        """根据前后场景选择转场"""
        key = (scene_a["type"], scene_b["type"])
        return self.TRANSITION_MAP.get(key, "crossfade")

class AudioProcessor:
    """音频处理"""
    
    def denoise(self, audio_path):
        """AI 降噪"""
        # 使用 DeepFilterNet 3
        import subprocess
        result = subprocess.run([
            "df3", "--model", "DeepFilterNet3",
            "-i", audio_path,
            "-o", "denoised.wav"
        ], capture_output=True)
        return "denoised.wav"
    
    def auto_level(self, audio_path):
        """自动音量平衡"""
        import librosa
        y, sr = librosa.load(audio_path, sr=48000)
        
        # 峰值归一化
        y_normalized = librosa.util.normalize(y)
        
        # 响度归一化（EBU R128）
        # 目标响度：-16 LUFS（社交媒体标准）
        y_loudness = self._normalize_loudness(y_normalized, sr, target_lufs=-16)
        
        return y_loudness, sr

class BGMMatcher:
    """根据视频内容自动匹配 BGM"""
    
    def __init__(self):
        self.music_library = self._load_library()
    
    async def match(self, video_analysis):
        """匹配 BGM"""
        # 根据视频内容确定音乐风格
        mood = video_analysis["mood"]  # happy/sad/energetic/calm
        genre = video_analysis["genre"]  # vlog/ad/education/drama
        tempo = video_analysis["tempo"]  # slow/medium/fast
        
        # 从音乐库筛选
        candidates = self._filter(mood, genre, tempo)
        
        # 排序
        ranked = self._rank(candidates, video_analysis)
        
        return ranked[0]  # 返回最佳匹配

class AutoColorist:
    """AI 智能调色"""
    
    def __init__(self):
        self.client = OpenAI()
    
    async def grade(self, video_path, style="cinematic"):
        """自动调色"""
        # 1. 抽取代表帧
        key_frames = self._extract_key_frames(video_path, n=10)
        
        # 2. GPT-4o 分析色调
        color_analysis = await self._analyze_colors(key_frames)
        
        # 3. 生成 LUT（Look-Up Table）
        lut = self._generate_lut(color_analysis, style)
        
        # 4. 应用 LUT
        graded_video = self._apply_lut(video_path, lut)
        
        return graded_video
    
    async def _analyze_colors(self, frames):
        """分析当前色调"""
        response = await self.client.chat.completions.acreate(
            model="gpt-4o",
            messages=[{
                "role": "user",
                "content": [
                    {"type": "text", "text": 
                        "分析这些截图的色彩特征，返回JSON："
                        "色温、饱和度、对比度、主色调、建议调色方向"},
                    *[{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{f}"}}
                      for f in frames]
                ]
            }]
        )
        return response.choices[0].message.content

class SubtitleGenerator:
    """自动字幕生成"""
    
    def __init__(self):
        self.client = OpenAI()
        self.translator = Translator()
    
    async def generate(self, video_path, languages=["zh", "en"]):
        """生成多语言字幕"""
        # 1. 提取音频
        audio_path = self._extract_audio(video_path)
        
        # 2. Whisper 3 语音识别（带时间戳）
        result = await self.client.audio.transcriptions.create(
            model="whisper-3",
            file=open(audio_path, "rb"),
            response_format="verbose_json",
            timestamp_granularities=["segment"]
        )
        
        # 3. 生成 SRT 字幕
        subtitles = {}
        for lang in languages:
            if lang == "zh":
                srt = self._to_srt(result.segments, lang="zh")
            else:
                # 翻译
                translated = await self.translator.translate_batch(
                    [seg["text"] for seg in result.segments],
                    target_lang=lang
                )
                srt = self._to_srt_translated(result.segments, translated, lang)
            
            subtitles[lang] = srt
        
        return subtitles
    
    def _to_srt(self, segments, lang):
        """转换为 SRT 格式"""
        srt_lines = []
        for i, seg in enumerate(segments, 1):
            start = self._format_timestamp(seg["start"])
            end = self._format_timestamp(seg["end"])
            srt_lines.append(f"{i}\n{start} --> {end}\n{seg['text']}\n")
        return "\n".join(srt_lines)

from langgraph.graph import StateGraph, END

class VideoEditingAgent:
    """完整的 AI 视频编辑智能体"""
    
    def __init__(self):
        self.workflow = self._build_workflow()
    
    def _build_workflow(self):
        """构建编辑工作流"""
        graph = StateGraph()
        
        # 定义节点
        graph.add_node("analyze", self._analyze_material)
        graph.add_node("rough_cut", self._rough_cut)
        graph.add_node("fine_cut", self._fine_cut)
        graph.add_node("audio", self._process_audio)
        graph.add_node("color", self._color_grade)
        graph.add_node("subtitle", self._generate_subtitles)
        graph.add_node("export", self._export_final)
        
        # 定义流程
        graph.set_entry_point("analyze")
        graph.add_edge("analyze", "rough_cut")
        graph.add_edge("rough_cut", "fine_cut")
        graph.add_edge("fine_cut", "audio")
        graph.add_edge("audio", "color")
        graph.add_edge("color", "subtitle")
        graph.add_edge("subtitle", "export")
        graph.add_edge("export", END)
        
        return graph.compile()
    
    async def edit(self, video_path, requirements):
        """执行自动编辑"""
        initial_state = {
            "video_path": video_path,
            "requirements": requirements,
            "style": requirements.get("style", "cinematic"),
            "target_duration": requirements.get("duration", None),
            "platform": requirements.get("platform", "youtube"),
            "languages": requirements.get("languages", ["zh"]),
        }
        
        result = await self.workflow.ainvoke(initial_state)
        return result["final_video"]