588 lines
24 KiB
Python
588 lines
24 KiB
Python
#!/usr/bin/env python3
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"""
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视频分析器模块 - 旧版本兼容模块
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支持多种分析模式：批处理、逐帧、自动选择
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包含Python原生的抽帧功能，作为Rust模块的降级方案
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"""
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import asyncio
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import base64
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import io
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import os
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from concurrent.futures import ThreadPoolExecutor
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from pathlib import Path
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from typing import Any
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import cv2
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import numpy as np
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from PIL import Image
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from src.common.logger import get_logger
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from src.config.config import global_config, model_config
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from src.llm_models.utils_model import LLMRequest
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logger = get_logger("utils_video_legacy")
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def _extract_frames_worker(
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video_path: str,
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max_frames: int,
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frame_quality: int,
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max_image_size: int,
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frame_extraction_mode: str,
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frame_interval_seconds: float | None,
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) -> list[Any] | list[tuple[str, str]]:
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"""线程池中提取视频帧的工作函数"""
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frames = []
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try:
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cap = cv2.VideoCapture(video_path)
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fps = cap.get(cv2.CAP_PROP_FPS)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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duration = total_frames / fps if fps > 0 else 0
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if frame_extraction_mode == "time_interval":
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# 新模式：按时间间隔抽帧
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time_interval = frame_interval_seconds
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next_frame_time = 0.0
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extracted_count = 0 # 初始化提取帧计数器
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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current_time = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000.0
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if current_time >= next_frame_time:
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# 转换为PIL图像并压缩
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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pil_image = Image.fromarray(frame_rgb)
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# 调整图像大小
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if max(pil_image.size) > max_image_size:
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ratio = max_image_size / max(pil_image.size)
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new_size = tuple(int(dim * ratio) for dim in pil_image.size)
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pil_image = pil_image.resize(new_size, Image.Resampling.LANCZOS)
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# 转换为base64
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buffer = io.BytesIO()
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pil_image.save(buffer, format="JPEG", quality=frame_quality)
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frame_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
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frames.append((frame_base64, current_time))
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extracted_count += 1
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# 注意：这里不能使用logger，因为在线程池中
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# logger.debug(f"提取第{extracted_count}帧 (时间: {current_time:.2f}s)")
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next_frame_time += time_interval
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else:
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# 使用numpy优化帧间隔计算
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if duration > 0:
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frame_interval = max(1, int(duration / max_frames * fps))
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else:
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frame_interval = 30 # 默认间隔
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# 使用numpy计算目标帧位置
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target_frames = np.arange(0, min(max_frames, total_frames // frame_interval + 1)) * frame_interval
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target_frames = target_frames[target_frames < total_frames].astype(int)
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for target_frame in target_frames:
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# 跳转到目标帧
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cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame)
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ret, frame = cap.read()
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if not ret:
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continue
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# 使用numpy优化图像处理
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# 转换为PIL图像并使用numpy进行尺寸计算
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height, width = frame_rgb.shape[:2]
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max_dim = max(height, width)
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if max_dim > max_image_size:
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# 使用numpy计算缩放比例
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ratio = max_image_size / max_dim
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new_width = int(width * ratio)
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new_height = int(height * ratio)
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# 使用opencv进行高效缩放
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frame_resized = cv2.resize(frame_rgb, (new_width, new_height), interpolation=cv2.INTER_LANCZOS4)
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pil_image = Image.fromarray(frame_resized)
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else:
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pil_image = Image.fromarray(frame_rgb)
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# 转换为base64
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buffer = io.BytesIO()
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pil_image.save(buffer, format="JPEG", quality=frame_quality)
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frame_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
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# 计算时间戳
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timestamp = target_frame / fps if fps > 0 else 0
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frames.append((frame_base64, timestamp))
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cap.release()
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return frames
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except Exception as e:
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# 返回错误信息
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return [("ERROR", str(e))]
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class LegacyVideoAnalyzer:
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"""旧版本兼容的视频分析器类"""
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def __init__(self):
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"""初始化视频分析器"""
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# 使用专用的视频分析配置
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try:
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self.video_llm = LLMRequest(
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model_set=model_config.model_task_config.video_analysis, request_type="video_analysis"
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)
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logger.info("✅ 使用video_analysis模型配置")
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except (AttributeError, KeyError) as e:
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# 如果video_analysis不存在，使用vlm配置
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self.video_llm = LLMRequest(model_set=model_config.model_task_config.vlm, request_type="vlm")
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logger.warning(f"video_analysis配置不可用({e})，回退使用vlm配置")
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# 从配置文件读取参数，如果配置不存在则使用默认值
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config = global_config.video_analysis
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# 使用 getattr 统一获取配置参数，如果配置不存在则使用默认值
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self.max_frames = getattr(config, "max_frames", 6)
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self.frame_quality = getattr(config, "frame_quality", 85)
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self.max_image_size = getattr(config, "max_image_size", 600)
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self.enable_frame_timing = getattr(config, "enable_frame_timing", True)
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# 从personality配置中获取人格信息
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try:
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personality_config = global_config.personality
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self.personality_core = getattr(personality_config, "personality_core", "是一个积极向上的女大学生")
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self.personality_side = getattr(
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personality_config, "personality_side", "用一句话或几句话描述人格的侧面特点"
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)
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except AttributeError:
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# 如果没有personality配置，使用默认值
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self.personality_core = "是一个积极向上的女大学生"
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self.personality_side = "用一句话或几句话描述人格的侧面特点"
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self.batch_analysis_prompt = getattr(
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config,
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"batch_analysis_prompt",
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"""请以第一人称的视角来观看这一个视频，你看到的这些是从视频中按时间顺序提取的关键帧。
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你的核心人设是：{personality_core}。
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你的人格细节是：{personality_side}。
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请提供详细的视频内容描述，涵盖以下方面：
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1. 视频的整体内容和主题
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2. 主要人物、对象和场景描述
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3. 动作、情节和时间线发展
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4. 视觉风格和艺术特点
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5. 整体氛围和情感表达
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6. 任何特殊的视觉效果或文字内容
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请用中文回答，结果要详细准确。""",
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)
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# 新增的线程池配置
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self.use_multiprocessing = getattr(config, "use_multiprocessing", True)
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self.max_workers = getattr(config, "max_workers", 2)
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self.frame_extraction_mode = getattr(config, "frame_extraction_mode", "fixed_number")
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self.frame_interval_seconds = getattr(config, "frame_interval_seconds", 2.0)
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# 将配置文件中的模式映射到内部使用的模式名称
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config_mode = getattr(config, "analysis_mode", "auto")
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if config_mode == "batch_frames":
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self.analysis_mode = "batch"
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elif config_mode == "frame_by_frame":
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self.analysis_mode = "sequential"
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elif config_mode == "auto":
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self.analysis_mode = "auto"
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else:
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logger.warning(f"无效的分析模式: {config_mode}，使用默认的auto模式")
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self.analysis_mode = "auto"
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self.frame_analysis_delay = 0.3 # API调用间隔（秒）
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self.frame_interval = 1.0 # 抽帧时间间隔（秒）
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self.batch_size = 3 # 批处理时每批处理的帧数
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self.timeout = 60.0 # 分析超时时间（秒）
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if config:
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logger.info("✅ 从配置文件读取视频分析参数")
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else:
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logger.warning("配置文件中缺少video_analysis配置，使用默认值")
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# 系统提示词
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self.system_prompt = "你是一个专业的视频内容分析助手。请仔细观察用户提供的视频关键帧，详细描述视频内容。"
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logger.info(
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f"✅ 旧版本视频分析器初始化完成，分析模式: {self.analysis_mode}, 线程池: {self.use_multiprocessing}"
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)
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async def extract_frames(self, video_path: str) -> list[tuple[str, float]]:
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"""提取视频帧 - 支持多进程和单线程模式"""
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# 先获取视频信息
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cap = cv2.VideoCapture(video_path)
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fps = cap.get(cv2.CAP_PROP_FPS)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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duration = total_frames / fps if fps > 0 else 0
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cap.release()
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logger.info(f"视频信息: {total_frames}帧, {fps:.2f}FPS, {duration:.2f}秒")
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# 估算提取帧数
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if duration > 0:
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frame_interval = max(1, int(duration / self.max_frames * fps))
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estimated_frames = min(self.max_frames, total_frames // frame_interval + 1)
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else:
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estimated_frames = self.max_frames
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logger.info(f"计算得出帧间隔: {frame_interval} (将提取约{estimated_frames}帧)")
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# 根据配置选择处理方式
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if self.use_multiprocessing:
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return await self._extract_frames_multiprocess(video_path)
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else:
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return await self._extract_frames_fallback(video_path)
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async def _extract_frames_multiprocess(self, video_path: str) -> list[tuple[str, float]]:
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"""线程池版本的帧提取"""
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loop = asyncio.get_event_loop()
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try:
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logger.info("🔄 启动线程池帧提取...")
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# 使用线程池，避免进程间的导入问题
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with ThreadPoolExecutor(max_workers=1) as executor:
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frames = await loop.run_in_executor(
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executor,
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_extract_frames_worker,
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video_path,
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self.max_frames,
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self.frame_quality,
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self.max_image_size,
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self.frame_extraction_mode,
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self.frame_interval_seconds,
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)
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# 检查是否有错误
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if frames and frames[0][0] == "ERROR":
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logger.error(f"线程池帧提取失败: {frames[0][1]}")
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# 降级到单线程模式
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logger.info("🔄 降级到单线程模式...")
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return await self._extract_frames_fallback(video_path)
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logger.info(f"✅ 成功提取{len(frames)}帧 (线程池模式)")
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return frames
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except Exception as e:
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logger.error(f"线程池帧提取失败: {e}")
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# 降级到原始方法
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logger.info("🔄 降级到单线程模式...")
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return await self._extract_frames_fallback(video_path)
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async def _extract_frames_fallback(self, video_path: str) -> list[tuple[str, float]]:
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"""帧提取的降级方法 - 原始异步版本"""
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frames = []
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extracted_count = 0
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cap = cv2.VideoCapture(video_path)
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fps = cap.get(cv2.CAP_PROP_FPS)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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duration = total_frames / fps if fps > 0 else 0
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logger.info(f"视频信息: {total_frames}帧, {fps:.2f}FPS, {duration:.2f}秒")
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if self.frame_extraction_mode == "time_interval":
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# 新模式：按时间间隔抽帧
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time_interval = self.frame_interval_seconds
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next_frame_time = 0.0
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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current_time = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000.0
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if current_time >= next_frame_time:
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# 转换为PIL图像并压缩
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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pil_image = Image.fromarray(frame_rgb)
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# 调整图像大小
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if max(pil_image.size) > self.max_image_size:
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ratio = self.max_image_size / max(pil_image.size)
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new_size = tuple(int(dim * ratio) for dim in pil_image.size)
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pil_image = pil_image.resize(new_size, Image.Resampling.LANCZOS)
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# 转换为base64
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buffer = io.BytesIO()
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pil_image.save(buffer, format="JPEG", quality=self.frame_quality)
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frame_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
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frames.append((frame_base64, current_time))
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extracted_count += 1
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logger.debug(f"提取第{extracted_count}帧 (时间: {current_time:.2f}s)")
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next_frame_time += time_interval
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else:
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# 使用numpy优化帧间隔计算
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if duration > 0:
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frame_interval = max(1, int(duration / self.max_frames * fps))
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else:
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frame_interval = 30 # 默认间隔
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logger.info(
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f"计算得出帧间隔: {frame_interval} (将提取约{min(self.max_frames, total_frames // frame_interval + 1)}帧)"
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)
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# 使用numpy计算目标帧位置
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target_frames = np.arange(0, min(self.max_frames, total_frames // frame_interval + 1)) * frame_interval
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target_frames = target_frames[target_frames < total_frames].astype(int)
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extracted_count = 0
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for target_frame in target_frames:
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# 跳转到目标帧
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cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame)
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ret, frame = cap.read()
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if not ret:
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continue
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# 使用numpy优化图像处理
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# 转换为PIL图像并使用numpy进行尺寸计算
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height, width = frame_rgb.shape[:2]
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max_dim = max(height, width)
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if max_dim > self.max_image_size:
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# 使用numpy计算缩放比例
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ratio = self.max_image_size / max_dim
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new_width = int(width * ratio)
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new_height = int(height * ratio)
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# 使用opencv进行高效缩放
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frame_resized = cv2.resize(frame_rgb, (new_width, new_height), interpolation=cv2.INTER_LANCZOS4)
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pil_image = Image.fromarray(frame_resized)
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else:
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pil_image = Image.fromarray(frame_rgb)
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# 转换为base64
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buffer = io.BytesIO()
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pil_image.save(buffer, format="JPEG", quality=self.frame_quality)
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frame_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
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# 计算时间戳
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timestamp = target_frame / fps if fps > 0 else 0
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frames.append((frame_base64, timestamp))
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extracted_count += 1
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logger.debug(f"提取第{extracted_count}帧 (时间: {timestamp:.2f}s, 帧号: {target_frame})")
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# 每提取一帧让步一次
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await asyncio.sleep(0.001)
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cap.release()
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logger.info(f"✅ 成功提取{len(frames)}帧")
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return frames
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async def analyze_frames_batch(self, frames: list[tuple[str, float]], user_question: str | None = None) -> str:
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"""批量分析所有帧"""
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logger.info(f"开始批量分析{len(frames)}帧")
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if not frames:
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return "❌ 没有可分析的帧"
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# 构建提示词并格式化人格信息，要不然占位符的那个会爆炸
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prompt = self.batch_analysis_prompt.format(
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personality_core=self.personality_core, personality_side=self.personality_side
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)
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if user_question:
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prompt += f"\n\n用户问题: {user_question}"
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# 添加帧信息到提示词
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frame_info = []
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for i, (_frame_base64, timestamp) in enumerate(frames):
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if self.enable_frame_timing:
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frame_info.append(f"第{i + 1}帧 (时间: {timestamp:.2f}s)")
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else:
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frame_info.append(f"第{i + 1}帧")
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prompt += f"\n\n视频包含{len(frames)}帧图像：{', '.join(frame_info)}"
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prompt += "\n\n请基于所有提供的帧图像进行综合分析，关注并描述视频的完整内容和故事发展。"
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try:
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# 尝试使用多图片分析
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response = await self._analyze_multiple_frames(frames, prompt)
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logger.info("✅ 视频识别完成")
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return response
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||
|
||
except Exception as e:
|
||
logger.error(f"❌ 视频识别失败: {e}")
|
||
# 降级到单帧分析
|
||
logger.warning("降级到单帧分析模式")
|
||
try:
|
||
frame_base64, timestamp = frames[0]
|
||
fallback_prompt = (
|
||
prompt
|
||
+ f"\n\n注意：由于技术限制，当前仅显示第1帧 (时间: {timestamp:.2f}s)，视频共有{len(frames)}帧。请基于这一帧进行分析。"
|
||
)
|
||
|
||
response, _ = await self.video_llm.generate_response_for_image(
|
||
prompt=fallback_prompt, image_base64=frame_base64, image_format="jpeg"
|
||
)
|
||
logger.info("✅ 降级的单帧分析完成")
|
||
return response
|
||
except Exception as fallback_e:
|
||
logger.error(f"❌ 降级分析也失败: {fallback_e}")
|
||
raise
|
||
|
||
async def _analyze_multiple_frames(self, frames: list[tuple[str, float]], prompt: str) -> str:
|
||
"""使用多图片分析方法"""
|
||
logger.info(f"开始构建包含{len(frames)}帧的分析请求")
|
||
|
||
# 导入MessageBuilder用于构建多图片消息
|
||
from src.llm_models.payload_content.message import MessageBuilder, RoleType
|
||
from src.llm_models.utils_model import RequestType
|
||
|
||
# 构建包含多张图片的消息
|
||
message_builder = MessageBuilder().set_role(RoleType.User).add_text_content(prompt)
|
||
|
||
# 添加所有帧图像
|
||
for _i, (frame_base64, _timestamp) in enumerate(frames):
|
||
message_builder.add_image_content("jpeg", frame_base64)
|
||
# logger.info(f"已添加第{i+1}帧到分析请求 (时间: {timestamp:.2f}s, 图片大小: {len(frame_base64)} chars)")
|
||
|
||
message = message_builder.build()
|
||
# logger.info(f"✅ 多帧消息构建完成，包含{len(frames)}张图片")
|
||
|
||
# 获取模型信息和客户端
|
||
model_info, api_provider, client = self.video_llm._select_model()
|
||
# logger.info(f"使用模型: {model_info.name} 进行多帧分析")
|
||
|
||
# 直接执行多图片请求
|
||
api_response = await self.video_llm._execute_request(
|
||
api_provider=api_provider,
|
||
client=client,
|
||
request_type=RequestType.RESPONSE,
|
||
model_info=model_info,
|
||
message_list=[message],
|
||
temperature=None,
|
||
max_tokens=None,
|
||
)
|
||
|
||
logger.info(f"视频识别完成，响应长度: {len(api_response.content or '')} ")
|
||
return api_response.content or "❌ 未获得响应内容"
|
||
|
||
async def analyze_frames_sequential(self, frames: list[tuple[str, float]], user_question: str | None = None) -> str:
|
||
"""逐帧分析并汇总"""
|
||
logger.info(f"开始逐帧分析{len(frames)}帧")
|
||
|
||
frame_analyses = []
|
||
|
||
for i, (frame_base64, timestamp) in enumerate(frames):
|
||
try:
|
||
prompt = f"请分析这个视频的第{i + 1}帧"
|
||
if self.enable_frame_timing:
|
||
prompt += f" (时间: {timestamp:.2f}s)"
|
||
prompt += "。描述你看到的内容，包括人物、动作、场景、文字等。"
|
||
|
||
if user_question:
|
||
prompt += f"\n特别关注: {user_question}"
|
||
|
||
response, _ = await self.video_llm.generate_response_for_image(
|
||
prompt=prompt, image_base64=frame_base64, image_format="jpeg"
|
||
)
|
||
|
||
frame_analyses.append(f"第{i + 1}帧 ({timestamp:.2f}s): {response}")
|
||
logger.debug(f"✅ 第{i + 1}帧分析完成")
|
||
|
||
# API调用间隔
|
||
if i < len(frames) - 1:
|
||
await asyncio.sleep(self.frame_analysis_delay)
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ 第{i + 1}帧分析失败: {e}")
|
||
frame_analyses.append(f"第{i + 1}帧: 分析失败 - {e}")
|
||
|
||
# 生成汇总
|
||
logger.info("开始生成汇总分析")
|
||
summary_prompt = f"""基于以下各帧的分析结果，请提供一个完整的视频内容总结：
|
||
|
||
{chr(10).join(frame_analyses)}
|
||
|
||
请综合所有帧的信息，描述视频的整体内容、故事线、主要元素和特点。"""
|
||
|
||
if user_question:
|
||
summary_prompt += f"\n特别回答用户的问题: {user_question}"
|
||
|
||
try:
|
||
# 使用最后一帧进行汇总分析
|
||
if frames:
|
||
last_frame_base64, _ = frames[-1]
|
||
summary, _ = await self.video_llm.generate_response_for_image(
|
||
prompt=summary_prompt, image_base64=last_frame_base64, image_format="jpeg"
|
||
)
|
||
logger.info("✅ 逐帧分析和汇总完成")
|
||
return summary
|
||
else:
|
||
return "❌ 没有可用于汇总的帧"
|
||
except Exception as e:
|
||
logger.error(f"❌ 汇总分析失败: {e}")
|
||
# 如果汇总失败，返回各帧分析结果
|
||
return f"视频逐帧分析结果：\n\n{chr(10).join(frame_analyses)}"
|
||
|
||
async def analyze_video(self, video_path: str, user_question: str | None = None) -> str:
|
||
"""分析视频的主要方法"""
|
||
try:
|
||
logger.info(f"开始分析视频: {os.path.basename(video_path)}")
|
||
|
||
# 提取帧
|
||
frames = await self.extract_frames(video_path)
|
||
if not frames:
|
||
return "❌ 无法从视频中提取有效帧"
|
||
|
||
# 根据模式选择分析方法
|
||
if self.analysis_mode == "auto":
|
||
# 智能选择：少于等于3帧用批量，否则用逐帧
|
||
mode = "batch" if len(frames) <= 3 else "sequential"
|
||
logger.info(f"自动选择分析模式: {mode} (基于{len(frames)}帧)")
|
||
else:
|
||
mode = self.analysis_mode
|
||
|
||
# 执行分析
|
||
if mode == "batch":
|
||
result = await self.analyze_frames_batch(frames, user_question)
|
||
else: # sequential
|
||
result = await self.analyze_frames_sequential(frames, user_question)
|
||
|
||
logger.info("✅ 视频分析完成")
|
||
return result
|
||
|
||
except Exception as e:
|
||
error_msg = f"❌ 视频分析失败: {e!s}"
|
||
logger.error(error_msg)
|
||
return error_msg
|
||
|
||
@staticmethod
|
||
def is_supported_video(file_path: str) -> bool:
|
||
"""检查是否为支持的视频格式"""
|
||
supported_formats = {".mp4", ".avi", ".mov", ".mkv", ".flv", ".wmv", ".m4v", ".3gp", ".webm"}
|
||
return Path(file_path).suffix.lower() in supported_formats
|
||
|
||
|
||
# 全局实例
|
||
_legacy_video_analyzer = None
|
||
|
||
|
||
def get_legacy_video_analyzer() -> LegacyVideoAnalyzer:
|
||
"""获取旧版本视频分析器实例（单例模式）"""
|
||
global _legacy_video_analyzer
|
||
if _legacy_video_analyzer is None:
|
||
_legacy_video_analyzer = LegacyVideoAnalyzer()
|
||
return _legacy_video_analyzer
|