|
|
@@ -0,0 +1,716 @@
|
|
|
+import os
|
|
|
+import cv2
|
|
|
+import base64
|
|
|
+import uuid
|
|
|
+import shutil
|
|
|
+import logging
|
|
|
+import requests
|
|
|
+from typing import Optional
|
|
|
+from fastapi import FastAPI, File, UploadFile, Form, HTTPException, Body
|
|
|
+from fastapi.responses import JSONResponse, StreamingResponse
|
|
|
+from fastapi.staticfiles import StaticFiles
|
|
|
+from fastapi.middleware.cors import CORSMiddleware
|
|
|
+from pydantic import BaseModel
|
|
|
+from openai import OpenAI
|
|
|
+from scenedetect import open_video, SceneManager
|
|
|
+from scenedetect.detectors import ContentDetector
|
|
|
+import asyncio
|
|
|
+from concurrent.futures import ThreadPoolExecutor
|
|
|
+from minio import Minio
|
|
|
+import traceback
|
|
|
+
|
|
|
+
|
|
|
+# 配置日志
|
|
|
+logging.basicConfig(
|
|
|
+ level=logging.INFO,
|
|
|
+ format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
|
|
+)
|
|
|
+logger = logging.getLogger(__name__)
|
|
|
+
|
|
|
+app = FastAPI()
|
|
|
+
|
|
|
+app.add_middleware(
|
|
|
+ CORSMiddleware,
|
|
|
+ allow_origins=["*"],
|
|
|
+ allow_credentials=True,
|
|
|
+ allow_methods=["*"],
|
|
|
+ allow_headers=["*"],
|
|
|
+)
|
|
|
+
|
|
|
+class Config:
|
|
|
+ # API_KEY = "sk-0bfcef4bcb124cba8484bb196a8befc6"
|
|
|
+ API_KEY = "empety"
|
|
|
+ # BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
|
|
+ BASE_URL = "http://192.168.3.33:11434/v1"
|
|
|
+ # MODEL_NAME = "qwen3-vl-flash"
|
|
|
+ MODEL_NAME = "qwen3-vl:8b"
|
|
|
+
|
|
|
+ # 视频处理参数
|
|
|
+ KEYFRAMES_PER_SCENE = 3 # 每个镜头抽取的关键帧数量
|
|
|
+ EXTRACT_INTERVAL = 3 # 无镜头检测时的抽帧间隔（秒）
|
|
|
+ TEMP_DIR = "./temp_file" # 临时文件目录
|
|
|
+ UPLOAD_DIR = "./uploads" # 上传文件目录
|
|
|
+
|
|
|
+ # 提示词模板
|
|
|
+ PROMPT_TEXT = "请回答用户的问题。"
|
|
|
+ # PROMPT_IMAGE = "请描述图像中的主要内容、场景与人物。"
|
|
|
+ PROMPT_VIDEO = "请描述镜头中的主要内容、场景、动作与事件。"
|
|
|
+
|
|
|
+ PROMPT_IMAGE = """
|
|
|
+ 你是一名专业的“应急灾害影像分析员”，需要根据图像内容对以下 6 类应急场景进行检测与风险分析：
|
|
|
+
|
|
|
+ 烟火 / 烟雾 / 热异常
|
|
|
+
|
|
|
+ 积水 / 涝情
|
|
|
+
|
|
|
+ 山体滑坡 / 崩塌 / 灾害迹象
|
|
|
+
|
|
|
+ 道路交通状况
|
|
|
+
|
|
|
+ 基础设施受损情况
|
|
|
+
|
|
|
+ 人员活动 / 救援迹象
|
|
|
+
|
|
|
+ 请严格按照以下规则执行任务：
|
|
|
+
|
|
|
+ 【任务要求】
|
|
|
+ 1. 对每一个类别输出一个 JSON 对象，共 6 个
|
|
|
+
|
|
|
+ 类别列表：
|
|
|
+
|
|
|
+ "fire_smoke_detection"
|
|
|
+
|
|
|
+ "water_accumulation"
|
|
|
+
|
|
|
+ "landslide_signs"
|
|
|
+
|
|
|
+ "road_traffic_status"
|
|
|
+
|
|
|
+ "critical_infrastructure"
|
|
|
+
|
|
|
+ "human_activity_signs"
|
|
|
+
|
|
|
+ 2. 每个类别内部必须包含以下字段（字段名固定）：
|
|
|
+ {
|
|
|
+ "category": "",
|
|
|
+ "is_detected": false,
|
|
|
+ "confidence": null,
|
|
|
+ "description": "",
|
|
|
+ "risk_assessment": "",
|
|
|
+ "location_reference": ""
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+ 字段含义如下：
|
|
|
+
|
|
|
+ 字段名 说明
|
|
|
+ category 以上六个类别之一，保持字符串一致
|
|
|
+ is_detected 是否检测到关键要素（true/false）
|
|
|
+ confidence [可选] 你对判断的置信度 0~1
|
|
|
+ description 详细描述：出现的对象、规模、范围、位置、特征
|
|
|
+ risk_assessment 风险等级："低" / "中" / "高"
|
|
|
+ location_reference 相对位置，如：道路北侧、桥梁附近、画面左下角等
|
|
|
+ 3. 必须严格输出 JSON 数组格式，不得输出解释性文字
|
|
|
+ [
|
|
|
+ {... 六个类别的 JSON ...}
|
|
|
+ ]
|
|
|
+
|
|
|
+ 4. 六类检测提示词（模型的视觉分析指南）
|
|
|
+ 【六类视觉检测指引】
|
|
|
+ 1. fire_smoke_detection（烟火监测）
|
|
|
+
|
|
|
+ 分析要点：
|
|
|
+
|
|
|
+ 是否有烟雾、明火、热异常、烧痕
|
|
|
+
|
|
|
+ 位置：如“山腰东南方向”“画面左侧”
|
|
|
+
|
|
|
+ 烟雾颜色：白/灰/黑
|
|
|
+
|
|
|
+ 火势规模：小片区 / 中等 / 大范围
|
|
|
+
|
|
|
+ 风险评估：低/中/高
|
|
|
+
|
|
|
+ 目标元素： 烟雾、明火、热异常区、过火痕迹
|
|
|
+ 评估维度： 有无、位置、范围、趋势、风险等级
|
|
|
+
|
|
|
+ 2. water_accumulation（积水）
|
|
|
+
|
|
|
+ 分析要点：
|
|
|
+
|
|
|
+ 路面积水、低洼区积水、河道上涨
|
|
|
+
|
|
|
+ 水面范围、估算深度
|
|
|
+
|
|
|
+ 是否影响道路、房屋、车辆
|
|
|
+
|
|
|
+ 是否有潜在次生灾害（淹没道路、冲刷边坡等）
|
|
|
+
|
|
|
+ 目标元素： 路面漫水、低洼积水、河道上涨、排水设施
|
|
|
+ 评估维度： 有无、位置、范围、深度、风险性
|
|
|
+
|
|
|
+ 3. landslide_signs（山体异常）
|
|
|
+
|
|
|
+ 分析要点：
|
|
|
+
|
|
|
+ 滑坡体、崩塌体、碎屑流、裂缝、树木倾斜
|
|
|
+
|
|
|
+ 新鲜裸露土体 / 老滑坡复活迹象
|
|
|
+
|
|
|
+ 是否影响道路、河道或建筑
|
|
|
+
|
|
|
+ 活动性（新滑坡 / 发展中）
|
|
|
+
|
|
|
+ 目标元素： 滑坡体、崩塌、裂缝、碎屑流、植被破坏
|
|
|
+ 评估维度： 有无、类型、规模、位置、活动性、影响范围
|
|
|
+
|
|
|
+ 4. road_traffic_status（道路交通）
|
|
|
+
|
|
|
+ 分析要点：
|
|
|
+
|
|
|
+ 道路是否通畅、受阻、中断
|
|
|
+
|
|
|
+ 是否有事故、拥堵、车辆滞留
|
|
|
+
|
|
|
+ 路面损坏：塌陷、裂缝、障碍物
|
|
|
+
|
|
|
+ 是否有救援力量
|
|
|
+
|
|
|
+ 目标元素： 通行中断、拥堵、车辆滞留、损坏、救援车辆
|
|
|
+ 评估维度： 通行状态 / 原因 / 影响程度 / 救援通道是否畅通
|
|
|
+
|
|
|
+ 5. critical_infrastructure（关键基建）
|
|
|
+
|
|
|
+ 分析要点：
|
|
|
+
|
|
|
+ 桥梁是否倾斜 / 断裂
|
|
|
+
|
|
|
+ 隧道口是否堵塞
|
|
|
+
|
|
|
+ 输电线塔是否倒伏
|
|
|
+
|
|
|
+ 通信基站是否损坏
|
|
|
+
|
|
|
+ 损伤程度及影响功能
|
|
|
+
|
|
|
+ 目标元素： 桥梁、隧道、线塔、重要设施
|
|
|
+ 评估维度： 结构完整性 / 功能状态 / 受损程度
|
|
|
+
|
|
|
+ 6. human_activity_signs（人员活动与救援）
|
|
|
+
|
|
|
+ 分析要点：
|
|
|
+
|
|
|
+ 有无人员：正常/受困
|
|
|
+
|
|
|
+ 救援队伍、工程车辆、临时安置点
|
|
|
+
|
|
|
+ 位置信息
|
|
|
+
|
|
|
+ 该区域是否安全
|
|
|
+
|
|
|
+ 目标元素： 受困人员、救援人员、工程车、临时设施
|
|
|
+ 评估维度： 活动类型 / 人员规模 / 位置 / 安全性
|
|
|
+
|
|
|
+ 最终输出格式（必须严格遵守）
|
|
|
+
|
|
|
+ 输出示例结构如下（内容仅示意）：
|
|
|
+
|
|
|
+ [
|
|
|
+ {
|
|
|
+ "category": "fire_smoke_detection",
|
|
|
+ "is_detected": false,
|
|
|
+ "confidence": 0.12,
|
|
|
+ "description": "",
|
|
|
+ "risk_assessment": "低",
|
|
|
+ "location_reference": ""
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "category": "water_accumulation",
|
|
|
+ "is_detected": true,
|
|
|
+ "confidence": 0.78,
|
|
|
+ "description": "画面右下角出现浅层路面积水，范围较小。",
|
|
|
+ "risk_assessment": "低",
|
|
|
+ "location_reference": "画面右下区域"
|
|
|
+ },
|
|
|
+ ...
|
|
|
+ ]
|
|
|
+
|
|
|
+ 禁止输出以下内容：
|
|
|
+
|
|
|
+ 解释文字
|
|
|
+ Markdown
|
|
|
+ 图片描述以外的闲聊
|
|
|
+ 未定义字段
|
|
|
+ JSON 外的任何文字
|
|
|
+ """
|
|
|
+
|
|
|
+
|
|
|
+ # MinIO配置
|
|
|
+ # MINIO_ENDPOINT = 'xia0miduo.gicp.net:9000'
|
|
|
+ # MINIO_ACCESS_KEY = 'minioadmin'
|
|
|
+ # MINIO_SECRET_KEY = 'minioadmin'
|
|
|
+ # MINIO_BUCKET = 'papbtest'
|
|
|
+ # MINIO_URL = "http://xia0miduo.gicp.net:9000"
|
|
|
+ # MINIO_SECURE = False
|
|
|
+
|
|
|
+ # MINIO_ENDPOINT = '127.0.0.1:30802'
|
|
|
+ # MINIO_ACCESS_KEY = 'admin'
|
|
|
+ # MINIO_SECRET_KEY = 'Ryu304307910'
|
|
|
+ # MINIO_BUCKET = 'file-storage-privatization'
|
|
|
+ # MINIO_URL = "http://127.0.0.1:30802"
|
|
|
+ # MINIO_SECURE = False
|
|
|
+
|
|
|
+ # MINIO_ENDPOINT = '192.168.3.33:9000'
|
|
|
+ # MINIO_ACCESS_KEY = 'minioadmin'
|
|
|
+ # MINIO_SECRET_KEY = 'minioadmin'
|
|
|
+ # MINIO_BUCKET = 'dji-fh'
|
|
|
+ # MINIO_URL = "http://192.168.3.33:9000"
|
|
|
+ # MINIO_SECURE = False
|
|
|
+
|
|
|
+ MINIO_ENDPOINT = 'minio.ryuiso.com:59000'
|
|
|
+ MINIO_ACCESS_KEY = 'oss_library'
|
|
|
+ MINIO_SECRET_KEY = 'yDkG9YJiC92G3vk52goST'
|
|
|
+ MINIO_BUCKET = 'dji-cloudapi'
|
|
|
+ MINIO_URL = "http://minio.ryuiso.com:59000"
|
|
|
+ MINIO_SECURE = False
|
|
|
+
|
|
|
+
|
|
|
+# 创建目录
|
|
|
+os.makedirs(Config.TEMP_DIR, exist_ok=True)
|
|
|
+os.makedirs(Config.UPLOAD_DIR, exist_ok=True)
|
|
|
+
|
|
|
+# 线程池用于异步处理
|
|
|
+executor = ThreadPoolExecutor(max_workers=4)
|
|
|
+
|
|
|
+# MinIO客户端初始化
|
|
|
+try:
|
|
|
+ minio_client = Minio(
|
|
|
+ Config.MINIO_ENDPOINT,
|
|
|
+ access_key=Config.MINIO_ACCESS_KEY,
|
|
|
+ secret_key=Config.MINIO_SECRET_KEY,
|
|
|
+ secure=Config.MINIO_SECURE,
|
|
|
+ )
|
|
|
+ logger.info("MinIO客户端初始化成功")
|
|
|
+except Exception as e:
|
|
|
+ logger.error(f"MinIO客户端初始化失败: {e}")
|
|
|
+ minio_client = None
|
|
|
+
|
|
|
+def build_image_content(image_path_or_url: str) -> dict:
|
|
|
+ """
|
|
|
+ 构建图像请求格式内容
|
|
|
+ """
|
|
|
+ if image_path_or_url.startswith("http://") or image_path_or_url.startswith("https://"):
|
|
|
+ try:
|
|
|
+ # 下载网络图片
|
|
|
+ logger.info(f"正在下载网络图片: {image_path_or_url}")
|
|
|
+ response = requests.get(image_path_or_url, timeout=30)
|
|
|
+ response.raise_for_status()
|
|
|
+ img_bytes = response.content
|
|
|
+
|
|
|
+ # 根据Content-Type或URL扩展名确定MIME类型
|
|
|
+ mime = "image/jpeg" # 默认MIME类型
|
|
|
+ content_type = response.headers.get('Content-Type', '')
|
|
|
+ if 'image/png' in content_type or image_path_or_url.lower().endswith(".png"):
|
|
|
+ mime = "image/png"
|
|
|
+ elif 'image/gif' in content_type or image_path_or_url.lower().endswith(".gif"):
|
|
|
+ mime = "image/gif"
|
|
|
+ elif 'image/webp' in content_type or image_path_or_url.lower().endswith(".webp"):
|
|
|
+ mime = "image/webp"
|
|
|
+
|
|
|
+ # 转换为base64编码
|
|
|
+ b64 = base64.b64encode(img_bytes).decode("utf-8")
|
|
|
+ logger.info(f"网络图片下载并转换成功: {image_path_or_url}")
|
|
|
+
|
|
|
+ return {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}}
|
|
|
+ except requests.exceptions.RequestException as e:
|
|
|
+ logger.error(f"下载网络图片失败: {image_path_or_url}, 错误: {str(e)}")
|
|
|
+ raise ValueError(f"无法下载网络图片: {image_path_or_url}, 错误: {str(e)}")
|
|
|
+ except Exception as e:
|
|
|
+ logger.error(f"处理网络图片时出错: {image_path_or_url}, 错误: {str(e)}")
|
|
|
+ raise ValueError(f"处理网络图片时出错: {image_path_or_url}, 错误: {str(e)}")
|
|
|
+
|
|
|
+ elif os.path.exists(image_path_or_url):
|
|
|
+ with open(image_path_or_url, "rb") as f:
|
|
|
+ img_bytes = f.read()
|
|
|
+ # 转码
|
|
|
+ b64 = base64.b64encode(img_bytes).decode("utf-8")
|
|
|
+
|
|
|
+ # 根据文件扩展名确定MIME类型
|
|
|
+ mime = "image/jpeg"
|
|
|
+ if image_path_or_url.lower().endswith(".png"):
|
|
|
+ mime = "image/png"
|
|
|
+ elif image_path_or_url.lower().endswith(".gif"):
|
|
|
+ mime = "image/gif"
|
|
|
+ elif image_path_or_url.lower().endswith(".webp"):
|
|
|
+ mime = "image/webp"
|
|
|
+
|
|
|
+ return {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}}
|
|
|
+ else:
|
|
|
+ raise ValueError(f"无效的图片路径或URL: {image_path_or_url}")
|
|
|
+
|
|
|
+def detect_scenes(video_path: str) -> list:
|
|
|
+ """
|
|
|
+ 使用PySceneDetect检测视频中的镜头切换
|
|
|
+ """
|
|
|
+ # 有关PySceneDetect的镜头检测，详见https://www.scenedetect.com/docs/latest/api/scene_manager.html#usage
|
|
|
+ video = open_video(video_path)
|
|
|
+ scene_manager = SceneManager()
|
|
|
+ scene_manager.add_detector(ContentDetector(threshold=20.0))
|
|
|
+ scene_manager.detect_scenes(video)
|
|
|
+ scenes = scene_manager.get_scene_list()
|
|
|
+ print(f"检测到 {len(scenes)} 个镜头片段")
|
|
|
+ return scenes
|
|
|
+
|
|
|
+def extract_frames_every(video_path: str, seconds: int = 3) -> list:
|
|
|
+ """
|
|
|
+ 按固定时间间隔从视频中提取帧（没有检测到镜头时）
|
|
|
+ """
|
|
|
+ cap = cv2.VideoCapture(video_path)
|
|
|
+ fps = cap.get(cv2.CAP_PROP_FPS)
|
|
|
+ interval = int(fps * seconds)
|
|
|
+ frame_paths = []
|
|
|
+ count = 0
|
|
|
+
|
|
|
+ while cap.isOpened():
|
|
|
+ ret, frame = cap.read()
|
|
|
+ if not ret:
|
|
|
+ break
|
|
|
+ if count % interval == 0:
|
|
|
+ path = f"{Config.TEMP_DIR}/frame_{count//interval+1}.jpg"
|
|
|
+ cv2.imwrite(path, frame)
|
|
|
+ frame_paths.append(path)
|
|
|
+ count += 1
|
|
|
+
|
|
|
+ cap.release()
|
|
|
+ print(f"基于间隔抽取 {len(frame_paths)} 张帧")
|
|
|
+ return frame_paths
|
|
|
+
|
|
|
+def extract_keyframes(video_path: str, scenes: list, num_frames: int = 3) -> list:
|
|
|
+ """
|
|
|
+ 从每个检测到的镜头中提取关键帧
|
|
|
+ """
|
|
|
+ cap = cv2.VideoCapture(video_path)
|
|
|
+ keyframes = []
|
|
|
+
|
|
|
+ for i, (start, end) in enumerate(scenes):
|
|
|
+ start_s, end_s = start.get_seconds(), end.get_seconds()
|
|
|
+ duration = end_s - start_s
|
|
|
+ frames = []
|
|
|
+
|
|
|
+ for j in range(num_frames):
|
|
|
+ # 在镜头时间范围内均匀抽帧
|
|
|
+ t = start_s + (j + 1) * duration / (num_frames + 1)
|
|
|
+ cap.set(cv2.CAP_PROP_POS_MSEC, t * 1000)
|
|
|
+ ret, frame = cap.read()
|
|
|
+
|
|
|
+ if ret:
|
|
|
+ gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
|
+ # 过滤过暗的帧
|
|
|
+ if gray.mean() > 30:
|
|
|
+ path = f"{Config.TEMP_DIR}/scene{i+1}_{j+1}.jpg"
|
|
|
+ cv2.imwrite(path, frame)
|
|
|
+ frames.append(path)
|
|
|
+
|
|
|
+ if frames:
|
|
|
+ keyframes.append(frames)
|
|
|
+
|
|
|
+ cap.release()
|
|
|
+ return keyframes
|
|
|
+
|
|
|
+def describe_scene(client: OpenAI, model_name: str, image_paths: list, scene_idx: int, prompt: str = None) -> str:
|
|
|
+ """
|
|
|
+ 调用多模态模型描述单个场景
|
|
|
+ """
|
|
|
+ if not prompt:
|
|
|
+ prompt = Config.PROMPT_VIDEO
|
|
|
+ contents = [build_image_content(p) for p in image_paths]
|
|
|
+ contents.append({"type": "text", "text": f"{prompt}（镜头 {scene_idx}）"})
|
|
|
+
|
|
|
+ response = client.chat.completions.create(
|
|
|
+ model=model_name,
|
|
|
+ messages=[{"role": "user", "content": contents}],
|
|
|
+ max_tokens=5120,
|
|
|
+ extra_body={"mm_processor_kwargs": {"fps": 2, "do_sample_frames": True}},
|
|
|
+ )
|
|
|
+ return response.choices[0].message.content
|
|
|
+
|
|
|
+def summarize_video(client: OpenAI, model_name: str, scene_descriptions: list) -> str:
|
|
|
+ """
|
|
|
+ 将所有场景描述汇总为整体视频摘要
|
|
|
+ """
|
|
|
+ prompt = "以下是视频中每个镜头的描述，请总结视频的主要内容与主题：\n\n"
|
|
|
+ for i, d in enumerate(scene_descriptions):
|
|
|
+ prompt += f"镜头{i+1}: {d}\n"
|
|
|
+ prompt += "\n请生成简洁的整体视频摘要。"
|
|
|
+
|
|
|
+ response = client.chat.completions.create(
|
|
|
+ model=model_name,
|
|
|
+ messages=[{"role": "user", "content": [{"type": "text", "text": prompt}]}],
|
|
|
+ max_tokens=5120
|
|
|
+ )
|
|
|
+ return response.choices[0].message.content
|
|
|
+
|
|
|
+def detect_content_type(input_path: str) -> str:
|
|
|
+ """
|
|
|
+ 自动检测输入内容的类型
|
|
|
+ """
|
|
|
+ # 仅通过文件扩展名判断
|
|
|
+ if os.path.isfile(input_path):
|
|
|
+ ext = os.path.splitext(input_path)[1].lower()
|
|
|
+ if ext in [".mp4", ".mov", ".avi", ".mkv"]:
|
|
|
+ return "video"
|
|
|
+ elif ext in [".jpg", ".jpeg", ".png", ".gif"]:
|
|
|
+ return "image"
|
|
|
+ else:
|
|
|
+ return "text"
|
|
|
+
|
|
|
+ # 通过URL判断
|
|
|
+ elif input_path.startswith("http://") or input_path.startswith("https://"):
|
|
|
+ if any(x in input_path.lower() for x in [".mp4", ".mov", ".avi", ".mkv"]):
|
|
|
+ return "video"
|
|
|
+ elif any(x in input_path.lower() for x in [".jpg", ".jpeg", ".png", ".gif"]):
|
|
|
+ return "image"
|
|
|
+ else:
|
|
|
+ return "text"
|
|
|
+ else:
|
|
|
+ return "text"
|
|
|
+
|
|
|
+def process_content(
|
|
|
+ content: str,
|
|
|
+ content_type: Optional[str] = None,
|
|
|
+ keyframes_per_scene: int = 3,
|
|
|
+ extract_interval: int = 3,
|
|
|
+ prompt_text: str = None,
|
|
|
+ prompt_image: str = None,
|
|
|
+ prompt_video: str = None
|
|
|
+) -> dict:
|
|
|
+ """
|
|
|
+ 处理内容分析
|
|
|
+ """
|
|
|
+ try:
|
|
|
+ logger.info(f"开始处理内容: {content[:100]}...")
|
|
|
+ # 使用默认提示词（如果未提供）
|
|
|
+ if not prompt_text:
|
|
|
+ prompt_text = Config.PROMPT_TEXT
|
|
|
+ if not prompt_image:
|
|
|
+ prompt_image = Config.PROMPT_IMAGE
|
|
|
+ if not prompt_video:
|
|
|
+ prompt_video = Config.PROMPT_VIDEO
|
|
|
+
|
|
|
+ logger.info("初始化OpenAI客户端...")
|
|
|
+ client = OpenAI(api_key=Config.API_KEY, base_url=Config.BASE_URL, timeout=3600)
|
|
|
+
|
|
|
+ # 自动检测内容类型
|
|
|
+ if not content_type:
|
|
|
+ content_type = detect_content_type(content)
|
|
|
+
|
|
|
+ logger.info(f"检测到内容类型: {content_type}")
|
|
|
+ print(f"内容类型: {content_type}")
|
|
|
+ except Exception as e:
|
|
|
+ logger.error(f"process_content初始化失败: {str(e)}")
|
|
|
+ logger.error(traceback.format_exc())
|
|
|
+ raise
|
|
|
+
|
|
|
+ # 根据类型处理
|
|
|
+ if content_type == "text":
|
|
|
+ try:
|
|
|
+ logger.info("处理文本内容...")
|
|
|
+ messages = [{"role": "user", "content": [{"type": "text", "text": f"{prompt_text}\n\n{content}"}]}]
|
|
|
+ resp = client.chat.completions.create(model=Config.MODEL_NAME, messages=messages, max_tokens=5120)
|
|
|
+ logger.info("文本内容处理成功")
|
|
|
+ return {
|
|
|
+ "status": "success",
|
|
|
+ "content_type": "text",
|
|
|
+ "result": resp.choices[0].message.content,
|
|
|
+ "scenes": None
|
|
|
+ }
|
|
|
+ except Exception as e:
|
|
|
+ logger.error(f"文本处理失败: {str(e)}")
|
|
|
+ raise
|
|
|
+
|
|
|
+ elif content_type == "image":
|
|
|
+ try:
|
|
|
+ logger.info("处理图像内容...")
|
|
|
+ content_obj = build_image_content(content)
|
|
|
+ messages = [{"role": "user", "content": [content_obj, {"type": "text", "text": prompt_image}]}]
|
|
|
+ resp = client.chat.completions.create(model=Config.MODEL_NAME, messages=messages, max_tokens=5120)
|
|
|
+ logger.info("图像内容处理成功")
|
|
|
+ return {
|
|
|
+ "status": "success",
|
|
|
+ "content_type": "image",
|
|
|
+ "result": resp.choices[0].message.content,
|
|
|
+ "scenes": None
|
|
|
+ }
|
|
|
+ except Exception as e:
|
|
|
+ logger.error(f"图像处理失败: {str(e)}")
|
|
|
+ raise
|
|
|
+
|
|
|
+ elif content_type == "video":
|
|
|
+ try:
|
|
|
+ # 视频处理流程
|
|
|
+ logger.info("开始视频处理...")
|
|
|
+ print("开始镜头检测...")
|
|
|
+ scenes = detect_scenes(content)
|
|
|
+
|
|
|
+ # 没检测到镜头
|
|
|
+ if not scenes:
|
|
|
+ logger.info("未检测到镜头，使用时间间隔抽帧")
|
|
|
+ print("未检测到镜头，使用时间间隔抽帧...")
|
|
|
+ frames = extract_frames_every(content, seconds=extract_interval)
|
|
|
+ keyframes = [[f] for f in frames]
|
|
|
+ else:
|
|
|
+ logger.info(f"检测到{len(scenes)}个镜头")
|
|
|
+ print("提取关键帧...")
|
|
|
+ keyframes = extract_keyframes(content, scenes, num_frames=keyframes_per_scene)
|
|
|
+
|
|
|
+ logger.info("开始场景分析...")
|
|
|
+ print("调用模型分析场景...")
|
|
|
+ scene_descriptions = []
|
|
|
+ for i, frames in enumerate(keyframes):
|
|
|
+ print(f"分析镜头 {i+1}/{len(keyframes)}...")
|
|
|
+ logger.info(f"分析镜头 {i+1}/{len(keyframes)}")
|
|
|
+ desc = describe_scene(client, Config.MODEL_NAME, frames, i + 1, prompt_video)
|
|
|
+ scene_descriptions.append(desc)
|
|
|
+ print(f"镜头{i+1}: {desc}")
|
|
|
+
|
|
|
+ logger.info("生成视频摘要...")
|
|
|
+ print("生成视频摘要...")
|
|
|
+ summary = summarize_video(client, Config.MODEL_NAME, scene_descriptions)
|
|
|
+ logger.info("视频处理完成")
|
|
|
+
|
|
|
+ return {
|
|
|
+ "status": "success",
|
|
|
+ "content_type": "video",
|
|
|
+ "result": summary,
|
|
|
+ "scenes": scene_descriptions
|
|
|
+ }
|
|
|
+ except Exception as e:
|
|
|
+ logger.error(f"视频处理失败: {str(e)}")
|
|
|
+ raise
|
|
|
+
|
|
|
+ else:
|
|
|
+ logger.error(f"不支持的内容类型: {content_type}")
|
|
|
+ raise ValueError(f"不支持的内容类型: {content_type}")
|
|
|
+
|
|
|
+# Pydantic模型定义请求体
|
|
|
+class AnalyzeRequest(BaseModel):
|
|
|
+ content: str
|
|
|
+ content_type: Optional[str] = None
|
|
|
+ keyframes_per_scene: Optional[int] = 3
|
|
|
+ extract_interval: Optional[int] = 3
|
|
|
+ prompt_text: Optional[str] = None
|
|
|
+ prompt_image: Optional[str] = None
|
|
|
+ prompt_video: Optional[str] = None
|
|
|
+
|
|
|
+@app.post("/analyze")
|
|
|
+async def analyze_content(request: AnalyzeRequest):
|
|
|
+ """
|
|
|
+ 分析文本、URL或已有路径的内容
|
|
|
+ """
|
|
|
+ try:
|
|
|
+ logger.info(f"收到/analyze请求: content={request.content[:100] if len(request.content) > 100 else request.content}...")
|
|
|
+ logger.info(f"请求参数: content_type={request.content_type}, keyframes_per_scene={request.keyframes_per_scene}, extract_interval={request.extract_interval}")
|
|
|
+
|
|
|
+ # 验证content参数
|
|
|
+ if not request.content:
|
|
|
+ logger.error("请求缺少content参数")
|
|
|
+ raise HTTPException(status_code=400, detail="缺少content参数")
|
|
|
+
|
|
|
+ # 在线程池中执行处理
|
|
|
+ loop = asyncio.get_running_loop()
|
|
|
+ logger.info("开始异步处理内容...")
|
|
|
+ result = await loop.run_in_executor(
|
|
|
+ executor,
|
|
|
+ process_content,
|
|
|
+ request.content,
|
|
|
+ request.content_type,
|
|
|
+ request.keyframes_per_scene,
|
|
|
+ request.extract_interval,
|
|
|
+ request.prompt_text,
|
|
|
+ request.prompt_image,
|
|
|
+ request.prompt_video
|
|
|
+ )
|
|
|
+ logger.info(f"处理完成: {result['status']}")
|
|
|
+ return result
|
|
|
+ except HTTPException as he:
|
|
|
+ logger.error(f"HTTP异常: {he.status_code} - {he.detail}")
|
|
|
+ raise
|
|
|
+ except Exception as e:
|
|
|
+ logger.error(f"处理请求时发生异常: {str(e)}")
|
|
|
+ logger.error(f"异常详情: {traceback.format_exc()}")
|
|
|
+ raise HTTPException(status_code=500, detail=str(e))
|
|
|
+
|
|
|
+@app.get("/minio/files")
|
|
|
+async def list_minio_files():
|
|
|
+ """
|
|
|
+ 获取MinIO中的文件列表
|
|
|
+ """
|
|
|
+ try:
|
|
|
+ objects = minio_client.list_objects(Config.MINIO_BUCKET, recursive=True)
|
|
|
+ files = []
|
|
|
+ for obj in objects:
|
|
|
+ # 只获取图片和视频文件
|
|
|
+ ext = os.path.splitext(obj.object_name)[1].lower()
|
|
|
+ if ext in ['.jpg', '.jpeg', '.png', '.gif', '.mp4', '.mov', '.avi', '.mkv']:
|
|
|
+ file_url = f"{Config.MINIO_URL}/{Config.MINIO_BUCKET}/{obj.object_name}"
|
|
|
+ files.append({
|
|
|
+ "name": obj.object_name,
|
|
|
+ "size": obj.size,
|
|
|
+ "url": file_url,
|
|
|
+ "type": "image" if ext in ['.jpg', '.jpeg', '.png', '.gif'] else "video"
|
|
|
+ })
|
|
|
+ print(f"文件: {obj.object_name}, 大小: {obj.size}, URL: {file_url}")
|
|
|
+ return {"files": files}
|
|
|
+ except Exception as e:
|
|
|
+ logger.error(f"获取MinIO文件列表时发生异常: {str(e)}")
|
|
|
+ raise HTTPException(status_code=500, detail=str(e))
|
|
|
+ except Exception as e:
|
|
|
+ raise HTTPException(status_code=500, detail=str(e))
|
|
|
+
|
|
|
+
|
|
|
+# @app.post("/upload")
|
|
|
+# async def upload_and_analyze(
|
|
|
+# file: UploadFile = File(...),
|
|
|
+# content_type: Optional[str] = Form(None),
|
|
|
+# keyframes_per_scene: Optional[int] = Form(3),
|
|
|
+# extract_interval: Optional[int] = Form(3),
|
|
|
+# prompt_text: Optional[str] = Form(None),
|
|
|
+# prompt_image: Optional[str] = Form(None),
|
|
|
+# prompt_video: Optional[str] = Form(None)
|
|
|
+# ):
|
|
|
+# """
|
|
|
+# 上传文件并进行分析
|
|
|
+# """
|
|
|
+# try:
|
|
|
+# # 生成唯一文件名
|
|
|
+# file_id = str(uuid.uuid4())
|
|
|
+# file_ext = os.path.splitext(file.filename)[1]
|
|
|
+# file_path = os.path.join(Config.UPLOAD_DIR, f"{file_id}{file_ext}")
|
|
|
+#
|
|
|
+# # 保存上传的文件
|
|
|
+# with open(file_path, "wb") as buffer:
|
|
|
+# shutil.copyfileobj(file.file, buffer)
|
|
|
+#
|
|
|
+# print(f"文件已保存: {file_path}")
|
|
|
+#
|
|
|
+# # 在线程池中执行处理
|
|
|
+# loop = asyncio.get_event_loop()
|
|
|
+# result = await loop.run_in_executor(
|
|
|
+# executor,
|
|
|
+# process_content,
|
|
|
+# file_path,
|
|
|
+# content_type,
|
|
|
+# keyframes_per_scene,
|
|
|
+# extract_interval,
|
|
|
+# prompt_text,
|
|
|
+# prompt_image,
|
|
|
+# prompt_video
|
|
|
+# )
|
|
|
+#
|
|
|
+# return JSONResponse(content=result)
|
|
|
+# except Exception as e:
|
|
|
+# raise HTTPException(status_code=500, detail=str(e))
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ import uvicorn
|
|
|
+ uvicorn.run(app, host="0.0.0.0", port=8000)
|
