369 lines
13 KiB
Python
369 lines
13 KiB
Python
import os
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import json
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import uuid
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from datetime import datetime, timedelta
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from fastapi import FastAPI, HTTPException, UploadFile, File
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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from kafka import KafkaProducer, KafkaConsumer
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from transformers import AutoTokenizer, AutoModel
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from decord import VideoReader, cpu
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from PIL import Image
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from redis import Redis
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import io
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import re
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import torch
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import asyncio
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from contextlib import asynccontextmanager
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import threading
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app = FastAPI()
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cpm_app = FastAPI()
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app.mount("/cpm", cpm_app)
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# CORS设置
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ALLOWED_ORIGINS = ['https://beta.obscura.work']
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app.add_middleware(
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CORSMiddleware,
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allow_origins=ALLOWED_ORIGINS,
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# 配置
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MODEL_PATH = "/home/zydi/worker_sys/OpenBMB/MiniCPM-V-2_6"
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KAFKA_BROKER = "222.186.10.253:9092"
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KAFKA_TOPIC = "cpm"
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KAFKA_GROUP_ID = "cpm_group"
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REDIS_HOST = "222.186.10.253"
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REDIS_PORT = 6379
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REDIS_PASSWORD = "Obscura@2024"
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REDIS_DB = 5
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UPLOAD_DIR = "/www/wwwroot/beta.obscura.work/upload_files/upload"
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RESULT_DIR = "/www/wwwroot/beta.obscura.work/upload_files/result"
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MAX_FILE_AGE = timedelta(hours=1)
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# 确保目录存在
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os.makedirs(UPLOAD_DIR, exist_ok=True)
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os.makedirs(RESULT_DIR, exist_ok=True)
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# 初始化 Kafka
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producer = KafkaProducer(bootstrap_servers=[KAFKA_BROKER])
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consumer = KafkaConsumer(
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KAFKA_TOPIC,
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bootstrap_servers=[KAFKA_BROKER],
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group_id=KAFKA_GROUP_ID,
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auto_offset_reset='earliest',
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enable_auto_commit=True,
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value_deserializer=lambda x: json.loads(x.decode('utf-8'))
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)
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# 初始化 Redis
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redis_client = Redis(
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host=REDIS_HOST,
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port=REDIS_PORT,
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password=REDIS_PASSWORD,
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db=REDIS_DB
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)
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# 设置 GPU 设备
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torch.cuda.set_device(0)
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# 初始化模型
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model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True)
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model = model.half().cuda().eval()
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
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class MediaAnalysisSystem:
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def __init__(self, model, tokenizer):
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self.model = model
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self.tokenizer = tokenizer
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self.device = torch.device("cuda:0")
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self.model = self.model.to(self.device)
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self.MAX_NUM_FRAMES = 16
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def encode_video(self, video_data):
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def uniform_sample(l, n):
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gap = len(l) / n
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return [l[int(i * gap + gap / 2)] for i in range(n)]
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video_file = io.BytesIO(video_data)
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vr = VideoReader(video_file, ctx=cpu(0))
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sample_fps = round(vr.get_avg_fps() / 1)
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frame_idx = list(range(0, len(vr), sample_fps))
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if len(frame_idx) > self.MAX_NUM_FRAMES:
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frame_idx = uniform_sample(frame_idx, self.MAX_NUM_FRAMES)
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frames = vr.get_batch(frame_idx).asnumpy()
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frames = [Image.fromarray(v.astype('uint8')) for v in frames]
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print('num frames:', len(frames))
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return frames
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def process_video(self, video_data, object_name):
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if not video_data:
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raise ValueError(f"Empty video data for {object_name}")
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print(f"Processing video: {object_name}, data size: {len(video_data)} bytes")
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frames = self.encode_video(video_data)
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question = "Describe the video in as much detail as possible in Chinese, including the setting, clear number of people, and changes in behavior."
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msgs = [
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{'role': 'user', 'content': frames + [question]},
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]
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params = {
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"use_image_id": False,
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"max_slice_nums": 1
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}
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answer = self.model.chat(
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image=frames, # 直接传递 frames
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msgs=msgs,
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tokenizer=self.tokenizer,
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max_length=512,
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temperature=0.7,
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top_p=0.9,
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**params
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)
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extracted_info = self.extract_info(answer)
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return {
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"original_answer": answer,
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"extracted_info": extracted_info,
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"num_frames": len(frames),
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# "start_time": start_time.strftime("%Y-%m-%d %H:%M:%S"),
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# "end_time": end_time.strftime("%Y-%m-%d %H:%M:%S")
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}
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def process_image(self, image_data, object_name):
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image = Image.open(io.BytesIO(image_data))
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question = "描述这张图片，包括场景、人物数量和行为等细节。"
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msgs = [
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{'role': 'user', 'content': [image] + [question]},
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]
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params = {
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"use_image_id": False,
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"max_slice_nums": 1
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}
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answer = self.model.chat(
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image=None,
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msgs=msgs,
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tokenizer=self.tokenizer,
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max_length=512,
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temperature=0.7,
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top_p=0.9,
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**params
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)
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extracted_info = self.extract_info(answer)
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return {
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"original_answer": answer,
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"extracted_info": extracted_info,
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"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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}
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@staticmethod
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def extract_time_from_filename(object_name):
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filename = os.path.basename(object_name)
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time_str = filename.split('_')[0] + '_' + filename.split('_')[1].split('.')[0]
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try:
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start_time = datetime.strptime(time_str, "%Y%m%d_%H%M%S")
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end_time = start_time + timedelta(seconds=10)
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return start_time, end_time
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except ValueError:
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print(f"无法从文件名 '{filename}' 解析时间。使用默认时间。")
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return datetime.now(), datetime.now() + timedelta(seconds=10)
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@staticmethod
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def extract_info(answer):
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info = {
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"environment": None,
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"num_people": None,
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"actions": [],
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"interactions": [],
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"objects": [],
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"furniture": []
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}
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environments = ["办公室", "室内", "室外", "会议室"]
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for env in environments:
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if env in answer.lower():
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info["environment"] = env
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break
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people_patterns = [
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r'(\d+)\s*(人|个人|位|名|员工|用户|小朋友|成年人|女性|男性)',
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r'(一|二|三|四|五|六|七|八|九|十)\s*(人|个人|位|名|员工|用户|小朋友|成年人|女性|男性)',
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r'(一个|几个)\s*(人|个人|员工|用户|小朋友|成年人|女性|男性)',
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r'几\s*(名|位)\s*(人|员工|用户|小朋友|成年人|女性|男性)?',
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r'(男|女)(性|生|士)',
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r'(成年|未成年|青少年|老年)\s*(人|群体)',
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r'(员工|职工|工人|学生|顾客|观众|游客|乘客)',
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r'(群众|民众|大众|公众)',
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r'(男女|老少|老幼|大人|小孩)'
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]
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for pattern in people_patterns:
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match = re.search(pattern, answer)
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if match:
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if match.group(1).isdigit():
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info["num_people"] = int(match.group(1))
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elif match.group(1) in ['一个', '一']:
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info["num_people"] = 1
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else:
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num_word_to_digit = {
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'二': 2, '三': 3, '四': 4, '五': 5,
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'六': 6, '七': 7, '八': 8, '九': 9, '十': 10
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}
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info["num_people"] = num_word_to_digit.get(match.group(1), 0)
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break
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actions = ["坐", "站", "摔倒", "跳舞", "转身", "摔", "倒", "倒下", "躺下", "转身", "跳跃", "跳", "躺", "睡", "说话"]
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for action in actions:
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if action in answer:
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info["actions"].append(action)
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interactions = ["互动", "交流", "身体语言", "交谈", "讨论", "开会"]
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for interaction in interactions:
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if interaction in answer:
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info["interactions"].append(interaction)
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objects = ["水瓶", "办公用品", "文件", "电脑"]
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furniture = ["椅子", "桌子", "咖啡桌", "文件柜", "床", "沙发"]
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for obj in objects:
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if obj in answer:
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info["objects"].append(obj)
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for item in furniture:
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if item in answer:
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info["furniture"].append(item)
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return info
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# 初始化 MediaAnalysisSystem
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media_analysis_system = MediaAnalysisSystem(model, tokenizer)
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async def process_file(file: UploadFile, file_type: str):
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content = await file.read()
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# 获取原始文件的后缀
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original_extension = os.path.splitext(file.filename)[1]
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# 生成新的文件名，包含 UUID 和原始后缀
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filename = f"cpm_{uuid.uuid4()}{original_extension}"
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file_path = os.path.join(UPLOAD_DIR, filename)
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with open(file_path, "wb") as f:
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f.write(content)
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producer.send(KAFKA_TOPIC, json.dumps({
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"filename": filename,
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"type": file_type
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}).encode('utf-8'))
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redis_key = f"{file_type}_result:{filename}"
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redis_client.set(redis_key, json.dumps({"status": "queued"}))
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return {"message": f"{file_type.capitalize()} uploaded and queued for processing", "filename": filename}
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@cpm_app.post("/upload")
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async def upload_file(file: UploadFile = File(...)):
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try:
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file_type = "image" if file.content_type.startswith("image") else "video"
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return await process_file(file, file_type)
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except Exception as e:
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return JSONResponse(content={"error": str(e)}, status_code=500)
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@cpm_app.post("/analyze_video")
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async def analyze_video(file: UploadFile = File(...)):
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try:
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return await process_file(file, "video")
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except Exception as e:
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return JSONResponse(content={"error": str(e)}, status_code=500)
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@cpm_app.post("/analyze_image")
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async def analyze_image(file: UploadFile = File(...)):
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try:
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return await process_file(file, "image")
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except Exception as e:
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return JSONResponse(content={"error": str(e)}, status_code=500)
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def process_task():
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for message in consumer:
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try:
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if isinstance(message.value, dict):
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task = message.value
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else:
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task = json.loads(message.value.decode('utf-8'))
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filename = task['filename']
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file_type = task['type']
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file_path = os.path.join(UPLOAD_DIR, filename)
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redis_key = f"{file_type}_result:{filename}"
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redis_client.set(redis_key, json.dumps({"status": "processing"}))
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with open(file_path, 'rb') as f:
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file_data = f.read()
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if file_type == "video":
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result = media_analysis_system.process_video(file_data, filename)
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elif file_type == "image":
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result = media_analysis_system.process_image(file_data, filename)
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# 保存结果到 JSON 文件
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result_file_path = os.path.join(RESULT_DIR, f"{filename}.json")
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with open(result_file_path, 'w', encoding='utf-8') as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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# 将结果存储在 Redis 中
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redis_client.set(redis_key, json.dumps({
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"status": "completed",
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"result": result
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}))
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except Exception as e:
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print(f"Error processing task: {str(e)}")
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if 'filename' in locals() and 'file_type' in locals():
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redis_key = f"{file_type}_result:{filename}"
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redis_client.set(redis_key, json.dumps({"status": "failed", "error": str(e)}))
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else:
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print("Error occurred before task details were extracted")
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@cpm_app.get("/result/{filename}")
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async def get_result(filename: str):
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for file_type in ["video", "image"]:
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redis_key = f"{file_type}_result:{filename}"
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result = redis_client.get(redis_key)
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if result:
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result_json = json.loads(result)
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if result_json.get("status") == "queued":
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return {"status": "queued", "message": "Your request is in the queue and will be processed soon."}
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elif result_json.get("status") == "processing":
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return {"status": "processing", "message": "Your request is being processed."}
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else:
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return result_json
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raise HTTPException(status_code=404, detail="Result not found")
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async def listen_redis_changes():
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pubsub = redis_client.pubsub()
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pubsub.psubscribe('__keyspace@5__:*_result:*')
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for message in pubsub.listen():
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if message['type'] == 'pmessage':
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key = message['channel'].decode('utf-8').split(':')[-1]
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print(f"Key changed: {key}")
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if __name__ == "__main__":
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# 在后台线程中启动Kafka消费者
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consumer_thread = threading.Thread(target=process_task, daemon=True)
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consumer_thread.start()
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=7000) |